Handbook Of Research On Science Education [Volume III] 9780367428884, 9780367428891, 9780367855758

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HANDBOOK OF RESEARCH ON SCIENCE EDUCATION Volume III

Volume III of this landmark synthesis of research ofers a comprehensive, state-of-the-art survey highlighting new and emerging research perspectives in science education. Building on the foundations set in Volumes I and II, Volume III provides a globally minded, up-to-the-minute survey of the science education research community and represents the diversity of the feld. Each chapter has been updated with new research and new content, and Volume III has been further developed to include new and expanded coverage on astronomy and space education, epistemic practices related to socioscientifc issues, design-based research, interdisciplinary and STEM education, inclusive science education, and the global impact of nature of science and scientifc inquiry literacy. As with the previous volumes, Volume III is organized around six themes: theory and methods of science education research; science learning; diversity and equity; science teaching; curriculum and assessment; and science teacher education. Each chapter presents an integrative review of the research on the topic it addresses, pulling together the existing research, working to understand historical trends and patterns in that body of scholarship, describing how the issue is conceptualized within the literature, how methods and theories have shaped the outcomes of the research, and where the strengths, weaknesses, and gaps are in the literature. Providing guidance to science education faculty, scholars, and graduate students, and pointing toward future directions of the feld, Handbook of Research on Science Education Research, Volume III ofers an essential resource to all members of the science education community. Norman G. Lederman (deceased) was the Distinguished Professor and Chair, Department of Mathematics and Science Education, Illinois Institute of Technology, USA. Dana L. Zeidler is the Distinguished University Professor and Program Coordinator for Science Education in the Department of Curriculum, Instruction, and Learning of the College of Education at the University of South Florida, USA. Judith S. Lederman is a Professor Emeritus in the Department of Mathematics and Science Education, Illinois Institute of Technology, USA.

HANDBOOK OF RESEARCH ON SCIENCE EDUCATION Volume III

Edited by Norman G. Lederman, Dana L. Zeidler, and Judith S. Lederman

First published 2023 by Routledge 605 Third Avenue, New York, NY 10158 and by Routledge 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN Routledge is an imprint of the Taylor & Francis Group, an informa business © 2023 Taylor & Francis The right of Norman G. Lederman, Dana L. Zeidler, Judith S. Lederman to be identifed as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identifcation and explanation without intent to infringe. ISBN: 978-0-367-42888-4 (hbk) ISBN: 978-0-367-42889-1 (pbk) ISBN: 978-0-367-85575-8 (ebk) DOI: 10.4324/9780367855758 Typeset in Bembo by Apex CoVantage, LLC

CONTENTS

Preface Acknowledgments Contributors

ix xi xii

SECTION I

Theory and Methods of Science Education Research Section Editor: William Boone 1 Paradigms in Science Education Research David F. Treagust and Mihye Won

1 3

2 Quantitative Research Designs and Approaches Hans E. Fischer, William J. Boone, and Knut Neumann

28

3 Qualitative Research as Culture and Practice Gregory J. Kelly

60

SECTION II

Science Learning Section Editor: Richard A. Duschl

87

4 Theories of Learning Clark A. Chinn and Kalypso Iordanou

89

5 Student Conceptions, Conceptual Change, and Learning Progressions Joseph Krajcik and Namsoo Shin

v

121

Contents

6 Student Attitudes, Identity, and Aspirations Toward Science Russell Tytler and Joseph Paul Ferguson

158

7 Learning Environments Barry J. Fraser

193

SECTION III

Diversity and Equity in Science Learning Section Editors: Cory A. Buxton and Okhee Lee

219

8 Unpacking and Critically Synthesizing the Literature on Race and Ethnicity in Science Education: How Far Have We Come? Felicia Moore Mensah and Julie A. Bianchini

221

9 Gender Matters: Building on the Past, Recognizing the Present, and Looking Toward the Future Anna Danielsson, Lucy Avraamidou, and Allison Gonsalves

263

10 Multilingual Learners in Science Education Cory A. Buxton and Okhee Lee

291

11 Special Needs and Talents in Science Learning Sami Kahn

325

12 Science Education in Urban and Rural Contexts: Expanding on Conceptual Tools for Urban-Centric Research Gayle A. Buck, Pauline W. U. Chinn, and Bhaskar Upadhyay

359

13 Culturally Responsive Science Education for Indigenous and Ethnic Minority Students Meshach Mobolaji Ogunniyi

389

SECTION IV

Science Teaching Section Editors: Jan van Driel and Charlene Czerniak

411

14 Discourse Practices in Science Learning Gregory J. Kelly, Bryan Brown, and María Pilar Jiménez-Aleixandre

413

15 Synergies Between Learning Technologies and Learning Sciences: Promoting Equitable Secondary School Teaching Marcia C. Linn, Dermot Donnelly-Hermosillo, and Libby Gerard

vi

447

Contents

16 Science Education During the Early Childhood Years: Research Themes and Future Directions Christina Siry, Kathy Cabe Trundle, and Mesut Saçkes

499

17 Elementary Science Teaching: Toward the Goal of Scientifc Literacy Valarie L. Akerson and Selina L. Bartels

528

18 Interdisciplinary Approaches and Integrated STEM in Science Teaching Carla C. Johnson and Charlene M. Czerniak

559

19 Teaching Biology: What Research Says Hernán Cofré, Claudia Vergara, David Santibáñez, Paola Núñez, and William McComas

586

20 Teaching Physics Hans E. Fischer and Knut Neumann

619

21 Chemistry Education Research: Recent Trends and the Onset of the Pandemic Era Sibel Erduran and Aybuke Pabuçcu Akış

657

22 Earth Science Education Nir Orion and Julie C. Libarkin

692

23 Environmental Education Justin Dillon and Benjamin Herman

717

24 Scientifc Inquiry Literacy: The Missing Link on the Continuum from Science Literacy to Scientifc Literacy Renée S. Schwartz, Judith S. Lederman, and Patrick J. Enderle

749

SECTION V

Curriculum and Assessment in Science Section Editors: Bronwen Cowen and Anders Jonsson

783

25 Science, Scientifc Literacy, and Science Education Jonathan Osborne

785

26 The Use of Content Standards for Curriculum Reform in the United States: A Historical Analysis George E. DeBoer 27 Research on Teaching, Learning, and Assessment of Nature of Science Fouad Abd-El-Khalick and Norman G. Lederman

vii

817 850

Contents

28 Exploring and Expanding the Frontiers of Socioscientifc Issues Dana L. Zeidler and Troy D. Sadler

899

29 Project Evaluation: Its History, Importance, and Current Practice Sarah Beth Woodruf and Qinghua Nian

930

30 Precollege Engineering Education Christine M. Cunningham and Cary Sneider

960

31 Review of Research About Science Education Program Evaluation Frances Lawrenz and Leslie Goodyear

993

32 An AI-Based Teacher Dashboard to Support Students’ Inquiry: Design Principles, Features, and Technological Specifcations Janice D. Gobert, Michael A. Sao Pedro, and Cameron G. Betts 33 Large-Scale Assessment in Science Education Xiaoming Zhai and James W. Pellegrino

1011 1045

SECTION VI

Science Teacher Education Section Editor: Saouma Boujaoude

1099

34 Science Teacher Attitudes and Beliefs: Reforming Practice M. Gail Jones and Soonhye Park

1101

35 Research on Science Teacher Knowledge and Its Development Jan H. van Driel, Anne Hume, and Amanda Berry

1123

36 Learning to Teach Science Tom Russell and Andrea K. Martin

1162

37 Research on Teacher Professional Development Programs in Science Gillian Roehrig

1197

Index

1221

viii

PREFACE

The third volume of this handbook builds on the seminal work of its predecessors. Volume I, published in 2007, was edited by Sandra K. Abel and Norman G. Lederman. This original volume provided the feld with the frst comprehensive synthesis of empirical and theoretical research represented by international scholars. The publication of Volume II, edited by Norman G. Lederman and Sandra K. Abell in 2014, carried this scholarship forward with attention to the coherent synthesis of newer research that informed theory, policy, and practice, as well as attention to emerging felds of research. Now, in 2023, we fnd ourselves building on the shoulders of our colleagues. In Volume III, edited by Norman G. Lederman, Dana L. Zeidler, and Judith S. Lederman, our aim is to build on past research, getting seminal works down to a science, and infuse it with the most insightful current research, raising it up to a state-of-the art collection of the most relevant themes and research to science education. We have confdence that the work in this volume will enrich our current understandings of theory, policy, and practice, as well as stimulate the growth of new directions of fruitful research that will inform our feld and as it continues to evolve with the zeitgeist and tenor of the times. This venture has not been without its unforeseen challenges on so many levels. The loss of a loving husband, colleague, and close friend made the development and production of this volume, to say the least, a difcult journey. We hereby dedicate this volume to Dr. Norman G. Lederman, who would have been quite disappointed in us had we not brought this work to its natural fruition! In a metaphorical, Aristotelian way, we can think of Norm as an unmoved mover – coalescing so many scholars around the globe to contribute their time and energy to something he deemed critical to the feld. To partake in this venture, with the collective goal of promoting human fourishing through the exercise of virtues of character and the quest for scientifc literacy, is Norm’s legacy. Volume III is dedicated to you, Norm! Of course, much of the development of this book took place as all of us confronted the ravishing global efects of COVID-19, and the many variants that followed. Many of the section editors and authors were faced with life-altering decisions about family, friends, professional colleagues, and rethinking how to efectively educate in the absence of the sociocultural contexts we had taken for granted. There may be topics that the reader would wish were included but could not be because of the personal hardships confronting all of us. However, it may count as a minor marvel that so many international scholars persevered to contribute to this volume, highlighting contemporary and emerging research perspectives. It may have taken a bit longer to bring this current project to conclusion than originally anticipated. We are grateful for the understanding and dedicated eforts

ix

Preface

of all who contributed to this collective endeavor, and we are confdent that Volume III represents a compendium of the best global research lines impacting science education research. The research in this volume is presented in six sections representing major themes in current research. They are as follows:

Section I. Theory and Methods of Science Education Research Section Editor: William Boone, Miami University Section II. Science Learning Section Editor: Richard A. Duschl, Southern Methodist University Section III. Diversity and Equity in Science Learning Section Editors: Cory A. Buxton, Oregon State University, and Okhee Lee, New York University Section IV. Science Teaching Section Editors: Jan van Driel, University of Melbourne, and Charlene Czerniak, University of Toledo Section V. Curriculum and Assessment in Science Section Editors: Bronwen Cowen, The University of Waikato, Hamilton, and Anders Jonsson, Kristianstad University Section VI. Science Teacher Education Section Editor: Saouma Boujaoude, American University of Beirut

x

ACKNOWLEDGMENTS

We want to acknowledge the work of our Managing Editors, who helped to right the ship if we meandered of course, worked closely with our publisher to make sure all the fles were consistent with publication requirements, and applied their technical expertise to formatting and indexing the many manuscripts that comprise this volume. They are Dr. Dionysius Gnanakkan, Illinois Institute of Technology, and Constantine Shuniak, University of South Florida. We could not have completed this task without their dedicated eforts.

xi

CONTRIBUTORS

Fouad Abd-El-Khalick, The University of North Carolina at Chapel Hill, USA Valarie Akerson, Indiana University, USA Aybuke Pabuçcu Akış, Dokuz Eylul University, Turkey Lucy Avraamidou, University of Groningen, The Netherlands Selina L. Bartels, Valparaiso University, USA Julie A. Bianchini, University of California, Santa Barbara, USA Amanda Berry, Monash University, Australia Cameron G. Betts, Apprendis LLC., USA William J. Boone, Miami University, USA Saouma Boujaoude, American University of Beirut, Lebanon Bryan Brown, Stanford University, USA Gayle A. Buck, Indiana University, USA Cory A. Buxton, Oregon State University, USA Clark Chinn, Rutgers University, USA Pauline W. U. Chinn, University of Hawaii, USA Bronwen Cowen, The University of Waikato, New Zealand Christine M. Cunningham, The Pennsylvania State University, USA Charlene Czerniak, University of Toledo, USA Anna Danielsson, Uppsala University, Sweden George E. DeBoer, Colgate University, USA Justin Dillon, University of Exeter, UK Dermot Donnelly-Hermosillo, University of California, Berkeley, USA Richard A. Duschl, Southern Methodist University, USA Patrick J. Enderle, Georgia State University, USA Sibel Erduran, University of Oxford, UK Joseph Ferguson, Deakin University, Australia Hans E. Fischer, Universität Duisburg-Essen, Germany Barry J. Fraser, Curtin University, Perth, Australia Libby Gerard, University of California, Berkeley, USA Janice Gobert, Rutgers University, USA Allison Gonsalves, McGill University, Canada Leslie Goodyear, Education Development Center, USA Benjamin Herman, Texas A&M University, USA Anne Hume, University of Waikato, New Zealand Kalypso Iordanou, University of Central Lancashire, Cyprus xii

Contributors

María Pilar Jiménez-Aleixandre, University of Santiago de Compostela, Spain Carla C. Johnson, North Carolina State University, USA M. Gail Jones, North Carolina State University, USA Anders Jonsson, Kristianstad University, Sweden Sami Kahn, Princeton University, USA Gregory J. Kelly, The Pennsylvania State University, USA Joseph S. Krajcik, Michigan State University, USA Frances Lawrenz, University of Minnesota, USA Judith S. Lederman, Illinois Institute of Technology, USA Norman G. Lederman, Illinois Institute of Technology, USA Okhee Lee, New York University, USA Julie C. Libarkin, Michigan State University, USA Marcia C. Linn, University of California, Berkeley, USA Hernán Cofré, Pontificia Universidad Católica de Valparaíso, Chile Andrea K. Martin, Queen’s University, Canada Felicia Moore Mensah, Columbia University, USA Knut Neumann, Leibniz-Institute for Science and Mathematics Education (IPN), Kiel, Germany Qinghua Nian, Johns Hopkins University, USA Paola Núñez, Pontificia Universidad Católica de Valparaíso, Chile Meshach M. Ogunniyi, University of the Western Cape, South Africa Nir Orion, Weizmann Institute of Science, Israel Jonathan Osborne, Stanford University, USA Soonhye Park, North Carolina State University, USA James Pellegrino, University of Illinois at Chicago, USA Gillian Roehrig, University of Minnesota, USA Tom Russell, Queen’s University, Canada Mesut Saçkes, Balikesir University, Turkey Troy Sadler, The University of North Carolina at Chapel Hill, USA David Santibáñez, Universidad Finis Terrae, Chile Michael A. Sao Pedro, Apprendis LLC., USA Kathleen Scalise, University of Oregon, USA Renée S. Schwartz, Georgia State University, USA Namsoo Shin, Michigan State University, USA Christina Siry, University of Luxembourg, Luxembourg Cary Sneider, Portland State University, USA David F. Treagust, Curtin University, Australia Kathy Cabe Trundle, Utah State University, USA Russell Tytler, Deakin University, Australia Bhaskar Upadhyay, University of Minnesota, USA Jan H. van Driel, University of Melbourne, Australia Claudia Vergara, Pontificia Universidad Alberto Hurtado, Chile Mihye Won, Curtin University, Australia Sarah Beth Woodruff, Miami University, USA Dana L. Zeidler, University of South Florida, USA

xiii

SECTION I

Theory and Methods of Science Education Research Section Editor: William Boone

1 PARADIGMS IN SCIENCE EDUCATION RESEARCH David F. Treagust and Mihye Won

Why Discuss Research Paradigms? From the nature of science studies, science education researchers are familiar with Thomas Kuhn’s (1962) theory of paradigm shifts. Kuhn’s main focus was on scientifc inquiry and the scientifc community, not on social or educational research, but his term “paradigm” provides a convenient reference point to talk about diferent sets of beliefs, values, and methodologies in educational research (Schwandt, 2001). A paradigm in educational research is recognized as a worldview that sets the value of research and asks such questions as: What is counted as social knowledge, action, and meaning? What are the main goals of educational research? What are the roles of educational researchers? How do we carry out our research projects? (Guba & Lincoln, 1994). Like G. Anderson (1998) notes, “How you see the world is largely a function of where you view it from” (p. 3). Consequently, the research paradigms guide the researchers throughout the empirical research process: from setting the research purpose to selecting data-collection methods to analyzing the data to reporting the fndings. As Kuhn noted, although the paradigm is frmly based on the philosophical stance and has a signifcant infuence over every aspect of the research procedures, researchers take for granted the paradigm in which they work, if they consider the paradigm at all. Indeed, researchers often have little or no knowledge of the historical grounding of the philosophical positions behind the paradigm, and consequently, they do not recognize the implications for conducting research. Indeed, in our recent informal review of research papers in science education when preparing for this chapter, the majority of authors do not refer explicitly to the paradigm that frames their research. In a similar manner, research methods books, particularly qualitative research methods books, discuss philosophical foundations and diferences between quantitative and qualitative studies without necessary mentioning paradigms. Generally, the focus is on “practical” aspects of data collection and analysis – that is, stepby-step how-to procedures, such as how to phrase survey questions, how to use statistical packages, or how to conduct efective interviews (e.g., Creswell & Guetterman, 2019; Fraenkel et al., 2019; Wiersma & Jurs, 2009). In such discussions of the research process, educational researchers view their studies mainly in terms of technicalities, without acknowledging worldviews that shape and validate their knowledge claims (Kincheloe & Tobin, 2009). In more recent editions, some comprehensive research methods texts do discuss research paradigms and philosophical backgrounds, but largely in terms of the procedural diferences between quantitative and qualitative data collection (Cohen et al., 2011; Punch & Oancea, 2014). Despite their importance, in most research papers and educational research methods books, research paradigms are rather hidden from plain view.

DOI: 10.4324/9780367855758-2

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David F. Treagust and Mihye Won

The fact that many people conduct studies without seriously considering research paradigms may be interpreted that the practical aspects of selecting a research paradigm are not as paramount as some researchers believe should be the case (Bryman, 2008). Some researchers even regard discussion of paradigms as a purely philosophical exercise, a remnant of the paradigm wars in the 1980s and 90s (Morgan, 2007). Refecting on this time period, the seminal article published by Gage (1989) (written as though it was 2009), described the situation of the paradigm wars from a vantage point of 20 years hence. As discussed in this article, positivist and post-positivist research fourished in the 1980s and was later challenged by alternative paradigms, namely, those taking an interpretivist and/ or critical stance. Much of what Gage wrote about has turned out to be what occurred in practice. However, initial antagonism of proponents of one paradigm toward another appears to have been somewhat moderated with the development and use of mixed-methods research (Bryman, 2008) and the wider acknowledgment of the contributions that research from diferent paradigms brings to the education community (Bredo, 2009). In recent years, there have been some heated discussion on the diversity of research paradigms and what it means in the practice of educational research (Moss et al., 2009). Many education philosophers and researchers have found that the education research guidelines and policies published in the United States by the National Research Council (NRC) (2002) and by other research-funding organizations dogmatically promote a certain type of research studies under the banner of evidencebased, scientifc research, implying quantitative experimental design studies. At the time, this position by the NRC was not well considered by researchers in education who believed that it is dangerous to have such a limited view on what “other” types of research could contribute to establishing better education (see especially Feuer et al., 2002). (A detailed discussion of this issue is available in Educational Researcher in 2002 [volume 31, issue 8] and 2009 [volume 38, issues 6–7] and Qualitative Inquiry in 2004 [volume 10, issue 1].) Furthermore, in the education community, and in the science education community in particular, there is still a tendency to ignore/dismiss research studies in other research paradigms (Kincheloe & Tobin, 2009). Post-positivists may think that interpretivist studies are anecdotal and not methodologically rigorous enough, and critical theory studies are too politically oriented. Interpretivists may regard that post-positivist studies are superfcial or limiting. Critical theorists may consider that post-positivist studies are exacerbating educational inequality. Yet, there is great need to have an open mind to learn from the diferences (Maxwell, 2004; Moss et al., 2009). The philosophical and practical diversity in the education research community not only supports building more balanced knowledge in education (St. Pierre, 2002), but also makes ways for more comprehensive research eforts with common goals (Bredo, 2009). In practice, this more balanced knowledge base is evident from an extensive review of 137,024 doctoral dissertations in education in US universities from 1980 to 2012, which showed an increased popularity of the interpretive research approaches during this period (Munoz-Najar Galvez et al., 2020). Further, there has been a willingness to move away from paradigm wars and examine the emergence of new approaches that address the complexities of educational research (Pivovarova et al., 2020). We intentionally did not use the common category distinction of quantitative and qualitative research in this chapter because the category could be misleading – as if paradigm is limited to the choice of data-collection methods. As mentioned earlier, we believe a research paradigm is much more encompassing than the choice of data types. It is not helpful when a US government report – Common guidelines for educational research and development (Earle et al., 2013) – presents six types of research without any mention of paradigms. In our view, without an analytical understanding of each research paradigm, it is easy to misjudge the quality and the value of research to be investigated and miss the opportunities to learn from them (Moss et al., 2009). In this chapter, we discuss four research paradigms  – positivist/post-positivist, interpretivist/ constructivist, critical theory, and mixed methods. While there are many diferent categorizations and

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Paradigms in Science Education Research

boundary drawings of research paradigms (Clandinin & Rosiek, 2007; Lincoln & Guba, 2000; Moss et al., 2009; Taylor et al., 2012), we chose those four to illustrate their own philosophical underpinnings or theoretical frameworks that guide research procedures and discuss how each paradigm is realized in various research studies in science education. Positivist/post-positivist researchers, based on realist worldviews, attempt to discover the truth by emulating “scientifc” research with solid literature backgrounds and “objective” and rigorous research methods. Interpretivist researchers, based on relativist or constructivist worldviews, endeavor to make sense of the social phenomena through a lens of participants, demanding researchers of fexibility, open-mindedness, and refexivity in design and execution of the research. Critical theory researchers, based on feminist, post-modernist, and other critical worldviews, challenge the status quo by questioning common assumptions and practices to create a more equitable, democratic society. In addition to research being conducted and framed within these three paradigms (even when not overtly mentioned), over the past three decades, a strong argument has emerged for what is referred to as the fourth pragmatic paradigm (Lukenchuk & Kolich, 2013). In this paradigm, mixed-methods researchers in education are not constrained by the underlying philosophies of the three paradigms referred to earlier and choose to not consider the philosophical underpinning of research, focusing on answering specifc research questions. By describing selected research articles that refect the diferent paradigms referenced in the science education research feld, we refect on our own research practices and facilitate a dialogue across paradigms among science education researchers.

Post-Positivist Research Paradigm Philosophical Backgrounds and Theoretical Frameworks of Post-Positivist Research Studies Positivism is understood as “any approach that applies scientifc method to the study of human action” (Schwandt, 2001, p. 199). Following the empirical science tradition, positivist researchers assert that in order to make a meaningful knowledge claim, research studies should be frmly supported by logical reasoning and empirical data that are self-evident and verifable (Schwandt, 2001). Many science education researchers may fnd this ideology of positivism familiar because it is well integrated within Western academic culture – such as the objective, scientifc, logical, evidencebased research as the most desirable form of research (Howe, 2009; Kincheloe & Tobin, 2009). In contemporary discourse, however, positivism carries some negative implications due to its link to naïve realism, but modifed forms of positivism are quite prevalent and infuential in the education feld. Diferent from positivists, post-positivists do admit that culture, personal value systems, and other surroundings infuence an individual’s perception of the world in both positive and negative ways (Phillips & Burbules, 2000) – positive because it guides what to look for and how to make a reasonable, logical explanation, but negative because it may lead to tunnel vision, limiting our understanding of the phenomenon in the truest form. Because of the negative infuence of our prejudices, we cannot be sure whether our knowledge claims really refect the truth or not. Yet, this does not mean that the truth does not exist or that the truth does not matter. For example, a group of teachers may personally prefer a didactic teaching method based on their experience. Their reluctance to recognize alternative teaching methods, however, does not mean that there could be certain teaching methods that are more efective and yield better outcomes with students. Here, the role of post-positivist researchers is, as objective investigators, to systematically approach the truth as best as they can. Rather than simply relying on prior experiences, the researchers endeavor to collect comprehensive empirical data methodically and compare the diferent teaching methods objectively. By conducting a systematic empirical inquiry, post-positivist researchers believe that they can approach

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David F. Treagust and Mihye Won

the truth (or warranted assertions to borrow from John Dewey [1938/1991]) and are able to inform the people of interest (teachers, policymakers, parents, students, etc.) in order to help make datadriven decisions, for example, on a new educational program or educational improvement plans (in this case, informing teachers which teaching method is better for improving student achievement).

Examples of Post-Positivist Research Studies Similar to research in the natural sciences or psychology, the post-positivist tradition focuses on seeking a scientifcally rational or correlational explanation – for example, the efectiveness of a new teaching method on students’ achievement, the relationship of students’ family background and their attitudes toward schooling, or the infuence of students’ perceptions toward science on their academic performance. Naturally, post-positivist researchers regularly adopt comparative experimental designs or survey designs to fnd a causal or correlational explanation. To help readers understand the distinct characteristics of post-positivist research, we introduce fve research studies from the science education literature with which we are familiar to illustrate the common features. These studies are not the result of an exhaustive review of the literature. Kihyun Ryoo and Marcia Linn (2012) followed this post-positivist research tradition and investigated the efectiveness of an educational program in terms of students’ conceptual achievement through pre- and post-tests. This study resembles much of an experiment report in the natural sciences. The authors conservatively designed their study in advance, strictly followed the research protocols, and methodically elaborated the research procedures in the report to convince the readers that they fulflled the quality standards of the post-positivist experimental design. At the beginning of their report, they posed their research question, “How do dynamic visualizations, compared to static illustrations, improve middle school students’ understanding of energy transformation in photosynthesis?” The researchers divided students into an experimental group with dynamic visualization and one control group with static visualization. While the researchers did put the efort in making the experimental education program attractive (in this case, dynamic visualization), they tried to make the control and experimental conditions similar as much as possible, except for the instruction materials (that is, independent variable of dynamic versus static visualization). To equalize those two conditions, the researchers adopted a few measures: they selected two teachers with similar teaching experience (fve years); within each teacher’s class, the students were randomly assigned into two groups after a pre-test; the students went through identical lessons and assessments, except for the visualization modes, and the number of students was large enough to make analytical claims based on statistics (200 students in total). After the lesson and assessments, the researchers categorized the students’ written answers based on an assessment rubric to decide on the improvements of students’ understanding of the concept. Once the data were in, the researchers used a set of statistical packages to analyze the data and backed up their research fndings using various sources of data and triangulation. In order to convince the reader that procedures have been followed faithfully, the researchers provided an extensive explanation of the research procedures with statistical signifcance, internal validity, and external validity of the study. After the data analysis, the researchers informed the readers of the educational implications of the fndings and the limitations of the study, such as where the results can and cannot be generalized to and possible ways to increase the educational efects for further studies. Another post-positivist study, Sunitadevi Velayutham et al. (2011) examined the afective domain. The researchers developed a survey instrument to measure students’ motivation and self-regulation in science learning. Based on a literature review, the researchers identifed a few key components that reportedly infuence students’ motivation in science learning, such as learning goal orientation, task value, self-efcacy, and self-regulation. Here, we notice the researchers’ frm belief that extensive utilization of previous research studies is the efective way to make a reliable instrument to measure

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students’ perception of themselves (Jaeger, 1997). They painstakingly identifed the possible factors and wrote the questionnaire items because the wording of the questions is regarded as very important to obtain the corresponding response. They conducted a pilot study and interviewed some teachers and students. The interviews were not a substantial part of the study, but were used to check whether students’ responses in the survey matched with what they said in their interview. After the confrmation, the researchers distributed the survey to a large number of students (1,360 students in 78 classes). The students were the data source, and any personal connection with them was neither necessary nor desirable to make an unbiased, scientifc claim. After the data collection, the researchers ran a series of statistical analyses to validate the instrument. With the numbers neatly organized in a table format, the researchers methodically claimed that their survey instrument has internal consistency reliability, concurrent validity, and predictive validity. They also claimed that they took stringent measures to safeguard themselves against methodical biases during their study. The researchers concluded the report with possible uses of the instrument for future studies. Another research domain that lends itself to a post-positivist research paradigm includes studies that assess national standards or competencies of learning. These competencies include understanding and application of science concepts, principles and views of the nature of science and evaluation, and judgment about the role of science knowledge in understanding key problems of society and the life world. Julia Holstenbach et al. (2011) developed a model of these competencies that is theoretically based and empirically validated by a test composed of items allowing large-scale assessment. The model included the following areas of competence: (1) science knowledge, (2) knowledge about science, (3) communication, and (4) evaluation and judgment. The work draws on earlier work on evaluation and judgment competence in the feld of biology education by Eggert and Bögeholz (2006), who presented a theoretically based competence model for decision-making in the area of sustainable development. This work discusses the difcult task to develop instructional settings and materials to guide students in achieving the complex competencies addressed. Secondary analyses where the research is presented as objective, logical, and evidence-based, with the researchers having no contact with the participants or the research sites, also ft within the post-positivist design. Hsin-Hui Wang et al. (2021) explored how specifc inquiry-related learning activities were related to student enjoyment of learning science and intended choice of future STEM careers. The data were from Taiwanese and Australian PISA 2015 results on three activities – debating and planning experiments, drawing conclusions and doing hands-on activities, and teachers and students explaining ideas. Taiwan and Australia were identifed as sharing a consensus on developing scientifc inquiry-related instruction to enhance the efectiveness of science education. The authors state that Taiwanese and Australian 15-year-old students have similar performances on science competency (4th and 14th out of 72 countries) but distinct cooperative behaviors – Taiwanese students emphasizing the product of cooperation compared to the Australian emphasis on the process of cooperation. Australian and Taiwanese high– and low–scientifc competency students were compared across the three activities. Contrary to reports that inquiry activities are negatively associated with student learning outcomes, “this study identifed specifc inquiry-related activities that are benefcial to high and low scientifc competency students in Australia and Taiwan” (p. 173). Education for Sustainable Development (ESD) is required as part of the primary and high school curricula in Germany, and these aims can be achieved by introducing students to systems thinking. However, systems thinking is not part of university teacher education in Germany. In this study, Daniela Fanta et al. (2020) conducted a study with preservice biology and geography teachers to investigate the efect of three diferent interventions – a technical course, a mixed course, and a didactic course – that difered on the proportion of systems science and content for teaching systems thinking. The goal was to measure the extent of fostering systems thinking in student teachers of biology and geography in contrast to a control group. A heuristic structural competence model for systems thinking comprising four dimensions of competence was developed and used as the basis for a test

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produced by the authors that was given to the preservice teachers. A quasi-experimental intervention study in a pre-, post-, and follow-up test control group design was employed and the instrument reliability, difculty, and discrimination values provided. Upon completion of the courses, systems thinking was evident in all three courses compared to the control group. The authors concluded that “courses in fostering and teaching systems thinking should become part of the curricula in university teacher education, especially in the ESD-related topics such as biology and geography” (p. 240).

Common Features of Post-Positivist Research Common Research Topics: The primary concern of post-positivist research is to provide a rational explanation for a variety of educational phenomena, but it is often linked with a scientifc test for efectiveness or efciency of a teaching program or educational system – in other words, investigating what works and why it works for evidence-based educational practice (Feuer et al., 2002). Studies that typically are within a post-positivist paradigm include: (1) intervention studies, as seen in Ryoo and Linn’s (2012) study and that of Fanta et al. (2020), and educational software studies such as the one by van Borkulo et al. (2012); (2) large-scale assessment studies, such as No Child Left Behind (NCLB) in the United States (Dee & Jacob, 2011), the National Assessment Program – Literacy and Numeracy (NAPLAN) in Australia (Dulfer et al., 2012), and the national competency study by Holstenbach et al. (2011); (3) international comparison studies, such as the Trends in International Mathematics and Science Study (TIMSS) (Thomson et al., 2012; Mullis et al., 2020) and the Programme for International Student Assessment (PISA) (Organisation for Economic Cooperation and Development, 2010; Thomson et al., 2016); and (4) secondary analyses, like those by Wang et al. (2021). Common Research Designs: Based on logical empiricism, post-positivists painstakingly focus on establishing formal research designs and data that can self-evidently explain what is happening within education programs/systems and why. In order to make their knowledge claim more scientifc and generalizable to other educational systems, post-positivists frequently choose experimental designs (Ryoo & Linn, 2012) or large-scale surveys (Velayutham et al., 2011) or interventions (Fanta et al., 2020). For such research designs, researchers adopt comprehensive sampling strategies (e.g., stratifed, systematic, or cluster sampling) to represent the target population, and they endeavor to control the variables (e.g., dependent, independent, or confounding variables) in various ways to establish a clear causal relationship (Porter, 1997). However, this level of control is constrained because educational researchers are limited by ethical considerations and in this way use quasi-experimental designs. These researchers also spend a signifcant amount of time methodically developing a quantitative instrument or rubric to record the research participants’ understanding, perceptions, or behaviors (Jaeger, 1997). The general standards of quantitative study, such as reliability, internal and external validity, and statistical precision, are faithfully addressed (Cohen et al., 2011). While qualitative data may be collected for such research designs through interviews, observations, or students’ essays, the data are typically converted into numbers to correspond to pre-set categories (Ryoo & Linn, 2012) or used to support or elaborate on the quantitative data (Velayutham et al., 2011) as a form of triangulation. Role of the Researcher in Relation to the Participants: Like natural scientists, post-positivist education researchers aim to be unbiased, knowledgeable experts who contemplate an educational phenomenon at a distance (Schwandt, 2001). The researchers primarily rely on the previously established body of knowledge, their intellectual reasoning power, and their impartiality to the study to make knowledge claims (Moss et al., 2009). Their personal values/beliefs or their involvement with the research participants may damage the objectivity of the study, and post-positivist researchers strive not to become too involved with the participants to proceed with the study fairly. In Ryoo and Linn’s (2012) study, the researchers were not directly involved in teaching the students themselves; rather, they were outsiders who sat in class to check the intervention protocols and collect the necessary data. They did not try to build any personal connection with the participating students. Similarly

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for the studies of Velayutham et al. (2011) and Fanta et al. (2020), the same basic relationship was established between the researchers and the participants with no personal attachment with the participants. In the secondary analysis study by Hsin-Hui Wang et  al. (2021), the researchers are far removed from the participants and the research sites. Because of the limited connections with the participants, the ethical obligations of the postpositivist researchers to the researched are seemingly straightforward. They follow the ethical guidelines outlined by the Institutional Review Board or Ethics Committee (see, for example, the ethics approval process of the American Educational Research Association [2011] and the Australian Association for Research in Education [n.d.] or similar institutional departments). These guidelines involve voluntary participation, informing participants about the research procedures in advance, being sure to avoid physical and psychological harm to the participants, safeguarding the anonymity of the participants, and reporting the data honestly (Fraenkel et al., 2019). Common Quality Standards: While many educational researchers characterize positivism/postpositivism in terms of rigorous research methods and verifable data (Kincheloe  & Tobin, 2009), D.C. Phillips (2005) argued that researchers in this tradition value not just the methods, but also how the overall case is made. He explains that a research study should be frmly based on objective, comprehensive data, but the arguments of the study should also be meticulously structured to present the main argument convincingly. Robert Floden (Moss et al., 2009) focuses on the connection of the research study to the research community and to the established body of knowledge and lists three important criteria to judge the quality of research in this tradition: (1) a clear defnition of concepts/ constructs that are employed in the study; (2) a strong, logical reasoning throughout the research process – from literature review to interpretation of the empirical data to drawing of its conclusions; and (3) signifcant contribution of the study fndings to educators or policymakers. Common Report Styles: Most post-positivist educational researchers follow the traditional scientifc research report format: starting from the literature review, research problem/questions, research design, data analysis, and discussion of research fndings, and fnishing with limitations and educational implications. The fow of the report is logically organized to demonstrate how scientifcally the study was conducted. The procedures are elaborately described to enable replications. The report is frequently written in a passive voice or third-person narrative to give an impersonal, objective tone.

Interpretivist/Constructivist Research Paradigm Philosophical Background and Theoretical Frameworks of Interpretivist/Constructivist Research Paradigm Studies Interpretivism emerged as the reaction against the prevalent “scientifc” positivism research. Diferent from post-positivists and their search for the objective, generalizable truth of the world, interpretivists focus on the localized meanings of human experience. Stemming from the relativist ontology and constructivist epistemology, the researchers in this tradition focus on the fact that people construct their understanding based on their experiences, culture, and context. Even one simple action of shaking hands could be interpreted diferently – as pleasant, too formal, or repulsive – depending on the social convention, location, time, and the company. Likewise, when an educational program is introduced, a young, enthusiastic, personable Ms. Alison may interpret and implement it diferently from an experienced, charismatic Mr. Buckley. Consequently, the “proven” efects of the educational program may have little relevance to the students in Ms. Alison’s class because of the local educational context. Thus, interpretivist researchers are scornful of the post-positivists’ efort to gloss over the specifcs of the teaching and learning context to generalize their research fndings. They argue that measuring and generalizing human understanding and behaviors  – as in post-positivist studies – do not tell the more important part of human action – the situated meanings that people

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make out of such social, educational interactions. Researchers in the interpretivist tradition thus do not overly claim generalizability of their fndings into other situations because people’s meanings and intentions are contextual, temporal, and particular. While academic researchers often feel the urge to make generalizable knowledge claims – that could go beyond the immediate context of the study to be widely applicable to address the situation at hand – interpretivists aim to describe in detail people’s lived experiences (Dewey, 1925/1981, 1938/1988) regarding educational phenomenon. If the audience of the study fnds the researcher’s interpretation plausible, informative, or thought-provoking, the research is regarded as worthwhile (Wolcott, 2009). Researching people’s localized, subjective interpretation of social phenomena, however, involves multiple layers of complication. For example, how do we know researchers identifed the true local meanings? Understanding people’s lived experience is not the same as interviewing and transcribing every word into a research paper. Researchers need to interpret what the research participants have shared with them, and the participants would share only what they want to share with the researchers. Based on the researchers’ own personal, social, and cultural experiences, the information from the participants could be interpreted quite diferently. In order for researchers to claim that they have a good understanding of the educational phenomenon or of the participants’ lived experiences, they usually spend an extended period of time with the participants, build rapport, empathize with the participants to make better sense of the situation, and review and share their own interpretation with the participants and against the literature. While the interpretivist researchers strive to examine their own values and experiences to establish better understanding of the situation by conducting member checks, audit trails, and other means (Guba & Lincoln, 1989; Merriam, 2009), the researchers do not claim that their knowledge claim is complete or the right one, but a sensible interpretation of the situation. The subjectivity issue becomes more complicated when considering the audience of the research report. When interpretivist researchers describe their understanding of the educational phenomenon and of the research participants, the audience has to reinterpret the research fndings. Based on the readers’ lived experience, the meaning drawn from the research report may be diferent. Aware of the multiple levels of subjectivity – from the social interaction to the research participants, from the research participants to the researcher, and from the researcher to the audience – the researchers in this tradition often ofer “thick descriptions” of the situation to communicate the researchers’ interpretation. Furthermore, as the researcher is often the instrument of interpretation, the researcher usually provides the reader with self-refections on the research process and provides evidence of any real or perceived biases that may have been part of the interpretation process.

Examples of Interpretivist/Constructivist Research Studies Similar to researchers in anthropology, science education researchers in the interpretivist/constructivist paradigm set out to examine in some detail the way that individuals – be they teachers, students, administrators, or parents  – develop an understanding of their experiences and activities. Consequently, researchers spend much time with the participants, whom they study and from whom they collect large amounts of (mostly) qualitative data from observations, interviews, and descriptive narratives. Interpretivist studies vary widely in the amount of structure, the length of time, and the level of engagement of the researchers with the participants. The following four examples are interpretivist/constructivist studies with which we are familiar that provide evidence of the variety of interpretivist studies. As with the selected post-positivist studies, these four studies are not the result of an exhaustive review of the literature. An example of a more methodical interpretivist research position is one by David Treagust et al. (2001). The study explored how a middle school teacher used assessment embedded within her teaching the topic of sound. In conducting this case study, the researchers regularly went to the

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research site – a Grade 8 science class with 23 students – to explore how the teacher “incorporated assessment tasks as an integral part of her teaching about the topic of sound” (p. 140). One of the authors was the teacher of the class, and the rest of the researchers interacted with the students as observer participants. After three weeks of intensive observations of science class and interviews with the teacher and the students, the researchers combed through the data to identify how the assessment strategies were used and contributed to or detracted from learning the sound concepts of the lessons. Consistent with the qualitative research design espoused by Erickson (1986, 2012), analysis of the data enabled the development of fve assertions that focused on the embedded assessment tasks. Each of the assertions was supported by detailed data from the classroom observations, as well as interviews and analysis of materials produced by the students during the lessons. The research showed “that nearly every activity had an assessment component integrated into it, that students had a wide range of opportunities to express their knowledge and understanding through writing tasks and oral questioning, and that individual students responded to and benefted from the diferent assessment techniques in various ways” (p. 137). Taking a more philosophical perspective, Beth Warren et al. (2001) at the Cheche Konnen Center illustrated how Haitian immigrant elementary school children develop scientifc discourse in relation to their everyday interactions. The science education researchers in the sociocultural tradition often regard science as a discourse of a scientifc community, and science learning as crossing borders or gaining control of multiple discourses (C. W. Anderson, 2007). Warren and her colleagues, however, argued that children’s everyday discourse and scientifc discourse are not dichotomous but are in a continuum. Using detailed descriptions of students’ and scientists’ interactions, the researchers in this study support their points. One of the episodes in the study was about Jean-Charles. He was a Haitian immigrant student, who spoke Haitian Creole (known not to contain technical, scientifc, abstract terms) as his frst language. The researchers had known the student and the class for a considerably long time, and they were able to describe the usual modes of Jean-Charles’s interactions with his peers, how it took a long time for him to speak about his ideas, and how his drawings were admired by others, etc. In analyzing a class dialogue on metamorphosis, the researchers dissected the meaning of each student’s sentences – both literal and contextual meanings in which they were understood by the members of the class – and how the casual language use and the class environment contributed to the sense-making of the metamorphosis of insects in relation to the human growth. In analyzing an interview with Jean-Charles, the researchers discovered how the use of his everyday language helped the young boy to distinguish growth and transformation in a unique way. Questioning the value of dichotomy between everyday language and scientifc language, the researchers concluded that educators need to observe more deeply and carefully how students’ negotiation of meanings could help their scientifc sense-making. What is taught in a genetics class depends on the teachers’ perspective on teaching genetics – some content is required and other content is optional, so it is dependent on the teachers’ willingness to teach it. In this qualitative case study, Tuomas Aivelo and Anna Uitto (2019) conducted open-ended semi-structured interviews with ten upper-secondary high school biology teachers in Finland to learn how these teachers justifed their choices for content and contexts when teaching genetics. The teachers were specifcally asked how they teach and what examples they used on three diferent human-related contexts: genetically modifed organisms (GMOs), human hereditary disorders, and human complex traits, such as intelligence. These three contexts functioned as a gradient in terms of how much freedom teachers had to choose what content and contexts they taught, with GMOs being part of the national core curriculum. Interviews lasted for 40 to 92 minutes. The teachers’ responses were categorized using a theory-guided content analysis. Trustworthiness of the data was based on teachers recording details of their teaching and the questions that students asked or for answers that needed clarifcation. Teachers’ discussion of the content taught was divided into three themes, and how they taught the use of GMOs and human genetics and dealt with controversial or

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sensitive issues in genetics. The analysis showed that there were fundamental diferences in these biology teachers’ perceptions of the most important themes in genetics and genetics teaching and hence what was taught. While many of these Finnish teachers discussed human traits and other sensitive issues, the researchers argue that the teachers would need more curriculum support to handle controversial and sensitive issues in the classroom. GMO was the most taught topic by all teachers, being specifcally mentioned in the curriculum. Heidi Carlone (2004) conducted an ethnographic case study, entering the feld with a research question: How do students, especially girls, make sense of science and being a good science participant in a reform-based physics class? The focus is on the female students’ experiences – the meanings they build from the instruction, and the local culture within which they operate. Science learning is understood not as a cognitive activity but as a sociocultural activity that integrates students’ identities, discourses, and values. Diferent from post-positivist researchers, Carlone actively sought to get to know the students and spent much time in their naturalistic setting – the physics classroom. Six weeks may not be regarded a long enough time to call this study an ethnography, but she stayed at school as a participant observer and collected an extensive data set utilizing ethnographic practices. She took feld notes in class, talked with students informally and in interviews, collected students’ documents, and interviewed the teacher and school administrators. Any verbal or behavioral data were entered into the data set. She might have had an initial research design, but as she was accumulating data, she redirected the research to follow up on the preliminary results of data analysis. Instead of summarizing students’ responses to the interview questions, Carlone endeavored to portray the participants’ experiences, values, and ways of thinking through their own words and actions. She allocated an extensive portion in the paper to demonstrate the subtle way the participants’ experiences are integrated into their way of communication by directly quoting them. Because of the thick description of the situation, readers feel as if they are sitting in the classroom or seeing through the participants’ minds. In conclusion, rather than giving a defnite answer to the research question, Carlone shows the complexities in implementing an inclusive science curriculum for diverse students and calls for more nuanced understanding of students’ participation in science learning. Consistent with recent policy initiatives in the United States, the goal of this study was to explore and further elucidate secondary teachers’ knowledge of students’ conceptions on the topic of evolution by natural selection. In this research, Margaret Lucero et al. (2020) conducted teacher interviews – using items from a known questionnaire as prompts, collected students’ artifacts and video recorded classroom observations. The research design employed a qualitative grounded theory approach to analyze data collected from four high school biology teachers. Recognizing that many US students hold non-scientifc explanations about evolution and natural selection, the researchers interviewed the teachers prior to and after formal instruction on evolution. Data from the videoed classroom observations and the students’ artifacts triangulated with the teacher interviews generated 18 concepts through open coding of the data, reconciliation of tentative categories and axial coding to establish the trustworthiness of the data. Five broad categories were identifed, two confrming prior research  – about students’ understanding of ecological and genetics concepts and students’ default ways of thinking – and three providing new ideas – related to students’ experience of the topic, non-science issues such as lack of vocabulary that may afect understanding, and students’ testtaking strategies. The authors argue that the fndings provide a better understanding of secondary students’ understanding of evolution that can help address reform-oriented instruction.

Common Features of Interpretivist Research Studies Common Research Topics: Interpretivist studies focus on the cultures (Carlone, 2004), language use (Warren et al., 2001), teachers’ decisions about content and contexts (Aivelo & Uitto, 2019), teachers’ knowledge of students’ conceptions (Lucero et al., 2020), classroom interactions (Gallas, 1995;

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Treagust et al., 2001), and lived experiences of students, teachers, scientists, and community members (Wong, 2002). Through the researcher’s empathic identifcation with the participants and through refection on the beliefs and values of the researcher and the society, researchers aim to understand the research participants’ meaning making around science teaching and learning. Even when a new educational intervention program is implemented, the researchers in this tradition highlight the dynamic interactions between the program and the local contexts, and consider how the local participants interact with and understand the new program (Erickson & Gutierrez, 2002). The interpretivists do not expect that their research results could be readily or directly translated into general science education policies or strategies (Bryman, 2012). Common Research Designs: As an interpretivist research study is perceived as a sensemaking process for the researchers involved, the research design itself can evolve, as illustrated by studies using grounded theory (Lucero et al., 2020). As the researchers immerse themselves in the situation, they get to know the “prominent” research questions better, develop a clearer focus, and may change the research design accordingly. The evolving research design is something that would be frowned upon in post-positivist research, but is a natural process of interpretivist research. Interpretivist/constructivist researchers tend to adopt qualitative research designs, such as case study, ethnography, narrative, and phenomenological research (Carlone, 2004). The qualitative data-collection methods tend to be interviews, observations, and document analysis. To capture the everyday experiences of the research participants, studies usually occur in naturalistic settings rather than experimental comparative settings as in post-positivist studies. Role of the Researcher in Relation to the Participants: Within the interpretivist paradigm, researchers do not aim to claim objectivity attained by disinterested, unbiased researchers. Because interpretivists believe that meanings are not pre-given but are co-created through hermeneutic dialogues (Schwandt, 2000), researchers often aim to study by engaging with the activities of the research participants (Aivelo & Uitto, 2019; Clandinin & Rosiek, 2007; Guba & Lincoln, 2005; Lucero et al., 2020; Wolcott, 2009). As the sense-maker and narrator of the situation under study, the researcher may solicit the views of the research participants and sometimes seeks to immerse in the situation to experience the situation him/herself. Because of the close relationship with the participants, researchers are obligated to consider many ethical issues beyond the Institutional Review Board guidelines, such as how to draw a boundary between the stories that are intriguing to readers and the stories that are too personal to pry into or too consequential to report, or how much to honor the participants’ willingness to share their stories when they do not fully grasp the meaning of participating in a research project (Clark & Sharf, 2007; Einarsdottir, 2007; Etherington, 2007; Jones & Stanley, 2008). Common Quality Standards: Interpretivist researchers admit that the quality of research depends on the skills, sensitivity, and integrity of the researcher because research itself is a sensemaking process. Frederick Erickson (Moss et al., 2009) categorizes the criteria to judge quality interpretivist research study into two: the technical aspects and the educational imagination. Technical aspects involve: (1) prolonged, meaningful interaction in the feld; (2) careful, repeated sifting through the data; (3) refective analysis of the data; and (4) clear, rich reporting. However, interpretivists focus more on the substance than on the methodical rigor by itself, and that is what Erickson meant by educational imagination. One of the criteria most interpretivist researchers uphold is crystallization (Denzin & Lincoln, 2011). Like a clear crystal that casts multiple colors, the researchers endeavor to create a strong image of the lived experiences of the participants through comprehensive deliberation and persuasive presentation (p. 5). As a general guideline for interpretivist research studies, Tracy (2010) ofers eight criteria: a worthy, relevant, signifcant topic; rich data and appropriate theoretical construct; researcher’s refexivity and transparency in value and biases; credible data through thick description and respondents’ validation; afects readers through resonance; signifcant contribution in theory and practice; ethical; and meaningful coherence of study. Interestingly enough, a few of these criteria sound very similar to the post-positivist quality standards we listed earlier.

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Common Report Styles: The most distinctive feature of interpretivist studies is that the data are qualitative, much of which is “thick description” of the situation (individuals, contexts, or events). Lengthy transcripts or rich, verbal descriptions of a situation often characterize interpretivist research. The report could take the form of a traditional empirical study with literature review, methods description, and data analysis (Aivelo & Uitto, 2019; Carlone, 2004; Lucero et al., 2020). Or it could take a story-like format of describing a daily procedure of a schoolteacher or children’s discussion in class (Gallas, 1995, 1997). In such story-like reports, some researchers do not make a long validity claim or methodological justifcation; they simply describe what they have done and explain why. Other researchers use member checking, follow audit trails, and other measures as a way to ensure that researchers are interpreting and communicating research participants’ perspectives fairly and refectively. Yet, the writing is not an easy task for interpretivist researchers. It is “endlessly creative and interpretive” (Denzin & Lincoln, 2011, p. 14). Researchers often ask questions such as: How much contextual description is enough for the readers? How much analysis and how much description are adequate? Through whose voice is the story told? (Wolcott, 2009). The rich description of research participants’ lived experience needs to be artfully weaved into researchers’ interpretations, and the researchers’ writing ability (or storytelling ability) is counted critical. Interpretivist researchers do not regard their interpretation of the situation as the absolute truth, so they tend not to provide the fnal words (or conclusions) of the study (Wolcott, 2009). However, in science education research journals, the extent of this thick description is often limited by the page requirements of the journal, and only short episodes can be reported. Depending on who reviews such work, these abbreviated thick descriptions or dialogues can be seen as not meeting the necessary criteria. In addition, many research reports lack the detailed description on how the researchers selected the participants, why they chose to focus on certain aspects or data-collection methods, what they did to ensure the quality data analysis, and how they considered alternative interpretations. However, the research by Munoz-Najar Galvez et al. (2020) that analyzed 137,024 dissertation abstracts in the feld of education from 1980 to 2010 showed that “topics associated with the interpretive approach rose in popularity while the outcomes-oriented paradigm declined” (p. 612). This detailed analysis of educational research abstracts is consistent with science education research articles where sociocultural, interpretivist research studies appear more frequently in major science education journals. In addition, Cultural Studies in Science Education, established in 2006, publishes articles with this particular focus and has greatly widened the scope of work that is designed to better understand science as a cultural practice.

Critical Theory1 Research Paradigm Philosophical Backgrounds and Theoretical Frameworks of Critical Theory Research Paradigm Studies Similar to interpretivist researchers, critical theory researchers acknowledge that people’s values, ideas, and facts are shaped by social, political, cultural, economic, gender, and ethnic experiences. Critical theory researchers, however, put more focus on the inequality and the power dynamics in human interactions because they understand that all ideas and social interactions are “fundamentally mediated by power relations” (Kincheloe & McLaren, 2005, p. 304). This tradition could be traced back to Marxism in terms of the exploration of unequal power relationships and power struggles. They view that “social reality is not always what it should or could be”, but the social arrangements make people feel comfortable with the status quo (Kincheloe & McLaren, 2005). Academia contributes to such social arrangements by making people develop false consciousness to believe the existing body of knowledge as neutral and scientifc (rather than a tool to serve a certain group of people), efectively preventing people from questioning the status quo (Kincheloe & Tobin, 2009). Clandinin

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and Rosiek (2007) observe that the critical theory researchers believe that “large scale social arrangements conspire not only to physically disempower individuals and groups but also to epistemically disempower people” (p. 47). Because the social narrative is conceptualized that way, researchers strive to examine the current social values and roles in historical and cultural contexts and problematize many taken-for-granted ideas for the beneft of socially marginalized people, such as: Is science learning or educational reform really benefcial for everyone (Barton & Osborne, 2001; Eisenhart et al., 1996)? Twenty years ago researchers were asking questions such as: Why don’t ethnic minority students or female students participate in school science as much as their white male counterparts (Lee, 2002; Noddings, 1998)? Isn’t there something that inherently discourages them to learn science at school (Aikenhead  & Jegede, 1999; Brickhouse et al., 2000; Harding, 1991)? Research has provided answers to many of these questions, leading to further questions about how to maintain females’ interest and engagement in science (see, for example, Prieto-Rodriguez et al., 2020; Stevenson et al., 2021). By asking such philosophical questions, researchers in this tradition focus on uncovering the unequal power relationship in societies and institutions. They aim not just to expand the knowledge of the society, but to contribute to transform the society and emancipate the disempowered people (Kincheloe, 2003). Critical researchers ask themselves how they should change, as teacher, educational researcher, and concerned community member, for society to be more equal, open, and democratic (Bouillion & Gomez, 2001; Elmesky & Tobin, 2005; Roth & Desautels, 2002; Tan & Barton, 2008). In order to enact changes in the lives of the socially, economically, and historically marginalized people, researchers often spend time in low-income, ethnic minority neighborhood schools and become involved in some type of action research project.

Examples of Critical Theory Research Studies Critical theory research studies may look quite diferent from more “traditional” research studies in terms of their (1) critique of the social discourse/structure; (2) orientation toward social action and change; (3) explicit analysis on the researchers’ identities, values, and intentions; and (4) experimental way of writing research reports (Kincheloe, 2003). The following fve examples are critical theory studies with which we are familiar that provide evidence of the variety of elements in such studies. As with the selected studies in the post-positivist and interpretive research, these fve studies are not the result of an exhaustive review of the literature. The frst two studies by Bouillion and Gomez (2001) and by Elmesky and Tobin (2005) illustrate how science education researchers attempted to change how schooling or social research is conducted. The researchers frst pointed out the limitations of the status quo and then enacted alternative ways. Their primary goal was not only to observe but to change the situation and empower the students and their community for the betterment of the people involved. The third study by Tan and Barton (2008) was conducted in the same vein as the frst two, but their study may look very similar to an interpretivist study in terms of their defense of research methods, presentation of results, and interpretations. The fourth study, by Eisenhart (2000), is a critical autoethnographic study where the author conveys her own experience and refections as “data.” The author made clear that her critical interpretation of the social phenomena was socially and politically motivated. The ffth study, by Hoeg and Bencze (2017), illustrates how government policy statements for STEM education are based on embedded practices that beneft and privilege certain people as efective citizens and the statements are not criticized. The fve studies briefy summarized next follow diferent research methods and reporting styles. Despite the diference, we put them in this critical research tradition because of their explicit focus on challenging the inequality of the status quo and the commitment toward social change (Maulucci, 2012). Lisa Bouillion and Louis Gomez (2001) conducted an action-oriented, transformative research study at an elementary school in a low-income urban neighborhood in Chicago, Illinois. Instead

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of following the traditional school learning model, the researchers, along with the teachers at the school, implemented a science project by which science was taught beyond the school walls and promoted the school–community partnership. The project was called the Chicago River Project. As students recognized illegally dumped garbage was a major community problem, they investigated the environmental issues scientifcally in terms of river pollution and water safety. They shared the results with other community members through writing. They organized a series of actions to change the situation. The project was not just one of interesting school activities for the teachers and students. It was their own community problem that they found intimately relevant and in need of action. As the project evolved, the researchers not only collected data for the research report, they also helped the students and teachers to make the action project successful. Here, the research report format is not much diferent from interpretivist research studies but has the important element of political orientation challenging the status quo. However, the focus of the study was not simply reporting a successful science activity. The researchers aimed to change the existing practice of science teaching at school and to break down several existing power relations or boundaries through the study: between students and science as they become users and producers of scientifc knowledge with the help from local community activist-scientists; between teachers and students as students’ ideas were actively incorporated into the activity planning and execution; between education researchers and school teachers as they became equal contributors in the collaborative project; between students and the city council as the students’ persistent efort persuaded the city to act on behalf of the community. While the research report may look similar to a qualitative study, a major goal of this study was to efect a change in the community and the identity of students and teachers within their learning environments. In conventional educational research, students are often the ones who supply data for the research project by flling out questionnaires, answering competency tests, or responding to interview questions, while researchers design, execute, and analyze the study. Instead of following the conventional model of objectifying students’ ideas, Rowhea Elmesky and Kenneth Tobin (2005) involved students as the collaborative researchers rather than as subjects trying to change the power imbalance in the research process. Elmesky and Tobin framed their research study as an alternative to the status quo educational research in American inner-city (low-income, ethnic minority neighborhood) schools. They started their study by questioning the efectiveness or the true intention of educational programs in improving the scientifc literacy of students in socially marginalized communities. Because they saw that the cultural defcit view on the marginalized is oppressive and hegemonic, the researchers adopted a research method that would value the students’ cultural resources and empower them. Following the model of Kincheloe and Steinberg (1998), the researchers recruited high school students as collaborative researchers so as to equip them with critical research skills and to challenge the conventional role of students as the researched. The students were not only provided with multiple research opportunities to refect on their own ideas and their school life, but also worked as a resource to shed a new light on the ways to appreciate their culture and educate how to teach in low-income neighborhood schools. When presenting their research project, the researchers used a transcript format (as if they were research participants) for their interpretation of students and sometimes they used a research narrative format (as if they were the authoritative researchers). The mixed formats of presenting their interpretations gave the impression that they were just telling their version of the stories, not the authoritative interpretation. Edna Tan and Angela Barton (2008) started their study in a similar tone to Elmesky and Tobin by critiquing the implementation of the American national initiative for scientifc literacy. Tan and Barton argued that the current education initiatives focus on the test scores and marginalize lowincome, ethnic minority students, by framing them as “problems” or “failures” and by depriving learning opportunities to make meaningful personal connections to science. After a discussion of the feminist stance on the global knowledge economy, the researchers carefully described how two sixth grade ethnic minority girls from a low-income neighborhood community school negotiated their

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identities through various school science activities and their interactions with the teacher and peers. While the researchers adopted the format of an ethnographic case study in analyzing and presenting the students’ interactions, they did so to problematize the status quo in school science and education research. Within the frame of a critical auto-ethnographic, refective research, Margaret Eisenhart (2000) told her own story of publishing a book on women’s participation in various venues of science. At the beginning of the paper, she explicitly mentioned that her story is not value neutral – rather, it is positioned with certain values and purpose. She intended to critically refect on how she, as an established academic, conceptualizes/practices science education research and how the larger sociocultural discourse shapes or constrains her practice. Retelling her story in two parts, she straightforwardly described why she wanted to investigate various science-related activities in which women were successfully participating, and how she designed a multiple-case study, including a case of the pro-choice and pro-life activist groups’ use of science. She portrayed that the participants in the pro-choice and pro-life groups were highly educated, politically charged, and strongly committed to learn and use science, but their use of science was “unsophisticated” and “divisive” (p. 48). In the second part of her story, she described a series of encounters of the strong discouragement to include the story of the pro-choice and pro-life groups in the book. Publishers and reviewers adamantly noted that those groups’ stories did not add anything new or valuable to the book. Initially, she blamed her inability to write persuasive, convincing arguments and tried to revise the writing. However, from the fear of not being able to publish the book, she conformed to the expectation of the publisher and the society. Eisenhart later refected on the reason why people isolated the pro-choice and pro-life groups’ stories, how the invisible boundary of what’s counted as scientifc activities played a role in their omission, and what she could have done diferently. In the paper, Eisenhart continuously reminded the reader what she was doing and why – for example, why she constructed her story in a more academically conventional way and how placing the blame of what happened to the larger social discourse eased her guilty conscience to her co-author and showed of an academic’s intellectual power. This refective, honest piece of writing leads us to reconsider the meaning of what we do and how we do it in a new light. The importance of science, technology, engineering, and mathematics education – collectively known as STEM education – for preparing citizens for the future is readily accepted by most nations. In the United States policy guidelines have been developed in STEM education. In their article, Darren Hoeg and Larry Bencze (2017) raise issues with the manner in which these policy guidelines are presented and critically discuss the “biopolitics in science education, notions of citizenship in contemporary school education and science education and citizenship and STEM Education” (p. 844). The review identifed themes and categories that became the basis of critical discourse analysis based on Foucault’s (2003) stages of biopolitical development associated with human action and practice. The authors illuminated the ways in which powerful social practices are embedded in the construction of STEM policy and education. They argue that these policy statements are designed to create positive attitudes toward solutions that prioritize and privilege citizen subjectivity such that responses are positive to STEM initiatives and little criticism is engendered. The dominant way of speaking in this discourse is that a scientifcally literate citizenship represents “progress”. The discourse connects scientifc literacy with efective citizenship – namely, those who participate efectively, implying that other citizenship roles and types of citizens are not as important to the problems that threaten a nation and specifcally the economy.

Common Features of Critical Theory Research Studies Common Research Topics: While a large portion of science education studies focuses on the technical aspects of how to teach science better, critical theory researchers concentrate on the political and

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historical aspects of education and educational inequality, seeking to challenge the status quo. The obvious topic for the critical researchers is investigating the multiple, subtle ways that discourage or marginalize the participation of socially disadvantaged people in schooling or science (Elmesky & Tobin, 2005) or challenges to policies that prioritize and privilege some citizens over others (Hoeg & Bencze, 2017). The study of power relationships is a common research topic. As an example, Teo and Tan (2020) provided a critical analysis of power, knowledge, and power relationships between a chemistry expert  – the school-based School Scientist  – and two apprentices  – two students in Grade 10 and another in Grade 9) – in a chemical synthesis project. “This study shows teaching and learning in the form of an apprenticeship model involved dynamism in the negotiations of power relationships during the apprenticeship process” (p. 672–673). Common Research Designs: The designs of critical theory research are often very similar to interpretivist studies, but with more explicit emphasis on larger social ideologies and power relationships. Critical theory researchers believe that empirical research and its data, no matter how rigorous the research methods are, cannot escape the dominant narrative of the society (Kincheloe, 2003). Because of this limitation, researchers in this tradition try to be critical of researchers’ own assumptions and their relationship with the researched. Interpretivist researchers often display refexivity in their relations with the research participants in terms of their values and experiences in understanding the participants. Critical theory researchers, on the other hand, show their refexivity in terms of power dynamics between the researchers and the researched, and even what the research participants have shared as their experiences. In critical ethnography, “[researchers] will be listening through the person’s story to hear the operation of broader social discourses shaping that person’s story of their experience” (Clandinin & Rosiek, 2007, p. 55). Listening to people’s stories is a way to uncover the larger social discourse and false consciousness to enlighten the public. Another common research design is participatory action research that actively addresses the inequalities in school and community. Researchers go into a low-income neighborhood and involve students and community members to recognize the issue of the community and take actions to change situations and their identities. Studies by Bouillion and Gomez (2001) and by Elmesky and Tobin (2005) are examples of such studies. Role of the Researcher: The main goal of research is not about expanding the body of knowledge but about challenging the given status quo with the aim of transforming the society and institution for the betterment of the people involved (Hoeg & Bencze, 2017). Rather than writing as a distant, unbiased scholar, critical theory researchers claim they are intellectuals and activists, working for social justice and for the people who are socially and politically disempowered (Fine et al., 2000). Common Quality Standards: Because critical theory researchers are skeptical of unbiased research through rigorous methodical measures, they do not provide a set of guidelines on how to ascertain quality research. Rather, they argue that by explicitly discussing the biases of researchers and societies, they are conducting more “objective” research studies because they are not operating under any “hidden agenda” or exacerbating social inequality. However, they highly value the democratic procedures in research (e.g., egalitarian relationships with research participants, democratic decisionmaking, and shared contributions to the study), and the social impact of the study in transforming society (e.g., greater/sophisticated understanding of the society, the empowerment of the participants, and prompting or enacting changes in social/personal practices) (G. Anderson et al., 1994; Grifths, 1998). Common Report Styles: Because they are consciously problematizing what is given or conventional, the authors intentionally do not follow the traditional fabric of a research report. Instead, they experiment with the reporting of the study, such as adopting a performance or writing the story as fction (Flores-González et al., 2006). Some social action-oriented research studies could be regarded

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as less methodically rigorous, thus not meeting the criteria of many academic journals. Consequently, to address this potential concern, many critical theory researchers adopt less radical, more traditional forms of ethnographic research reports, such as those by Tan and Barton (2008) and by Eisenhart (2000).

Mixed-Methods Research Paradigm Discussions about research paradigms often result in responses like, “Research paradigms do not matter anymore” and “We can mix and match multiple paradigms to answer research questions”. While it is true that research studies do need to address the research questions thoroughly, and some researchers use both quantitative and qualitative data, can we really mix and match research paradigms? How can realism (positivist), relativism (interpretivist), and feminism (critical theory) as worldviews be integrated into a research study? How can a researcher be a distant scientist (positivist) without direct connection to research participants and at the same a passionate interpreter (interpretivist) and advocate (critical theory) of research participants with intimate knowledge of their lived experiences? To many contemporary education researchers, research paradigms are not considered complex belief systems with philosophical underpinnings and practical implications. Rather, paradigms are regarded the same as research methods or designs. According to Cohen et al. (2017), researchers such as Creswell and Plano Clark (2011) appear to make the distinction linked to types of data  – post-positivism (quantitative research), constructivism (qualitative research), participatory/transformative (qualitative research), and pragmatism (quantitative research and qualitative research). Alise and Teddlie (2010) also identifed a strong tendency for post-positivist researchers to use quantitative methods and for interpretivist and critical theory researchers to use qualitative methods. Many researchers do not regard paradigms as complex belief systems, and they do not see any problems mixing quantitative and qualitative data in a research study. Mixed-methods researchers believe that dichotomizing quantitative and qualitative data is not only unproductive but fallacious (Ercikan & Roth, 2006). As researchers tend to focus on practical aspects of research design and methods rather than worldviews or paradigms when designing and executing research, some researchers question the practical value of research paradigms anyway (Morgan, 2007). Others (e.g., Greene, 2008) focus on the practical value of a problem-solving approach without the restrictions of theory. Discussions about the relationship between paradigms and data types is an ongoing issue. As Bryman (2008) notes, combining diferent research methods is an area where researchers still have diferent views. While many post-positivist researchers welcome such adjustment as a way to increase the validity of research fndings, constructivist researchers are rather critical of such approaches. Denzin and Lincoln (2011), for example, regard mixed methods as a remnant of positivist legacies that relies on numbers as scientifc evidence, resisting to acknowledge the value of interpretivist qualitative studies and the political issue of what counts as evidence. Without an explicit philosophical framework and guiding principles within it and confating research paradigms with methods, the mixed-methods approach is a diferent way of framing research compared to the other three research traditions. Nevertheless, we decided to include mixed methods as the fourth paradigm, despite our initial reservation against it. In many ways, discussion of the mixed-methods approach relates to our earlier comments about Kuhn’s discussion of the invisibility of paradigms where researchers do not consider the history that frames their current practice. This tendency to ignore historical and philosophical considerations such as paradigms when designing and discussing our research is a limitation of the reported research. For a brief comparison of four diferent research traditions, please see Table 1.1.

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David F. Treagust and Mihye Won Table 1.1 Similarities and Diferences of Common Features Between the Four Paradigms Common Features  Post-Positivist Research Studies

Interpretivist/ Constructivist Research Studies

Critical Theory Research Studies

Mixed Methods Research Studies

Research Topics

Evaluation of efectiveness/ efciency of intervention teaching programs Large-scale assessments or surveys

Lived experiences of teachers and students with focus on culture, language use, and daily classroom/school interactions

Political and historical aspects of education Lived experiences of disadvantaged population to highlight educational inequality Activist movement to challenge the status quo

Evaluation of efectiveness/ efciency of intervention teaching programs

Research Designs

Explanatory designs (e.g., experiment or survey research) with representative sampling, quantitative measurement, and multiple validation processes of data

Exploratory designs (e.g., grounded theory, ethnography, phenomenology research) in naturalistic settings with evolving research methods involving thick description

Similar to interpretivist studies or participatory action research with explicit emphasis on social ideologies and power relationships

Explanatory or exploratory mixedmethods research designs

Role of the Researcher in Relation to the Participants

Objective, unbiased collector and interpreter of data without close connection to research participants

Insightful and passionate meaning maker and storyteller of research participants’ lived experiences

Strong advocates of the socially marginalized to challenge status quo

Objective data collector and interpreter with some connection to research participants

Quality Standards Clear defnition of constructs/concepts Rigorous research methods for objective, comprehensive data Reliability of measurement Internal and external validity of knowledge claims

Prolonged meaningful Democratic and interaction in the feld catalytic value of the and repeated re-analysis research and refections on data Depends on researcher’s skills, sensitivity, and integrity Member check, audit trail, peer review, triangulation

Comprehensive answer to the research questions

Report Styles

Use of thick description Can be as a traditional empirical study Or as storytelling, weaving participants’ lived experience with researchers’ interpretations

Similar styles as in post-positivist studies with participants’ interview quotes

Traditional scientifc research format, written in third person

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Consciously problematizing what is given or conventional. Some researchers write a fctional story, others more traditional ethnographic reports

Paradigms in Science Education Research

Examples of Mixed-Methods Studies Next, we present fve studies that adopt mixed-methods approaches, providing readers with the types of data collected and how these are analyzed in mixed-methods research studies. As noted in Table 1.1, mixed-methods researchers’ reports have the main features of reports by researchers using post-positivist and interpretivist paradigms but, as already stated, have essentially ignored the philosophical and epistemological frameworks or backgrounds. Using an overtly described two-phase, sequential mixed-methods study, Sedat Ucar et al. (2011) examined the efects of an intervention with preservice teachers at various educational levels in terms of their conceptual understanding. Following inquiry-based instruction using archived, online data about tides, a total of 79 preservice teachers completed a questionnaire and subsequently a subset of 29 participants was interviewed. From the qualitative and quantitative data, the authors described and measured the impact of the intervention. The manner in which the quantitative and qualitative data were analyzed was described in detail, including reliability and trustworthiness measures. The fndings were presented as a response to the research questions and discussed in relation to previous literature with implications made for teacher education and future research. As an example of another clearly described mixed-methods study, Liesl Hohenshell and Brian Hand (2006) investigated whether diferences in student performance on science tests was a direct result of the implementation of a science writing program when the students in Grades 9 and 10 were learning cell biology. In this “mixed-method, quasi-experimental [study] . . . with a non-random sample” (p. 267), the researchers investigated the students’ performance and explored students’ perceptions of the writing activities using a survey and semi-structured interviews. The authors emphasized the complementary role of quantitative and qualitative methods by using the quantitative results to document science achievement while using the qualitative data to enhance their interpretation of any fndings arising from the quantitative data. The data interpretation was presented separately for the quantitative and qualitative analyses, as were the initial results. In drawing fve assertions arising from the study, the authors integrated the analysis of the quantitative and qualitative data. In a similar manner, Renee Clary and James Wandersee (2007) used a concurrent mixed-methods research design to investigate whether or not an integrated study of petrifed wood could help students gain an improved geobiological understanding of fossilization, geologic time, and evolution. The researchers adopted Creswell’s QUAL and QUAN approaches “to cross validate, confrm or corroborate the fndings” (p. 1016). A survey about petrifed wood was used pre- and post-instruction in a quasi-experimental setting, with the treatment class receiving the integrated petrifed wood instruction. In addition to the quantitative data from the survey, qualitative data were collected from the content analysis of students’ free responses on the survey as well as from the discussion board feedback and researchers’ feld notes. Some of the qualitative data were later quantifed. Although there were quantitative and qualitative data from this investigation, the qualitative data were used to support the fndings from the quantitative data. The students who experienced the integrated petrifed wood instruction showed greater knowledge about aspects of petrifed wood and geologic time; fossilization of geochemistry remained problematic for both groups. Vaughan Prain and Bruce Waldrip (2006) conducted research with a group of teachers and their Year 4–6 students when they engaged with multiple representations of the same science concepts in electrical circuits and collisions and vehicle safety. Using “a mixed-methods approach entailing collection and analysis of both qualitative and qualitative data within the same study, including triangulation of diferent data sources” (p. 1848), the authors identifed teachers’ and students’ practices and beliefs in using multimodal representations of science concepts. Based on survey responses from 20 teachers and their students, 6 teachers and their classes were selected for a case study of their classroom practice with a multi-modal focus. The data included classroom observations and interviews with students when they were involved in classroom activities. Two science classes were reported.

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While these two teachers used various modes to engage students, the researchers observed that the teachers were not systematic in developing students’ knowledge integration and their efective use of diferent modes. Students who demonstrated conceptual understanding were those who recognized the relationships between modes. In a similar manner, using a mixed-methods study described as a quasi-experimental control group design with a pre- and post-test questionnaire involving both quantitative and qualitative data collection and analysis procedures, Emine Adadan (2020) investigated the role of metacognitive awareness in preservice chemistry teachers’ level of understanding of gas behavior in a multirepresentational instruction setting. The quantitative data were from a survey administered to a group of 34 preservice teachers and qualitative data from an open-ended questionnaire. Reliability measures for the quantitative data were reported, and the qualitative responses were coded using the constant comparative method. The results were reported in response to three research questions, and the numerical and interpretive data analyses provided consistent and integrated evidence (p. 271). Similar to other related studies, “the participants with high metacognitive awareness appeared to outperform the participants with low metacognitive awareness in terms of developing a more scientifc understanding of gas behaviour immediately after the multi-representational instruction” (p. 271).

Common Features of Mixed-Methods Research Studies Common Research Topics: Mixed-methods studies involve a wider range of research topics, from an evaluation of a teaching intervention with some research participants’ insights integrated into the research report, to a case study of classroom interactions and dialogues with some complementary quantitative measures. Common Research Designs: Creswell’s (2012) common mixed-methods designs, both explanatory and exploratory, are adopted so long as quantitative and qualitative data are used in a complementary manner. These designs encompass post-positivist researchers adding qualitative data, such as short interviews or feld notes, to a quantitative experimental research design, as seen in Ucar et al. (2011) and Clary and Wandersee (2007). They also include interpretivist researchers borrowing quantitative techniques, such as achievement test scores or survey results, to a case study design, as seen in Hohenshell and Hand (2006) and Prain and Waldrip (2006). Common Quality Standards: One of the justifcations of mixed-methods approaches is that the mixed use of quantitative and qualitative data enables a thorough triangulation of the data to make stronger knowledge claims (Creswell, 2012; Mathison, 1988; Reeves, 1997). Role of the Researcher and Common Report Styles: As mixed-methods researchers use the methods from the other three paradigms, the role of the researcher and the reporting styles are similar to those described for the other three paradigms.

Conclusion Science education researchers have strived to establish solid knowledge claims in their studies. Locating their studies within a particular research tradition or paradigm gives researchers philosophical, methodological, and practical guidelines to design and conduct a persuasive and convincing research project. In this chapter, we have described four distinctive research traditions, identifed relevant studies, and highlighted commonly shared features within each tradition. Our aim has been to show how a research paradigm frames the research efort by conditioning the research topics to be studied, the research designs used, the role of the researcher in relation to the participants, the common quality standards, and the common report styles presented. The landscape of conducting research within these paradigms has gradually changed over the years, though all paradigms, in keeping with the ideas of Thomas Kuhn, are largely hidden from

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view. In the years ahead, we can imagine that approaches to research will continuously evolve to incorporate new issues and ideas. We hope this review will contribute to productive discussion by science education researchers working within and across these four diferent research paradigms in science education.

Acknowledgments This chapter extends the published chapter in the Handbook of Research in Science Education (Volume 2) authored by Treagust et al. (2014). We would like to thank Reinders Duit (now retired from the IPN – Leibniz Institute for Science and Mathematics Education, University of Kiel, Germany) for his contribution to that earlier chapter. We would like to thank Xiufeng Liu and William Romine, who carefully and critically reviewed this chapter. We hope that we have done justice to their critiques and constructive suggestions.

Note 1

Critical theory studies include several research traditions, such as feminism, postcolonialism, post-structuralism, emancipatory/participatory, postmodernism, etc.

References Adadan, E. (2020). Analyzing the role of metacognitive awareness in preservice chemistry teachers’ understanding of gas behavior in a multirepresentational instruction setting. Journal of Research in Science Teaching, 57, 253–278. https://doi.org/10.1002/tea.21589 Aikenhead, G. S.,  & Jegede, O. J. (1999). Cross-cultural education: A cognitive explanation of a cultural phenomenon. Journal of Research in Science Teaching, 36(3), 269–287. https://doi.org/10.1002/(SICI) 1098-2736(199903)36:33.0.CO;2-T Aivelo, T., & Uitto, A. (2019). Teachers’ choice of content and consideration of controversial and sensitive issues in teaching of secondary school genetics. International Journal of Science Education, 41(18), 2716–2735. https:// doi.org/10.1080/09500693.2019.1694195 Alise, M. A., & Teddlie, C. (2010). A continuation of the paradigm wars? Prevalence rates of methodological approaches across the social/behavioral sciences. Journal of Mixed Methods Research, 4(2), 103–126. http://doi. org/10.1177/1558689809360805 American Educational Research Association. (2011). Code of ethics. Educational Researcher, 40(3), 145–156. Anderson, C. W. (2007). Perspectives on science learning. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 3–30). Lawrence Erlbaum Associates. Anderson, G. (1998). Fundamentals of educational research (2nd ed.). Routledge. Anderson, G., Herr, K., & Nihlen, A. S. (1994). Studying your own school: An educator’s guide to qualitative practitioner research. Corwin Press. Australian Association for Research in Education. (n.d). AARE code of ethics. www.aare.edu.au/assets/documents/policies/AARE-Code-of-Ethics.pdf Barton, A. C., & Osborne, M. D. (2001). Introduction Teaching science in diverse settings: Marginalized discourses and classroom practice. Peter Lang Publishing, Inc. Bouillion, L. M.,  & Gomez, L. M. (2001). Connecting school and community with science learning: Real world problems and school-community partnerships as contextual scafolds. Journal of Research in Science Teaching, 38(8), 878–898. https://doi.org/10.1002/tea.1037 Bredo, E. (2009). Comments on Howe: Getting over the methodology wars. Educational Researcher, 38(6), 441–448. http://doi.org/10.3102/0013189x09343607 Brickhouse, N. W., Lowery, P., & Schultz, K. (2000). What kind of a girl does science? The construction of school science identities. Journal of Research in Science Teaching, 37(5), 441–458. https://doi.org/10.1002/ (SICI)1098-2736(200005)37:53.0.CO;2-3 Bryman, A. (2008). The end of paradigm wars? In P. Alasuutari, L. Bickman, & J. Brannen (Eds.), SAGE handbook of social research methods (pp. 12–26). Sage. Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press. Carlone, H. B. (2004). The cultural production of science in reform-based physics: Girls’ access, participation, and resistance. Journal of Research in Science Teaching, 41(4), 392–414. https://doi.org/10.1002/tea.20006

23

David F. Treagust and Mihye Won Clandinin, D. J., & Rosiek, J. (2007). Mapping a landscape of narrative inquiry: Borderland spaces and tensions. In D. J. Clandinin (Ed.), Handbook of narrative inquiry: Mapping a methodology (pp. 35–75). Sage. Clark, M. C., & Sharf, B. F. (2007). The dark side of truth(s): Ethical dilemmas in researching the personal. Qualitative Inquiry, 13(3), 399–416. http://doi.org/10.1177/1077800406297662 Clary, R. M., & Wandersee, J. H. (2007). A mixed methods analysis of the efects of an integrative geobiological study of petrifed wood in introductory college geology classrooms. Journal of Research in Science Teaching, 44(8), 1011–1035. http://doi.org/10.1002/tea.20178 Cohen, L., Manion, L., & Morrison, K. R. B. (2011). Research methods in education (4th ed.). Routledge. Cohen, L., Manion, L., & Morrison, K. R. B. (2017). Research methods in education (8th ed.). Routledge. Creswell, J. W. (2012). Controversies in mixed methods research. In N. K. Denzin & Y. S. Lincoln (Eds.), Strategies of qualitative inquiry (4th ed.). Sage Publishers. Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (6th ed.). Pearson. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Sage. Dee, T. S., & Jacob, B. (2011). The impact of no child left behind on student achievement. Journal of Policy Analysis and Management, 30(3), 418–446. https://doi.org/10.1002/pam.20586 Denzin, N. K., & Lincoln, Y. S. (2011). Introduction: The discipline and practice of qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), SAGE handbook of qualitative research (4th ed., pp. 1–19). Sage. Dewey, J. (1925/1981). Experience and nature. In J. A. Boydston (Ed.), John Dewey: The later works, 1925–1953 (Vol. 1). Southern Illinois University Press (Reprinted from: 1997). Dewey, J. (1938/1988). Experience and education. In J. A. Boydston (Ed.), John Dewey: The later works, 1925– 1953 (Vol. 13, pp. 1–62). Southern Illinois University Press (Reprinted from: 1997). Dewey, J. (1938/1991). Logic: The theory of inquiry. In J. A. Boydston (Ed.), John Dewey: The later works, 1925–1953 (Vol. 12). Southern Illinois University Press. Dulfer, N., Polesel, J., & Rice, S. (2012). The experience of education: The impacts of high stakes testing on school students and their families. Whitlam Institute, University of Western Sydney. Earle, J., Maynard, R., & Neild, R. C. (Co-chairs). (2013). Common guidelines for education research and development. Institute of Education Science, US Department of Education and National Science Foundation. Eggert, S., & Bögeholz, S. (2006). Göttinger Modell der Bewertungskompetenz – Teilkompetenz „Bewerten, Entscheiden und Refektieren“ für Gestaltungsaufgaben nachhaltiger Entwicklung [The Göttingen Model of evaluation and judgement competence – evaluating, deciding, and refecting in the area of sustainable development]. Zeitschrift für Didaktik der Naturwissenschaften, 12, 177–197. https://archiv.ipn.uni-kiel.de/zfdn/ pdf/010_12.pdf Einarsdottir, J. (2007). Research with children: Methodological and ethical challenges. European Early Childhood Education Research Journal, 15(2), 197–211. http://doi.org/10.1080/13502930701321477 Eisenhart, M. (2000). Boundaries and selves in the making of science. Research in Science Education, 30(1), 43–55. https://doi.org/10.1007/BF02461652 Eisenhart, M., Finkel, E., & Marion, S. (1996). Creating conditions for scientifc literacy: A re-examination. American Educational Research Journal, 33(2), 261–295. https://doi.org/10.3102/00028312033002261 Elmesky, R., & Tobin, K. G. (2005). Expanding our understandings of urban science education by expanding the roles of students as researchers. Journal of Research in Science Teaching, 42(7), 807–828. http://doi. org/10.1002/tea.20079 Ercikan, K., & Roth, W.-M. (2006). What good is polarizing research into qualitative and quantitative? Educational Researcher, 35(5), 14–23. http://doi.org/10.3102/0013189X035005014 Erickson, F. (1986). Qualitative methods in research on teaching. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 119–161). MacMillan. Erickson, F. (2012). Qualitative research methods for science education. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (Vol. 2, pp. 1451–1469). Springer. Erickson, F., & Gutierrez, K. (2002). Comment: Culture, rigor, and science in educational research. Educational Researcher, 31(8), 21–24. http://doi.org/10.3102/0013189x031008021 Etherington, K. (2007). Ethical research in refexive relationships. Qualitative Inquiry, 13(5), 599–616. http:// doi.org/10.1177/1077800407301175 Fanta, D., Braeutigam, J., & Riess, W. (2020). Fostering systems thinking in student teachers of biology and geography – An intervention study. Journal of Biological Education, 54, 226–244. http://doi.org/10.1080/00 219266.2019.1569083 Feuer, M. J., Towne, L.,  & Shavelson, R. J. (2002). Scientifc culture and educational research. Educational Researcher, 31(8), 4–14. http://doi.org/10.3102/0013189x031008004 Fine, M., Weis, L., Weseen, S.,  & Wong, L. (2000). For whom?: Qualitative research, representations, and social responsibilities. In N. K. Denzin  & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 107–131). Sage.

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Paradigms in Science Education Research Flores-González, N., Rodriguez, M., & Rodriguez-Muniz, M. (2006). From hip-hop to humanization: Batey Urbano as a space for Latino youth culture and community action. In S. Ginwright, P. Noguera, & J. Cammorota (Eds.), Beyond resistance! Youth activism and community change (pp. 175–196). Routledge. Foucault, M. (2003). Society must be defended: Lectures at the College de France 1975–1976 (D. Macey, Trans.). Picador. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to design and evaluate research in education (10th ed.). McGraw-Hill. Gage, N. L. (1989). The paradigm wars and their aftermath: A “historical” sketch of research on teaching since 1989. Educational Researcher, 18(7), 4–10. https://doi.org/10.3102/0013189X018007004 Gallas, K. (1995). Talking their way into science: Hearing children’s questions and theories, responding with curricula. Teachers College Press. Gallas, K. (1997). Sometimes I can be anything: Power, gender, and identity in a primary classroom. Teachers College Press. Greene, J. C. (2008). Is mixed methods social inquiry a distinctive methodology? Journal of Mixed Methods Research, 2(1), 7–22. http://doi.org/10.1177/1558689807309969 Grifths, M. (1998). Educational research for social justice: Getting of the fence. Open University Press. Guba, E. G., & Lincoln, Y. S. (1989). Judging the quality of fourth generation evaluation. Sage. Guba, E. G., & Lincoln, Y. S. (1994). Competing paradigms in qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 105–117). Sage. Guba, E. G., & Lincoln, Y. S. (2005). Paradigmatic controversies, contradictions, and emerging confuences. In N. K. Denzin & Y. S. Lincoln (Eds.), SAGE handbook of qualitative research (3rd ed., pp. 191–215). Sage. Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Cornell University Press. Hoeg, D., & Bencze, L. (2017). Rising against a gathering storm: A biopolitical analysis of citizenship in STEM policy. Cultural Studies in Science Education, 12, 843–861. http://doi.org/10.1007/s11422-017-9838-9 Hohenshell, L., & Hand, B. (2006). Writing-to-learn strategies in secondary school cell biology: A mixed method study. International Journal of Science Education, 28(2–3), 261–289. http://doi.org/10.1080/09500690500336965 Holstenbach, J., Fischer, H. E., Kauertz, A., Mayer, J., Sumfeth, E., & Walpuski, M. (2011). Modellierung der Bewertungskompetenz in den Naturwissenschaften zur Evaluation der Nationalen Bildungsstandards [Modeling the evaluation and judgement competence in science to evaluate national educational standards]. Zeitschrift für Didaktik der Naturwissenschaften, 17, 261–287. https://archiv.ipn.uni-kiel.de/zfdn/pdf/17_Hostenbach.pdf Howe, K. R. (2009). Positivist dogmas, rhetoric, and the education science question. Educational Researcher, 38(6), 428–440. http://doi.org/10.3102/0013189X09342003 Jaeger, R. M. (1997). Survey research methods in education. In R. M. Jaeger (Ed.), Complementary methods for research in education (2nd ed., pp. 449–476). American Educational Research Association. Jones, M., & Stanley, G. (2008). Children’s lost voices: Ethical issues in relation to undertaking collaborative, practice-based projects involving schools and the wider community. Educational Action Research, 16(1), 31–41. http://doi.org/10.1080/09650790701833089 Kincheloe, J. L. (2003). Critical research in science education. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education (Vol. 2, pp. 1191–1205). Springer. Kincheloe, J. L., & McLaren, P. (2005). Rethinking critical theory and qualitative research. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (3rd ed., pp. 303–342). Sage. Kincheloe, J. L., & Steinberg, S. R. (1998). Students as researchers: Critical visions, emancipatory insights. In S. R. Steinberg & J. L. Kincheloe (Eds.), Students as researchers: Creating classrooms that matter (pp. 2–19). Falmer Press. Kincheloe, J. L., & Tobin, K. G. (2009). The much exaggerated death of positivism. Cultural Studies of Science Education, 4, 513–528. http://doi.org/10.1007/s11422-009-9178-5 Kuhn, T. S. (1962). The structure of scientifc revolution. University of Chicago Press. Lee, O. (2002). Promoting scientifc inquiry with elementary students from diverse cultures and languages. Review of Research in Education, 26, 23–69. https://doi.org/10.3102/0091732X026001023 Lincoln, Y. S., & Guba, E. G. (2000). Paradigm controversies, contraditions, and emerging confuences. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 163–188). Sage. Lucero, M., Delgado, C., & Green, K. (2020). Elucidating high school teachers’ knowledge of students’ conceptions regarding natural selection. International Journal of Science and Mathematics Education, 18, 1041–1061. https://doi.org/10.1007/s10763-019-10008-1 Lukenchuk, A., & Kolich, E. (2013). Paradigms and educational research: Weaving the tapestry. In A. Lukenchuk (Ed.), Counterpoints paradigms of research for the 21st century: Perspectives and examples from practice (Vol. 436, pp. 61–87). Peter Lang. Mathison, S. (1988). Why triangulate? Educational Researcher, 17(2), 13–17. https://doi.org/10.3102/ 0013189X017002013 Maulucci, M. S. R. (2012). Social justice research in science education: Methodologies, positioning, and implications for future research. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (Vol. 1, pp. 583–594). Springer.

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David F. Treagust and Mihye Won Maxwell, J. A. (2004). Reemergent scientism, postmodernism, and dialogue across diferences. Qualitative Inquiry, 10(1), 35–41. http://doi.org/10.1177/1077800403259492 Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. Jossey-Bass. Morgan, D. L. (2007). Paradigms lost and pragmatism regained: Methodological implications of combining qualitative and qualitative methods. Journal of Mixed Methods Research, 1(1), 48–76. http://doi.org/ 10.1177/2345678906292462 Moss, P. A., Phillips, D. C., Erickson, F. D., Floden, R. E., Lather, P. A., & Schneider, B. L. (2009). Learning from our diferences: A dialogue across perspectives on quality in education research. Educational Researcher, 38(7), 501–517. http://doi.org/10.3102/0013189X09348351 Mullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. Boston College, TIMSS & PIRLS International Study Center. https://timssandpirls. bc.edu/timss2019/international-results/ Munoz-Najar Galvez, S., Heiberger, R., & McFarland, D. (2020). Paradigm wars revisited: A cartography of graduate research in the feld of education (1980–2010). American Educational Research Journal, 57(2), 612– 652. https://doi.org/10.3102/0002831219860511 National Research Council. (2002). Scientifc research in education. The National Academies Press. https://doi. org/10.17226/10236 Noddings, N. (1998). Perspectives from feminist philosophy. Educational Researcher, 27(5), 17–18. http://doi. org/10.3102/0013189X027005017 Organisation for Economic Cooperation and Development. (2010). PISA 2009 results: What students know and can do: Student performance on reading mathematics and science (Vol. 1). OECD Publishing. Phillips, D. C. (2005). The contested nature of empirical educational research (and why philosophy of education ofers little help). Journal of Philosophy of Education, 39(4), 577–587. https://doi.org/10.1111/j.1467-9752.2005.00457.x Phillips, D. C., & Burbules, N. C. (2000). Postpositivism and educational research. Rowman & Littlefeld Publishers. Pivovarova, M., Powers, J. M.,  & Fischman, G. E. (2020). Moving beyond the paradigm wars: Emergent approaches for educational research. Review of Research in Education, 44(1), vii–xvi. https://doi.org/ 10.3102/0091732X20909400 Porter, A. C. (1997). Comparative experiments in educational research. In R. M. Jaeger (Ed.), Complementary methods for research in education (2nd ed., pp. 523–544). American Educational Research Association. Prain, V., & Waldrip, B. (2006). An exploratory study of teachers’ and students’ use of multi-modal representations of concepts in primary science. International Journal of Science Education, 28(15), 1843–1866. http://doi. org/10.1080/09500690600718294 Prieto-Rodriguez, E., Sincock, K., & Blackmore, K. (2020). STEM initiatives matter: Results from a systematic review of secondary school interventions for girls. International Journal of Science Education, 42(7), 1144–1161. http://doi.org/10.1080/09500693.2020.1749909 Punch, K. F., & Oancea, A. (2014). Introduction to research methods in education. Sage. Reeves, T. (1997). Educational paradigms. In C. R. Dills & A. J. Romiszowski (Eds.), Instructional development paradigms (pp. 163–178). Educational Technology Publications. Roth, W.-M., & Desautels, J. (2002). Science education as/for sociopolitical action: Charting the landscape. In W.-M. Roth & J. Désautels (Eds.), Science education as/for sociopolitical action (pp. 1–16). Peter Lang. Ryoo, K., & Linn, M. C. (2012). Can dynamic visualization improve middle school students’ understanding of energy in photosynthesis? Journal of Research in Science Teaching, 49(2), 218–243. http://doi.org/10.1002/ tea.21003 Schwandt, T. A. (2000). Three epistemological stances for qualitative inquiry: Interpretivism, hermeneutics, and social constructioinism. In N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (2nd ed., pp. 189–214). Sage. Schwandt, T. A. (2001). Dictionary of qualitative inquiry (2nd ed.). Sage. Stevenson, K. T., Szczytko, R. S., Carrier, S. J., & Peterson, M. N. (2021). How outdoor science education can help girls stay engaged with science. International Journal of Science Education, 43(7), 1090–1111. http://doi. org/10.1080/09500693.2021.1900948 St. Pierre, E. A. (2002). Comment: “Science” rejects postmodernism. Educational Researcher, 31(8), 25–27. http://doi.org/10.3102/0013189x031008025 Tan, E.,  & Barton, A. C. (2008). Unpacking science for all through the lens of identities-in-practice: The stories of Amelia and Ginny. Cultural Studies of Science Education, 3(1), 43–71. http://doi.org/10.1007/ s11422-007-9076-7 Taylor, P. C., Taylor, E., & Luitel, B. C. (2012). Multi-paradigmatic transformative research as/for teacher education: An integral perspective. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (Vol. 1, pp. 373–387). Springer.

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Paradigms in Science Education Research Teo, T. W., & Tan, Y. L. K. (2020). Examining power, knowledge and power relations in a science research apprenticeship. Cultural Studies of Science Education, 15, 659–677. https://doi.org/10.1007/s11422-019-09936-9 Thomson, S., De Bortoli, L., & Underwood, C. (2016). PISA 2015: A frst look at Australia’s results. Australian Council for Educational Research (ACER). https://research.acer.edu.au/ozpisa/21 Thomson, S., Hillman, K., & Wernert, N. (2012). Monitoring Australian year 8 student achievement internationally: TIMSS 2011. Australian Council of Educational Research Ltd. Tracy, S. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837–851. http://doi.org/10.1177/1077800410383121 Treagust, D. F., Jacobowitz, R., Gallagher, J. L., & Parker, J. (2001). Using assessment as a guide in teaching for understanding: A case study of a middle school science class learning about sound. Journal of Research in Science Teaching, 85(2), 137–157. https://onlinelibrary.wiley.com/doi/10.1002/1098-237X(200103) 85:2%3C137::AID-SCE30%3E3.0.CO;2-B Treagust, D. F., Won, M., & Duit, R. (2014). Paradigms in science education research. In N. Lederman & S. K. Abell (Eds.), Handbook of research on science education Volume II (pp. 3–17). Routledge. Ucar, S., Trundle, K. C., & Krissek, L. (2011). Inquiry-based instruction with archived, online data: An intervention study with preservice teachers. Research in Science Education, 41(2), 261–282. http://doi.org/10.1007/ s11165-009-9164-7 van Borkulo, S. P., van Joolingen, W. R., Savelsbergh, E. R., & de Jong, T. (2012). What can be learned from computer modeling? Comparing expository and modeling approaches to teaching dynamic systems behavior. Journal of Science Education and Technology, 21(2), 267–275. http://doi.org/10.1007/s10956-011-9314-3 Velayutham, S., Aldridge, J., & Fraser, B. J. (2011). Development and validation of an instrument to measure students’ motivation and self-regulation in science learning. International Journal of Science Education, 33(15), 2159–2179. http://doi.org/10.1080/09500693.2010.541529 Wang, H.-H., Lin, H.-S., Chen, Y.-C., Pan, Y.-T., & Hong, Z.-R. (2021). Modeling relationships among students’ inquiry-related learning activities, enjoyment of learning and their intended choice of a future career. International Journal of Science Education, 43(1), 157–178. http://doi.org/10.1080/09500693.2020.1860266 Warren, B., Ballenger, C., Ogonowski, M., Rosebery, A. S., & Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching, 38(5), 529–552. https://doi.org/10.1002/tea.1017 Wiersma, W., & Jurs, S. G. (2009). Research methods in education: An introduction (9th ed.). Pearson. Wolcott, H. F. (2009). Writing up qualitative research (3rd ed.). Sage. Wong, E. D. (2002). To appreciate variation between scientists: A perspective for seeing science’s vitality. Science Education, 86(3), 386–400. https://doi.org/10.1002/sce.10023

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2 QUANTITATIVE RESEARCH DESIGNS AND APPROACHES Hans E. Fischer, William J. Boone, and Knut Neumann

Introduction The main aim of science education research is to improve science learning. In order to do so, as researchers, we need to understand the complex interplay of teaching and learning in science classrooms. In the reality of the classroom, there is a tremendous number of variables that afect the teaching and learning of science. Some of these include variables on (1) the individual level, such as students’ knowledge, cognitive abilities, and cognitive development; (2) the classroom level, such as the teachers’ pedagogical content knowledge, content knowledge, and pedagogical knowledge; and (3) the system level, such as school funding and school governance (Fischer et al., 2005; Honingh et al., 2020; Keller et al., 2017). However, research is about evidence. Therefore, many established and necessarily oversimplifed models of teaching and learning science must be iteratively refned in order to account for increasingly more specifc variables. Furthermore, the quality of evidence is of particular importance. On the one hand, qualitative research seeks to improve science education through developing an understanding of the complexity of the teaching and learning of science, often starting with highly selected and isolated cases. On the other hand, quantitative research often is characterized by investigating commonalities across all teaching and learning of science. Both qualitative and quantitative communities have particular rules for estimating the quality of evidence in their feld. As one approach to the question of evidence, researchers have to answer the decisive question: “Cui bono?” What does the researcher want to investigate, to whom are the results addressed, and what are the consequences when the results are applied and put into practice? Research results can be used for diferent levels of decision-making. Again, we can identify three diferent levels of variables describing and infuencing teaching and learning at schools: 1. 2. 3.

The level of individual teaching and learning processes, including knowledge, competencies, beliefs, motivation, and interest The classroom level, including teacher and student activities and interactions, and the quality of instruction as a measure of those activities and interactions related to learning outcomes The system level, including system conditions of schools and larger social units

For research in science education, the quality of in-class instruction and, to a lesser extent, the quality of out-of-class instruction, are the main foci. However, we want to inform frst and foremost

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DOI: 10.4324/9780367855758-3

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teachers and policymakers of how they can improve and/or control the quality of instruction and the output of the educational system. Teachers want to know how to support students so that they can increase their abilities individually or improve the quality of lessons to increase the average ability of a class. However, principals also want to know how they can improve average student achievement at their school. In addition, policymakers want to know how to allocate resources most efciently in order to increase educational achievement across their district, state, or nation in order to support the development of economic and social afairs. To perform quantitative studies, one has to start with a research question, including the operational defnitions of variables and the purpose of the study. The results of investigating each of the levels of the educational system and the research-based conclusions should be of a kind that other researchers can rely upon; and such conclusions should be a base for extending their research. At this point, it is particularly important to consider the empirical character of science education research. Results of empirical research are always provisional. The provisional aspects of empirical research can be clearly seen as COVID-19 research results are presented and revised. Therefore, the trustworthiness of empirical research is a particularly important criterion when we conduct our own research but also for practical applications of our results.

Evidence Trustworthiness of Evidence There are many issues that must be addressed as one seeks to ensure the trustworthiness of research results. As researchers, we have to be aware of the diversity of variables and conditions that possibly infuence research quality. For instance, it may appear easy to compare the average achievement of two classes in one school. However, without taking into account issues such as the classes’ average pre-knowledge, reading ability, socioeconomic background, and cognitive abilities, the potential for mismeasurement is possibly too large for drawing conclusions of certainty. This is true for quantitative as well as for theory-based qualitative studies, both of which are addressed in this chapter. For a description of grounded theory and design-based research, see Chapter 3 in this handbook. For example, the ability of a participant to express scientifc features in a certain language or the preknowledge about a specifc scientifc concept might infuence the participant’s responses in a semistructured interview. Theory-based qualitative researchers should also consider these factors even in case studies in order to produce reliable interpretations of the observed dialogues between teachers and students, or between students, in classroom activities. Therefore, as a frst step, we have to think very carefully about possible confounding variables and possible infuences of such variables upon assessment, motivation, or other categories with regard to the focus of the investigation. Overall, the question of trustworthiness and generalizability of research results must be answered to address the diferent ways in which the aforementioned stakeholders consider the results of research for improving teacher education and research. Additionally, knowing about the confounding variables means understanding the limitations of the study, as this aids a researcher in their interpretation of research results. With regard to teacher education, it is abundantly clear how important it is to communicate and to teach content in a way that utilizes state-of-the-art teaching based upon empirical research results. Given the provisionality and evolution of empirical research, researchers should be able to explain to future teachers how to increase the likelihood that their own lessons will be of high quality. Often, teacher education does not build on empirical fndings; this therefore reproduces intuitive beliefs and myths that often do not correspond to scientifc fndings or have not been empirically examined. For example, a great amount of time is spent on teaching future teachers about students’ everyday conceptions or misconceptions, although there is little evidence that such teacher knowledge is necessary for students’ learning and thus for the improvement in science instruction. In

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fact, a study comparing teaching and learning physics in Finland and Germany showed that Finnish teachers knew fewer misconceptions held by students than German teachers; still, Finnish students learned signifcantly more than German students (Olszewski, 2010). Despite these and other fndings, that knowledge of misconceptions is not as important for high-quality teaching as had been assumed (Hammer, 1996); there are a multitude of studies on students’ misconceptions. Some universities have advocated that the knowledge about misconceptions in all sciences is important for a science teacher (e.g., New York Science Teacher, 2013). However, Chen et al. (2020) found that students’ physics content knowledge was correlated with their physics teachers content knowledge, an observation that had already been made by Ball (1991), and Galili and Lehavi (2006) found that the ability of teachers to apply physics concepts by themselves broadly and correctly is a critical contributor to their content knowledge and pedagogical content knowledge. According to Etkina et al. (2018), teachers’s content knowledge must contain fundamentally diferent components than the content knowledge normally provided for physicists and physics teachers at universities. Teachers’ content knowledge should take into account insights from research on teaching and learning and be operationalized accordingly for the diferent content areas of physics that are relevant in school. In our view, this includes a comprehensive knowing of physics concepts that includes the ability to construct students’ learning processes in these content areas. This begs the question of whether misconceptions are as important as what science educators have considered and whether training on knowing misconceptions might be a waste of limited learning time in science teacher education. As authors, we believe that this leads to the natural conclusion that in science teacher education, teacher educators should teach only, or at least mostly, those content areas that can be trusted from the standpoint of the commonly agreed conclusions of the research community. In the aforementioned cases, concentrating on student teachers’ content knowledge might have more impact upon student learning than teaching student teachers about misconceptions. A review of the relevant literature on misconceptions also reveals that many studies cannot be generalized due to methodological issues. This also applies to other research in the feld of science education. Furtak et al. (2012), for example, found in their meta-analysis study that of 1,625 studies on scientifc inquiry only 59 papers remained after excluding those that focused specifcally on students with disabilities, special education, or interventions outside K–12 science classrooms – and, in particular, studies that did not employ experimental or quasi-experimental designs. After further applying features of quality research – such as pre-post-design, two-group, cognitive outcome measures, and efect size calculations were applied as a further selection criteria, only 10 papers from the original 1,625 studies remained. After asking some authors of the excluded articles for data and additional calculations, Furtak et al. (2012) found that four of the excluded studies could be included in their meta-analysis. Repeating this procedure with slightly diferent search criteria yielded an additional 4,239 papers, of which only 8 could eventually be included. In another meta-analysis, M. A. Ruiz-Primo et al. (2008) analyzed the impact of innovations in physics education. They started with more than 400 papers and found that only 51 papers that reported the studies could be used for a quantitative synthesis of the efects. Meta-analysis such as those detailed earlier abide by the rules of empirical research and serve as an excellent example of applied trustworthiness. The larger number of papers that had to be excluded due to statistical weaknesses suggests that it is necessary to pay even more attention to the observance of the respective discussed standards of teaching and learning research in the domain of science education research.

Obtaining Evidence One of the main problems of research in science education in general is to classify diferent types of cognition. The direct sensory experience of human beings is generally incomplete and not dependable because of the restricted sensitivity range of diferent types of human organs of perception.

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Everyday communication and other social interaction rely on agreed common knowledge and intuition. Therefore, everyday communication is fuzzy by nature and can even be wrong in certain situations. In a scientifc discourse, these inaccuracies are not tolerated, but in everyday communication they contribute to enabling social interaction in the frst place, even if inaccuracies sometimes lead to misunderstandings and disasters. The important point is that researchers are not able to guarantee conclusions regarding communication and interaction, but they are able to describe their inaccuracies and doubts. If we are not sure about research results, we can ask experts about their opinion. However, experts’ opinions can be wrong and mistaken, and their reasoning, even if it is based on certain logical systems and rules, can be based on false premises. To avoid mistakes and to obtain trustworthy conclusions, we have to use scientifc methods and procedures that are agreed upon by researchers and experts and that allow more trustworthy statements to be made than those based upon a few experts’ opinions. Studies must be linked to the whole range of relevant prior studies and conducted using scientifc methods and utilizing quality criteria. Therefore, planning and performing a study must include a theoretical model with regard to relevant past work, rigorous sampling, wellelaborated instrument construction that involves piloting of the instruments, adequate experiment design, up-to-date psychometrics, carefully captured data, and rigorous interpretation of results. Within the research community, these criteria and the process planning and performing a study must again be discussed and agreed upon. Doing so will then allow for estimating the quality of the results of all investigations, and this will also provide research results that can guide further research and practice. The necessary agreement in a community of researchers requires publicity and discussion, such as that which has taken place with regard to the nature of science (e.g., Lederman, 2019) or the professional knowledge of science teachers (e.g., Gess-Newsome et al., 2019). In addition, it is important in science education that researchers be able to replicate studies. In natural sciences, replication and public discussion are an indispensable part of evaluating the trustworthiness of scientifc investigations. We strongly suggest that national and international associations for science education should encourage publishing and reporting of replications of research in their journals and at conferences. Theories and models of empirical research have their limitations and may even be proven wrong. There are many prominent examples in physics, such as Bohr’s atomic model, which despite its limitations is still being utilized to interpret the results of spectroscopy (Garrett et al., 2018) or Newtonian mechanics, which it is not suitable for describing the micro or macro world, but is still being used to describe causal phenomena in the meso world (Casadio & Scardigli, 2020). Empirical research can never prove anything to be correct (Popper, 1959). The interrelation between theory development and empirical results leads to the fact that both felds are aligned with each other, and therefore they have to change permanently. Therefore, there is no proof in empirical research. It must always be assumed that the theory does not adequately describe the facts, which may be because the theory contains logical or conceptual errors or because the empirical results contradict the theoretical assumptions. Therefore, all results have to be discussed and interpreted. Conclusions should be viewed as tentative and open to revision. Therefore, it is necessary that all published work include a detailed description of the project data with a clear explanation of the process of constructing the respective measures and the analysis of the measures. Therefore, raw data must be available, at least by request.

Theory and Evidence In the feld of science education, research attempts have been made to utilize theories or models to evaluate and describe the quality of what is taking place in classroom teaching. Of great importance is the use of theories or models to serve as a guide to research investigations and to allow for the

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development and use of consistent methodologies (e.g., Carlson  & Daehler, 2019; K. Neumann et  al., 2012; Opfermann et  al., 2017). Attempts to identify and describe the quality of instruction and its components were already undertaken in the 1960s  – mostly as observational studies in which classroom instruction was observed and criteria identifed regarding what was thought to be the teaching of a high-quality teacher. In the late 1960s and throughout the 1970s extensive research programs investigated teacher efectiveness. This type of research – oftentimes termed process-product-research – investigated relations between characteristics of good teachers (or good teaching) as components of the process outcomes, such as student achievement, which is viewed as a product. In the late 1970s and throughout the 1980s, researchers attempted to systematize the results of teacher efectiveness research in an efort to establish more comprehensive models of instructional quality; such research mainly comprised meta-analyses. One shortfall in these eforts was not explaining instructional outcomes. This void was apparent as the TIMSS study attempted to investigate instruction and to relate instructional characteristics to students’ achievements. One factor fueling this shift was that video analysis of lessons had become technically possible. Video analyses allowed researchers to record and analyze classroom instruction in an extensive and thorough manner in multiple iterations. This led to a further refnement of models of instructional quality as more methodologies that are complex became available for capturing and researching the complex reality of the classroom (K. Neumann et al., 2012). This brief review of the history of research of quality of science instruction highlights the importance of building a study upon previous research in order to advance a feld. Building a research study upon a sound theoretical framework is indispensable for obtaining clear and relevant results. There must be a linkage between theory and research experiments – theory informs and provides a framework for experiments, and research fndings allow acceptance, modifcation, or total rejection of the theoretical model used. The essence of research in science education is to fnd regular patterns in social situations, which in our case, refers to teaching and learning sciences. In the end, researchers should be able to conclude that a certain educational activity, or setting, most likely results in the intended learning process, student behavior, or increase of learning gains for a particular group of students. If a newly developed theoretical model is not tested, it does not permit generalization to other cases. As already described, building theoretical models of aspects of teaching and learning science at school has to take into account multiple variables, the interdependencies of variables, and hierarchically ordered system structures. Perhaps it is not surprising that controlling as many infuencing variables of classroom settings as possible is absolutely necessary when researchers wish to reach conclusions with regard to the impact of changes in teaching and learning recommended by some science educators. The efect of a newly developed unit, for example, for quantum mechanics, surely depends on the design and strategy of presenting the subject matter adequately regarding its scientifc content and structure. However, scientifc content taught at schools is not identical with that taught at universities because the learners at schools and universities are diferent in so many ways that teachers have to think about the teaching and learning process specifcally suited for the recipients. For example, the academic content structure of a unit is moderated by features of the teacher’s personality, his or her pedagogical content knowledge, students’ socioeconomic background, the structure of student– teacher interactions in the classroom; and in particular, by students’ cognitive abilities. This holds true for every description of learning and teaching processes and for every intervention aiming to change teaching practice. Consistent with the idea of science instruction as a complex system, it should be obvious that an adequate model is needed to describe as many infuences as possible on the teaching and learning processes. Clausen (2002) suggested three critical aspects for developing and validating a theory (or model) for assessing instructional quality: selection of representative constructs (variables of theoretical models), formulation of hypothetical correlations (structure models), and the

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development of adequate indicators for operationalization (measuring models) (Fischer & Neumann, 2012, p. 118; K. Neumann et al., 2012).

Quantitative Research: Theoretical and Methodological Considerations As discussed in the preceding section, strong research has to be built upon strong theory; the stronger the theoretical framework, the stronger the research. The theoretical framework should be based upon a synthesis of previous research studies and new ideas regarding unknown and/or innovative infuences and variables. In quantitative research, a theoretical framework is oftentimes presented as a model, that is, a conglomerate of variables and relations between these variables. The model identifes the variables that are assessed or surveyed respectively through an empirical study. Depending on the theoretical framework and the particular research questions, the empirical study may utilize one of a number of diferent designs. In addition to selecting a research design, the sampling of participants of the study is a major consideration of quantitative research. Sample size and sample composition (e.g., what type of students are to be studied) are tightly connected to the theoretical framework, the respective research design, and the proposed analysis procedures. For example, classical approaches, such as an analysis of variance (ANOVA), typically require smaller sample sizes than more sophisticated approaches, such as structural equation models (SEM). In principle and irrespective of whether researchers are performing theory-based qualitative research or quantitative research, there are four criteria of trustworthiness of data and results that have to be clarifed: objectivity, reliability, validity, and signifcance. Important features of research design and the goal of research instrumentation can be derived from formulating the purpose of the study as precisely as possible, identifying the levels of the analysis, considering all possible confounding variables, and utilizing these four criteria of trustworthiness.

Objectivity Objectivity refers to the reduction, or at best the elimination, of any external infuences on a measurement or an observation. The idea of objectivity was already expressed by Bacon (2000). He demanded researchers avoid becoming four so-called idols: 1. 2. 3. 4.

The Idols of the Tribe (Idola tribus) point out that a researcher must consider that all perception is made by the human mind and not by the objective universe The Idols of the Cave (Idola specus) are the specifc perspectives of an individual caused by his or her education, socialization, and individual life experience The Idols of the Market (Idola fori) can be translated as acting as socially desired The Idols of the Theatre (Idola theatri) refer to dogmata and ideologically guided principles

We suggest that observations and results shall be verifable independently of subjective infuences (intersubjectivity). It is often helpful if researchers can consider performing, evaluating, and interpreting objectively. Objectivity in quantitative research is possible when well-established rigorous steps of performance standardization (e.g., scripts for classroom videos, detailed manuals to administrate a test), psychometrics (e.g., ensuring the marking of items along a variable is maintained over time), statistics (e.g., routine computation of efect size, addressing the hierarchical nature of educational data), and interpretation are utilized. Within the realm of qualitative studies, the issue of objectivity is more diffcult to address, for it is not possible to distinguish easily between an observer and a participant as between the procedure of testing and a participant. Therefore, regardless of the type of measurement, reliability and validity are decisive criteria for the quality of a measurement if objectivity is asserted.

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Reliability Reliability refers to the error of a measurement. Sociological theorists often use concepts that are formulated at a rather high level of abstraction. These are quite diferent from the variables that are in the stock-in trade of empirical sociologists. . . . The problem of bridging the gap between theory and research is then seen as one of measurement error. (Blalock, 1968, pp. 6–12) This is the basic idea of theory-guided measurement – the existence of a true value T is assumed (e.g., of motivation or school achievement in chemistry or physics). A measurement should be able to provide an assessment about the level of probability if the true value can be expressed as observed value corrected by the error. To explain T, we have to consider diferent error types. First, each measurement contains a random error Ei, for example, variations in the subject’s attention, ambiguous answers, disturbances of classroom settings from the outside, mistakes during data entry, errors in data coding, and so on. Ei = T-xi where xi is observed, T is the estimated true value, Ei is the random error of a single measurement, and M is the mean value of all measurements xi. Further procedures of empirical measures and calculations, such as criteria that confront nonnormal distributed data, are described in this chapter. Since random error will have an infuence on the reliability and validity of a measurement, the measurement has to be conducted and data have to be processed as carefully as possible. Careful means that the criteria of objectivity of the implementation and the evaluation, the validity of the measurement models and measurement instruments and the assurance of reliability are observed and documented as well as possible. The second type of error is the systematic error S. If objectivity is given as a precondition, they result from a bias in measurement as over- or underestimation of key parameters or sample mistakes. If xi is an observed value, the true value T can be expressed as T= xi+E+S, where E denotes the random error and S the systematic error (see Figure 2.1). Reliability is defned theoretically as the ratio of the variance of the true values xT to the variance of the measured values xi. A careful planning and execution of design and measurement and probability-based sample selection are necessary, especially referring to sample selection and response bias, such as social desirability. A comparison of random and occasional samples makes this clear. For example, let us consider two

Figure 2.1 Diferent scenarios of reliability and validity.

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research projects on the motivation of ninth graders of a certain country to learn physics. One of the projects is able to compile the sample randomly, and the respondents are selected from all ninth graders in a country. Since this is mostly not possible, a second project has selected an opportunity sample made up of school classes that are available at a given time with the consent of the headmasters of the schools and the parents of the students. An occasional sample may, for example, contain more girls than boys and thus generate a bias. It is important to note that a systematic error does not change the reliability of a measurement and can therefore not be detected through poor values of reliability. Systematic errors can only be avoided by a very careful approach to sample selection, design, and measurement and through a critical evaluation of the whole investigation. In case of qualitative analyses such as categorizations or interpretations of specifc classroom situations, typical measures of reliability include interrater reliability (Gwet, 2014) or intraclass correlation (ICC) (Shrout & Fleiss, 1979). These procedures compare the assessments of diferent independent raters for the same situation. The raters are trained for the specifc task and function as quasiindependent measuring devices. The comparison tests the accuracy of the interpretation procedure and the category system, which must be derived from physical, pedagogical, and/or psychological models and already established results of empirical research. Further procedures of empirical measures and calculations, such as criteria that confront nonnormal distributed data are described in this chapter. A detailed description of further procedures and criteria of trustworthiness can be found in Gravetter et al. (2020). A central aspect of quantitative research is to measure constructs such as students’ science achievement or students’ interest in science; and not surprisingly, the reliability of the measures obtained through achievement tests or questionnaires is of particular importance. Typically, a Cronbach’s alpha (or coefcient α) is computed using raw score data and used to provide information about the consistency with which a given set of items measures the same construct. High to perfect reliability is indicated by values of Cronbach’s alpha between 0.7 and 1.0. Cronbach’s alpha is typically lower than that, when there is only a small correlation among the items or a small number of items of a test or questionnaire, indicating that the items correctly measure the underlying theoretical construct (e.g., intelligence or competence) only to a small extent. As is the case with any measurement, Cronbach’s alpha and thus the reliability of the overall score may be improved by adding more items to the test or questionnaire by removing items of low quality and by selecting a large sample. As for reliability of test scores, it often is observed that the reliability (i.e., Cronbach’s alpha as internal constituency) is much lower for pretest scores than for posttest scores of intervention studies. This is not necessarily considered as bad because a test designed to assess student learning should represent students’ pre-knowledge and – to a larger extent – what students should learn as a result of the intervention and not what they already know before.

Validity Validity is a very important commonly evaluated category for evaluating the trustworthiness of all kinds of qualitative or quantitative observations. An instrument, which measures something, is called valid if it measures what it is supposed to measure. To evaluate validity, frst of all, as researchers, we have to ask if the design and the instruments of our observation or measurement correspond with our theoretical construct or at least our theoretical assumptions. For example, our theory should predict a particular item ordering from easy to difcult, the theory should match what is observed in the instrument’s use, and our instruments and our design can detect specifc characters of the construct with high accuracy. There are diferent types of validity, some of which are briefy outlined in the following sections. Face validity is a superfcial type of validity. Mostly an expert in the feld, just through a document review, evaluates face validity. There is no statistical procedure for evaluating; therefore, face validity is the weakest form of validity (Nevo, 1985).

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External validity, or generalizability, must be discussed and taken into account at the very beginning of a study. The sample must be representative of the population tested, and the sampling must be random and not accidental. Therefore, all types of schools, all regions of the country, and all social diferences and cognitive diferences of students should be included in a representative sample. This makes it possible to generalize the results of a test on scientifc literacy to all students at the end of Grade 10 in the educational system of the respective country. In some cases, the spacing and ordering of items as a function of item difculty may difer as a function of subgroups, such as gender or ethnicity. Such issues can be investigated and interpreted using diferential item functioning (DIF) using a latent class (LC) analysis approach. It is important to note that the more often the results of a study are replicated and confrmed with diferent subsamples, the higher is its external validity (Tsaousis et al., 2020). Content validity can only be discussed in the context of a theoretical framework as a matter of semantic accordance of the measuring instrument and the theoretical assumptions or theoretical construct (Haynes et al., 1995). All diferent aspects of the construct should be represented in the instruments. If the professional knowledge of science teachers to be validated contains, for example, three elements – content knowledge (CK), pedagogical content knowledge (PCK), and pedagogical knowledge (PK) – they have to be each theoretically described and given an operational defnition that can serve as a starting point for the construction of the three corresponding test instruments. If the theoretical model contains, for example, the assumption of CK, PCK, and PK being three independent dimensions of the professional knowledge, the researcher needs to consider a threedimensional model. Criterion validity is also known as concurrent validity and prognostic validity. Concurrent validity compares the test results with a criterion assessed at the same time. For example, a student tells an interviewer that he or she is not able to solve physics tasks about force. To validate the students’ statement, the interviewer administers to the student a short, previously validated test on force, such as one on parts of the force concept inventory (Hestenes et al., 1992; McLure et al., 2020). For prognostic validity, the criterion is assessed at a later date, for example, high school examination results of individuals can be compared with their success at university. Internal validity considers causal relationships between independent and dependent variables of the construct to eliminate or control the infuence of control variables or confounding variables. For example, in most studies on science teaching and learning the motivation of students and their knowledge of physics correlate, but their knowledge also correlates with the pedagogical content knowledge (PCK) of the teacher. In addition, the relation between students’ knowledge and their motivation is not a causal efect. It is not clear whether students are motivated because of their good performance or whether their motivation leads to good performance. As a result, the correlation between motivation and student performance cannot be fully elucidated. Construct validity can be viewed as a component of internal validity. If it can be confrmed that, as theoretically predicted, a test and the items are able to discriminate between high- and low-achieving students, a test has construct validity. For example, by correlating the results of a test on scientifc literacy with results of an already existing test on scientifc literacy is a check of convergent validity (high correlation expected). To decide if a test for scientifc literacy measures science and, e.g., not reading competence, the results of the science literacy test has to be correlated in the same sample with a reading ability test (discriminant validity, low correlation expected). Construct validity considers, in essence, if and how a theoretical construct or model correlates with the results measured by an instrument that purports to measure the construct. If, for example, the construct of physics competency is operationalized to be measured by a test, the test must demonstrate how its items measure the construct. Construct validity can be assessed by comparing the predicted difculty ordering and spacing of items on a test to that which is observed in terms of item difculty. To approach construct validity, researchers usually determine the correlation between

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the results of their test instrument and those of other instruments that also purport to measure the same construct. In order to achieve construct validity, content validity is a necessary precondition (O’Leary-Kelly & J. Vokurka, 1998). To check if a test can validly measure the operationalized form of a construct, for example, pedagogical content knowledge in chemistry (PCK-Ch) researchers can compare the results measured by the instrument with those by another previously used test that also claims to test PCK-Ch administered to the same sample at the same time. Convergent and discriminant validity are seen as part of construct validity. Concurrent validity is a type of evidence that can be gathered to defend the use of a test for predicting other outcomes. Concurrent validity indicates whether a test result agrees with other observations from the environment. If the correlation is high, both instruments are valid (convergent validity) and the probability that they both measure PCK-Ch increases. On the contrary, researchers can compare the results of one instrument using chemistry teachers and mathematics teachers and expect only a low correlation (discriminant validity) between the results of the two samples. If this is the case, the probability that the test validly measures the PCK of chemistry teachers increases. Reliability and validity criteria are needed to assess the quality of a measurement if objectivity is asserted.

Signifcance Signifcance refers to the trustworthiness of results obtained through procedures of data reduction. As quantitative research usually involves consideration of larger samples, procedures of data reduction are very important in analyzing data and obtaining results that can be used to answer research questions. For example, as researchers, we examine the relation between two variables in terms of correlation and want to claim that the two variables are correlated with each other at a medium level. Then, we would have to obtain a measure that informs us – on the basis of the data we have collected – as to how sure we can be that there is in fact a correlation between the two variables or whether it may be that we were mistaken in concluding that there is a correlation. In test theory, four possible cases with respect to testing a hypothesis through empirical data can be diferentiated. 1. 2. 3. 4.

The hypothesis is in fact true, and the data confrm the hypothesis The hypothesis is true, but the data suggest the hypothesis be rejected The hypothesis is false, but the data confrm the hypothesis The hypothesis is false, and the data suggest the hypothesis be rejected

Case 3, where a false hypothesis is falsely accepted, is called error of the frst kind (Type I error) that researchers typically try to avoid. For example, if a new science curriculum is falsely found to be more efective than the traditional curriculum, it may not be useful for student learning of science. Thus, in quantitative research, a researcher wants to ensure that a hypothesis is only accepted if the chance for it being false is small. The error of the frst kind is sometimes also referred to as alpha error because alpha in test theory is used to denote the probability that a false hypothesis is erroneously accepted as true based on the data. The researcher and the community determine what is considered as an acceptable value for the alpha error. For example, as students are not expected to know much about what they are supposed to learn, the students who may attempt to answer items by guessing do not correctly answer many of the items in the pretest. Students may randomly know the answers to some individual items of a test, but do not yet have a coherent understanding of the domain (see, for example, Geller et al. (2014). As such, when piloting an instrument, the sample should include students across diferent ability levels in the pretest prior to and the posttest after instruction.

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These criteria of the alpha error apply in principle to all kinds of measurement, and as quantitative (and qualitative) research builds on the measurement of various constructs, they also apply to research in general. However, these criteria must be individually adapted or expanded depending on the concrete designs, samples, and methods of analysis. Typically, in science education, only an alpha error level of below 5% is considered acceptable. However, with an alpha level of 5%, we would expect to wrongly reject the null hypothesis in one out of 20 tests – i.e., commit a Type I error. There are many ways to correct Type I error infation. For example, the Bonferroni correction can be used to limit alpha error accumulation in multiple comparisons (Sinclair et al., 2013), and the Type II error can be decreased by increasing the sample size (VanVoorhis & Morgan, 2007).

Designs, Sample Size, Effect Size, and Methods of Analysis One important decision following the establishment of a strong theoretical framework as well as the identifcation of research questions and hypotheses based on that framework is the choice of an appropriate research design. There are a multitude of research designs and categorizations of research designs. One principal categorization divides research designs into experimental and nonexperimental designs.

Design Experimental designs are those setups in which at least one group is treated in a particular manner and the expected outcomes are compared to data and measures collected from a control group, which receives a diferent treatment. To identify the efect of a treatment is not an easy problem to solve. Besides designing the treatment as carefully as possible, the control group is often taken as it appears in the feld. For example, researchers want to measure the impact of teachers’ process-oriented sequencing of a physics lesson on student learning in a study in which the research team trains the teachers. The researchers have to design a lesson for the teachers in the experimental group including classroom management and provide, or even construct, learning material for the students. The teachers of the control group have to be supported in the same way on all aspects of the lesson planning, except for sequencing the lesson. Otherwise, the researchers are not able to decide if the measured efect is a result of the treatment. Nonexperimental research designs typically include studies that seek to identify characteristics of a population and relations between such characteristics. For example, this would include a study that investigates college freshmen’s interest in science and how that interest is related to students’ choice of their master’s degree specialization. Another design classifcation addresses the diferences between descriptive, correlative, and experimental studies. Descriptive studies are thought to mainly aim to describe particular characteristics within a population, correlative studies seek to identify correlations among such characteristics, and experimental studies aim to establish causal relations between at least one independent and one dependent variable. Other research design classifcations are diferentiated based on sampling (e.g., cross-sectional and longitudinal studies). Note that there are statistical procedures, such as regression discontinuity analysis or fxed efects models, that allow for drawing causal conclusions from observational data (Schneider et al., 2007). Irrespective of design classifcation, a selected research design for a project should be one that leads the researchers to addressing the research questions and hypotheses. Both research questions and hypotheses emerge from previous research and science education theory. Design and methodological choices should be made with theory, research questions, and hypotheses in mind. The interest of social institutions to learn more about school or learning in general is of secondary importance. However, recent methodological developments allow for better research designs, resulting in more trustworthy fndings. Specifc considerations that arise from these developments are discussed in the following section.

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Sample Size and Effect Size However, there are some general guidelines for planning a sample size appropriate to the research method. In general, N200 as a large sample size. A sample size of 20 students per combination of grouping variables is commonly considered the minimum; group sizes of 40 students are commonly recommended (Memon et al., 2020). If two diferent treatments are to be compared under control of the efect of gender, one group of at least 20 female and 20 male students are required per treatment. However, in this example the minimum sample size of 20 students per each of the four groups requires at least a large efect for drawing conclusions. According to Salkind (2010) Cohen’s efect size is as follows: small efect: r ≥ 0, 10; medium efect: r ≥ 0, 30; and large efect: r ≥ 0, 50. The recommended sample size of 40 students per group would allow for identifying medium efects (f >.25), and for identifying small efects (f >.1) a sample size of 400 per group would be required. Note that these sample sizes are unique to the previous example, as the sample size depends on many factors related to the design of a study. If, for example, random assignment of students to treatment or control groups is not possible, then whole classes are sampled instead. That means that instead of a minimum of 20 students per group, 20 classes per group would be required for sound conclusions about the efect of the teaching strategy under investigation. The estimation of the required sample size pending efect sizes, statistical power, etc. is commonly referred to as power analysis and can be performed using software such as g*power (Faul et al., 2009). All considerations regarding the design, sampling, and procedures of analysis should be based on a theoretical framework, that is, the research questions and the expected fndings (i.e., tested hypotheses). If the conclusions are expected to only extend to selected ninth graders in Melbourne, Australia, the sample will need to comprise exactly that – selected ninth graders from Melbourne. In order to be able to extend the conclusions to the population of students across the whole of Australia, a representative sample for Australian students (i.e., a sample of particularly large size) is required.

Specifc Methodological Considerations In quantitative research, statistical procedures play a central role. If the theoretical framework is fxed, and the research questions and hypotheses are formulated, a research design has to be chosen. Depending on the area of research, diferent designs are employed to obtain evidence to answer the specifc research questions. However, before answering the research questions, the data obtained – usually data from a large sample of participants – need to be analyzed by means of statistical procedures. That is, the full set of information – as represented by every single answer of every single participant to every single question in all the instruments utilized in the study – needs to be reduced to the actual information in which the researchers are interested. This is how statistical procedures help quantitative researchers. For example, if 50 treatment-group students receive teaching on mechanics in everyday contexts and the same number of students in a control group receives regular teaching, then both have to answer the same test, for example, the Force Concept Inventory (FCI), a 30-item test instrument. The full data set contains a unique identifer for each student, the group the student belonged to, and the student’s answer to each of the 30 items. In the frst step, ideally the Rasch measure (e.g., Granger, 2008; Wright, 1995) is calculated for each student. Then, a t-test is applied in order to determine whether students from the treatment group had a higher measure than did students from the control group in order to fnd out how sure we, as researchers, can be about our fndings. But, what if the t-test gave us a false positive? What if we were not allowed to actually use the t-test? Certainly, the t-test for N=2 gives questionable results that cannot be generalized. In fact, statistical procedures come with certain requirements, which need to be checked (Badagnani et al., 2017).

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Central requirements of statistical procedures: One of the central requirements of parametric statistical procedures such as the t-test is normal distribution of the measures, such as the students’ scores on the FCI test in the previous example. Another requirement is the linearity of measures. That is, that students not only can be ranked by their measures, indicating that those with a measure of 2 logits had a higher ability than a student with a measure of 1 logit. The increase in the measure from 1 logit to 2 logits corresponds to the same increase in the knowledge about mechanics as the increase of a measure from 0 logits to 1 logit. For a long time, researchers have been concerned about the normal distribution of measures (which usually is a sound assumption) and have neglected the consideration of the linearity of measures. Linearity can, in part, be considered an appraisal of the accuracy of the measurements over the full range of the instruments (for an in-depth discussion of this issue, see I. Neumann et al., 2011). Rasch analysis allows researchers to obtain linear measures from ordinal scores, such as test scores or sum scores from Likert scales (Rasch, 1980). A more complete treatment of Rasch analysis follows later in this chapter. In a similar way, researchers in science education have begun to use other methodologies developed in, for example, educational psychology or educational research that can help to ensure that the results obtained through statistical procedures capture the complexity of the teaching and learning about science inside and outside of the classroom.

Obtaining Linear Measures and Rasch Analysis Up to this point in our chapter, we have described the necessary considerations and requirements for conducting science education research. In this section, we present introductory details as an example of how Rasch measurement theory can be utilized by science education researchers in the course of investigations utilizing instrumentation such as tests and survey questionnaires. Many examples of the application of statistical techniques (e.g., ANOVA, regression, correlation) have been presented in the science education literature. However, here we concentrate upon detailing selected aspects of applying the Rasch theory because this theory provides measures of the quality needed for statistical analysis. Core themes of earlier sections of our chapter are revisited in our discussion about the Rasch method. Rasch measurement techniques were developed by the Danish mathematician George William Rasch (1901–1980) and greatly expanded by the University of Chicago’s Benjamin Wright in his books Best Test Design (Wright & Stone, 1979) and Rating Scale Analysis (Wright & Masters, 1982). Rasch and Wright both noticed that raw test data (e.g., scores for an answer, dichotomous and partial credit) and raw rating scale data are ordinal. This means that, among many implications, the use of parametric statistics to evaluate raw ordinal data violates assumptions of such statistical tests. Additional problems involve the weakness of quality control with regard to data quality and instrument functioning, an inability to express how an individual student performed on a test (or the attitude expressed on a survey) with respect to instrument items, and the inability to track development of students over time in detail with a single scale. In addition, in a classical analysis, the raw score person measures are item dependent. The Rasch person measures are not item dependent, as long as the items taken by a respondent involve the same trait as the items taken by another respondent. Rasch analysis is now being utilized worldwide to 1. 2. 3. 4.

Guide and facilitate the development of rigorous measurement instruments Express student performance with respect to item difculty Compute linear student measures, which can be used for parametric tests Develop alternative forms of tests and survey questionnaires (multi-matrix design), which can be linked (e.g., Kolen & Brennan, 2004; K. Neumann et al., 2013)

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Rasch analysis is not simply the application of the Rasch model in a computer program, but it is also about thinking of what it means to measure. When Rasch measurement is utilized, there is frequent refection upon and the assessment of many pieces of evidence for reaching a conclusion, for example, the overall functioning of an instrument. To determine the quality of evidence in a feld, one requirement is to be able to evaluate the quality of the measurement instruments utilized to collect data. Rasch measurement ofers a variety of techniques that can be used to estimate the functionality of an instrument. For those who have utilized Rasch measurement, it is abundantly clear that the steps taken to evaluate instrument functioning greatly parallel the steps taken by biologists, physicists, and other scientists as they create laboratory instruments. Measurement error and validity: Let us frst begin with the issue of reliability. It is common in the feld of science education that a researcher computes reliability as a way of attempting to evaluate, to some degree, the quality of results collected with an instrument. There are a number of pitfalls in the computation of a Cronbach’s alpha or a Kuder–Richardson formula (KR-8, -20, or -21), as is detailed in Smith et al. (2003, pp. 198–204): Reliability was originally conceptualized as the ratio of the true variance to the observed variance. Since there was no method in the true score model of estimating the SEM [standard error of the mean] a variety of methods (e.g., KR-20, Alpha) were developed to estimate reliability without knowing the SEM. In the Rasch model it is possible to approach reliability the way it was originally intended rather than using a less than ideal solution. Thus, when Rasch analysis is used, improved reliability can be computed rather than utilizing other methods to estimate the reliability without taking into account the standard error of mean (SEM). An additional problem in the computation of Cronbach’s alpha involves raw data, which are nonlinear and causes a well-known ceiling efect for large samples. There are also additional weaknesses in the traditional computation of Cronbach’s alpha in science education studies that involve the lack of investigation of both the reliability of test items and the reliability of persons, for example, in the use of internal consistency indices, such as Cronbach’s alpha (e.g. Clauser & Linacre, 1999; Linacre, 1997). When Rasch analysis is used, there are reliabilities computed for persons and items. Rasch measurement provides a number of indices and techniques by which instrument quality and trustworthiness can be assessed. Some examples involve item ft and person ft assessed through mean square residual (MNSQ) criteria (e.g., Smith, 1986, 1991, 1996). Item ft can be seen as a technique to identify items that may not lie on the same measurement scale as the majority of items (items not part of a construct). Person ft can be used to identify idiosyncratic responses to the test (or survey questionnaire) by the respondents, such as poorly performing students who unexpectedly give correct answers to very difcult items on a test. Rasch analysis also includes indices such as person separation/person strata and item separation/item strata, which can be used to evaluate additional aspects of instrument functioning (Fisher, 1992). We now consider the topic of measurement error, which impacts the trustworthiness of data collected with instruments and the trustworthiness of statistical results. In any laboratory in which data are collected, the scientist is acutely aware of the possible errors in each measurement. In addition, the scientist will know that the error is not necessarily the same for each measure taken. When a Rasch analysis is conducted, measurement errors are computed for each respondent and each item. When science education researchers review the errors of respondent measures, they will better understand a number of possible errors. For example, a test taker who has earned a perfect raw score on a test will have a much larger Rasch error of measurement than in the case of another test taker who has only correctly answered half of the test items. This diference in error can be understood by reasoning that a perfect score does not reveal how much more the test taker knows. The test taker

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with the perfect score could have been presented with 10 more very difcult items and have correctly answered all, some, or none of these items. This is similar to a voltmeter that can measure a maximum of 12 volts. When it is known that the voltage is higher than 12 volts it could be 12.5 volts or 1,000 volts. Numerous articles in science education have presented details that further discuss the computation of Rasch item/person error and the use of error in Rasch measurement for analysis of data (e.g., Granger, 2008; Wright, 1995). In earlier sections of this chapter, we discuss the important issue of considering what a researcher wants to investigate and which method he or she has to use for the best answer. With regard to Rasch measurement, the question becomes: Does an instrument measure what is needed to be measured, and how well does the instrument measure? When Rasch measurement is used, great care is taken to defne the variable of interest and to defne what qualitative diferences of the variable occur as the researcher moves from one end of the variable’s range to the other. In Figure 2.2 we present a common tool used by those applying Rasch analysis to the development of a measurement device. Moreover, we provide the predicted location and order of eight biology test items. When one understands what it means to measure and what is needed for a good measurement tool, be it a ruler, a thermometer, or a knowledge test, it should make sense that the variable is arranged on a scale that marks a qualitative diference in the measured characteristic. In the case of a test on physics knowledge this may mean that the items should range from easy to difcult. In the case of an attitudinal survey questionnaire with a rating scale of agreement, there should be items that range from “easier to agree with” to items that are “more difcult to agree with”. In our example, the gaps between the test items will mean that some respondents who difer in their traits (e.g., ability or attitude) cannot be distinguished or diferentiated. For example, a student John may have an ability level just above that of item 6, while a student Paul may have an ability level just below the portion of the trait measured by item 5. By utilizing the Rasch idea of how items measure a variable, a researcher is able to avoid using an instrument inappropriately for the goal of the study or avoid utilizing an existing instrument for which the researcher may not have clearly defned a range of values of the variable being measured to distinguish and diferentiate respondents. Without consideration of a single variable and the range of levels along one variable, a new instrument may fall short of measurement precision, often to the extent that it makes no sense to attempt any statistical analysis. As presented in Figure 2.2, the identifcation of items that measure a variable (which items fall on the line) clearly involves content validity as a theoretical argument for the correctness of the assessment; and the prediction of item ordering and spacing along a variable (where items fall on the line) involves construct validity. Diferential item functioning: In addition to considering construct validity by predicting the order and spacing of items along the measurement scale of a variable using a guiding theory, Rasch analysis provides many analytical techniques by which construct validity can be evaluated, the most common of which is perhaps the investigation of diferential item functioning (DIF). DIF can be used to determine whether the items of the test represent the variable consistently in diferent subgroups of the sample. If an item defnes the variable in a diferent manner, it may not measure the construct (or the defned variable) in the same manner across the groups. Identifcation of such an item may mean that the item needs to be removed from the instrument. Nevertheless, if the quality parameters of the item are good enough, it can be interpreted qualitatively and can be of great interest for the researcher. For example, if the content diferences of the DIF items are interpreted, the efect can be

Figure 2.2 Example for a latent variable defned by eight items.

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related to the content of the intervention’s lessons. The work of Brown et al. (2009) provides a good example of the use of these and other Rasch measures in the Rasch evaluation of construct validity of a measurement instrument. Scales of person measure and variable: In science education research, we have argued that science educators should be able to provide meaningful and accurate guidance to teachers, policymakers, and researchers to improve and/or control the quality of instruction or the output of the educational system. Rasch analysis provides meaningful guidance to teachers, policymakers, and researchers. Since Rasch analysis facilitates the computation on the same scale of a person measure and an item measure, it is possible to express the relation between person measures and item measures. In Figure 2.3, we provide a Wright map in which person measures are plotted on the left side and item measures on the right side. Each item is identifed with the phrase Q Item Number (e.g., Q5). Items higher up on the Wright map are items that are more difcult. This enables the performance of each student (or group of students), to be expressed by which item (or items) the student(s) would be predicted to have correctly answered or not answered. For example, to make use of this map to study gender diferences, the mean of males and females on the test have to be marked. To understand which items the typical male could do, we simply draw a

Figure 2.3 Wright map of person measures and item difculty ordered on one scale.

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horizontal line from the average measure of the males across the Wright map. Those items below the male line are those items a typical male would be predicted to have correctly answered but not those items above the line. This technique allows explaining the meaning of a measure by interpreting the related items as content dependent. Now, one is able to understand what the performance means (what a test taker, or a group of test takers, can or cannot do). An additional way in which this Wright map can be used can best be understood by drawing a horizontal line from the average measure of female test takers, and then evaluating the items between the line for the males and the line for the females. Those items that fall between the two lines are the items that describe the diferences observed between the two groups. It may be that t-test results have shown a statistically signifcant diference between the average male and female measures. However, now it is possible to explain the meaning of the diference between the two groups by interpreting and comparing items that are correctly answered and those predicted to be correctly answered in relation to diferent individuals and diferent groups. The meaning of the statistical diference between groups can be detailed in terms of what students are able to do and how they difer. Versions of such Wright maps have been widely used in Australia for a number of decades, and such Wright maps are now often used to write parent reports and to compare diferent groups in science education research (e.g., Kauertz & Fischer, 2006; I. Neumann et al., 2011; K. Neumann et al., 2013). Boone et al. (2014) described the use of Rasch analysis for science education research and Boone and Staver (2020) has two chapters on detailed discussion of Wright maps in particular. Wright maps have also been frequently used in medical felds to communicate instrument validity as well as patient measures (Stelmack et al., 2004). Hierarchical structure of data: As detailed in the frst part of this chapter, of immense importance to science education researchers is the hierarchical structure of school systems. There are a number of models that can be used to provide guidance for conducting studies on school systems. One such example was the study conducted by the Consortium on Chicago School Research (CCSR) in which hierarchical linear modeling (HLM), Rasch analysis, and other statistical techniques were utilized to facilitate an analysis of a large school system (see Kapadia et  al., 2007). In addition, every part of a school system contains many variables that are dependent upon each other. For example, it is well known that student learning (i.e., the increase in the scores on a test of student competence in a domain) depends on cognitive ability. For example, if a teacher uses the same complex diagrams in two diferent classes, the students in one class – a larger number of whom exhibit a lower average cognitive ability – will, on average, learn less compared to the students in the other class. However, the pertinent issue is that although in both classes the students with lower cognitive ability will learn less, since there are fewer students with a low cognitive ability in the class with a higher average cognitive ability. This will not become apparent when the knowledge of both classes is compared. The bottom line is that in order to capture such complex relations between various factors on the individual level and the aggregate level (i.e., the classroom or school system), more complex procedures than t-tests or ANOVA are required. Although ANOVA allows for accounting for variables on the classroom level, it does not allow for modeling the efect of a classroom variable that the variable has on another variable on the individual level. For example, if a teacher alters his/her teaching style by presenting specifc, less complex diagrams to students with lower cognitive abilities, the students may learn better, and this learning may in turn afect the overall correlation between their cognitive abilities and their learning outcomes. One method to account for such a complex situation is the method of hierarchical linear modeling. In addition to utilizing HLM, two other related analysis techniques structural equation models (SEM) or path analysis, are also of importance for the rigorous analysis of data in science education research. There is one particularly important aim of quantitative science education research for researchers to consider. That is to describe the complex situation in classrooms and how variables are correlated with each other and to identify the nature of the relation

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between such information. For example, to analyze if two variables, such as student achievement and student interest, afects the other when they are correlated. The question arises, does higher student interest lead to better achievement, or is it the other way around and a higher achievement leads to more interest? Or is it that both achievement and interest progress alongside each other as specifc features of the teacher–student interaction mediates or moderates both, depending on a third variable (e.g., students’ self-efcacy)? In the discussion section of papers, it is oftentimes pointed out how correlation may not be mistaken as indicating causal relations and that identifying causal relations requires experimental designs. In fact, with path analysis or its more sophisticated variant, SEM, it becomes possible to specify the directions of relations in a model and to test the validity of the model against a given data set. There are a variety of methodologies (many new or further developed) addressing quantitative science education research. Examples of such methodologies include Rasch analysis, video analysis, HLM, and SEM. These methodologies and their potential for quantitative science education research are discussed in the following section.

Quantitative Approaches in Science Education Research As discussed earlier in this chapter, three major areas of research in science education may be identifed: 1.

2. 3.

Research on evaluating science teaching strategies, including any kind of research that evaluates diferent approaches for enhancing student learning (e.g., students’ knowledge, interest, or skills) Research on classroom-based learning, including research on any kind of learning processes with regards to science in larger groups, such as science feld trips Research on how people learn about science, including research on constructs related to learning (e.g., interest in science or beliefs about science related to subjects/individuals)

Evaluating Strategies of Science Teaching Research in science education very often concerns the investigation of changes in outcome variables, such as students’ achievement, interest, or motivation. However, if the focus is on evaluating diferent ways of teaching science and/or more precisely evaluating strategies to foster improvement in science achievement, interest, or motivation of diferent groups (which require group comparisons), diferent research designs other than the ones described previously are required. These designs are commonly referred to as experimental or quasi-experimental designs. In such designs, instruments are needed to assess changes in students’ achievement, interest, or motivation as a result of treatment or intervention. The instruments need to be administered to students before and after students are taught according to a particular strategy (an approach commonly referred to as a pre-post-design). Using such a design, the efect of the particular teaching strategy on students’ achievement may be investigated. However, the problem of using a simple pre-post-design is that it does not provide conclusive evidence that the change in students’ achievement is related to the new teaching strategy. It might be that the achievement change may be the result of other teaching strategies or that there is a regression to the mean. In order to obtain evidence for the conclusion that the observed change in student achievement is in fact due to teaching with a particular strategy, a second group of students is required. This second group needs to be taught with a diferent strategy, or at the minimum have received regular instruction. Another teaching strategy instead of regulated instruction is usually preferred, as it can rule out alternative explanations for students’ potential improvement, for example, a novelty efect. Therefore, evaluation of teaching strategies – that is, any strategies that foster an

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increase in any science learning–related variables, including soft skills such as interest or motivation – requires more sophisticated experimental designs than a pre-post-design. In experimental designs, students should ideally be randomly assigned to one of the groups that are compared to each other (usually at least one treatment and one control group). Consequently, these designs are referred to as randomized experimental designs. Such randomization should be a goal of a study using experimental designs, as randomization is expected to rule out efects of diferences – in cognitive abilities and any other variables – which may afect the results of the study. Randomization obviously requires a large enough sample size, and in some cases it is either not possible or not feasible to randomly assign students to treatment and control groups. In this case, the variables expected to infuence the variables targeted for assessment need to be controlled. This means added variables should be assessed as well and included in an analysis to determine their efects. We fnd that these added variables are very often not addressed in science education studies when the limitations of a study are discussed. When randomization is not possible, we suggest a strategy is to sample a large enough group of students to rule out particular unwanted infuences. We cannot provide exact numbers for sample sizes, but the sample size would have to be large enough to allow for the assumption that the distribution of the variables – that may cause unwanted infuences confounding the results upon an analysis – is similar in both groups. Experimental designs that do not use randomized samples are also commonly referred to as quasi-experimental designs. In a quasi-experimental design researchers should make sure that the two groups have similar profles, either by matching the samples (e.g., using procedures such as propensity score matching) or by carefully comparing the groups with respect to relevant variables. Procedures of data analysis in experimental designs typically include a variety of analyses of variance (ANOVA), where the grouping variable serves as the independent variable. As discussed earlier, in the case of just one dependent variable (e.g., change in student achievement) and just two diferent values of the independent (grouping) variable (i.e., one treatment and one control group), an ANOVA yields the same results as does a t-test. One strength of the ANOVA approach to analysis, however, is that it allows for more than one independent variable, which may be considered as in the case of experimental pre-post-designs and in longitudinal studies. ANOVA is also applicable as repeated measures ANOVA. In addition to the grouping variable that distinguishes students from treatment and control groups, there may be other grouping variables, such as gender. The so-called analysis of covariance (ANCOVA), moreover, allows for adding continuous variables, such as students’ cognitive abilities or interest that are not of primary interest, known as covariates. ANCOVA is a general linear model to combine regression and ANOVA to control the efects of the covariates. An ANCOVA adjusts the dependent variable (e.g., students’ competence) as if all comparison groups are equal on the covariates (Best & Kahn, 2006).

Investigating Classroom Instruction As science education research aims at improving instruction, even if a randomized experimental design cannot be applied to obtain evidence of how teaching works in diferent classrooms with diferent teachers, the starting point for determining the sample size is the number of characteristics teachers are expected to difer in. If, for example, teachers only vary with respect to gender, a minimum of 20 male and 20 female teachers are needed to detect a large efect (see section “Sample Size and Efect Size” for a detailed discussion of the relationship between sample and efect sizes). For every additional parameter 20 more individuals should be added. Teacher efectiveness research has shown that despite a particular teaching strategy that may have been found to work with one group of students, the strategy may not work with another group, as there are a variety of characteristics of teachers, students, and instruction that afect the learning

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outcomes. Because many strategies have been found to only work with a particular group of students, such as high or low achievers, there are nuances in selecting a sample size and a research design, which are impacted by a particular group of students (e.g., high achievers). One example of this issue involves the infuence of students’ socioeconomic status (SES). SES has been found to infuence students’ mathematics and science achievement (Cooper & Berry, 2020; Li et al., 2020). According to Prenzel (2003, p. 251), on average 16.8% of variance in students’ mathematics achievement was found to be explained by their SES across participating countries in the Programme for International Student Assessment (PISA). In Germany’s case, SES accounted for an above-average variance in students’ mathematics achievement at 21.1% (Prenzel  & Deutsches, 2004, p.  275). Consequently, SES needs to be controlled in studies investigating instructional quality and be included as a moderator or mediator when analyzing the efects of instructional characteristics on student achievement. In addition, any other factors, such as political, organizational, or institutional conditions must be controlled as well, as these may infuence instruction, student achievement, or the relation between the two. That is, in order to improve instruction as a whole, it is of utmost importance to understand the complex processes in regular classrooms and how such processes are afected by diferent characteristics of students, teachers, and the classroom environment, as well as other factors, such as funding of the schools. At present, little is known about the complex processes of instruction and how such processes are afected by framework characteristics such as those mentioned earlier. Consequently, it is not yet possible to detail and describe a theoretical framework of classroom teaching and learning from previous research with respect to instructional quality. For example, the efect of certain aspects of professional knowledge of teachers on the quality of lessons is not known, nor is the kind of content knowledge teachers should learn at universities for best practice in schools (Cauet et al., 2015). Certainly, knowing more about the interdependencies between teaching and learning would lead to a more precise formulation of goals for teacher education and would result in a more efective use of the limited learning time of future teachers at universities. Some examples of quality criteria that should be utilized to detail a theoretical framework are classroom management (Fricke et al., 2012), cognitive activation (Baumert et al., 2010; Teig et al., 2019), and feedback (Hattie & Timperley, 2007; Maria Araceli Ruiz-Primo & Li, 2013). Once the constituents of a model of professional knowledge have been established, a test instrument can be selected if one exists for the model. If appropriate instruments do not exist, as researchers we need to validate our self-developed instruments, and even if appropriate instruments exist, eforts should be undertaken to provide an argument for the validity of the instrument (Kane, 2006). This is what is required whether the instruments are for tests, surveys, or video analysis. In case an instrument exists, we must consider convergent validity and discriminant validity. Therefore, we need either another instrument that was already used for the same purpose (convergent validity), or we have to choose samples of participants with diferent characteristics to validate our instrument by correlating the results of these samples and those of our target sample (discriminant validity). For example, in the validation of an instrument for measuring the PCK of physics teachers, mathematics or English teachers should perform signifcantly lower than physics teachers. As a result, the correlation between the samples should be low too. Ultimately, we are able to describe the professional knowledge of a certain sample of teachers in terms of the mean and variance, and we are able to compare these measures between diferent samples, for example, a number of schools and those schools’ teachers, or representative samples of two countries. The mere description of data does not allow one to draw conclusions. Thus, the description of the quality of teacher education or the quality of teaching does not allow one to make a conclusion. To draw conclusions or to investigate directions of an efect, one needs criteria to classify instructional quality, and one must have a theory of how professional knowledge contributes to a framework of instruction quality. An example of a criterion might be measures of students’ learning outcomes, including knowledge, competences,

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motivation, and interest (controlled for cognitive abilities and SES). To measure outcomes, we must have appropriate tests and questionnaires. As we advance in our analysis using our selected instruments, we should be able to discriminate between low- and high-achieving classes and their teachers. In case the correlation between students’ and teachers’ test results is moderately signifcant or higher, a connection between the two can be assumed. Otherwise, we have to think about possible external infuences that were not taken into account but may have infuenced our data. Our point is that external infuences have to be taken into consideration and have to be refected upon to improve ongoing and future research. To answer the research question of whether our construct of PCK matters for classroom performance and students’ outcomes, an experimental study should be designed to determine the direction of the efect before measuring correlation. Once the efect of PCK is known from such a study, we can proceed to investigate how teachers’ PCK afects instructional quality in real classrooms. In the best situation, such studies can target a representative sample of students in a particular setting. To complete our discussion on the complexity of science education research, and how/why this research must be improved, we wish to summarize some caveats and issues that must be considered by science education research. In order to capture the complex processes of classroom teaching and learning, the use of statistical tools such as t-tests, ANOVA, or regression analyses has their limitations. In order to consider a variety of diferent characteristics on diferent aggregation levels with respect to classroom instruction (i.e., the individual level of the students, the classroom level, the level of schools, or even whole countries) requires sophisticated procedures of analysis. This starts with the fact that in the reality of the classroom, aptitude-treatment-interaction efects (Koran & Koran Jr., 1984; Snow, 1991; Yeh, 2012) must be considered and taken into account. There are, for example, individual characteristics of students that resonate with characteristics of the teacher, the school, and/or the countries’ school system. Procedures typically applied to the analysis of more complex instructional settings are path analysis, HLM, or SEM. Some of these procedures are discussed in the following section.

Longitudinal and Cross-Sectional Panel Studies Understanding how students learn about science is a central requirement of research for improving science education. Generally, this research is related to questions of how students proceed in making sense of a particular domain, for example, learning the energy concept (e.g., Duit, 2014; Lee & Liu, 2010; K. Neumann et al., 2013; Opitz et al., 2019). This research is also related to the development of concepts or learning progressions (for energy concept and learning progression see, for example, Liu, 2013; Nordine et al., 2011; Romine et al., 2020). To analyze the development of concepts or learning progressions, students must be tracked over a long period. Instructional components and students’ outcomes must be assessed using assessment instruments (Opitz et al., 2017; Won et al., 2017). As such, this research most likely utilizes longitudinal designs, which means sampling students from diferent points in the learning process over time (panel design). When the same instruments must be evaluated for use with students with diferent ages and diferent subsamples, the design is called cross-sectional design. However, cross-sectional studies do not produce data about cause-andefect relationships because such studies only ofer a snapshot of a single moment; and such studies do not allow tracking development over time. As a result, one weakness of cross-sectional studies is that one cannot know with certainty if students develop certain energy concepts as a consequence of the completed lessons or students’ cognitive development. Both panel and cross-sectional (called trend studies) are observational ones. Researchers collect data about their samples without changing the environment of the study, for example, through an intervention or a selection of specifc individuals. Typical research questions for panel studies focus on how students proceed in their understanding of a concept, skill, or domain based on a particular

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curriculum, that is, the existing or a new curriculum (cf. K. Neumann et  al., 2013). However, panel studies come with a particular number of constraints. They require test instruments that can compare students and questionnaires that are understood in the same way by students of diferent ages and diferent cognitive development. In addition, longitudinal studies require assigning an individual ID to each student’s performance at each measurement time point to allow collation of data from the diferent measurements. However, this procedure is prone to errors, as students may not know their mother’s birth date or other dates chosen as identifers. Therefore, so-called panel mortality is increased. This particular term refers to the complete set of data measures obtained from a panel study compared to the number of theoretically possible full data measures. Common causes of missing data are students’ absence due to diferent reasons. This issue is exacerbated when a study extends over a longer period (e.g., several years) because a crucial sample size is needed at the fnal measurement time point (the sample must be large enough for statistical analysis). Typical procedures of analyses on students’ understanding include t-tests, diferent kinds of ANOVA, linear regression analyses, or combinations of these. Given the great cost in time and money associated with longitudinal studies, it is often reasonable to carry out a cross-sectional study frst, particularly when the period exceeds a reasonable amount of time. The period is determined by students’ expected progression (e.g., Grades 6, 7, 8, and 9). Based on students’ (linear) measures of ability obtained through Rasch analysis, students’ progression in mastering the particular concept, skill, or domain may be analyzed as a function of schooling. In a longitudinal study, traditionally repeated measures of ANOVA are utilized. This allows for analyzing whether there are statistically signifcant diferences in measures of students’ ability depending on the time of measurement. In cross-sectional studies, typically regular ANOVA is utilized. Using ANOVA, measures of students’ ability are investigated as a function of schooling (e.g., grades). With regard to independent variables, linear regression analysis should be used. For example, the variable denotes the amount of schooling received or learning that has taken place respectively or spanning across a larger number of values, when learning about science is investigated across the life span and age is utilized as an indicator of the amount of learning that has taken place. In cases where there are only two diferent groups for comparison with respect to measures of students’ ability, the t-test may be used. Generally, an ANOVA with only two groups (students at the beginning and end of Grade 8) will yield the same results as in a t-test. Comparison of more than two groups requires the use of an ANOVA or regression analysis. In case additional variables (e.g., students’ interest in science) are to be included in the analysis, two- or more-factor ANOVAs or more complex regression models may be applied (for a detailed discussion of t-tests, diferent ANOVAs, or regression analyses, see Field, 2005). It is important to note that whereas regression analysis provides information about the (linear) relation between the independent and dependent variable, ANOVA does not. ANOVA only informs the researcher about the diferences between diferent values of the independent variable (e.g., diferent groups of students). The exact nature of the relation needs to be investigated through further analyses, such as the use of predefned contrasts (Field, 2009; Weinberg et al., 2020). The intended statistical technique to analyze the data greatly afects the procedures and details of sampling. A simple ANOVA requires a minimum of 10 persons per value of the grouping variable (Breitsohl, 2018; Chi et al., 1994). However, usually a minimum of 20 persons is suggested (Clayson et al., 2019; Döring & Bortz, 2016). In general, the sample size depends on the size of the expected efects. To resolve a medium efect (d = 0.5) for a regression of college students’ semester achievement, a sample size of 34 students is required. More complex procedures naturally require a higher sample size. If, for example, Rasch analysis is to be used to obtain linear measures of students’ ability, a minimum of 80 students per task is suggested, and in the case of SEM a minimum of 50 students per variable included in the model is suggested for obtaining stable parameter estimates. This provides an estimate of the required initial sample size of a longitudinal study.

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Analyzing Teaching and Learning Processes by Means of Video Analysis Of course, the quality criteria also apply to qualitative investigations, the basics of which are only briefy outlined here. Grounded theory, for example, assumes that a teaching–learning situation in a biology lesson can be interpreted without a theoretical assumption at the beginning of the study; instead, a parallel process of constructing an interpretation and a theoretical model of the situation is utilized during data capturing (Glaser & Strauss, 1967). A similar idea called objective hermeneutics was considered in German-speaking countries in the late 1970s and refers mainly to the work of Oevermann et al. (1979). These researchers refer to scientifc criteria where reliability is the expression of the interpretation process of a group of experts interpreting a situation. The agreement of the experts on one interpretation in the end is seen as an expression of person reliability as well as item reliability (Breuer & Reichertz, 2002; Oevermann et al., 1987; Strauss & Corbin, 2008) and therefore must be documented precisely. One particular method is the analysis of lessons by means of analyzing video recordings of the lessons. Although at frst glance this method may not appear to be a statistical procedure, it is tremendously useful in capturing teaching and learning about science in real classrooms. For example, in analyzing student interviews, the transcription of video recordings and their analysis were utilized in science education research as early as the 1970s. However, as advanced technological developments have made video equipment more afordable and easier to use, it became possible to standardize the way recordings were made and analyzed such that video analysis could be used in quantitative research (e.g., Janík & Seidel, 2009; Stigler & Hiebert, 1999). This development was partly due to the development of the method of category-based analysis of text, including video recordings or category-based analysis of videotapes (for details, see Niedderer et al., 2002). However, as is often the case, the availability of a new methodology not only led to better answers regarding long-standing research questions, but also resulted in the identifcation of new theoretical and methodological defcits. One of these theoretical defcits that became apparent from video analysis is how little is known about complex classroom processes. Video analysis is a method that originally was developed in qualitative research. Certainly, video analysis as such is a qualitative process because a social interaction has to be interpreted. However, the development of the CBAV has made video analysis available as a tool for quantitative research. CBAV provides a standardization of the process of interpretation and the introduction of measures of reliability and validity of the category systems used. Needless to say, the category system is of central importance when analyzing video data. Video data may be considered as raw data from lessons, which once recorded, needs to be analyzed based on a particular theoretical framework; and thereafter, it is used for developing research questions. However, before the lessons can be analyzed, they have to be recorded. That is, the teaching and learning in the classroom need to be captured by a video camera. This procedure involves decisions such as where to position the cameras and microphones in the classroom and how many to use. Naturally, these decisions have to be made based on an underlying theoretical framework. Even the location of the video cameras in the classroom is part of a research question. Investigation of group work requires camera locations diferent from those for the investigation of teacher–student interactions (for a description of diferent arrangements of how to video-record lessons, see Fischer and Neumann, K. (2012). Once the actual arrangement of the recording equipment is decided upon, recording guidelines have to be specifed to ensure that the video recordings of diferent classrooms are comparable. In principle, these guidelines are supposed to ensure standardized and comparable recordings independent of the actual person doing the video recording, that is, to ensure objectivity of the fndings. When video recordings are made, even the cameraperson has to be familiar with the guidelines. Once video data have been properly recorded and processed (e.g., digitized for digital analysis utilizing computer-based analysis software), the analysis can commence. The frst step when analyzing

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video data is to decide on the detail level of the analysis. In the second step, a coding procedure has to be developed and applied to the data. Finally, in the third step, quality measures have to be determined. Obtaining quantitative data from video analysis requires exact rules on how to observe, secure, and categorize the observable features – based on the respective theoretical framework or model. This set of rules must be applied to the observation material to obtain quantitative data in a process called coding. The set of rules is termed the coding or the category system (Mayring, 2019) that emphasizes the necessity of a sound category system. If the category system is not developed with the greatest of care, the obtained quantitative data will be rendered meaningless. Mayring (2014) diferentiated between two approaches to obtain a category system: an inductive and a deductive approach. Whereas the inductive approach is of particular importance for qualitative content analysis, a deductive, theory-guided process is more appropriate for quantitative video analysis of instruction. This is because an inductive approach will allow for only a strictly qualitative analysis of video data. A deductive approach, on the other hand, will allow for a qualitative or a quantitative analysis, as well as a combination of the two. A deductive approach always starts from theory, whereas a qualitative approach involves developing theoretical statements inductively during the process of data analysis. Based on theoretical considerations, the decisions on the exact research questions and hypotheses have to be made. This would include, for example, decisions about the segmentation of data or further processing of data, such as transcripts of the video recordings. In the case of an analysis of teacher–student dialoguess, the raw recorded data would be segmented into turns (e.g., teacher speaking or student speaking). To analyze students’ time on tasks, segmenting the recording into intervals (e.g., deciding every 30 seconds whether students are on or of task) would be reasonable. A transcript can also be helpful, especially when a decision in favor of segmenting data into completed sections of interaction (so-called turns) is made. In the case of the investigation of teacher–student dialogues, it is better to identify individual turns than to analyze the interval-based situation. When video analysis is conducted, transcription requires clear and precise rules to consider activities of interest. For example, gestures and facial expressions, as well as afective expressions or transformed everyday language or dialect have to be carefully examined during transcription. Examples of rules for transcription are given in (Mayring, 2007, p. 49). Once the material has been prepared accordingly, the development of the category system itself can begin. Development of the category system begins by identifying the theoretical constructs of relevance and operationalizing the constructs into categories. A construct may be represented by one or more categories. The categories smartphone, blackboard, overhead projector, or textbook, for example, may represent the theoretical construct teacher use of media. Each of the categories comes with a series of indicators that provide guidance as to whether the respective category should be coded. An example of an indicator could be the teacher writes or draws on the blackboard or the teacher asks students to open their textbooks. The key here is that the indicators have to characterize the related category as precisely and completely as possible using a potentially extensive collection of examples. Once categories and indicators are created, a coding manual should be written, thoroughly describing categories and indicators. With the coding manual, this initial version of the category system can be applied to coding real data in order to test its practicability and determine the quality measures. According to Özcan and Gerçek (2019), and many others, appropriate software such as Videograph helps to track all assumed indicators of the categories, to organize a time frame for turns or intervals, and gives the basis for statistical analyses (see Figure 2.4). In the next step, the data obtained from the coding are analyzed. In this step, investigation of the reliability of the coding is of central importance. In addition to determining the validity of data analysis, ensuring the validity of data interpretation is also needed. In a fnal step, the fndings, and in particular the quality measures and estimates, need to be carefully examined with respect to the formulated research questions and hypotheses, and particularly to

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Figure 2.4 Tracking of Indicators for the Category Method Competence (Özcan & Gerçek, 2019, p. 89).

the expectations regarding quality measures. If the quality measures do not meet established requirements, that is, reliability remains unsatisfactorily low and validity cannot be established, the coding procedure needs to be refned in a new iteration of the previously described sequence of steps. A detailed discussion of quality measures is discussed in the next section. It should be emphasized that developing a coding procedure is an intense and demanding process, but for a sound video analysis, it is a mandatory step. Although reliability, in the context of video analysis, is often a primary concern of researchers, validity is also important as a quality criterion. Reliability of the coding obtained through video analysis depends on the coders. In many ways, the human coders can be considered as the measurement instrument in video analysis. In the context of video analysis, reliability is usually determined by means of intercoder reliability. To determine intercoder reliability, selected video material has to be coded independently by diferent coders following the (same) coding procedure. The diferent codings are compared on a per-unit-of-analysis basis. Diferent techniques can be used to determine reliability. The simplest measure is the percentage of agreement p; whereas the percentage of agreement p is easily calculated, it unfortunately cannot be used to consider random agreements. More sophisticated measures that are corrected for random agreement are suitable for use, for example, Cohen’s κ in the case of nominal data or Goodman–Kruskal’s γ in the case of ordinal data. However, there is no agreement upon the cut-of values that have to be met by these measures in order to consider intercoder reliability to be sufcient. One issue is that the criterion for sufcient reliability depends on how much interpretation is involved in the coding, that is, the level of inference inherent in a particular category. Higher inference entails higher infuence of a coder’s knowledge, expectations, and beliefs on the coding process. High inference is therefore tied to lower intercoder reliability. For example, when coding the addressee(s) of a teacher’s communication, three mutually exclusive categories of addressees may be diferentiated: individual, group, or class. These categories involve high inference because asking one student in front of the class does not necessarily indicate communication that is directed to only one individual. Diferent cut-of values are needed to determine whether the coding is sufciently reliable. Typically for high-inference codings, a reliability of Cohen’s κ > .6 is considered sufcient. Video analysis can be utilized in many areas of research to obtain empirical evidence: in case studies to investigate learning processes, in experimental studies to check implementation of a 52

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treatment, or in feld studies to describe characteristics of interest. As such, video analysis is a valuable tool for analyzing teaching and learning about science in very diferent scenarios in science education research. However, linking video analysis with data and measures from tests and questionnaires is also necessary to obtain better results for estimating the quality of observed instruction. One measure of the quality of a lesson is students’ outcomes, for example, competences and interest they gained during a certain newly developed unit or other intervention. Another research aim can be to fnd out if teachers’ professional knowledge has infuenced classroom interaction and students’ outcomes. In this case, to calculate correlations, linear regressions, or path analyses, a measure of quality of the diferent categories of the video analysis must be developed (Fischer & Neumann, K., 2012; Klieme et al., 2006; Ohle, 2010; Ohle et al., 2011; Seidel & Prenzel, 2006; Seidel et al., 2005).

Accounting for the Complexity of Classrooms As researchers, we always have to take into account that in studies on teaching and learning at schools, at least three diferent levels of organization have to be considered. For example, when analyzing the efect of teaching, we should be aware of the individual teachers and the individual students being at diferent levels in a teaching–learning process. From a statistical point of view when designing a study, we need at least 20 diferent teachers and their classes to obtain trustworthy results with confdence and to calculate by means of multilevel analysis, for example, using HLMs (Hox & Moerbeek, 2018). Because of diferent contextual efects or socioeconomic backgrounds, observations within one group are in some cases not independent from each other, that is, the similarities between individuals in one group regarding one variable can be larger than the similarities between the individuals of diferent groups. To test the variables, we must partition the sum of squares. Because the variables are correlated, there are sums of squares that could be attributed to more than one variable; therefore, the variables are not as independent as they are expected to be. The overlaps between the variances of the variables depend on the type of sum of squares that the researcher uses. To estimate these variances of independent variables, standard methods, such as simple regressions, cannot quantify the share of the variance between groups and the total variance of all groups, but an intraclass correlation can (Hox & Moerbeek, 2018). In the case of students nested within classrooms, the intraclass correlation tells the analyst how much of the variance in the error can be attributed to diferences between classrooms (the level-2 unit) as opposed to diferences between students (the level-1 unit). Thus, multilevel analysis is needed to appropriately analyze hierarchically organized problems, for example, HLMs. For HLM objects of diferent order – such as individuals on the lowest level and collectives on higher levels – can be simultaneously assigned to each other, whereas the highest level embeds all lower levels. Students are embedded within classes, and classes are in turn embedded within schools. The idea of a related analysis is that the achievement of students in science is dependent on parameters of the science lessons, and these parameters are infuenced by conditions of the school and so on. Ignoring the multilevel character of classroom settings is likely to produce mistakes since classroom assignment may produce diferential efects in the outcome that are not directly related to the intervention (see Raudenbush & Bryk, 2002). Figure 2.5 shows the student mean test scores in the assessment of a sample of two classes in one school as a standard regression, with one mean and one slope coefcient, and also separately for the two classes. The information difers substantially between the two ways of looking at the individual students of two diferent classes. Using a multilevel analysis turns out to dissipate the variance of a distribution of the classes as a whole in diferent classes as diferent levels of the system. In this example (see Figure 2.5), the starting level can be described as a school level, or a year level, of a school; and the embedded level is the classes that can be resolved in more detail. The measure of knowledge in physics as the mean test score is plotted against the social index of students in the two classes of one school. Now the 53

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Figure 2.5 Standard regression of two classes on the relation between physics knowledge and social index. The graph for Class 1 & 2 hides information about the sample, which is uncovered in the graphs for Class 1 and Class 2 when those data are plotted separately.

classes can be distinguished according to the connection between physics knowledge and social index.

Conclusion for Modeling the Complex Teaching and Learning Processes In line with research on science education in general, numerous attempts have been made to link investigations to theories or models for describing the quality of classroom teaching and learning in order to guide research and to allow for the development and use of consistent methodologies (Ditton, 2000; K. Neumann et al., 2012). As we have discussed in this chapter, building theoretical models of aspects of teaching and learning at school has to take into account multiple variables, their interdependencies, and hierarchically ordered system structures. This helps researchers to control as many infuencing variables of classroom settings as possible. The efect of a newly developed instructional unit (e.g., quantum mechanics) surely depends on its adequate design and strategy in presenting the subject matter to students regarding its scientifc content and structure. However, the learning outcomes of an instructional unit may also be moderated by features of the teacher’s personality, his or her pedagogical content knowledge, students’ socioeconomic backgrounds, the structure of student–teacher interactions in the classroom, and in particular, by students’ cognitive abilities and their respective pre-knowledge. This holds true for every description and every intervention. Following the idea of science instruction as a complex system, it is now clear that an adequate model is needed to describe most of the infuences on the teaching–learning process as far as possible in order to understand the actual state of research on science education. To advance our feld, investigations must carefully make use of the tools and techniques we have outlined in this chapter – ranging from techniques (e.g., HLM) that consider the hierarchical structure of educational systems to techniques that provide linear measures (Rasch) and to the appropriate use of multilevel statistics. Doing so helps science education researchers confdently draw conclusions from the efects of intended changes on teaching and learning in studies in science education and to guide future teachers in how to improve the quality of their own teaching. Often enough there is little evidence that such teachers’ knowledge is benefcial for students’ learning and teacher education cannot refer to empirically based results; this therefore reproduces intuitive beliefs and myths. With the careful use of these and other tools, advances can be made in science education research,

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and in turn, teachers and policymakers can be informed of useful guidance for achieving highquality science teaching and learning.

Acknowledgments We would like to thank two anonymous reviewers who carefully and critically reviewed this chapter.

References Bacon, F. (2000). Francis Bacon: The New Organon (Cambridge Texts in the History of Philosophy) (L. Jardine & M. Silverthorne, Eds.). Cambridge: Cambridge University Press. doi:10.1017/CBO9781139164030 Badagnani, D., Petrucci, D., & Cappannini, O. (2017). Evidence on the coherence – Pieces debate from the force concept inventory. European Journal of Physics, 39(1), 015705. http://doi.org/10.1088/1361-6404/ aa940f Ball, D. L. (1991). Research on teaching mathematics: Making subject matter part of the equation. In J. Brophy (Ed.), Advances in research on teaching. Teachers’ knowledge of subject matter as it relates to their teaching practices (Vol. 2, pp. 1–48). JAI. Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann U., Krauss S., Neubrand, M., & Tsai, Y.-M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. http://doi.org/10.3102/00028312 09345157 Best, J. W., & Kahn, J. V. (2006). Research in education (10th ed.). Pearson Education, Inc. Blalock, H. M. (1968). The measurement problem: A gap between the languages of theory and research. In H. M. Blalock & A. B. Blalock (Eds.), Methodology in social research (pp. 1–27). McGraw-Hill. Boone, W. J., & Staver, J. (2020). Advances in Rasch analysis in the human science. Springer. Boone, W. J., Staver, J., & Yale, M. (2014). Rasch analysis in the human sciences. Springer. Breitsohl, H. (2018). Beyond ANOVA: An introduction to structural equation models for experimental designs. Organizational Research Methods, 22(3), 649–677. http://doi.org/10.1177/1094428118754988 Breuer, F., & Reichertz, J. (2002). FQS debates: Standards of qualitative social research. Historical Social Research, 27(4), 258–269. Brown, T., Unsworth, C., & Lyons, C. (2009). An evaluation of the construct validity of the developmental test of visual-motor integration using the Rasch measurement model. Australian Occupational Therapy Journal, 56(6), 393–402. http://doi.org/10.1111/j.1440-1630.2009.00811.x Carlson, J., & Daehler, K. R. (2019). The refned consensus model of pedagogical content knowledge in science education. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 77–92). Springer. Casadio, R., & Scardigli, F. (2020). Generalized uncertainty principle, classical mechanics, and general relativity. Physics Letters, B, 807, 135558. Cauet, E., Liepertz, S., Borowski, A.,  & Fischer, H. E. (2015). Does it matter what we measure? Domainspecifc professional knowledge of physics teachers. [Messen wir relevantes Wissen? Fachspezifsches Professionswissen von Physiklehrkräften]. Schweizerische Zeitschrift für Bildungswissenschaften, 37(3), 462–479. Chen, C., Sonnert, G., Sadler, P. M., & Sunbury, S. (2020). The impact of high school life science teachers’ subject matter knowledge and knowledge of student misconceptions on students’ learning. CBE – Life Sciences Education, 19(1), ar9. http://doi.org/10.1187/cbe.19-08-0164 Chi, M. T. H., De Leeuw, N., Chiu, M.-H.,  & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. https://doi.org/10.1016/0364-0213(94)90016-7 Clausen, M. (2002). Qualität von Unterricht – Eine Frage der Perspektive? [Quality teaching – A question of perspective?]. Waxmann. Clauser, B., & Linacre, J. M. (1999). Relating cronbach and Rasch reliabilities. Rasch Measurement Transactions, 13. Clayson, P. E., Carbine, K. A., Baldwin, S. A., & Larson, M. J. (2019). Methodological reporting behavior, sample sizes, and statistical power in studies of event-related potentials: Barriers to reproducibility and replicability. Psychophysiology, 56(11), e13437. http://doi.org/10.1111/psyp.13437 Cooper, G., & Berry, A. (2020). Demographic predictors of senior secondary participation in biology, physics, chemistry and earth/space sciences: Students’ access to cultural, social and science capital. International Journal of Science Education, 42(1), 151–166. http://doi.org/10.1080/09500693.2019.1708510 Ditton, H. (2000). Qualitätskontrolle und Qualitätssicherung in Schule und Unterricht: Ein Überblick zum Stand der empirischen Forschung [Quality control and quality management for school and instruction: An overview of the state of empirical research]. In A. Helmke, W. Hornstein, & E. Terhart (Eds.), Qualität und Qualitätssicherung im Bildungsbereich: Schule, Sozialpädagogik, Hochschule [Quality and quality management in education: School, social pedagogy, university] (Vol. 41, Beiheft, Ed., pp. 73–92). Beltz.

55

Hans E. Fischer, William J. Boone, and Knut Neumann Döring, N.,  & Bortz, J. (2016). Datenanalyse [Analysis of data]. In Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften [Research methods and evaluation in social- and human sciences] (pp. 597–784). Springer Berlin Heidelberg. Duit, R. (2014). Teaching and learning the physics energy concept. In R. F. Chen, A. Eisenkraft, D. Fortus, J. Krajcik, K. Neumann, J. Nordine, & A. Schef (Eds.), Teaching and learning of energy in K – 12 education (pp. 67–85). Springer International Publishing. Etkina, E., Gitomer, D., Iaconangelo, C., Phelps, G., Seeley, L.,  & Vokos, S. (2018). Design of an assessment to probe teachers’ content knowledge for teaching: An example from energy in high school physics. Physical Review Physics Education Research, 14(1), 010127. http://doi.org/10.1103/ PhysRevPhysEducRes.14.010127 Faul, F., Erdfelder, E., Buchner, A., & Albert-Georg Lang. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. https://doi. org/10.3758/BRM.41.4.1149 Field, A. (2009). Discovering statistics using SPSS. Sage Publications. Field, A. P. (2005). Intraclass correlation. In B. S. Everitt & D. C. Howell (Eds.), Encyclopedia of statistics in behavioral science. John Wiley & Sons, Ltd. https://doi.org/10.1002/0470013192.bsa313 Fischer, H. E., Klemm, K., Leutner, D., Sumfeth, E., Tiemann, R., & Wirth, J. (2005). Framework for empirical research on science teaching and learning. Journal of Science Teacher Education, 16(4), 309–349. www.jstor. org/stable/43156373 Fischer, H. E., & Neumann, K. (2012). Video analysis as a tool for understanding science instruction. In D. Jorde & J. Dillon (Eds.), Science education research and practice in Europe: Retrosspective and prospecctive (pp. 115– 139). Sense Publishers. Fisher, W. J. (1992). Reliability, separation, strata statistics. Rasch Measurement Transactions, 6. Fricke, K., Ackeren, I. V., Kauertz, A., & Fischer, H. E. (2012). Students’ perceptions of their teachers’ classroom management in elementary and secondary science lessons and the impact on student achievement (p. 167). Brill | Sense. Furtak, E. M., Seidel, T., Iverson, H., & Briggs, D. (2012). Experimental and quasi-experimental studies of inquiry-based science teaching: A meta-analysis. Review of Educational Research, 82(3), 300–329. Galili, I., & Lehavi, Y. (2006). Research report. International Journal of Science Education, 28(5), 521–541. http:// doi.org/10.1080/09500690500338847 Garrett, P. E., Wood, J. L., & Yates, S. W. (2018). Critical insights into nuclear collectivity from complementary nuclear spectroscopic methods. Physica Scripta, 93(6), 063001. http://doi.org/10.1088/1402-4896/ aaba1c Geller, C., Neumann, K., Boone, W. J., & Fischer, H. E. (2014). What makes the fnnish diferent in science? Assessing and comparing students’ science learning in three countries. International Journal of Science Education, 36(18), 3042–3066. http://doi.org/10.1080/09500693.2014.950185 Gess-Newsome, J., Taylor, J. A., Carlson, J., Gardner, A. L., Wilson, C. D.,  & Stuhlsatz, M. A. M. (2019). Teacher pedagogical content knowledge, practice, and student achievement. International Journal of Science Education, 41(7), 944–963. http://doi.org/10.1080/09500693.2016.1265158 Glaser, B. G.,  & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Aldine Transaction. Granger, C. (2008). Rasch analysis is important to understand and use for measurement. Rasch Measurement Transactions, 21, 1122–1123. Gravetter, F. J., Wallnau, L. B., Forzano, L.-A. B., & Wittnauer, J. E. (2020). Essentials of statistics for the behavioral sciences (10th ed.). Cengage. Gwet, K. L. (Ed.). (2014). Handbook of inter-rater reliability: The defnite guide to measuring the extent of agreement (4th ed.). Gaithersburg Advanced Analytics. Hammer, D. (1996). More than misconceptions: Multiple perspectives on student knowledge and reasoning, and an appropriate role for education research. American Journal of Physics, 64, 1316. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. http:// doi.org/10.3102/003465430298487 Haynes, S. N., Richard, D. C. S.,  & Kubany, E. S. (1995). Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment, 3, 238–247. https://doi. org/10.1037/1040-3590.7.3.238 Hestenes, D., Wells, M., & Swackhamer, G. (1992). Force concept inventory. The Physics Teacher, 30(3), 141– 158. http://doi.org/10.1119/1.2343497 Honingh, M., Ruiter, M., & Thiel, S. V. (2020). Are school boards and educational quality related? Results of an international literature review. Educational Review, 72(2), 157–172. http://doi.org/10.1080/00131911.2 018.1487387

56

Quantitative Research Designs and Approaches Hox, J. J., & Moerbeek, M. (2018). Multilevel analysis: Techniques and applications (3rd ed.). Routledge, Taylor & Francis Group. Janík, T., & Seidel, T. (Eds.). (2009). The power of video studies in investigating teaching and learning in the classroom (1st ed.). Waxmann. ISBN 978-3-8309-2208-7. Kane, M. T. (2006). Validity. Educational Measurement, 4, 17–64. Kapadia, K., Coca, V., & Easton, J. Q. (2007). Keeping new teachers: A frst look at the infuences of induction in the Chicago public schools. Research report. Consortium on Chicago School Research, University of Chicago. Kauertz, A., & Fischer, H. E. (2006). Assessing students’ level of knowledge and analyzing the reasons for learning difculties in physics by Rasch analysis. In X. Liu & W. J. Boone (Eds.), Applications of Rasch measurement in science education (pp. 212–246). JAM Press. Keller, M. M., Neumann, K., & Fischer, H. E. (2017). The impact of physics teachers’ pedagogical content knowledge and motivation on students’ achievement and interest. Journal of Research in Science Teaching, 54(5), 586–614. http://doi.org/10.1002/tea.21378 Klieme, E., Pauli, C.,  & Reusser, K. (Eds.). (2006). Dokumentation der Erhebungs- und Auswertungsinstrumente zur schweizerisch-deutschen Videostudie “Unterrichtsqualität, Lernverhalten und mathematisches Verständnis” [Documentation of the instruments of the Swiss-German video study, lesson quality, learning behavior, and mathematical understanding] (Vol. 15). Deutsches Institut für internationale pädagogische Forschung [German Institute for International Educational Research]. Kolen, M. J., & Brennan, R. L. (2004). Test equating, scaling, and linking: Methods and practices (2nd ed.). Springer. Koran, M. L., & Koran Jr., J. J. (1984). Aptitude-treatment interaction research in science education. Journal of Research in Science Teaching, 21(8), 793–808. http://doi.org/10.1002/tea.3660210804 Lederman, N. G. (2019). Contextualizing the relationship between nature of scientifc knowledge and scientifc inquiry. Science & Education, 28(3), 249–267. http://doi.org/10.1007/s11191-019-00030-8 Lee, H.-S., & Liu, O. L. (2010). Assessing learning progression of energy concepts across middle school grades: The knowledge integration perspective. Science Education, 94(4), 665–688. http://doi.org/10.1002/sce.20382 Li, H., Liu, J., Zhang, D., & Liu, H. (2020). Examining the relationships between cognitive activation, selfefcacy, socioeconomic status, and achievement in mathematics: A multi-level analysis. British Journal of Educational Psychology, n/a(n/a). http://doi.org/10.1111/bjep.12351 Linacre, J. M. (1997). KR-20 or Rasch reliability: Which tells the “truth”? Rasch Measurement Transactions, 3, 580–581. Liu, X. (2013). Difculties of items related to energy and matter: Implications for learning progression in high school chemistry. Educación Química, 24(4), 416–422. Mayring, P. (2007). Mixing qualitative and quantitative methods. Brill | Sense. Mayring, P. (2014). Qualitative content analysis: Theoretical foundation, basic procedures and software solution. Klagenfurt. For print on demand see www.beltz.de Mayring, P. (2019). Qualitative content analysis: Demarcation, varieties, developments. Forum: Qualitative Social Research, 20(3), 1–26. https://doi.org/10.17169/fqs-20.3.3343 McLure, F., Won, M., & Treagust, D. F. (2020). A sustained multidimensional conceptual change intervention in grade 9 and 10 science classes. International Journal of Science Education, 42(5), 703–721. http://doi.org/10. 1080/09500693.2020.1725174 Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: review and recommenations. Journal of Applied Structural Equation Modeling, 4(2), i–xx. http:// doi.org/10.47263/jasem.4(2)01 Neumann, I., Neumann, K., & Nehm, R. (2011). Evaluating instrument quality in science education: Raschbased analyses of a nature of science test. International Journal of Science Education, 33(10), 1373–1405. http:// doi.org/10.1080/09500693.2010.511297 Neumann, K., Kauertz, A., & Fischer, H. E. (2012). Quality of instruction in science education. In B. Fraser, K. Tobin, & C. McRobbie (Eds.), Second international handbook of science education (pp. 247–258). Springer. Neumann, K., Viering, T., Boone, W. J., & Fischer, H. E. (2013). Towards a learning progression of energy. Journal of Research in Science Teaching, 50(2), 162–188. http://doi.org/10.1002/tea.21061 Nevo, B. (1985). Face validity revisited. Journal of Educational Measurement, 22(4), 287–293. Niedderer, H., Aufschnaiter, Stefan v., Tiberghien, A., Buty, Ch, Haller, K., Hucke, L., Sander, F., & Fischer, H. E. (2002). Talking Physics in Labwork Contexts – A Category Based Analysis of Videotapes. In: D. Psillos & H. Niedderer (Eds.), Teaching and Learning in the Science Laboratory. Science & Technology Education Library, (Vol. 16, pp. 31–40). Springer, Dordrecht. https://doi.org/10.1007/0-306-48196-0_5 Nordine, J., Krajcik, J.,  & Fortus, D. (2011). Transforming energy instruction in middle school to support integrated understanding and future learning. Science Education, 95(4), 670–699. http://doi.org/10.1002/ sce.20423

57

Hans E. Fischer, William J. Boone, and Knut Neumann Oevermann, U., Allert, T., Konau, E., & Krambeck, J. (1979). Die Methodologie einer “objektiven Hermeneutik” und ihre allgemeine forschungslogische Bedeutung in den Sozialwissenschaften [The methodology of an „objective hermeneutic“ and its general research logical signifcance in social sciences]. In H.-G. Soefner (Ed.), Interpretative Verfahren in den Sozial- und Textwissenschaften [Interpretative methods in social sciences and text analysis] (pp. 352–434). Metzler. Oevermann, U., Allert, T., Konau, E., & Krambeck, J. (1987). Structures of meaning and objective hermeneutics. In V. Meja, D. Misgeld, & N. Stehr (Eds.), Modern German sociology, European perspectives: A series in social thought and cultural criticism (pp. 436–447). Columbia University Press. Ohle, A. (2010). Primary school teachers’ content knowledge in physics and its impact on teaching and students’ achievement. Logos. Ohle, A., Fischer, H. E., & Kauertz, A. (2011). Der Einfuss des physikalischen Fachwissens von Primarstufenlehrkräften auf Unterrichtsgestaltung und Schülerleistung [The impact of physics content knowledge of primary school teachers on lesson design and students’ performance]. Zeitschrift für Didaktik der Naturwissenschaften, 17, 357–389. O’Leary-Kelly, S. W., & Vokurka, R. J. (1998). The empirical assessment of construct validity. Journal of Operations Management, 16(4), 387–405. https://doi.org/10.1016/S0272-6963(98)00020-5 Olszewski, J. (2010). The impact of physics teachers’ Pedagogical content knowledge on teacher action and student outcomes (Vol. 109). Logos. Opfermann, M., Schmeck, A.,  & Fischer, H. E. (2017). Multiple representations in physics and science education – Why should we use them? In D. F. Treagust, R. Duit, & H. E. Fischer (Eds.), Multiple representations in physics education (pp. 1–22). Springer International Publishing. Opitz, S. T., Neumann, K., Bernholt, S., & Harms, U. (2017). How do students understand energy in biology, chemistry, and physics? Development and validation of an assessment instrument. Eurasia Journal of Mathematics, Science and Technology Education, 13, 3019–3042. https://doi.org/10.12973/eurasia.2017.00703a Opitz, S. T., Neumann, K., Bernholt, S.,  & Harms, U. (2019). Students’ energy understanding across biology, chemistry, and physics contexts. Research in Science Education, 49(2), 521–541. http://doi.org/10.1007/ s11165-017-9632-4 Özcan, Ö., & Gerçek, C. (2019). Multidimensional analyzing of the microteaching applications in teacher education via videograph. European Journal of Teacher Education, 42(1), 82–97. http://doi.org/10.1080/026197 68.2018.1546285 Popper, K. R. (1959). The logic of scientifc discovery. Basic Books. Prenzel, M. (2003). Unterrichtsentwicklung auf der Grundlage empirisch fundierter Diagnosen und Interventionskonzepte [Lesson development based on empirically grounded diagnoses and intervention concepts]. In E. J. Brunner, P. Noack, G. Scholz, & I. Scholl (Eds.), Diagnose und Intervention in schulischen Handlungsfeldern [Diagnose and intervention in school activities] (pp. 29–46). Waxmann. Prenzel, M., & Deutsches, P. K. (2004). Pisa 2003. Waxmann Verlag. Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. University of Chicago Press. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models (2nd ed.). Sage Publications. Romine, W. L., Sadler, T. D., Dauer, J. M., & Kinslow, A. (2020). Measurement of socio-scientifc reasoning (SSR) and exploration of SSR as a progression of competencies. International Journal of Science Education, 42(18), 2981–3002. http://doi.org/10.1080/09500693.2020.1849853 Ruiz-Primo, M. A., Briggs, D., Shepard, L., Iverson, H., & Huchton, M. (2008). Evaluating the impact of instructional innovations in engineering education. In M. Duque (Ed.), Engineering education for the XXI Century: Foundations, strategies and cases (pp. 241–274). ACOFI Publications. Ruiz-Primo, M. A., & Li, M. (2013). Analyzing teachers’ feedback practices in response to students’ work in science classrooms. Applied Measurement in Education, 26(3), 163–175. http://doi.org/10.1080/08957347.2 013.793188 Salkind, N. J. (Ed.). (2010). Encyclopedia of research design (Vols. 1-0). SAGE Publications, Inc. https://dx.doi. org/10.4135/9781412961288 Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W. H., & Shavelson, R. J. (2007). Estimating causal efects using experimental and observational designs. American Educational Research Association. Seidel, T., & Prenzel, M. (2006). Stability of teaching patterns in physics instruction: Findings from a video study. Learning and Instruction, 16(3), 228–240. http://doi.org/10.1016/j.learninstruc.2006.03.002 Seidel, T., Prenzel, M., & Kobarg, M. (Eds.). (2005). How to run a video study. Technical report of the IPN video study. Waxmann. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428. http://doi.org/10.1037/0033-2909.86.2.420

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Quantitative Research Designs and Approaches Sinclair, J. K., Taylor, P. J.,  & Hobbs, S. J. (2013). Alpha level adjustments for multiple dependent variable analyses and their applicability – A review. International Journal of Sports Science and Engineering, 7(1), 17–20. Smith, R. M. (1986). Person ft in the Rasch model. Educational and Psychological Measurement, 46, 359–372. Smith, R. M. (1991). The distributional properties of Rasch item ft statistics. Educational and Psychological Measurement, 51, 541–565. Smith, R. M. (1996). Polytomous meansquare ft statistics. Rasch Measurement Transactions, 10, 516–517. Smith, R. M., Linacre, J. M., & Smith, E. V. (2003). Guidelines for manuscripts. Journal of Applied Measurement, 4, 198–204. www.rasch.org/guidelines.htm Snow, R. E. (1991). Aptitude-treatment interaction as a framework for research on individual diferences in psychotherapy. Journal of Consulting and Clinical Psychology, 59(2), 205–216. http://doi.org/10.1037/ 0022-006X.59.2.205 Stelmack, J. A., Szlyk, J. P., Stelmack, T. R., Demers-Turco, P., Williams, R. T., Moran, D. A., & Massof, R. W. (2004). Psychometric properties of the veterans afairs low-vision visual functioning questionnaire. Investigative Ophthalmology & Visual Science, 45(11), 3919–3928. http://doi.org/10.1167/iovs.04-0208 Stigler, J., & Hiebert, J. (1999). The teaching gap: Best ideas from the world’s teachers for improving education in the classroom. The Free Press. http://lst-iiep.iiep-unesco.org/cgi-bin/wwwi32.exe/[in=epidoc1.in]/?t2000= 011347/(100) Strauss, A. L., & Corbin, J. (2008). Basics of qualitative research: Grounded the- ory procedures and techniques. Sage. Teig, N., Scherer, R., & Nilsen, T. (2019). I know I can, but do I have the time? The role of teachers’ selfefcacy and perceived time constraints in implementing cognitive-activation strategies in science. Frontiers in Psychology, 10(1697). http://doi.org/10.3389/fpsyg.2019.01697 Tsaousis, I., Sideridis, G. D., & AlGhamdi, H. M. (2020). Measurement invariance and diferential item functioning across gender within a latent class analysis framework: Evidence from a high-stakes test for university admission in Saudi Arabia. Frontiers in Psychology, 11(622). http://doi.org/10.3389/fpsyg.2020.00622 VanVoorhis, C. R. W.,  & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43–50. Weinberg, S. L., Harel, D., & Knapp Abramowitz, S. (2020). Statistics using R: An integrative approach. Cambridge University Press. Won, M., Krabbe, H., Ley, S. L., Treagust, D. F., & Fischer, H. E. (2017). Science teachers’ Use of a concept map marking guide as a formative assessment tool for the concept of energy. Educational Assessment, 22(2), 95–110. http://doi.org/10.1080/10627197.2017.1309277 Wright, B., & Stone, M. (1979). Best test design. MESA Press. Wright, B. D. (1995). Which standard error? Rasch Measurement Transactions, 9. Wright, B. D., & Masters, G. N. (1982). Rating scale analysis: Rasch measurement. MESA Press. Yeh, Y.-C. (2012). Aptitude-treatment interaction. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 295–298). Springer US.

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3 QUALITATIVE RESEARCH AS CULTURE AND PRACTICE Gregory J. Kelly

What is qualitative research? Why does understanding qualitative research matter for science education? My approach to this chapter is grounded in a consideration of issues of importance for the various genres that count as “qualitative research” in science education.1 This includes asking questions about the nature and value of such research and how such work contributes to ongoing conversations about sociocultural practices that come to defne learning, teaching, curricula, and students in various educational contexts. There are already entire handbooks dedicated to qualitative research (Denzin & Lincoln, 2011; Delamont, 2012; Leavy, 2014). This chapter cannot delve into all the range, types, and nuances of the many forms of qualitative research. Furthermore, there are many books dedicated to procedures and detailed approaches for how to conduct various kinds of qualitative research (e.g., Emerson et al., 2011; Glesne, 2016; Heath & Street, 2008; Kelly et al., 2008; Weis & Fine, 2000). This chapter will neither ofer a “how to” guide to qualitative research nor review the many ways that qualitative research enters into the feld of science education. Instead, I will explore ways of thinking about approaches to research that take seriously the cognitive, sociocultural, political, and ideological manifestations of science and education in science education. To do so, I begin by posing a series of epistemological questions about what constitutes qualitative research, the nature of claims in the qualitative research, and the standards for excellence for work that constitute the genres of qualitative research. To address these questions, I provide a short review of some key studies in a few of the many genres of qualitative research in science education. These studies provide illustrative examples that make visible salient topics for consideration. The chapter does not provide a comprehensive review of qualitative research in science education. Rather, my review of research is selective, focused on recent studies that examine a range of topics in science education across settings. Through a consideration of some key studies in a few genres, I examine questions about the purposes and meaning of qualitative research. Thus, the purpose of the chapter is to identify some key areas for consideration and open up discussions about ways to advance thinking about qualitative research in science education. In this way, I argue for a view of qualitative research as culture and practice, constructed by epistemic cultures for the purposes of generating wisdom, solidarity, and hope.

Framing Qualitative Research: Questions About Questions One question that arises in writing such a chapter concerns the very naming of a genre (or set of genres) of research as “qualitative”. What is qualitative research? And why is it named so? Qualitative

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research as a term emerged in contrast to statistical studies that used quantitative measures of sociocultural phenomena. In some ways, the term “qualitative” is poorly chosen – careful study of culture, social practices, and ways of being in the world may include quantities. Furthermore, coding and counting are not prohibited by most qualitative research. Yet, the emergence of qualitative studies in education, and science education in particular, centered on understandings of salient topics that were not easily captured by studies using statistical inference. For example, nuanced studies of students’ understanding of science phenomena (e.g., light, electricity, relativity) required extended clinical interviews without the beneft of knowing how students would respond. Similarly, studies of classroom discourse focused on the ways that the semantics of teacher talk defned, framed, and portrayed science as a discipline (Kelly, 2014). These studies required entering the social setting without a prescribed set of categories or understandings prior to the initial data collection. The emergent nature of qualitative studies is further evidenced by an expanded sets of genres of research, including case studies, ethnographies, and design-based research. So why, then, is qualitative research needed? Could at least some of the salient sociocultural phenomena be captured (perhaps after initial qualitative studies) by a more defned approach with specifc statistical measures? Perhaps in some instances, but qualitative research is grounded in a set of assumptions about epistemological foundations of sociocultural phenomena that requires approaches open to learning about the relevant phenomena as it is studied. An example is illustrative of the complexity of such phenomena: By participating and afliating with a social group (e.g., sixth grade science classroom, afterschool club, environmental activist group), participants come to develop an identity associated with the group practices. Through concerted activity the participants come to understand themselves, and get positioned and understood by others, diferently. In such instances, the emergent identity construction is a progressive process grounded in the specifc discourse processes, social practices, and cultural assumptions of the group. From the analysts’ perspective, understanding the identity work requires investigating these phenomena in detail, over time, and without an initial set of categories. Indeed, the changing nature of the social and cultural phenomena under study, and the reactive efects of the research process, evince the need for research approaches that are adaptive and contextualized in the settings. In such instances, an open approach to the local culture that attends to the emergent nature of the sociocultural phenomena is needed to understand issues salient to the education of the participants. Although this is not the only reason to use qualitative research, the example points to the nuanced, refexive approach needed to understand human experience. The in-depth qualitative approach to research entails studying activity in naturalistic settings, recognizing context(s), and attending to marginalized voices. What is the science of qualitative research in science education? Teachers of science, teacher educators, and science education researchers are advocates of science to some degree, even if this includes serious critiques of traditional views of science. Although the intended purposes of science education may vary among practitioners, there remains a goal among educators of developing knowledge among learners. In science education, the aims, purposes, procedures, and outcomes in each instance rely on defnitions, explicit or assumed, of science. But what counts as science, for whom, and under what conditions? Who decides what counts as science? These questions are further complicated by stance of researchers of science education: Are the research processes investigating the science teaching, learning, and policy themselves scientifc? Are the qualitative research methods scientifc, and should they even aspire to be so? These questions rest on various defnitions of science and how science operates within the discourses of academia, government, and society (e.g., National Research Council, 2002; Eisenhart & Towne, 2003). While there are many ways to conceptualize science, I will use a quotidian defnition of science as “the systematic studies of the natural and social world” (Durbin, 1988, p, 270). Importantly, whether qualitative research counts as a systematic study of the social world in any instance is always situationally defned among practitioners of research. In this way, the legitimization of an approach to research is subject to the intersubjective scrutiny of an

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epistemic community. This occurs through the many institutional forms of demarcating, shaping, and recognizing claims of knowledge about educational phenomena. Debates about defnitions are part of this ongoing conversation, not a way to end the conversation through an imposed consensus. Qualitative research, therefore, is the result of sociocultural practices, the coordinated and concerted activities of people (Smith, 1996), much like felds well recognized as science, such as physics (Traweek, 1988) or biology (Longino, 2002). These sociocultural practices shape and are shaped by the participants in the epistemic cultures of educational research. Qualitative research is a culture with ways of doing, being, and speaking tied to a history of ideas. This culture emerged from studies of human interaction and eventually constructed various anthropological and sociological theories (Harris, 2001). As culture and practice, members of communities that engage in qualitative research have norms and expectations for the stance toward the research subject, data collection and analysis, standards of evidence, and genres of communication. In this way, qualitative research is always emergent, situationally defned, and transforming according to the exigent circumstances of the era. Qualitative research in its many forms is the product of the various researchers working within various epistemic cultures. By learning to be a member of a research community (or theory group, see Murray, 1998), members of the epistemic culture construct repertoires of practices that become the ways of doing, writing, and evaluating qualitative research. In this way, learning to be a member of a group includes developing a set of values, beliefs, attitudes, and ways of being in the world that shape, and are shaped by, the specialized discourse of a specifc epistemic community (Green & Harker, 1988; Lave & Wenger, 1991; Kelly & Cunningham, 2019). The apprenticeship into such a community means learning to make sense of the publicly recognized meanings of key terms, theories, and ways of conducting research. Furthermore, these ways of being are not fxed but evolve and change as members build knowledge, redefne constructs, and refne methods of data collection and analysis (Kelly & Green, 2019a). In this way, qualitative research is itself culture and practice. To examine the nature of this culture and epistemic practices of qualitative research in science education, I consider a limited range of the domains of research (ethnography, design-based research, and case studies) to illustrate how qualitative research manifests in science education as culture and practice. By considering some illustrative cases, I distill some methodological themes and consider standards of success in qualitative research.

Applications of Qualitative Research: Illustrative Examples From Science Education To address some of the questions posed in the earlier sections of this chapter, I chose to review selective studies in three genres of qualitative research: educational ethnography, case studies, and design-based research. There are other qualitative research methods that merit discussion and consideration (e.g., research interviewing, narrative inquiry, phenomenology); I chose these due to their prominence in science education and the value the studies have for unearthing key ideas related to the intersection of the cognitive, sociocultural, political, and ideological manifestations of science and education in science education. I decided to answer the questions posed in the introductory section of this chapter and to provide the bases for discussion of salient topics for qualitative research in science education. These three approaches thus form contrastive cases that can be used to explore issues in qualitative research.2 Qualitative research often involves extended feldwork, participant observation, artifact analysis, and interviews with members of the community. Such participant observation typically entails situating and understanding the reactive role of the researcher embedded in a local culture. For the three chosen qualitative research approaches considered in detail in this chapter, i.e., educational ethnography, case studies, and design-based research, there is a commitment to extensive data sets, close

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examination of interactions, and a refexive stance toward participants. In this way, the qualitative approaches share common commitments developed in anthropology. Across the qualitative traditions, the researchers themselves become part of the everyday practices of the local culture as participants. Thus, understanding and positioning oneself as researcher entails refecting on how learning occurs within the group and for oneself. The theoretical premises of research form an orienting theory for understanding the construction of everyday life from an emic point of view (Green & Bridges, 2018; Kelly & Green, 2019b). Orienting theories frame the research approach to the educational phenomena. Examples of orienting theories would be interactional ethnography (Green et al., 2012), critical discourse analysis (Rogers, 2004), positionality (Parsons, 2008), critical race theory (CRT) (Mensah, 2019), or cultural-historical activity theory (CHAT) (Salloum & BouJaoude, 2020), among many others. An orienting theory guides the nature of the research process by focusing interpretative activity in certain ways. In science education and other felds, qualitative researchers study a variety of substantive issues, producing knowledge that contributes to explanatory theories that make sense of sociocultural phenomena. In this way, the substantive fndings of the qualitative inquiry inform the explanatory theories defning the locus of attention. Thus, the orienting theory informing the research approach may be applied to understand and make sense of local cultures, and in doing so, examine education issues leading to fndings about educational phenomena (e.g., peer social groups, resistance to school authority, epistemic practices). Accordingly, the analytic focus may vary, informing diferent explanatory theories. To make sense of the three contrastive cases highlighted in this chapter, I consider the primary orienting and explanatory theories informing the work, the research design and methods, and the topics and issues under consideration for each. Mapping the articles in this way allows the reader to pose a series of related questions about the contributions: What does this research, as a qualitative study, contribute to understandings of the psychological, social, cultural, or educational phenomena relevant to science education? How does this advance thinking in science education as a qualitative research approach? What does the study allow us to understand? In each case, the reader can also ask the methodological question: What lessons are learned from the study for advancing methodology in qualitative research in science education? Delving into to these questions allows for considerations of how qualitative research contributes to the overall feld of science education.

Ethnographies of and in Science Education To identify some salient issues for discussion and refection in science education, I drew from a sample of recent ethnographic studies of science education local cultures. My choices were made to examine and understand the methodological inferences and consequences for theoretical positions regarding research, rather than a comprehensive or even representative sample. The ethnographies described here are a purposeful sample of studies with salience to the goals of this chapter. Ethnography is the study of culture. Ethnographers study what people make, do, and say. Such studies attempt to take an emic point of view  – that is, the perspective of the participants. This orientation is acquired by the ethnographers through extended participation in the local setting, negotiating access among the participants in the cultural group under study, and engaging in extensive data collection and analysis. Developing understandings of a local culture is not straightforward. Rather, the ethnographers position themselves refexively, recognizing that their role and emerging understandings are only partial, situated, and limited. Although the ethnographers are not able to understand everything about the local culture, they are able to understand something (Geertz, 1973). The following examples from science education are illustrative of this approach to research. Table 3.1 provides a summary of the core theories, research design, methods of analyses, and topic of investigation for the selected, illustrative ethnographic studies.

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Gregory J. Kelly Table 3.1 Illustrative Examples of Ethnographies of and in Science Education Study

Theories

Carlone et al. (2011)

Rahmawati and Taylor (2018)

Research Design

Methods for Analysis

Topics and Issues

Orienting theory: Ethnographies of Cultural anthropology two fourth grade classrooms Explanatory theory: Sociocultural theories of identity

Theoretically informed review of feld notes of observations, video recordings, studentwritten assessments and interviews

Epistemic, communicative, and investigative practices infuence on local defnitions of science and smartness

Orienting theory: Critical auto/ Cultural self-knowing ethnography of and critical refection teacher educator Explanatory theory: Postcolonial theory Cultural hybridity

Narrative telling, refexive inquiry

Complex nature of teacher identity

Constant comparative of feld notes, video recordings, and artifacts interviews

Exposing, critiquing, and addressing unjust experiences in middle school science classrooms through engineering design

Calabrese Barton Orienting theory: and Tan (2019) Community ethnography, critical justice Explanatory theory: Rightful presence, consequential learning

Critical and participatory design with middle school science students engaged in engineering projects

Page-Reeves et al. (2019)

Orienting theory: Participant-observation and community ethnography Explanatory theory: Cultural negotiations

Ethnographic Team-based interviews with collaborative analysis Native American of the interviews science professionals

Sherman et al. (2019)

Orienting theory: Refexive, dialogical Cultural anthropology meta-ethnography Explanatory theory: Bakhtinian dialogic theory

Interpretive synthesis of multiple qualitative explorations of culture

Understanding the experiences of the Native Americans in professional STEM careers Challenges and strengths of ethnographic research across disciplines in education

An important and infuential ethnography in science education was conducted by Carlone et al. (2011). This study focused on what it meant to be scientifc in a reform-based science curricula in two diverse fourth grade classrooms in the southeast United States. Using thick description and an extensive data set, the ethnography examined situationally and locally defned meanings of “scientifc” and “smart science person”. Consistent with an ethnographic stance, the study asked about the local meanings of key terms among the participants. Thus, rather than using taken-for-understood meanings, the ethnographic analysis looked to the local participants’ meaning of “struggling” and “promising” students in two fourth grade classrooms. Adopting a cultural analysis orientation to the classrooms informed the ways the authors analyzed an extensive data set of observations, artifacts, video recordings, and interviews. Together these data created robust descriptions of the classrooms and the ways that science and students’ perceptions of themselves and others were defned in the local settings. These situationally defned meanings were examined across two school settings. The comparative contexts allowed for how local cultures came to defne ways of participating, the situated interpretation of terms, and ways students’ identity potentials were made available. To properly consider the normative meanings for “being scientifc,” the analysis focused on epistemic, communicative, and investigative practices as enacted in the two classrooms.

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As a qualitative study, the contribution to science education centers on ways that the local culture of the classrooms come to defne and place valance of meaning of key terms. Instead of assuming common, taken-for-granted defnitions for “science”, “smart”, and “promising”, the ethnographers examine how the situated meanings had real consequences for students in the settings. With this approach, student identity is not a given characteristic intrinsic to the mind of an individual student, but rather the discursive work of the participants that creates potentials for identity development. One lesson learned from the study for advancing methodology in qualitative research in science education concerns the nature of the construction of identity and equity over time. The momentto-moment interactions aggregate to form patterns of activity, identifed in the ethnographic analysis, that construct the culture across scales (Bloome et al., 2005; Castanheira et al., 2001; Wortham & Reyes, 2015). The critical auto/ethnography of Rahmawati and Taylor (2018) explores the changing cultural identity of Yuli Rahmawati as she comes to redefne herself and her understanding of her role as an educator. This study builds on postcolonial theory and examines the complexity of hybridity in the making and remaking of cultural identity. Through refexive narratives of her experiences in science and education, Rahmawati examines her own assumptions as she comes to learn about the ways that classroom discourse and practice proceed in Australian secondary schools. The contrast of the Australian school approach with that of her own experience in Indonesia provide the backdrop for examining the complex nature of teacher identity and its ties to emergent cultural experiences and understandings. For example, as Rahmawati refects on her expectations for student behavior, she draws in the cultural assumptions from her parents’ cultures, noting diferences in her Javanese father and her Bimanese mother’s perspectives. Layers of complexity include her understanding of Islam and the legacy of the Dutch colonial rule in Indonesia. As a qualitative study in science education, the critical auto/ethnography makes visible the ways that personal life experiences, upbringing, and afliations, such as religion or nation, contribute to the making of a science teacher. The teacher/author recognized herself as living a multicultural life, and she learned to negotiate the many cultures that comprise her everyday experience. Furthermore, the intersectionality of the life as a science teacher is evident through the refections of the ways she comes to participate in diferent settings. This study ofers many lessons for qualitative researchers, including about importance of contexts, author positionality, and refexivity. One prominent lesson is the way that the theoretical orientation is informed by the substantive, explanatory theories. The authors use postcolonial theory to make sense of the refexivity of the teacher/author. This highlights how the choices of theory lead to decisions about how to conduct research in education. Calabrese Barton and Tan (2019) conducted a community ethnography with ethnically diverse middle school science students engaged in engineering projects in a Midwestern city in the United States. The theoretical orientation and methodological approach coalesced in this study around the commitment to a justice-oriented science education. The authors draw from the notion of rightful presence as they seek to create inviting experiences for participating students and address the often exclusionary practices of STEM education. The authors drew from feld notes, selected video episodes, and interviews focused on the students’ engineering devices to identify ways that a student-led educational experience could be used to promote afliation with science and engineering. Their ethnography of the students led to considerations of ethnography by the students. The community ethnography orientation provided the students opportunities to investigate problems identifed by members of the greater school environment as they sought to address sustainability issues in their engineered technologies. The middle school students conducted surveys and interviews of members of their school to learn about sustainability concerns, analyzed data, and represented results in data displays, such as graphs. In this way, the students’ community ethnographies informed their engineering designs as the students sought to fgure out causes of social problems in their classroom, such as bullying. As they began building their design and testing, they checked in with community members

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to gain insights into the design features. Thus, the ethnographic experience of the students help identify (sometimes ignored) issues of concern, develop better technologies, and attend to solving concerns within the community. In doing so, they shifted defnitions of “what counts” from externally defned criteria to youth-defned criteria – what students wanted to learn and what problems they wanted to solve. As a qualitative study of science education, the research contributes to documenting the value of considering the students’ agency and local community. These are not variables to be measured, but rather emergent from the patterned activities of the participants over time. A lesson learned about qualitative research concerns how community ethnography speaks to the issue of theory/method relationships. The authors sought to help create more equitable STEM education and identify ways that this can be studied, documented, and legitimized. The focus on students’ rightful presence oriented attention to the goals of consequential learning and brought attention to engagement in community and disciplinary knowledge. The community ethnography identifed ways of understanding the classroom cultures and also modeled for the students ways of understanding community practices. Participant-observation and community ethnography are important ways of understanding an endogenous culture; in-depth ethnographic interviewing can similarly contribute to understanding culture from the participant’s point of view. Page-Reeves et al. (2019) conducted 41 ethnographic interviews with 21 Native American science professionals. The researchers used a collaboratively organized process for the conduct and analysis of the interviews. Through this team approach that drew from multiple theoretical traditions, the ethnographers provided perspective for the extensive processes of interpreting the interview transcripts. The analysis identifed conceptual categories and patterns from the interviews. These data became the basis for more refned analysis and synthesis aimed at understanding the experiences of the Native Americans in professional STEM careers. Emergent coding categories, such as “identity”, “wayfnding”, “perspective”, “giving back”, “resilience”, and “Native organizations” were organized into conceptual themes. The themes of navigating and wayfnding were illustrated through a set of critical cases. Using these methods the authors were able to bring together theoretical perspectives derived from literature around cultural and Native American experiences. The critical cases foregrounded the cultural experiences of the STEM professionals and provided contextualization to the intervening quotes from interviews. As ethnographers, the authors demonstrated how the details of the extensive interviews can be interpreted, depicted, and recontextualized into a cultural account of the Native American experience in STEM felds. In doing so, the ethnographic interviews contribute to knowledge about the lived experiences of the participants, but also adhere to the intended goals, satisfying the criteria of plausibility, credibility, and relevance (following Hammersley, 2008). As a qualitative study of science education, the in-depth ethnographic interviews provide insights into the ways that cultural experiences contribute to and provide strategies for, participation in STEM. The local cultural knowledge of the participants became the focus of the study, thus demonstrating the ways that cultural knowledge can form a basis for understanding ways of being in science (Aikenhead, 2006). There are many lessons to be learned from this study about qualitative research, most notably how the detailed data from the interviews can be constructed to form themes informed by the theoretical orientation of the researchers. Insights into ways that ethnographic research informs science education can be enhanced through contrastive analysis across studies. Sherman et al. (2019) examined issues of research methodology through meta-ethnography across four studies showing contrast in topic and settings: standardized tests, technology, and classroom instruction in middle school; English as a foreign language in postsecondary college; gender and race in advanced placement biology; and early literacy in a community learning space. The meta-ethnography was driven by dialogic analysis by the researchers who designed and conducted the four ethnographies. The analysis centered on the methodological tensions that arose in each of the specifc studies. The orientation was not focused on the fndings of the

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studies, but rather on methodological issues of conducting ethnographic research and what can be learned through this contrastive, dialogical approach. Through a three-phase analysis the researchers identifed methodological tensions. These fndings are instructive for this chapter. Tensions arising from the contrastive meta-ethnography of Sherman et al. (2019) speak to issues of doing ethnographic and ethnographically informed research in science education. One tension for this sort of research is the positioning of the researcher with respect to the cultural setting and participants. Studies in science education face this tension, as many researchers are familiar with the schooling or other educational contexts, and associated cultural practices, through previous experiences as students, scientists, and teachers. Thus, while anthropological studies of culture often seek to understand cultures signifcantly diferent from that of the ethnographer, educational studies often face a familiarity with the settings and cultural practices. This both allows for the insider knowledge to inform the researchers’ perspectives and can also limit their ability to make the familiar strange. This tension is complicated by the assumptions of the participants in the ethnography about the knowledge of the researcher and what needs to be explained and what is taken for granted. A second tension arose around the nature and scope of ethnography. The authors drew from Green and Bloome’s (1997) distinctions between doing ethnography, taking an ethnographic perspective, and using ethnographic tools. In the four cases, and for all ethnographies in education, there are questions about the scope and validity of the study. What counts as an ethnography? As ethnographic? As using ethnographic tools? How does an ethnographer, or qualitative researcher, know when they are sufciently informed to be able to speak to the cultural practices of the participants? Although this tension was not resolved in the meta-ethnography, the authors provided their refections and ways of resolving the tensions in the individual studies. Through questions about positionality and ethnographic analysis, the authors trouble the notion of doing ethnography. A critical stance toward the research approach and research methods questions the assumptions of the systematicity of common ways of making inferences from raw data sources to claims about the cultures in the studies. The authors acknowledge that researchers are informed by theory long before making choices about the foci of the data collection and analysis. This leads to a discussion of how sociocultural phenomena come to be recognized and command attention for the researcher – building on MacLure’s (2013) notion of glowing data. The meta-ethnography ofers a contrastive analysis to provide insights into ways that methodological tensions can inform conversations about the nature and processes of conducting ethnographic research. For this reason, and others, the methodological lessons center on the ways that researchers can learn from each other through dialogue about theories of culture, positionality, and accountability in reporting research. The fve ethnographic studies reviewed in this section place importance on participantobservation, the sociocultural contexts of the setting, and understanding of the cultural dimensions of the educational phenomena under study. Through this work, ethnographers recognized the difculty in assuming and identifying shared meanings in the generally heterogeneous settings of many educational studies. Case study research, described in the next section, often draws from ethnographic methods, but tends to place emphasis on specifc foci of the education phenomena.

Making the Case for Case Studies in Science Education A case study, as a method of qualitative research, investigates contemporary phenomena in depth, in a real-world context. Understandings developed through such research are mediated by multiple contextual factors pertinent to the case and dependent on the ways boundaries are established defning the case (Stake, 2005; Thomas, 2015). Case studies can involve multiple sources of information for collecting data, including but not limited to observations, interviews, audiovisual material, documents, and reports (Creswell, 2013). In this way, case studies have similarities to ethnographic studies, but rather than bringing the perspective of cultural anthropology as found in ethnography, case

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study research has “its own logic of design, data collection techniques, and specifc approaches to data analysis” (Yin, 2018, p. 16). The fve case studies presented here are illustrative of methodological considerations. They were chosen to show the variety and value of case studies across topics, settings, research techniques, and orientation. Table 3.2 provides a summary of the core theories, research design, methods of analyses, and topic of investigation for the selected, illustrative case studies. González-Howard and McNeill (2019) provide an example of how case study research can be efectively applied to understanding nuances in classroom activity. This study was designed to examine ways that two seventh grade teachers in the United States sought to engage students in scientifc practices leading to epistemic understandings, i.e., how to use evidence to construct, evaluate, and revise knowledge (p. 822). The study is grounded in a view that argumentation practices are dialogic as students learn to engage with evidence through reasoning. The authors used a multiple-case study methodology “to explore the relationship between how teachers framed an argumentation task, and their students’ engagement in this science practice” (p.  825). Two classrooms provided a basis for comparisons across implementation and engagement. The analysis was organized as an exploratory sequential design, frst with open coding to examine the framing of the argumentation practices of the teachers, and second, with social network analysis (SNA) to examine student interactions. The mixed-methods nature of the case study provided the basis for understanding the framing and Table 3.2 Making the Case for Case Studies With Illustrative Examples From Science Education Study

Theories

Research Design

Methods

Topics and Issues

GonzálezHoward and McNeill (2019)

Orienting theory: Participation frameworks Explanatory theory: Scientifc practices, argumentation

Multiplecase study methodology

Mixed methods, exploratory sequential design

Framing and taking up of argumentation practices in science

Haverly et al. (2020)

Orienting theory: Classroom cultures Explanatory theory: Teacher noticing and learning

Qualitative (contrastive) case study

Open coding and constant comparative analysis

Sensemaking moments of novice science teachers facilitating classroom discussions

McNew-Birren et al. (2018)

Orienting theory: Critical sociocultural research Explanatory theory: Democratic equality, social mobility

Interpretive case study

Grounded theory, constant comparative approach

Contrast of perspectives regarding social justice across two forms of teacher education

Shea and Sandoval (2020)

Orienting theory: Ethnography Explanatory theory: Equity-oriented pedagogy

Ethnographic case study

Participant observation, coding, member checks

Design of after-school science program for equity by community educators

Avraamidou (2020)

Orienting theory: Cultural-historical activity theory Explanatory theory: Intersectionality, gender performativity, identity, and recognition

Qualitative single case study

Life history, semi-structured, extended interviews. Matrix and intersection analysis

Intersectionality and identity for immigrant Muslim woman studying

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take up of the desired practices. For example, the open coding of the teacher discourse drew from the theoretical perspective of participation frameworks, focused on actions and goals of the events. This provided a basis for understanding the framing of the scientifc practices. The SNA provided a quantitative interpretation of the interactional patterns. Taken together, these analyses identifed the diferent ways the two teachers interpreted the value of classroom discourse; one teacher saw interactions as a means for improving individual understanding, while the other saw interactions as a means for cultivating a communal understanding. These expectations were refected in the organization of the subsequent student interactions. The contrastive analysis across the two cases demonstrated how teacher framing of practices led to diferences in how students communicated with one another during classroom discussions. As a qualitative study, this research contributes to understandings of how students’ discourse processes and practices are tied to the ongoing history of discourses of the classroom. The analysis of student discourse, argumentation, or other forms of productive discourse should always be understood as a consequence of the norms, expectations, and positions set by the over-time discourse practices of the classroom. By grounding the study in the classroom discourse, and examining the framing and take up of argumentation tasks across analytic methods, the study demonstrates how qualitative research can be informed by a multimethodological approach. The role of teacher discourse was also examined in the next case study. Haverly et  al. (2020) conducted a case study that examined the sensemaking moments of novice science teachers located in the Midwestern United States facilitating elementary classroom discussions. Students’ engagement with ideas and sensemaking of science play an important role in science learning. Such engagement is potentially empowering to students when the sensemaking takes into consideration the “students’ ideas, experiences, and cultural resources while disrupting power structures” (p. 63). The cases in this research provide contrasting examples of how participants made space for students’ sensemaking. The cases are examined through a theoretical lens that considered how the sensemaking moments were contextualized in time and social practice. The inputs (student and teacher resources, external factors) and science storylines were considered relevant to the analysis of the sensemaking moments (see the fgures presented in Haverly et al., 2020, pp. 65, 69, 71, 74). The cases are illustrated with depictions of the ways that the teachers sought to organize the classroom discourse, in the form of a model of teacher noticing and responding to sensemaking. The data sources included teacher portfolios, video records, and research interviews. Through open coding and constant comparative analysis, the research team interpreted the sensemaking events in the video records, how the teachers talked about these events, the tensions with the intended science storylines, and the consequences of these moments in the ongoing instructional conversations. The contrastive cases identifed how equitable sensemaking in classroom discourse was fostered by distributing epistemic authority from the teacher to the students in the instructional conversations. Contributions of this case study to science education include understanding the nuanced ways that equity needs to be considered in the everyday life of participants in science education. The case study drew from multiple data sources but provided ways of understanding the contrastive cases with data representation. These representations ofer a valuable lesson for qualitative research – the nuanced, detailed, and highly contextual data is often difcult to summarize and communicate. In this case study, the cases make visible important, albeit sometimes subtle, diferences in the teaching approaches, leading to the recognition of the re-assignment of epistemic authority as a key variable for achieving more equitable classroom dialogues. Another contrastive study further advances the methodological value of case study research. McNew-Birren et al. (2018) provide a case study contrasting views of social justice in a Teach for America (TFA) teacher preparation program with the views promulgated in a traditional teacher education program located in a US-based university. As explained by the authors, TFA uses an accelerated program for high-achieving students interested in learning to teach through experience

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and concurrent teacher education. The program seeks to “eliminate the achievement gap” (p. 439) through the development of efective teaching strategies and leadership skills, and thus has a social mobility orientation to social justice. This view of social justice contrasts that of university teacher education programs, which tend to emphasize democratic equality. The collaborative, interpretative case study sought to understand the contrasting ideologies of a cohort of TFA teachers (“corps members”) in a secondary science teaching methods course. Course artifacts, including weekly journal entries submitted by corps members, comprised the data set. The analyses followed the grounded theory approach of Strauss and Corbin (1998). The case study identifed a key tension in the corps of TFA teachers, noting the contrasts of the conficting narratives of defcit thinking and seemingly high expectations for students. The tension in the narratives emerged as the core values of the democratic equity perspective from the sociocultural view of the science education scholars (leveraging students’ lived experiences to support learning and engaging students for democratic citizenship), contrasted with the achievement orientation from the previous TFA training. This case study contributes to science education by showing how ideological diferences among teachers can sometimes be masked by the use of common language. The two local cultures (TFA teachers and university teacher education), with their corresponding histories and commitments, both had (ostensibly) a social justice orientation. The contrastive case made visible how social justice can be diferentially understood and applied to the classroom. From a qualitative research perspective, the theoretical orientation of the researchers, recognizing diferences in democratic equality and equity, allowed for interpreting the narratives in a particular manner to make visible the tensions across the educational goals. Science education occurs in multiple settings and across a lifetime. While the previous cases reviewed considered formal education and schooling, Shea and Sandoval (2020) sought to understand how community educators designed out-of-school-time (OST) programs for equity. This case study acknowledges the need to link the microinteractions of everyday educational experience with the macro-level inequities in society toward the goal of redesigning science education for equity and justice. The ethnographic case study was situated in a drop-in after-school science program in a working-class, Latinx community, in California, USA. The unit of analysis for the case study was the activity system of the after-school science studio. Consistent with the ethnographic orientation, the authors drew from multiple data sources, including participant observation, video records of interactions, archival records, and ethnographic and focus group interviews. The lead ethnographer, Shea, spent fve years working with the OST educators and supported the validity of her claims by sharing her interpretations and initial claims with the educators. Through extensive coding, data sharing, and theorizing, the research team developed a stable set of codes around the “cultural repertoires of practice”, “implicit explicit links to political and historical discourse and practices”, “science practices”, and “social practices and interaction” (see Table 3.2, in Shea & Sandoval, 2020, p. 35). The study found that the OST educators saw their work as a political act. The inquiry-oriented science and engineering after-school program ofered the Latinx students an intellectual home, addressed issues of poverty, and provided an alternative to test-heavy school science. To understand how the educators were able to accomplish these goals, the research team sought to understand the micro moments of interaction within the broader sociopolitical contexts. They were able to document the momentto-moment afrming interactions of educators with students, recognize ways the educators brought cultural practices to science, and show how material abundance of the local sites provided opportunities for learning science and engineering. Importantly, the OST activities also extended into the local community and in this way demonstrated the value of science and engineering for solving problems. This case study makes a valuable contribution to science education by applying an orienting theory (ethnography) and an explanatory theory (centered on equity-oriented pedagogy) to the out-of-school-time (OST) settings. The extensive data collection as well as the connection to, and inclusion of, the local community members provide a valuable model for understanding how the

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research team sought to position themselves among the learners of the cultural context. In this study, the students, community members, and educational researchers built on each other’s strengths to create more equitable science and engineering education. Avraamidou (2020) examined the trials of an immigrant Muslim woman working as a physics instructor in a higher education institution in Western Europe. The author frames her approach to understanding the salient issues of the intersectionality of ethnicity, gender, religion, and social class as a life history, single case study. The case study provides insights into the underrepresentation of women in STEM felds in Europe and beyond. It draws from theories of intersectionality and gender performativity while making the case for the social construction of identity. This theoretical orientation is particularly important given the barriers to entry to the physics community that include an assumed masculine nature of physics, assumptions about those holding a religious perspective, and ways that social class and race limit recognition as a scientist. This case study is insightful from a methodological point of view, as the orienting theory (in this instance CHAT) informed the case study approach in unique ways. Framed within a life history research design, the case examines identity from a CHAT point of view. This theoretical grounding provides an orientation to the phenomena comprising the life history and identity and thus guides the nature of the data collection, analysis, and interpretation. The qualitative single case study design focused on the experiences of one Muslim, female, physics instructor, Amina. Extended interviews were used to build a thorough understanding of Amina’s experiences in physics. These understandings were presented in a chronological narrative of Amina’s life history. The trajectory through schooling and experiences in various educational settings provides a fascinating story about the ways that gender, religion, and social class intersect with her identity formation, and how these vary across contexts and time. The case shows how Amina’s construction of self as a competent physicist intersected with, and often contrasted with, the ways she was recognized by others, leading to diferential feelings of belonging. The methodological approach, grounded in social theory, was able to make clear the ways that intersectionality played into the politics of recognition for this physicist. As Avraamidou noted, the methodology illustrated that “to become a physicist is a distinctly personal, emotional, and intimate involving in which multiple identities are intersecting and at times contesting” (p. 337). As a case study in science education, this research contributes to the understanding of the experiences of immigrants participating in science. The analytic focus on intersectionality and drawing from the life history of Amina makes visible the complex ways understandings of self and other, by her and by the community, of her experience. It highlights that science learning, participation, and belonging are more than knowing the disciplinary ideas and practices. An important lesson from a methodological point of view is the care taken to understand the emic point of view and to build empathy and understanding, which allowed the researcher to raise important sociocultural issues to the foreground of the case study. The case study examples presented in this section provide additional insights into the conduct and value of qualitative research in science education. The examples document how attention to the specifc features of the educational context led to understandings that inform broader educational issues. Much like the ethnographic studies, these case studies make use of varied research methods to build narratives informing understanding, empathy, and recognition of the ideologies pervasive in science across settings.

Design-Based Research in Science Education An emerging area of qualitative and mixed-methods research in science education is design-based research. Design-based research is grounded in a number of commitments to theory, practice, and research: The development of educational programs and research are co-developed through cycles of design, the research and practice occur in authentic settings, and researchers and practitioners develop

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and share theories relevant to improving practice (Design-Based Research Collective, 2003). To identify how design-based research (DBR) contributes to the culture and practice of qualitative research in science education, I consider recent studies spanning a range of topics and variations in DBR. Much like the section regarding ethnography and case studies, my choices resulted from a purposeful sample to examine and understand the methodological inferences and consequences for theoretical positions regarding qualitative research in science education. Table 3.3 provides a summary of the core theories, research design, methods of analyses, and topic of investigation for the selected, illustrative design-based studies. Land and Zimmerman (2015) applied the DBR approach to assess the affordances of mobile devices for outdoor science learning. In this study the distributed cognitive learning theory

Table 3.3  Illustrative Examples of Design-Based Research (DBR) in Science Education Study

Theories

Research Design

Methods

Topics and Issues

Land and Zimmerman (2015)

Orienting theory: Design-based research forming collective case Explanatory theory: Socio-technical systems, distributed cognitive learning theory

Three iterative cycles of design around four theoretical conjectures

Analysis of the discourse of the learning experiences and measures of knowledge

Use mobile devices to support families’ scientific talk

Boda and Brown Orienting theory: (2020) Design-based research Explanatory theory: Relationality theory

Mixed-methods approach, including attitudinal surveys and research interviews

Two-way MANCOVA; qualitative analysis of observations and interviews

Explanation of science learning opportunities through the use of VR360 videos

DeLiema et al. (2019)

Orienting theory: Design-based research Explanatory theory: Roles, rules, and keys of participation in play activity

Interactional analysis informing a larger design-based research program

Discourse analysis of activity in a mixedreality (MR) learning environment

Examination of affordances of a play-based mixedreality (MR) learning environment

Wiblom et al. (2019)

Orienting theory: Participatory, Discourse analysis of Design-based research collaborative design classroom interaction approach Explanatory theory: Scientific literacy, social justice

Development of an evaluation tool to improve students’ interpretation and application of internet-based resources for making decisions about health

Anderson et al. (2018)

Orienting theory: Design-based implementation research, researchpractice partnerships Explanatory theory: School change, environmental science literacy

Iterative design cycle, examination of classroom communities

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Mixed-method School reform at scale analysis of tests, for classroom learning surveys, videos, and communities interviews; qualitative analysis of case study data

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supports our understandings of the ways that the mobile device built capacity for children and families to engage in scientifc observation and explanation in out-of-school settings, which took place in two arboreta and a nature center in the northeast United States. The DBR approach informed the design, theory, and practice of the socio-technical system infuencing the interactions in these settings. Consistent with DBR (Sandoval, 2014), the authors conducted three iterative cycles of design around four theoretical conjectures. These conjectures centered around ways to support student learning as they engaged with a guiding naturalist and the supportive technology. To examine the conjectures, the primary data sources were observational in the form of video recordings. Each of the three iterations was considered a case and informed the design, theory, and practice moving forward into subsequent iterations of design. The frst iteration concerned ways of supporting learners to observe and explain scientifcally relevant characteristics of trees and make distinctions between evergreen and deciduous trees; the data analysis focused on the discourses of learning events. In this iteration, the learners used primarily perceptual talk and were less focused on goals directed at developing conceptual, connecting, and afective talk. Thus, iteration two was designed to place a greater emphasis on the conceptual understanding of the scientifc phenomena. For this cycle of analysis, the researchers continued with the discourse analysis of the interactions of the learners, naturalists, and technologies, and added a pre- and post-test to assess knowledge gains. The change in the approach led to a more balanced use of perceptual and conceptual talk. Building on the results of the second iteration, the third cycle of design made tighter connections between the observations and the photo capture tool provided for the learners and redesigned the technology to help the learners distinguish between seedlings and saplings. These changes were implemented at a summer camp at an environmental center. Through analysis of the discourse of the learning experiences and measures of knowledge, the results identifed the mediating role of the naturalist in developing productive perceptual and conceptual talk among learners. Taken together the three iterative cycles of design and research identifed ways to use mobile devices to support families’ scientifc talk. The design-based nature of the research approach contributed to understanding about the substantive learning of science concepts and ways of knowing. The iterative cycles of design, implementation, and assessment allowed educational products to be refned. Methodologically, the study advances qualitative research methodology in science education by showing how learning occurred through cycles of design and how these were informed by multiple research methods. Boda and Brown (2020) used a DBR approach to provide racially, ethnically, and socioeconomically diverse groups of ffth grade elementary students unique science learning opportunities using VR360 videos (interactive and immersive virtual reality). Students participated in a set of activities, grounded in their locales and communities (northern California, USA) to engage with three-dimensional learning advocated by the Next Generation Science Standards (National Research Council, 2013). The VR videos allowed students to experience their local communities without leaving the classroom. The study examined how technologically enhanced learning of science content may infuence students’ attitudes toward science. Interestingly, this study used a mixed-methods approach, combining an attitudinal survey and post-intervention research interviews. Consistent with the DBR approach, however, the study spanned three years and drew from three design cycles. To diferentiate the value of the connection of VR to the local community, the study provided different interventions for a control and experimental group in the second year of the study. The control group ofered students a VR experience that did not look and sound like the students or their communities. Under these conditions the students showed uneasiness toward science. In contrast, the experimental group experienced contextualizing science content in students’ local communities using VR360 videos. Under these conditions, there were measurable positive improvements in students’ attitudes about the relevancy of science. Emerging from the lessons learned in the second cycle of the study (with control and experimental groups), the third iteration of design ofered a diferent

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type of learning experience. In this way, the DBR approach provided ways for the researchers to refne the uses of the VR videos to foster positive attitudes among the students. Although this was a mixed-methods approach to DBR that included statistical analysis, it provides an example of how researchers can draw from diferent research traditions around a substantive topic with coherence. The research sought to understand complex social phenomena in varied settings, and through the learning processes of design cycles developed position outcomes for students. The study highlights the importance of pragmatic uses of research methods to address the central research question, rather than adopting an a priori commitment to certain research approaches. Boda and Brown (2020) draw from mixed methods and set conditions for the interventions with practitioners. Using a diferent approach to DBR, DeLiema et al. (2019) demonstrated how close analysis of interactions in settings can be part of a larger DBR program. This study examined how students and teachers from the west coast of the United States introduce and enact roles, rules, and keys in two diferent play spaces in mixed-reality simulations. The science involved understanding change of state (liquid to solid) as the children “shrank” to enact water molecules. The tasks were set to contrast two approaches – a modeling activity characterized by a more fexible and open-ended approach, and a game activity, with a more rigid structure governing the activities around winning the game. Through careful analysis of the students and teachers in these two conditions, the researchers developed a framework for tracking the collaborative instances of roles, rules, and keys. The roles, rules, and keys were accomplished interactively among the participants; their interpretation by the analysts was informed by discourse analysis and social theory. In this way, the situationally defned terms were identifed and examined as aspects of the environment supporting learning. The roles and rules of the activities were found to be partially infuenced by how participants key (alter in distinct ways) an activity (Gofman, 1974) – the ways that participants signal to each other the nature of the activity, such as distinguishing play, which comes with re-interpretable expectations for interaction from other types of activity. This qualitative study is embedded in ongoing cycles of DBR. In this case, the authors “zoom in” for close, interactional analysis of the ways that the designed environment fosters learning of the science concepts. Although the study itself does not (and does not intend to) document the cycles of design and redesign, it illustrates a valuable dimension of DBR – fexibility in approach to access the key learning issues in a local setting. The interactional analysis of the moment-to-moment events provide evidence for the overall efcacy of the learning theory and its application of the mixed-reality environment (fusing of physical and virtual worlds). From a methodological point of view, the study demonstrated the value of embedding interactional analysis in the cycles of DBR. Thus, the  interactional analysis of an individual study contributes to an overall research program aimed at the typical goals of DBR. DBR provides a set of processes that foster collaboration among researchers and practitioners. Wiblom et  al. (2019) sought to develop an evaluation tool to improve upper secondary students’ interpretation and application of internet-based resources for making decisions about health. The tool was designed to build students’ capability to critically reason about online health information. The processes of learning to reason extend beyond just health information, thus developing scientifc literacy skills that could be applied more generally. The approach was based in a set of moral commitments to democratic values and social justice and consistent with the capabilities expected in Swedish secondary schools. This study makes clear the ways that DBR “resembles how teachers work in everyday practice” (p. 1765). The study was conducted with two science teachers in a secondary school in Sweden. The frst author of the study, employed as a teacher at the school, was familiar with the school practices and culture. The authors describe how the DBR approach was similar to the iterative cycles of refective teaching “including stages of design, implementation, and analysis” (p. 1765). Much like the study by DeLiema et al. (2019), close analysis of the interactions

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in the setting informed the DBR framework. Video recordings of the classroom constituted the primary data source. These video data were analyzed with a qualitative content analysis framework developed by Graneheim and Lundman (2003). Through detailed discourse analysis and coding of the transcripts, the authors identifed four themes, two prior to student use of the internet evaluation tool and two related to the use of the tool to assess the health information. Throughout the processes of fnding and evaluating health information, the students posed questions about the trustworthiness of the vast information. The evaluation tool led the students to question all sources of information (rather than looking for objective sources) and to discuss the scientifc origin and content of the sources of information. Through these analyses, grounded in the practices of the discussions in the classrooms, and consistent with the DBR approach, the study advances two principles for designing learning activities using online searches regarding ways of supporting students’ evaluation and scrutiny of sources of information and strategies for encouraging students to examine the trustworthiness of online resources. As a qualitative study, this research makes visible the importance of two central commitments: to a social justice orientation for democratic values, and to participatory and collaborative research. These commitments contributed to the development of the evaluation tool for assessing health resources. The joint problem formation was developed through the close collaboration among practitioners and researchers, including the dual role of one researcher as teacher. The lesson for qualitative research includes the importance of the ethical commitment to joint understanding of multiple educators and to development of educational interventions. Building on features of DBR, Anderson et al. (2018) advanced this paradigm by considering ways to apply “design work and knowledge building about learning and teaching at scale” (p. 1027). Their study incorporated key features of DBR, including an iterative design cycle, a focus on classroom communities, and research-practice partnerships. This approach took the unit of analysis to be classrooms rather than individual teachers or students. The study is ambitious in scope, taking on three challenges of implementation of educational reform: three-dimensional learning as articulated in the Next Generation Science Standards, reform at scale, and adherence to the diversity of US schools. The authors called this approach design-based implementation research (DBIR), as the goal was to address issues of teaching and learning for enacting change by considering the schooling as a system. Consistent with the design-based approach, the study examined ways to implement change through an iterative design cycle. The scope of implementation entailed approximately 160 teachers over two years, including 94 schools and over 900 diferent middle and high school classrooms. The data collection was oriented to learn about the implementation of a curriculum to foster environmental literacy (Carbon TIME). To address the teaching and learning dimensions of the implementation, the research team collected extensive data of diferent types (tests, surveys, videos) and engaged in a variety of analyses. The ways that three-dimensional learning was fostered through productive discourse was examined by using the classroom community as the key unit of analysis. The results from the mixed approach point to a number of successes in the implementation in terms of teacher and student gains in knowledge. Importantly, the outcomes for students included signifcant reduction in achievement gaps between students with lower and higher pre-test scores. Nevertheless, despite the active engagement with practitioners, the benefts of the Carbon TIME program were less efective for classrooms from schools with higher percentages of students qualifying for free and reduced lunch (a proxy for family socioeconomic status). The qualitative dimensions of the data analysis provided insights into structures, norms, and organization of the professional learning communities. This DBIR study included qualitative research procedures. This approach examined how to bring about educational change at scale. This work assessed the value and contributions of partnerships. As a qualitative study, the research identifed the range and types of data relevant to informing the practitioner-researcher partnerships. For this reason, and the ambitious examination of research at scale, the study makes a valuable contribution.

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The DBR studies presented in this section draw from a repertoire of research methods to actively pursue change in education. Each of the studies ofered unique insight into how empirical research can inform theory and practice. Much like the ethnographies and case studies, the DBR research provides understanding of the nuanced, local knowledge of participants. In the next section, I pull out common themes across the three types of studies examined in this chapter.

Cross-Cutting Themes for Comparative Cases in Qualitative Methods in Science Education Common themes are evident in the three approaches (ethnography, case study, DBR) in the selected, contrastive cases of qualitative science education research chosen for review in this chapter; however, the themes are prevalent in other forms of qualitative research as well. There are fve themes I explore in this section. First, qualitative inquiry recognizes that sociocultural phenomena of education are situated in time and place and within social, cultural, and political contexts. Second, qualitative research entails the use of extensive, situated data sets. Third, qualitative research makes use of evidence to produce claims about psychological, social, cultural, and educational phenomena. Fourth, positionality and refexivity are integral to understanding the reactive nature of qualitative inquiry. And ffth, qualitative studies seek to recognize and understand intersectionality to support a commitment to social justice. The frst theme concerns the nature of education in research: Sociocultural phenomena of education are constructed in the moment-to-moment interactions of everyday life. These interactions can be among people, with texts or technologies, or experiential in nature (Green et al., 2020). These moments of everyday life are constructed with cultural assumptions, tools, practices, norms, and technologies and span time and place (Carlone et al., 2011; Gee & Green, 1998). Of the focal papers reviewed in this chapter, examples of this sort of detailed analysis are found in studies by GonzálezHoward and McNeill (2019) and Wiblom et  al. (2019). In these and other cases, even though sociocultural phenomena of education are constructed through moment-to-moment interactions, they are situated in time and place and within social, cultural, and political contexts (Kelly & Green, 2019a; Rogers, 2004). Importantly, studies of qualitative research seek to identify, understand, and take into account these broader contexts that shape the nature of interactions. A second theme emerging across studies of qualitative research concerns the nature and types of data sets. The research approaches examined in this chapter (ethnography, case study, and DBR) used extensive data sets. Data often included video records, interviews, and artifacts. The range and types of these data, while tailored to the particular research interests of a given study, provide a basis for the in-depth, contextually recognized analyses needed to understand the complexity of every life in educational settings. The focal papers reviewed in this chapter each drew from varied data sets. Indepth analysis can be drawn primarily from one type of data, such as the ethnographic interviews in the study by Page-Reeves et al. (2019), or a variety of data sources, such as by the study of Haverly et al. (2020). Through the processes of analyses, qualitative researchers interpret and produce multiple texts (spoken, written, symbolic, embodied). The texts generated by participants are situated in ongoing sociocultural practices of the local cultural group, with associated histories, discourses, intertextual references, social relationships, positions, and obligations of members (Kelly, 2014). Texts make reference and make use of other texts, and such intertextuality provides resources for analysts to understand the situated nature of discourse in sociocultural practices (Bazerman, 2004; Bloome et al., 2005). Similarly, data-rich analyses have led qualitative researchers to examine how local social contexts build on, and are infuenced by, other contexts. Examining such intercontextuality for and by participants serves as a tool for making sense of these extended data sets (Bloome et al., 2009). Third, qualitative research produces evidence. The three types of qualitative research highlighted in this chapter make use of evidence to support assertions about the phenomena in question. In this

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way, evidence is formulated, produced, and communicated about the psychological, social, cultural, and educational phenomena. The ethnographic studies place a premium on the use of participant observation. Being present matters for ethnographers. The study of the enacted cultural practices of a group requires active participation (minimally as an observer) in these practices, as evidenced in the artifacts, discourse, and actions of the participants. Ethnography seeks understanding and makes eforts to understand educational issues from the emic (or insiders’) point of view. The case studies share many similarities, again drawing on extensive data sets, but also identify how to delimit a case for analysis. The evidence for case studies makes clear the understandings derived from the analyses. Cases studies may be varied in substance and stance, but aim to develop knowledge of the phenomena defned by the limits of the case. DBR, while often drawing from methods used in ethnography and case study research, has a goal of changing practices. Of the three approaches, DBR studies are most likely to consider theory and practice together and aim toward a defned educational goal. These studies seek researcher-practitioner collaborative arrangements so that the research into the practice in question produces knowledge for the betterment of the practice in subsequent cycles of research. The uses of evidence from qualitative analysis to inform practice occurred in each of the focal studies informing this chapter, and well-articulated in the DBR studies by Boda and Brown (2020) and Land and Zimmerman (2015). Fourth, qualitative researchers recognize that research is done from a particular perspective. The researcher has an intellectual, cultural, educational, and methodological history. The researcher has a position. It is important to recognize that this position has an infuence on the ways that research is conducted – the point of view of the inquirer sets up and infuences the orientation and direction of the study, the nature of the research questions, the observations of the phenomena (recognized as theoretically salient), the analyses, and the type and form of reporting from a particular position. Because of this recognition, the positionality of the researcher is acknowledged and taken into account through the construction of the research itself, but also in the interpretation of the research accounts (van Langenhove & Harré, 1999). This issue is well illustrated by the critical auto/ethnography of Rahmawati and Taylor (2018). Just as the analyst seeks to understand the perspective of the participants in a study, the analyst too must consider ways positionality infuences the nature of the researcher. Thus, qualitative research places value on refexivity – understanding that the research itself is a sociocultural phenomenon, infuenced by many factors, and in need of introspection to examine underlying assumptions. Finally, qualitative inquiry ranges from highly descriptive to explanatory to activist. Across these varied purposes, qualitative researchers seek to understand sociocultural phenomena with the hope of bringing about change. This is especially true for educational research. Few educational researchers seek merely to document education as it is. Often they are motivated by a desire to improve the systems of education through understanding and advocating for change. The commitment to social justice takes on diferent forms; as a thorough understanding through ethnography and/or an explicit goal-orientated change agenda through participatory-action research, each provide approaches to change. The focal studies by Calabrese Barton and Tan (2019) and Shea and Sandoval (2020) make visible diferent ways the participants themselves, along with the researcher, view the common work as bringing about social change. By bringing to the fore the stories and circumstances of participants that might not otherwise be heard, qualitative inquiry can contribute to social justice in education. Such analyses need to take into account how oppression often occurs diferentially across race, class, gender, sexuality, language use, immigration status, and nationality, among other forms of discrimination.

Criteria for Success: Epistemological Considerations for Methods Across the three types of qualitative research depicted in this chapter, and for all types of qualitative research, there are epistemological considerations. Qualitative research seeks to develop knowledge

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about psychological, social, cultural, and educational phenomena and share this knowledge through accounts. The assumptions about the purposes of the research, the validity of the assertations produced in the accounts, and the nature of the narratives instantiate theories of knowing and knowledge. Authors of qualitative inquiry construct relationships with the participants in their studies; the data collected, analyzed, and produced; and the audiences of the narratives as they make the research public. The types and range of these qualitative inquiries and their articulation vary widely and show marked diferences from other types of research (c.f., Denzin & Lincoln, 2011; Camilli et al., 2006). Qualitative research in education generally has an orientation to produce accounts that have validity concerning the nature of the phenomena under study and a goal to improve practice. This orientation manifests in a variety of accounts of qualitative research. Narrative accounts of qualitative inquiry vary in purpose and form. Some accounts are largely descriptive in nature. These studies ofer close-up, detailed views into the lives of the participants. Descriptive accounts tell the story of people and the social, cultural, linguistic, and political situations of their experience. Such accounts make visible the local knowledge and how interpretations of experience are constructed by participants (Geertz, 1983). Another type of narrative in qualitative inquiry are normative accounts. These inquiries lead the researcher to make moral arguments and often recommendations for practice. Normative accounts are often derived from contextualized studies of everyday action and identify ways that life circumstances can be improved from a moral point of view. Descriptive and normative accounts are not often easily distinguishable. Descriptive accounts tell the reader what is, but often imply what should be. Normative accounts build on description and make the case for what should be. Furthermore, the very nature of the descriptive accounts, the vocabulary, metaphors, and point of view of the account is steeped in moral decisions. The decisions about the design and methods of inquiry are similarly not neutral, but derived from a theoretical point of view, grounded in an orientation to the world. In this way, even accounts that aim for careful description are normative. Finally, there are qualitative research studies that are activist in nature. These studies aim to bring about social change, often stating this intent at the outset. Such research makes visible the moral point of view of the authors of the work and seek to bring about change through the research. Although such studies identify as aiming to bring about social change, this is really a matter of emphasis. Descriptive and normative accounts of research similarly often represent a goal of improving the human condition through construction of knowledge. Research accounts from qualitative inquiry, like all research reports, are produced with an audience in mind. The production of research results, particularly in felds like education with consequences for practice, are judged based on quality. There are a number of considerations for such judgments. Qualitative research, like all research, is judged by norms and expectations of the feld, often appearing in the form of peer review, readership, citations, and application. These judgments are undoubtedly variously interpreted and applied with the epistemic cultures of research communities. To clarify the judgments of quality, the feld from time to time considers the standards for research. The heterogeneity of the nature and practice of qualitative research suggests that developing a consensus about a set of quality considerations for standards for research that applies uniformly across all qualitative genres is unlikely; furthermore, such imposition may limit the value of qualitative research by narrowing a robust debate (Freeman et al., 2007). Nevertheless, considerations of quality in research are important and the feld of educational research, like all research, needs to hold itself accountable to others in society. Nevertheless, discussions of standards should be part of a robust critical discourse about the nature, practice, and value of research. Discussions of standards can be part of this ongoing conversation, particularly if the discussion opens up consideration of alternative viewpoints, rather than limit debate with a putative consensus. Howe and Eisenhart (1990) were among the frst to lay out some considerations for standards for qualitative research. I use these as an example of a way to advance thinking about the cultural and practice of qualitative inquiry. They noted that such standards are abstract, and therefore open

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to interpretation, before they ofered four suggestions. First, they advocated for a good match of research questions to data collection and analysis. This suggests that the nature of the inquiry and the purposes of the research should drive decisions about the choices of research methods to apply to the study. Consideration should be given to the type of claims that can be made from the research methods and the overall goal of the research project. Second, the procedures for data collection and analysis need to be applied with efective use of the techniques of the research approach. Although there can be stark diferences across approaches to social science research, each approach has a tradition of ways of going about the business of conducting a proper study. Adhering to these genrespecifc norms provides potential insight into the phenomena in question and credibility among the readers of the work. Third, Howe and Eisenhart place emphasis on the overall warrant for the empirical claims. This is an important dimension to any empirical research. The warrant in qualitative research is established through each of the steps of the research process, from negotiating access to an educational setting, to defning (and modifying) the research questions, to refning procedures for data collection, representation, and analysis to creating accounts of the research experience that are persuasive. Evidence is thus created, but nonetheless grounded in the everyday life of participants of the research. Fourth, educational research is value laden. Qualitative research is often conducted with participants in settings with learners, and often vulnerable populations, and thus needs to recognize the ethical implications of the work. Choices about the stance toward participants and the research approach itself derive from the researcher(s)’s values. The American Educational Research Association (2006) created “Standards for Reporting on Empirical Social Science Research in AERA Publications”. Two overarching principles underlie the standards: reports of empirical research should be warranted and transparent. As qualitative research is empirical (that is, derived from investigations in the social world), these principles apply. Indeed, qualitative research demonstrates particular attention to the nature of the evidence warranting empirical claims and the means of developing transparency in the processes of knowledge construction and communication through inquiry. Much like the standards proposed by Howe and Eisenhart (1990), the AERA standards for empirical work, derived from the two overarching principles, contribute to the feld by identifying points for discussion, critique, and emergent formulations of how to conduct the work of empirical social science, including qualitative inquiry. The standards are organized into eights areas: problem formulation; design and logic of the study; sources of evidence; measurement and classifcation; analysis and interpretation; generalization; ethics in reporting; and title, abstract, and headings (p. 33). These standards provide a starting point for considerations of quality in empirical social science and educational research. Discussion of the value and usefulness of standards is itself a critical discourse that can inform the feld of education. In the next section, I provide a framework to propose three types of critical discourses for advancing educational research. The goal of such work is to develop the collective wisdom of the feld regarding the practices constituting the work of qualitative research. Discussion of standards, to the extent that they open up critical discourses about warrantability and transparency, is useful, particularly when tied to considerations valued in the various epistemic cultures of qualitative research, such as learning from being there, representation of everyday life, insights about participants, plausibility of results, actionable results, and political implications for change.

Qualitative Research as Culture and Practice Critical Dialogues for Education Research My chapter in the Handbook for Complementary Methods in Education Research (Camilli et al., 2006) proposed three sorts of epistemological conversations needed to inform debates about research methodology (Kelly, 2006). The framing of debates about methods is relevant, particularly when the

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criteria for successful research themselves are in question. These conversations place emphasis on diferent dimensions of approaches to educational research and recognize diferent audiences for the ongoing conversations. Based on a framework for moral discourse proposed by Strike (1995), I proposed three types of critical discourse about research methods to explain, compare, assess, and improve research methodologies in educational research: critical discourse within group, critical discourse regarding public reason, and hermeneutical conversations across groups (Kelly, 2006). Taken together, the critical discourses ofer a collective method for refning thinking about methodologies for research and developing efective ways of communicating across paradigms. I describe here how these critical dialogues can be applied to qualitative research within the context of research methods within science education. The frst type of critical dialogue specifes commitments within a research tradition. Critical discourse within group are conversations concerning developmental and defnitional work regarding the creation, specifcation, and extension of a research group’s central theories, assumptions, and key constructs (Kelly, 2006). Within-group critical discourse provides a forum for development of a research area’s core theories and commitments. For example, case study research can debate the nature and purposes of case study research, the kinds and types of cases, and the various ways that cases are defned and delimited. In this manner, the scholars working within the research tradition of case study research refne, extend, and re-invent their collective work. In these critical dialogues within group, new ideas, metaphors, and redescriptions may be developed to advance the thinking within the feld (Rorty, 1989). For example, the contrastive case study by McNew-Birren et  al. (2018) (focal study, reviewed previously) identifed the ways that various interpretations of social justice were construed among diferent stakeholders in the educational system. The purposes of constructive cases and descriptive language used in this and other case studies form the basis for refning the methodological approaches in case study research. The developmental and defnitional work of these critical dialogues defne the nature of the research tradition (e.g., case study) and contribute to judgments of quality within the tradition. Similarly, the metaethnography of Sherman et al. (2019) sought to examine tensions within ethnographic research and seeks new ways of doing research through dialogue within the ethnographic tradition. I refer to the second form of critical discourse as critical discourse regarding public reason. These dialogues focus on the development of epistemological commitments to assess the value of educational research within and across diferent research traditions (Kelly, 2006). These conversations concern the criteria used to judge research and provide the bases for evaluating diferent forms of research. Across methodological approaches, candidates for quality criteria could be insightfulness, empirical warrant, theoretical salience, consistency with other knowledge, transparency, and usefulness for practitioners. These dialogues concern public reason as they strive for some overlapping consensus among scholars. For example, the studies reviewed in this chapter based in the design-based tradition aim to improve practice through iterations of design, research, refnement, and redesign (e.g., Anderson et al., 2018). Their value may be compared to other sorts of research also aimed at improving practice, such as random control trials. The diferent approaches may lead to diferent types of knowledge, which may be complementary, or may provide conficting visions of applied educational research. Critical discourses regarding public reason would seek to establish the basis for the quality criteria for making decisions about the respective value and future investment in applied research. Such discourse would examine the epistemological commitments of the feld of education regarding research methods. What counts as valid and useful research for advancing knowledge and improving practice? To what standards are researchers accountable? The third form of critical discourse concerns hermeneutical conversations across groups. These critical discourses are designed to foster learning from diferences across research traditions. Such conversations can occur within qualitative research in focused areas, for example, examining how studies grounded in sociolinguistics can learn from conversational analysis. They may also learn and combine

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ideas from across broader qualitative traditions. For example, DeLiema et  al. (2019) (focal study, reviewed previously) brought together interactional analysis within a larger set of studies grounded in DBR. In this way, the study demonstrated what can be learned across research approaches. Such critical dialogues can be extended further to consider ways that mixed-methods approaches to educational phenomena can be created to provide complementary insights into the relevant psychological, social, cultural, and educational dimensions under consideration. Bringing together diferent traditions may also entail a consideration of the epistemological commitments of the various ways of conducting research and a re-examination of the bases for research as found in the critical discourse regarding public reason. Viewing qualitative research as culture and practice suggests that progress for the feld entails building a dialogic community of scholars open to discussion and debate. Fostering such an epistemic culture depends on opening up the feld to new voices and shifting the epistemic subject from that of the inquirer to that of the local, collective group of inquirers (Longino, 2002). The critical dialogues about research methods will beneft from learning from alternative points of view and accounts of social reality. Examination of the assumptions of everyday practices, whether they be those of science instruction, or those constructing what counts as qualitative inquiry, will beneft from perspectives derived from diferent theoretical and subject positions. Science education has begun to be informed by an increasingly broader set of theoretical frameworks, such as feminist scholarships, critical race theory, indigenous knowledge, decolonial theory, queer theory, among others (e.g., Emeagwali, 2020; Letts & Fifeld, 2019; Mensah, 2019; Scantlebury et al., 2019; Zidny et al., 2020). As these and other frameworks challenge what counts as qualitative research in science education and education more broadly, the feld will be strengthened through a construction of public reason from multiple perspectives.

Ongoing Conversations: Collective Wisdom, Solidarity, and Hope I have argued that the dialogic community of inquirers defnes and transforms qualitative inquiry as culture and practice. As the scope of the chapter concerns science education, the question about science and qualitative inquiry is an avenue for understanding the nature of claims in qualitative inquiry and the purposes of qualitative research. The science question for qualitative research in science education and beyond concerns whether causal relationships can be ascertained from the results of qualitative inquiry (Lagemann, 2000; National Research Council, 2002). It would be convenient for school leaders if educational research could deliver ready-made results, relatively context free, that informed policy decisions, particularly as related to resource allocation. For example, generalized empirical statements such as “a w percent increase for x years in middle school science teacher professional learning centered on y type of instructional strategies for biology will yield a z percent increase in student learning as measured by standardized statewide tests.” It’s debatable about whether any educational research produces causal claims as specifc in detail and wide-ranging in implications this (invented) hypothetical statement. Qualitative research most certainly does not produce such causal statements, nor does it aspire to do so. Rather, claims emanating from qualitative inquiry tend to be what Heap (1995) calls a priori, normative claims articulated in propositional form. Contingent, empirical claims do not aggregate to generalization, but rather provide insights into the cultural practices of educational phenomena. For example, consider the following statement from the conclusion of Avraamidou (2020): “The fndings of this study have ofered insights into how intrapersonal, interpersonal, sociocultural factors, relationships, and a myriad of experiences nurtured Amina’s intersecting identities, and what this may mean for Muslim women’s participation in physics” (p. 337). The claim, while grounded in empirical work, does not form a generalization derived from accumulation of facts. Rather, it makes a normative claim about the sociocultural condition of the case study participant, and in doing so, provides insights into how this woman’s experiences in

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physics can be understood and appreciated. This is not to say that the empirical work did not inform the normative claim; it was the careful analysis and insights derived from the case study that made clear ways that the intrapersonal, interpersonal, sociocultural factors, relationships, and experiences nurtured Amina’s intersecting identities. Rather than aim for generalizations about all women’s or Muslim women’s experiences in physics, the claim brings understandings of the experiences that are likely to resonate with others who have had similar experiences, or with those who seek to understand the lived experiences of others in the physics culture. The value of the empirical work is to provide understanding and empathy to the cultural dissonance in this instance, recognizing that such insights might inform thinking about similar experiences for learners entering a new culture. To recognize the value of these sorts of claims, we need to understand qualitative inquiry as contributing to the collective wisdom, solidarity, and hope (to borrow liberally from Rorty, 1982), rather than producing generalizable, empirical claims. This is not to argue that qualitative inquiry does not produce assertions about psychological, social, cultural, and educational phenomena. Qualitative inquiry does this – for example, DBR provides and enacts specifc recommendations. The empirical bases of understanding are derived from explorations in the worlds as they appear to us. Avraamidou’s study ofers insight into a culture and contributes to the collective wisdom (learned from experience, other ethnographies, inference from similar cultures) that can inform how education is understood as sociocultural phenomena. In addition to collective wisdom, qualitative inquiry builds understanding and empathy for others, in diferent life circumstances. This type of understanding has the potential to build solidarity among people and to recognize each other as part of greater whole. For example, Shea and Sandoval’s (2020) study identifed the importance of afrming practices of not only the Latinx students’ interest and ideas but also their stories from Mexico. Accumulating understanding and building solidarity among and across cultures has the potential to foster hope for improving education through collective understanding and work. In this way, qualitative inquiry helps us understand each other, the structures that limit the realization of human potential, and the ways that oppression limits educational opportunity. Through the development of wisdom, solidarity, and hope we can strive to improve educational systems for the betterment of societies – freer from oppression, more equitable, and kinder.

Conclusion In this chapter, I have presented the case for considering qualitative research as culture and practice. Rather than worrying about whether qualitative research is scientifc or rigorous, or even whether instances of qualitative inquiry can be judged by a set of standards, I propose to shift the conversation to recognize that qualitative research, like all research in education, is the product of specifc epistemic cultures. These cultures defne membership through common ways of acting, speaking, and being; construct norms and expectations for viewing and interpretating each other, our artifacts, and discourse; socialize new members through ritualized ceremonies defning the folklore about how to be in the culture; and evolve through concerted activity and redefning of the vocabulary, metaphors, and ways of taking action. In addition to being epistemological in the sense of generating, communicating, and critiquing empirical knowledge claims, I have argued that the goal for qualitative inquiry is also ethical in the sense of producing collective wisdom, solidarity, and hope. Qualitative inquiry constructs collective wisdom through understanding, empathy, and inquisitiveness. Such inquiry is empirical, learns from investigations in the world, and as such seeks ways of understanding. By understanding and building a web of beliefs, and breaking down the distinction of participant and researcher, this approach helps us understand the other, whether they are a struggling student, overworked school leaders, or any person sufering from oppression. Wisdom helps us come to recognize the others as part of us. This research can help us understand the plight and challenges faced by others in the educational sector and beyond, and in doing so has the potential to build solidarity (Rorty, 1991). Understanding and empathy have

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the potential to construct ways of being together that improve educational practices. Finally, the work of qualitative research provides social hope (Rorty, 1999). The recognition of oppression in science classrooms from ethnographic research, refnement of science lessons through DBR, or developing a better understanding of a reform in education practices in a classroom through a case study, provide ways of making education better. Such improvement is measured from within the knowledge system of the researchers, practitioners, and students – this is all we have, the reasoned judgment of participating members of a social group. Furthermore, these social groups are constituted by members, each with a positionality. As scholars we must recognize the contingency of our own vocabulary, of our own perspective, point of view, and lived experience. We each interpret the world from a stance. Our ways of knowing are contingent on our experiences, backgrounds, intellectual histories, and importantly, the ever-evolving epistemic cultures of our research communities. My own intellectual history, life experience, and scholarly orientation deeply infuenced this text, claiming qualitative research as culture and practice. Regardless, we can build hope for change by recognizing the contingencies of how any one of us views a situation, but knowing that others can and do interpret experience diferently provides bases for deeper understanding. Qualitative inquiry builds such solidarity, through which we have hope for making education better.

Acknowledgments I would like to thank my critical friends Kathryn Bateman, Christine Cunningham, Ashwin Mohan, and Amy Ricketts for comments on an earlier version of this chapter, and the chapter reviewers, Lucy Avraamidou and Heidi Carlone, for their suggestions for improving the chapter.

Notes 1

2

Taylor (2014) provides an excellent review of the philosophical, sociocultural, historical, and political infuence shaping qualitative research in science education. Taylor’s chapter sets qualitative research in a historical context and identifes the epistemological commitments of the feld over time. Rather than rehearse those perspectives, this chapter builds on the work already done by Taylor to extend the perspective in qualitative research in science education. This chapter builds on the epistemological foundation from Taylor and treats contemporary issues for qualitative research in science education in this context. Creswell (2013) provides a thorough comparison of fve qualitative traditions of inquiry from a methodological point of view. The book provides an analysis and commentary on the theoretical framing, data collection, analysis and representation, writing qualitative inquiry, and standards for quality.

References Aikenhead, G. S. (2006). Science education for everyday life: Evidence-based practice. Teachers College Press. American Educational Research Association. (2006). Standards for reporting on empirical social science research in AERA publications. Educational Researcher, 35, 33–40. Anderson, C. W., de los Santos, E. X., Bodbyl, S., Covitt, B. A., Edwards, K. D., Hancock, J. B., Lin, Q., Thomas, C. M., Penuel, W. R., & Welch, M. M. (2018). Designing educational systems to support enactment of the Next Generation Science Standards. Journal of Research in Science Teaching, 55, 1026–1052. Avraamidou, L. (2020). “I am a young immigrant woman doing physics and on top of that I am Muslim”: Identities, intersections, and negotiations. Journal of Research in Science Teaching, 57, 311–341. Bazerman, C. (2004). Intertextualities: Volosinov, Bakhtin, literary theory, and literacy studies. In A. F. Ball & S. Warshauer Freedman (Eds.), Bakhtinian perspectives on language, literacy, and learning (pp. 53–65). Cambridge University Press. Bloome, D., Beierle, M., Grigorenko, M., & Goldman, S. (2009). Learning over time: Uses of intercontextuality, collective memories, and classroom chronotopes in the construction of learning opportunities in a ninthgrade language arts classroom. Language and Education, 23, 313–334. Bloome, D., Carter, S., Christian, B., Otto, S., & Shuart-Faris, N. (2005). Discourse analysis and the study of classroom language and literacy events: A microethnographic approach. Erlbaum.

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Gregory J. Kelly Boda, P. A., & Brown, B. (2020). Priming urban learners’ attitudes toward the relevancy of science: A mixedmethods study testing the importance of context. Journal of Research in Science Teaching, 57, 567–596. Calabrese Barton, A., & Tan, E. (2019). Designing for rightful presence in STEM: The role of making present practices. Journal of the Learning Sciences, 28, 616–658. Camilli, G., Elmore, P. B., & Green, J. L. (Eds.). (2006). Handbook of complementary methods for in education research. Lawrence Elbaum. Carlone, H. B., Haun-Frank, J., & Webb, A. (2011). Assessing equity beyond knowledge-and skills-based outcomes: A comparative ethnography of two fourth-grade reform-based science classrooms. Journal of Research in Science Teaching, 48, 459–485. Castanheira, M. L., Crawford, T., Dixon, C. N., & Green, J. L. (2001). Interactional ethnography: An approach to studying the social construction of literate practices. Linguistics and Education, 11(4), 353–400. Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among fve traditions (3rd ed.). Sage. Delamont, S. (Ed.). (2012). Handbook of qualitative research in education. Edward Elgar Publishing. DeLiema, D., Enyedy, N., & Danish, J. A. (2019). Roles, rules, and keys: How diferent play confgurations shape collaborative science inquiry. Journal of the Learning Sciences, 28, 513–555. Denzin, N. K., & Lincoln, Y. S. (Eds.). (2011). The Sage handbook of qualitative research. Sage. Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8. Durbin, P. T. (1988). Dictionary of concepts in the philosophy of science. Greenwood Press. Eisenhart, M., & Towne, L. (2003). Contestation and change in national policy on “scientifcally based” education research. Educational Researcher, 32(7), 31–38. Emeagwali, G. (2020). African indigenous knowledges and the decolonization of education in Africa. In J. Abidogun & T. Falola (Eds.), The Palgrave handbook of African education and indigenous knowledge (pp. 335–349). Palgrave Macmillan. Emerson, R. M., Fretz, R. I., & Shaw, L. L. (2011). Writing ethnographic feldnotes (2nd ed.). University of Chicago Press. Freeman, M., DeMarrais, K., Preissle, J., Roulston, K., & St. Pierre, E. A. (2007). Standards of evidence in qualitative research: An incitement to discourse. Educational Researcher, 36(1), 25–32. Gee, J. P., & Green, J. L. (1998). Discourse analysis, learning, and social practice: A methodological study. Review of Research in Education, 23, 119–169. Geertz, C. (1973). The interpretation of cultures; selected essays. Basic Books. Geertz, C. (1983). Local knowledge: Further essays in interpretive anthropology. Basic Books. Glesne, C. (2016). Becoming qualitative researchers: An introduction (5th ed.). Pearson. Gofman, E. (1974). Frame analysis: An essay on the organization of experience. Harvard University Press. González-Howard, M., & McNeill, K. L. (2019). Teachers’ framing of argumentation goals: Working together to develop individual versus communal understanding. Journal of Research in Science Teaching, 56, 821–844. Graneheim, U. H., & Lundman, B. (2003). Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24, 105–112. Green, J. L., Baker, W. D., Chian, M., Vanderhoof, C., Hooper, L., Kelly, G. J., Skukauskaite, A., & Kalainof, M. (2020). Studying the over-time construction of knowledge in education settings: A microethnographicdiscourse analysis approach. Review of Research in Education, 44, 161–194. Green, J. L., & Bloome, D. (1997). Ethnography and ethnographers of and in education: A situated perspective. In J. Flood, S. B. Heath, & D. Lapp (Eds.), Research on teaching and literacy through the communicative and visual arts (pp. 181–202). Macmillan. Green, J. L., & Bridges, S. M. (2018). Interactional ethnography. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 475–488). Routledge. Green, J. L., & Harker, J. O. (Eds.). (1988). Multiple perspective analyses of classroom discourse. Ablex. Green, J. L., Skukauskaite, A., & Castanheira, M. (2012). Studying the discursive construction of learning lives for individuals and the collective. In O. Erstad & J. Sefton-Green (Eds.), Identity, community, and learning lives in the digital age (pp. 126–145). Cambridge University Press. Hammersley, M. (2008). Questioning qualitative inquiry: Critical essays. Sage Press. Harris, M. (2001). The rise of anthropological theory: A history of theories of culture. AltaMira Press. Haverly, C., Calabrese Barton, A., Schwarz, C. V., & Braaten, M. (2020). “Making space”: How novice teachers create opportunities for equitable sense-making in elementary science. Journal of Teacher Education, 71, 63–79. Heap, J. L. (1995). The status of claims in “qualitative” research. Curriculum Inquiry, 25(3), 271–291. Heath, S. B., & Street, B. V. (2008). On ethnography: Approaches to language and literacy research. Teachers College Press. Howe, K., & Eisenhart, M. (1990). Standards for qualitative (and quantitative) research: A prolegomenon. Educational Researcher, 19(4), 2–9.

84

Qualitative Research as Culture and Practice Kelly, A. E., Lesh, R. A., & Baek, J. Y. (Eds.). (2008). Handbook of design research methods in education: Innovations in science, technology, engineering, and mathematics learning and teaching. Routledge. Kelly, G. J. (2006). Epistemology and educational research. In J. Green, G. Camilli, & P. Elmore (Eds.), Handbook of complementary methods in education research (pp. 33–55). Lawrence Erlbaum Associates. Kelly, G. J. (2014). Discourse practices in science learning and teaching. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education, volume 2 (pp. 321–336). Lawrence Erlbaum Associates. Kelly, G. J., & Cunningham, C. M. (2019). Epistemic tools in engineering design for K-12 education. Science Education, 103, 1080–1111. Kelly, G. J., & Green, J. L. (Eds.). (2019a). Theory and methods for sociocultural research in science and engineering education. Routledge. Kelly, G. J., & Green, J. L. (2019b). Framing issues of theory and methods for the study of science and engineering education. In G. J. Kelly & J. L. Green (Eds.), Theory and methods for sociocultural research in science and engineering education (pp. 1–28). Routledge. Lagemann, E. C. (2000). An elusive science: The troubling history of education research. University of Chicago Press. Land, S. M., & Zimmerman, H. T. (2015). Socio-technical dimensions of an outdoor mobile learning environment: A three-phase design-based research investigation. Educational Technology Research and Development, 63(2), 229–255. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. Leavy, P. (Ed.). (2014). The Oxford handbook of qualitative research. Oxford University Press. Letts, W., & Fifeld, S. (Eds.). (2019). STEM of desire: Queer theories and science education. Brill Sense. Longino, H. E. (2002). The fate of knowledge. Princeton University Press. MacLure, M. (2013). Researching without representation? Language and materiality in post-qualitative methodology. International Journal of Qualitative Studies in Education, 26, 658–667. McNew-Birren, J., Hildebrand, T., & Belknap, G. (2018). Strange bedfellows in science teacher preparation: Conficting perspectives on social justice presented in a teach for America – university partnership. Cultural Studies of Science Education, 13, 437–462. Mensah, F. M. (2019). Finding voice and passion: Critical race theory methodology in science teacher education. American Educational Research Journal, 56, 1412–1456. Murray, S. O. (1998). American sociolinguistics: Theorists and theory groups. John Benjamins Publishing. National Research Council. (2002). Scientifc research in education. In R. Shavelson & L. Towne (Eds.), Committee on scientifc principles for educational research. National Academy Press. National Research Council. (2013). Next generation science standards: For states, by states. The National Academies Press. https://doi.org/10.17226/18290 Page-Reeves, J., Marin, A., Mofett, M., DeerInWater, K., & Medin, D. (2019). Wayfnding as a concept for understanding success among Native Americans in STEM: “Learning how to map through life”. Cultural Studies of Science Education, 14, 177–197. Parsons, E. R. C. (2008). Positionality of African Americans and a theoretical accommodation of it: Rethinking science education research. Science Education, 92, 1127–1144. Rahmawati, Y., & Taylor, P. C. (2018). “The fsh becomes aware of the water in which it swims”: Revealing the power of culture in shaping teaching identity. Cultural Studies of Science Education, 13, 525–537. Rogers, R. (2004). An introduction to Critical Discourse Analysis in education. In R. Rogers (Ed.), An introduction to Critical Discourse Analysis in education (pp. 1–18). Lawrence Erlbaum Associates. Rorty, R. (1982). Method, social hope, and social science. In R. Rorty (Ed.), Consequences of pragmatism (pp. 191–210). University of Minnesota Press. Rorty, R. (1989). Contingency, irony, and solidarity. Cambridge University Press. Rorty, R. (1991). Objectivity, relativism, and truth. Cambridge University Press. Rorty, R. (1999). Philosophy and social hope. Penguin. Salloum, S.,  & BouJaoude, S. (2020). Understanding interactions in multilingual science classrooms through Cultural-Historical Activity Theory (CHAT): What do contradictions tell us? International Journal of Science and Mathematics Education, 1–23. Sandoval, W. (2014). Conjecture mapping: An approach to systematic educational design research. The Journal of the Learning Sciences, 23, 18–36. Scantlebury, K., Hussenius, A., & Ivarsson, J. (2019). Learning about matter and the material, struggling with entanglement and staying with the trouble to raise up feminist science education. In J. Bazzul & C. Siry (Eds.), Critical voices in science education research (pp. 235–245). Springer. Shea, M. V., & Sandoval, J. (2020). Using historical and political understanding to design for equity in science education. Science Education, 104, 27–49.

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Gregory J. Kelly Sherman, B. J., Bateman, K. M., Jeong, S. K., & Hudock, L. A. (2019). Dialogic meta-ethnography: Troubling methodology in ethnographically informed qualitative inquiry. Cultural Studies of Science Education, 1–24. Smith, D. (1996). Telling the truth after postmodernism. Symbolic Interaction, 19(3), 171–202. Stake, R. E. (2005). Qualitative case studies. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (pp. 443–466). Sage. Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Procedures and techniques for developing grounded theory (2nd ed.). Sage. Strike, K. A. (1995). Discourse ethics and restructuring. Presidential address. In M. Katz (Ed.), Philosophy of education (pp. 1–14). Philosophy of Education Society. Taylor, P. C. (2014). Contemporary qualitative research: Toward an integral research perspective. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education, volume 2 (pp. 38–54). Lawrence Erlbaum Associates. Thomas, G. (2015). How to do your case study (2nd ed.). Sage. Traweek, S. (1988). Beamtimes and lifetimes: The world of high energy physicists. Harvard University Press. van Langenhove, L., & Harré, R. (1999). Positioning and the writing of science. In R. Harré & L. van Langenhove (Eds.), Positioning theory (pp. 102–115). Blackwell. Weis, L., & Fine, M. (2000). Speed bumps: A student-friendly guide to qualitative research. Teachers College Press. Wiblom, J., Rundgren, C. J., & Andrée, M. (2019). Developing students’ critical reasoning about online health information: A capabilities approach. Research in Science Education, 49(6), 1759–1782. Wortham, S., & Reyes, A. (2015). Discourse analysis beyond the speech event. Routledge. Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage. Zidny, R., Sjöström, J., & Eilks, I. A. (2020). Multi-perspective refection on how indigenous knowledge and related ideas can improve science education for sustainability. Science & Education, 29, 145–185.

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SECTION II

Science Learning Section Editor: Richard A. Duschl

4 THEORIES OF LEARNING Clark A. Chinn and Kalypso Iordanou

Overview Efective science instruction requires an understanding of how students learn within various learning environments. To this end, theorists develop and investigate theories of learning. In this chapter, we review the most prominent theories of learning and their intersections with science education. Because learning and instruction are closely intertwined, our discussion extends to address features of instructional learning environments that successfully foster students’ learning. The scope of research on learning and instruction is vast; to manage this scope, we must focus selectively on certain issues and fndings. Accordingly, this chapter explores applications of two foundational theories of learning to science education. The two “theories” are really clusters of theories, which we refer to as cognitive theories and participationist theories (cf. Sfard, 1998). After providing an overview of these theories, we explain how each has been applied to address a series of important issues relevant to learning and teaching in science education: transfer, the generality vs. situativity of learning, equity, scafolding learning, collaborative learning, self-regulated learning, motivation, conceptual change, views of inquiry, and learning through direct instruction versus inquiry. In the second section of the chapter, we focus on one particularly important contemporary topic in science education: how to promote epistemic growth. We focus especially on how to promote better thinking in the so-called post-truth information society, in which misinformation is rife and it is difcult to know what is accurate. We discuss approaches that seek to integrate some of the elements of cognitive and participationist approaches to learning and instruction. The theories of learning we discuss have been applied to advance a variety of educational goals. Throughout most of the history of science education, the core goal was for students to master science content  – the currently accepted theories, laws, models, explanations, and principles (Duschl & Tahirsylaj, 2021; Duschl & Grandy, 2008). This need not entail mere memorization; educators have advocated that students understand the content so that they can apply ideas to solve new problems (Smith & Siegel, 2004). More recently, other goals have emerged. Prominent among them is the goal of learning science as a process of inquiry (Duschl & Tahirsylaj, 2021; Duschl & Grandy, 2008). This goal focuses on the development of competence in inquiry practices. Students learn to engage in practices such as explaining, modeling, and arguing (National Research Council, 2012) or in practices of thinking about scientifc issues as a layperson (Feinstein & Waddington, 2020). Some conceptualizations of the goals of science education merge these two approaches. For instance, the

DOI: 10.4324/9780367855758-6

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USA’s Next Generation Science Standards set out standards that meld practices of inquiry with the development of content knowledge and understanding (NGSS Lead States, 2013). In addition to these two core goals, educators have advocated other signifcant goals for science education. These include the goal of developing an identity as someone who cares about and uses science, or (for some) an identity as a scientist or future scientist (Hand & Gresalf, 2015; Robinson et al., 2019). Other important goals are cultivation of interest in science (Renninger & Hidi, 2016) and developing curiosity about the natural world (Muis et al., 2018). Recent scholarship has advocated the promotion of scientifc virtues such as intellectual open-mindedness, intellectual courage, and meticulousness (Baehr, 2021; Chinn et al., 2011). Further, educators have advocated the goal that science students learn to engage with science efectively as a layperson or as a scientifcally literate citizen, given that most students will not become scientists (Feinstein & Waddington, 2020; Norris & Phillips, 2003). These various educational goals are pursued in research grounded in the two theoretical perspectives. As we will discuss, these goals may be diferentially emphasized across theories, and the ways that the goals are conceptualized may also vary.

Theories of Learning In this section, we sketch two broadly used theories of learning – cognitive and participationist – which we will subsequently apply to examine particular educational issues and challenges. First, however, we briefy note two theories once dominant in education but no longer prevalent: behaviorism and Piagetian theory. Behaviorism held sway over learning theory in psychology from the 1920s or 30s to the 1960s or 70s (Lagemann, 2000). Behavioral theories disallowed any constructs referring to mental entities – memory, understanding, ideas, metacognition, mental models, and so on. Two forms of learning were dominant. The frst was operant conditioning, in which rewards (e.g., food, grades) increased the likelihood of organisms producing particular behaviors (e.g., pecking a lever, rehearsing information for a test). The second was classical conditioning, in which a behavioral response associated with one stimulus (e.g., salivating when provided with food) could be elicited by a diferent stimulus if that stimulus was paired regularly with the frst stimulus (e.g., the salivation that occurs when food is presented can arise strictly from hearing a bell when that bell is regularly rung whenever food is provided). The cognitive revolution of the 1960s and 1970s supplanted behaviorism in most areas of research. Researchers produced a growing array of phenomena that could not be explained without positing mental constructs such as metacognition and memories. For example, Chomsky (1959) showed that behaviorism was incapable of explaining language phenomena, which required internal cognitive structures to adequately explain how people attain competence in their native languages. Brewer (1974) showed that even classical conditioning does not occur in humans unless humans become mentally aware of the association. Even the core phenomenon of using rewards to increase behaviors has run into challenges, as motivation researchers have argued that such rewards undermine intrinsic motivation (Ryan & Deci, 2020). Behaviorism continues to be infuential, however, in special education. Piaget’s cognitive developmental theory has strongly infuenced science education work on learning and instruction. Piaget (1974) proposed that development occurred through a series of defned stages of increasing cognitive and social capabilities. Piaget (1974) also proposed that cognitive development takes place through the processes of accommodation and assimilation; these constructs served as the basis of some of the early work on conceptual change. According to Posner et al. (1982), when the learner’s existing conceptual schema are efcient in solving problems, no conceptual change takes place. In the case that the current conceptual structure fails to solve some problems, moderate

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changes take place in one’s conceptual schema (accommodation). However, when the learner faces major anomalies and consistent failures in solving problems, one proceeds to radical revision of existing conceptions (assimilation). Central to cognitive growth, according to Piaget, are interactions with others. Being exposed to the thought of others, refecting on others’ ideas and reasoning, and communicating one’s own thought through language are fundamental for developing awareness and control of one’s thinking (Piaget, 1928/1959; Piaget, 1964/1968). Piaget’s ideas of constructivism and the role of peer interaction to cognitive and metacognitive development have made an enduring contribution to developmental psychology and science education (Beilin, 1992; Fox & Riconscente, 2008). Piaget has also infuenced science educators’ ideas about the sequencing of certain learning goals. For instance, because the conceptual structures needed to support the ability to control variables in experiments was supposed to emerge in adolescence in the “formal operations” stage of development, science educators held of on encouraging these forms of reasoning until adolescence (see a critique of this in Metz, 1995). But science educators have subsequently provided evidence that elementary-aged children, and even kindergartners and early elementary school students, can engage in sophisticated scientifc reasoning involving controlling variables, measuring and representing phenomena, and so on (Lehrer  & Schauble, 2004; Metz, 1995). For reasons such as these, Piagetian frameworks no longer dominate science education. Nonetheless, elements of Piaget’s theory lie at the heart of current conceptualizations of learning in science education, such as the role of interaction with peers and objects for learning and for developing metacognitive control of one’s own thinking (Fox & Riconscente, 2008).

Two Theoretical Approaches: Overview Many specifc theories of learning have been advanced to explain how children and adults learn, and it is of course impossible to review all of them in a single chapter. Instead, building on analyses by Sfard (1998), Dohn (2016), and Danish and Gresalf (2018), we discuss two broad theoretical approaches that have been especially infuential: cognitive and participationist theories. We begin with an overview of these theories in this section. Cognitive theories: Sfard (1998) and Dohn (2016) classifed cognitive theories as acquisitionist theories. Acquisitionist theories have in common the underlying metaphor of getting or acquiring something (such as knowledge, understanding, concepts, or mental models). The predominant acquisition theories are cognitive learning theories (also called information processing theories). Cognitive learning theories generally explain learning phenomena through positing that learners construct mental representations to represent the world. According to some theories, these are abstract symbolic representations like a general statement of the critical concepts and processes of evolutionary theory. According to other theories, they are more concrete and specifc, such as representations of specifc instances of natural selection rather than a general, abstracted representation. But common to both is the idea that, following learning processes, students have gained or constructed new or revised mental representations. Cognitive theories frequently emphasize that learning occurs when learners store information in a long-term memory store. Learners can then retrieve that information and use it to solve problems, to reason, or to learn new information. The active processing of information occurs within another memory store, called working memory. Working memory holds information and processes that are currently active; it contains the information of which people are currently processing. The size of working memory is extremely limited, and this has strong implications for learning. Because learning is viewed as requiring connecting old and new information, this can occur only if there is space in working memory’s current capacity for the old and new to be brought together and linked in some

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way. If learners’ working memory is overloaded with things to think about and consider, this can therefore preclude learning (Anmarkrud et al., 2019; Sweller & Chandler, 1994). Cognitive theories typically assume that learners’ strategic engagement is an important driver of learning (Greene & Azevedo, 2007). For instance, Chi’s ICAP (interactive, constructive, active, and passive) framework (e.g., Chi & Wylie, 2014) explains learning in terms of how diferent kinds of information-processing strategies promote memory and understanding. Passive strategies of just reading or listening to information without doing anything else are least likely to promote learning. Active strategies involve making some kind of manipulation to information but without drawing any new inferences (e.g., repeating it, copying it, taking verbatim notes); these promote greater learning than passive strategies, but less than constructive or interactive strategies. Constructive engagement involves forming new inferences that go beyond the stated information; this can involve, for example, drawing concept maps, explaining ideas, comparing information across texts, or supporting ideas with evidence-based arguments. Such constructivist strategies promote greater learning than active or passive strategies. Finally, interactive strategies promote the greatest learning. Interactive engagement involves learners in groups who are engaged in the give and take of sharing diferent ideas. This may involve argumentation in which students have diferent perspectives to argue for or asking and answering comprehensive questions with a partner (as long as this involves sharing of unique ideas and uptake of some of these ideas from each other). In the ICAP framework, as in many other cognitive theories, social dimensions of learning are central. But at the same time, learning in this framework is individual  – the focus is on individuals acquiring new memories and understandings through social interaction. For example, individuals might receive new ideas and information from partners and then use this to build their own new internal representations. Cognitive theories can vary in terms of the extent to which they assume that students are active agents who construct, discover, or invent ideas. At one end of a continuum, teachers are providers of knowledge, and students are predominantly the recipients, although they may need to engage in cognitive strategies, such as elaborating or explaining information they receive, in order to learn (Kirschner et al., 2006). In other, more constructivist approaches to learning within this framework, teachers are the facilitators in environments that enable students to build knowledge largely on their own (Hmelo-Silver et al., 2007). At the other endpoint, some approaches assume that learners must actively develop or invent most ideas themselves based on their own experiences, which of course can include social interactions with others (such as the radical constructivism of von Glasersfeld, 1995). Participationist theories: In contrast to cognitive theories, participationist theories eschew metaphors that rely on acquiring mental representations (Sfard, 1998). On participationist accounts, learning involves changes in how people participate with others. The focus is not on the things learned, but rather changes in the ways of interacting and acting with others. This can involve, for example, changing the way one talks with others: One learns new ways of talking as one engages in joint activity, such as designing experiments (e.g., developing discourse about fnding the best control condition, how to establish reliability of measures, and so on). Although participationist theorists may allow that learners develop cognitive representations (Greeno & van de Sande, 2007), participationist theorists tend not to focus on the structure of people’s knowledge representations (e.g., their mental representations of experimentation), but rather on how people engage and interact with each other over time (e.g., how they talk about alternative explanations for fndings and about how experiments can be redesigned to rule out these alternatives). In addition, participationist theorists tend to write of “knowing” as an action instead of knowledge as an object that people possess. A central goal of participationist learning is the building of communities (Sfard, 1998). Learning involves becoming a participant in a community (e.g., becoming a participant in the community of a microbiology lab, and at a larger level, in the community of microbiologists as a whole).  In

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schools, students may be regarded as apprentices in a community, and their initial participation may be peripheral – that is, they may be observing at frst more than acting within the community (Lave & Wenger, 1991). More expert participants enculturate learners into shared engagement in community activities. Knowing is not a matter of “having” knowledge, but a matter of belonging to and participating in a community, and of developing new ways of communicating and behaving within this community (see Sfard, 1998, Table 4.1). Because learning is a change in ways of participating, learning cannot be separated from the settings and communities that enable participation. Learning activities therefore always occur in specifc contexts (Greeno, 1989). Two concepts that are central to participationist theories are positioning and identity (Dohn, 2016). Consider a simple classroom scenario in which a physics teacher asks the class how to set up an experiment. One student (Rachel) raises her hand but is ignored (a frequent occurrence with her). Another student (Dan) is called on but ofers an answer that the teacher considers unacceptable, so the teacher moves on without comment to another student, Charles, whose answer is considered correct and is praised. In this scenario, the teacher has been positioned as the ultimate authority. Rachel has been positioned as unworthy to express ideas in this community – perhaps, given what is known about bias in classrooms, because of her gender. Dan has been positioned as a student with a wrong answer not even worthy of comment, and Charles as a student who is good at physics. Over time, Dan could conceivably develop a stable identity as “not a science person” and Charles as a “science person”, maybe even a future scientist. Rachel and Dan may tire of being positioned as non-contributors and could shift over time toward taking on identities as resistors or slackers. Thus, a simple episode that might be seen as a straightforward matter of asking and answering questions in reality positions students in ways that can infuence their identities as well as their ways of participating in the class. There is a range of theories that can be considered participationist, including sociocultural theories, situated cognition, cultural-historical activity theory, and social constructivism (Danish  & Gresalf, 2018). We cannot distinguish all of these within the scope of this chapter; like Sfard (1998) and Danish and Gresalf (2018), we will treat them as sharing most or all of the elements discussed earlier. However, we will briefy comment on the most prominent theory in this tradition, that of Vygotsky. According to Vygotsky (1978), the social environment plays a pivotal role in learning. All the higher-order functions frst appear in the social plane (the level or space of human interactions) between human individuals and then at the individual plane (the level or space of individual thinking). A process which originates in the social level, between people (interpsychological), is internalized and then appears inside the individual (intrapsychological). For instance, students might fnd that other people in their science class repeatedly ask them for evidence when they make statements. After experiencing this in the social plane, they may come to demand evidence of themselves when they are thinking on their own or in other social contexts. A widely used construct in Vygotsky’s sociocultural theory of learning is the zone of proximal development. Vygotsky proposed a distinction between two developmental levels for a child at any given time. One (the actual developmental level) refers to what children can do on their own when asked to solve problems. The other level is the level of potential development, which refers to what children can achieve with adult guidance or in collaboration with a more capable peer. The zone of proximal developmental refers to the distance between the actual developmental level and the potential developmental level. Vygotsky proposed that learning awakens a variety of internal developmental processes that can operate only when there is interaction between the child and more capable individuals in the child’s environment. Once those processes are internalized, they become part of what the child can achieve when working independently. One implication of Vygotsky’s theory is to ask children to engage in tasks that are beyond their actual developmental level – “the only ‘good learning’ is that which is in

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advance of development” (Vygotsky, 1978, p. 100). Another implication is that assessments that probe learning within the zone of proximal development are needed to properly appraise learning levels. Two students may perform identically on a test of unaided learning, whereas one performs much better with some limited assistance; these students are at diferent levels of competence that are not picked up by traditional assessments (Vygotsky, 1978). Appraising the two theories: The two sets of theories have diferent strengths and weaknesses for explaining learning phenomena and grounding the design of efective instruction (Sfard, 1998). The diferences between the theories also provide impetus to theorists in each “camp” to attend to issues and phenomena uncovered by their counterparts. For example, cognitive theories (such as Chi’s ICAP framework) have increasingly emphasized the importance of social interaction in supporting learning. Going further, some theorists have sought a more comprehensive integration of elements of the theories (Greeno & van de Sande, 2007; Reznitskaya & Wilkinson, 2021). But even so, diferences persist. For example, the focus of cognitive theorists on internal cognitive processes continues to drive research and design on learning by individual students, such as how individuals learn through studying worked examples (e.g., Mara et al., 2021). In contrast, although individuals learning alone is not ruled out by participationist theories (Greeno, 1989), participationist theorists generally investigate learning in collaborative and community settings. We will discuss other divergences in the following sections. Sfard (1998) argued that, while partly mixed approaches are possible and desirable, the two theories take fundamentally diferent perspectives on learning. She argued that both are also essential to a complete picture of how people learn. Analogously to the dual nature of light (particle and wave), she argued that human learning can be viewed alternatively as participationist or acquisitionist but not as both simultaneously. Any particular analysis could foreground elements from one, but at the inevitable cost of backgrounding some elements that are prominent in the other.

Applying These Two Theoretical Frames to Topics Related to Learning and Instruction We further unpack these theories in the following sections by examining how they can be applied to diverse topics relevant to science education. Transfer: Transfer refers to extending what is learned from one situation to another, such as applying what one has learned about experimentation to determine if a study one reads about the internet is properly controlled. The two theoretical perspectives take diferent approaches to transfer (Danish & Gresalf, 2018). Cognitive approaches generally treat transfer as a matter of acquiring an abstract representation and then retrieving that abstract representation to solve a relevant problem, modifying the representation as needed to ft the specifcs of the problem. For example, while solving acceleration problems in diferent contexts, a student could abstract a general solution procedure that can then be used across all such problems. Cognitive studies have shown that encouraging students to form abstract representations (e.g., identifying what two examples have in common at an abstract level) fosters transfer of a concept or principle (e.g., Gick & Holyoak, 1983). Further, when there is greater overlap in the procedures used to solve problems, transfer is easier (Day & Goldstone, 2012). It is easier to transfer skills of conducting experiments with bacteria to conducting experiments with viruses than to conducting feld experiments in ecology because there is more overlap in the cognitive representations of the steps needed to conduct experiments within the microbiology felds. In contrast, participationist theories address transfer by focusing on how the practices in which people participate in one setting overlap with practices in other settings in which they also participate. When there is such overlap in practices, there is potential for transfer (Danish & Gresalf, 2018). There is thus a strong emphasis that the transfer must be explained not just in terms of the individual’s

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cognitions but in terms of the overlap in the practices, the settings, and the other people involved in those settings. Many scholars within both the cognitive and participationist perspectives have emphasized that successful transfer of learning is often difcult to achieve (Detterman, 1993; Lobato, 2012). For example, learners who can successfully ofer a natural selection explanation for the acquisition of anatomical features of animals may be unable to explain the loss of features or any changes at all in plants (Nehm & Schonfeld, 2008). However, instead of treating poor transfer as the main fnding, participationist theorists emphasize instead what learners do use from prior experiences when tackling new tasks. Even if they struggle to fnd the “right” knowledge to apply, learners do take bits and pieces of prior learnings and experiences and piece them together to try to address the new problems (Wagner, 2010). Both cognitive and participationist researchers have observed that both theoretical approaches have uncovered important phenomena and yielded valuable insights into diferent varieties of transfer (Day & Goldstone, 2012; Lobato, 2012). Sfard (1998) argued that a crucial weakness of participationist theories is the challenge they face in explaining transfer. Transfer seems to require carrying something over from one situation to another as an individual human moves from one situation to another. It is hard to see what that something could be except for something that is cognitive – some knowledge representation that endures across situations and can therefore be retrieved and used in a new situation. Yet participationists can also note that, when transfer occurs, it is not merely a matter of cognitive representations transferring. The patterns of participation must also overlap in a way that afords use of new knowledge in the new setting. For example, if people learn to participate in practices of grounding decisions in empirical evidence within one community, they are unlikely to engage in those practices in a new setting with people who are unaccustomed or even hostile to speaking of evidence during decision-making. Generality versus situativity of learning: A recurring tension in educational research is the extent to which learning should be viewed as learning general content and skills that can be applied broadly across diferent topics, versus learning content and skills that are specifc to disciplines and even felds within disciplines (Fischer et al., 2018). Taking argumentation as an example, the generalist approach treats argumentation as a general skill that is fundamentally similar across even diferent disciplines, such as history and science, and certainly similar across diferent subfelds of science. In all of these felds, argumentation involves using evidence to support claims with warrants that link the claims to the evidence, as well as advancing counterarguments and rebuttals. According to the generalist approach, it is thus valuable for students to learn this general structure of argumentation so that they can apply it across many topics. Support for this view comes from studies that show that students who learn general principles or skills can frequently apply them in other contexts (Hetmanek et al., 2018) as well as from developmental studies demonstrating links between younger children’s performance on general reasoning tasks and reasoning on scientifc tasks (Sodian, 2018). In contrast, the situative view posits that both science itself and learning science are highly specifc to situations  – including both the particular topic of study and the setting (particular classrooms, museums, etc.) in which learning occurs. Situative theorists note that constructs and skills that are seemingly similar across disciplines and subdisciplines are actually quite distinct. For instance, argumentation in history relies on testimonial evidence from sources that is very diferent from the detailed experiments that provide evidence in microbiology. Further, the types of arguments that appear in written reports difers; indeed, historians often write narratives, not arguments, in their publications (see Chinn & Sandoval, 2018). Even within diferent subdomains of science, constructs and skills that seem superfcially similar difer in many details. For instance, experiments in a particular arena of feld ecology difer strikingly from experiments in a particular arena of microbiology (different kinds of controls, diferent issues to worry about in terms of controls, and so on). Therefore, much of what needs to be learned to engage in argumentation based on experiments in these two felds is specifc to that setting. Although there is some evidence of transfer in argumentation (see

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Iordanou & Rapanta, 2021 for a review of studies), it is often difcult to achieve high levels of transfer from one domain to another, especially when specifc knowledge is needed (Iordanou, 2010), which can be viewed as evidence that the demands of argumentation across settings with diferent topics and diferent participants may difer. Because participationist theories view learning as occurring in particular settings and involving the development of particular practices within those settings, participationist theories generally fall on the situated side of this issue (Greeno, 1989). Many cognitive theorists have often adopted a more general view, advocating teaching generalized knowledge that can be applied across diferent settings (Hetmanek et al., 2018). Nevertheless, some cognitive approaches treat science learning as highly situated, such as viewing students as having small “bits” of knowledge that can be fexibly assembled in unique arrangements across diferent situations (diSessa, 1993). Despite ongoing diferences between these two positions, there has been movement among theorists in both camps to acknowledge points made by the other camp. On the generalist side, there has been increasing acknowledgment that there are diferences between diferent domains of knowledge (e.g., mathematics versus science versus history) and even between diferent topics (e.g., climate change versus immunology) (e.g., Bråten & Strømsø, 2010). On the situative side, there have been moves to acknowledge the need to explain how people can develop stable ways of thinking across settings (Elby et al., 2016). For example, according to a situative perspective, people are expected to use diferent ways of knowing in diferent settings: They might appeal to scientifc evidence in one setting, to anecdotal experiences in another, and to authority in a third. Yet people may come to use one or more of these ways of knowing in a broader range of settings, such as scientifc evidence across a wider range of settings (Elby et al., 2016). In these ways, each theoretical approach has over time sought to explain a more comprehensive set of phenomena. Equity: Sfard (1998) argued that acquisitionist theories are beset with the problem that because knowledge is seen as internal to individuals, it becomes easy – even inevitable – for acquisitionist theories to treat knowledge as something that is the property of individuals. Further, because knowledge is a good thing to have, it can become natural to view those who “have” more knowledge as advantaged over those who “have” less. This might lead to harmful “defcit” views of learning, in which one group of individuals (e.g., minoritized students) are viewed as unsuccessful because they “lack” knowledge or motivation, or because their families or cultural groups lack certain values (e.g., valuing education) that more successful (frequently majority group members) “have” (Gay, 2018). In contrast, participationist theories avoid positing that knowledge is the property of individuals; rather, knowledge is manifested in the joint activity of groups (Hall & Jurow, 2015). The location of knowledge is groups and communities, not individuals alone. For example, whereas acquisitionist theories would posit that members of a microbiological research team each “possesses” knowledge (such as knowledge of the experimental methods and theories being tested), participationist theories would view that the knowledge lies in the patterns of interactions that members of the research group have with each other and with the materials and tools that they work with. Further, much of their “knowledge” is contained in the algorithms of the computer programs or in the workings of their technical equipment. Knowledge claims appearing in written reports emerge not from the heads of single individuals but from the interactions and dialogic argumentation of group members as they negotiate what their data are and how the data should be interpreted. Thus, the knowledge lies in the artifacts, the tools, and in the patterns of participation (Lehrer & Schauble, 2006). Much research has pointed to the inaccuracy and harm of defcit models (e.g., Yosso, 2005). Such models neglect the knowledge (or ways of participating) that students do bring with them to school. The knowledge valued in school assessments is often fact-based bits of information and terminology that say little about students’ understanding of key concepts or theories, or about their profciency in thinking scientifcally about real problems. The focus of traditional science education may discount minoritized students’ many competencies (Bang et al., 2012). Yosso (2005) identifed “funds

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of knowledge” or “capital” shared by members of minoritized communities that are frequently discounted and ignored in schools. For instance, although research has established the cognitive and social value of multilingualism, most schools in the United States fail to value the dual-language abilities of emerging bilinguals (Yosso, 2005). Participationist theories directly address issues of equity in learning environments through their focus on how learners are positioned in learning settings. For example, as we have discussed, when teachers ignore students or correct them, they may be positioning them as “not knowing science” and “not able to contribute to the discussion” (Dohn, 2016). A repeated pattern of such events could contribute toward the development of student identities as “good at science” and “bad at science.” Battey and McMichael (2021) discussed how teachers can – even inadvertently – position students of color as good or poor at mathematics through class discussions. These analyses go beyond a prevalent kind of cognitive analysis of discourse that focuses on properties of questions and responses (such as whether teacher questions and student responses involve higher-order thinking or elaborated ideas) but do not consider how patterns of questions and answers may position students (Murphy et al., 2018). The two approaches provide essential, complementary lenses on discourse. Cognitive theorists can avoid defcit framing by emphasizing the value of the varieties of knowledge and skills that students “have” and “bring with them” to science class. They will do best at this, however, if they emphasize the cultural assets of knowledge that students share with their communities and fnd ways to incorporate these assets into classrooms (Moll et al., 2005). Cognitive theories can also account for issues of equity through incorporating participationist lenses to examine issues of positionality and power relationships that fgure in many participationist accounts. Scafolding learning: Scafolds are aids provided to learners that enable them to accomplish tasks that they would not be able to accomplish unaided (Belland et al., 2013; Quintana et al., 2004). Scafolds can be “hard” or “soft” (Quintana et al., 2004). Hard scafolds are provided as a built-in part of a curriculum, such as via handouts that direct students to evaluate evidence along specifed dimensions or computer software that helps students construct arguments. Soft scafolds are more fexibly provided in response to how students are working, such as when a teacher gives hints to a collaborative group to help them advance more efectively (Hogan et al., 1999). In a seminal article on scafolding, Wood et al. (1976) examined how parents worked with infants to solve problems together. They found that, rather than telling the infants how to solve problems or just modeling solutions directly, parents engaged in joint problem solving, using six strategies: “recruitment, reduction in degrees of freedom, frustration control, direction maintenance, marking critical features, and demonstration” (Wood et al., p. 98). Thus, when learners are engaged in solving problems, scafolds provide supports that enable the learners to solve problems that they would not be able to solve unsupported. This is closely connected to Vygotsky’s zone of proximal development, which we discussed earlier. In the zone of proximal development, learners solve problems with the help of others (e.g., through scafolds) that they cannot solve without such help. A core idea in scaffolding theory is that scafolds are intended to be gradually withdrawn or faded (Collins et al., 1989). Once learners advance to the point that they do not need the support anymore, then the scafold can be gradually withdrawn. Subsequent research has examined the types of scafolds that can support science students’ reasoning and conceptual development. In a classic paper, Quintana et al. (2004) proposed guidelines for developing diferent types of scafolds, including: •

Organize tools and artifacts around the semantics of the discipline. For example, students can record in a diagram each piece of evidence they encounter and how each evidence relates to the explanations they are considering (Rinehart et al., 2016); such diagrams help students learn to emphasize explanation-evidence relations as core.

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•

• •

Use representations that learners can inspect in diferent ways to reveal important properties of underlying data. For example, the BGuILE system (Sandoval & Millwood, 2005; Sandoval & Reiser, 2004) provides students with computer-based tools to examine graphically the relations among variables bearing on population changes among fnches during a drought on a Galapagos island. Embed expert guidance about scientifc practices. For instance, a tutoring system can provide expert hints when students run into difculties conducting experiments (Gobert et al., 2015). Facilitate ongoing articulation and refection during the investigation. Quintana et al. (2004) recommended that systems provide guidance for the key metacognitive regulation processes: planning, monitoring, articulation of ideas, and noting epistemic features of science.

The scafolds designed by cognitive theorists and participationist theorists frequently overlap. Cognitive theorists conceive of scafolds as supporting the cognitive processes needed to construct knowledge. Participationist theorists emphasize the role of scafolds in supporting participation in a community’s thinking, and in enculturating students into productive ways of interacting around science. In practice, these goals can overlap considerably. Collaborative learning: There is a great deal of research on how to foster productive collaborative learning in science classes. As with research on scafolds, there is overlap in recommendations for collaborative learning by cognitive and participationist theorists, despite diferences in how collaborative learning is characterized. Reviewing a wide spectrum of research, Hmelo-Silver and Chinn (2016) concluded that efective groups evince the following core processes: (1) There is positive interdependence, which means that it is essential that students work together in order to succeed (Johnson & Johnson, 1992). If one student can succeed on their own without help from others, then positive interdependence does not exist. (2) The students have and display mutual respect for each other (Rogat et al., 2013). (3) Students engage in high-quality thinking together, such as engaging in argumentation using appropriate reasons and counter-reasons (Asterhan  & Schwarz, 2016) or co-constructing knowledge through sharing ideas and giving explanations (Webb, 2013). (4) There is uptake of each others’ ideas (Barron, 2003; Webb & Farivar, 1994). (5) Students develop shared norms that regulate their joint work together (Duncan et al., 2021; Saleh et al., 2021). Cognitive approaches to collaborative learning frequently emphasize the goal of fostering highquality strategy use during collaborative interactions, such as giving explanations and reasons (Webb, 2013). To encourage these, theorists have developed means such as scripted cooperation in which students take turns engaging in summaries and checks of each others’ summaries (Fischer et  al., 2013; O’Donnell, 2006) or question stems, which give students starter language for asking questions of each other (King, 2002). Group investigation is an efective collaborative learning method that structures students’ work in research groups as they plan and carry out investigations by gathering, organizing, and analyzing information from multiple sources (Sharan et al., 2013). Participationist approaches to collaborative learning emphasize the joint community goals and practices of creating knowledge together. One example is communities of learners (e.g., Bielaczyc & Collins, 1999; Brown  & Campione, 1994; Fong  & Slotta, 2018; Kali, 2021), in which students work together as a collective group of learners to produce new knowledge. Students can revise and improve this knowledge through ongoing inquiry. Teachers play an orchestration role rather than providing explicit instruction. Another example is knowledge building (KB) (Chen & Hong, 2016; Kali, 2021; Lei & Chan, 2018). The primary focus in KB is on the collective achievements of the group in producing jointly established knowledge. Students identify and work on problems of authentic interest to them and work together to post and establish knowledge claims in an interactive technological environment called a knowledge forum. As in real knowledge-producing communities, students’ work is distributed so that diferent students make diferent contributions to the class’s community knowledge, which is embodied in the notes that are posted in the knowledge forum.

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Cohen and her colleagues (Cohen, 1994; Cohen et  al., 2002) pointed out that collaborative learning activities can aggravate status diferences that exist between students. If less knowledgeable students interact with more knowledgeable students, the diferential displays of knowledge on that topic may reify the students’ senses of diferences in profciency. Cohen’s instructional remedy for this is twofold. First, teachers must prepare students who are less ready to contribute to a collaborative project by helping them build their relevant background knowledge prior to launching the collaborative work. Second, teachers should emphasize to students that the problems they are working on are complex and that achieving success will require the insights and skills of everyone in the group. In addition, collaborative problems need to be complex enough to foster positive interdependence because the problems are indeed too complex for individual students with their single and limited perspectives to solve on their own. All the scafolds discussed in the previous section have been used extensively with collaborative groups, and in fact most were designed to support reasoning and knowledge construction in collaborative groups. Additional scafolds that have been designed specifcally for collaborative groups include cognitive roles and self-evaluation. In a project in which sixth graders were conducting science investigations, Herrenkohl and Guerra (1998) developed three cognitive roles that improved students’ interactions when responding to group presentations made to the whole class after each investigation. The three roles were: (1) making a prediction and building a theory, (2) summarizing results of investigations, and (3) relating the results to the prediction and theory. White and Frederiksen (1998) found it was efective to have groups of seventh, eighth, and ninth graders evaluate their own group work on dimensions such as being systematic and writing and communicating well. Self-regulated learning: Many cognitive theorists have focused on self-regulated learning (SRL), which refers to the efective orchestration of motivational and learning strategies to monitor and control one’s learning. Self-regulated learners “are generally characterized as active, efciently managing their own learning through monitoring and strategy use” (Greene & Azevedo, 2007, pp. 334– 335). Theories of SRL aim to model how a range of cognitive (e.g., prior knowledge and beliefs), motivational, and contextual factors shape the learning process (Greene & Azevedo, 2007). An infuential SRL model was developed by Winne and Hadwin (1998) and elaborated by Greene and Azevedo (2007). In this model, learners are viewed as engaged in a task. The task conditions (resources, instructional cues, time, and social context) and cognitive conditions (beliefs, dispositions, and styles; motivational factors; domain knowledge, knowledge of task, and knowledge of study tactics and strategies) infuence the standards used to evaluate products, as well as the operations used to develop products. The products include, at diferent phases of the project, outputs such as a defnition of the task, goals and plans, studying tactics, and adaptations; these are performed iteratively until a fnal product is produced. The products along the way are evaluated against the learner’s standards (e.g., if the student is developing an explanation, the learner might use standards such as a feeling of understanding (a standard) to determine if the developed explanation is acceptable. Recent work from a cognitive perspective has continued to develop SRL theories, identifying an increasingly large range of factors that infuence SRL processes and strategies used during them (Schunk & Greene, 2018). Participationist approaches to learning do not center on internal cognitions as SRL theories do, but some analogous constructs appear in participationist accounts. For example, rather than focus on individual standards for evaluating products, participationist approaches emphasize that standards are socially developed and sustained (Cobb et al., 2003; Duncan et al., 2021). Similarly, participationists emphasize that the knowledge produced by a group is negotiated socially within the group and is not created strictly by individuals independent of the group; this would entail that regulatory processes be at least partly social. Research strongly supports an important role of metacognition in learning (Barzilai & Chinn, 2018; Barzilai  & Zohar, 2014). Metacognition refers to regulation of and knowledge of one’s

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cognitive processes. Metacognitive skills involve the control of cognitions through forming plans, monitoring and evaluating progress, and deploying productive strategies. Metacognitive knowledge involves knowledge about cognition, such as knowledge of what scientifc knowledge is, what strategies are reliable for achieving knowledge, and so on. The facilitative role of metacognition in promoting learning has been extensively documented in education (e.g., Barzilai & Ka’adan, 2017; Efklides, 2011; Frazier et al., 2021). For example, instruction to promote meta-strategic knowledge has a positive efect not only on participants’ meta-strategic knowledge but also on their ability to apply these strategies, such as the control of variables strategy (Zohar & Ben David, 2009). In participationist accounts of learning, there is often a corresponding emphasis on discursive refection by learners on their individual and collective learning and thinking processes. For instance, Ryu and Sandoval (2012) found that an elementary school class that increasingly centered evidence in their scientifc modeling practices across a year engaged regularly in discussions that refected on these practices (e.g., articulating that they were using data they gathered to convince each other). From an SRL perspective, these are metacognitive refections on practice. Chinn et al. (2020) provided examples of such refective (and metacognitive) discourse. Recent research  – both from cognitive and participationist perspectives  – has extended SRL constructs to social situations (Järvelä et al., 2016; Lobczowski et al., 2020; Rogat & LinnenbrinkGarcia, 2011; Schoor et al., 2015). This research seeks to understand how groups of learners regulate their joint work through constructs such as socially shared regulation (developing joint goals, plans, and practices) and co-regulation (e.g., one student helps another regulate their individual processing [Panadero & Järvelä, 2015]). A high quality of socially shared regulated learning is also associated with greater learning (Panadero & Järvelä, 2015). Motivation: Research on motivation has proceeded in parallel and in combination with research on learning for many decades. Current theories of motivation are too numerous to review in this brief section; a recent comprehensive review can be found in Anderman and Anderman (2020). Here we note just a few infuential theories and constructs. Many motivational theories can be viewed as falling broadly within the cognitive tradition because they emphasize orientations or motivations that learners typically “have.” For example, students may be characterized as having learning or mastery goals to understand what they are learning and to gain competence, versus having performance goals to perform better than other students or to secure grades or other external markers of success (Hulleman et al., 2010). Most researchers agree that mastery goals are advantageous for students’ learning and should be fostered in schools. There is more debate over the value of performance goals, with some researchers emphasizing they have a positive role to play alongside mastery goals, and others believing they should be strongly deemphasized (Senko et al., 2011). It is worth noting that much schooling is organized around performance goals with much less emphasis on mastery goals in many school environments. Participationist approaches have also emerged within motivational theories (Nolen, 2020; Nolen et al., 2015). Participationist approaches emphasize that motivation emerges as an interaction between people and environments and is closely tied to identities within various situations. For instance, a student who is positioned by teachers and other students as competent in inquiry may exhibit high degrees of engagement in a discussion about how to develop evidence to test a model, whereas the same student may disengage on another day when the context changes to a more lecture-oriented format (Hand & Gresalf, 2015). One infuential motivational theory is self-determination theory (Deci & Ryan, 1985; Ryan & Deci, 2000, 2020). Self-determination theory (SDT) makes the well-known distinction between intrinsic and extrinsic motivation. Intrinsic motivation occurs with activities done for their own sake, or for their inherent interest or enjoyment; most learning through the life span is driven by intrinsic motivation. In contrast, extrinsic motivation occurs when people perform behaviors “for reasons other than their inherent satisfactions” (Ryan & Deci, 2020), such as for external grades. SDT

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views humans as tending to strive toward psychological growth and integration. But for these to be realized, the environment must support three fundamental human needs: autonomy, competence, and relatedness. Autonomy involves initiative, choice, and ownership over what one does; it means that people are able to engage in activities based on interests and values rather than external control. People are more engaged when the goals they are pursuing have value to them (Wigfeld & Eccles, 2000). Competence involves the feeling of mastery, which is best supported in environments with optimal levels of challenge, supportive feedback, and opportunities for growth. Relatedness involves a sense of belonging to a community and connection to others, with respect and caring. These three needs have loomed large in recent science education research. First, the notion of autonomy has been enlarged in science education research advocating epistemic agency by students (Duncan et  al., 2021; González-Howard  & McNeill, 2020; Stroupe et  al., 2018). Students with epistemic agency have the autonomy to make up their own minds about what ideas to develop and adopt, and even which questions to address and which epistemic standards to use to evaluate questions and ideas (Chinn et al., 2018; Duncan et al., 2021). Researchers have also recommended increasing engagement in activities that connect with students’ interests and values, such as developing projects for minoritized students that enable them to pursue intimate concerns with equity and social justice (e.g., Nasir & Vakil, 2017). Second, science education researchers have also conducted research on students’ self-efcacy (Chen et al., 2016; Zeldin et al., 2008) – their belief that they have the competence to succeed at tasks. Self-efcacy positively predicts students’ science learning (Chen et al., 2016). And third, science educators have emphasized relatedness particularly in their emphasis on enculturing students into communities of learners in which students are engaged collectively in engagement in the disciplinary practices of science (Engle & Conant, 2002). Teachers can also promote relatedness through their own caring. Battey et al. (2016) defned fve dimensions of caring discourse in classrooms: addressing students’ behavior in positive ways, framing STEM ability positively, acknowledging students’ contributions, attending supportively to students’ language and culture, and setting a positive emotional tone. Classrooms in which teachers engage in these behaviors can better support students’ need for relatedness. Conceptual change: Chapter 5 in this volume addresses learning progressions and conceptual change, and we will leave most of the discussion of these issues to that chapter. In this section we will focus more narrowly on how cognitive and participationist theories characterize and foster conceptual change. Research on conceptual change emphasizes that some topics in science are especially difcult to learn because they involve more fundamental changes in ideas, or how ideas are applied, than others. It is harder to learn about the molecular nature of matter than it is to learn some facts about snakes or foxes. Within broadly cognitive traditions, two very diferent approaches to conceptual change have been advanced. One of these posits that during conceptual change, children move from one coherent conception to another (Vosniadou et  al., 2001). For example, children who have a coherent conception of the earth as fat may change to the view that the earth is round. Often, students may invent intermediate theories in between the initial (naïve) and fnal (normatively scientifc) theory in order to make sense of the information they are encountering. For example, some students develop the view that there are two “earths” – the ground we live on and a second, separate earth up in the sky that they see in photographs and other media (Samarapungavan et al., 1996; Vosniadou & Brewer, 1992). Chi (2005) explained challenging changes as requiring students to shift from viewing phenomena as direct processes (e.g., heat moving through materials) to viewing them as emergent processes (e.g., heat as an emergent property of molecular motion). An alternative approach views knowledge as much less coherent. Rather than having coherent theories or frameworks for understanding phenomena, students deploy many diferent, highly contextual elements of knowledge that diSessa called p-prims (diSessa, 1993; diSessa et al., 2004). For

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example, when thinking about phenomena related to forces and motion, one situation may elicit the p-prim that force is a mover  – that a directed impetus causes something to move; another may elicit the p-prim of dying away – that all motion dies away over time. These p-prims need not be assembled coherently; rather, they may be assembled in quite diferent ways from situation to situation. From this point of view, what some have called conceptual change is a gradual process of learners shifting the range of use of existing p-prims rather than developing entirely new ideas. Participationist accounts of conceptual change treat all these changes as shifts in group activity: “Conceptual growth by a community or group is change in the concepts and conceptions it uses in communicating, understanding, reasoning, solving problems, and making decisions, or in the distribution of participation in these activities across members of the community or group” (Greeno & van de Sande, 2007, p. 12). This approach emphasizes that conceptual change should not be sought merely in response to test or interview items, but in changing patterns of using principles and concepts for purposes such as joint problem solving. Participationist accounts thus center group and community action as settings both to promote change and to determine whether change has occurred. These accounts also emphasize that patterns of discourse should change (such as patterns of eliciting and sharing ideas with others), alongside change in how learners use concepts and principles in their discourse. Further, even traditional individual measures of cognitive understanding are discursive interactions in which the social setting of the test or interview can afect performance. For instance, the specifcs of interactions between an interviewer and a student on the seasons can strongly afect what students say and therefore infuence researchers’ interpretations of what their conceptions are (Halldén et al., 2007; Sherin et al., 2012). Much of the current instructional work on conceptual change – whether from a cognitive or sociocultural perspective – emphasizes the role of social communities of learners in promoting growth. One exception to this is the more individualistic instructional approach using refutation texts to promote change. Refutation texts are texts that address students’ common mistaken ideas by explicitly identifying what the common mistaken ideas are (e.g., explaining to students that many people mistakenly believe that heat and temperature are the same), and then presenting the normative scientifc explanation. However, Zengilowski et al. (2021) have argued that the efects of refutation texts tend to be short-lived; in addition, refutation texts are less likely to be efective for deeply held conceptions such as conceptions about climate change than for less consequential conceptions. As would be expected if fundamental forms of conceptual change are very challenging to achieve, it can take an extended period of time and many opportunities to interact and work with new ideas before conceptual change will take hold. In contrast to refutation text–based instruction, most curricular eforts to promote conceptual change have engaged students in extended units in which they work with hands-on evidence, simulation environments, and secondhand reports of evidence to develop and evaluate scientifc models or explanations (Chinn et al., 2013). Further, most research has sought to scafold students’ social processes of inquiry to support conceptual change using approaches like those that we described earlier (e.g., Amin & Levrini, 2018; Chinn et al., 2013; Smith & Wiser, 2015). Learning environments also encourage the formation of communities that adhere, and even develop, community norms. For instance, the PRACCIS project provides students with opportunities to develop their own criteria for what count as good models and evidence and then hold each other accountable to their own publicly agreed-on criteria (Chinn et al., 2018). Designers of learning environments also include many approaches to enhance active engagement, such as hands-on experimentation and data collection, use of simulation environments, and addressing problems of immediate import to students’ communities (Morales-Doyle et al., 2019). Many designs involve students learning to coordinate multiple representations, such as representing evaporation at both the level of observable phenomenon and the level of microscope particles, and seeing how these are related; technological and other scafolds have been developed to help students coordinate these representational levels (e.g., Wu et al., 2001).

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One point of agreement between cognitive and participationist theorists is that, to achieve conceptual change, it is important for students to be able to understand the distinct, alternative conceptions under consideration (such as their own conception of heat transfer and the alternative conception proposed by a classmate in class or group discussions (Greeno & van de Sande, 2007; Vosniadou, 2007). In other words, learners need to step back and refect metacognitively on the different and competing conceptions and how they relate to the available evidence in order to evaluate them. Students need to be able to “bracket” these conceptions as objects of thought that can be evaluated (Kuhn, 2021). Recent empirical work has also revealed the role of metacognition in inhibiting misconceptions from being activated (Mason et al., 2019; Ronfard et al., 2021). Views of inquiry: As science education has increasingly incorporated goals of learning to engage in inquiry, it has become important to articulate what inquiry involves so that instruction can be developed to promote inquiry. Historically, cognitive and participationist approaches to fostering students’ scientifc reasoning have tended to conceptualize scientifc inquiry and scientifc reasoning in diferent ways. Cognitivists tended to have an “image” of “science-as-logical-reasoning” (Lehrer & Schauble, 2006, p. 156). Scientifc reasoning was viewed as a set of heuristics and skills (such as the skill of controlling variables in experiments). These heuristics and skills were viewed as quite generally applicable, as noted earlier (e.g., if students learn to control variables, they can apply this to conduct and evaluate experiments across many diferent scientifc topics). In contrast, participationist approaches have tended to advance an image of “science-as-practice” (Lehrer & Schauble, 2006, p. 158). Building on historical, philosophical, anthropological, and psychological studies of how scientists work, this image emphasizes the collaborative work of scientists as they grapple with the material world (using a variety of instruments) to produce evidence and models that can account for this evidence. This perspective emphasizes the various inscriptions or representations that scientists create and share to make their work visible and shareable. “Science-as-practice emphasizes the complicated and variable nature of science” (Lehrer & Schauble, 2006, p. 158). Science unfolds quite diferently in every domain as scientists wrestle with the contingencies of producing, interpreting, and combining evidence in each distinct domain (Latour, 1999). There is ongoing debate over these diferent images of science (Fischer et al., 2018). Chinn and Malhotra (2002) argued that much of science education has focused on oversimplifed rules of reasoning, such as an algorithmic view of controlling variables as a matter of checking of which of several provided variables are kept constant and which are varied. In actual practice, it is formidably difcult for scientists to work out what the best controls should be when conducting experiments; actual practice requires extensive domain knowledge about a wide range of possible causal confounds that can afect experimental results. Further, authentic science does not provide a set menu of methodological procedures that can be taught to students; rather, science involves constant tension and argumentation over what the best methods are in a particular domain, as scientists grapple with making the material world yield usable evidence (Duncan et al., 2018). In addition, collaboration in scientifc work is thoroughgoing, as scientists engage in ongoing cycles of co-construction and critique of ideas (Ford, 2008). For such reasons, a view of science as practice has become ascendent in recent research and standards (e.g., NGSS Lead States, 2013). Cognitive approaches can certainly embrace such views, but there is a need to acknowledge the thoroughly social and contextual nature of scientifc inquiry, and that knowledge development and evaluation is not an individual process but a community process, as demonstrated by research across many felds of science studies (Oreskes, 2019). Direct instruction versus learning through inquiry: Science educators have for decades expressed an interest in promoting learning through engaging in inquiry (Duschl & Tahirsylaj, 2021), and much research in science education today explores approaches and processes of learning through inquiry (Duncan et  al., 2021; Duncan  & Chinn, 2021). However, other scholars have argued that learning through inquiry is inefectual. Scholars who argue for direct instruction are almost invariably

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cognitivists, although many cognitivists do not advocate direct instruction. In contrast, participationist theorists generally support the use of collaborative inquiry environments, given their view that learning involves participating in collaborative activity as a means to change patterns of participation. Kirschner et al. (2006) argued for the superiority of direct instruction over constructivist (including inquiry-based) approaches on the grounds that constructivist instruction overburdens working memory, leaving too little space in working memory for learners to consolidate new concepts and understandings. However, there is a broad body of research supporting the efcacy of various inquiry-based methods of instruction, by which we include methods termed problem-based learning, project-based learning, inquiry-based learning, guided discovery, and others. Diferent authors use these terms in diferent ways, but what they have in common is afording students the epistemic agency to work out explanations, problem solutions, policy solutions, and so on. Empirical research in support of this position has been reviewed by Hmelo-Silver et al. (2007), Schmidt et al. (2009), Chinn et al. (2013), Kapur (2016), and Duncan et al. (2021). Many arguments supporting direct instruction appeal to empirical evidence showing that direct instruction is superior to pure, unguided discovery, in which students are given little or no guidance at all (Kirschner et  al., 2006; Klahr  & Nigam, 2004). However, proponents of learning through inquiry have acknowledged that pure discovery methods are inefective and that guided (or scafolded) forms of discovery learning are needed. Nonetheless, arguments in favor of direct instruction often continue to make the straw-person case against pure discovery (e.g., Zhang et al., 2021). Many studies that are cited as supporting direct instruction target the learning of only very simple concepts or tasks that bear little resemblance to real science (the “science-as-logical-reasoning” view discussed in the previous section) (e.g., Chen & Klahr, 1999; Klahr & Nigam, 2004). Many such studies have targeted teaching elementary or middle school students how to control variables, using extremely simple tasks. Such tasks can mislead students about the epistemology of science – leading them to see science as simple, algorithmic, and cut and dried (Chinn & Malhotra, 2002). Then they are not prepared to grasp real scientifc experiments, in which constructing proper controls is difcult and often contentious. Ford (2005) demonstrated that direct instruction in how to control variables on simple tasks produced higher performance than inquiry-oriented instruction on tests that treated control of variables as algorithmic, but inquiry-oriented instruction was much more efective at promoting high levels of performance on a complex task with the answers not so obvious. Finally, newer lines of research argue that, even when it is desirable to eventually explain things to students, the best instructional approach is to guide students in frst trying to discover or invent what is to be learned (Kapur, 2016; Kapur & Bielaczyc, 2012; Schwartz & Bransford, 1998; Schwartz et  al., 2011). For instance, when learning about density, students learn more if they try to solve presented density puzzles frst before hearing an explanation about density than if they receive the explanation frst and then practice using it (Schwartz et al., 2011). Thus, even when telling students is valuable, it does not follow that inquiry does not also have a vital place in the learning process. Finally, real inquiry-oriented learning environments are in practice complex mixtures of multiple forms of instruction (Duncan et al., 2021). The debate between inquiry learning and direct instruction is a false dichotomy; both forms of learning have their place in a comprehensive curriculum and should be weighted according to the context (National Research Council, 2012). Over extended periods of time, even in predominantly inquiry-based approaches, there will be many elements of direct instruction incorporated. For example, teachers might directly explain the structural components of arguments to support students’ inquiry (e.g., McNeill & Krajcik, 2008). What is needed is research that contrasts instructional approaches that are actually considered viable alternative forms of instruction spanning weeks or months. Smaller-scale studies, such as brief laboratory or classroom studies that use a single form of instruction on isolated tasks, do not speak to how to organize these larger-scale curricula.

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Epistemic Growth To this point in the chapter, we have discussed cognitive and participationist theories and how they view a spectrum of issues in science learning and instruction. We have observed some ways in which elements of cognitive and participationist theories can be integrated to account for phenomena uncovered by both approaches. In this second section of the chapter, we illustrate two specifc eforts at integration. Specifcally, we discuss two contemporary approaches to promoting scientifc reasoning and discuss how they can be viewed as integrating some elements of the two theoretical perspectives. The two approaches are: instruction in argumentation and instruction to promote apt epistemic performance. Then we discuss how these two approaches can be used to address an important contemporary problem – the problem of how to promote better thinking by citizens in the modern, complex information society.

Argumentation Argumentation is a central practice of science (NGSS Lead States, 2013). Among the many lines of research on argumentation, those that adopt a view of argumentation as a social, norm-governed practice have become more prevalent (Kuhn et al., 2013). This work has sought to promote changes in students’ competence to participate in argumentation with others, while also considering the cognitive structures that students develop (e.g., Reznitskaya & Wilkinson, 2021). There is a growing line of research in science education examining how to support students’ argument skills (Osborne et al., 2019). For example, a line of research using the “Argue with Me” method shows that engagement in goal-based dialogic practice with peers, using collaborative activities and scafolded prompts for refection, fosters argument skills and epistemic growth (see Iordanou & Rapanta, 2021). Rooted in Vygotsky’s idea of the complementarity of social and internal thinking, students engaging in dense dyadic argumentation with peers show a progressive development of their argumentation skills, which is frst evident in their dialogic interaction and then in their individual written arguments. Argumentation at the social plane is a facilitative condition for developing and sharing the norms of argumentation (Kuhn et al., 2013), consistent with the participationist perspective. Engagement in argumentation at the social level could also be a fruitful condition for promoting scientifc learning (see Asterhan & Schwarz, 2016; Iordanou, 2022). For scientists, argumentation is at the heart of the process they employ for producing new reliable knowledge (Duschl, 2008). Given that argumentation is a means of advancing knowledge in science, can argumentation be deployed as a means of learning science content knowledge through inquiry? Indeed it can: Many researchers view engagement in argumentation as a means for promoting learning and conceptual change in science education (Asterhan  & Schwarz, 2016; Murphy et  al., 2018). During argumentation students have to present their views verbally, ofer explanations and their reasons for holding those views, and defend their views after being challenged by others. Students are also exposed to and actively engaged in alternative views, comparing views, trying to identify strengths and weaknesses of each view, address discrepancies and reconcile them or choose the stronger one. Engagement in argument-writing tasks, especially from multiple sources, supports understanding of the subject matter and promotes more inferences and greater integration of central ideas (Wiley & Voss, 1999). All these processes are facilitative conditions for supporting students’ learning (Asterhan & Schwarz, 2016; Newton et al., 1999). Asterhan and Schwarz (2016) presented a comprehensive analysis of the role of argumentation in learning content, including science topics, such as evolution, in which conceptual change is involved. Thus, participation in the practice of argumentation can promote goals of advancing ideas, measured in cognitive terms through cognitive assessments as well as through changes in participation.

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Peer interaction during argumentation, as well as engagement in metacognitive refection, are facilitative conditions for promoting argumentation skills. Iordanou (2022) compared students who engaged in argumentation with additional refective activities (e.g., reviewing an electronic transcript of their dialogue to address questions such as whether they had used evidence in their counterarguments) with students who engaged in argumentation without the refective activities. The students who engaged in argumentation practice with additional refective activities showed greater gains in developing argument skill – particularly in employing evidence to weaken an opposing position  – compared to a control group who only engaged in the argumentation practice. Microgenetic analysis of dialogues during the interventions revealed that experimental condition participants exhibited overall greater improvements at both strategic and meta-strategic growth, as well as epistemic growth, compared to control condition participants. This study provides evidence that development in metacognition – and epistemic growth, as well – underlies the development of argument skills. Another instructional method integrating cognitive and participationist approaches has been proposed by Reznitskaya et al. (2012), who viewed learning arguments as a matter of developing argument schemas, which include elements such as claims, reasons, warrants (the reasoning that connects reasons with claims). These schemas can be applied generally in diferent situations, though of course the particular reasons and claims will difer from topic to topic. In addition to this schema, people have a schema for knowledge that treats knowledge as tentative, relative, and contextual; and yet it is possible for some knowledge claims to be judged stronger than others because they are supported by better arguments. Reznitskaya et al. (2012) then applied constructs from Vygotsky’s sociocultural theory to explain the development of schemas. They argued that argument schemas are developed, at least in part, through participation in a dialogic group activity, where students can observe, try out and, eventually, internalize novel language and thought practices. Although each dialogic discussion diferent, participants will engage with common “cultural tools” . . . characteristic of argumentation, including taking a public position, supporting it with reasons, challenging other discussion participants, and responding to counterarguments with rebuttals. Over time, the rational processes externalized through group argumentation become part of an individual argument schema. (Reznitskaya et al., 2012, pp. 289–290) Thus, Reznitskaya et al. (2012) took key constructs from Vygotsky’s sociocultural theory to explain how a cognitive object (a schema) could develop. The core idea was that what is encountered in participation with others becomes appropriated or internalized (cultural tools, participation in dialogic discussions, appropriating dialogue at the interpersonal plane to internalized use).

Apt Epistemic Performance Another approach to epistemic growth that integrates elements of participationist and cognitivist views is the Apt-AIR framework developed by Barzilai and Chinn (2018). The Apt-AIR framework builds on the philosophy of Sosa (2011, 2015), who focused on apt epistemic performance as the objective of human thinking and reasoning. An epistemic performance is an action related to developing knowledge, such as forming a belief or making a knowledge claim. An epistemic performance is fully apt if three conditions are met: (1) The performance is successful (e.g., the belief that the climate is warming is accurate or extremely well grounded in the scientifc evidence). (2) The performance is produced through the competence of the person or community (e.g., the belief that climate is warming is grounded in a person’s ability to appraise scientifc evidence and the consensus of climate scientists on this issue, and not in the individual’s political commitments). (3) The performance

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refects an individual’s or community’s meta-knowledge that there is good enough justifcation to form a belief, rather than withholding belief for lack of evidence. Barzilai and Chinn (2018) further unpacked apt epistemic performance in terms of three components of epistemic thinking and fve aspects of engagement. We briefy explain each of these next. This framework can be viewed as seeking to synthesize and integrate many of the conceptualizations of epistemic growth that have been advanced in the feld (Barzilai & Chinn, 2018), including insights from both cognitive and participationist approaches. Components of epistemic thinking: Aims, ideals, and reliable processes: Chinn et  al. (2014) proposed that epistemic thinking can be conceptualized as having three components: Aims and value, Ideals, and Reliable processes (summarized as the AIR model). Aims are the epistemic goals that people set – goals involving developing some kind of representation of how the world is, such as developing accurate beliefs, understanding, knowledge, or explanations. Value refers to the worth assigned to diferent goals; for instance, developing an understanding of cancer is valuable to many people because of its worth in supporting better treatments for cancer. Epistemic ideals are the standards or criteria used to evaluate whether epistemic aims have been achieved. For instance, ideals for evaluating scientifc models, theories, and explanations traditionally include criteria such as ft with evidence, explanatory power, parsimony, and fruitfulness (Longino, 1996). Reliable epistemic processes are the social and individual processes that are likely to achieve epistemic aims. For instance, validation of measures is a methodological process that makes it more likely that scientists will achieve well-grounded theories and models. Critique of ideas by the scientifc community and serious consideration of these critiques are reliable processes of the community of science (Longino, 1990). Epistemic communities typically developed shared aims and ideals that they apply to their practice, as well as a shared sense of which methods and other processes are reliable for the community to use. Five aspects of engagement in aims, ideals, and reliable processes: The second dimension of the Apt-AIR framework comprises fve aspects of engagement with epistemic performance – that is, engagement with appropriate aims, ideals, and reliable processes. The fve aspects are: 1.

2.

3.

4.

Cognitive engagement in epistemic performance. This involves engagement in addressing problems of science, such as understanding whether and why climate is changing. Cognitive engagement involves selecting valuable epistemic products to aim for, selecting and using appropriate epistemic ideals, and using processes (individual and social) that enable people to realize their epistemic aims. Regulating and understanding epistemic performance. Apt epistemic performance involves metacognitive regulation of epistemic performance. For instance, people can select aims, ideals, and processes based on which are most appropriate for a particular situation. They can plan how to fnd out what they want to know. They can monitor and evaluate how well they are progressing based on how well their product is meeting their ideals. All of this is facilitated by an understanding of the appropriate aims, ideals, and processes and the conditions under which their use is appropriate (e.g., appreciating that ft with evidence is a good ideal for scientifc knowledge, understanding what counts as strong scientifc evidence, and so on). Participating in epistemic performance with others. This aspect involves social engagement in epistemic performance with other people. Scientists of course engage as teams and communities of scientists to develop knowledge. Students can learn some of these social practices by forming communities of inquirers within their classrooms. But beyond this, laypeople also need to act together to develop knowledge as laypeople, interacting with each other and with their communities. Caring about and enjoying epistemic performance. This aspect involves developing motivations and dispositions to pursue valuable epistemic aims, treating them as valued and worth pursuing even if they are efortful. In addition, the aspect encompasses emotional aspects of epistemic

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5.

performance; the goal is for students to develop positive emotional responses to engaging in epistemic performance, particularly enjoyment. Adapting epistemic performance. Students need to learn to adjust their epistemic performance appropriately across diferent contexts and tasks. This involves adapting epistemic aims, ideals, and processes depending on tasks afordances and constraints. It could involve, for example, deciding when one has the knowledge to evaluate evidence oneself (such as recognizing that a study touted as evidence for the use of hydroxychloroquine to treat COVID-19 is seriously fawed due to nonrandom assignment to treatment) and when one needs to trust experts (e.g., when it comes to evaluating the technical quality of studies supporting claims that global climate is changing).

The Apt-AIR framework thus incorporates elements from cognitive approaches, such as the analyses of cognitive goals and processes, as well as metacognition. It also emphasizes the centrality of the social aspect of knowing, so that changes in ways that people interact with each other are part of what is involved in epistemic growth. The metacognitive aspect of the Apt-AIR framework can encompass students’ understanding of the nature of science, such as their understanding of the diference between theories and laws, that science is social, that science is creative, and so on (Lederman et al., 2002; Summers et al., 2019). But it especially emphasizes the importance of knowledge about reasoning that is close to the practice of sciences (cf. Allchin, 2011; Höttecke & Allchin, 2020). The metacognitive understanding of aims, ideals, and practices in the Apt-AIR framework are not primarily highly abstract understandings, such as the idea that science is a social process. Rather, the understanding is of specifc social processes (e.g., peer review, mutual critique of knowledge claims, subjecting hypotheses to specifc empirical tests) that are reliable, as well as the conditions under which they are reliable (e.g., there needs to be a diversity of perspectives among the community of scientists, and the community must advance and uptake critiques) (Longino, 1990; Oreskes, 2019). According to the Apt-AIR framework, there should also be a strong focus on appreciating and using the ideals that can guide the lay evaluation of science claims (e.g., trustworthiness of scientists, and the grounds for considering them trustworthy, as well as conditions that can undermine trustworthiness). A critical focus must be appreciating reasons why all these processes are reliable – not just knowing that science involves social processes but also appreciating why these particular social processes can be regarded as reliable ways of producing knowledge (Chinn et al., 2021).

Education for a Post-Truth World Taking the ideas we have discussed in the previous sections on argumentation and apt epistemic performance, we now turn to a discussion of how science educators can prepare students to reason well in the so-called post-truth world. These instructional applications also incorporate elements of cognitive and participationist theories. In the so-called post-truth world, misinformation abounds, and science is distrusted by many as a way to know about topics such as climate change, vaccinations, and pandemics. An increasing number of scholars have addressed ways of better preparing students to think about scientifc issues in such a world (Barzilai & Chinn, 2020; Chinn et al., 2020, 2021; Feinstein & Waddington, 2020; Höttecke & Allchin, 2020; Kienhues et al., 2020; Leung, 2020a, 2021; Sinatra & Lombardi, 2020). Some key post-truth challenges include (Barzilai & Chinn, 2020; Kavanagh & Rich, 2018): the growing prevalence and impact of misinformation and disinformation; the growing rejection of well-established facts and claims; prioritizing personal beliefs, feelings, and experiences over facts and evidence; declining trust in science and journalism as institutions providing scientifc information; and increasing fragmentation and polarization of information consumption. All of these challenges make it difcult to form reliable judgments about scientifc matters.

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Chinn et  al. (2021) applied the Apt-AIR framework to analyze post-truth challenges and to generate fve recommendations for instruction that can prepare students for more apt epistemic performance in a post-truth world. These recommendations are compatible with the contemporary research on argumentation that we outlined earlier in this section. We summarize these recommendations next. The frst recommendation is to engage students regularly in more complex, “unfriendly” epistemic settings that approximate those of the post-truth world that prevails outside of school, with all its disinformation, misinformation, and rampant conficting arguments. School typically presents students with “friendly” epistemic environments – that is, environments that are carefully curated and have vetted information, so that the students are not exposed to the informational confusion that prevails out of school. Students will never learn to deal with the real-world challenges if they are not exposed to them so that they can learn how to address them more efectively. This will require reforming curricula so that students are exposed to sources and evidence of widely varying quality, reliability, and trustworthiness (Barzilai et al., 2020; Chinn et al., 2021). Students’ engagement in these environments should encompass all fve aspects of apt epistemic performance. Several lines of research support the conclusion that for students to learn to reason in realistic ways, they should experience inquiry in ways that problematize the aims, ideals, and processes they are using (Ford, 2005). For instance, Lehrer and Schauble (2004) and Manz (2016; Manz & Renga, 2017) have engaged frst and second graders in trying to work out on their own how to measure plant growth, as well as how to represent growth graphically. This leads them to work through many complex issues of establishing valid and reliable measures – afording understandings that would have never arisen if teachers had simply directed them to use a particular method to measure the plants. Thus, a critical way to grasp real science is to engage in the complex negotiation and working out of reliable methods needed to deal with the complexity of the actual world (Duncan et al., 2018; Manz, 2016; Manz & Renga, 2017). Learning environments like this will need to be carefully sequenced and scafolded. In a review of inquiry-based learning, Lazonder and Harmsen (2016) identifed a set of productive scafolds for inquiry, including process constraints, status overviews, prompts, presentation of heuristics, taking over part of the process for learners so that learners only do part of the process, and explanations (sometimes in advance, usually after learners were working on problems). Such scafolds would need to be adapted to support learners in more complex learning environments. The second recommendation is to help students learn how to adapt their epistemic performance across diferent situations. For laypeople, this includes developing a metacognitive awareness of when they have sufcient expertise to evaluate evidence themselves and when they need to defer to experts. Laypeople unavoidably lack the technical expertise to evaluate much of the evidence that scientists develop to support and counter various claims (Allchin, 2011; Chinn & Duncan, 2018; Duncan et al., 2018). Further, they do not have awareness of all or even most of the evidence bearing on an issue; developing command over a large body of evidence requires years of reading, attending conferences, and interchanges within a feld. In such cases, apt epistemic performance for laypeople will inevitably involve deferring to experts, and it will become critical for people to learn processes such as determining the extent to which scientists have consensus on a topic, whether sufcient scientifc consensus exists to support decisions, and so on (Chinn et al., 2021). This means that curricula should focus on the nature, afordances, and limitations of expertise as well as on enabling students to evaluate evidence themselves. Thus, the ways in which competent outsiders interact with science are not simpler or partial versions of how scientists interact with science; they are diferent. Further, laypeople engage with social media and with other communities in knowing about scientifc matters, and they need to learn efective ways of participating in such practices. Learning how to evaluate evidence oneself can be benefcial in some circumstances, as well. For instance, learning about common methodological problems in scientifc studies could enable learners

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to understand the fatal methodological weaknesses of the retracted study by Wakefeld that alleged a link between vaccinations and autism. It may also be sufcient to understand why early studies in 2020 that reported benefts of hydroxychloroquine were sufciently fawed methodologically to warrant caution in the use of this drug. Yet it is also important for people to gain metacognitive awareness of bounds of their knowledge and how to respond in various circumstances in light of these bounds (Bromme & Goldman, 2014). Inquiry and argumentation activities should be designed to promote lay ways of knowing, as well as a grasp of when it is appropriate to defer to others. This recommendation is in accord with the recommendations of Feinstein (2011; Feinstein & Waddington, 2020) to develop citizens who are competent outsiders with respect to science. Nonetheless, it may be valuable for students who will not become scientists to nonetheless come to appreciate how scientists work (their practices) and what their core achievements are (the content of science). Through understanding how science reliably produces useful knowledge, students can come to appreciate why science is trustworthy (Chinn & Duncan, 2018; Duncan et al., 2018). The third recommendation is to engage students in explorations into ways of knowing, which are inquiries into the strengths and weaknesses of diferent aims, ideals, and reliable processes for achieving knowledge. To begin with, one source of post-truth challenges is that people simply may not care about the truth. For example, political partisans may be willing to set the truth aside if it advances their political ends. Schools may provide few opportunities for students to deliberate on why truth matters or to consider the problems that arise when epistemic aims are discarded in order to achieve aims of power (Barzilai & Chinn, 2020). Thus, they should engage in explorations into why epistemic aims are valuable. Another cause of post-truth problems is that people have fundamental disagreements not only about scientifc conclusions but how to come to know about scientifc matters. For example, they do not merely disagree about whether vaccinations are safe and efective; they also disagree about how about how to know about vaccine safety and efcacy. Vaccine skeptics believe that experimental tests are deeply biased by the interests of the researchers and that personal experiences and interpretations should outweigh systematic scientifc evidence (Chinn et al., 2020). Similarly, people disagree about which sources of information are most trustworthy. Chinn et al. (2020, 2021) argued that in such cases, the needed next step is for people to engage in discussions and deliberations of how to know (see Lynch, 2016). Indeed, students should regularly engage in argumentation about proper aims and ideals of knowing, and about the reliability of processes for knowing. Learning to engage in such argumentative practices as members of a society is vital for productive deliberations within a democracy (Lynch, 2016). Although schools have not prepared citizens to engage in such metacognitive deliberations, there is evidence that students can readily learn to engage in such discourse. Chinn et al. (2020) argued that the discourse involves describing aims, ideals, and processes; justifying them (that is, giving reasons why they are valuable or reliable); and engaging in inquiry to investigate them through a dialogic give and take of reasons for and against using diferent aims, ideals, and processes. For example, students might engage in inquiry into cognitive biases and how they might be attenuated. This approach also opens science curricula to welcome alternative epistemologies into classroom consideration; students can discuss when diferent ways of knowing can be appropriately used (e.g., Bang, 2015; Medin & Bang, 2014). Students may come to recognize that there can be a role for multiple ways of knowing in diferent settings. The fourth recommendation is to promote caring and positive emotions. There is less research on how to accomplish these goals. Some have argued for promoting a commitment to intellectual virtues such as intellectual courage, intellectual honesty, and indeed a love of truth (Battaly, 2015; Lapsley & Chaloner, 2020). Current recommendations often focus on adopting direct instructional methods advocated by character education (Lapsley & Chaloner, 2020), but these could be extended to allow students to explore virtuous intellectual character through metacognitive refection and  inquiry,

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following the approaches outlined earlier. Researchers have also proposed ways to enhance positive epistemic emotions such as curiosity and to avoid negative emotions such as frustration when answers are not immediately forthcoming. Muis et al. (2018) identifed instructional practices that can promote productive epistemic emotions. Educators should develop learning environments that encourage curiosity through engagement in complex but attainable tasks. Students should also have many opportunities to refect on their thinking and the associated emotions, thus combining metacognitive refection with meta-emotional refection. They can also learn to reappraise emotions such as frustration as a response to conficting, difcult information and to use more efective strategies for addressing conficting information. Emphasizing to students that confusion is normal and appropriate during inquiry can also be valuable. Another element of instruction to promote caring about epistemic aims, ideals, and processes is centering epistemic agency. Students with epistemic agency have the authority within the learning settings to make up their own minds about what explanations to develop or support, how to evaluate and interpret evidence, and so on. Epistemic agency can also extend even to authority over determining what the epistemic aims of the classroom should be (e.g., what topics to investigate) (Bang, 2015; Miller et al., 2018), what ideals should be used to evaluate scientifc explanations and evidence (such as deciding on their own that models should ft the evidence and “show all the steps”) (Chinn et al., 2018), and which processes are considered reliable and which to use (Chinn et al., 2021). Epistemic agency may be crucial to promoting caring about epistemic practices. If students are simply told how to think, they will have little reason to use the instructed reasoning practices outside of settings where they are told to use them. But if they co-develop them themselves, they may instead develop a commitment to use them, not only in the classroom but beyond (Chinn et al., 2020, 2021). If students freely develop and commit to the epistemic ideals and processes that they are using, they may be more likely to care about using them. Developing community norms can be central to both cognitive goals of developing new conceptions of how to know and participationist goals of learning to interact in new ways when developing knowledge. As students engage in argumentation and other knowledge-producing interactions, they can hold themselves and each other accountable to the aims, ideals, and processes they identify, such as ensuring that their ideas ft all the available evidence, determining whether they are in a position to evaluate the evidence themselves, and evaluating whether there is scientifc consensus on a topic. The goal is for these ways of knowing and interacting to be extended to lay practices out of school as well. The ffth and fnal recommendation is to enable students to develop a working knowledge of how social institutions of knowledge production produce knowledge (and when and why they fail to do so). This should include an understanding of a range of institutional practices (Höttecke & Allchin, 2020; Howell & Brossard, 2021): (1) An understanding of the real processes by which science operates – in all its uncertainty, negotiation, and messiness. Students should learn not only that science is social, but what its specifc social practices are and how they make science reliable (and how science can often be regarded as reliable despite some optimal processes. (2) An understanding of how science media processes work – how these can change the way information is presented and the conditions under which they are more or less reliable (Maier et al., 2014). (3) An understanding of how social media produce and disseminate information and misinformation, from social media to Wikipedia (Höttecke & Allchin, 2020). The instructional methods for achieving these goals could include explorations into knowing (discussed as part of the third recommendation) directed at social systems of knowledge production. For instance, students could examine existing evidence and even develop their own evidence on the reliability of Wikipedia as a social source of scientifc knowledge. Or they could examine and develop evidence from science communications relating to how diferent processes of science journalism enhance or detract from reliability of science media. Another approach engages students in case studies of when these various systems work (cf. Clough, 2011) and when they do not (e.g., cases

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of racial and gender bias in science (Longino, 1990). Finally, students can engage in social systems themselves in their school communities and analyze them to understand how social production of knowledge works. These activities could include games and simulations in which they experience knowledge production systems. In all these activities, the goal should be to help students understand how these social systems really work, in all their “messiness” as well as conditions that undermine knowledge (e.g., incentives for sharing misinformation in some social media). These eforts should certainly encompass enabling students to understand how and why the processes of science – as it is actually practiced, not the sanitized version that is presented in school – are generally reliable, as well as conditions under which they are more or less reliable (Kienhues et al., 2020).

Conclusions This chapter has provided a broad overview of learning theories and issues raised by learning theories for science education. The chapter has pointed to several tensions that continue to exist across learning theories that have been prevalent in science education over the last several decades. Most issues in education can be – and have been – approached from either of two opposing yet indispensable perspectives: the cognitive and participationist perspectives. As we discussed these theoretical perspectives, we also discussed a range of issues that are important to understanding learning and instruction. These included the challenge of transfer, the situativity or generality of learning, promoting equity, views of inquiry, the roles of inquiry learning and direct instruction, and more. In the last section, we have discussed instructional methods to promote growth in reasoning that do indeed incorporate elements of both theories. We have also argued that developing lay reasoning in the contemporary, “post-truth” information world is a critical goal for science education.

References Allchin, D. (2011). Evaluating knowledge of the nature of (whole) science. Science Education, 95, 1–25. Amin, T. G., & Levrini, O. (Eds.). (2018). Converging perspectives on conceptual change: Mapping an emerging paradigm in the learning sciences. Routledge. Anderman, E. M., & Anderman, L. H. (2020). Classroom motivation: Linking research to teacher practice. Routledge. Anmarkrud, Ø., Andresen, A., & Bråten, I. (2019). Cognitive load and working memory in multimedia learning: Conceptual and measurement issues. Educational Psychologist, 54(2), 61–83. https://doi.org/10.1080/0 0461520.2018.1554484 Asterhan, C. S. C., & Schwarz, B. B. (2016). Argumentation for learning: Well-trodden paths and unexplored territories. Educational Psychologist, 51(2), 164–187. https://doi.org/10.1080/00461520.2016.1155458 Baehr, J. (2021). Deep in thought: A practical guide to teaching for intellectual virtues. Harvard Education Press. Bang, M. (2015). Culture, learning, and development and the natural World: The infuences of situative perspectives. Educational Psychologist, 50(3), 220–233. Bang, M., Warren, B., Rosebery, A. S.,  & Medin, D. (2012). Desettling expectations in science education. Human Development, 55(5–6), 302–318. https://doi.org/10.1159/000345322 Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12, 307–359. Barzilai, S., & Chinn, C. A. (2018). On the goals of epistemic education: Promoting apt epistemic performance. Journal of the Learning Sciences, 27, 353–389. Barzilai, S., & Chinn, C. A. (2020). A review of educational responses to the “post-truth” condition: Four lenses on “post-truth” problems. Educational Psychologist, 55(3), 107–119. https://doi.org/10.1080/00461520.2020. 1786388 Barzilai, S., & Ka’adan, I. (2017). Learning to integrate divergent information sources: The interplay of epistemic cognition and epistemic metacognition. Metacognition and Learning, 12(2), 193–232. Barzilai, S., Mor-Hagani, S., Zohar, A. R., Shlomi-Elooz, T., & Ben-Yishai, R. (2020). Making sources visible: Promoting multiple document literacy with digital epistemic scafolds. Computers & Education, 157. https:// doi.org/10.1016/j.compedu.2020.103980 Barzilai, S., & Zohar, A. (2014). Reconsidering personal epistemology as metacognition: A multifaceted approach to the analysis of epistemic thinking. Educational Psychologist, 49(1), 13–35. https://doi.org/10.1080/ 00461520.2013.863265

112

Theories of Learning Battaly, H. (2015). Responsibilist virtues in reliabilist classrooms. In J. Baehr (Ed.), Intellectual virtues and education: Essays in applied virtue epistemology (pp. 163–183). Routledge. Battey, D., & McMichael, E. W. (2021). Inquiry-based practices: Opening possibilities for (in)equitable interactions in classrooms. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 189–203). Routledge. Battey, D., Neal, R. A., Leyva, L.,  & Adams-Wiggins, K. (2016). The interconnectedness of relational and content dimensions of quality instruction: Supportive teacher  – Student relationships in urban elementary mathematics classrooms. The Journal of Mathematical Behavior, 42, 1–19. https://doi.org/10.1016/j. jmathb.2016.01.001 Beilin, H. (1992). Piaget’s enduring contribution to developmental psychology. Developmental Psychology, 28, 191–204. Belland, B. R., KIm, C., & Hannafn, M. J. (2013). A framework for designing scafolds that improve motivation and cognition. Educational Psychologist, 48, 243–270. Bielaczyc, K., & Collins, A. (1999). Learning communities in classrooms: Advancing knowledge for a lifetime. In C. M. Reigeluth (Ed.), Instructional design theories and models: A new paradigm of instructional theory (Vol. II, pp. 269–292). Erlbaum. Bråten, I., & Strømsø, H. I. (2010). When law students read multiple documents about global warming: Examining the role of topic-specifc beliefs about the nature of knowledge and knowing. Instructional Science, 38, 635–657. Brewer, W. F. (1974). There is no convincing evidence for operant or classical conditioning in adult humans. In W. B. Weimer & D. S. Palermo (Eds.), Cognition and the symbolic processes (pp. xx–xx). Lawrence Erlbaum. Bromme, R., & Goldman, S. R. (2014). The public’s bounded understanding of science. Educational Psychologist, 49(2), 59–69. Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice (pp. 229–270). MIT Press. Chen, B., & Hong, H.-Y. (2016). Schools as knowledge-building organizations: Thirty years of design research. Educational Psychologist, 51(2), 266–288. https://doi.org/10.1080/00461520.2016.1175306 Chen, J. A., Tutwiler, M. S., Metcalf, S. J., Kamarainen, A., Grotzer, T., & Dede, C. (2016). A multi-user virtual environment to support students’ self-efcacy and interest in science: A latent growth model analysis. Learning and Instruction, 41, 11–22. https://doi.org/10.1016/j.learninstruc.2015.09.007 Chen, Z., & Klahr, D. (1999). All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development, 70, 1098–1120. Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. The Journal of the Learning Sciences, 14, 161–199. Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49, 219–243. Chinn, C. A., Barzilai, S., & Duncan, R. G. (2020). Disagreeing about how to know: The instructional value of explorations into knowing. Educational Psychologist, 55(3), 167–180. https://doi.org/10.1080/00461520. 2020.1786387 Chinn, C. A., Barzilai, S., & Duncan, R. G. (2021). Education for a “post-truth” world: New directions for research and practice. Educational Researcher, 50(1), 51–60. https://doi.org/10.3102/0013189x20940683 Chinn, C. A., Buckland, L. A., & Samarapungavan, A. (2011). Expanding the dimensions of epistemic cognition: Arguments from philosophy and psychology. Educational Psychologist, 46, 141–167. https://doi.org/10 .1080/00461520.2011.587722 Chinn, C. A., & Duncan, R. G. (2018). What is the value of general knowledge of scientifc reasoning? In F. Fischer, C. A. Chinn, K. Engelmann, & J. Osborne (Eds.), Scientifc reasoning and argumentation: The roles of domain-specifc and domain-general knowledge (pp. 77–101). Routledge. Chinn, C. A., Duncan, R. G.,  & Av-Shalom, N. A. (2021). Promoting better thinking in an epistemically unfriendly world: Applying the grasp of evidence framework to evaluate learning environments. Information & Technology in Education & Learning, 1, 1–23. Chinn, C. A., Duncan, R. G., Dianovsky, M., & Rinehart, R. (2013). Promoting conceptual change through inquiry. In S. Vosniadou (Ed.), International handbook of conceptual change (2nd ed., pp. 539–559). Taylor & Francis. Chinn, C. A., Duncan, R. G., & Rinehart, R. W. (2018). Epistemic design: Design to promote transferable epistemic growth in the PRACCIS project. In E. Manalo, Y. Uesaka,  & C. A. Chinn (Eds.), Promoting spontaneous use of learning and reasoning strategies: Theory, research, and practice for efective transfer (pp. 242–260). Routledge. Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic reasoning in schools: A theoretical framework for evaluating inquiry tasks. Science Education, 86, 175–218.

113

Clark A. Chinn and Kalypso Iordanou Chinn, C. A., Rinehart, R. W., & Buckland, L. A. (2014). Epistemic cognition and evaluating information: Applying the AIR model of epistemic cognition. In D. Rapp & J. Braasch (Eds.), Processing inaccurate information: Theoretical and applied perspectives from cognitive science and the educational sciences (pp. 425–453). MIT Press. Chinn, C. A., & Sandoval, W. A. (2018). Epistemic cognition and epistemic development. In F. Fischer, C. Hmelo-Silver, S. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 24–33). Routledge. Chomsky, N. (1959). A review of B. F. Skinner’s Verbal Behavior. Language, 35, 26–58. Clough, M. P. (2011). The story behind the science: Bringing science and scientists to life in post-secondary science education. Science & Education, 20(7–8). https://doi.org/10.1007/s11191-010-9310-7 Cobb, P., McClain, K., & Gravemeijer, K. (2003). Learning about statistical covariation. Cognition and Instruction, 21, 1–78. Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64, 1–35. Cohen, E. G., Lotan, R. A., Abram, P. L., Scarloss, B. A., & Schultz, S. E. (2002). Can groups learn? Teachers College Record, 104, 1045–1068. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Erlbaum. Danish, J. A., & Gresalf, M. (2018). Cognitive and sociocultural perspective on learning: Tensions and synergy in the learning sciences. In F. Fischer, C. Hmelo-Silver, S. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 34–43). Routledge. Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting fndings and theories of transfer of learning. Educational Psychologist, 47, 153–176. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. Detterman, D. K. (1993). The case for the prosecution: Transfer as an epiphenomenon. In D. K. Detterman & R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition, and instruction (pp. 1–24). Ablex. diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225. https://doi. org/10.1207/s1532690xci1002&3_2 diSessa, A. A., Gillespie, N. M., & Esterly, J. B. (2004). Coherence versus fragmentation in the development of the concept of force. Cognitive Science, 28, 843–900. Dohn, N. B. (2016). Explaining the signifcance of participationist approaches for understanding students’ knowledge acquisition. Educational Psychologist, 51(2), 188–209. https://doi.org/10.1080/00461520.2016. 1160321 Duncan, R. G., Av-Shalom, N. A., & Chinn, C. A. (2021). Inquiry and learning in science. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 325–344). Routledge. Duncan, R. G., & Chinn, C. A. (Eds.). (2021). International handbook of learning and inquiry. Routledge. Duncan, R. G., Chinn, C. A.,  & Barzilai, S. (2018). Grasp of evidence: Problematizing and expanding the next generation science standards’ conceptualization of evidence. Journal of Research in Science Teaching, 55(7), 907–937. https://doi.org/10.1002/tea.21468 Duschl, R. A. (2008). Quality argumentation and epistemic criteria. In S. Erduran & M. P. Jiménez-Aleixandre (Eds.), Argumentation in science education (pp. 159–175). Springer. Duschl, R. A., & Grandy, R. E. (2008). Reconsidering the character and role of inquiry in school science: Framing the debate. In R. A. Duschl & R. E. Grandy (Eds.), Teaching scientifc inquiry: Recommendations for research and implementation (pp. 1–37). Sense Publishers. Duschl, R. A.,  & Tahirsylaj, A. (2021). Evolving conceptions of educational research and inquiry. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 17–38). Routledge. Efklides, A. (2011). Interactions of metacognition with motivation and afect in s-elf-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. Elby, A., Macrander, C., & Hammer, D. (2016). Epistemic cognition in science. In J. A. Greene, W. A. Sandoval, & I. Bråten (Eds.), Handbook of epistemic cognition (pp. 113–127). Routledge. Engle, R. A., & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: Explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20, 399–483. Feinstein, N. (2011). Salvaging science literacy. Science Education, 95(1), 168–185. https://doi.org/10.1002/ sce.20414 Feinstein, N. W., & Waddington, D. I. (2020). Individual truth judgments or purposeful, collective sensemaking? Rethinking science education’s response to the post-truth era. Educational Psychologist, 55(3), 155–166. https://doi.org/10.1080/00461520.2020.1780130

114

Theories of Learning Fischer, F., Chinn, C. A., Engelmann, K., & Osborne, J. (Eds.). (2018). Scientifc reasoning and argumentation: The roles of domain-specifc and domain-general knowledge. Routledge. Fischer, F., Kollar, I., Stegmann, K., & Wecker, C. (2013). Toward a script theory of guidance in computersupported collaborative learning. Journal of Educational Psychology, 48(1), 56–66. https://doi.org/10.1080/0 0461520.2012.748005 Fong, C., & Slotta, J. D. (2018). Supporting communities of learners in the elementary classroom: The common knowledge learning environment. Instructional Science, 46(4), 533–561. https://doi.org/10.1007/ s11251-018-9463-3 Ford, M. J. (2005). The game, the pieces, and the players: Generative resources from two instructional portrayals of experimentation. The Journal of the Learning Sciences, 14, 449–487. Ford, M. J. (2008). “Grasp of practice” as a reasoning resource for inquiry and nature of science understanding. Science & Education, 17, 147–177. Fox, E., & Riconscente, M. (2008). Metacognition and self-regulation in James, Piaget, and Vygotsky. Educational Psychology Review, 20, 373–389. Frazier, L. D., Schwartz, B. L., & Metcalfe, J. (2021). The MAPS model of self-regulation: Integrating metacognition, agency, and possible selves. Metacognition and Learning, 16(2), 297–318. Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38. Gobert, J. D., Baker, R. S.,  & Wixon, M. B. (2015). Operationalizing and detecting disengagement within online science microworlds. Educational Psychologist, 50(1), 43–57. https://doi.org/10.1080/00461520.2014. 999919 González-Howard, M., & McNeill, K. L. (2020). Acting with epistemic agency: Characterizing student critique during argumentation discussions. Science Education, 104(6), 953–982. https://doi.org/10.1002/sce.21592 Greene, J. A., & Azevedo, R. (2007). A theoretical review of Winne and Hadwin’s model of self-regulated learning: New perspectives and directions. Review of Educational Research, 77, 334–372. Greeno, J. G. (1989). On claims that answer the wrong questions. Educational Researcher, 26, 5–17. Greeno, J. G., & van de Sande, C. (2007). Perspectival understanding of conceptions and conceptual growth in interaction. Educational Psychologist, 42, 9–23. Hall, R., & Jurow, A. S. (2015). Changing concepts in activity: Descriptive and design studies of consequential learning in conceptual practices. Educational Psychologist, 50(3), 173–189. https://doi.org/10.1080/004615 20.2015.1075403 Halldén, O., Haglund, L., & Strömdahl, H. (2007). Conceptions and contexts: On the interpretation of interview and observational data. Educational Psychologist, 42, 25–40. Hand, V., & Gresalf, M. (2015). The joint accomplishment of identity. Educational Psychologist, 50, 190–203. Herrenkohl, L. R., & Guerra, M. R. (1998). Participant structures, scientifc discourse, and student engagement in fourth grade. Cognition and Instruction, 16, 431–473. Hetmanek, A., Engelmann, K., Opitz, A., & Fischer, F. (2018). Beyond intelligence and domain knowledge: Scientifc reasoning and argumentation as a set of cross-domain skills In F. Fischer, C. A. Chinn, K. Engelmann, & J. Osborne (Eds.), Scientifc reasoning and argumentation: The roles of domain-specifc and domain-general knowledge (pp. 203–226). Routledge. Hmelo-Silver, C. E., & Chinn, C. A. (2016). Collaborative learning. In E. M. Anderman & L. Corno (Eds.), Handbook of educational psychology (3rd ed., pp. 349–363). Routledge. Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scafolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42, 99–107. Hogan, K., Nastasi, B. K., & Pressley, M. (1999). Discourse patterns and collaborative scientifc reasoning in peer and teacher-guided discussions. Cognition and Instruction, 17, 379–432. Höttecke, D., & Allchin, D. (2020). Reconceptualizing nature-of-science education in the age of social media. Science Education, 104(4), 641–666. https://doi.org/10.1002/sce.21575 Howell, E. L., & Brossard, D. (2021). (Mis)informed about what? What it means to be a science-literate citizen in a digital world. PNAS, 118(15). https://doi.org/10.1073/pnas.1912436117 Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Diferent labels for the same constructs or diferent constructs with similar labels? Psychol Bull, 136(3), 422–449. https://doi.org/10.1037/a0018947 Iordanou, K. (2010). Developing argument skills across scientifc and social domains. Journal of Cognition and Development, 11, 293–327. Iordanou, K. (2022). Supporting strategic and meta-strategic development of argument skill: The role of refection. Metacognition and Learning, 17, 399–425. https://doi.org/10.1007/s11409-021-09289-1

115

Clark A. Chinn and Kalypso Iordanou Iordanou, K., & Rapanta, C. (2021). “Argue with me”: A method for developing argument skills. Frontiers in Psychology, 12, 631203. https://doi.org/10.3389/fpsyg.2021.631203 Järvelä, S., Malmberg, J., & Koivuniemi, M. (2016). Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL. Learning and Instruction, 42, 1–11. https://doi.org/10.1016/j. learninstruc.2015.10.006 Johnson, D. W., & Johnson, R. T. (1992). Positive interdependence: Key to efective cooperation. In R. HertzLazarowitz & N. Miller (Eds.), Interaction in cooperative groups: The theoretical anatomy of group learning (pp. 174– 199). Cambridge University Press. Kali, Y. (2021). Guiding frameworks for the design of inquiry learning environments. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 39–59). Routledge. Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51, 289–299. Kapur, M., & Bielaczyc, K. (2012). Designing for productive failure. Journal of the Learning Sciences, 21(1), 45–83. https://doi.org/10.1080/10508406.2011.591717 Kavanagh, J., & Rich, M. D. (2018). Truth decay: An initial exploration of the diminishing role of facts and analysis in American public life. Rand Corporation. Kienhues, D., Jucks, R.,  & Bromme, R. (2020). Sealing the gateways for post-truthism: Reestablishing the epistemic authority of science. Educational Psychologist, 55(3), 144–154. https://doi.org/10.1080/0046152 0.2020.1784012 King, A. (2002). Structuring peer interaction to promote high-level cognitive processing. Theory Into Practice, 41, 33–39. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86. Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction. Psychological Science, 15, 661–667. Kuhn, D. (2021). Metacognition matters in many ways. Educational Psychologist, 57(2), 73–86. https://doi.org/ 10.1080/00461520.2021.1988603 Kuhn, D., Zillmer, N., Crowell, A., & Zavala, J. (2013). Developing norms of argumentation: Metacognitive, epistemological, and social dimensions of developing argumentive competence. Cognition and Instruction, 31(4), 456–496. Lagemann, E. C. (2000). An elusive science: The troubling history of education research. University of Chicago Press. Lapsley, D., & Chaloner, D. (2020). Post-truth and science identity: A virtue-based approach to science education. Educational Psychologist, 55(3), 132–143. https://doi.org/10.1080/00461520.2020.1778480 Latour, B. (1999). Pandora’s hope. Harvard University Press. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Efects of guidance. Review of Educational Research, 86(3), 681–718. Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: Toward valid and meaningful assessment of learners’ conceptions of nature of science. Journal of Research in Science Teaching, 39, 497–521. Lehrer, R., & Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41, 635–679. Lehrer, R., & Schauble, L. (2006). Scientifc reasoning and science literacy. In R. M. Lerner, W. Damon, K. A. Renninger, & I. E. Sigel (Eds.), Handbook of child psychology (6th ed., pp. 153–196). Wiley. Lei, C., & Chan, C. K. K. (2018). Developing metadiscourse through refective assessment in knowledge building environments. Computers & Education, 126, 153–169. https://doi.org/10.1016/j.compedu.2018.07.006 Leung, J. S. C. (2020a). Promoting students’ use of epistemic understanding in the evaluation of socioscientifc issues through a practice-based approach. Instructional Science, 48(5), 591–622. https://doi.org/10.1007/ s11251-020-09522-5 Leung, J. S. C. (2020b). Students’ adherences to epistemic understanding in evaluating scientifc claims. Science Education, 104(2), 164–192. https://doi.org/10.1002/sce.21563 Lobato, J. (2012). The actor-oriented transfer perspective and its contributions to educational research and practice. Educational Psychologist, 47, 232–247. Lobczowski, N. G., Allen, E. M., Firetto, C. M., Greene, J. A., & Murphy, P. K. (2020). An exploration of social regulation of learning during scientifc argumentation discourse. Contemporary Educational Psychology, 63. https://doi.org/10.1016/j.cedpsych.2020.101925 Longino, H. E. (1990). Science as social knowledge: Values and objectivity in scientifc inquiry. Princeton University Press.

116

Theories of Learning Longino, H. E. (1996). Cognitive and non-cognitive values in science: Rethinking the dichotomy. In L. H. Nelson & J. R. Nelson (Eds.), Feminism, science, and the philosophy of science (pp. 39–58). Kluwer. Lynch, M. (2016). The Internet of us: Knowing more and understanding less in the age of big data. Norton. Maier, M., Rothmund, T., Retzbach, A., Otto, L., & Besley, J. C. (2014). Informal learning through science media usage. Educational Psychologist, 49(2), 86–103. https://doi.org/10.1080/00461520.2014.916215 Manz, E. (2016). Examining evidence construction as the transformation of the material world into community knowledge. Journal of Research in Science Teaching, 53(7), 1113–1140. Manz, E., & Renga, I. P. (2017). Understanding how teachers guide evidence construction conversations. Science Education, 101(4), 584–615. Mara, K. R., Kaplan, A., Balsai, M. J., Cromley, J. G., Perez, T., & Dai, T. (2021). How instructors can enhance biology students’ motivation, learning, and grades through brief relevance writing and worked-example interventions. Journal of Microbiology & Biology Education, 22(2). https://doi.org/10.1128/jmbe.00110-21 Mason, L., Zaccoletti, S., Carretti, B., Scrimin, S., & Diakidoy, I. A. N. (2019). The role of inhibition in conceptual learning from refutation and standard expository texts. International Journal of Science and Mathematics Education, 17(3), 483–501. McNeill, K. L., & Krajcik, J. (2008). Scientifc explanations: Characterizing and evaluating the efects of teachers’ instructional practices on student learning. Journal of Research in Science Teaching, 45, 53–78. Medin, D. L., & Bang, M. (2014). Who’s asking? Native science, Western science, and science education. MIT Press. Metz, K. E. (1995). Reassessment of developmental constraints on children’s science instruction. Review of Educational Research, 65, 93–127. Miller, E., Manz, E., Russ, R., Stroupe, D., & Berland, L. (2018). Addressing the epistemic elephant in the room: Epistemic agency and the next generation science standards. Journal of Research in Science Teaching, 55(7), 1053–1075. https://doi.org/10.1002/tea.21459 Moll, L., Amanti, C., Nef, D., & González, N. (2005). Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. In N. González, L. C. Moll, & C. Amanti (Eds.), Funds of knowledge: Theoretizing practices in households, communities, and classrooms (pp. 71–87). Routledge. Morales-Doyle, D., Childress Price, T., & Chappell, M. J. (2019). Chemicals are contaminants too: Teaching appreciation and critique of science in the era of Next Generation Science Standards (NGSS). Science Education, 103(6), 1347–1366. https://doi.org/10.1002/sce.21546 Muis, K. R., Chevrier, M., & Singh, C. A. (2018). The role of epistemic emotions in personal epistemology and self-regulated learning. Educational Psychologist, 1–20. https://doi.org/10.1080/00461520.2017. 1421465 Murphy, P. K., Greene, J. A., Firetto, C. M., Hendrick, B. D., Li, M., Montalbano, C., & Wei, L. (2018). Quality Talk: Developing students’ discourse to promote high-level comprehension. American Educational Research Journal, 55(5), 1113–1160. https://doi.org/10.3102/0002831218771303 Nasir, N. I. S., & Vakil, S. (2017). STEM-focused academies in urban schools: Tensions and possibilities. Journal of the Learning Sciences, 26(3), 376–406. https://doi.org/10.1080/10508406.2017.1314215 National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. Nehm, R. H., & Schonfeld, I. S. (2008). Measuring knowledge of natural selection: A comparison of the CINS, an open-response instrument, and an oral interview. Journal of Research in Science Teaching, 45(10), 1131–1160. https://doi.org/10.1002/tea.20251 Newton, P., Driver, R., & Osborne, J. (1999). The place of argumentation in the pedagogy of school science. International Journal of Science Education, 21, 553–576. NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press. Nolen, S. B. (2020). A situative turn in the conversation on motivation theories. Contemporary Educational Psychology, 61. https://doi.org/10.1016/j.cedpsych.2020.101866 Nolen, S. B., Horn, I. S., & Ward, C. J. (2015). Situated motivation. Educational Psychologist, 50, 234–247. Norris, S. P., & Phillips, L. M. (2003). How literacy in its fundamental sense is central to scientifc literacy. Science Education, 87, 224–240. O’Donnell, A. M. (2006). The role of peers and group learning. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 781–802). Erlbaum. Oreskes, N. (2019). Why trust science? Princeton University Press. Osborne, J. F., Borko, H., Fishman, E., Gomez Zaccarelli, F., Berson, E., Busch, K. C., Reigh, E., & Tseng, A. (2019). Impacts of a practice-based professional development program on elementary teachers’ facilitation of and student engagement with scientifc argumentation. American Educational Research Journal, 56(4), 1067–1112. https://doi.org/10.3102/0002831218812059 Panadero, E., & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20, 190–203. https://doi.org/10.1027/1016-9040/a000226

117

Clark A. Chinn and Kalypso Iordanou Piaget, J. (1928/1959). Judgment and reasoning in the child (M. Warden, Trans.). Littlefeld, Adams, & Co. Piaget, J. (1964/1968). Six psychological studies (A. Tenzer, Trans.). Random House. Piaget, J. (1974). Understanding causality (D. Miles & M. Miles, Trans.). W.W. Norton. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientifc conception: Toward a theory of conceptual change. Science Education, 66, 211–227. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R. G., Kyza, E., Edelson, D., & Soloway, E. (2004). A scafolding design framework for software to support science inquiry. The Journal of the Learning Sciences, 13, 337–386. Renninger, K. A., & Hidi, S. (Eds.). (2016). The power of interest for motivation and engagement. Routledge. Reznitskaya, A., Glina, M., Carolan, B., Michaud, O., Rogers, J.,  & Sequeira, L. (2012). Examining transfer efects from dialogic discussions to new tasks and contexts. Contemporary Educational Psychology, 37(4), 288–306. https://doi.org/10.1016/j.cedpsych.2012.02.003 Reznitskaya, A., & Wilkinson, I. A. G. (2021). How best to argue? Examining the role of talk in learning from a sociocultural perspective. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 204–220). Routledge. Rinehart, R. W., Duncan, R. G., & Chinn, C. A. (2016). Critical design decisions for successful model-based inquiry in science classrooms. International Journal of Designs for Learning, 7, 17–40. Robinson, K. A., Perez, T., Carmel, J. H., & Linnenbrink-Garcia, L. (2019). Science identity development trajectories in a gateway college chemistry course: Predictors and relations to achievement and STEM pursuit. Contemporary Educational Psychology, 56, 180–192. https://doi.org/10.1016/j.cedpsych.2019.01.004 Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulatoin in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29, 375–415. Rogat, T. K., Linnenbrink-Garcia, L., & DiDonato, N. (2013). Motivation in collaborative groups. In C. E. Hmelo-Silver, C. A. Chinn, C. K. K. Chan, & A. M. O’Donnell (Eds.), International handbook of collaborative learning (pp. 250–267). Routledge. Ronfard, S., Brown, S., Doncaster, E., & Kelemen, D. (2021). Inhibiting intuition: Scafolding children’s theory construction about species evolution in the face of competing explanations. Cognition, 211, 104635. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic defnitions and new directions. Contemporary Educational Psychology, 25, 54–67. Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Defnitions, theory, practices, and future directions. Contemporary Educational Psychology, 61. https://doi. org/10.1016/j.cedpsych.2020.101860 Ryu, S., & Sandoval, W. A. (2012). Improvements to elementary children’s epistemic understanding from sustained argumentation. Science Education, 96, 488–526. Saleh, A., Hmelo-Silver, C. E., & Glazewski, K. D. (2021). Collaborative interactions in inquiry learning. In R. G. Duncan & C. A. Chinn (Eds.), International handbook of learning and inquiry (pp. 239–255). Routledge. Samarapungavan, A., Vosniadou, S., & Brewer, W. F. (1996). Mental models of the earth, sun, and moon: Indian children’s cosmologies. Cognitive Development, 11, 491–521. Sandoval, W. A., & Millwood, K. A. (2005). The quality of students’ use of evidence in written scientifc explanations. Cognition and Instruction, 23, 23–55. Sandoval, W. A., & Reiser, B. J. (2004). Explanation-driven inquiry: Integrating conceptual and epistemic scaffolds for scientifc inquiry. Science Education, 88, 345–372. Schmidt, H. G., Van Der Molen, H. T., Te Windel, W. W. R., & Wijnen, W. H. F. W. (2009). Constructivist, problem-based learning does work: A meta-analysis of curricular comaprisons involving a single medical school. Educational Psychologist, 44, 227–249. Schoor, C., Narciss, S., & Körndle, H. (2015). Regulation during cooperative and collaborative learning: A theory-based review of terms and concepts. Educational Psychologist, 50, 97–119. Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of self-regulation of learning and performance (2nd ed.). Routledge. Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16, 475–522. Schwartz, D. L., Chase, C. C., Oppezzo, M. A.,  & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The efects of telling frst on learning and transfer. Journal of Educational Psychology, 103, 759–775. Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement goal theory at the crossroads: Old controversies, current challenges, and new directions. Educational Psychologist, 46(1), 26–47. https://doi.org/ 10.1080/00461520.2011.538646

118

Theories of Learning Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 4–13. Sharan, S., Sharan, Y., & Tan, I. G.-C. (2013). The Group Investigation approach to cooperative learning. In C. E. Hmelo-Silver, C. A. Chinn, C. K. K. Chan, & A. M. O’Donnell (Eds.), International handbook of collaborative learning (pp. 351–369). Routledge. Sherin, B. L., Krakowski, M., & Lee, V. R. (2012). Some assembly required: How scientifc explanations are constructed during clinical interviews. Journal of Research in Science Teaching, 49, 166–198. Sinatra, G. M., & Lombardi, D. (2020). Evaluating sources of scientifc evidence and claims in the post-truth era may require reappraising plausibility judgments. Educational Psychologist, 55(3), 120–131. https://doi.org/10. 1080/00461520.2020.1730181 Smith, C. L., & Wiser, M. (2015). On the importance of epistemology-disciplinary core concept interactions in LPs. Science Education, 99, 417–423. https://doi.org/10.1002/sce.21166 Smith, M. U., & Siegel, H. (2004). Knowing, believing, and understanding: What goals for science education? Science & Education, 13, 553–582. Sodian, B. (2018). The development of scientifc thinking in preschool and elementary school age. In F. Fischer, C. A. Chinn, K. Engelmann, & J. Osborne (Eds.), Scientifc reasoning and argumentation: The roles of domainspecifc and domain-general knowledge (pp. 227–250). Routledge. Sosa, E. (2011). Knowing full well. Princeton University Press. Sosa, E. (2015). Judgment and agency. Oxford University Press. Stroupe, D., Caballero, M. D., & White, P. (2018). Fostering students’ epistemic agency through the co-confguration of moth research. Science Education, 102(6), 1176–1200. https://doi.org/10.1002/sce.21469 Summers, R., Alameh, S., Brunner, J., Maddux, J. M., Wallon, R. C., & Abd-El-Khalick, F. (2019). Representations of nature of science in U.S. science standards: A historical account with contemporary implications. Journal of Research in Science Teaching, 56(9), 1234–1268. https://doi.org/10.1002/tea.21551 Sweller, J., & Chandler, P. (1994). Why some material is difcult to learn. Cognition and Instruction, 12, 185–233. von Glasersfeld, E. (1995). Radical constructivism: A way of knowing and learning. Falmer Press. Vosniadou, S. (2007). The cognitive-situative divide and the problem of conceptual change. Educational Psychologist, 42, 55–66. Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood. Cognitive Psychology, 24, 535–585. Vosniadou, S., Ioannides, C., Dimitracopoulou, A., & Papademetriou, E. (2001). Designing learning environments to promote conceptual change in science. Learning and Instruction, 11, 381–419. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. Wagner, J. F. (2010). A transfer-in-pieces consideration of the perception of structure in the transfer of learning. Journal of the Learning Sciences, 19(4), 443–479. https://doi.org/10.1080/10508406.2010.505138 Webb, N. M. (2013). Information processing approaches to collaborative learning. In C. E. Hmelo-Silver, C. A. Chinn, C. K. K. Chan, & A. M. O’Donnell (Eds.), International handbook of collaborative learning (pp. 19–40). Routledge. Webb, N. M., & Farivar, S. (1994). Promoting helping behavior in cooperative small groups in middle school mathematics. American Educational Research Journal, 31, 369–395. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3–118. Wigfeld, A., & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68–81. Wiley, J., & Voss, J. F. (1999). Constructing arguments from multiple sources: Tasks that promote understanding and not just memory for text. Journal of Educational Psychology, 91, 301–311. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Erlbaum. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry and Allied Disciplines, 17, 89–100. Wu, H.-K., Krajcik, J. S., & Soloway, E. (2001). Promoting understanding of chemical representations: Students’ use of a visualization tool in the classroom. Journal of Research in Science Teaching, 38, 821–842. Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91. https://doi.org/10.1080/1361332052000341006 Zeldin, A. L., Britner, S. L., & Pajares, F. (2008). A comparative study of the self-efcacy beliefs of successful men and women in mathematics, science, and technology careers. Journal of Research in Science Teaching, 45(9), 1036–1058. https://doi.org/10.1002/tea.20195

119

Clark A. Chinn and Kalypso Iordanou Zengilowski, A., Schuetze, B. A., Nash, B. L., & Schallert, D. L. (2021). A critical review of the refutation text literature: Methodological confounds, theoretical problems, and possible solutions. Educational Psychologist, 1–21. https://doi.org/10.1080/00461520.2020.1861948 Zhang, L., Kirschner, P. A., Cobern, W. W., & Sweller, J. (2021). There is an evidence crisis in science educational policy. Educational Psychology Review, 34, 1157–1176. https://doi.org/10.1007/s10648-021-09646-1 Zohar, A., & Ben David, A. (2009). Paving a clear path in a thick forest: A conceptual analysis of a metacognitive component. Metacognition and Learning, 4(3), 177–195.

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5 STUDENT CONCEPTIONS, CONCEPTUAL CHANGE, AND LEARNING PROGRESSIONS Joseph Krajcik and Namsoo Shin

Introduction Attention to improving science education has grown nationally and globally, as science and policy communities fnd themselves challenged by complex real-world problems that have neither straightforward nor ultimate solutions (Anderson & Li, 2020). Science, technology, engineering, and mathematics (STEM) permeate nearly every facet of modern life; indeed, STEM holds the key to meeting many of the problems facing humans today and in the future (OECD, 2019). Moreover, understanding science can fundamentally improve people’s lives (National Research Council, 2012b; OECD, 2019). Yet, unfortunately, many people in our society lack the fundamental knowledge of science needed to live fruitful lives and contribute productively to a global community. Providing healthy food that is sustainable serves as a powerful example. While nations like the United States have sufcient food supplies, many third-world countries throughout the globe do not. But even in the United States, poor communities, which are often composed of people of color, don’t have access to healthy, afordable, and sustainable food sources. Many children of color sufer from type 2 diabetes. As a global community, we need to learn how to provide food that is healthy, afordable, and sustainable to all individuals throughout the globe. People worldwide are now experiencing the efects of climate change as more extreme weather occurs, exacerbating food shortages. The potential to use alternative fuels (e.g., solar and wind) to mitigate climate change exists; however, these problems require a scientifcally knowledgeable citizenry to support policy changes and understand future consequences due to lack of action. The global food problem and climate change have demonstrated that most individuals have little understanding of how scientifc knowledge develops, how scientifc models can change as new evidence unfolds, the importance of evidence in supporting scientifc claims, and the role of peer review in establishing the validity of scientifc knowledge. Moreover, our students are not fully prepared to solve problems and make informed decisions about their lives because school curricula, instruction, and assessment lack coherence and depth to provide the necessary learning opportunities for students to develop and apply such knowledge (OECD, 2019). The last ten years have also seen unprecedented technological changes that have reshaped how people communicate, fnd information, and even purchase household items. The following ten years will continue to bring about unforeseen changes in how we live and interact with each other and the environment. To prepare students for this world, a new approach to K–12 science education for a wide range of students is needed (National Research Council, 2012b).

DOI: 10.4324/9780367855758-7

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All Students Need to Develop Knowledge-in-Use in Science The goals of science education stress that all students need to explain scientifc phenomena, make informed decisions about science and technology related to their life, and have the motivation (or interest) to consider science-related occupations. As such, science education is not only for those pursuing a career in science but for all students. To develop citizens who understand science, educators must consider what students should ultimately know (big ideas) and be able to do (scientifc and engineering practices) and what paths they can take to reach that goal (National Research Council, 1999). It is critical for students to understand science as a specifc way of knowing and to develop the capability to understand and appropriately apply the strategies of scientifc inquiry, called “knowledge-in-use” (National Research Council, 2012a). Science education worldwide has moved to performance-based learning goals in which learners need to use their knowledge.1 Knowledge-in-use refers to students demonstrating and applying their knowledge rather than recalling what they know (National Research Council, 2012b; Next Generation Science Standard [NGSS] Lead States, 2013). Knowledge-in-use learning goals allow students to explain what causes phenomena to occur, solve complicated real-world problems,2 and make challenging decisions using evidence, scientifc ideas, and reasoning. It also involves incorporating ideas from many areas of science (e.g., the structure of matter, energy, and natural selection) to learn more when needed. Knowledge-in-use requires that learners selectively incorporate ideas from various knowledge domains and apply them to new contexts. To reach excellence in science education, students need to develop usable knowledge of the big ideas and practices of science (American Association for the Advancement of Science, 1989; National Research Council, 1999); however, to do so, they require opportunities to learn. Although students develop understanding of science in diferent ways and at diferent rates, opportunities to learn entail that all students have the chance to create deep and usable knowledge so they can apply ideas to explain compelling phenomena, solve challenging problems, and learn more when needed. These opportunities should occur throughout an individual’s life span because learning challenging big ideas (e.g., evolution, energy, or conservation of mass) is not developmentarily inevitable; moreover, it takes time and is dependent on experiences and education (National Research Council, 2007). Furthermore, deep understanding only develops when individuals make sense of challenging situations or complex problems (National Research Council, 2012b). For instance, think of climbing a very challenging mountain. There are many paths to the top: some may be easier and require less energy to climb, while some may be harder but ofer more beautiful views. Since students’ learning depends on appropriate instructional materials and practices, learning environments require explicit instruction to provide rich opportunities for a wide range of students to learn big ideas and scientifc and engineering practices. Such teaching and learning might be more challenging, but the results are also more benefcial. Therefore, teachers need to develop their instructional strategies to support students in developing knowledge-in-use. In addition, teachers need to recognize individual diferences among students: they will develop an understanding of scientifc ideas in diferent ways, at diferent depths, and at varying rates of progress.

Learning Progressions Provide a Guide for Developing Knowledge-in-Use A learning progression (LP) describes how big ideas can develop over time and become more sophisticated so that learners can use their knowledge in new and challenging situations. As such, LP research provides a promising venue to support various learners to develop and monitor usable knowledge (Corcoran et al., 2009; NRC, 2012b). Learning environments should facilitate students’ learning of big ideas of science in and across disciplines and developing capabilities to apply those ideas in real situations. These big ideas are essential because they can explain a range of phenomena in our world. The science education community has adopted learning progressions to organize

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and align science content, assessment, instruction, and learning experiences to provide learners the opportunities to develop deep knowledge of big scientifc ideas and practices (National Research Council, 2007; Smith et al., 2006). To serve as valuable guides for such learning, LPs should provide a progression of integrated sets of ideas and capabilities across disciplines, from early elementary grades through secondary education and into college. In addition, LPs focus on how learners need to connect and relate relevant ideas and experiences to use them appropriately, as opposed to knowing individual, discreet facts. A vital aspect of a LP is showing how to support learners in developing knowledge of big ideas and practices over time. Most adults have not developed the depth of expertise to use big ideas and scientifc practices to understand everyday phenomena. For example, most adults do not know that a force must be applied to change the speed or direction of a moving object; instead, most individuals think a force needs to be consistently applied to keep an object moving (Osborn & Freyberg, 1985). This lack of understanding of disciplinary ideas is unsurprising. Although topics like energy, natural selection, growth and development of organisms, plate tectonics, and nature of matter are typically part of the K–12 science education curriculum and taught across diferent grade levels, they often are dealt with superfcially. These ideas do not systematically develop across grade levels, in contexts that require learners to apply the ideas more deeply and in more sophisticated situations. While there is no single trajectory or pathway that all learners will follow, learners need to develop certain scientifc ideas before they can learn other, more sophisticated ideas, as some ideas are dependent on others. For instance, before learners understand that substances can interact to form new substances, they need to learn that (1) matter can be identifed by its properties and (2) that material composed of the same matter will have the same properties. Now imagine a learner attempting to develop profciency in a high school standard without the profciencies of the prior learning developed in earlier years. Without prior knowledge and experiences, attempting to develop high school–level profciency would be exceptionally challenging. Figure 5.1 presents an example that focuses on learners

Progression for Big Idea: Structure of Ma˜er

Highest level By the end of 12th grade

Atomic Structure Model – provides a mechanis˜c model for chemical reac˜on

By the end of 8th grade

Atomic/Molecular Model – explains proper˜es and diversity of materials

Ideas build across the school years to become successively more sophis˜cated. Par˜cle Model – explains phase changes and phases

By the end of 5th/6th grade

Macroscopic Model – describes ma°er

By the end of 2nd grade Lowest level

Figure 5.1 Progression of a big idea for the structure of matter.

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developing ideas over time; in this example, the developmental sequence is related to an idea for the structure of matter. Learners develop very descriptive knowledge of matter at the early grades (i.e., metals are shiny and can bend). As they progress in their thinking, learners move from a macroscopic description of matter to an atomic structure model that can provide a mechanistic model for understanding the nature of a chemical reaction (i.e., atoms that make molecules rearrange to form new molecules with diferent confgurations).

Chapter Overview This chapter is about learning and how learners from the earliest grades can develop more sophisticated understandings of scientifc ideas and practices that are necessary to understand the world in which they live. More specifcally, we are interested in learners developing understanding of scientifc ideas and practices across time so that their knowledge becomes more sophisticated and usable. How this development occurs can be thought of as a LP by providing a comparatively small set of scientifc ideas and practices throughout a grade level and revisiting those ideas in later grades in new situations. However, we are not talking about just repeating the idea but supporting learners in developing deeper and more sophisticated understandings of these ideas across grade levels. Students can learn some scientifc ideas and practices in the elementary grades, and then these ideas can become progressively refned, elaborated, and extended throughout an individual’s educational experience. The Framework for K–12 Science Education (National Research Council, 2012b) and (NGSS Lead States, 2013) embrace this developmental view of learning. They provide a coherent vision for developing and testing assessment, instruction, and curriculum materials throughout K–12 education. But LPs are not inevitable; they depend upon learners experiencing situations where they need to build and use an appropriate sequencing of ideas. The development of LPs should therefore be based on research and on what we know about student development (Lehrer & Schauble, 2015). Furthermore, since learning is a complicated process that is nonlinear and has multiple pathways depending on an individual learner’s background, ongoing research eforts are needed to explore how students learn in science to develop research-based LPs. We examine some learning theories related to LPs. We delve deeper into what is meant by knowledge-in-use and explore the meaning of conceptual change and how it can occur. We then examine the LP literature, focusing on two aspects: historical views of LPs and LP research by discipline (e.g., physics, life, earth, and space sciences), curriculum materials and instruction, and assessment and measurement. Building on LP research and related learning theories, we put forth a view of LPs and features and components of LPs. We conclude by discussing challenges and suggesting future directions for LP research.

Theoretical Framework Next, we discuss the learning theories and principles related to LPs, including concepts, cognition, conceptual changes, deep learning, and building ideas across time.

Concepts and Cognition Central to learning is the development of concepts. Concepts can be thought of as mental representations of ideas that serve as fundamental knowledge structures of understanding and emotions. Individuals have their own concepts based on their prior experiences and emotions (Bransford et al., 2000; Chi  & Ceci, 1987; National Academies of Sciences, E.  & Medicine, 2018). For example, when we think of an object in the world, such as a dog, various ideas come to mind. This is an

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individual’s concept of a dog. Some of us have more ideas linked to dogs than others, and the more ideas connected to dogs, the richer one’s understanding of “dog.” Emotions are also connected to our concepts. Some individuals, when they think of a dog, have positive concepts, such as their dog cuddled in their laps. Others might have negative associations and emotions, like a big dog chasing them. The more connections a learner makes between these concepts, the more easily they can access that knowledge (information) and use it in a new situation (Bransford et al., 2000; Chi & Ceci, 1987). Concepts play a critical role in learning and cognition. Cognition refers to a mental process in which an individual develops understanding and builds knowledge through thinking, experiencing, and sensing. Cognition heavily overlaps with “learning”, with the two terms often used interchangeably. Learning is the process of forming new ideas and connections among those ideas to obtain new understanding, knowledge, behaviors, values, and beliefs (National Academies of Sciences, E.  & Medicine, 2018). Through a series of learning experiences, we acquire new knowledge that we can use in other situations. Individuals learn concepts by interacting and interpreting their world, including essential interactions with others. When a child is born, she (supported through interactions with others) constructs knowledge of her environment. Knowledge is not memorized; individuals continuously build and refne their knowledge as they use the ideas and experiences in the world. Not all learning, however, is the same. Although we often say an individual has learned an idea if they can recall what it means, just because an individual can recall an idea does not mean she can use that idea in a new situation – or even use it to learn something else (Roseman et al., 2008). This condition is referred to as “inert knowledge” (Whitehead, 1967). For instance, a middle school student might be able to recite that a chemical reaction occurs when two substances interact to form a new substance with diferent chemical composition and new properties. But this does not mean she can apply these ideas in new situations, like explaining why a bicycle will rust if left in the rain or the diference between a chemical and a phase change. Individuals with inert knowledge cannot retrieve or use knowledge in appropriate situations or solve problems, as they lack connections and relationships between the ideas (Perkins, 1992). Learners often do not systematically assemble ideas derived from prior schooling and life experiences (diSessa, 1988). Learners, therefore, are likely to end up with disconnected and fragmented structures that do not form well-organized knowledge structures that allow them to use their ideas. Fragmented knowledge structures make it difcult for learners to apply their knowledge to new situations (Sirhan, 2007). These fragmented knowledge pieces are often referred to as “knowledge-in-pieces.” When students learn bits of factual information and memorize how to solve problems using formulas, they develop knowledge-in-pieces or inert knowledge, but not knowledge-in-use. Despite the importance of knowledge-in-use, in school systems across the globe, the learning of scientifc facts and algorithmic problem solving dominate the science curriculum. Most students memorize scientifc ideas and procedures to solve problems rather than developing the underlying conceptual understanding of fundamental ideas of science and how to integrate and use those ideas (OECD, 2019). LPs focus on learners using their knowledge to understand natural phenomena or solve complex problems. A LP is not a listing of all concepts in a domain that students need to memorize (e.g., defnition of ideas); instead, a LP includes a small set of big ideas important across disciplines to explain phenomena that learners experience. A LP describes how a set of concepts integrates so learners can apply them when needed (knowledge-in-use, deep understanding).

Knowledge-in-Use Requires Deep Learning Deep learning is a process of making connections among ideas. The more connections a learner makes, the deeper and more usable their knowledge becomes (Bransford et al., 2000). When students develop ideas that richly connect to each other, they form usable knowledge (National Research

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Council, 2012a). When new ideas are richly connect to ideas and experiences, learners can access those ideas and apply them in new situations (Chi & Ceci, 1987). Research shows that experts organize their knowledge around fundamental ideas of the discipline that guide their thinking about solving problems and explaining phenomena, rather than a simple list of facts and formulas (Bransford et al., 2000). To develop knowledge-in-use, learners must do more than just accumulate knowledge; they must also integrate those ideas with various experiences (Esposito & Bauer, 2017). Deep learning occurs when learners actively construct knowledge as they explore, observe, and interact with phenomena, take in new information, and discuss and interact with others. Well-organized and well-integrated knowledge structures enable learners to easily access ideas (Bransford et al., 2000; Chi et al., 1981). When learners can use their knowledge to generalize, categorize, apply, synthesize, and solve problems, they develop the capacity to generate novel and creative solutions to persistent problems when needed, and in so doing, they make new connections among ideas. Learners who develop more connections among ideas have greater integrated understanding and can more readily apply their knowledge in novel situations (Bransford et al., 2000; National Academies of Sciences, E. & Medicine, 2018). They will also learn new, related material more efectively. Curriculum developers, including teachers, therefore need to design learning activities to support students in making connections among new and old ideas and experiences. Building on students’ prior knowledge, self-refection, and frsthand experiences that are meaningful to the learner promotes deep learning. The more these learning activities are grounded in real-world experiences that the learner fnds essential, the more connections learners can make. LPs can guide this efort by illustrating how ideas can develop over time. A LP includes teaching and learning materials to support learners in having rich learning experiences to connect ideas appropriately and meaningfully.

Learners Build Ideas Across Time Learning concepts and building knowledge structures evolve as learners experience more situations and problems (National Academies of Sciences, Engineering & Medicine, 2018). As learners have more experiences, their conceptions become richer – this, in turn, infuences the way they make sense of the world. Building a rich network of concepts is a knowledge-integration process through which learners put together diferent sorts of information from various experiences: identifying and establishing relationships and expanding knowledge-building structures for connecting them (Linn  & Eylon, 2006). Chi (1976) argues that signifcant diferences among children of diferent ages lie in the complexity of their knowledge base (i.e., integrated knowledge structure). As learners grow and have more relevant experiences, the number of ideas and connections in their knowledge structure increases. Because knowledge increases over time with relevant experience, this raises the question of how science instruction can support a wide range of learners to develop more tightly connected ideas over time that can guide their thinking, observing, and searching for new information. Research on how experts develop knowledge sheds light on this efort. The learning of experts unfolds over time in a continuous process as they engage in new experiences and make connections between old and new ideas. Learning complex ideas takes time and often happens as learners work on challenging tasks that require them to synthesize and make connections. Throughout their lifetime, learners continue to refne their abilities to use information at various levels of abstraction. Once a learner begins to form a network of ideas, it is much easier for them to continually make connections to this network to build deeper and more connections over time. As the knowledge structures become more connected and deeper, learners become even more sophisticated at understanding the nature of good explanations, methods of inquiry, and the role of evidence. A LP is not for obtaining simple facts in a short period of instruction but is for developing usable knowledge of big ideas over time.

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Conceptual Change Conceptual change is a learning process in which individuals develop their knowledge structures by adding more concepts and relationships throughout their lifetime (National Academies of Sciences, E. & Medicine, 2018). Changes in individuals’ knowledge structure often occur when they have new experiences or new information is made available to them (Bransford et al., 2000). The process allows learners to make connections with a new concept, or new connections to prior concepts in new ways; this, then, enables them to solve more challenging and complex problems that were too difcult in the past and explain compelling phenomena in new ways (Chi & Ceci, 1987). For instance, in early elementary school, a student might learn about energy, but her understanding of energy will change over her lifetime, hopefully becoming more refned and usable. She might frst think of energy as an object having more energy the faster it moves, but as she has more experiences (including those in school), that knowledge becomes deeper and more sophisticated. She realizes that energy can transfer among objects. This process, in which an idea becomes deeper and more sophisticated, is termed “conceptual change”. As learners work to make sense of new ideas, they incorporate and connect new information to existing knowledge, which should enable them to develop complex, integrated knowledge structures for using and organizing what they learned (e.g., Ausubel et al., 1968; Linn & Eylon, 2006; Novak, 2002). Picture a network of ideas connected by appropriate relationships: a richly elaborated concept map can serve as a good analogy for integrated knowledge structures. Appendix 1 presents graphical images of little to no understanding (i.e., inert knowledge), thin understanding, and integrated understanding of scientifc ideas related to chemical reactions. Conceptual change can occur in many diferent ways. Some conceptual change is much harder to achieve than others. Some kinds of conceptual change occur naturally as a consequence of a learner’s everyday experiences. For example, young children learn to avoid touching hot surfaces when their fngertips burn slightly when a hot surface is touched. This beginning idea might lead to further refnement – i.e., that fre can cause injury to living organisms. Interestingly, instruction can use such experiences to develop ideas related to energy transfer. Other, more challenging ideas may require intentional efort, often through formal school experiences. For instance, the understanding that substances in the world can rearrange to make new substances would be hard – if not impossible – for most learners to develop without formal instruction. Yet, there is no simple, concrete path in an individual’s development; often, what an individual learns seems to regress. In other words, changes in a learner’s knowledge do not necessarily develop linearly across time, and an individual’s understanding can vary across contexts. Nevertheless, science education researchers should explore and learn how best to promote the development of fundamental ideas of science across time. The best scenario for growth occurs when an individual needs to add to their existing knowledge structures to make sense of new phenomena or solve a complex problem. For instance, a learner may understand that a substance can be identifed by its properties and may learn some new, more advanced properties. More challenging, however, would be learning that new substances result from the recombination of atoms from previous substances into new substances. However, this understanding is attainable with appropriate experiences and a need to know. If a learner begins by understanding, through relevant experiences, that substances are made up of smaller parts that can break apart and recombine to form new materials, they can build on this knowledge to develop new ideas if they experience appropriate new and challenging learning experiences. However, if a learner begins with a knowledge structure that does not have this idea of rearrangement but instead holds the idea that materials can go in and out of existence, helping this learner develop a more appropriate understanding will be more challenging. This form of conceptual change is often referred to as “radical restructuring” or “radical conceptual change” (Keil, 1981; Carey, 1988; Chi, 1992). Radical restructuring occurs when a learner not only needs to add new concepts to

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her current knowledge structure but when existing concepts change their meaning in fundamental ways. Hence, a critical issue in supporting learning across the life span is what constitutes radical restructuring – and how to bring about such changes. LPs describe conceptual change as a developmental process. The LP level is not adding one more idea but seeing the phenomena from a new perspective (e.g., frst level: volume or mass [external properties], next level: density or melting/ boiling points [unique characteristics]). However, supporting learners from the beginning to have appropriate understandings in which to attach new ideas is an easier path than knowledge restructuring when ideas do not conform to scientifc thinking. Developing appropriate understanding at earlier ages provides a solid rationale for why learners need to experience science at the elementary level. Elementary teachers need to support students in building appropriate building blocks of science ideas so that ideas build on solid knowledge structures so that learners do not need to restructure their ideas in later grades.

Mechanisms of Conceptual Change Indeed, radical restructuring is a more challenging mechanism. If we can help learners build more appropriate knowledge structures early on in their life spans, through meaningful experiences and educational opportunities, much more sophisticated understandings can occur across numerous individuals. Suppose the science education community can develop LPs from the early years forward for challenging concepts like energy, the structure and properties of matter, and evolution. In that case, more individuals from various ethnic and socioeconomic groups across the globe will have deeper and more usable knowledge of science that will promote equity in education. Of course, individuals will still take diferent pathways, but we can make progress if a wide range of learners is developing more usable knowledge at an early age. By providing a wide range of children opportunities to learn challenging scientifc ideas, more children will develop appropriate ideas that will allow them to develop sophisticated understanding because their knowledge does not need to be restructured but instead just added to using relevant experiences. This is the vision promoted by the Framework for K–12 Science Education and the NGSS to improve science learning (National Research Council, 2012b; National Science Teaching Association, 2019; NGSS Lead States, 2013). Supporting young learners in building an initial foundation of big ideas related to the fundamental concepts of science is an essential part of the process of conceptual development (Carey, 1988; Chi, 1992). If learners can develop a foundation on which big ideas can develop, it is much easier to support learners to add to and elaborate on their existing knowledge structure than to restructure and build new knowledge structures. Learners can more readily learn new ideas that ft into existing knowledge structures if they have structures developed from rich and appropriate experiences. Developing learning environments that provide learners with opportunities to revisit critical ideas (diSessa & Minstrell, 1998; Minstrell, 1984; Minstrell & Kraus, 2005), elaborate on these ideas (Clark & Linn, 2003), and use them across years (Arzi, 1988) promotes the development of strong knowledge structures that learners can use in new situations. At times, learners need to restructure their knowledge to make a more thorough and sophisticated sense of phenomena or problems. When learners need to restructure rather than add new information to existing structures, the situation is more challenging. To reorganize their knowledge structure, the learner needs to have critical experiences on which to anchor ideas, and they need to make connections to other related ideas. Appropriate learning experiences that build coherently over time are crucial to support this change (Fortus & Krajcik, 2012). But even learners with appropriate knowledge structures often need to undergo some form of restructuring when new problems, new information, or new evidence arise. As such, conceptual change usually requires learners to imagine new and alternative ways of structuring problems (Strike & Posner, 1985; Inagaki & Hatano, 2002; Carey, 1999). For instance, when a learner is trying to understand why they feel sick when they

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overeat, deeper levels of understanding occur when he comes to realize that the human body consists of systems that communicate with each other and that these systems are made up of even smaller, microscopic entities that work together.

Factors Infuencing Conceptual Change Several key factors are essential to promoting conceptual change. These include prior knowledge, curriculum coherence, meaningful and engaging contexts, development over time, and selfrefection. Next, each of these factors is briefy discussed. Prior knowledge is key to building integrated understanding (i.e., appropriate knowledge structures). Learning occurs when an individual links new ideas to prior knowledge and experiences. This process occurs regardless of the “appropriateness” of the original ideas. This is why it is so critical to ensure that children have appropriate learning experiences from a young age and build a foundation on which to link new ideas. Failure to use initial ideas leads to knowledge that is fragmented and disconnected. Research has shown that a learner cannot use such superfcial, fragmented knowledge (i.e., knowledge-in-pieces) for problem solving, explaining their world, or future learning (Bransford et al., 2000). Children entering school already have substantial knowledge of the natural world. But the resources that learners bring must be built further to develop their understanding of the fundamental ideas and practices of science. Some areas of knowledge may provide more robust foundations to build on than others because they appear very early and have some universal characteristics across cultures and worldwide. This is why constructive play is so vital in the early years of a child’s development. For example, having a child experience that seeds grow with sufcient and appropriate sunlight and water is critical to supporting their learning about factors that infuence the growth of plants. Coherent curriculum materials are a critical factor for successful student learning. To be coherent, curriculum materials must develop a set of ideas, topics, and skills within a discipline, that progress from relatively simple to more complex and that explicitly support the learner to make connections (Fortus & Krajcik, 2012; Schmidt et al., 2005; Roseman et al., 2008). In their seminal work, Kesidou and Roseman (2002) demonstrate that most textbooks in the United States deal with very broad topics, do not focus on age-appropriate learning goals, and do not systematically build ideas across time. Although curriculum designers and researchers since the release of the NGSS in 2013 have made progress on developing more appropriate materials, more work needs to occur. Schmidt et al. (2005) found that curriculum coherence was the most dominant predictor of student achievement. Moreover, Fortus and Krajcik (2012) argue that mere coverage of material (i.e., scientifc ideas) does not lead to learners building conceptual, integrated knowledge structures; instead, learners need to experience science in meaningful contexts in which they have opportunities to link ideas to experiences. Meaningful contexts support learners in developing deeper understanding by placing them in situations that will cognitively involve them in (1) the exploration of phenomena and (2) solving everyday problems that spark a need to know (Bransford et al., 2000). Research shows that the most efective learning occurs when learners actively use their understanding by working with and using ideas in real-world contexts that they fnd meaningful (Blumenfeld et al., 1991; National Academies of Sciences, E.  & Medicine, 2018; National Research Council, 2007). Although some individuals can learn about water quality by reading about it, most learners need to observe the impact of water quality on organisms, collect water quality data to determine the water quality, and examine how humans have impacted water quality. These frsthand experiences provide learners with contexts in which they can link new ideas, helping to build their conceptual network. Many school districts that reside in older and poor urban areas provide environments to study water quality because the infrastructure to deliver clean water is outdated.

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Learning over time is a key to developing deep understanding, particularly of challenging ideas, because these take time to build and require diferent experiences; moreover, the understanding must gradually develop as learners solve challenging problems and make sense of compelling phenomena. If we want learners to develop sophisticated ideas (e.g., biological evolution), then we need to introduce these ideas at an early age and continue to build understanding across grade levels. For example, learners at an early age need to experience that organisms have particular needs and that if not met, the species will either die or adapt to the new conditions. Self-refection, the ability to refect on one’s learning, is a critical factor in conceptual change. Scholars organize self-refection into three components (Weissberg et al., 2015; Patti et al., 2015). First, self-refection involves students’ ability to consider personal and academic experiences to make sense of situations. Second, self-refection might help a person decide whether there is sufcient evidence to support a claim; it may also aid students in articulating their goals and aspirations. This might include expressing a plan for developing an investigation to show how one variable impacts another. Lastly, self-refection involves careful contemplation of one’s emotions; here, a learner might know that they do not learn well under pressure. Learning environments can help support selfrefection by including tasks that ask learners to make decisions about whether they have sufcient, appropriate evidence to support a claim. Overall, self-refection helps learners decide what they must do to build a model, use evidence to support an argument, or design an investigation. These tasks are critical to support knowledge building.

A History of Learning Progressions In this section, we discuss the history of LPs to understand how and why LPs were initiated and how they evolved and examine the research fndings associated with LPs. LPs were initiated and guided by the vision of No Child Left Behind (NCLB, 2002) and Systems for State Science Assessment (National Research Council, 2006) to improve student learning across gender, race, ethnicity, and income level. The underlying principles of LPs focus on the importance of long-term changes in students’ integrated knowledge and thinking about and with the big ideas of science. Taking Science to School (National Research Council, 2007) focused on LP development with a science-as-practice framework by emphasizing applying big ideas alongside scientifc practices. A Framework for K–12 Science Education (National Research Council, 2012b) further stresses the importance of students developing knowledge they can use to make sense of phenomena and solve problems.

No Child Left Behind Act The inequities in science education worldwide have received critical attention over the last several decades, with recent global challenges only exacerbating the situation. For instance, science and engineering have been driving the economy and well-being of nations; however, the inequities mentioned earlier are even more evident in 2023, when less-developed nations struggle to provide vaccinations to overcome the COVID-19 pandemic. In the United States, the NCLB Act of 2001 mandated improving science education by providing learning opportunities for all students, regardless of gender, ethnicity, socioeconomic status, disability, and English-language profciency. The main idea underlying NCLB is that, while “individual diferences” exist and learners develop understanding in diferent ways (e.g., visual, verbal, and psychomotor) and at diferent rates, every student needs equal opportunities and resources to develop deep, usable knowledge of science; moreover, this must happen through meaningful experiences in a range of learning contexts (National Research Council, 1999). Exploring how learners develop ideas across time is critical to realizing this vision.

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Atlas for Science Literacy In response to NCLB, Project 2061 (American Association of the Advancement of Science, 2001), took a giant step forward in providing a structure for developing science ideas across time. Based on the Benchmarks for Science Literacy (American Association for the Advancement of Science, 1993), the Atlas for Science Literacy (American Association for the Advancement of Science, 2007) presents conceptual maps of the domains of science knowledge and how this knowledge should develop across time. The conceptual maps show how the key ideas developed across time and the connections among these ideas within and across domains. The maps provide a thoughtful examination of how ideas develop based primarily on the structure of the discipline. However, while these conceptual maps pushed the feld forward in thinking about how fundamental ideas can develop over time, they did not consider the literature on student learning. Moreover, the maps are based primarily on the structure of expert knowledge rather than evidence-based research fndings on how K–12 students develop ideas using well-developed, principled, and research-based curriculum materials, as these materials were not developed at this time. Even so, the work of Project 2061 and the Atlas for Science Literacy provide important work in helping the feld think of how students’ ideas develop over time to become more sophisticated.

Systems for State Science Assessment The NCLB Act required science standards that could measure students’ knowledge, considering individual diferences across a wide range of students. Furthermore, NCLB mandated that states express standards in a coherent manner and specify how students’ knowledge and skills should develop throughout instruction. Systems for State Science Assessments (SSSA) (National Research Council, 2006) provided guidance to state departments of education on what should be measured and how to measure it at the state level. One critical piece of advice they ofered was that assessment tasks needed to move beyond using the verbs “to know” and “to understand” – instead, tasks needed to use verbs that would require learners to demonstrate their knowledge in terms of performance. In addition to this guidance, SSSA introduced the idea of LPs as “descriptions of successively more sophisticated ways of thinking (or moving towards more expert understanding) about an idea”. To further explore the use of LPs, the SSSA committee commissioned Smith and her colleagues to develop a LP of the atomicmolecular theory across K–8 (Smith et al., 2006) and Lehrer’s research team to create a LP for evolution (Catley et al., 2005). These two groups also included example assessment tasks. SSSA and the seminal work of these two research groups led to the initial design principles for LPs in K–12 science education.

Taking Science to School To respond to the vision of NCLB and the seminal ideas of SSSA, Taking Science to School (National Research Council, 2007) proposed the science-as-practice framework, which contains three key learning processes: (1) obtaining ideas, (2) building meaningful knowledge structures by incorporating these ideas, and (3) applying these knowledge structures using scientifc practices (e.g., explaining, interpreting, constructing, investigating, arguing, or modeling). The science-as-practice framework theorized that learners would develop deep, usable understanding of science by using scientifc ideas in a similar manner as that of scientists and practices (Gotwals & Songer, 2006; Songer et al., 2009b). This framework, supported by research from the learning science community, highlights the value of teaching and learning scientifc content integrated with scientifc practices (Fortus et al., 2015; Krajcik et al., 2008). NCLB did not argue to assess content and practices together; however, it did state that, to learn science, students must use ideas and practices in concert when involved in scientifc tasks (Gotwals & Songer, 2006).

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Based on the science-as-practice framework, LPs serve as “descriptions of the successively more sophisticated ways of thinking about a topic that can follow one another as children learn about and investigate a topic over a broad span of time (e.g., 6 to 8 years)”. The authors of Taking Science to School emphasized that LPs depend on instruction and learning opportunities. Since appropriate instruction, curricula, and assessments are essential, science education needs a reasonable set of learning goals that integrate scientifc ideas with practices to support student learning in science. Such LPs are a promising direction for organizing science instruction, curricula, and assessment. To illustrate this promise, Songer and colleagues (Songer et al., 2009a) developed a LP by integrating scientifc ideas in the biological sciences (biodiversity) and scientifc practices (constructing science explanations) (Gotwals et al., 2012; Songer et al., 2009a).

Framework for K–12 Science Education and NGSS The Framework for K–12 Science Education (National Research Council, 2012b) articulates the three dimensions of scientifc knowledge needed for learners to explain compelling phenomena and solve complex problems. Building on the work of Project 2061 (American Association for the Advancement of Science, 2001), SSSA (National Research Council, 2006), and Taking Science to School (National Research Council, 2007), the Framework proposed three-dimensional learning, which integrates the use of disciplinary core ideas (DCIs), crosscutting concepts (CCCs), and scientifc and engineering practices (SEPs) across grades to make sense of phenomena and solve problems. The Framework specifes big ideas by identifying DCIs and CCCs as separate dimensions. DCIs are the fundamental ideas within a discipline that have broad importance within or across disciplines for explaining and investigating a wide range of phenomena and solving complex real-world problems (Duncan et al., 2017). For example, energy is a DCI, as it is needed to explain various phenomena, from plant growth to the damage caused by the collision of two vehicles. Gene-environment interactions is a DCI in life science. This idea is necessary for understanding many phenomena related to the traits organisms develop from interacting with their environments. CCCs are important ideas (National Research Council, 2012b; NGSS Lead States, 2013; Nordine & Lee, 2021) that have application across all science disciplines (e.g., physical and life sciences) but are not specifc to any one discipline. CCCs include cause and efect, patterns, structure, function, and stability and change. However, while CCCs are not discipline-specifc, they are big ideas of science and are essential to making sense of phenomena and solving problems. This means asking questions like: What could be causing that phenomenon? What patterns do I notice in the data? What is it about the structure of the animal that causes it to run so swiftly? How does the structure of a molecule give it its properties? The CCCs thus serve as tools for connecting diferent disciplines, as well as a unique lens through which to explore phenomena. When scientists take part in inquiry, they use a series of practices to explore the question they have about the natural world. These scientifc practices are how scientists and engineers make sense of phenomena or solve problems. SEPs include using evidence to support claims, asking questions, constructing models, and designing and carrying out investigations (Schwarz et al., 2017). Though each dimension matters, a central argument of the Framework is that profciency is demonstrated through performances that require learners to make sense of phenomena or solve complex problems using the integration of all three dimensions of scientifc knowledge. The Framework elaborates on what students should know about each DCI at the end of each grade band: K–2, 3–5, 6–8, and 9–12. The Framework also described how to put together these ideas to form a set of performance-based standards that integrates all three dimensions across the grade levels. The Framework, in its use of a LP approach, thus supports a coherent guide for organizing and aligning scientifc content, instruction, and assessment.

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Based on the Framework, NGSS developed a set of standards integrating the three dimensions that build across the K–2, 3–5, 6–8, and 9–12 grade levels. At the K–2 and 3–5 levels, the standards were written to be grade specifc (i.e., grade level), while at the 6–8 and 9–12 levels, the standards were written for applicability across the grade band. The NGSS specifed what students should know and be able to do for each grade level, K–5, and for each grade band, 6–8 and 9–12. By combining the three dimensions, these standards are expressed as performance expectations, i.e., learning goals expressed as performances by integrating a SEP, DCI, and CCC. The NGSS developed LPs for each DCI, SEP, and CCC. Each LP, in turn, describes how understanding within a single DCI, SEP, and CCC can develop over time; moreover, they each specify how students should be able to use their knowledge at various grade levels.3 The NGSS performance expectations provide opportunities for students to develop conceptual understanding of a relatively small set of DCIs, SEPs, and CCCs across grades, in which ideas increase in sophistication. Performance expectations for K–12 students move beyond developing standards in vague terms, such as “to know” and “to understand”, to more specifc expressions that include performances like “build a model”, “develop an explanation”, or “ask a question”, integrated with aspects of DCIs and CCCs. Since the release of the NGSS in 2013, 48 states and the District of Columbia have adopted or adapted the standards. A substantial proportion of the US student population needs to develop profciency in these performance standards.4 The Framework and NGSS describe LPs as roadmaps to guide the development of coherent curriculum materials, assessments, and instructional approaches to develop usable knowledge (National Research Council, 2012b). To understand the status of LP research and help direct future LP research, we review the LP literature in science education.

Research Studies About Learning Progressions Research in science education and the learning sciences provides promising evidence for using LPs to promote teaching and learning in science education. We review LP research in four categories: the big ideas of science, scientifc practices, curriculum materials and instruction, and assessment and measurement. The main goal of our review is twofold: frst, to explore the gaps between learning theories and LP research in K–12 science education and, second, to propose future directions for a comprehensive LP research agenda to reduce those gaps. Here, the aim is to support learning deep, usable knowledge for all students. In this section, we discuss the literature that has guided the development of LPs. As a complete review of the literature on LP research is beyond the scope of this chapter, we summarize some of the research that has impacted the feld. Table 5.1 contains a summary of the studies.

Big Ideas Physical Science Research Several research groups have focused on developing LPs about the particle nature of matter for K–12 (Johnson, 1998; Smith et al., 2006; Stevens et al., 2010). Cooper and colleagues (2012) designed and studied LPs for introductory college chemistry, and Morell and colleagues (2017) and Talanquer (2009) developed a LP for atomic-molecular structures. Stevens and colleagues (2010) developed a LP for the nature of matter that explores the growth of grade 7–14 (sophomores in college) learners’ understanding of the structure, behavior, and properties of matter in the context of nanoscale science and engineering. Stevens and colleagues also emphasized that a LP describes how learners incorporate and connect ideas within and across domains (e.g., atomic structure and the electrical forces at the nano, molecular, and atomic scales) to explain a broad range of phenomena. Cooper

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Joseph Krajcik and Namsoo Shin Table 5.1 Summary of the Studies in Learning Progression Research Content

Authors

One Dimension: DCIs, SEPs, or CCCs Physical sciences Energy Forces and motion Matter, atomic-molecular theory

Lee & Liu, 2010 (M); Neumann et al., 2013 (6th, 8th, and 10th); Fortus et al., 2015 (M) Alonzo & Steedle, 2009 (K–12) Corcoran et al., 2009 (K–12);Cooper et al., 2012 (C); Johnson, 1998 (ages 11–14, M); Margel et al., 2008 (H); Merritt, 2010 (M); Merritt & Krajcik, 2013 (M); Smith et al., 2006 (K–12); Stevens et al., 2010 (K–16); Morell et al., 2017 (M); Talanquer, 2009 (K–16)

Life sciences Evolution

Catley et al, 2005 (K–12)

Environmental literacy

Mohan et al., 2009 (4th–12th)

Carbon cycling

Jin et al., 2012 (K–12): Mohan et al., 2009 (E)

Genetics

Duncan et al., 2009 (5th–10th)

Earth and space sciences Celestial motion, seasons Water

Plummer et al., 2010 (10–11-year-olds: E); Plummer et al., 2014 (8th) Gunckel et al., 2012 (K–12)

Science practices Argumentation

Berland & McNeill, 2010 (K–12); Osborne et al., 2016 (6th–8th)

Scientifc explanations

Gotwals et al., 2013 (4th–6th)

Modeling

Schwarz et al., 2009 (5th and 6th)

Multi-Dimensions Physical sciences

Kaldaras et al, 2021a & 2021b (H): DCI – Electrical Interaction, SEP – Modeling, explanation, CCCs – Cause and efect, structure, and function He et al., 2020 & 2021 (M): DCI-Energy, SEP – Varies (mainly modeling), CCCs – Varies (mainly energy and matter, and system and system models)

Life sciences

Gotwals et al, 2006 (6th): DCI – Biodiversity SEP – Science explanation Songer et al., 2009 (6th): DCI – Biodiversity SEP – Reasoning

Interdisciplines

Anderson et al., 2020, (M, H): DCIs – Transformations in Matter and Energy, SEP-Varies, CCCs – Varies

Curriculum and Instruction Physical sciences

Cooper et al., 2012 (C); Fortus et al., 2012 (M), 2015 (6th–8th); Krajcik et al., 2009, 2012 (6th–8th); Margel et al., 2008 (H); Merritt et al., 2008, 2010, 2013 (6th–8th); Schwarz et al., 2009 (5th and 6th); Wiser et al., 2012 (E)

Life sciences

Jin et al., 2012 (K–12)

Earth sciences

Plummer et al., 2010 (10–11-year-olds: E); Plummer et al., 2014 (8th)

Assessment Physical sciences

Cooper et al., 2012 (C); Lee & Liu, 2010 (M); Smith et al., 2006 (K-12); Shin et al., 2019 (6th–8th); Neumann et al., 2013 (6th, 8th, and 10th)

Life sciences

Jin et al., 2012 (K–12); Gotwals et al., 2012 (6th)

Measurement Physical sciences

Alonzo et al., 2009 (K–12); Briggs et al., 2006; Morell et al., 2017 (M); Steedle et al., 2009; Wilson, 2009

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and colleagues (2012) extended the learning progress in this area into higher education; their LP on molecular structure and properties included assessments and was validated using a control/treatment group comparisons study. Other research teams developed LPs in the area of energy (Lee & Liu, 2010; Fortus et al., 2015). Neumann and colleagues (2013) developed and tested a LP for basic energy ideas for grades 6, 8, and 10. They provide evidence that sixth grade learners can build an understanding of energy forms and energy sources; by the eighth grade, learners show understanding of energy transfer and transformation, and by the tenth grade, some learners demonstrate understanding of energy conservation. Alonzo and Steedle (2009) used an iterative process to develop a LP for forces and motion with corresponding assessment tasks.

Life Sciences Research Duncan and colleagues (2009) developed a LP for modern genetics for grades 5–10. Their approach follows the view that LPs are grounded in research on student learning in the domain and organized around big ideas (National Research Council, 2007). Duncan and colleagues (2009) identifed eight big ideas in genetics. They described how these develop across grade bands based on research on student thinking and learning in genetics, a content analysis of modern genetics, and the National Science Education Standards (National Research Council, 1996). They unpacked this information to develop performance-based learning goals5 and assessments aligned with their LP. Performance-based learning goals, developed by combining scientifc practices with big ideas, guide the development of assessment items by specifying the type of performances or products students should produce to demonstrate profciency at each level in the LP (Harris et al., 2019b; Krajcik et al., 2008). Mohan and colleagues (Mohan et al., 2009) developed and empirically tested a LP for carbon cycling in socio-ecological systems for upper elementary through high school. They used designbased research by revising their LP using cross-sectional data from students at diferent grade levels in three diferent locations. Their approach was iterative and included developing a hypothetical LP and associated assessments, revising the initial LP based on the results of the assessments, and revising or developing new assessments. For the latter, they constructed three criteria to guide them: (1) conceptual coherence – depicting a comprehensive progression from naive to mastery understanding in a domain; (2) compatibility with current research – focusing on using research fndings and theories about student learning to develop the LP; and (3) empirical criteria – validating the LP using data gathered from students in classrooms. Jin and colleagues and Mohan and her research team also developed LPs for carbon cycling and the fow of energy in socio-ecological systems (Jin & Anderson, 2012; Mohan et al., 2009). Finally, Catley et al. (2005) developed a LP on evolution in K–12, as described in the section on SSSA.

Earth and Space Science Research Plummer and her colleagues developed a LP on core ideas in earth and space science – e.g., for celestial motion (Plummer & Krajcik, 2010) and the seasons (Plummer & Maynard, 2014) – based on student interviews across grades. The authors analyzed the content of the domain and applied prior research to identify the endpoint of the LP. They specify low and intermediate concepts of the LP based on the results of the student interviews. The LP was used to develop assessments and track students’ understanding of celestial motion before and after an intervention. Gunckel and colleagues (2012) investigated elementary, middle, and high school students’ accounts of water in environmental systems to develop a LP for water in environmental systems. They explored students’ ideas of water and substances in water moving through the atmospheric, surface, and soil/groundwater systems, including human-engineered components of these systems. Through an iterative process of

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analyzing assessments, the results of students’ interpretations, and a model of student development, they identifed four levels of student reasoning about water in environmental systems.

Scientifc Practices Other research groups focused on the design of LPs for scientifc practices. Schwarz and colleagues (Schwarz et al., 2009) developed a LP that took into consideration two aspects of scientifc modeling: modeling as a scientifc practice (e.g., models as generative tools for predicting and explaining) and metamodeling knowledge (e.g., models change as understanding improves with the use of new evidence). The authors illustrate possible paths and characterize students’ models within their LP by providing examples collected from ffth and sixth grade students in six-week science units. Berland and McNeill (2010) developed a LP that focused on argumentation in science. The authors used the LP to design assessments to gather student understanding of the ideas and conduct interviews to test and refne the LP. They also used the LP to characterize students’ arguments collected from four classroom settings across grade levels. Osborne and colleagues (2016) developed a LP of argumentation in science at the middle school level. Their LP has three components: claim, evidence, and warrants. They argue that, given the centrality of argumentation in the NGSS (NGSS Lead States, 2013), there is an urgent need for an empirically validated LP of this practice and the development of high-quality assessment items. As such, the authors provide validity evidence for their LP based on large-scale testing and analysis using item response theory.

Multi-Dimensions The examples of LPs described earlier focused on developing single dimensions (either big ideas or practices) rather than incorporating the three dimensions of scientifc knowledge as an integrated whole. Songer and colleagues were the frst research group to develop and explore a multidimensional LP. They developed a LP for biodiversity with the practice of scientifc explanation targeting sixth graders (Gotwals et al., 2012; Songer et al., 2009a) prior to the publication of the NGSS. Some recent work by Kaldaras and colleagues (2021a) explores the development of threedimensional LP. Kaldaras and colleagues developed and validated a LP with the following three dimensions of scientifc knowledge: (1) DCIs – electric interactions and energy; (2) SEPs – modeling and explanation; and (3) CCCs – patterns and systems and system models. They argue that a threedimensional LP can be used as a “ruler” to measure how much knowledge a given student or group of students know and can do with the knowledge and what supports are required to help them attain higher levels of understanding. Anderson, working with the Carbon: Transformations in Matter and Energy (Carbon: TIME) team,6 used design-based implementation research to develop a threedimensional LP for middle and high school classrooms (2020). This LP focuses on carbon cycling at various scales. Although researchers are conducting important work in this area, more research is needed in developing, testing, and using three-dimensional LPs.

Curriculum Materials and Instruction There is promising evidence on using LP research to promote teaching and learning in science education (Duncan et al., 2009; Krajcik et al., 2009; Margel et al., 2008; Merritt, 2010; Merritt & Krajcik, 2013). Prior to LPs as an important area of research in science education, Johnson (1998) applied the idea to support learners’ understanding of a “basic” particle theory of matter using a three-year longitudinal study of students aged 11–14. His results show that students move from one level to the next but at times fall between two levels (however, not in a linear manner). Since

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then, curriculum developers have used a LP approach and research to develop materials that provide opportunities for learners to progress to deeper, more usable levels of understanding over an instructional period – e.g., the Inquiry Project (Wiser et al., 2012) and Investigating and Questioning our World through Science and Technology (IQWST, Krajcik et al., 2009). Wiser and her colleagues (2012) designed curriculum materials (the Inquiry Project) for the elementary grades based on a LP for matter developed by Smith and colleagues (Smith et al., 2006); Wiser and her team implemented the curriculum materials and monitored students’ understanding in two schools over a three-year span. Krajcik and colleagues (2012) designed and tested middle school curricula (IQWST) that focus on big ideas and scientifc practices developing through the middle grades to support learning interconnected knowledge structures of big ideas and scientifc practices over time using prior experiences. The approach focuses on comprehensively developing LP levels by unpacking a big idea into key subconcepts and relevant scientifc practices. Their LP – associated assessments, guided by learning goals expressed as performances, specifed what students need to do to demonstrate their understanding (i.e., evidence for students meeting a performance learning goal) (Harris et al., 2019a; Krajcik et al., 2008). Design-based research was employed to iteratively develop instructional materials by tracking 3,000 students’ performances through middle school in four states. Their fndings suggest that curriculum coherence around LPs helps students develop deep understanding of core ideas. Fortus and Krajcik (2012) developed principles and guidelines based on LP research for developing coherent curriculum materials. They emphasized four coherences: (1) content standards coherence to support deep, interconnected understanding of concepts in a domain; (2) learning goal coherence to support deep understanding over time; (3) intra-unit coherence for alignment among learning goals, scientifc practices, and learning and assessment tasks; and 4) inter-unit coherence for coordinating larger sets of the intra-unit coherence (multiple domains and practices) within and across years. Fortus and colleagues (2015) examined the contribution of inter-unit coherence of energy in the IQWST materials to promote learning of this idea over time. The designers interwove the concept of energy in six diferent IQWST units. Their results provide evidence that inter-unit coherence of the units enables students to develop deeper understanding of energy. They argue that the deeper usage of the energy concept that resulted from repeated exposures across years rather than weeks allowed learners to build prior knowledge necessary for additional learning. This inter-unit coherence allowed revisiting ideas and experiencing new phenomena that required the use of more sophisticated ideas of energy in new contexts. Authors also suggest that professional learning for teachers needs to align with the coherent features of the curriculum materials; in this way, teachers can learn how to teach in a developmental manner. Cooper and colleagues (2012) developed and monitored a LP for the molecular structures of matter and their properties (Lewis structures and the connections among structure and properties) for college students, over one year of instruction. They developed curriculum materials to promote robust learning of Lewis structures based on the LP perspective and explored the efect on student learning using control and treatment groups over two semesters. Although the previous studies show promise, the LPs discussed focus on only one or two dimensions, instead of integrating the three dimensions of scientifc knowledge.

Assessment and Measurement Learning progressions require validated and reliable assessment tasks to place students on the learning progression. Such information is valuable because it provides teachers and students with what students know and what teachers need to do to move students to the next level on the learning progression. Some scholars have used LP research to develop assessments to trace developmental growth in a domain, such as carbon-transforming processes in socio-ecological

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systems (Jin  & Anderson, 2012) and constructing scientifc explanations (Gotwals et  al., 2012). Shin and her research team (2019) developed assessment items using a LP framework to track how students move toward deep understanding of chemistry core ideas over time. They followed 1,225 middle school students from sixth through eighth grade in four states to explore the efects of coherent curriculum materials. They found that students who used coherent curriculum materials that aligned learning goals of the curriculum, instruction, and assessment developed from LP research outperformed students who used traditional curriculum materials. Other researchers have proposed measurement models to empirically test and validate LPs: for example, a Bayesian network approach (West et  al., 2012) and the Psychometric Modeling of Ordered Multiple-Choice (OMC) items (Briggs et al., 2006; Alonzo & Steedle, 2009). The latter approach uses progressive levels of understanding concepts rather than one correct response among four choices in a test item (Briggs et al., 2006). Briggs and colleagues developed OMC items associated with their LP for Earth in the Solar System. Alonzo and Steedle (2009) developed OMC items along with their forces and motion LP; they found that OMC items appear to provide more accurate assessments of students’ level of performance based on the LP – and elicit more valid students’ ideas. Steedle and Shavelson (2009) used LPs as a framework to guide the development of assessments to characterize students’ level and nature of understanding in a domain. The foundation of this study was to explore a systematic approach for empirically testing a LP using students’ observable products. They argued that allocating students’ understanding in a certain level along a LP requires that students consistently express the ideas associated with one specifc LP level. In their approach, the LP levels serve as a rubric for tracing students’ progress toward more sophisticated understanding. They employed OMC items and latent class analysis to investigate whether patterns of students’ responses to the items provide valid evidence for assigning them to LP levels. Their results suggest that it is not feasible to develop valid LPs that describe the paths and levels of how students develop within a domain. However, these ideas require further research to validate LPs empirically. Wilson (2009) describes a measurement approach (the BEAR Assessment System) to assign and monitor student understanding along a LP throughout instruction. His methods follow four principles: (1) a developmental perspective  – assessing student understanding over time; (2) a match between instruction and assessment – developing curriculum materials and assessment in a coherent manner; (3) the generating of quality evidence – providing reliable, valid, fair, consistent, and unbiased assessments; and (4) management by teachers – providing useful information to judge students’ level of understanding and to prepare appropriate, timely feedback. While Wilson and his colleagues have published numerous manuscripts on LP research and have made many contributions to the design and validation of LPs, Wilson’s most signifcant contribution is the idea of a ConstructMap. A ConstructMap is a graphical, menu-driven software package that uses a multidimensional item response theory engine to estimate item and person variables; it also includes tools to manage crosssectional and longitudinal student response data and interpret fndings. Morell and colleagues (2017) illustrate Wilson’s methodology using the ConstructMap approach and multidimensional Rasch model for investigating the progression of how middle grade students understand the structure of matter.

Our Views on Learning Progressions Based on our review of selected LP-related learning theories and research in K–12 education, we elaborate next on our conceptual background of learning, views on LP, and outline how to develop and validate a LP. Specifcally, we describe how our approach to designing and studying LPs incorporates the learning theories to support students in creating deep, usable knowledge.

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Conceptual Background of Learning The Three Dimensions of Scientifc Knowledge To support learners in developing deep, usable knowledge, we need to know what students need to learn and how to support them in learning those ideas. Science is more than just a compilation of facts. Scientifc knowledge is organized around three dimensions: disciplinary core ideas (DCIs), scientifc and engineering practices (SEPs), and crosscutting concepts (CCCs). To learn science, students of all ages need sustained efort and focus. They also need appropriate instruction to help them develop deep, usable knowledge centered on DCIs, CCCs, and SEPs across time. Learning science is a complex, challenging, and multifaceted efort because developing sophisticated knowledge in one area of science requires an in-depth understanding of other areas. For instance, photosynthesis and respiration are typically considered DCIs of the biological sciences, but to understand these two reciprocal processes, one needs to understand the structure and function of matter and energy transfer. Developing a usable understanding of scientifc ideas is inextricably linked to the context in which the learner develops that understanding (National Research Council, 2007, 2012b). As such, to support learners in developing usable knowledge, it is essential to link SEPs with the DCIs and CCCs for instructional and assessment purposes. Just as science is a body of knowledge and the process whereby that body of knowledge develops, the learning of science is similar. One cannot learn scientifc ideas without using scientifc practices, and the reverse also holds: one cannot learn scientifc practices separate from scientifc ideas. To support learners in developing deep, usable knowledge, they need to engage with scientifc ideas and scientifc practices; if we want students to learn scientifc practices, they must use them alongside scientifc ideas to make sense of phenomena. In other words, individuals need to use all three dimensions to make sense of phenomena or solve problems. In the Framework for K–12 Science Education, the integration of the three dimensions (DCIs, CCCs, and SEPs) is referred to as three-dimensional learning. Just as a rope becomes stronger when individual strands are braided together, when SEPs, DCIs, and CCCs are integrated, learners can use that knowledge to solve problems, make decisions, think innovatively, make sense of phenomena, and learn more when needed.

Introducing Big Ideas Over Time Learning big ideas requires introducing them to students over time, from early grades through college. Because of their importance to various domains of science, DCIs need to be taught across grade levels and subjects (e.g., biology, physics, chemistry, and earth science) with increased depth and sophistication (National Research Council, 2013). Learning challenging big ideas takes time, as learners grapple with making sense of complex phenomena or problems. As students progress in their learning, how they explain phenomena changes and advances (e.g., how plants use energy from the sun for growth, the transfer of energy when two objects collide, and how the body makes use of energy to sustain life and repair). The study of energy might begin by exploring how moving objects, which children can see, can transfer energy in a collision; it might then advance to more microscopic-based and mechanistic explanations as a learner advances to explain phenomena like energy transfer in chemical reactions. For example, science ideas like balanced and unbalanced forces are quite challenging for students and for teachers trying to support all their students in learning these ideas. The NGSS has balanced and unbalanced forces as a performance expectation at the third grade level. Students at the third grade level are, therefore, expected to develop competency in the following learning performance:

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“Plan and conduct an investigation to provide evidence of the efects of balanced and unbalanced forces on the motion of an object.” In elementary school, this is not the frst time children experience ideas related to force and motion. In the K–2 range, children engage in learning the idea of force as a push and a pull. The performance expectation reads: “Analyze data to determine if a design solution works as intended to change the speed or direction of an object with a push or a pull.” A beginning idea is thus introduced and is further developed in the third to ffth grade range. Balanced and unbalanced forces causing an object to start, stop, slow down, or change direction is a challenging idea for many third graders – and even for adults. At the middle school level, students delve deeper into understanding forces to develop profciencies in the following middle school performance expectations: “Plan an investigation to provide evidence that the change in an object’s motion depends on the sum of the forces on the object and the mass of the object” and “Apply Newton’s third law to design a solution to a problem involving the motion of two colliding objects”. Notice how the ideas are becoming progressively more sophisticated and challenging from K–2 through middle school. Without their previous experiences and deep knowledge from the K–5 grades, students in middle school would fnd this idea exceptionally challenging to learn. However, even in K–2 students can use the ideas of forces and motion to make sense of phenomena. At the high school level, the ideas of forces and motion become even more sophisticated as students grapple with developing a more quantitative understanding: “Analyze data to support the claim that Newton’s second law of motion describes the mathematical relationship among the net force on a macroscopic object, its mass, and its acceleration”. Notice how the ideas develop across the grade levels, becoming more sophisticated and challenging but allowing learners to build richer explanations of phenomena – such as why a moving object starts and stops.

Features of Three-Dimensional Learning Progressions Based on the literature, LPs contain four essential features: (1) research-based development; (2) DCIs in science and three-dimensional integration of  DCIs, SEPs, and CCCs; (3) multiple paths; and (4) monitoring developmentally appropriate growth over time. Table 5.2 summarizes how each LP feature relates to the learning theories we discussed in the theoretical background. We describe each of these features next.

Research-Based Development LPs should be research (evidence) based. The LP approach is diferent from a conventional approach – the traditional approach focuses on a series of process skill development by identifying and breaking into simpler elements of a learning task and providing specifc, associated exercises for students to practice these skills. For example, in a conventional approach, learners practice making measurements without understanding why they are measuring and how their measurements link to prior and subsequent tasks. Such instruction results in students carrying out meaningless procedures (Baroody et al., 2004; Mintzes et al., 1997), in which they ignore fndings on how individuals build knowledge structures based on meaningful experiences. In contrast, LPs develop by using the results from extensive research on student learning. The development of LPs should use research fndings about how learners develop knowledge in science. Researchers can use the experience of teachers and other classroom experiences to develop LPs when research in science learning is incomplete. However, further research and development are needed to identify and elaborate the progressions of learning and instruction that can support students’ understanding of the three dimensions of scientifc knowledge across the various disciplines. As mentioned earlier, LPs can change: as we learn how to support students in understanding complex ideas and gathering more evidence. Because LPs coincide with research on supporting learners to build knowledge structures of interrelated scientifc

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3D Learning Progression in Science

3-Dimensional integration Concepts Mental representation of abstract ideas

LP characterizes learning in terms of sets of related big ideas, including DCIs and CCCs to develop more sophisticated understandings to explain phenomena and solve problems

Cognition Mental process of connecting new and old ideas through a series of learning experiences by interacting with and interpreting the world

LP integrates sets of DCIs and CCCs with SEPs so that learners can apply them when they are needed

Multiple paths Knowledge-in-use, deep learning The more connections among ideas based on prior experience and instruction result in greater integrated understanding and deeper and more usable knowledge to apply in novel situation

LP delineates progress levels to guide curriculum and instruction development to support an individual constructing deeper understanding following various paths – as opposed to progress along a single path

Monitor developmentally appropriate growth over time Knowledge build across time Concepts and knowledge structures evolve over time as learners experience more situations and problems

LP introduces DCIs and CCCs to learners from early grades through college for developing knowledge-in-use of science ideas over time with various experiences

Radical conceptual change Radical conceptual change occurs when learners not only need to add new concepts to their current knowledge structure, but when existing concepts change their meaning in fundamental ways

LP describes critical conceptual change as a developmental process. The LP level is not adding one more idea but seeing the phenomena in a new perspective. Each level describes a qualitatively diferent level of understanding

concepts and practices (Mohan et al., 2009), LPs represent a promising direction for organizing science instruction, curriculum materials, and professional learning across grade levels for deeper, more usable understanding.

Multiple Paths Students can follow more than one path to develop sophisticated understandings. Like the earlier mountain analogy, learners can take diferent paths to learn ideas, and along the way, just like there are beautiful views, reaching new levels allow learners to explain more phenomena. LPs are not contextfree, rote descriptions of learning but depend upon experiences and instruction. Because they depend upon instruction, learners can take diferent paths in developing disciplinary knowledge. In this way, LPs are diferent from the conventional approach. Many schools have sequencing charts for various subjects, but these do not focus on the development of big ideas and scientifc practices – nor do they take a developmental perspective in which ideas and experiences build upon one another across time. Moreover, LPs can change and develop as we learn more about how to support learners in developing more sophisticated understandings. This is why professional learning and supporting teachers in understanding important instructional strategies and fundamental ideas of how learning occurs is

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so critical. Teachers may need to adapt their instruction to align with students’ experiences, interests, and knowledge in their classroom. Many factors lead to diferent pathways that individual students might follow as they move from novice to more sophisticated understanding: these include prior knowledge and experience, curriculum materials, instruction, interests, and daily life (Smith et al., 2006). However, there are some key ideas that all students need to understand so that deeper understanding can develop from them. For instance, students need to understand that properties can identify substances before understanding that substances can interact to form new substances with diferent properties. Some learners will follow some typical paths more often than others. These key ideas and typical paths can provide a foundation for developing LPs, associated learning materials, and assessment tasks that support students’ understanding at each level along the progression. Moreover, developing sophisticated understanding is dependent on learners having opportunities to learn. While learners can take many paths, an instructional-aligned, three-dimensional LP can guide the development of meaningful learning over time. LPs must also incorporate corresponding assessment items and instructional ideas to move students to the next level (Davis & Krajcik, 2005). As a feld, however, we know little about how to develop complex ideas like forces, evolutions, and plate tectonics across grade levels. Some progress is being made, particularly with the adoption and adaptation of the NGSS in the United States, but more work across grade levels is needed – especially on how to tailor LPs to learners’ backgrounds, experiences, and resources.

Monitor Developmentally Appropriate Growth Over Time LPs depict how students’ knowledge of DCIs, CCCs, and SEPs grows (Lehrer & Schauble, 2015) over a long period of time (K–16) or across a semester or several weeks. Because LPs focus on complex DCIs essential for understanding the world, they take time to develop. Deep understanding of complex core ideas – such as evolution, geological time, and the transfer of energy – takes time and often happens as individuals work on challenging tasks that require them to synthesize and make connections to ideas related to the tasks. Given the complexity and difculty of understanding DCIs integrated with SEPs and CCCs, such learning occurs over a long period and in multiple contexts. LPs are developmentally appropriate, as they build over time based on learners’ prior knowledge and experiences (Lehrer & Schauble, 2015; National Research Council, 2007; Smith et al., 2006). LPs build from students’ prior knowledge and experiences to move them further in their understanding. In sum, LPs should describe how students develop more sophisticated understanding of threedimensional knowledge with the integration of DCIs, SEPs, and CCCs over a defned time period. LPs need to clearly defne what it means to move toward a more advanced understanding of a DCI using SEPs and CCCs. LPs represent not only how deep knowledge develops but also predict how students’ knowledge builds over time and how to support that development. Thus, the focus is on how new ideas build on intermediate understanding of ideas to reach an understanding of the target DCIs, CCCs, and SEPs (Duncan  & Rivet, 2013; National Research Council, 2007). As learners build knowledge, they may begin with a few simple ideas, but as their understanding becomes more complex – as new ideas are introduced and integrated with prior knowledge – earlier ideas are refned (National Academies of Sciences, E. & Medicine, 2018).

Components of Learning Progressions LPs include four components: (1) upper and lower anchors of LPs; (2) developmental and comprehensible levels; (3) description of progress across levels; and (4) aligned curriculum, instruction, assessment, and professional learning.

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Upper and Lower Anchors of LPs A LP addresses a clearly defned range of content within a discipline (National Research Council, 2007; Smith et al., 2006; Stevens et al., 2010) from what students should know and be able to do at the beginning of a LP to what they should know and be able to do at the end. The lower anchor of a LP describes the prior understanding of the dimensions of scientifc knowledge required for students to begin to achieve the learning goals set out in the LP. The scope of the LP – which infuences the prior knowledge and experiences a learner will determine the lower anchor. The knowledge and skills students are expected to develop by the end of the LP defne the upper anchor, which relates to three key elements: (1) what learning research has found to be developmentally feasible, (2) established learning goals of science education, and (3) the needs and expectations of society (Mohan et al., 2009). The upper anchor consists of the predictions of what level of understanding a student can reach with appropriate instructional opportunities. The physical science performance expectations (PEs) associated with matter and its interactions from the NGSS at the middle school level, for instance, can represent the upper anchor of ideas that students should develop profciency by the end of middle school. The lower anchors stem from the physical science PEs associated with matter and its interactions from the elementary grades. The challenging aspect is flling in the levels in between the lower and upper anchors.

Developmental and Comprehensible Levels LPs defne developmental and comprehensible steps or levels needed to move toward a more sophisticated understanding of DCIs in the context of SEPs and CCCs. Each level describes a qualitatively diferent understanding, but that understanding always links to previous levels, creating a path by which students can progress as they move toward the upper anchor (see Figure 5.1). Learning research should guide the descriptions of the levels and not necessarily the logic of the discipline. Learning research should also guide when and how it might be appropriate for students to learn scientifc ideas and practices (National Research Council, 2007). While some researchers may use non-canonical ideas in their descriptions of student progress, and students may use less sophisticated or non-normative ideas as they struggle to understand new ideas, we suggest not including noncanonical ideas in the LP – unless research reveals that the non-canonical ideas can serve as productive stepping stones in building more appropriate understandings toward reaching the upper anchor.

Description of Progress Across Levels LPs represent a potential path that is coherent, developmentally appropriate, and empirically verifable by which learners may develop knowledge and scientifc practices and may include multiple instructional pathways (Shin et al., 2019; Stevens et al., 2010). However, a LP includes not only an ordered description of how big ideas can develop over time, but also evidence-based accounts of how students should use and apply that knowledge and instructional components to support students in their growth and assessment tasks to provide indications of student understanding. LPs thus give a sequence of successively more complex and refned ways of thinking about a DCI, along with SEPs and CCCs that might reasonably follow one another as learners develop more sophisticated understanding. In other words, LPs describe the pathways and the instruction to guide learners by which they can bridge their starting point and the desired endpoint. As such, LPs provide descriptions of student learning as well as providing appropriate experiences and phenomena that can support students in building toward the next level of sophistication. He and colleagues developed a threedimensional LP by integrating DCIs, SEPs, and CCCs to make sense of energy-related phenomena over time (He et al., 2020; Shin et al., 2021). Appendix 2 illustrates their three-dimensional LP and

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examples of associated phenomena, assessment tasks, and students’ responses to delineate students’ progress in three-dimensional learning over time (see Appendix 2 for details).

Alignment of LPs With Curriculum Materials, Instruction, Assessment, and Professional Learning Mastering a level requires students to develop understanding of ideas in multiple disciplines (e.g., energy, chemical reaction, photosynthesis) and provide them with opportunities to connect and relate those ideas to develop integrated knowledge structures. Such learning should result in usable knowledge and skills to allow learners to select and connect ideas and apply them to explain phenomena and solve problems. Although multiple pathways are possible, LPs provide a framework to guide curriculum materials, instruction and assessment development, and teacher professional learning. What is critical is that researchers and educators agree on the big ideas and practices students should learn. In the United States, the Framework for K–12 Science Education provides this step forward. Next, researchers need to design curriculum materials and associated assessments to test whether students can reach the various levels with appropriate instruction and learning materials. Supporting students to build understanding of DCIs along with SEPs and CCCs to make sense of phenomena and solve problems can be daunting. Teacher professional learning, along with curriculum materials and assessment, is critical to promoting student learning. Professional learning to support teachers in understanding LPs and development over time is critical in helping students develop more sophisticated understanding. Professional learning is important to support teachers in building a developmental perspective of learning and to support teachers to learn (1) new instructional strategies, (2) how to help students develop the connections and relationships needed to build integrated knowledge structures, and (3) use assessments to evaluate student performance and move them further. Curriculum sequences aligned with a LP should focus on developing understanding over time about what might be truly foundational and most important to teach to explain a wide variety of compelling phenomena and challenging problems.

Future Direction for Learning Progression Research Although the Framework and NGSS (NGSS Lead States, 2013) outline possible theoretical LPs, fully designed, researched, and validated LPs are outside the scope of both documents. This is due in part to the substantial amount of time, expertise, and resources needed to (1) develop high-quality curriculum materials that support development across time, (2) construct assessments capable of probing LP levels, and (3) generate evidence to validate the LP (Kaldaras et al., 2021a, 2021b). To respond to the importance of knowledge-in-use learning in science, LP research needs to be conducted actively across disciplines and grade levels, using integrated three-dimensional LPs to identify what students should know and be able to do in each grade band.

Research on Developing and Monitoring Students’ Deep Learning of Three-Dimensional LPs What science education researchers must fgure out are the mechanisms of conceptual change for developing understandings of DCIs, CCCs, and SEPs, not individually but how they work together to build knowledge-in-use. LP research includes developing, validating, and monitoring three-dimensional LPs on student learning to capitalize on the impact of LP to promote deep, usable knowledge in science education. Research is needed on tracking student understanding of an idea or cluster of ideas integrated with practices and based on curriculum materials purposefully designed, developed, and tested using the principle of coherence and other ideas known

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to support student learning (i.e., situated in meaningful contexts, frsthand experiences with phenomena, collaborations) (Krajcik  & Shin, 2022). Linking to prior knowledge and building stronger connections to new ideas and experiences should result in meaningful learning; this will increase students’ ability to select and combine ideas to apply to new situations, providing richer, multifaceted explanations of phenomena. Because LPs make connections among ideas explicit, they provide a framework to guide curriculum development, assessment, instruction, and professional learning  – all of which support students in building integrated knowledge structures, which students can use to solve novel problems, explain more complex phenomena, and learn more when needed. In the research on developing and monitoring LPs, multiple research groups have developed, tested, and used various LPs (Duschl et  al., 2011). As we discussed earlier, the most common approach is to build and test a LP solely in terms of content ideas or practices (i.e., one-dimensional). Researchers have developed LP-aligned curriculum materials to characterize and follow the growth of students’ understanding as they experience specifc instruction (Alonzo & Steedle, 2009; Duncan et  al., 2009; Krajcik et  al., 2012). For example, researchers create a sequence of instructions and learning materials aligned with a LP’s levels to track students’ learning across a period of time (e.g., 4–8 weeks of instruction). Merritt and Krajcik (Merritt & Krajcik, 2013) tracked the development of student understanding of the particle nature of matter over an 8–10 week period. The curriculum materials for IQWST (Krajcik et al., 2009) were designed based on research in science education and learning science. It is important to specify how students should apply knowledge, as it provides insight into learners’ reasoning (Smith et al., 2006; Jin & Anderson, 2012). Merritt and colleagues (Merritt & Krajcik, 2013) developed a LP on the particle nature of matter consistent with the Framework (National Research Council, 2012b) and NGSS (NGSS Lead States, 2013) that details how learners can develop an understanding of core ideas across the primary and secondary grades. Cooper and colleagues (2012) tracked the development of student understanding of the molecular structures and properties at the introductory college level using control and treatment group comparisons. Their curriculum development was also based on the research fndings in science education. However, their curriculum-focused LPs are two-dimensional, combining DCIs and SEPs but not CCCs (Merritt & Krajcik, 2013; Cooper et al., 2012; Gotwals & Songer, 2006). More research is needed on how to develop and test three-dimensional LPs to foster deep, usable student learning. The research by Kaldaras and colleagues (Kaldaras et al., 2021a, 2021b) provides insights into developing and testing three-dimensional LPs (see next section for further information). As discussed earlier, the work by Fortus and colleagues (Fortus et  al., 2015) tracked students’ developing understanding of the big ideas with the integration of CCCs and SEPs, but additional work monitoring the integration of the three dimensions over time is still needed. As such, a critical area for research in science education and the learning sciences includes tracking students’ learning across time, based on curriculum materials designed to foster students’ learning in a particular area that integrate the three dimensions of scientifc knowledge.

Research on Testing and Validating Three-Dimensional LPs Current debates in the feld center on whether one can measure the three dimensions of scientifc knowledge as a unifed construct or if the measurement of the three dimensions must occur separately. Research is needed to test and validate three-dimensional LPs. Although the Framework describes the theoretical basis of three-dimensional learning, and the NGSS proposes LPs for the three dimensions across grades, there is limited empirical evidence to show that a LP for threedimensional learning can be validated in classrooms. Kaldaras and colleagues (2021a) demonstrate the feasibility of developing a three-dimensional LP supported by qualitative and quantitative evidence

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for the areas of intermolecular forces and energy. They present multiple sources of evidence for the three-dimensional LP to provide evidence for the validity of their LP. Finally, they demonstrate the feasibility of using assessment tasks to probe levels and assign learners to levels based on student responses. In a second paper, Kaldaras and colleagues (2021b) suggest that the most plausible latent structure to account for students’ responses to three-dimensional assessment tasks is that the instrument is three-dimensional instead of separating them into a single dimension. As such, the LP measures a unidimensional construct (i.e., the integrations of DCI, SEPs – in their case modeling and explanations – and CCCs). While this work is promising, more three-dimensional LPs need to be developed and tested in various domains using various practices. In particular, advanced or innovative measurement models are necessary to validate three-dimensional LPs.

Developing a Three-Dimensional Design-Based Research Agenda To pursue an empirically tested three-dimensional LP requires a principled research agenda that will (1) ensure coherent alignment among the three dimensions of the LP, (2) monitor the threedimensional LP regarding promoting students’ deep learning, and (3) validate the three-dimensional LP. We propose that a design-based research approach with a design-test-revise cycle is an appropriate methodology for developing, testing, and studying three-dimensional LPs and associated curriculum materials and assessments. Much of the LP research has been conducted by developing LPs that align with traditional forms of instruction. Lehrer and Schauble (2015) describe how such LP work could perpetuate the current education system. Here, the LPs would align more with traditional forms of instruction rather than using evidence-based curricula and a design-based approach to push students to more sophisticated levels of understanding. Merely describing how students learn, based on current instructional approaches and curricula used in schools, prevents the feld from reexamining how to help all students develop deep, usable knowledge and how teaching and learning occur in science classrooms is necessary to provide all students opportunities to learn. As argued by Lehrer and Schauble (2015), LPs are a way to rethink, restructure, and push forward “the content and sequencing of the subject matter taught in schools”. They argue that LPs serve as proposals or models to shift teachers’ and students’ views of what it means to understand a big idea of science (like evolution) or to engage in a practice (like scientifc modeling). They also argue that using LPs involves shifting our view of what it means to learn big ideas along with scientifc practices so that learners can make sense of complex phenomena. For this reason, we promote their position that LPs need to be developed in design-based research settings where the research team – along with practitioners – play a critical role in generating the context, developing learning tasks, and supporting instruction that will encourage deep learning (Cobb et al., 2003). In a design-based research approach to developing LPs, researchers collaborate with teachers to design and support instructional tasks, tools, frsthand experience, simulations, and assessment tasks. The research is driven by researchers making conjectures or hypotheses about how to support students in obtaining deeper knowledge of DCIs integrated with SEPs and CCCs to make sense of phenomena or solve complex problems, given the instruction intervention specifed in the design of the LP. First, researchers investigate the conjectures in classroom settings. They use their fndings to inform revisions to the design of the instructional materials and suggested teacher practices and to modify the conjectures about the levels of students’ development. Next, the design is retested in an iterative fashion and in new and more classrooms. The goal is to obtain evidence of students reaching particular levels of performance described in the LP. The various levels capture a range of student learning, from using the ideas in a relatively intuitive or everyday way to make sense of phenomena to using the ideas in increasingly sophisticated and complex ways (similar to more knowledgeable

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and experienced individuals). Each level of student learning is demonstrated by student performance (Harris et al., 2019a; Krajcik et al., 2008) – building a model, designing an investigation to fgure out phenomena, providing an evidence-based explanation, and asking new questions. These performances ofer insights into students’ depth of knowledge and how the LP functions to promote student learning. Researchers iteratively apply the design-test-revise cycle so that the educational design of the LP and the fndings provide feedback to refne the LP further. A good demonstration of this design-based research model to develop and test LPs and associated three-dimensional assessment tasks comes from the work of Kaldaras et al. (2021b). They used a design-based research approach to develop and test a high school LP on intermolecular forces and energy, using the scientifc practices of modeling and explanation with the CCCs of cause and efect, and structure and function that aligned with a curriculum unit.7 Kaldaras and colleagues also demonstrate the feasibility of developing a three-dimensional LP through design-based research by obtaining qualitative and quantitative validity evidence for the structure of the LP and the corresponding assessments. Their research illustrates the evolving design: they show how fndings regarding students’ reasoning with science ideas and evidence evolve together to improve the LP, the assessments, and the curriculum materials. Their work also demonstrates the usefulness of design-based research to validate a three-dimensional LP for organizing the learning that can occur based on the Framework and NGSS performance expectations, and it also shows how a LP can serve as a diagnostic tool, think of a ruler, to measure student learning. Anderson and colleagues (Gunckel et al., 2012; Jin & Anderson, 2012) also used a design-based research model for middle and high school classrooms to describe the development of a LP for the transformations in matter and energy. Moreover, they are currently using a design-based research approach to modify their LP on carbon recycling to incorporate SEPs and CCCs (Scott et  al., 2022).

Conclusions Learning progressions hold promise in supporting all learners to develop deep, usable scientifc knowledge that learners can use to make sense of phenomena, solve problems, and learn more when needed. All learners need to have a deep, usable knowledge of science, as the world they live in is scientifc and technology based. Moreover, science and technology are critical for understanding and solving the problems that exist in our world. But as Lehrer and Schauble (2015) argue, LPs cannot be designed with traditional curricula or business-as-usual approaches. Instead, researchers need to design LPs through iterative development and testing, using what we know works to promote student learning. As discussed earlier, design-based research will allow researchers to refne LPs, curriculum materials, assessment tasks, instruction, and professional learning. Given the perspective described, we recommend using LPs to rethink and restructure how students learn scientifc ideas and practices in schools. However, the feld still has much to learn regarding how to appropriately sequence the development of DCIs, SEPs, and CCCs in LPs. Next, we present a summary of the chapter’s highlights: 1.

2.

LPs build on students’ prior knowledge: Because curriculum materials designed on LP research build on learners’ prior knowledge, more students should develop deeper and more sophisticated understandings of a complex idea. As such, more students worldwide will be prepared for STEM-related careers but also to make informed decisions in today’s scientifcally and engineering-driven environment. LPs need to focus on three-dimensional learning: When researchers build and test LPs around three-dimensional learning, students develop deep, usable knowledge that they can apply to novel situations, which students have not yet experienced.

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3.

4.

5.

6.

7.

LPs promote equal learning opportunities: Developing sophisticated understandings depends on learners’ having opportunities to learn. While learners can take many paths in their learning, LP-based instruction can guide the development of meaningful learning over time. LPs promote growth over time: Developing deep understanding of complex core disciplinary ideas (e.g., evolution, geological time, and energy) takes time and often happens as students work on challenging tasks that require them to synthesize and make connections among ideas related to those tasks. Given the complexity of DCIs, particularly when integrated with SEPs and CCCs, such learning must occur over a long period of time and in multiple contexts. LPs are thus developmentally appropriate, as they build over time based on learners’ prior knowledge and experiences. Three-dimensional LP research across disciplines and grade levels: To respond to the importance of three-dimensional learning in science, researchers need to investigate LP across disciplines (biology, chemistry, physics, earth science, and engineering) and grade levels. Further research on developing and validating LPs that integrate the three dimensions of scientifc knowledge across the disciplines of science needs to occur. Such research will point to promising directions for organizing science instruction, curriculum materials, and assessment across K–16. Three-dimensional LP design-based research: There is a need for more design-based research that tracks students’ learning across time. This research needs to occur using curriculum materials, assessment, and professional learning purposely and principally designed to foster student learning in a particular area or across areas. Designed-based research demonstrates the usefulness of validating three-dimensional LPs for organizing the learning process in classrooms. Validated LPs represent diagnostic tools or a “ruler” to indicate what a student knows and can do – i.e., a measure of three-dimensional understanding. LPs can also suggest next steps on how to push students to a higher and deeper level of understanding.

Providing instruction across each grade on a comparatively small set of powerful, foundational big ideas along with scientifc practices would provide coherence for developing assessments, instruction, curriculum materials, and professional learning. However, as noted earlier, we are not talking about just repeating the big ideas. Instead, we want to support students in developing more sophisticated understandings of these ideas throughout their schooling. To accomplish this, researchers need to build LPs based on what we know about student learning, test these LPs with context-based curriculum and assessments, and support teachers through corresponding professional learning. It is then that we can provide all learners across the globe opportunities to develop deep, usable knowledge to help solve today’s problems.

Acknowledgments The authors thank Shawn Y. Stevens, James Pellegrino, and the late Aaron Rogat, who provided insight and expertise that greatly assisted the book chapter.

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Appendices Appendix 1 A graphical illustra˜on of li°le, if any, understanding: Learners cannot use ideas

Thin understanding: Some connec˜ons

between ideas with poten˜al to make new connec˜ons

for further learning or problem solving. Bond breaking

Physical change Chemical change

Compounds

Physical change

Bond breaking

Chemical change

Electrons Atoms

Atoms

Electrons

Integrated understanding: Ideas linked together new and exis˜ng knowledge is structured around a big idea. Knowledge is useful for problem solving.

Chemical change

involves

Atoms rearrange

can be

can be Covalent Ionic

does not involve

Share electrons

Physical change

Lose electrons

Gain electrons

Electron transfer

Metals

Non-metal

Figure 5.1 Graphical illustration of little, if any, understanding; thin understanding; and integrated understanding.

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Appendix 2 An Example of Three-Dimensional Learning Progressions and Associated Phenomena, Assessment Tasks, and Student Responses Science and Engineering Practices

Micro system and energy transfer ** DUM + EnM

Level IV

Energy and Matter (EnM)

* CoE + EnM

Level IIIa

* AID + CaE

System and System Models (SPQ)

System of moving particles

150

** DUM + CaE

** PCI + CaE

Constructing Explanations (CoE)

Macro system and energy transfer * DUM + CaE

Level II Developing and Using Models (DUM)

Level Ia

** DUM + SSM

Scale, Proportion, and Quantity (SPQ)

* AID + Pat Patterns (Pat)

* CoE + EnM

System of a moving object * DUM + CaE

Disciplinary Core Ideas

Figure 5.2A

** EAE + EnM

System of two interacting objects Level Ib

Analyzing and Interpreting Data (AID)

Microlevel

System of interacting particles Level IIIb

Engaging in Argument from Evidence (EAE)

* DUM + SSM

Cause and Effect (CaE) ** AID + Pat

Joseph Krajcik and Namsoo Shin

Planning and Carrying out Investigations (PCI)

** CoE + EnM

Crosscutting Concepts

* CoE + CaE

Manifestations of Energy

Energy Transfer

Three-dimensional learning progression of middle school students’ knowledge-in-use of energy.

Macrolevel

Student Conceptions, Conceptual Change, Learning Progressions

Level

Disciplinary Core Ideas (DCIs) in each level

Learning Performance

Level 1

Force and motion • When objects collide, the forces exert with each other, *the strength of the forces are equal, but the direction is opposite. (MS-PS2-1)

Students construct models to show that when a moving object collides with another object, it exerts a force on that object.

Level 2

Force and motion • When objects collide, the forces exert with each other, the strength of the forces are equal, but the direction is opposite. (MS-PS2-1) Kinetic Energy • Motion energy is properly called kinetic energy. (MS-PS3-1) Energy Transfer (not include the DCI) • When the motion of an object changes, the kinetic energy of the object is transferred to some other changes in energy (such as potential energy, temperature, or kinetic energy) (MS-PS3-5).

Students construct models to explain that the contact force exerted on one object to another object will change the motion of the object upon collision, and that the kinetic energy of the moving object was transferred to another object involved in the collision.

Level 3

Force and motion • When objects collide, the forces exert with each other, *the strength of the forces are equal, but the direction is opposite. (MS-PS2-1) • When two objects interact at a distance, each one can exert a force on the other. The strength of the forces is equal, but the direction of the forces is opposite. (MS-PS2-1) • Gravitational forces are always attractive. (MS-PS2-4) Kinetic Energy • Motion energy is properly called kinetic energy. (MS-PS3-1) Energy Transfer (not include the DCI) • When the motion of an object changes, the kinetic energy of the object is transferred to some other changes in energy (such as potential energy, temperature, or kinetic energy) (MS-PS3-5).

Students construct models to explain that the contact and non-contact forces changed the motion of the object, and the kinetic energy of the object was transferred to another object as result of collision.

Level 4

Force and motion • When objects collide, the forces exert with each other, the strength of the forces are equal, but the direction is opposite. (MS-PS2-1) • When two objects interact at a distance, each one can exert a force on the other. The strength of the forces is equal, but the direction of the forces is opposite. (MS-PS2-1) • Gravitational forces are always attractive. (MS-PS2-4) • A system of objects exists in a feld that causes the objects to exert forces at a distance (electrical, magnetic, and gravitational). (MS-PS2-5) Kinetic Energy • Motion energy is properly called kinetic energy. (MS-PS3-1) Potential Energy • PE1: A system of objects may contain stored energy (potential energy), depending on their relative positions. (MS-PS3-2) Energy Transfer (not include the DCI) • When the motion of an object changes, the kinetic energy of the object is transferred to some other changes in energy (such as potential energy, temperature, or kinetic energy) (MS-PS3-5). • When objects interact at a distance, each one exerts a force on the other that transfers potential energy to change the kinetic energy of objects. (MS-PS3-2)

Students construct models to explain that the contact and non-contact forces changed the motion of the object, the potential energy of the object and the earth was transferred to the kinetic energy of the object and was transferred to another object as result of collision.

Figure 5.2B Description of three-dimensional learning progression level on energy with the associated ideas. Note: The gray color aspects of ideas were omitted in the ILP.

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Figure 5.2C

Phenomenon and assessment item.

Stage 1: Before basketball rolling down

Speed = 0 Has energy on the top of slide

Stage 2: During basketball rolling down

Basketball

Speed Speed = 0

Stage 3: Before basketball hitting Yoga ball

Speed = 0

KE Stage 4: During collision Contact Force

Speed = 0

Has speed and KE

Stage 5: After collision

Yoga Ball

Contact Force

Decrease

Speed

Speed

KE

KE

Increase

Directions of Forces

Stage 6: Final Stage

KE (Kinetic Energy) Friction

Friction

Speed

Speed

KE

KE

Speed = 0

Figure 5.2D An exemplar model and explanation for level 2. 

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Speed = 0

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Notes 1 2

3 4 5 6 7

Throughout this manuscript, we use the phrases “knowledge-in-use” and “usable knowledge” interchangeably. When we use the phrase “solve problems” in this chapter, we are referring to problems that learners encounter in their lives. These problems are meaningful to the learner, have real consequences, and are complex, often having more than one solution. A real-world problem can range from “Where is the best location to place my bluebird boxes?” to “What can I do to help clean up the pond in my local park?” In other words, the phrase “solve problems” does not mean “answer questions at the back of a chapter”. See the Next Generation of Science Standards (NGSS, 2013) Appendix E: Disciplinary Core Idea Progressions, Appendix F: Science and Engineering Practices, and Appendix G: Crosscutting Concepts. See https://ngss.nsta.org/ Researchers refer to these performance-based learning goals as learning performance. But to prevent confusion with learning progressions, throughout this document we refer to them as performance-based learning goals. http://carbontime.bscs.org/ https://concord.org/our-work/research-projects/interactions/

References Alonzo, A. C., & Steedle, J. T. (2009). Developing and assessing a force and motion learning progression. Science Education, 93(3), 389–421. American Association for the Advancement of Science. (1989). Science for all Americans: A project 2061 report on literacy goals in science, mathematics, and technology (Vol. 89). American Association for the Advancement of Science. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. Oxford University Press. American Association for the Advancement of Science. (2001). Project 2061. https://www.aaas.org/programs/ project-2061. American Association for the Advancement of Science. (2007). Atlas of science literacy (Vol. 2). AAAS Project, p. 2061. Anderson, J., & Li, Y. (2020). Integrated approaches to STEM education: An international perspective. Springer Nature. Arzi, H. J. (1988). From short-to long-term: Studying science education longitudinally. Studies in Science Education, 15(1), 17–53. Ausubel, D. P., Novak, J. D., & Hanesian, H. (1968). Educational psychology: A cognitive view (Vol. 6). Holt, rinehart and Winston. Baroody, A. J., Cibulskis, M., Lai, M.-L.,  & Li, X. (2004). Comments on the use of learning trajectories in curriculum development and research. Mathematical Thinking and Learning, 6, 227–260. https://doi. org/10.1207/s15327833mtl0602_8 Berland, L. K., & McNeill, K. L. (2010). A learning progression for scientifc argumentation: Understanding student work and designing supportive instructional contexts. Science Education, 94(5), 765–793. Blumenfeld, P., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating projectbased learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26, 369–398. Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn (Vol. 11). National Academy Press. Briggs, D. C., Alonzo, A. C., Schwab, C., & Wilson, M. (2006). Diagnostic assessment with ordered multiplechoice items. Educational Assessment, 11(1), 33–63. Carey, S. (1988). Conceptual diferences between children and adults. Mind and Language, 3(3), 167–181. Carey, S. (1999). Sources of conceptual change. Conceptual Development: Piaget’s Legacy, 293–326. Catley, K., Lehrer, R., & Reiser, B. (2005). Tracing a prospective learning progression for developing understanding of evolution. Paper Commissioned by the National Academies Committee on Test Design for K-12 Science Achievement, 67. Chi, M. T. (1976). Short-term memory limitations in children: Capacity or processing defcits? Memory & Cognition, 4(5), 559–572. Chi, M. T. (1992). Conceptual change within and across ontological categories: Examples from learning and discovery in science. In R. Giere & H. Feigl (Eds.), Cognitive models of science (pp. 129–186). University of Minnesota Press.

153

Joseph Krajcik and Namsoo Shin Chi, M. T., & Ceci, S. J. (1987). Content knowledge: Its role, representation, and restructuring in memory development. Advances in Child Development and Behavior, 20, 91–142. Chi, M. T., Feltovich, P. J.,  & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152. Clark, D., & Linn, M. C. (2003). Designing for knowledge integration: The impact of instructional time. The Journal of the Learning Sciences, 12(4), 451–493. Cobb, P., Confrey, J., DiSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. Cooper, M. M., Underwood, S. M., Hilley, C. Z., & Klymkowsky, M. W. (2012). Development and assessment of a molecular structure and properties learning progression. Journal of Chemical Education, 89(11), 1351–1357. Corcoran, T. B., Mosher, F. A., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform. Report of the center on continuous instructional improvement, teachers college. Columbia University. Davis, E. A., & Krajcik, J. S. (2005). Designing educative curriculum materials to promote teacher learning. Educational Researcher, 34, 3–14. https://doi.org/10.3102/0013189X034003003 diSessa, A. A. (1988). Knowledge in pieces. In G. Forman & P. B. Pufall (Eds.), Constructivism in the computer age (pp. 49–70). Psychology Press. diSessa, A. A., & Minstrell, J. (1998). Cultivating conceptual change with benchmark lessons. In J. G. Greeno & S. V. Goldman (Eds.), Thinking practices in mathematics and science learning (pp. 155–187). Lawrence Erlbaum Associates Publishers. Duschl, R., Maeng, S., & Sezen, A. (2011). Learning progressions and teaching sequences: A review and analysis. Studies in Science Education, 47(2), 123–182. https://doi.org/10.1080/03057267.2011.604476 Duncan, R. G., Krajcik, J. S., & Rivet, A. E. (Eds.). (2017). Disciplinary core ideas: Reshaping teaching and learning. NTSA Press, National Science Teachers Association. Duncan, R. G., & Rivet, A. E. (2013). Science learning progressions. Science, 339(6118), 396–397. Duncan, R. G., Rogat, A. D., & Yarden, A. (2009). A learning progression for deepening students’ understandings of modern genetics across the 5th – 10th grades. Journal of Research in Science Teaching: The Ofcial Journal of the National Association for Research in Science Teaching, 46(6), 655–674. Esposito, A. G., & Bauer, P. J. (2017). Going beyond the lesson: Self-generating new factual knowledge in the classroom. Journal of Experimental Child Psychology, 153, 110–125. Fortus, D., Adams, L., Krajcik, J. S., & Reiser, B. (2015). Assessing the role of curriculum coherence in student learning about energy. Journal of Research in Science Teaching, 52, 1408–1425. https://doi.org/10.1002/ tea.21261 Fortus, D., & Krajcik, J. (2012). Curriculum coherence and learning progressions. In Second international handbook of science education (pp. 783–798). Springer. Gotwals, A. W., & Songer, N. B. (2006). Measuring students’ scientifc content and inquiry reasoning. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.), The international conference of the learning sciences: Indiana university 2006 (Proceedings of ICLS 2006, Vol. 1, pp. 196–202). International Society of the Learning Sciences. Gotwals, A. W., Songer, N. B., & Bullard, L. (2012). Assessing students’ progressing abilities to construct scientifc explanations. In Learning progressions in science (pp. 183–210). Brill. Gunckel, K. L., Covitt, B. A., Salinas, I., & Anderson, C. W. (2012). A learning progression for water in socioecological systems. Journal of Research in Science Teaching, 49(7), 843–868. Harris, C. J., Krajcik, J. S., Pellegrino, J. W., & DeBarger, A. H. (2019a). Designing knowledge-in-use assessments to promote deeper learning. Educational Measurement: Issues and Practice, 38(2), 53–67. https://doi. org/10.1111/emip.12253 Harris, C. J., Krajcik, J. S., Pellegrino, J. W., & DeBarger, A. H. (2019b). Designing knowledge-in-use assessments to promote deeper learning. Educational Measurement: Issues and Practice, 38(2), 53–67. https://doi. org/10.1111/emip.12253 He, P., Shin, N., Li, T., & Krajcik, J. (2020, March). Developing an integrated learning progression and assessments to measure middle school student profciency of energy. Paper presented at the annual meeting of the NARST-A Worldwide Organization for Improving Science Teaching and Learning through Research, Portland, OR. Conference canceled. www.researchgate.net/publication/348635154 Inagaki, K., & Hatano, G. (2002). Young children’s naive thinking about the biological world. Psychology Press. Jin, H., & Anderson, C. W. (2012). A learning progression for energy in socio-ecological systems. Journal of Research in Science Teaching, 49(9), 1149–1180. Johnson, P. (1998). Progression in children’s understanding of a “basic particle theory”: A longitudinal study. International Journal of Science Education, 20(4), 393–412. Kaldaras, L., Akaeze, H., & Krajcik, J. (2021a). Developing and validating Next Generation Science Standardsaligned learning progression to track three-dimensional learning of electrical interactions in high school physical science. Journal of Research in Science Teaching, 58(4), 589–618.

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Student Conceptions, Conceptual Change, Learning Progressions Kaldaras, L., Akaeze, H., & Krajcik, J. (2021b). A methodology for determining and validating latent factor dimensionality of complex multi-factor science constructs measuring knowledge-in-use. Educational Assessment, 26(4), 241–263. Keil, F. C. (1981). Constraints on knowledge and cognitive development. Psychological Review, 88(3), 197–227. Kesidou, S., & Roseman, J. E. (2002). How well do middle school science programs measure up? Findings from Project 2061’s curriculum review. Journal of Research in Science Teaching, 39(6), 522–549. Krajcik, J. S., McNeill, K. L., & Reiser, B. J. (2008). Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy. Science Education, 92(1), 1–32. https://doi.org/10.1002/sce.20240 Krajcik, J. S., Reiser, B., Sutherland, L., & Fortus, D. (2009). Investigating our world through science and technology (IQWST). Unpublished Curriculum. Krajcik, J. S., & Shin, N. (2022). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (3rd ed.). Cambridge. Krajcik, J. S., Sutherland, L., Drago, K., & Merritt, J. (2012). The promise and value of learning progression research. Making it Tangible: Learning Outcomes in Science Education, 261–284. Lee, H. S., & Liu, O. L. (2010). Assessing learning progression of energy concepts across middle school grades: The knowledge integration perspective. Science Education, 94(4), 665–688. Lehrer, R., & Schauble, L. (2004). Modeling natural variation through distribution. American Educational Research Journal, 41(3), 635–679. Lehrer, R., & Schauble, L. (2015). Learning progressions: The whole world is NOT a stage. Science Education, 99(3), 432–437. Linn, M. C., & Eylon, B. S. (2006). Science education: Integrating views of learning and instruction. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (pp. 511–544). Lawrence Erlbaum Associates Publishers. Margel, H., Eylon, B. S., & Scherz, Z. (2008). A longitudinal study of junior high school students’ conceptions of the structure of materials. Journal of Research in Science Teaching, 45(1), 132–152. Merritt, J. D. (2010). Tracking students’ understanding of the particle nature of matter [Doctoral dissertation], ProQuest Dissertations and Theses Database (UMI No. 3441321). Merritt, J. D., & Krajcik, J. (2013). Learning progression developed to support students in building a particle model of matter. In Concepts of matter in science education (pp. 11–45). Springer. Minstrell, J. (1984). Teaching for the development of understanding of ideas: Forces on moving objects. Observing Science Classrooms: Observing Science Perspectives from Research and Practice, 55–74. Minstrell, J., & Kraus, P. (2005). Guided inquiry in the science classroom. How Students Learn: History, Mathematics, and Science in the Classroom, 475–513. Mintzes, J. J., Wandersee, J. H., & Novak, J. D. (1997). Teaching science for understanding. Academic Press. Mohan, L., Chen, J.,  & Anderson, C. W. (2009). Developing a multi-year learning progression for carbon cycling in socio-ecological systems. Journal of Research in Science Teaching, 46(6), 675–698. Morell, L., Collier, T., Black, P., & Wilson, M. (2017). A construct-modeling approach to develop a learning progression of how students understand the structure of matter. Journal of Research in Science Teaching, 54(8), 1024–1048. National Academies of Sciences, E.,  & Medicine. (2018). How people learn II: Learners, contexts, and cultures. National Academies Press. National Research Council. (1996). National science education standards. National Academies Press. National Research Council. (1999). How people learn: Bridging research and practice. National Academies Press. National Research Council. (2006). Systems for state science assessment. National Academies Press. National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. National Academies Press. National Research Council. (2012a). Education for life and work: Developing transferable knowledge and skills in the 21st century. National Academies Press. National Research Council. (2012b). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. National Research Council. (2013). Next generation science standards: For states, by states. National Academies Press. https://doi.org/10.17226/18290 National Science Teaching Association. (2019). About the next generation science standards. https://ngss.nsta.org/ About.aspx Neumann, K., Viering, T., Boone, W. J., & Fischer, H. E. (2013). Towards a learning progression of energy. Journal of Research in Science Teaching, 50(2), 162–188. Next Generation Science Standard Lead States. (2013). Next generation science standards: For states, by states. National Academies Press.

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Joseph Krajcik and Namsoo Shin No Child Left Behind (NCLB). (2002). Act of 2001, Pub. L. No. 107-110, § 115, Stat. 1425. Nordine, J.,  & Lee, O. (Eds.). (2021). Crosscutting concepts: Strengthening science and engineering learning. NSTA Press. Novak, J. D. (2002). Meaningful learning: The essential factor for conceptual change in limited or inappropriate propositional hierarchies leading to empowerment of learners. Science Education, 86(4), 548–571. OECD. (2019). PISA 2018 results (volume I): What students know and can do. PISA, OECD Publishing. https:// doi.org/10.1787/5f07c754-en Osborne, J. F., Henderson, J. B., MacPherson, A., Szu, E., Wild, A., & Yao, S. Y. (2016). The development and validation of a learning progression for argumentation in science. Journal of Research in Science Teaching, 53(6), 821–846. Osborne, R., & Freyberg, P. (1985). Learning in science. The implications of children’s science. Heinemann Educational Books, Inc., 70 Court Street, Portsmouth, NH 03801. Patti, J., Holzer, A. A., Brackett, M. A., & Stern, R. (2015). Twenty-frst-century professional development for educators: A coaching approach grounded in emotional intelligence. Coaching: An International Journal of Theory, Research and Practice, 8(2), 96–119. Perkins, D. N. (1992). Smart schools: Better thinking and learning for every child. Free Press. Plummer, J. D., & Krajcik, J. (2010). Building a learning progression for celestial motion: Elementary levels from an earth-based perspective. Journal of Research in Science Teaching, 47(7), 768–787. Plummer, J. D., & Maynard, L. (2014). Building a learning progression for celestial motion: An exploration of students’ reasoning about the seasons. Journal of Research in Science Teaching, 51(7), 902–929. Roseman, J., Linn, M., & Koppal, M. (2008). Characterizing curriculum coherence. Designing Coherent Science Education: Implications for Curriculum, Instruction, and Policy, 13–36. Schmidt, W. H., Wang, H. C.,  & McKnight, C. C. (2005). Curriculum coherence: An examination of US mathematics and science content standards from an international perspective. Journal of Curriculum Studies, 37(5), 525–559. Schwarz, C. V., Passmore, C., & Reiser, B. J. (2017). Helping students make sense of the world using next generation science and engineering practices. NSTA Press. Schwarz, C. V., Reiser, B. J., Davis, E. A., Kenyon, L., Achér, A., Fortus, D., Shwartz, Y., Hug, B., & Krajcik, J. (2009). Developing a learning progression for scientifc modeling: Making scientifc modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632–654. Scott, E. E., Edwards, K. D., & Anderson, C. W. A. (2022). How students interpret claims, analyze data, and link evidence to claims about carbon-transforming processes. In H. J. A. J. K. Duanli Yan (Ed.), Handbook of learning progressions. Taylor and Francis. Shin, N., Choi, S.-Y., Stevens, S. Y.,  & Krajcik, J. S. (2019). The impact of using coherent curriculum on students’ understanding of core ideas in chemistry. International Journal of Science and Mathematics Education, 17(2), 295–315. Shin, N., He, P., Li, T., & Krajcik, J. (2021, April). A three-dimensional integrated learning progression and aligned assessments to monitor middle school student profciency of energy, modeling and cause and efect. Paper presented at the annual meeting of the NARST-A Worldwide Organization for Improving Science Teaching and Learning through Research, Virtual Conference. www.researchgate.net/publication/350756338 Sirhan, G. (2007). Learning difculties in chemistry: An overview. Journal of Turkish Science Education, 4, 2–20. Smith, C. L., Wiser, M., Anderson, C. W., & Krajcik, J. S. (2006). Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic-molecular theory. Measurement: Interdisciplinary Research and Perspectives, 4(1–2), 1–98. https://doi.org/10.1080/15366367.200 6.9678570 Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009a). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 610–631. https://doi.org/10.1002/tea.20313 Songer, N. B., Kelcey, B., & Gotwals, A. W. (2009b). How and when does complex reasoning occur? Empirically driven development of a learning progression focused on complex reasoning about biodiversity. Journal of Research in Science Teaching, 46(6), 610–631. Steedle, J. T., & Shavelson, R. J. (2009). Supporting valid interpretations of learning progression level diagnoses. Journal of Research in Science Teaching, 46, 699–715. https://doi.org/10.1002/tea.20308 Stevens, S. Y., Delgado, C.,  & Krajcik, J. S. (2010). Developing a hypothetical multi-dimensional learning progression for the nature of matter. Journal of Research in Science Teaching, 47(6), 687–715. https://doi. org/10.1002/tea.20324 Strike, K., & Posner, G. (1985). A conceptual change view of learning and understanding, cognitive structure and conceptual change (pp. 189–210). LHTWest and AL Pines, Press, Academic.

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Student Conceptions, Conceptual Change, Learning Progressions Talanquer, V. (2009). On cognitive constraints and learning progressions: The case of “structure of matter”. International Journal of Science Education, 31(15), 2123–2136. Weissberg, R. P., Durlak, J. A., Domitrovich, C. E., & Gullotta, T. P. (2015). Social and emotional learning: Past, present, and future. In J. A. Durlak, C. E. Domitrovich, R. P. Weissberg, & T. P. Gullotta (Eds.), Handbook of social and emotional learning: Research and practice (pp. 3–19). Guilford. West, P., Rutstein, D. W., Mislevy, R. J., Liu, J., Levy, R., Dicerbo, K. E., Crawford, A., Choi, Y., Chapple, K., & Behrens, J. T. (2012). A Bayesian network approach to modeling learning progressions. In Learning progressions in science (pp. 255–292). Brill Sense. Whitehead, A. N. (1967/1929). The aims of education and other essays. Free Press. Wilson, M. (2009). Measuring progressions: Assessment structures underlying a learning progression. Journal of Research in Science Teaching, 46(6), 716–730. Wiser, M., Smith, C. L., & Doubler, S. (2012). Learning progressions as tools for curriculum development: Lessons from the Inquiry Project. In Learning progressions in science (pp. 357–403). Brill Sense.

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6 STUDENT ATTITUDES, IDENTITY, AND ASPIRATIONS TOWARD SCIENCE Russell Tytler and Joseph Paul Ferguson

Introduction Research on attitudes in science has been an increasing focus over the last decades, driven in large part by concerns with student responses to science as a subject (Gardner, 1975; Osborne & Collins, 2001) and more recently with concern internationally with the take up of science and the STEM (science, technology, engineering, and mathematics) disciplines more generally in post-compulsory years. This latter concern is related to increasing acknowledgment of the crucial importance of the STEM disciplines to national wealth creation (Marginson et  al., 2013). Alongside this neoliberal agenda that emphasizes the utilitarian value of science for economic purposes, there has been an increasing policy focus on equity considerations concerning access to productive futures in sciencerelated work, and a recognition that citizens’ responses to science and science research are bound up with national well-being. Within the science education research community, this concern for equity has spawned research drawing on critical and post-critical research traditions focusing on afect in relation to the response of minority communities to participation in science. With both these agendas, a key driver of research into afect is the concern for students’ engagement with science studies and their aspirations concerning science-related futures. In the previous edition of this handbook and chapter (Tytler, 2014), it was pointed out that the particular interest in attitudes to science stems from the tension in the science curriculum between two major roles: to prepare future citizens to engage with science in their lives, and to prepare the next generation of scientists. Most subjects in the school curriculum do not have this dual mandate (Millar & Osborne, 1998). A further and recently emphasized corollary of these mandates is the concern to engage with minority groups with a history of non-participation in those science pathways that can lead to productive work futures (Archer et al., 2015; Hilts et al., 2018). Increasingly, the focus on attitudinal research has shifted over the last decade from a consideration of attitudes, motivation, and interest as governing engagement with the conceptual aspects of science learning, to a recognition of afect as an important end of a science education in itself. Nevertheless, Fortus (2014) pointed out that of the research articles published in the period 2001–2011, less than 10% were focused on attitudes, indicating that attitudinal research still plays a relatively minor role in the feld, compared with research that focuses on conceptual learning. Since 2011 there have been an escalating number of studies on afect and identity, reviewed in this chapter. The point, however, still holds. Within the research focused on afect, researchers have grappled with a number of problems that we will canvas in this chapter.

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In the previous edition (Tytler, 2014) we referred to the important distinction between “attitudes toward science”, such as enjoyment or interest, and “scientifc attitudes”, such as objectivity. In the research since then, we see evidence that this distinction is one of a number in the feld in need of clarifcation, concerning the object of the attitude. This can variously refer to the broad subject of science, topics in school science, aspects of the pedagogies used to teach science, or the classroom learning environments and interactions with the teacher and other students. Attitudes/afective constructs can also relate to commitments to particular ways of participating or acting, afective commitments to science ideas and practices, or identifcation with scientifc ways of thinking and working. Allied to these distinctions are diferences in the nature of the relationship between the person and the object to which the afective response refers. In much of the attitudinal research, the person sits outside the object in the role of evaluator: how do I feel about school science? What is my preferred topic? For the “scientifc attitudes” construct, however, the response is conceived of as part of the person’s values and disposition to the world that shapes ways of operating, such that afect (such as curiosity or objectivity) is a developed habit (Dewey, 1922). These diferences have developed into a more complex set of afective constructs and conceptualization of their interrelations since these earlier studies. In this chapter, we will briefy reprise what was reported in the last edition in 2014 before exploring where research on afect in science has gone since then and highlighting the key points of advancement. The feld is complex, with many constructs developed and refned in ways that do not encourage a defnitive taxonomy. Terms include attitude (like/dislike); motivation and interest (situational, dispositional); emotions (fear, anger, enjoyment, disgust, confusion, frustration, pleasure – feelings parsed by Lemke [2015] as fundamental or evaluative); afect or feeling; and longer-term, more person-oriented positional constructs, such as identity, self-efcacy, and disposition; and aesthetic terms, such as taste. There are three major distinctions we will make in reviewing the literature around this variety of afective constructs that allow us to make some sense of how these diferent constructs arose, are used in research, and how they can and do inform practice. The frst is one of purpose – what question or agenda underpins interest in the particular construct. We trace the major questions in the science education feld that have driven attitudinal research, ranging from concern about student motivation and engagement with science as a subject – refned in the last 20 years with concerns about students’ participation in the science and STEM “pipeline” – to concerns about equity issues in relation to socioeconomic groups, Indigenous populations, ethnicity, and gender. With these concerns, the identity construct continues to bear fruit as a descriptive and explanatory lens. Thus, we argue that the shifting focus of research to afect must be seen in relation to broader movements in the way science is conceived and justifed in the curriculum. Second, we draw on Wickman’s (2017) distinction between analytic and synthetic categories. Analytic traditions strive to defne afective constructs tightly, in reliably measurable ways, such that relations between them and causal factors can be explored and theorized. Synthetic traditions, on the other hand, acknowledge the inherent messiness and ambiguity of language and contextual complexities involved in afective responses, and tend to focus on the sociocultural settings and determinants of participation and practice, drawing for instance on the theoretical work of Bourdieu (1986, 1990, 1992) and Dewey (1997). The distinction is an issue of language; whether terms we use, such as “confusion” or “interest”, can be understood as distinct and tightly defned entities, or whether there is an inevitable bleeding of meaning between such constructs. There are recent examples of research that coordinate these traditions, with the construction and validation of instruments using factor analysis or structural equation modeling (SEM) to provide measures of complex constructs developed in the synthetic tradition, such as identity or science capital. Sfard and Prusak (2005, p. 15) saw identity as the perfect candidate for the missing link in the complex dialectic between learning and its sociocultural context. Identity thus becomes an interesting “middle ground” that has done work in a variety of contexts, refecting and reprising analytic

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categories such as self-efcacy, interest, or attitude, as well as the more complex afective categories related to personal stance and context. Third is the object or circumstance of the afect. As indicated earlier, much traditional attitudinal research is of student response to science as a subject, topics in science, or features of the science classroom experience. Research on emotions tends to focus on the response of students to aspects of tasks or interactions with the teaching and learning environment. There is renewed current interest in pragmatist framings of afect, drawing on Peirce (1907/1998) and Dewey (1938/1997), that conceive of a fundamental link between “feeling and meaning” (Lemke, 2013, 2015), and which frame afective outcomes in terms of the development of longer-term habits. Here, there is a tight binding of afect to the object of the feeling. Anderhag et al. (2016) argue, for instance, that the drop in interest toward school science over the elementary to secondary school transition measured in many studies may be explained by the fact that the “science” in primary schools is perceived as a diferent entity to that in secondary schools. What is being measured is students’ response to very diferent “objects”: activity in elementary schools with limited conceptual overlay on the one hand, and in secondary schools abstracted conceptual structures incompletely connected to students’ worlds on the other. This focus on the object of afect is represented by an “aesthetics turn” in the literature, with researchers focusing on students’ values, preferences, or “taste” (Anderhag, 2017) in relation to the objects or practices of science and the science classroom. Aesthetics positions afect as inextricably bound to the conceptual theories and practices of science, and hence engages with that aspect of “attitudes” fagged by Gardner (1975) as scientifc attitudes. This review will focus on the various afective constructs used to make sense of the engagement of students with various aspects of science learning, their aspirations toward science-related careers, and the factors that infuence these. The review will attempt to describe what we have learned from attitudinal research since 2014 and the theoretical and methodological advances that have occurred. In reviewing research involving the various afective constructs, it will explore the determinants of, or factors afecting these, and the implications of the studies for: student engagement with learning science, the framing of teaching and learning, the links to aspirations toward science-related futures, and equity issues around participation. A number of key themes will structure the chapter. 1.

2. 3.

4.

The nature of particular attitudinal constructs (attitude, interest, motivation) and how these are defned, measured, and used to explore diferent research agendas, including the response of diferent sociocultural groups, change in attitudes over time, and the nature of teaching and learning approaches that engage students. This theme will also encompass theories, such as those of self-efcacy, reasoned action, and expectancy value theory (EVT), that have provided insights into the links between attitudes, behavior, and aspirations. Within this theme we will canvas the issue of the lack of clarity in the nature of and relations between these multiple constructs used in the feld, the diferent methodological traditions represented, and the extent to which these speak productively to each other. The “turn to afect” and emotions research in relation to students’ responses to the particularities of classroom activity. The construct of identity, which engages with the complexity of responses to a range of features of experience in science classrooms, over time. Sociocultural framings of identity underpin qualitative studies that attempt to capture the complexity of classrooms and open examination of the history of the person intersecting with the discursive practices of science and the science classroom. This theme will examine identity in relation to minority groups, gender, and sociocultural factors framing student participation and learning. We will also examine recent research in the analytic, quantitative tradition, which has developed instruments for the measurement, comparison, and tracking of identity. Research into attitudes in relation to aspirations. These have revealed a range of factors that afect student participation and learning in science, with diferences between social groups in the patterns

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5.

of infuence on career intentions. We review how Bourdieusian theory is used in the construct of “science capital” to tease out the dimensions that relate to student aspirations, including self-efcacy and self-concept, as well as family science capital. Analytic research has developed measures of science capital with the aim to explore the predictive power of this construct in relation to aspirations. Recently there has been interest in the pragmatic semiotic perspectives of Peirce and Dewey, where afect or “feeling” is fundamentally tied to meaning, or the conceptual. This notion of aesthetics is closely tied to the constructs of taste and interest, as well as students’ and teachers’ identities in relation to science.

Defning, Coordinating, and Measuring Attitudinal Constructs A variety of attitudinal constructs exist in the science education literature, and psychology literature more generally. While there are defnitions that exist for the various constructs – attitudes, motivation, emotion, etc. – these are not generally agreed upon. Much of the literature in the psychological tradition focuses on clarifying these constructs and their interrelations, but these vary from study to study. Each construct has its own history of research, theoretical formulations of interrelations, and types of issues that they are commonly associated with. In science education research, the distinctions between constructs are often underacknowledged. As we pointed out in the previous version of this chapter (Tytler, 2014), major attitudinal constructs often run together in instruments and can be quite diferent in time scale, focus (on school science, or science), their relation to cognition, and whether they refer to attitudes toward science or scientifc attitudes. Since then, Blalock’s (2008) critique of the feld in terms of clarity of constructs continues to hold sway, and researchers have defned attitudinal constructs and their interrelations inconsistently. Much of this is a function of the particular focus of studies and does not necessarily invalidate the fndings of any particular study, but the lack of clarity continues to slow the growth of the feld in terms of the development of agreed positions and instruments. Fortus (2014) similarly questions the value of the wide array of instruments used, making it difcult to link across studies. We will continue this critique next, for recent research. In a later synthetic and systematic description of 228 research articles published between 2000 and 2012, Potvin and Hasni (2014) covered the key constructs of interest, motivation, and attitude. They used a composite construct (I/M/A) through which they were able to survey some major trends and fndings in the feld, despite the loss of diferentiation between constructs this involved. These three constructs were again determined by Wickman (2017) to be the most prevalent in the feld based on the number of peer-reviewed articles from the ERIC database with emotional or aesthetic constructs in their titles (Table 6.1). Table 6.1 Count of Articles With Emotional or Aesthetic Constructs in Their Title (Adapted from Wickman, 2017) Construct in Title

Total Number

Percentage

Attitude(s)

6,911

62

Motivation

2,079

19

Interest(s)

1,647

15

Emotion(-s, -al)

204

2

Aesthetic(s)

171

1.5

84

0.8

Afect Taste

5

Total

11,101

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Savelsbergh et al. (2016), in undertaking a meta-analysis of the efects of innovative science and mathematics teaching on student attitudes and achievement, distinguished between the constructs of attitudes, motivation, and interest as such (p. 160): •

•

•

An attitude is a “psychological tendency expressed by evaluating a particular entity” or a “summary evaluation of a psychological object” that has some favor/disfavor degree or “attribute dimension”, such as good-bad, likable-dislikeable, pleasant-unpleasant. Motivational theories “attempt to answer the question about what gets an individual moving (energization) and toward what activities or tasks” and tend to focus on external factors that infuence energization for the particular task, such as engagement with science learning. Interest theories also focus on person-object behaviors. Hidi and Renninger (2006, p. 112) refer to interest as “the psychological state of engaging or the predisposition to reengage with particular classes of objects, events, or ideas over time”. This applies to either short-term situational interest (a state) or long-term personal interest (a disposition).

We see that there are distinct diferences between these constructs in their relation to the object of afect and their relation to action, whether it be continued engagement (interest) or moving toward a task or end goal (motivation). Therefore, one would expect that which construct is foregrounded would depend on the focus of the study, but the danger exists that in combining these constructs in surveys or interviews, clarity is lost. Nevertheless, in bringing together these constructs, Potvin and Hasni (2014) were able to construct a picture of the types of topic focus of their 228 studies that were not purely associated with development and validation of instruments. Table 6.2 shows the profle of topics that were subject to interest/motivation/attitude studies. From this analysis, a number of fndings of interest emerged (p. 110): • •

• • •

More than half the articles did not propose an explicit defnition of the focus constructs. Boy/girl diferences were slight, giving a sense of “overkill” of study. I/M/A studies for science and technology overall, or comparing disciplines, were not as insightful or productive as studying for fner-grained themes, such as the efect of particular day-to-day interventions, on a smaller-than-disciplinary scale. Teacher variables were important, with enthusiastic, encouraging, and close-to-their-students teachers linked to positive afect. Classroom features associated with positive afect included “inquiry-based” and “problem-based” interventions, collaborative work, linking science and technology to reality, and practical work. Intentions to pursue science/technology studies were better predicted by self-esteem/selfefcacy than by I/M/A.

Table 6.2 Number of Topics That Were Subject to Interest/Motivation/Attitude Studies (Adapted from Potvin and Hasni’s [2014] analysis of articles between 2000 and 2012) Topic

Number of Articles

Boy/girl diferences School-related variables

50 31

Decline of I/M/A with age or school year Performance and self-efcacy S&T careers International diferences Sociological variables Correlations with other variables

24 23 13 12 8 8

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Potvin and Hasni noted that most studies yielded positive results and questioned whether researchers tended to design interventions that could be confdently expected to succeed, often with multiple dimensions. We argue from this a need for more sharply defned interventions that generate more precision in our knowledge of specifc determinants of afect.

Measuring Attitudinal Constructs It has been previously argued (Osborne et al., 2003; Tytler & Osborne, 2012; Tytler, 2014) that a key issue in constructing attitudinal instruments is that of construct validity, concerning whether the associated constructs (attitude, motivation, interest, emotion, etc.) are grounded in a clear and sound theoretical basis. Without this, it is likely that the items sitting within the construct will not yield a consistent representation and that disparate items put together in a single scale will be theoretically indefensible (Gardner, 1975). This critique comes from meta-analyses (Blalock et al., 2008; Potvin & Hasni, 2014) that found a tendency to design ft-for-purpose instruments and a lack of attention to psychometric rigor. Similarly, Aydeniz and Kotowski (2014) critiqued a small number of well-regarded instruments, fnding fault, for instance, with the confation of distinct constructs and lack of use of confrmatory factor analysis (CFA) to ensure conceptually diferent components. Aydeniz and Kotowski used CFA to develop an instrument consisting of seven measures within the attitude construct: subjects’ attitudes toward science, motivation toward learning science, utility of science, self-efcacy in science learning, normative beliefs about science involvement, and intention to pursue science-related activities. More recently, a comprehensive review of the psychometric properties of 18 attitudes toward science instruments, published between 2005 and 2019, was undertaken by Toma (2022), who reviewed the measures for “attitudes toward science” against currently accepted assessment standards involving six key validity and reliability measures: 1. 2. 3. 4. 5. 6.

Content validity – whether items adequately refect the construct of interest Construct validity – whether items capture the dimensionality of the target trait Predictive validity – whether scores predict outcomes convincingly Discriminant validity – whether scores can diferentiate between groups (e.g., gender diferences) Internal consistency reliability – intercorrelation of items Temporal stability – whether scores are stable over time when the trait has not changed

Toma (2022) found that only 2 of the 18 instruments they surveyed were consistent with currently accepted standards, raising questions about validity consistent with previous reviews across many decades, described earlier. Only fve instruments clearly described procedures for establishing content validity, seven used the recommended practice of sequentially applying exploratory factor analysis (EFA) and CFA to establish construct validity, and only three attended to predictive validity. In short, they paint a disappointing picture that raises questions about the meaningfulness of fndings in the science education literature regarding attitudinal shifts and comparisons. They call for the development of trustworthy instruments that can be meaningfully used in intervention and longitudinal studies or used across disciplinary and age-level contexts and countries.

Recent Types of Research Focusing on Affect Since 2014, studies have explored diferent approaches to validating instruments, including using item response theory (IRT) methods or SEM, or grounding their development in established theories, sometimes translating existing instruments to be used for diferent language groups. Here we summarize a few signifcant studies to illustrate the diferent methodologies used, the types of constructs that are incorporated, and the varying contexts for which these instruments are developed.

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Attitude correlate studies: The relationship between attitudes and observable and measurable outcomes such as classroom behavior, learning behaviors and outcomes, and intentions to continue with science continue to energize researchers. These relationships are explored through correlational studies and modeling, in some cases with self-efcacy or self-concept as mediating factors. Wan and Lee (2017) used SEM to develop a three-factor structure with cognitive, afective, and behavioral domains, showing that cognition and afect were linked. Höft and colleagues (2019) undertook a refned investigation of the relationship between interest and conceptual understanding across Grades 5–11 to fnd that interest in activities likely to promote cognitive activation (e.g., problem solving) or involving communication of knowledge were more strongly connected to conceptual understanding, especially in the upper secondary grades, while interest in guided hands-on activities show only small correlations with conceptual understanding, compared to interest in investigative, “minds-on” aspects of practical work. The study thus charts the refning of particular interests associated with conceptual learning over the secondary school years. In terms of behavioral correlates, Sha et al. (2015) developed and validated an instrument using IRT that measured science choice preferences, fnding correlations with science interest, self-efcacy, and learning achievement. Toma (2020) explored the cost domain from the expectancy-value theory (EVT) of achievement motivations (Eccles, 2009) to show how the attitudinal dimensions of perceived difculty of science infuences the perceived cost and efort needed to continue in a science pathway. Wan (2021) also used EVT to explore the extent to which the interaction between motives for learning science and self-efcacy in science learning can predict students’ behavioral tendency to learn science in the classroom. The study found signifcant links between utilitarian motives and self-efcacy in predicting science learning, whereas intrinsic motives were not linked. These studies thus continue to explore and reinforce the links between diferent attitudinal constructs, cognition, and behaviors relating to subject learning and to aspirations. Group comparison and temporal trends: Other studies focus on diferences between groups (gender, cross-country comparisons, sociocultural factors), or changes over time, drawing on a suite of attitudinal instruments. Navarro and colleagues (2016) adapted the TOSRA (Test of Science-Related Attitudes) instrument for Spanish-speaking students, analyzing its psychometric properties to fnd adequate construct validity and good reliability indexes. In applying it, they found minimal gender diferences. A number of studies have developed attitudinal scales and instruments for diferent language groups. Abd-El-Khalick and colleagues (2015) drew on Ajzen and Fishbein’s (2005) theories of reasoned action and planned behavior (TRAPB) to construct and validate a fve-factor model and instrument for assessing Arabic-speaking students’ attitudes to science: attitudes toward science and school science, unfavorable outlook on science, control beliefs about ability in science, behavioral beliefs about the consequences of engaging with science, and intentions to pursue science. In research using this instrument, Said et al. (2016) found a decreasing attitude with age, marginal gender diferences, and signifcant diferences in student nationality and type of school. Summers and Abd-El-Khalik (2018) refned this work to develop and validate a BRAINS (Behaviors, Related Attitudes, and Intentions toward Science) survey using CFA, mindful of the criticisms of existing instruments. This survey consists of fve constructs: attitudes toward science, behavioral beliefs about science, intentions to engage in science, normative beliefs, and control beliefs. There is a growing number of studies using international assessments to examine diferences across countries. Some studies use IRT to improve the psychometric properties of attitude measures. Oon and Fan (2017) applied Rasch analysis to the TIMSS 2011 data set to argue that this can yield insights into test design and interpretation. SEM is also used to develop factor structures for students’ attitudes, allowing two-level attitudinal structures. Oon and Subramaniam (2018) used TIMSS data to compare attitudes of students from Singapore and the UK, using Rasch analysis to verify the reliability of the TIMSS instrument and showing signifcant diferences in UK and Singapore students’

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confdence in science, liking to learn science, and perceptions of the utility of science. Oon et al. (2020) used Rasch modeling to examine gender diferences in attitudes toward and intentions to pursue physics in Asian learners. They found that intrinsic factors, mainly interest, had the largest gender diferences on students’ intentions to pursue physics, rather than utilitarian or recognition factors. Similarly, Lau and Ho (2020) analyzed PISA 2015 data of high-performing countries and identifed enjoyment of science learning as the strongest attitudinal predictor of performance for all regions. Self-efcacy of students in Hong Kong and China was found to be lower than in Canada and Finland, and less positively associated with performance, raising questions about the efect of cultural infuences on attitude scales. The issue of cultural infuences on cross-country comparisons of attitude and achievement has been raised by fndings that on international comparisons, there is an inverse correlation between attitudes toward science and achievement and also between attitudes and countries’ economic indices (Tytler & Osborne, 2012). One feature of this “contradiction” is that students in Asian-Chinese countries have relatively low interest and self-efcacy levels yet perform at a high achievement level, in a culture where classroom learning environments are assumed not to conform to Western researchbased views of good classroom environments leading to quality learning, for instance, featuring large classes, expository teaching, and passive learners, compared to student collaborative activity and an open classroom climate advocated by the Western research community. Cheng and Wan (2016) undertook a comprehensive meta-analysis of research into attitude-achievement relations for students from diferent Asian-Chinese countries, exploring this seeming contradiction. Their analysis revealed diferent patterns of relations across these Asian countries between self-efcacy, interest, motives, learning strategies, and epistemological views, yet a broadly similar relation to achievement as found in the West. Cheng and Wan argue that low interest can be balanced by diferent patterns of motivational constructs and that there are signifcant cultural infuences in the way these attitudinal constructs are exhibited. They further argue that observed teacher practices and teacher–student interactions in Asian-Chinese classrooms have quite diferent afective entailments when seen from within the culture, than interpreted through a Western lens. Cross-cultural analyses such as these raise important questions about cultural infuences on these analytic attitudinal constructs, signalling that we need to approach cultural comparisons of attitudes and their relation to conceptual learning with care. Afect as an outcome: Other studies have treated afect as an outcome, examining features of classrooms linked to positive afect, examining the efect of an intervention, or treating afect as a mediator in modeling outcomes. As an example, Hassan (2018) also drew on the theory of reasoned action to develop a fve-factor model using CFA to study teaching characteristics in Malaysian schools, fnding signifcant efects from inquiry teaching, group work, and spiritual focus on the promotion of interest and engagement. Further instruments have been developed more recently, using a wider framing around identity, science capital, and aspirations, but drawing on a similar suite of constructs to those described earlier. These will be discussed in later sections of the chapter, including qualitative, synthetic approaches to afect that combine interview data or ethnographic analysis of classroom interactions, sometimes longitudinally in case studies, to inform the complex sociocultural and cognitive meanings underlying the statistical correlates. Next, however, we continue our review of research in the analytic tradition, reviewing studies focused on the particular afective constructs of interest and self-efcacy.

Studies Focused on the Interest Construct Interest has been an important construct with signifcant theoretical development underpinning its use and measure. It was a major construct in the PISA 2006 and 2015 non-cognitive assessments. Infuential in this development were Hidi and Renninger (2006), who argue that interest is a critical

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cognitive and afective variable that guides attention. Their four-phase model describes four stages of interest development that moves from a situational interest triggered by an event or object, through to a well-developed individual interest that is similar to a disposition, or trait. Their model situates interest as having components of knowledge, afect, and value, thus foreshadowing current interest in aesthetics as a construct that links feeling and meaning (Lemke, 2015), described further in this chapter. Associated with this bridging aspect of interest, Krapp and Prenzel (2011) argue that “the decisive criterion of the interest construct which enables it to be clearly distinguished from several neighbouring motivational concepts is its content specifcity. An interest is always directed towards an object, activity, feld of knowledge or goal” (p. 33). Thus, the interest construct, distinct from other afective constructs, is multidimensional. Researchers have difered in whether interest is considered a subconstruct of attitude or separate to it. The fact that one can be interested in something even though one fnds it reprehensible can be taken as indicating it should be treated as a separate construct. Swarat et al. (2012) reviewed the diferent theoretical perspectives used to defne the interest construct, revealing a complex typography, for instance, in relation to motivation or engagement. They argue that “interest researchers tend to view interest as the precondition for intrinsic motivation and mastery goal orientation, whereas the motivation researchers . . . often see interest as the outcome of mastery goal adoption” (p. 518). Their study found that student interest in science was more afected by the form of activity rather than the content topic or learning goal and suggested that manipulating learning environments could best improve student interest. Jack and Lin (2017) reviewed 18 articles studying students’ views about what instructional strategies made learning interesting. They developed a TEDI instructional framework representing four interdependent but distinct strategies for making learning interesting: Transdisciplinary Connections, Mediated Engagement, Meaningful Discovery, and Self-directed Inquiry. They argue that “the dynamic character of interest refers to the instinctive, spontaneous, self-determined attention or impulse to action necessary to self-preservation, personal well-being or satisfaction” (p. 140) and that “motivates the child to seek experiences which provide continuing or repeated interaction with objects or activities associated with feelings of personal satisfaction” (p. 141). Reviewing studies of determinants of students’ interest, Krapp and Prenzel (2011) found complexity in student responses to topics, pedagogies, and aspects of science classes, and distinctions between interest in the domain of science and in science classes. This casts doubt on the generalization that interest in science sinks dramatically at the secondary level. A more nuanced and complex view of afect that attends to diferent features of students’ science experiences, and the relation between afect and cognitive aspects of these experiences, would allow us to go beyond generalizations about diminishing student attitudes to science with age and begin to look at afect in relation to particularities of the elementary and secondary science experiences of students. This echoes the fndings of Höft et al. (2019) described earlier. This examination of the interaction of students’ afective response to school science over time and the nature of the subject is taken up in detail by Anderhag and colleagues (2016) to argue that “studies relying on interviews and questionnaires make it difcult to ascertain what the actual object of interest is when students act in the science classroom” (p. 791). They argue that in relation to the interest and enjoyment that we fnd with primary school students, “we cannot with certainty conclude that it is this same interest that is lost in secondary school” (p. 809). Rather, the examination of students’ experiences across the primary–secondary divide through the lens of an interaction between cognition and aesthetics allows a more nuanced interpretation of how students’ existing interests interact with the changing nature of the science that is presented in school, and the increasing move toward more abstracted science that is not presented to encourage a continuity with their interest in everyday objects. This aesthetic interpretation of afective responses that foreground the continuity with cognition will be taken up later in the chapter. Anderhag and colleagues argued that

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a key to efective engagement with learning in the science classroom was to support the transition from students’ natural interests to interest in scientifc objects and practices. This is consistent with Jack and Lin’s (2017) framework. A number of studies have focused on enhancing student interest in science. Hagay and BaramTsabari (2015) explored ways of incorporating students’ interest-based questions into the science classroom, identifying a variety of pedagogies triggered by this, leading to improvements in students’ responses across the themes of autonomy, relatedness, and competence specifed within selfdetermination theory (SDT). Cheung (2018) in a mixed-methods study utilizing interviews followed by instrument design and validation using SEM, found the signifcant factors afecting individual interest in science lessons to be science self-concept, individual interest in science, and situational infuences in the lessons themselves. Hong and colleagues (2019) traced the pathway from situational to individual interest in a STEAM competition, arguing that well-designed competitions can stimulate sustained individual interest. Drymiotou and colleagues (2021) found that active engagement in scientifc practices and interactions with experts enhanced situational interest in STEM careers. From these studies over at least a decade we see that student interest can relate to multiple aspects of teaching and learning science, that the object of interest changes across the schooling years in relation to changing aspects of disciplinary knowledge, and that there are a variety of strategies focused on interest that can lead to improved outcomes in both interest and learning.

Motivation Motivation, involving a “movement” toward some goal or activity, is often associated with autonomy. SDT (Deci & Ryan, 2012) is a theory that diferentiates motivation in terms of being autonomous and controlled. SDT elaborates on the distinction between intrinsically and extrinsically motivated behavior. Studies of motivation to learn in science classrooms have tended to focus on the ways teachers explicitly support student autonomy. Hoferber and colleagues (2016) showed that autonomy-supportive teaching increased Grade 6 students’ intrinsic motivation and fow-experience in a biology class. Großmann and Wilde (2020) showed advantages for students’ interest in biological topics of such teaching approaches, especially for those students with initially low individual interest. Adler et al. (2018) used SDT to examine the teacher’s role in providing motivational support in an online open inquiry setting, fnding a positive correlation between this (which they labeled “guided autonomy”) and students’ unfolding expressions of motivation. Fortus and Vedder-Weiss (2014) developed an instrument to measure “continuing motivation” to examine the diferential efect on motivation for science for students from democratic compared to traditional schools in Israel. Democratic schools explicitly focus on supporting students’ autonomy, and the study found that compared to traditional schools, the continuing motivation of students in these schools was maintained from ffth to eighth grade, whereas it decreased signifcantly in traditional schools. They hypothesize that the diference may relate to encouragement of autonomy in the teaching approaches of democratic schools but may be related also to family factors. The Science Motivation Questionnaire developed by Glynn and colleagues (2009) and modifed in 2011 (Glynn et al., 2011) consists of fve components: intrinsic motivation, self-determination, self-efcacy, career motivation, and grade motivation. They found a link between college students’ motivation scores and science achievements, and gender diferences in self-efcacy in favor of men, and in self-determination in favor of women. This fnding was repeated in Salta and Koulougliotis’s (2015) study of Greek secondary school students, and Schumm and Bogner’s (2016) study of German secondary school students. The instrument has been recently translated, trialed, and validated across diferent age groups and science disciplines in a range of countries, including Indonesia (Aini et al., 2020; Dong et al., 2020).

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Engagement Claims around student engagement can be potentially misleading because of the diferent ways the construct is used. Engagement has, like other afect-related constructs, been conceptualized as having multiple dimensions. Fredericks et al. (2004) distinguished three forms of engagement: (1) afective-emotional engagement, including attitude, interest, and a sense of belonging; (2) cognitive engagement, including persistence and motivation; and (3) behavioral engagement, including participation in activity. Thus, the engagement construct draws on a number of afective constructs. Within these, it fags overlap with not only the literature on attitudes, interest, and motivation, but also the growing literature on identity, and the link between afect, intention, and behavior. Fredricks and colleagues (2018) used a mixed-methods sequential exploratory design, starting with interviews to identify perceptions of motivational and contextual factors infuencing level of engagement in mathematics and science. They found both boys and girls expressed higher engagement in classrooms with more student-centered instruction and in classes with highly engaged peers, with girls more likely to emphasize teacher support and personally relevant instruction. From this analysis, they designed, applied, and validated an instrument designed to test associations between motivational beliefs (drawing on EVT), social support, and student-centered and relevant instructional practice with engagement in mathematics and science. Phillips and colleagues (2019), in documenting engagement in citizen science, developed a Dimensions of Engagement framework based on cognitive, afective, social, behavioural, and motivational dimensions.

Self-Effcacy and Self-Concept Many attitudinal instruments include self-efcacy (Bandura, 2006) and/or self-concept as companion factors, exploring links between attitudes, self-efcacy, and aspirations. Fortus (2014) describes selfefcacy as “an expectancy about one’s capabilities to learn or perform a given task” and self-concept as the “beliefs, hypotheses, and assumptions that an individual has about himself”. Further, “selfefcacy is specifc to a task; self-concept is not” (p. 824). Studies incorporating self-efcacy show a complex interplay between this construct, sociocultural infuences, and attitudinal constructs. There is debate concerning whether there is a causal relationship between self-efcacy or self-concept, interest, and aspirations. Kang et  al. (2019) found no reciprocal relationship between interest and self-concept in predicting students’ science aspirations. On the other hand, in a study examining the relationship between under- and over-confdence (i.e., confdence bias) in science and attitudes and intentions, Sheldrake (2016) applied the EVT of motivated behavioral choice (Eccles, 2009), fnding that degree of confdence afects the way students consider their intentions toward studying science. They found that parental infuences predicted science intentions for over-confdent students but not for under-confdent students. In a longitudinal sample of students’ intentions to study non-compulsory physics in Years 8 and 10, Sheldrake et al. (2019) used predictive modeling to fnd that perceived advice, perceived utility of physics, interest in physics, self-concept beliefs (students’ subjective beliefs of their current abilities and performance), and home support specifcally orientated to physics were key predictors of students’ intentions. Their fndings showed considerable stability in students’ attitudinal and belief profles. These relationships can depend on the particular cohort of students. Hilts et al. (2018) studied predictors of achievement and retention in a science major using a multi-group path analysis. They highlighted the importance of social supports that contribute to perceived competence (self-efcacy) and relatedness (the extent to which students identifed as related with others in STEM), especially for female, ethnically underrepresented, or frst-generation student cohorts. They identifed classmate contact and peer mentoring as important for encouraging feelings of competence and relatedness.

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Teaching Practices, Attitudes, and Achievement As we described earlier, studies have shown positive attitudinal outcomes related to classroom practices that emphasize inquiry or problem-based activities, practical work, links with meaningful context, and opportunity for refection. In this section we review studies that have explicitly examined the interaction between particular teaching practices and attitudes, which draw on a similar range of attitudinal constructs described earlier. Many of these studies also examine achievement outcomes associated with these teaching practices and attitudinal fndings. Studies focused on attitudinal outcomes resulting from particular interventions tend to produce positive results, with some indication that pedagogical novelty in itself improves attitudes (Potvin et al., 2020). Aguilera and Perales-Palacios (2020) undertook a meta-analysis of studies reporting the efect of didactic interventions on attitudes to fnd most had a positive and substantial efect size, with the greatest efects being for cooperative learning, project-based instruction, context-based instruction, and technology-multimedia materials. Inquiry and dramatization had moderate efect sizes (see also Villanueva Baselga et al., 2022), consistent with Vossen et al.’s (2018) fndings with respect to research and design activities. Studies have confrmed the generally positive efects of particular features of outreach activities on afect. Vennix et al. (2018) investigated 12 disparate outreach activities and found positive efects on autonomous motivation, particularly associated with perceptions of personal relevance focused on understanding science, out-of-school activities with classmates that were related to a school subject, and workshops compared to projects or lectures. Savelsbergh and colleagues (2016) conducted a meta-analysis of the efect of interventions involving innovative teaching designed to improve student attitudes. They “distinguished fve types of commonly advocated educational approaches: context-based, inquiry-based, ICT-enriched, collaborative, and extra-curricular” (p. 167). They considered attitude “a multidimensional construct comprising: perceived relevance (personal and societal), interest (school, leisure, career), self-efcacy, and normality of scientists” (p. 167). From this, they found signifcant positive efects of the interventions on overall attitude, general interest, and career interest. They found the efects of these innovations on achievement considerably larger than the efects for attitude. They argue that there is no evidence that a focus on attitude-oriented teaching compromises the possibility of improved achievement outcomes. They pointed out the signifcant challenge for attitudinal research of the problematic link between attitudes and behavior, where measured attitudes are often poor predictors of what students actually do in relation to science. From this they argue that behaviors are infuenced by many contextual factors as well as other psychological factors, such as perceptions of degree of control. In response to this concern, one can argue a need when studying attitudes to be very specifc about the object to which the attitude refers, a point we made earlier regarding the interest construct. For instance, for attitudes to science in school, this might encompass attitudes to the science teaching approach, doing practical work, doing science as a leisure activity, support for scientifc work in society, and choosing a science study pathway. Even if students think science is an important and interesting endeavor, they may or may not enjoy their school science, and this may be independent of whether they feel positively about a future in science for themselves. This raises a signifcant question concerning whether attitudinal constructs can be usefully combined into a single “attitude towards science”.

Analysis of International Assessments to Explore Teaching-Affect Associations A number of studies over the last decade analyzed international assessments to examine the efects of diferent types of instruction on attitudes and achievement. Generally, the evidence points to

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inquiry approaches being associated with enhanced afect, with conceptual achievement best supported by guided, rather than open, inquiry. Jiang and McComas (2015) used the PISA 2006 results to investigate the efect of inquiry teaching on achievement and attitudes. They found the highest achievement levels associated with level-2 inquiry characterized by students conducting activities and drawing conclusions from data, but not with level 3 involving designing or asking their own questions. Higher attitude scores were, however, associated with these higher levels of inquiry. Kang and Keinonen (2017) analyzed PISA 2015 data to fnd that inquiry learning experiences were a positive predictor of students’ career aspirations, mediated by outcome expectations. In an analysis of PISA 2015 data for Taiwanese students, Liou (2021) found that teacher-directed instructional practices, compared to inquiry practices, had a signifcant positive efect on students’ achievement but an inverse relation on attitudes toward science. Questions have been raised, however, about the validity of the instrument in distinguishing teaching practices as distinctly “inquiry” or “direct instruction”. Kang and Keinonen (2018) also used PISA 2006 Finnish data and SEM to explore the links between interest, achievement, and teaching approaches. The data indicated that guided inquiry and using topics that were seen as relevant by students positively predicted students’ achievement, but this was not the case with open inquiry and discussion-based approaches. Jack et  al. (2014) performed an SEM analysis of the 2006 PISA data to explore the interaction between afective and self-related factors (science-related interest, enjoyment, self-efcacy, self-concept, leisure time engagement, and future intended interest) and environmental awareness and responsibility among Taiwanese and Canadian students. They found that science self-concept was weakly associated with future intended interest and engagement in science learning for both groups. Their results suggest that students do not view science learning, environmental awareness and responsibility as connected to their personal core sense of self and argue a need for an emphasis on environmental education in the next-generation science education frameworks and standards and to increase the priority and explicit consideration of afective factors, identity and self-in-science, and environmental education instruction.

Sociocultural Infuences on Affect and Aspirations There are some crucial sociocultural factors afecting students’ attitudes and aspirations toward science that have been studied extensively, including gender, family infuences, and cultural infuences pertaining to minority groups. Such studies have been driven by increasing concerns over equity. Many studies have used analytic approaches, using the types of instruments and theoretical framings described earlier, but over the last decades, signifcant insights have been garnered through synthetic approaches, using ethnographic or interview data, case study, and sometimes mixed methods with smaller-scale qualitative research operating together with instrument development and application. Gendered responses to science have been recognized, in particular, as a signifcant equity issue for decades, yet only limited inroads have been made into addressing these inequities. Women’s engagement difers considerably depending on within-science diferences (biology vs. physics), context, and profession (medicine vs. engineering). While there have been numerous recent studies undertaken in the analytic tradition on gendered attitudinal efects, as with Potvin and Hasni’s (2014) review, these tend not to have broken new ground in understanding the deeper mechanisms at play, with gender diferences being more apparent when one looks at particular disciplines, topics within disciplines, or particular features of pedagogy and interactions, compared to looking at attitudes to science overall. Gender diferences in afect tend to wash out when looking at larger-scale phenomena. For instance, Reilly et al. (2019) drew on TIMSS 2011 data to show small to medium gender efects on attitudes and self-efcacy beliefs for individual countries but a complex picture and no overall gender diferences. Further, in this chapter, we will report a more extensive review of gender diferences in afect

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and aspirations through the lens of identity and science capital, involving synthetic methodological approaches that in many cases combine with analytic survey research.

Family and Cultural Infuences on Attitudes and Aspirations Studies of sociocultural infuences on attitudes and aspirations toward science have utilized a variety of methodologies and afective constructs and instruments. These consistently show substantial infuence of family and family culture. Studies have used data from the PISA international assessment to explore the efect of family factors on students’ attitudes, achievement, and aspirations toward science. Shin and colleagues (2015) used Korean PISA 2006 data to show positive efects for parents’ higher valuing of science and socioeconomic status on motivation, science-career pursuit intentions, and motivations for this career pursuit for both girls and boys. Perera (2014) used data from 15 countries to show that parents’ attitudes toward science have a positive and statistically signifcant efect on science achievement across diferent socioeconomic groups. Summers and Abd-El-Khalick (2019), used multivariate, multilevel modeling using constructs based on theories of reasoned action and planned behavior to explore Grades 5–10 students’ attitudes. They found that students’ perceived science ability and the frequency of talk within families about school were more infuential on attitudes than teacher- and school-related variables. In terms of the mechanisms by which family attitudes operate to shape young children’s learning, engagement, and interest development in science (science-learning dispositions), Kewalramani and Phillipson (2020), through interviews with immigrant parents, showed how children internalize parents’ cultural values and ideas, which refect their home culture and aspirations for their children. They argued that parents with cultural values and aspirations for their children regarding science engaged their children in science activities and discussions that are signifcant in shaping their children’s scientifc aptitudes and aspirations. This is consistent with Liu et al. (2015) fndings that ffth grade Chinese children’s career aspirations were infuenced by their parents responding positively to children’s interests, emphasizing education, and conveying career values. Sheldrake (2020) found students who consistently expressed science-related aspirations at 14 tended to have more advantaged family backgrounds, higher proportions of parents working within science-related felds, higher self-confdence (in science, mathematics, and English), higher school motivation, and higher self-esteem. Sáinz and Müller (2018) drew on EVT to examine predictors of aspirations toward STEM among Spanish 16-year-old students. Their results suggest the important role of the mother’s educational level in shaping their children’s aspirations and the value they attach to these. They found that students with non-Spanish-born parents had higher extrinsic motivation related to STEM than those with Spanish-born parents, consistent with an Australian study showing also the greater participation of frst-generation and foreign-background students in post-16 science (Cooper et al., 2020). Nguyen and colleagues (2021) identifed, through interviews, the forces that shape the achievement and choices of Black women undergraduate STEM students, including: parental engagement shaping interests and aspirations, teachers modeling excitement and providing learning pathways, and extracurricular pipeline programs. Also important was the afrming nature and support from the institution and peer interaction and support, highlighting the importance of structures that acknowledge students and welcome them into STEM pathways.

Emotions The research on emotions, distinct from research that examines students’ feelings about science classes or science as a discipline, focuses on afective dynamics in the science class (Jaber & Hammer,

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2016). This research difers from that described earlier on attitudinal constructs in terms of the focus on particulars of a classroom experience, and the short-term temporal nature of this focus. Emerging research highlights the salience of afect, such as annoyance, excitement, wonder, frustration, pride, and triumph (Bellocchi & Ritchie, 2015; Siry & Brendel, 2016; Bellocchi et al., 2017), in shaping scientifc activity and learning outcomes or coupling with personal epistemologies (Gupta et al., 2018). Control-value theory (Pekrun, 1992; Pekrun et al., 2007, 2011) was developed as an integrated framework for explaining the interactions between afect, behavior, and achievement in academic settings. The basis of this interest is that learning is an emotional experience, and emotions therefore infuence learning and development. The extent to which emotions are infuential depends on a person’s perceived value and control of their learning. In Pekrun’s theory, there are positive activating achievement emotions (enjoyment, hope, pride), negative activating emotions (anger, anxiety, shame), positive deactivating emotions (relief), and negative deactivating emotions (hopelessness, boredom). Research in this tradition continues. For instance, Raker and colleagues (2019) developed and validated an instrument using SEM for measuring achievement emotions in post-secondary chemistry courses and related these to subscales of academic motivation and regulation of learning. Recent research has used sociological approaches to focus on emotions in naturalistic settings. Bellocchi (2015) describes emotions as biological and cultural processes involving physiological states of arousal. He distinguishes between emotions experienced in the moment by individuals and emotional climate (EC) arising from bidirectional interactions (Tobin et al., 2013; Bellocchi et al., 2014). There are a number of approaches that have been recently developed for micro-level analyses of emotions in response to classroom activity, including facial expression analysis, gestural analysis, and vocalizations, all of which can be coded against emotion constructs, such as irritation, assertivenessanger, satisfaction-happiness, or disgust. A range of self-report techniques are used (Pekrun & Bühner, 2014), including emoticon selection or clicker buttons, which can be used for studies of EC (Ritchie et al., 2016; Tomas et al., 2016). Zembylas and Schutz (2016) provide an overview of conceptual and theoretical frameworks and research methods used by emotions researchers, discussing the implications of methodological choices for understanding emotions. Recent theorization around emotions (Patulny et al., 2019) points to increasing complexity in the way we view emotions, challenging binaries such as emotional/rational and positive/negative emotional valence, and recognizing tensions between individualized and collectivized emotions. Zembylas (2021) describes the “afective turn” in the social sciences marking “a shift in thought in critical theory through an exploration of the complex interrelations of discursive practices, the human body, social and cultural forces, and individually experienced but historically situated afects and emotions” (p. 1). Some of these critical perspectives are represented in the science education literature around identity, aspirations, and aesthetics, discussed in the following section. As an indicative example of recent research directions, Davis and Bellocchi (2018) analyzed classroom micro-social practices to show how objectivity came to exist in a school science inquiry task with subjectivity as crucially important through social encounters, shaped by the taken-forgranted emotion of respect, which intensifed as emotional energy during the inquiry. Bellocchi and Ritchie (2015) analyzed links between the emotions of pride and triumph within classroom interactions and instructional tasks, drawing on Turner’s (2007) classifcation of emotions. Their results indicate that emotions such as pride are not only relevant for students’ responses to success in tasks, but also play a role in strengthening bonds between students after successful collaboration. Sanctioning by the teacher or peers was a key element associated with these emotions and contributed signifcantly to creating a positive emotional climate, student engagement, and stimulating motivation.

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In this emotions research we see the possibility of identifcation of the moment-by-moment interactions and experiences through which longer-term afective orientations, such as interest or motivation, are crafted. Sociocultural studies of identity, and aesthetics, are also pursued at this level.

The Identity Construct in Science Education To understand complexities in engagement with learning and longer-term participation patterns in science pathways, there has been for the last two decades increasing interest in exploring students’ identity in relation to science and science learning. A range of studies have shown that science identity plays a signifcant role in students’ persistence in learning science subjects and their selection of science careers (Aschbacher et al., 2014; Hazari et al., 2010). The identity construct goes beyond considerations of short-term attitudes and afective responses to classroom events, to frame engagement with science and longer-term aspirations in terms of self-processes that are bounded by social structures and interactions with others that shape the organization and content of self. The move toward an identity framing of afective responses constitutes a sociocultural turn in studies of afect and aspirations, to sit alongside the psychological, analytic framing that has tended to dominate the area. As Hazari and colleagues (2010) assert: “We believe that this [identity] focus provides a basis for understanding students’ long-term personal connection to physics and is a more meaningful measure than a general assessment of students’ attitudes” (p. 979). However, recent research shows an increasing number of studies where sociocultural, synthetic framings have been combined with analytic psychological categories in mixed-methods designs. This poses challenges for how the identity construct is interpreted, with sociocultural framings viewing identity as discursively and socially produced, malleable, and multiple, compared to more positivist renderings of identity as fxed and measurable. Avraamidou (2020b), in reviewing the conceptual history of the identity construct, points out how it ofers an ontological approach to learning as an identity experience and its socially framed nature in communities of practice. A number of seminal studies have laid the groundwork for research on identity in science education (Avraamidou, 2014; Calabrese-Barton et al., 2013; Carlone & Johnson, 2007; Varelas, 2012). Carlone and Johnson (2007) built on the work of Gee (2000) to develop an identity construct built on three dimensions: competence, performance, and recognition. Avraamidou (2014) probed the construct of science teacher identity. Calabrese Barton and colleagues (2013) focus on analyses of the processes of identity “work” as individuals interact over time with the complexity of their “fgured” social worlds, rather than treating identity as fxed and defnable at any point in time. Despite the distinction between research into attitudes in science and identity research, there are many ways in which these traditions overlap, for instance, in the clear role of afect in students’ identity work, including the overlap with analytic constructs of self-concept, self-efcacy, and interest. We will describe next the incorporation of analytic attitude categories in recent quantitative research around identity. In the sociocultural tradition, Avraamidou (2020a, 2020b) echoes Rivera Maulucci’s (2013) perspectives on the central role of emotions in identity and identity development, particularly with regard to misrecognition and response to oppression. The identity construct has been particularly fruitful in determining equity considerations regarding participation in science, with growing recognition of how the cultural framing of science interacts with and can disturb the culturally framed identities of the diverse young people we aim to induct into scientifc ways of thinking and doing. Thus, identity has been a focus for understanding: how female students’ everyday identities respond to the “cultural arbitrary” of science subjects (DeWitt et al., 2019), how Indigenous students’ engagement with science learning involves “border crossing” (Aikenhead, 2001) or “wayfnding” (Howard & Kern, 2019) into Western science as a cultural practice, how minority students’ productive participation in science involves sustained “identity work”

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(Calabrese Barton et  al., 2013), or how family background signifcantly frames students’ response to school science (Dou et al., 2019; Dou & Cian, 2021). A key plank in this identity scholarship is the realization that science and school science are not culturally neutral enterprises but at their core represent values, perspectives, and assumptions that can be at odds with those of young people and challenge their sense of self. As a corollary of this insight, the implications of this work speak not only to approaches that are invitational for students, for instance, through pedagogical practices or choice of contextual activities, but also raise questions about the presumptions of the disciplines themselves and customary ways of teaching them. Archer et al. (2015) argue that science education is an important form of symbolic capital (Bourdieu, 2010), which can facilitate agency and the shaping of forms of privilege. Identity/agency studies represent a strong social justice agenda, in terms of fnding ways of moving toward more equitable patterns of participation.

Science Classrooms and Identity Over the last few years, a number of signifcant studies have explored the way science classroom practices shape or constrain students’ science identity work. Kim (2018) used positioning theory (Harré & Van Langenhove, 1999) to interpret through an identity lens the way a teacher positioned students in inquiry-based classroom activities “to develop students’ science identity imbued with the qualities of curiosity, wonder, perseverance, scepticism, and open-mindedness” (Kim, 2018, p. 40). As part of this positioning, the teacher modeled the identity of a scientist and explicitly included students in a storyline as scientists. Hazari et  al. (2015) examined diferences in physics students’ behavioral, afective, and cognitive engagement as infuenced by teachers’ pedagogical moves. They found that teachers’ social cues (those aimed at narrowing the social distance between student and teacher) seemed to be the most important for afective and cognitive engagement, and subsequently physics identity development (using an identity proxy that combined the subconstructs of physics recognition, interest, and performance/competence, echoing Gee’s [2000] identity dimensions of recognition, interest, competence, and performance). Studying an intervention designed to bridge identity challenges, Birmingham et al. (2017) constructed case studies of females from nondominant communities taking the “science that matters” and bridging to the community. They argue that repurposing the science to their communities empowered the girls and gave hope, and that learning science for them was about connecting its purposes to “who they are and who/where they care about” (p. 839). Outreach experiences have been studied for their potential role in supporting students’ science identity development (Carlone et al., 2015). Millar et al. (2019) studied the role that communities of practice play in an astronomy outreach program for low-SES schools in developing students’ science identity. They base their framing on the recognition that identity development and enactment requires the participation of others in recognizing a particular kind of identity (Archer et al., 2010) and that practice is identity work. They found “the communities that emerged in schools around the TiS (Telescopes in Schools) program allowed for shared afective experiences of astronomical phenomena” (p.  2596). As a counter-example, Shaby and Vedder-Weiss (2020) analyzed identity development in three students in visits to a science museum, fnding that the museum reproduced positioning and roles found in the classroom, thus questioning the capacity of feld trips to shift identity trajectories. Studies have also drawn on Butler’s (1993) idea of identity performance, exploring, for instance, the afordances and limitations of a science museum learning space for girls (Dawson et al., 2020). Dawson et al. identifed four types of performance (e.g., good behavior, being cool) enacted by the girls (aged 12–13), arguing that a science museum space puts girls in a difcult position for learning and for enacting the identities they are invested in. They argue a need for science learning spaces “that disrupt rather than reproduce social inequalities” (p. 664). A further study (Godec et al., 2020)

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extends this performativity focus to physical and digital materiality, arguing for a greater focus on multimodality in STEM identity research and the importance of space, including material technologies for enacting/limiting identity performance.

Recent Identity/Equity-Focused Studies Carlone et al. (2014) followed three students through from fourth to sixth grade in science, using classroom observations and interviews to examine the cultural and structural elements of the classrooms, using the lens of celebrated subject positions that enabled or constrained identity work, the nature of students’ identity work, and the ways larger social structures (race, class, gender) infuenced this identity work and positioning. They relate their fndings to the problem of diminishing interest in science over the middle school years, ofering insight into the enabling/constraining efects of the ways in which “celebrated subject positions”, or the construction of “ideal student”, serve to enable or constrain students’ identity work in science, when these constructions are raced, classed, and gendered (Archer, 2012). Further studies have drawn on this notion of “celebrated” classroom identities. Archer, Dawson et al. (2017) characterize school classrooms as spaces that are constituted by complex power struggles involving contestation between the institution, teachers, and students, which has profound consequences for students’ science identity and participation. They analyzed data from nine months of London classroom observations and discussion group data to identity three dominant “celebrated identity performances” (“tick box” learning, behavioral compliance, and muscular intellect), with signifcant equity implications for the way these afect the participation of students from diverse backgrounds. The study drew on the feminist poststructuralist work of Judith Butler (1993), who proposed that gender identity is not the natural corollary of a person’s sex but should be understood as socially constructed through discursive and bodily acts. Archer et al. thus treat identity as “performative, nonessentialized, fuid, contested, and produced through discourse” (p. 744). The identity construct has proven powerful for developing insights into the cultural hurdles experienced by minority groups in navigating school science, as part of an increasing concern with equity of access to STEM futures. Studies of minority group identity experience of science have variously focused on the way structures are intertwined with students’ self-authoring of identity and the importance of social and emotional participation (Kane, 2016), and the diverse ways in which minority ethnic students participate in science (Wong, 2016), including the conscripting of dance identities of Black youth for science identity narration (Chappell & Varelas, 2020). Such studies have helped us recognize the strong cultural framing of school science classrooms and subjects, suggesting a need for change that has thus far eluded us at a system level.

Identity and Gender Exploring the dialectic between social structures and individual agency (Varelas et al., 2015) in shaping identity, Carlone et al. (2015) explored the constraints on girls’ agency by examining the efects of social structures such as race, class, and gender, and classroom structures that narrowly defne participation practices. The focus on structures enabled recognition of “the narrowly constructed classroom subject positions that left virtually no room to be simultaneously ‘girly’ and ‘scientifc’ and the prominence of heteronormative versions of femininity” (p. 474). The focus on agency “made evident that girls were less engaged with how to become scientifc and more concerned with fguring out what kind of girl to be, given what was acceptable in the setting” (p. 474). Avraamidou (2020a) explored the experience of a young immigrant Muslim woman in her trajectory in physics. Cultural expectations, sociopolitical barriers, and negative stereotypes were obstacles to Amina’s sense of belonging in physics, and her social class, religion, gender performance, and

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ethnic status positioned her as Other throughout her trajectory in physics. Avraamidou argued a need to understand how to transform physics to be more invitational of diverse authentic identity performances (see also Godwin & Potvin, 2017). Waide-James and Schwartz (2019) examined how macro-level discourses (“ways of saying, doing, and being that are recognized as certain types of people”, p. 679) available in the fgured world of school science acted to limit the engagement of African American girls to acceding to identities as good science students who are quiet, polite, passive, and fast workers. They noted an absence of discourses that would support learning and development in science for these students

Indigenous Students Participation of Indigenous students in science pathways is identifed as a signifcant equity issue in many countries and has been the focus of identity research for some decades (e.g., Aikenhead, 2001; McKinley, 2005). Aikenhead (2001) coined the term “border crossing” to highlight the identity challenges experienced by First Nations students in engaging with school science as a cultural entity. McKinley (2016) makes the point that PISA data shows that Indigenous students have interest in science equal to their non-Indigenous peers and asks, “Why have STEM educators and schools not been able to capitalise on this interest?” (p. 64). The identity perspective would indicate a need to develop classroom practices that acknowledge “the nature of knowledge and the importance of cultural identity to Indigenous communities” and to “identify ways to support teachers and students to better leverage on the funds of knowledge that each bring to the STEM classroom” (p. 64). In this spirit, a range of recent studies have explored ways of honoring Indigenous knowledges and practices. Rioux and Smith (2019) described an initiative that “engaged Indigenous preservice teachers in border-crossing pedagogical practices as a way to recognise the legitimate use of the Indigenous concepts of place” (p. 90). Fakoyede and Otulaja (2020) explored the use of cultural artifacts to teach and learn science for Indigenous learners in South Africa. The study “showed that learners’ capitals, namely cultural, social and symbolic capitals, enhance the development of cognitive capital leading to the emergence of more knowledgeable other(s) within Indigenous science classrooms” (p. 193).

Defning and Measuring Identity Qualitative identity-focused research has yielded rich insights into students’ experience of and participation in science and science futures, and the way these experiences are framed within broader social structures and the structures of science disciplines and classrooms. In the case of minority groups, gender, and ethnicity/class, we have come to understand better the sociocultural settings that can constrain students’ productive participation in science. This research has drawn strength from and had profound efects on issues of equity in science education. Following this work, researchers in the analytic tradition have more recently been engaged in constructing instruments to measure the identity construct to explore patterns and infuences in relation to diferent groups, diferent experiences, and diferent sociocultural variables. In moving from synthetic to analytic framings of identity, there are, however, challenges. In constructing measures of identity, aspects of the rich complexity of the sociocultural underpinnings are necessarily essentialized and frozen in time and interrelations. Nevertheless, the research is yielding interesting results, and researchers such as Archer previously steeped in qualitative approaches are exploring mixed-mode studies that capture both large-scale patterns and narrative complexity (Archer et al., 2020). Many of the instruments developed include constructs from the attitudes literature, such as interest, curiosity, and self-efcacy. It is easy to see why constructs such as “self-concept” are related to identity, but one of the issues on which researchers disagree is whether the identity construct should refer only to notions of recognition in relation to self and others or whether a composite defnition

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is more useful. Next we describe some of the recent research that involves development of measures of identity. Dou and colleagues (2019, 2020) developed an instrument with the identity construct consisting of three primary factors: interest, recognition, and performance/competence, building on the earlier work of Gee (2000), Carlone and Johnson (2007), and Hazari et al. (2010). Carlone and Johnson had used a three-dimensional identity construct: competence, performance, and recognition, to which Hazari et al. added interest as a fourth dimension (see also Chen & Wei, 2022). Of these factors, both sets of researchers had concluded that “recognition” (by others) was most correlated with identity. Vincent-Ruz and Schunn (2018) make the point that the identity construct often includes a range of attitudinal constructs, and their study sought to test its distinctiveness and coherence. They used EFA to explore whether self-recognition (“I am a science person”) and recognition by others (“my family/friends/teachers think of me as a science person”) can be considered part of one coherent identity construct. Their survey included three attitudinal constructs: fascination (interest and positive afect, curiosity, and goals of acquiring scientifc skills and ideas), values (importance placed on knowing and being able to do science), and competency beliefs. They found that (1) science identity overall was a strong predictor of students’ science-related choices, and (2) science identity behaves separately from the other attitudinal factors and has a unique contribution to our understanding of students’ choices. Distinct from previous qualitative research fndings (e.g., Calabrese Barton et al., 2013), they found that perceived personal and perceived recognized science identity components loaded into a single factor and were not distinct. A large body of qualitative research has connected early informal experiences to science identity development (Maltese et al., 2014; Rahm & Moore, 2016), ofering a powerful lens for understanding students’ career choices (Godwin et al., 2016). Dou and colleagues (2019) studied the predictive power for STEM identity outcomes for fve categories of childhood experiences: engaging in disciplinary-based performances such as stargazing, participating in science programs such as camps, participating in STEM competitions, consuming STEM media, and talking about science with friends or family. They found that only these last two factors – consuming STEM media and talking about science – were predictive of a STEM identity in college, with talking about science with family and friends having a much more signifcant efect. In a follow-up study (Dou & Cian, 2021), they further explored these science talk experiences of STEM majors in a Hispanic-serving institution and found that “talking about science with friends and family was the only informal learning experience associated with students’ STEM identity” (p. 1093). Vedder-Weiss (2018), in a micro-ethnographic study of a family rich in science habitus (Bourdieu, 2010), explored the complexity of how individuals respond to the opportunities ofered and the crucial but subtle nature of family interactions for shaping recognition and roles that intertwine with everyday science engagement to shape the forming of identities. In recent years a number of quantitative studies have developed multi-factor models to explore a range of determinants of students’ science identity development. Kang et al. (2019) surveyed middle school girls across a number of low-income communities, using multigroup SEM. They found selfperception in relation to science to be the strongest predictor of identifcation with STEM-related careers, and this varied by race/ethnicity. They found girls’ identifcation with STEM and STEM careers was infuenced by home, school, and outside activities and that “attention ought to be paid to the kinds of family and support that leverage the assets of the girls, their families, and their communities” (Hazari et al., 2020, p. 434), arguing that unlike qualitative research into identity, quantitative work had so far failed to incorporate the efect of context on identity development. Using the constructs of performance/competence, recognition, interest, and a sense of belonging, they used structural equation modeling to show how the relationship of these constructs to physics identity of college students varied with gender and seniority. They argue for the importance of examining context and variations in student experience when studying disciplinary identity development. Again,

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we see how one must be careful with analytic constructs to not overgeneralize, given the important roles of context and culture.

Aspirations and Science Capital Archer (2014) unpacked the construct of aspiration in some detail in terms of its predictive accuracy, its discursively constructed nature, and the variety of infuences that shape it. In particular, she draws on the work of Bourdieu (1986, 1992) to examine the infuence of family through the construct of family habitus, defned as “a framework of dispositions, developed through a family’s sense of its collective identity, that guides action, shapes perceptions of choice and provides family members with a practical feel for the world” (p. 27), which interacts with various forms of capital to shape young people’s desirable and possible futures. In this way, aspirations are closely linked to identity construction and discursively shaped by socioeconomic status and gender. The work of the UK ASPIRES project (DeWitt et al., 2014; DeWitt & Archer, 2015; Archer et al., 2020) is signifcant in combining the identity construct in relation to experience of school science with sociological notions of family habitus and capital, focusing attention on the notion of “feld” in relation to science and the cultural work that needs to be done by students in negotiating the complexities of the intersections between these (Godec et al., 2018). Along with critical identity perspectives, it raises questions about the cultural arbitrariness of school science as a barrier to equitable participation. The ASPIRES project involved large-scale surveys of students across the elementary–secondary school transition, supplemented by interview data. While the majority of students were found to enjoy school science (very often linked to practical work) and hold positive views of scientists, this did not translate into interest in “being” a scientist. This again raises questions about the distinctions between interest, enjoyment, and aspirations. DeWitt et al. (2014) argued that “a narrow view of the ‘kinds of people’ who pursue science operates against individuals coming to consider science as something that is ‘for me’” (p. 1612). They suggest that the problem of aspirations is not so much linked to “attitudes” as to a mismatch of self-perceptions and identities, conceptualized as nonessentialized, fuid, contested, and discursively produced (Archer & deWitt, 2015). In a related study, Wong (2015) explored reasons why careers from science are much more highly sought after than careers in science for 11–14-year-old students. He argued that scientists’ science is constructed by students as a highly gendered and racialized profession, compared to careers from science, particularly medicine. Thus, identity can be a key factor through which to understand aspirations. The ASPIRES project has suggested “science capital” as a construct that identifes the multiple factors impacting students’ aspirations toward science futures, alongside educational factors and practices and dominant representations of science (Archer, Dawson et al., 2015; Archer et al., 2020). This extends Bourdieusian notions of cultural and social capital to recognize scientifc forms that command high symbolic and exchange value. Analysis of survey fndings showed that students’ diferent levels of science capital predicted diferent future science intentions, diferent levels of self-efcacy in science, and diferences in the extent to which they felt others saw them as a “science person” (Dewitt et al., 2016). Their fndings point to the complex array of factors infuencing aspirations, including attitudes to school science and parental attitudes, mediated by possession of science capital. ASPIRES pursued a range of studies focusing on gender and ethnicity efects on science engagement and aspirations (Archer, DeWitt et al., 2014; Archer & DeWitt, 2015; Archer, Moote et al., 2017; Moote et al., 2019, 2020), in particular the engagement and aspirations of minority groups, gendered participation, and the nexus between science capital, family habitus, and science as a feld (Archer, Moote et al., 2020; Wong, 2015). Archer et al. (2015), comparing survey results on science

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aspirations for diferent ethnic groups and conducting a longitudinal study of a subset of Black African/Caribbean students from ages 10–14, argued that these students are situated at the nexus of intersecting inequalities that render science “unthinkable” because of incompatibility with dominant representations of scientists, Othering through association of science with “braininess”, economic and social disadvantage, and associations of science careers with White masculinity. Science capital was conceptualized in ASPIRES using principal component analysis (Archer, Dawson et al., 2015) based on large-scale survey data, as consisting of nine components, which have been gathered together under eight dimensions as part of a science capital teaching approach (Godec et al., 2017) that attends explicitly to equity issues in framing teaching and learning. A number of recent studies have involved the development of quantitative instruments designed to measure science capital. For instance, Du and Wong (2019) used PISA 2015 survey items as a proxy to investigate Chinese and UK students’ patterns of infuence of science capital on career aspirations, fnding a strong infuence. The research pathways followed with the identity, and science capital constructs, provide interesting insights into the relation between synthetic and analytic framings. This point will be discussed further in the concluding section.

Aesthetics and Science Education The early part of the 21st century involved a growing appreciation for the importance of exploring and valuing the role of aesthetics in students’ and teachers’ encounters and relationships with science. Aesthetics here is understood in the Kantian sense of “judgments of taste about qualities of objects in the world” (Wickman, 2017, p. 10), which has been almost exclusively explored through sociocultural, synthetic approaches. This notion of aesthetics is strongly present in science education in Dewey’s pragmatist ideas, grounded in his notion that aesthetic experience signifes “experience as appreciative, perceiving and enjoying” (1934/1980, p. 4). This challenges the traditional separation of afect from cognition, instead considering these as two sides of the same coin (Wickman, 2006). Conceptual learning and students’ appreciation of science – how they feel about what science can do for them – are intimately intertwined and underpin student engagement with science (Jakobson & Wickman, 2008). As students are inducted into the practices of science, they need teacher support to appreciate and value those objects and processes of science that aford the solving of scientifc problems and engaging with the world in a scientifc way (Pugh & Girod, 2007). Learning to know and learning to value are inseparable. The focus on developing aesthetic understandings can be linked to the notion of scientifc attitude. Students’ appreciation of scientifc phenomena and processes impact their interest in science and identifcation with the discipline.

Pragmatist Approaches to Feeling and Meaning This promising pragmatist approach to aesthetics, which can uncover fresh insights into some trenchant issues of attitudes toward science, is playing out in a research context that is still dominated by approaches that seek to separate emotion/afect/feeling from cognition/conceptual learning. Wickman (2017) pointed out in a systematic literature review that “the most common constructs used to study aesthetic experience in science education are (1) attitude, (2) motivation, and (3) interest . . . less common are the constructs (4) emotion, (5) aesthetic, (6) afect, and (7) taste” (p. 15). These constructs refect the analytic research tradition in science education, which as Wickman (2017) argues, “relies on a conception of learning as change of mental states and as chains of causal lawbound processes” with a focus on determining the “structural factors causing emotions within individuals” (p. 12). Emotions are considered as “afective inner mental structures” (p. 12), with learning framed as a reward/arousal system.

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The situation seems to have changed little since 2017. Wickman et  al. (2022) report that the synthetic approaches to aesthetics in science education, in particular pragmatist approaches, have still not made their mark on the research landscape to the same extent as those studies that are grounded in neuroscientifc, cognitive, and psychological accounts. Such pragmatist perspectives approach aesthetics as a matter of studying emotions/afect/feeling “in action, as social transactions, in historically constituted settings” (Wickman, 2017, p. 2). They focus on “how the various feelings, as in a science education class, are transacted in furthering action and so giving them certain meanings” (p. 2). Following the pragmatism of Peirce (1905/1998), this approach unifes feeling with meaning so that “feeling and meaning are coeval, coevolved, functionally complementary, co-determined, and codeterminative” (Lemke, 2015, p. 602). As Prain (2020) points out, “feelings are constitutive of, and inseparable from, meaning-making”, with feelings considered as “semiotic signs with interpretable meanings” (p. 22). Wickman et al. (2022) argue, “viewing aesthetics, afect and disciplinary learning as intertwined implies new challenges for the what, how and why of classroom research on this topic” (p. 14). There is a need for in situ, empirical research that makes use of methods and methodologies that aford the study of the moment-to-moment constitution of feeling and meaning in the science classroom, for example, the use of classroom video (Prain, 2020; Wickman, 2017). This necessitates a focus on feeling/meaning in teaching and learning science (Lemke, 2015), that is aesthetic experiences as the development of habits in the Rortian sense of “a matter of acquiring habits of action for coping with reality” (Rorty, 1991, p. 1). Students’ dispositions to science in the form of habits are the ways in which they are likely to relate to and encounter science as particular feelings and meanings (Wickman et al., 2022). More research is required to determine what tasks, sign-making, and teacher support are conducive to students developing this appreciation for the processes and practices of science (Prain, 2020). Research is starting to emerge that focuses on teachers supporting students to develop a taste for science, as built on a growing appreciation for the alignment of afect and interest and the semiotic nature of aesthetic experiences.

A Taste for Science Anderhag et al. (2015a) show that most accounts of students losing interest in science are framed by analytic research that primarily makes use of interviews and questionnaires. These are limited in providing insights into the moment-to-moment interactions between students and teachers, and between students and the objects of interest that are pivotal to constituting their aesthetic experiences of science. The “object of interest” (physical, conceptual, human) is key to this Deweyan (1934/1980) perspective; it is what the aesthetic experience “centres on through the anticipation and expectations of its outcomes or in savouring the actual outcome of being involved” (Wickman et al., 2022, p. 727) as students seek to solve the problem at hand and consummate their experiences. As such, “interest in science class . . . is understood as the fruitful prosecution of some course of action in which elements . . . are discerned and appreciated as objects of interest on the basis of what they do along the way in carrying this process to fulflment” (Anderhag et al., 2016, p. 796). Anderhag et al. (2016) propose that interest is constituted in the science classroom through what students and teachers say and do, which is linked to what they feel and think (Anderhag et al., 2015). They employ practical epistemology analysis (PEA) (Wickman  & Östman, 2002a, 2002b), based on Dewey’s pragmatism (1938/1997) and Wittgenstein’s (1953/1967) notion of language games, to unpack the meaning-making potential of encounters that occur in the Year 7 and Year 9 science classrooms. They show that it is not a case of students losing interest in science as they transition from primary to secondary school, but rather that the object of interest – science – alters in character across the primary–secondary school divide. In primary school, students tend to relate to and value everyday objects of interest and are not aware of the diferent objects of interest of science. While secondary students are often aware of the presence of both everyday and scientifc objects of interest,

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they also struggle to appreciate scientifc objects of interest (Anderhag et al., 2016). Students need support to establish continuity between these diferent types of objects of interest as manifested in the primary and secondary school settings (Anderhag et al., 2015; Anderhag et al., 2016). As Anderhag et al. (2015) argue, this is particularly important for making science welcoming for students from disadvantaged socioeconomic backgrounds. Anderhag (2017), drawing on his previous PEA studies (Anderhag et al., 2015; Anderhag et al., 2015), proposes that teachers need to work with students to develop “a taste for science”. To align their personal interests and the associated everyday objects of interest with those of science in a disciplinary-specifc and disciplined way. Anderhag uses the “construct of taste for science as a social and communicative operationalization, or proxy, to the more psychologically oriented construct of interest” (Anderhag et al., 2015a, p. 339). Taste is action-oriented interest, that is, interest that is constituted in what students say and do. It is of the nature of a habit in that it is a set of normative value judgments and associated feelings and meanings as to what objects of interest should be excluded and included from the scientifc process of resolving the problem at hand and consummating experience (Anderhag, 2017). This notion of taste is grounded in both Dewey’s (1938/1997) account of habit as well as Bourdieu’s notion of habitus (1986). Bourdieu here represents a turn to the sociological, such that taste relates to students’ dispositions to the world around them, in particular appreciating and valuing what particular objects can do for them in making sense of the world (Anderhag, 2017). Taste provides a complementary view to the development of science capital, also strongly inspired by Bourdieu, such that a focus on interest as taste means a focus on equity and inclusion. The science classroom becomes a place in which all students, including those traditionally marginalized, need to be supported to develop a taste for science through aligning their personal/everyday tastes with those of science (Anderhag et al., 2015). Anderhag et al. (2015b) make clear that the teacher needs to establish continuity between this taste of science in the classroom and the associated personal tastes of students, and the way in which taste is constituted in the world of science beyond the school. Hobbs and Kelly (2017) show that if teachers do not have a taste for science and model this for their students, then it is unlikely that their students will appreciate the value of science. Lima Junior and colleagues (2022) trace the development of a prize-winning teacher’s taste for science teaching, showing the strong sociocultural framing of his commitments. This pragmatist approach to science education that focuses on taste is closely aligned with research that explores students’ curiosity and wonder in science. Luce and Hsi (2015) qualitatively analyzed Year 6 students’ photo journals and interviewed students about their expressions of curiosity regarding objects of science. They discovered that students expressed this curiosity in various ways, including wonderment for science. Lindholm (2018) proposed a conceptual framework for curiosity-based science education, working with the notion of wonder as applicable to preschool, primary school, and secondary school contexts. He argued that in preschool, “curiosity and wonder are triggered by perceptive beauty rather than by facts”, while in primary school “curiosity is encouraged by exploring the diversity of the world”, and in high school “curiosity is ignited by means of a better balance between models and phenomenology” (p. 987). Gilbert and Byers (2017), working with preservice teachers, showed that fostering teachers’ wonder in science, particularly among those teachers who have negatively experienced science and lack interest in science, is a powerful way to support them to develop positive relationships with science and associated pedagogies that foster wonder in their students as part of a positive disposition toward science. The pragmatist approach to aesthetics in science education is building evidence that what students know about science and feel about science are intimately intertwined. Science education is about “learning to feel like a scientist” (Jaber  & Hammer, 2016, p.  216). Jaber’s and Hammer’s notion of “epistemic-afect” (2016, p. 190) is similar in meaning to “aesthetics” as used in the pragmatist research. Both approaches highlight what Alsop (2015) refers to as “the fact that afect and cognition are inseparable and mutually constitutive” (p.  22). This aligns with synthetic approaches to

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understanding the role of feeling and meaning in science that builds on the afective turn in science education to closely align what students feel about science with their conceptual learning (Alsop, 2016). Alsop’s (2016, 2017, 2018) research on afect highlights the way in which students’ interest in science is closely aligned with students’ encounters with science – the objects and processes of science – and more specifcally the way in which students’ minds/bodies are afected by and afect science. Alsop and Dillon (2018), refecting on their encounters with a (preserved) Narwhal at the Royal Ontario Museum, contend that “education is a way of attending with, a way of feeling and relating to” (p. 65) science.

Semiotic and Interdisciplinary Nature of Aesthetics This pragmatist aesthetic account is ofering new and potentially productive means to explore the variety of ways (conceptual, afective/aesthetic) in which teachers and students relate to science in interdisciplinary contexts, which is an increasingly important issue as STEM and, even more-so, STEAM become favored ways for engaging students in authentic science in school (Mejias et al., 2021). The increasing recognition of the semiotic nature of aesthetics, and thus the semiotic nature of feeling and meaning as understood through Peirce’s semiotics (1894/1998), Wittgenstein’s ideas on language (1953/1958), as well as Halliday’s systemic functional linguistics (1978), is making it possible to appreciate more of the complexity and richness of students’ and teachers’ aesthetic experiences of science. Due to art’s historical links to aesthetics, it has become a favored context in which to explore the aesthetics of science and more specifcally how each discipline can aesthetically inform students’ experiences of learning (Wickman et al., 2022). Jakobson and Wickman (2015), in a PEA analysis of video data of Year 1 students’ explorations of leaves through a science activity and an art activity, demonstrated that art and science mediated diferent aesthetic experiences for the students. The nature of these aesthetic experiences depended on the “mediating artefacts” (Jakobson & Wickman, 2015, p. 325) that the students encountered as they explored the leaves in diferent ways. Caiman and Jakobson (2019) showed, through another PEA analysis of video data, that Year 1 students’ understanding of and interest in animal ecology was enhanced when their art practices and science practices were combined through drawing to provide a rich aesthetic experience. Students’ drawings as artistic artifacts were valued as creatively and imaginatively meaningful for learning the science of animal ecology. Recent studies (Ferguson et al., 2021; Hannigan et al., 2021; Tytler et al., 2022) have started to draw more explicitly on the semiotics of Peirce (1894/1998) to show ways in which practices of art and science can be productively intertwined in the secondary science classroom. Students create and work with multimodal representations in order to make value judgments that align with the aesthetics of art and science. Through microgenetic analyses of video data, these studies point to an “emotionally-infused semiotic” or “semiotically-infused aesthetic” (Ferguson et al., 2021, p. 18) at play when students feel and think in the moment. They are supported by their teachers to develop a habitual appreciation for what the practices of science and art can do for them in the science classroom, their daily lives, and the world of science beyond.

Concluding Thoughts The last two decades have involved a proliferation of interest in research on afect in science education, driven initially by a realization that learning is crucially shaped by afective factors, then by concern at falling participation in science in the post-compulsory years and evidence of diminishing interest across the middle years, and more recently by a sharper focus on equity and social justice considerations around these trends. This shift in context has widened the focus of research on afect from framing in psychological terms to an increasing recognition of its sociological drivers and of the

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culturally framed structures of school science that serve to limit equity of participation. Methodologically, some of this shift has been accompanied by a greater visibility for synthetic alongside analytic research approaches. This chapter has been broadly structured to follow these evolving themes, and here we attempt a reprise of the key insights into the productive framings of afect, the factors that determine it, and its entailments for learning and participation. There are a range of constructs, particularly “attitude”, “interest”, and “motivation”, that dominate analytic research in science education. However, measures of these constructs tend to be inconsistent across the feld, and questions continue to be raised concerning the psychometric processes used by researchers, leading to advocacy for greater attention to a variety of aspects of validity. However, one of the challenges with constructs developed in the analytic tradition is the assumption of fxity of the underlying language, an assumption that is needed if we are to investigate diferences between groups, yet it is also clear that responses to such language are culturally framed. We need to develop sharper agreements about the operationalization of attitudinal constructs, but also a more sophisticated way of attending to cultural diferences in how these are expressed. Generally, improvements in attitudes are associated with a variety of student-centered pedagogies, but with guided rather than open inquiry associated with improved learning outcomes. More positive afect is associated with increased learning outcomes. However, the causal pathway between attitudes and engagement, learning and future learning intentions, is not straightforward; a number of theories that model these continue to be used by analytic researchers. Attitudes and aspirations in science are strongly mediated by a range of sociocultural factors, including gender, race/ethnicity, and socioeconomic status. Gender diferences tend to be specifc to topics, pedagogies, and classroom interactions. Family talk about science and family values are consistently found to be highly infuential. Increasingly, our views on afect are being refned to be more specifc as we develop theoretically informed languages to talk about afect. Including a greater sensitivity to the objects of afect, to the sociocultural settings of learning related to the classroom environment, and to the embedded cultural presumptions of school science and its symbolic entailments and the disjunctions this creates for a range of groups of students. For each construct, there is a history of both analytic and synthetic research traditions that can relate bidirectionally, and in some cases are combined in mixed-methods studies. Constructs change, however, with changing methodological framings. Emotions research has a history in the psychological literature, but recently there has been interest in a sociological perspective, for instance, around classroom emotional climate. In constructs such as interest, identity or “science capital”, we see this movement between analytical and synthetic traditions. For the generation of afective constructs related to particular aspects of student engagement or learning, and identifcation of the complexities underpinning afect–context–conceptual learning relations, synthetic approaches have progressed our thinking. For system-level explorations or comparison between groups or diferent approaches and contexts, an analytic framing is needed. The construct of identity has been useful for unpacking the distinction between attitudes toward science and intentions to take up scientifc futures and has been particularly powerful in developing insights into sociocultural factors afecting learning and short- and longer-term engagement with science. In particular, framings of identity as multiple, shifting, and culturally/discursively/bodily produced has alerted researchers to the large-scale and classroom structural features that interact to frame student agency. Synthetic research into identity, and the more recent science capital framing, has been used to construct a range of analytic instruments to measure these constructs for comparison across groups and time. These inevitably involve a theoretical shift in the way these constructs are understood, and more research is needed to better align these instruments and constructs with the sociocultural complexities identifed in the synthetic research. Research into aesthetic responses to science have powerfully drawn on pragmatist semiotic perspectives that link afect and conceptual meaning as mutually constituted. Afect is central rather than

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antecedent to learning. The construct of “taste” introduces a sociological dimension to the aesthetic experience of science, aligning this work with that of identity and science capital. The aesthetic construct holds promise of a sharper analysis of student afective responses to a variety of aspects of a science education, with more research needed based on this. Currently, the analytic instruments developed for measuring identity and science capital have similar challenges outlined for attitudinal instruments in the earlier part of this chapter, on which they are based. Nevertheless, the emergence of these constructs in their synthetic form and their subsequent representation in analytic categories have been and continue to be generative tools for (1) widening the construct of “attitudes” from the psychological to include the sociocultural context of learning in science classrooms, (2) alerting us to the particular challenges for a range of learners in learning science, to support a more informed focus on equity in science education, and (3) providing a theoretical framing for analysis of the broader sociocultural factors impacting on learning science for diferent groups. There is a need for ongoing research to translate these fndings into efective system policy and teacher practice to create a tradition of efective and equitable classroom science teaching.

References Abd-El-Khalick, F., & Summers, R., Said, Z., Wang, S., & Culbertson, M. (2015). Development and large-scale validation of an instrument to assess Arabic-speaking students’ attitudes toward science. International Journal of Science Education, 37(16), 2637–2663. Adler, I., Schwartz, L., Madjar, N., & Zion, M. (2018). Reading between the lines: The efect of contextual factors on student motivation throughout an open inquiry process. Science Education, 102(4), 820–855. Aguilera, D., & Perales-Palacios, F. (2020). What efects do didactic interventions have on students’ attitudes towards science? A meta-analysis. Research in Science Education, 50(2), 573–597. Aikenhead, G. (2001). Students’ ease in crossing cultural borders into school science. Science Education, 85(2), 180–188. Aini, R. Q., Rachmatullah, A., Roshayanti, F., Shin, S., Lee, J. K., & Ha, M. S. (2020, March). Testing validity inferences of science motivation questionnaire (SMQ-II) instrument: Rasch-based analysis with Indonesian secondary students. Journal of Physics: Conference Series, 1521(4), 042101. IOP Publishing. Ajzen, I., & Fishbein, M. (2005). The infuence of attitudes on behavior. In D. Albarracín, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173–221). Erlbaum. Alsop, S. (2015). Afect in learning science. In R. Gunstone (Ed.), Encyclopedia of science education (pp. 20–24). Springer. Alsop, S. (2016). Encountering science education’s capacity to afect and be afected. Cultural Studies of Science Education, 11(3), 551–565. Alsop, S. (2017). Afterword: Science education and promises of aesthetics, emotion and wellbeing. In A. Bellocchi, C. Quigley, & K. Otrel-Cass (Eds.), Exploring emotions, aesthetics and wellbeing in science education research (pp. 269–285). Springer. Alsop, S. (2018). Science education and promises and prospects of interest. In J. Yeo, T. Teo, & K. S. Tang (Eds.), Science education research and practice in Asia-Pacifc and beyond. Springer. Alsop, S., & Dillon, J. (2018). Encounters with a Narwhal: Revitalising science education’s capacity to afect and be afected. In D. Corrigan, C. Buntting, A. Jones, & J. Loughran (Eds.), Navigating the changing landscape of formal and informal science learning opportunities. Springer. Anderhag, P. (2017). Taste for science: A Bourdieu-pragmatism approach to interest, aesthetics and learning. In A. Bellocchi, C. Quigley, & K. Otrel-Cass (Eds.), Exploring emotions, aesthetics and wellbeing in science education research (pp. 39–54). Springer. Anderhag, P., Hamza, K. M., & Wickman, P. O. (2015). What can a teacher do to support students’ interest in science? A study of the constitution of taste in a science classroom. Research in Science Education, 45(5), 749–784. Anderhag, P., Wickman, P.-O., Bergqvist, K., Jakobson, B., Hamza, K. M., & Säljö, R. (2016). Why do secondary school students lose their interest in science? Or does it never emerge? A possible and overlooked explanation. Science Education, 100(5), 791–813. Anderhag, P., Wickman, P. O., & Hamza, K. M. (2015a). Signs of taste for science: A methodology for studying the constitution of interest in the science classroom. Cultural Studies of Science Education, 10(2), 339–368. Anderhag, P., Wickman, P. O., & Hamza, K. M. (2015b). How can teaching make a diference to students’ interest in science? Including Bourdieuan feld analysis. Cultural Studies of Science Education, 10(2), 377–380.

184

Student Attitudes, Identity, and Aspirations Toward Science Archer, L. (2012). “Between authenticity and pretension”: Parents’, pupils’ and young professionals’ negotiations of minority ethnic middle-class identity. The Sociological Review, 60(1), 129–148. Archer, L. (2014). Conceptualising aspiration. In A. Mann, J. Stanley, & L. Archer (Eds.), Understanding employer engagement in education – Theories and evidence (pp. 23–35). Routledge. Archer, L., Dawson, E., DeWitt, J., Godec, S., King, H., Mau, A., Nomikou, E., & Seakins, A. (2017). Killing curiosity? An analysis of celebrated identity performances among teachers and students in nine London secondary science classrooms. Science Education, 101(5), 741–764. Archer, L., Dawson, E., DeWitt, J., Seakins, A., & Wong, B. (2015). “Science capital”: A conceptual, methodological, and empirical argument for extending Bourdieusian notions of capital beyond the arts. Journal of Research in Science Teaching, 52(7), 922–948. Archer, L., & DeWitt, J. (2015). Science aspirations and gender identity: Lessons from the ASPIRES project. In E. K. Henrisken, J. Dillon, & J. Ryder (Eds.), Understanding student participation and choice in science and technology education (pp. 89–102). Springer. Archer, L., DeWitt, J., & Osborne, J. (2015). Is science for us? Black students’ and parents’ views of science and science careers. Science Education, 99(2), 199–237. Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2010).“Doing” science versus “being”a scientist: Examining 10/11-year-old schoolchildren’s constructions of science through the lens of identity. Science Education, 94(4), 617–639. Archer, L., DeWitt, J., & Willis, B. (2014). Adolescent boys’ science aspirations: Masculinity, capital, and power. Journal of Research in Science Teaching, 51(1), 1–30. Archer, L., Moote, J., Francis, B., DeWitt, J., & Yeomans, L. (2017). The “exceptional” physics girl: A sociological analysis of multimethod data from young women aged 10–16 to explore gendered patterns of post-16 participation. American Educational Research Journal, 54(1), 88–126. Archer, L., Moote, J., Macleod, E., Francis, B., & DeWitt, J. (2020). ASPIRES 2: Young people’s science and career aspirations, age 10–19. UCL Institute of Education. Aschbacher, P. R., Ing, M., & Tsai, S. M. (2014). Is science me? Exploring middle school students’ STEM career aspirations. Journal of Science Education and Technology, 23(6), 735–743. Avraamidou, L. (2014). Studying science teacher identity: Current insights and future research directions. Studies in Science Education, 50(2), 145–179. Avraamidou, L. (2020a). “I am a young immigrant woman doing physics and on top of that I am Muslim”: Identities, intersections, and negotiations. Journal of Research in Science Teaching, 57(3), 311–341. Avraamidou, L. (2020b). Science identity as a landscape of becoming: Rethinking recognition and emotions through an intersectionality lens. Cultural Studies of Science Education, 15(2), 323–345. Aydeniz, M., & Kotowski, M. R. (2014). Conceptual and methodological issues in the measurement of attitudes towards science. The Electronic Journal for Research in Science & Mathematics Education, 18(3), 1–24. Bandura, A. (2006). Guide for constructing self-efcacy scales. Self-efcacy Beliefs of Adolescents, 5(1), 307–337. Bellocchi, A., Quigley, C., & Otrel-Cass, K. (Eds.). (2017). Exploring emotions, aesthetics and wellbeing in science education research (Vol. 13). Springer. Bellocchi, A., & Ritchie, S. M. (2015). “I was proud of myself that I didn’t give up and I did it”: Experiences of pride and triumph in learning science. Science Education, 99(4), 638–668. Bellocchi, A., Ritchie, S. M., Tobin, K., King, D. T., Sandhu, M., & Henderson, S. (2014). Emotional climate and high quality learning experiences in science teacher education. Journal of Research in Science Teaching, 51(10), 1301–1325. Birmingham, D., Calabrese Barton, A., McDaniel, A., Jones, J., Turner, C., & Rogers, A. (2017). “But the science we do here matters”: Youth-authored cases of consequential learning. Science Education, 101(5), 818–844. Blalock, C. L., Lichtenstein, M. J., Owen, S., Pruski, L., Marshall, C., & Toepperwein, M. (2008). In pursuit of validity: A comprehensive review of science attitude instruments. International Journal of Science Education, 30(7), 961–977. Bourdieu, P. (1986). Distinction: A social critique of the judgement of taste. Routledge and Kegan Paul. Bourdieu, P. (1990). The logic of practice. Stanford University Press. Bourdieu, P. (1992). Language and symbolic power. Polity Press. Bourdieu, P. (2010). Sociology is a martial art. New Press. Butler, J. (1993). Bodies that matter: On the discursive limits of sex. Routledge. Caiman, C., & Jakobson, B. (2019). The role of art practice in elementary school science. Science & Education, 28(1–2), 153–175. Calabrese-Barton, A., Kang, H., Tan, E., O’Neill, T. B., Bautista-Guerra, J., & Brecklin, C. (2013). Crafting a future in science: Tracing middle school girls’ identity work over time and space. American Educational Research Journal, 50(1), 37–75.

185

Russell Tytler and Joseph Paul Ferguson Carlone, H. B., Hufing, L. D., Tomasek, T., Hegedus, T. A., Matthews, C. E., Allen, M. H., & Ash, M. C. (2015). “Unthinkable” selves: Identity boundary work in a summer feld ecology enrichment program for diverse youth. International Journal of Science Education, 37(10), 1524–1546. Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187–1218. Carlone, H. B., Johnson, A., & Scott, C. M. (2015). Agency amidst formidable structures: How girls perform gender in science class. Journal of Research in Science Teaching, 52(4), 474–488. Carlone, H. B., Scott, C. M., & Lowder, C. (2014). Becoming (less) scientifc: A longitudinal study of students’ identity work from elementary to middle school science. Journal of Research in Science Teaching, 51(7), 836–869. Chappell, M. J., & Varelas, M. (2020). Ethnodance and identity: Black students representing science identities in the making. Science Education, 104(2), 193–221. Chen, S., & Wei, B. (2022). Development and validation of an instrument to measure high school students’ science identity in science learning. Research in Science Education, 52(1), 111–126. Cheng, M. H. M., & Wan, Z. H. (2016). Unpacking the paradox of Chinese science learners: Insights from research into Asian Chinese school students’ attitudes towards learning science, science learning strategies, and scientifc epistemological views. Studies in Science Education, 52(1), 29–62. Cheung, D. (2018). The key factors afecting students’ individual interest in school science lessons. International Journal of Science Education, 40(1), 1–23. Cooper, G., Berry, A., & Baglin, J. (2020). Demographic predictors of students’ science participation over the age of 16: An Australian case study. Research in Science Education, 50(1), 361–373. Davis, J. P., & Bellocchi, A. (2018). Objectivity, subjectivity, and emotion in school science inquiry. Journal of Research in Science Teaching, 55(10), 1419–1447. Dawson, E., Archer, L., Seakins, A., Godec, S., DeWitt, J., King, H., Mau, A., & Nomikou, E. (2020). Selfes at the science museum: Exploring girls’ identity performances in a science learning space. Gender and Education, 32(5), 664–681. Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. In P. A. M. Van Lange, A. W. Kruglanski, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 416–436). Sage Publications Ltd. Dewey, J. (1922). Habits as social functions. In Human nature and conduct: An introduction to social psychology (pp. 14–23). Modern Library. Dewey, J. (1934/1980). Art as experience. Perigee Books. Dewey, J. (1938/1997). Experience and education. Touchstone, Simon and Schuster. DeWitt, J., & Archer, L. (2015). Who aspires to a science career? A comparison of survey responses from primary and secondary school students. International Journal of Science Education, 37(13), 2170–2192. DeWitt, J., Archer, L., & Mau, A. (2016). Dimensions of science capital: Exploring its potential for understanding students’ science participation. International Journal of Science Education, 38(16), 2431–2449. DeWitt, J., Archer, L., & Moote, J. (2019). 15/16-year-old students’ reasons for choosing and not choosing physics at A level. International Journal of Science and Mathematics Education, 17(6), 1071–1087. DeWitt, J., Archer, L., & Osborne, J. (2014). Science-related aspirations across the primary – Secondary divide: Evidence from two surveys in England. International Journal of Science Education, 36(10), 1609–1629. Dong, Z., Li, M., Minstrell, J., & Cui, Y. (2020). Psychometric properties of science motivation questionnaire II-Chinese version in two waves of longitudinal data. Psychology in the Schools, 57(8), 1240–1256. Dou, R., Bhutta, K., Ross, M., Kramer, L., & Thamotharan, V. (2020). The efects of computer science stereotypes and interest on middle school boys’ career intentions. ACM Transactions - Computer Education, 20(3), 1–15. Dou, R., & Cian, H. (2021). The relevance of childhood science talk as a proxy for college students’ STEM identity at a Hispanic serving institution. Research in Science Education, 51(3), 1093–1105. Dou, R., Hazari, Z., Dabney, K., Sonnert, G.,  & Sadler, P. (2019). Early informal STEM experiences and STEM identity: The importance of talking science. Science Education, 103(3), 623–637. Drymiotou, I., Constantinou, C. P.,  & Avraamidou, L. (2021). Enhancing students’ interest in science and understandings of STEM careers: The role of career-based scenarios. International Journal of Science Education, 43(5), 717–736. Du, X., & Wong, B. (2019). Science career aspiration and science capital in China and UK: A comparative study using PISA data. International Journal of Science Education, 41(15), 2136–2155. Eccles, J. (2009). Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78–89. Fakoyede, S. J.,  & Otulaja, F. S. (2020). Beads and beadwork as cultural artifacts used in mediating learners’ agentic constructs in science classrooms: A case for place-based learning. Cultural Studies of Science Education, 15(1), 193–210.

186

Student Attitudes, Identity, and Aspirations Toward Science Ferguson, J. P., Tytler, R., & & White, P. (2021). The role of aesthetics in the teaching and learning of data modelling. International Journal of Science Education, 44(5), 753–774. Fortus, D. (2014). Attending to afect. Journal of Research in Science Teaching, 51(7), 821–835. Fortus, D., & Vedder-Weiss, D. (2014). Measuring students’ continuing motivation for science learning. Journal of Research in Science Teaching, 51(4), 497–522. Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. Fredricks, J. A., Hofkens, T., Wang, M.-T., Mortenson, E.,  & Scott, P. (2018). Supporting girls’ and boys’ engagement in math and science learning: A mixed methods study. Journal of Research in Science Teaching, 55(2), 271–298. Gardner, P. L. (1975). Attitudes to science. Studies in Science Education, 2(1), 1–41. Gee, J. P. (2000). Identity as an analytic lens for research in education. Review of Research in Education, 25(1), 99–125. Gilbert, A., & Byers, C. (2017). Wonder as a tool to engage preservice elementary teachers in science learning and teaching. Science Education, 101(6), 907–928. Glynn, S. M., Brickman, P., Armstrong, N.,  & Taasoobshirazi, G. (2011). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 48(10), 1159–1176. Glynn, S. M., Taasoobshirazi, G., & Brickman, P. (2009). Science motivation questionnaire: Construct validation with nonscience majors. Journal of Research in Science Teaching, 46(2), 127–146. Godec, S., King, H., & Archer, L. (2017). The science capital teaching approach: Engaging students with science, promoting social justice. UCL Institute of Education. Godec, S., King, H., Archer, L., Dawson, E., & Seakins, A. (2018). Examining student engagement with science through a Bourdieusian notion of feld. Science & Education, 27(5–6), 501–521. Godec, S., Patel, U., Archer, L., & Dawson, E. (2020). Young people’s tech identity performances: Why materiality matters. International Journal of STEM Education, 7(1), 1–12. Godwin, A., & Potvin, G. (2017). Pushing and pulling Sara: A case study of the contrasting infuences of high school and university experiences on engineering agency, and participation. Journal of Research in Science Teaching, 54(4), 439–462. Godwin, A., Potvin, G., Hazari, Z., & Lock, R. (2016). Identity, critical agency, and engineering: An afective model for predicting engineering as a career choice. Journal of Engineering Education, 105(2), 312–340. Großmann, N., & Wilde, M. (2020). Promoting interest by supporting learner autonomy: The efects of teaching behaviour in biology lessons. Research in Science Education, 50(5), 1763–1788. Gupta, A., Elby, A., & Danielak, B. A. (2018). Exploring the entanglement of personal epistemologies and emotions in students’ thinking. Physical Review Physics Education Research, 14(1), 010129. Hagay, G., & Baram-Tsabari, A. (2015). A strategy for incorporating students’ interests into the high-school science classroom. Journal of Research in Science Teaching, 52(7), 949–978. Halliday, M. (1978). Language as social semiotic. Edward Arnold. Hannigan, S., Wickman, P.-O., Ferguson, J. P., Prain, V., & Tytler, R. (2021). The role of aesthetics in learning science in an art-science lesson. International Journal of Science Education, 44(5), 797–814. Harré, R., & Van Langenhove, L. (1999). Positioning theory: Moral contexts of international action. Wiley. Hassan, S. S. S. (2018). Measuring attitude towards learning science in Malaysian secondary school context: Implications for teaching. International Journal of Science Education, 40(16), 2044–2059. Hazari, Z., Cass, C., & Beattie, C. (2015). Obscuring power structures in the physics classroom: Linking teacher positioning, student engagement, and physics identity development. Journal of Research in Science Teaching, 52(6), 735–762. Hazari, Z., Chari, D., Potvin, G., & Brewe, E. (2020). The context dependence of physics identity: Examining the role of performance/competence, recognition, interest, and sense of belonging for lower and upper female physics undergraduates. Journal of Research in Science Teaching, 57(10), 1583–1607. Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M.-C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: A gender study. Journal of Research in Science Teaching, 47(8), 978–1003. Hidi, S., & Renninger, K. A. (2006). The four phase model of interest development. Educational Psychologist, 41(2), 111–127. Hilts, A., Part, R., & Bernacki, M. L. (2018). The roles of social infuences on student competence, relatedness, achievement, and retention in STEM. Science Education, 102(4), 744–770. Hobbs, L., & Kelly, L. (2017). The heart of the educator: Aesthetic experience shaping knowledge, identity, and passion. In A. Bellocchi, C. Quigley, & K. Otrel-Cass (Eds.), Exploring emotions, aesthetics and wellbeing in science education research (pp. 55–82). Springer.

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Russell Tytler and Joseph Paul Ferguson Hoferber, N., Basten, M., Großmann, N., & Wilde, M. (2016). The efects of autonomy-supportive and controlling teaching behaviour in biology lessons with primary and secondary experiences on students’ intrinsic motivation and fow-experience. International Journal of Science Education, 38(13), 2114–2132. Höft, L., Bernholt, S., Blankenburg, J., & Winberg, M. (2019). Knowing more about things you care less about: Cross-sectional analysis of the opposing trend and interplay between conceptual understanding and interest in secondary school chemistry. Journal of Research in Science Teaching, 56(2), 184–210. Hong, J. C., Chang, C. H., Tsai, C. R., & Ta, K. H. (2019). How situational interest afects individual interest in a STEAM competition. International Journal of Science Education, 41(12), 1667–1681. Howard, M. A., & Kern, A. L. (2019). Conceptions of wayfnding: Decolonizing science education in pursuit of Native American success. Cultural Studies of Science Education, 14(4), 1135–1148. Jaber, L. Z., & Hammer, D. (2016). Learning to feel like a scientist. Science Education, 100(2), 189–220. Jack, B. M., & Lin, H.-S. (2017). Making learning interesting and its application to the science classroom. Studies in Science Education, 53(2), 137–164. Jack, B. M., Lin, H.-S., & Yore, L. D. (2014). The synergistic efect of afective factors on student learning outcomes. Journal of Research on Science Teaching, 51(8), 1084–1101. Jakobson, B., & Wickman, P.-O. (2008). The roles of aesthetic experience in elementary school science. Research in Science Education, 38(1), 45–66. Jakobson, B., & Wickman, P. O. (2015). What diference does art make in science? A comparative study of meaning-making at elementary school. Interchange, 46(4), 323–343. Jiang, F., & McComas, W. F. (2015). The efects of inquiry teaching on student science achievement and attitudes: Evidence from propensity score analysis of PISA data. International Journal of Science Education, 37(3), 554–576. Kane, J. M. (2016). Young African American boys narrating identities in science. Journal of Research in Science Teaching, 53(1), 95–118. Kang, H., Calabrese Barton, A., Tan, E., Simpkins, S. D., Rhee, H.-Y., & Turner, C. (2019). How do middle school girls of color develop STEM identities? Middle school girls’ participation in science activities and identifcation with STEM careers. Science Education, 103(2), 418–439. Kang, J.,  & Keinonen, T. (2017). The efect of inquiry-based learning experiences on adolescents’ sciencerelated career aspiration in the Finnish context. International Journal of Science Education, 39(12), 1669–1689. Kang, J., & Keinonen, T. (2018). The efect of student-centered approaches on students’ interest and achievement in science: Relevant topic-based, open and guided inquiry-based, and discussion-based approaches. Research in Science Education, 48(1), 865–885. Kang, J., Keinonen, T., & Salonen, A. (2019). Role of interest and self-concept in predicting science aspirations: Gender study. Research in Science Education, 51(2), 513–535. Kewalramani, S., & Phillipson, S. (2020). Parental role in shaping immigrant children’s subject choices and career pathway decisions in Australia. International Journal for Educational and Vocational Guidance, 20(1), 79–99. Kim, M. (2018). Understanding children’s science identity through classroom interactions. International Journal of Science Education, 40(1), 24–45. Krapp, A., & Prenzel, M. (2011). Research on interest in science: Theories, methods and fndings. International Journal of Science Education, 33(1), 27–50. Lau, K. C., & Ho, S. C. (2020). Attitudes towards science, teaching practices, and science performance in PISA 2015: Multilevel analysis of the Chinese and Western top performers. Research in Science Education, 51(2), 415–426. Lemke, J. (2013). Thinking about feeling: Afect across literacies and lives. In O. Erstad & J. Sefton-Green (Eds.), Learning lives: Transactions, technologies, and learner identity (pp. 57–69). Cambridge University Press. Lemke, J. (2015). Feeling and meaning: A unitary bio-semiotic account. In P. P. Trifonas (Ed.), International handbook of semiotics (pp. 589–616). Springer. Lima Junior, P., Anderhag, P., & Wickman, P. O. (2022). How does a science teacher distinguish himself as a good professional? An inquiry into the aesthetics of taste for teaching. International Journal of Science Education, 44(5), 815–832. Lindholm, M. (2018). Promoting curiosity? Possibilities and pitfalls in science education. Science & Education, 27(9–10), 987–1002. Liou, P.-Y. (2021). Students’ attitudes toward science and science achievement: An analysis of the diferential efects of science instructional practices. Journal of Research in Science Teaching, 58(3), 310–334. Liu, J., McMahon, M., & Watson, M. (2015). Parental infuence on mainland Chinese children’s career aspirations: Child and parental perspectives. International Journal for Educational and Vocational Guidance, 15(2), 131–143. Luce, M. R., & Hsi, S. (2015). Science-relevant curiosity expression and interest in science: An exploratory study. Science Education, 99(1), 70–97.

188

Student Attitudes, Identity, and Aspirations Toward Science Maltese, A. V., Melki, C. S., & Wiebke, H. L. (2014). The nature of experiences responsible for the generation and maintenance of interest in STEM. Science Education, 98(6), 937–962. Marginson, S., Tytler, R., Freeman, B., & Roberts, K. (2013). STEM: Country comparisons. Australian Council of Learned Academies. www.acola.org.au McKinley, E. (2005). Brown bodies, white coats: Postcolonialism, Maori women and science. Discourse: Studies in the Cultural Politics of Education, 26(4), 481–496. McKinley, E. (2016). STEM and indigenous learners [Paper presentation]. Research Conference 2016 - Improving STEM learning: What will it take? https://research.acer.edu.au/research_conference/RC2016/8august/14 Mejias, S., Thompson, N., Sedas, R. M., Rosin, M., Soep, E., Peppler, K., Roche, J., Wong, J., Hurley, M., Bell, P., & Bevan, B. (2021). The trouble with STEAM and why we use it anyway. Science Education, 105(2), 209–231. Millar, R., & Osborne, J. F. (Eds.). (1998). Beyond 2000: Science education for the future. King’s College London. Millar, V., Toscano, M., van Driel, J., Stevenson, E., Nelson, C., & Kenyon, C. (2019). University run science outreach programs as a community of practice and site for identity development. International Journal of Science Education, 41(18), 2579–2601. Moote, J., Archer, L., DeWitt, J.,  & MacLeod, E. (2019). Who has high science capital? An exploration of emerging patterns of science capital among students aged 17/18 in England. Research Papers in Education, 36(4), 402–422. Moote, J., Archer, L., DeWitt, J., & MacLeod, E. (2020). Comparing students’ engineering and science aspirations from age 10 to 16: Investigating the role of gender, ethnicity, cultural capital, and attitudinal factors. Journal of Engineering Education, 109(1), 34–51. Navarro, M., Förster, C., González, C., & González-Pose, P. (2016). Attitudes toward science: Measurement and psychometric properties of the Test of Science-Related Attitudes for its use in Spanish-speaking classrooms. International Journal of Science Education, 38(9), 1459–1482. Nguyen, T.-H., Gasman, M., Washington Lockett, A., & Peña, V. (2021). Supporting Black women’s pursuits in STEM. Journal of Research in Science Teaching, 58(6), 879–905. Oon, P. T., Cheng, M. M. W., & Wong, A. S. L. (2020). Gender diferences in attitude towards science: Methodology for prioritising contributing factors. International Journal of Science Education, 42(1), 89–112. Oon, P. T., & Fan, X. (2017). Rasch analysis for psychometric improvement of science attitude rating scales. International Journal of Science Education, 39(6), 683–700. Oon, P. T., & Subramaniam, R. (2018). Comparative study of middle school students’ attitudes towards science: Rasch analysis of entire TIMSS 2011 attitudinal data for England, Singapore and the U.S.A. as well as psychometric properties of attitudes scale. International Journal of Science Education, 40(3), 268–290. Osborne, J. F., & Collins, S. (2001). Pupils’ views of the role and value of the science curriculum: A focus-group study. International Journal of Science Education, 23(5), 441–468. Osborne, J. F., Simon, S.,  & Collins, S. (2003). Attitudes towards science: A review of the literature and its implications. International Journal of Science Education, 25(9), 1049–1079. Patulny, R., Bellocchi, A., Olson, R. E., Khorana, S., McKenzie, J.,  & Peterie, M. (2019). Emotions in late modernity. Routledge. Peirce, C. S. (1894/1998). What is a sign? In N. Houser, A. De Tienne, J. R. Eller, C. L. Clark, A. C. Lewis, & D. B. Davis (Eds.), The essential Peirce – Selected philosophical writings (1893–1913) (Vol. 2, pp. 4–10). Indiana University Press. Peirce, C. S. (1905/1998). What pragmatism is. In N. Houser, A. De Tienne, J. R. Eller, C. L. Clark, A. C. Lewis, & D. B. Davis (Eds.), The essential Peirce – Selected philosophical writings (1893–1913) (Vol. 2, pp. 331– 345). Indiana University Press. Peirce, C. S. (1907/1998). Pragmatism. In N. Houser, A. De Tienne, J. R. Eller, C. L. Clark, A. C. Lewis, & D. B. Davis (Eds.), The essential Peirce – Selected philosophical writings – Volume 2 (1893–1913) (pp. 398–433). Indiana University Press. Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359–376. Pekrun, R., & Bühner, M. (2014). Self-report measures of academic emotions. In R. Pekrun & L. LinnenbrinkGarcia (Eds.), International handbook of emotions in education (pp. 561–579). Routledge. Pekrun, R., Frenzel, A. C., Goetz, T., & Perry, R. P. (2007). The control-value theory of achievement emotions: An integrative approach to emotions in education. In P. A. Schutz & R. Pekrun (Eds.), Emotions in education (pp. 13–36). Academic Press. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questinnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. Perera, L. D. H. (2014). Parents’ attitudes towards science and their children’s science achievement. International Journal of Science Education, 36(18), 3021–3041.

189

Russell Tytler and Joseph Paul Ferguson Phillips, T. B., Ballard, H. L., Lewenstein, B. V., & Bonney, R. (2019). Engagement in science through citizen science: Moving beyond data collection. Science Education, 103(3), 665–690. Potvin, P., & Hasni, A. (2014). Interest, motivation and attitude towards science and technology at K-12 levels: A systematic review of 12 years of educational research. Studies in Science Education, 50(1), 85–129. Potvin, P., Hasni, A., Sy, O., & Riopel, M. (2020). Two crucial years of science and technology schooling: A longitudinal study of the major infuences on and interactions between self-concept, interest, and the intention to pursue S&T. Research in Science Education, 50(5), 1739–1761. Prain, V. (2020). Methodological challenges in researching students’ aesthetic responses in science inquiry. In P. White, R. Tytler, J. C. Clark, & J. Ferguson (Eds.), Methodological approaches to STEM education research – Volume 1 (pp. 21–33). Cambridge Scholars. Pugh, K. J., & Girod, M. (2007). Science, art, and experience: Constructing a science pedagogy from Dewey’s aesthetics. Journal of Science Teacher Education, 18(1), 9–27. Rahm, J., & Moore, J. C. (2016). A case study of long-term engagement and identity-in-practice: Insights into the STEM pathways of four underrepresented youths. Journal of Research in Science Teaching, 53(5), 768–801. Raker, J. R., Gibbons, R. E., & Cruz-Ramírez de Arellano, D. (2019). Development and evaluation of the organic chemistry-specifc achievement emotions questionnaire (AEQ-OCHEM). Journal of Research in Science Teaching, 56(2), 163–183. Reilly, D., Neumann, D. L., & Andrews, G. (2019). Investigating gender diferences in mathematics and science: Results from the 2011 Trends in Mathematics and Science Survey. Research in Science Education, 49(1), 25–50. Rioux, J., & Smith, G. (2019). Both-ways science education: Place and context. Learning Communities: International Journal of Learning in Social Contexts, 25, 90–105. Ritchie, S. M., Hudson, P., Bellocchi, A., Henderson, S., King, D.,  & Tobin, K. (2016). Evolution of selfreporting methods for identifying discrete emotions in science classrooms. Cultural Studies of Science Education, 11(3), 577–593. Rivera Maulucci, M. S. (2013). Emotions and positional identity in becoming a social justice science teacher: Nicole’s story. Journal of Research in Science Teaching, 50(4), 453–478. Rorty, R. (1991). Objectivity, relativism, and truth – Philosophical papers (Vol. 1). Cambridge University Press. Said, Z., Summers, R., Abd-El-Khalick, F.,  & Wang, S. (2016). Attitudes toward science among grades 3 through 12 Arab students in Qatar: Findings from a cross-sectional national study. International Journal of Science Education, 38(4), 621–643. Sáinz, M.,  & Müller, J. (2018). Gender and family infuences on Spanish students’ aspirations and values in STEM felds. International Journal of Science Education, 40(2), 188–203. Salta, K., & Koulougliotis, D. (2015). Assessing motivation to learn chemistry: Adaptation and validation of Science Motivation Questionnaire II with Greek secondary school students. Chemistry Education Research and Practice, 16(2), 237–250. Savelsbergh, E. R., Prins, G. T., Rietbergen, C., Fechner, S., Vaessen, B. E., Draijer, J. M.,  & Bakker, A. (2016). Efects of innovative science and mathematics teaching on student attitudes and achievement: A meta-analytic study. Educational Research Review, 19(1), 158–172. Schumm, M. F., & Bogner, F. X. (2016). Measuring adolescent science motivation. International Journal of Science Education, 38(3), 434–449. Sfard, A., & Prusak, A. (2005). Telling identities: In search of an analytic tool for investigating learning as a culturally shaped activity. Educational Researcher, 34(4), 14–22. Sha, L., Schunn, C., & Bathgate, M. (2015). Measuring choice to participate in optional science learning experiences during early adolescence. Journal of Research in Science Teaching, 52(5), 686–709. Shaby, N., & Vedder-Weiss, D. (2020). Science identity trajectories throughout school visits to a science museum. Journal of Research in Science Teaching, 57(5), 733–764. Sheldrake, R. (2016). Confdence as motivational expressions of interest, utility, and other infuences: Exploring under-confdence and over-confdence in science students at secondary school. International Journal of Educational Research, 76(1), 50–65. Sheldrake, R. (2020). Changes in children’s science-related career aspirations from age 11 to age 14. Research in Science Education, 50(4), 1435–1464. Sheldrake, R., Mujtaba, T., & Reiss, M. J. (2019). Students’ changing attitudes and aspirations towards physics during secondary school. Research in Science Education, 49(6), 1809–1834. Shin, J., Lee, H., McCarthy-Donovan, A., Hwang, H., Yim, S., & & Seo, E. (2015). Home and motivational factors related to science-career pursuit: Gender diferences and gender similarities. International Journal of Science Education, 37(9), 1478–1503.

190

Student Attitudes, Identity, and Aspirations Toward Science Siry, C., & Brendel, M. (2016). The inseparable role of emotions in the teaching and learning of primary school science. Cultural Studies of Science Education, 11(3), 803–815. Summers, R., & Abd-El-Khalick, F. (2018). Development and validation of an instrument to assess student attitudes toward science across grades 5 through 10. Journal of Research in Science Teaching, 55(2), 172–205. Summers, R., & Abd-El-Khalick, F. (2019). An exploration of Illinois students’ attitudes toward science using multivariate multilevel modelling with a cross-sectional sample of responses from grades 5 through 10. Journal of Research in Science Teaching, 56(8), 1106–1134. Swarat, S., Ortony, A., & Revelle, W. (2012). Activity matters: Understanding student interest in school science. Journal of Research in Science Teaching, 49(4), 515–537. Tobin, K., Ritchie, S. M., Hudson, P., Oakley, J., & Mergard, V. (2013). Relationships between EC and the fuency of classroom interactions. Learning Environments Research, 16(1), 71–89. Toma, R. B. (2022). Perceived difculty of school science and cost appraisals: A valuable relationship for the STEM pipeline? Research in Science Education, 52(2), 567–582. Tomas, L., Rigano, D., & Ritchie, S. M. (2016). Students’ regulation of their emotions in a science classroom. Journal of Research in Science Teaching, 53(2), 234–260. Turner, J. H. (2007). Human emotions: A sociological theory. Routledge. Tytler, R. (2014). Attitudes, identity, and aspirations toward science. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 82–103). Routledge. Tytler, R., & Osborne, J. (2012). Student attitudes and aspirations towards science. In B. Fraser, K. Tobin & C. McRobbie (Eds.), Second international handbook of science education (pp. 597–625). Springer. Tytler, R., Prain, V.,  & Hannigan, S. (2022). Expanding the languages of science and how they are learnt. Research in Science Education, 52(1), 379–392. Varelas, M. (Ed.). (2012). Identity construction and science education research. Sense Publishers. Varelas, M., Settlage, J., & Mensah, F. M. (2015). Explorations of the structure-agency dialectic as a tool for framing equity in science education. Journal of Research in Science Teaching, 52(4), 439–447. Vedder-Weiss, D. (2018). Won’t you give up your snack for the sake of science Emerging science identities in family everyday interaction. Journal of Research in Science Teaching, 55(8), 1211–1235. Vennix, J., den Brok, P., & Taconis, R. (2018). Do outreach activities in secondary STEM education motivate students and improve their attitudes towards STEM? International Journal of Science Education, 40(11), 1263–1283. Villanueva Baselga, S., Marimon Garrido, O., & González Burón, H. (2022). Drama-based activities for STEM education: Encouraging scientifc aspirations and debunking stereotypes in secondary school students in Spain and the UK. Research in Science Education, 52(1), 173–190. Vincent-Ruz, P., & Schunn, C. D. (2018). The nature of science identity and its role as the driver of student choices. International Journal of STEM Education, 5(1), 1–12. Vossen, T. E., Henze, I., Rippe, R. C. A, Van Driel, J. H., & De Vries, M. J. (2018). Attitudes of secondary school students towards doing research and design activities. International Journal of Science Education, 40(13), 1629–1652. Waide-James, K., & Schwartz, R. (2019).“I don’t think it’s science:” African American girls and the fgured world of school science. Journal of Research in Science Teaching, 56(6), 679–706. Wan, Z. H. (2021). Exploring the efects of intrinsic motive, utilitarian motive, and self-efcacy on students’ science learning in the classroom using the expectancy-value theory. Research in Science Education, 51(3), 647–659. Wan, Z. H., & Lee, J. C. K. (2017). Hong Kong secondary school students’ attitudes towards science: A study of structural models and gender diferences. International Journal of Science Education, 39(5), 507–527. Wickman, P. O. (2006). Aesthetic experience in science education: Learning and meaning-making as situated talk and action. Routledge. Wickman, P. O. (2017). Back to the drawing board: Examining the philosophical foundations of educational research on aesthetics and emotions. In A. Bellocchi, C. Quigley, & K. Otrel-Kass (Eds.), Exploring emotions, aesthetics and wellbeing in science education research (pp. 9–37). Springer. Wickman, P.-O., & Östman, L. (2002a). Learning as discourse change: A sociocultural mechanism. Science Education, 86(5), 601–623. Wickman, P.-O., & Östman, L. (2002b). Induction as an empirical problem: How students generalize during practical work. International Journal of Science Education, 24(5), 465–486. Wickman, P.-O., Prain, V., & Tytler, R. (2022). Aesthetics, afect, and making meaning in science education: An introduction. International Journal of Science Education, 44(5), 717–734. Wittgenstein, L. (1953/1958). Philosophical investigations (G. E. M. Anscombe, Trans.). Basil Blackwell Limited.

191

Russell Tytler and Joseph Paul Ferguson Wong, B. (2015). Careers “from” but not “in” science: Why are aspirations to be a scientist challenging for minority ethnic students? Journal of Research in Science Teaching, 52(7), 979–1002. Wong, B. (2016). Minority ethnic students and science participation: A qualitative mapping of achievement, aspiration, interest and capital. Research in Science Education, 46(1), 113–127. Zembylas, M. (2021). The afective turn in educational theory. In G. W. Noblitt (Ed.), Oxford research encyclopedia of education. Oxford University Press. Zembylas, M., & Schutz, P. A. (Eds.). (2016). Methodological advances in research on emotion and education. Springer.

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7 LEARNING ENVIRONMENTS Barry J. Fraser

Introduction There has been growing recognition worldwide that the learning environment is centrally important in education (OECD, 2013, 2017; Zandvliet & Fraser, 2019). As identifed in the executive summary of the OECD’s (2013) Innovative Learning Environments, a learning environment is “an organic, holistic concept – an ecosystem that includes the activity and the outcomes of the learning. Some of the innovations examined are in places called schools and others are not”. Undoubtedly, science education researchers have contributed enormously to the establishment, evolution, growth, and international spread of the feld of learning environments. These many contributions  – those particularly providing convincing evidence that the learning environment infuences a broad range of students’ outcomes and therefore that a positive learning environment is both a worthwhile end itself and a means to a valuable end – have been extensively reviewed (Fraser, 1994, 2012a, 2012b, 2014; Zandvliet & Fraser, 2019). It is important to delineate clearly what the central focus of this chapter is and is not. The central purpose is to review publications in the English language that report research from the feld of learning environments. Therefore, the chapter excludes descriptions of novel learning environments, however interesting, unless they are the foci of published learning environment studies. The chapter deliberately builds upon Fraser’s (2014) corresponding chapter in the previous version of this handbook to provide a consolidated, updated, holistic, integrated, and complete, not an add-on, portrayal of learning environments research. In structuring this chapter’s review of past learning environment research, the following two major sections are used: • •

Assessment of learning environments Established, emerging, and future lines of research on learning environments

The long-term success of any new feld requires not only strong research traditions but also strong support structures. Therefore, in 1984, the American Educational Research Association (AERA) Special Interest Group (SIG) on Learning Environments was established. This highly successful and widely international SIG has its own program each year at the AERA’s annual meeting and, currently, each AERA annual meeting program has over 100 entries in its index under the heading “learning environments”. The next landmark in the evolution of the feld was the establishment

DOI: 10.4324/9780367855758-9

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in 1998 of Learning Environments Research: An International Journal published by Springer Nature (previously Kluwer). In its 25th volume in 2022, this journal is rated in Scimago Quadrant 1 in several felds, including education, and is included in Web of Science. A decade after the birth of this journal, the next landmark was the establishment in 2008 of the book series Advances in Learning Environments Research, published by Brill Sense (e.g., Aldridge & Fraser, 2008; Wubbels et al., 2012; Zandvliet & Fraser, 2019).

Assessment of Learning Environments A wide range of research methods can be used for assessing and investigating learning environments, including direct observations, surveys of students’ and teachers’ perceptions, ethnographic and phenomenological approaches, natural inquiry, case studies, narratives, and behavior settings (Fraser, 2012b). A comprehensive and insightful discussion of numerous qualitative research methods is provided by Erickson (2012). These methods can be diferentiated into “low-inference” measures that tap into specifc explicit phenomena and “high-inference” measures that involve respondents making judgments about the meaning of events within a learning environment (Rosenshine, 1970). Tobin and Fraser (1988) advocate combining qualitative and quantitative approaches in mixedmethods approaches to learning environment research. Using multiple methods not only allows the unique strengths of each diferent method to emerge, but also reduces or overcomes each method’s weaknesses. As well, triangulating fndings emerging from the use of diferent methods strengthens confdence in those fndings. “We cannot envision why learning environment researchers would opt for either qualitative or quantitative data, and we advocate the use of both in an efort to obtain credible and authentic outcomes” (Tobin & Fraser, 1998, p. 639). Although the take-up of this recommendation to use mixed methods in learning environment research was somewhat slow in the ensuing years, there were notable exceptions. As part of a crossnational investigation of learning environments in Taiwan and Australia, Aldridge et  al. (1999) combined the use of a popular questionnaire (quantitative data) with observations, interviews, and narrative stories (qualitative data). It is noteworthy that Aldridge et al.’s article is reproduced in Creswell and Plano Clark’s (2007) seminal book Designing and Conducting Mixed Methods Research as an exemplary application of mixed methods. Guided by Deci and Ryan’s (2002) self-determination theory, Cho et al. (2021) used a sequential explanatory mixed-methods approach in exploring how autonomy-supportive learning environments promoted 356 Asian international students’ academic adjustment. The quantitative phase focused on relationships between autonomy-supportive environments and afective, behavioral, and cognitive learning components. When results from the quantitative component were explored further through follow-up interviews, autonomy-supportive environments were found to satisfy international students’ basic psychological needs by decreasing their language anxiety and increasing their classroom participation and adaptive perspectives about classroom assessment. Also drawing on the self-determination theory of Ryan and Deci (2000), Glassner (2022) designed a self-determined learning environment for an innovative teacher education program in Israel. When data were gathered via semi-structured interviews and presented as students’ stories, together with students’ presentations that were subjected to deductive thematic analysis, Glassner concluded that this program helped students to advance their self-capacity, autonomy, connections with others, and responsibility for learning. A later subsection of this chapter entitled “Improving Learning Environments” identifes as an important more-recent trend teachers’ action research aimed at improving their learning environments. This involves mixed methods in that questionnaires are used to assess students’ perceptions of actual and preferred learning environment, after which this is supplemented by classroom observations, case studies, vignettes, and narratives.

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Another advantage of using mixed methods in learning environment research is that direct observations by external observers are widely recognized in psychological literature as being somewhat diferent and distinct from milieu inhabitants’ apprehension of an environment (Jessor & Jessor, 1973). For this reason, Murray (1938) introduced the terms “alpha press” and “beta press” to distinguish between externally observed and internally perceived environments. Because of these diferences between observed and perceived environments, there are merits in using both in mixed-methods designs and triangulating the data obtained. Despite the desirability of using mixed methods in learning environment research, reviews show that the most frequently used method of assessment in past studies has been the use of questionnaires that tap students’ and teachers’ perceptions (Fraser, 2012b, 2014, 2019). In addition to considerations of economy with large samples, assessing learning environments in terms of the shared perceptions of students and teachers characterizes settings through the eyes of the participants and captures data that observers possibly could miss or consider unimportant (Fraser, 2014). “Few felds of educational research have such a rich diversity of valid, economical and widely-applicable assessment instruments as the feld of learning environments” (Fraser, 1998, p. 7). Because of the central role that questionnaires have played in past learning environment research, both as the sole method of assessment and in mixed-methods studies, this section provides readers with ready access to some of these economical and validated surveys by providing a historical review of their development and use. The Appendix lists eight historically signifcant or currently used classroom learning environment questionnaires and provides the name of each scale in each instrument, the level (primary, secondary, higher education) for which each instrument is suited, the number of items in each scale, and the classifcation of each scale according to the Moos (1974) highly regarded scheme for classifying human environments into three basic types of dimension: relationship dimensions (which identify the nature and intensity of personal relationships within the environment and assess the extent to which people are involved in the environment and support and help each other), personal development dimensions (which assess basic directions along which personal growth and self-enhancement tend to occur), and system maintenance and system change dimensions (which involve the extent to which the environment is orderly, clear in expectation, maintains control, and is responsive to change). The historical and chronological development of these surveys is highlighted in the following subsections.

Learning Environment Inventory (LEI) and Classroom Environment Scale (CES) in the United States The historical beginnings of contemporary learning environments research usually are attributed to independent research in the United States conducted by Rudolf Moos, who pioneered the use of participant perceptions of various human environments, and Herbert Walberg. Research and evaluation related to Harvard Project Physics led to the development of the Learning Environment Inventory (LEI, Anderson & Walberg, 1968; Walberg, 1979), whereas Moos developed the Classroom Environment Scale (CES, Moos, 1979; Moos & Trickett, 1974), which applied his work in other human environments to school settings. One of Moos’s (1974) enduring contributions is that the same three fundamental dimensions characterize diverse human environments, including hospital wards, work settings, families, and schools. Although historically signifcant, the LEI is seldom used in contemporary learning environment research. However, the My Class Inventory (MCI), a simplifed version of the LEI for children aged 8–12 years, is still used today because of its low reading level. The MCI, which has only fve scales (cohesiveness, friction, satisfaction, difculty, and competitiveness) and simplifed wording, has been modifed and cross-validated in Australia (D. L. Fisher  & Fraser, 1981), in Texas (Scott Houston et al., 2008), in Brunei with large samples of 1,565 lower-secondary students (Majeed et al., 2002),

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in Washington state with 2,835 grade 4–6 students (Sink & Spencer, 2005), and in Singapore with 1,512 grade 5 students (S. C. Goh & Fraser, 1998). Early Australian research with the CES, involving 116 grade 8 and 9 science classes, made several signifcant contributions. First, the CES was cross-validated for use in Australia (D. L. Fisher & Fraser, 1983a). Second, interesting patterns of diferences were identifed between teachers’ and students’ perceptions of actual and preferred learning environments (D. L. Fisher & Fraser, 1983b): both students and teachers preferred a more favorable learning environment than the one perceived as being actually present; and teachers perceived the same learning environment more favorably than did their students (the “rose-colored glasses” phenomenon). Third, associations were established between student attitudes and inquiry skills and the learning environment, especially order/organization, teacher support, and innovation (Fraser & D. L. Fisher, 1982). Fourth, Fraser and D. L. Fisher (1983) utilized a person–environment ft perspective to establish that students achieve cognitive and afective outcomes better when in their preferred learning environment. This research not only supported the importance of the actual learning environment, but also suggested that changing actual learning environments to better align with students’ preferences also could lead to additional improvements in student outcomes.

Individualized Classroom Environment Questionnaire (ICEQ) and College and University Environment Inventory (CUCEI) in Australia Before long, the ideas of Walberg and Moos spread to Australia. The frst major new questionnaire development undertaken in the feld of learning environments in Australia focused on the Individualised Classroom Environment Questionnaire (ICEQ), which grew out of awareness that the LEI and CES assess the environments of teacher-centered settings and therefore are not ideal for student-centered, individualized and inquiry-based settings. The ICEQ’s fve scales of personalisation, participation, independence, investigation, and diferentiation assess dimensions that distinguish individualized settings from conventional ones (Fraser, 1990). In a literature review entitled “What about tertiary climate”, Alansari and Rubie-Davies (2020) laments the underrepresentation of a higher-education studies in learning environment research compared with the volume of research at the primary- and secondary-school levels. Therefore, the development of the College and University Classroom Environment Inventory (CUCEI) is signifcant (Fraser & Treagust, 1986; Fraser et al., 1986). The CUCEI was validated originally with 372 Australian university students in 34 classes (Fraser & Treagust, 1986) and later cross-validated and used in the United States to evaluate inverted/fipped instruction (Strayer, 2012) and in the United Arab Emirates to evaluate teaching strategies for adults who experienced childhood difculties in learning mathematics (Hasan & Fraser, 2015).

Questionnaire on Teacher Interaction (QTI) in the Netherlands Simultaneously with the research in Australia, programmatic learning environment research emerged in the Netherlands with a focus specifcally on the interaction between teachers and students in the classroom (i.e., only the relationship dimension of Moos’s classifcation) and involving use of the Questionnaire on Teacher Interaction (QTI; Wubbels  & Brekelmans, 2012; Wubbels et al., 2012; Wubbels & Levy, 1993). This questionnaire is based on a model of teacher interpersonal behavior with the two dimensions of infuence (dominance–submission) and proximity (opposition–cooperation) and its eight scales assess leadership (e.g., “This teacher explains things clearly”), helping/friendly (e.g., “This teacher is friendly”), understanding (e.g., “If we have something to say, this teacher will listen”), student responsibility/freedom (e.g., “This teacher gives us much free time in class”), uncertain (e.g., “This teacher seems uncertain”), dissatisfed (e.g., “This teacher is

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suspicious”), admonishing (“This teacher is impatient”), and strict (“This teacher is strict”) behavior. It is noteworthy that some QTI scales have been renamed recently (leadership to directing, student responsibility/freedom to compliant, admonishing to confrontational, and strict to imposing) by Wubbels et al. (2016). An extensive study identifed changes in teacher interpersonal behavior during the professional career, with teachers with 6–10 years of experience having the best relationships with their students (in terms of exhibiting leadership, friendly, and strict behaviors) and promoting achievement and positive attitudes (Brekelmans et al., 2005). Research on teacher–student interaction involving use of the QTI spread to many countries, including Australia (Henderson et al., 2000), Korea (Kim et al., 2000), and Singapore (S C. Goh & Fraser, 1998; Quek et al., 2005). In Brunei Darussalam, a Malay version of the QTI was validated with 3,104 primary-school science students in 136 classrooms. When associations between students’ perceptions of their teachers’ interpersonal behaviors and their end-of-year science achievement were investigated, students’ perceptions of cooperative behaviors were positively correlated with achievement, while submissive behaviors were negatively correlated (den Brok et al., 2005; Scott & D. L. Fisher, 2004). When Koul and D. L. Fisher (2005) cross-validated English versions of the QTI and WIHIC in Jammu, India, among 1,021 students from 31 classes, they found that Kashmiri students perceived their learning environments more positively than did students from other cultural groups. Recently, Sivan and Cohen (2021) used smallest space analysis to examine the structure of a Chinese version of the QTI among 612 secondary-school students in Hong Kong. Results generally replicated the QTI’s circumplex structure, but six sectors rather than the original eight sectors emerged. Use of a nonlinear dynamical systems (NDS) approach was pioneered to investigate how teacher–student relationships develop in real time at the micro level (Pennings et al., 2014). These researchers concluded that the NDS approach has potential for further future research into the connection between teacher–student relationships and real-time teacher interpersonal behavior.

Science Laboratory Environment Inventory (SLEI) and Constructivist Learning Environment Survey (CLES) Because of the importance of laboratory settings in science education, the Science Laboratory Environment Inventory (SLEI) was developed specifcally for laboratory settings at the senior high school or higher-education levels (Fraser et al., 1995). The SLEI has fve seven-item scales (student cohesiveness, open-endedness, integration, rule clarity, and material environment). The initial feld testing and validation of the SLEI was unusual in that it simultaneously involved 5,477 students in 269 classes in six diferent countries (the United States, Canada, England, Israel, Australia, and Nigeria) (Fraser et al., 1995). Later, it was cross-validated with Australian students (Fisher et al., 1997; Fraser & McRobbie, 1995), 761 American high-school biology students (Lightburn & Fraser, 2007) and 1,592 grade 10 Singaporean chemistry students (Wong & Fraser, 1996). A translated and validated Korean-language version of the SLEI (Fraser & Lee, 2009) was used with a sample of 440 grade 10 and 11 students in 13 classes. As well as replicating past fndings of outcome–environment associations, diferences were identifed between the learning environment perceptions of science students in three diferent streams (science-independent, science-oriented, and humanities). Teacher–student interactions in Korean senior high-school science refected the general image of the youth–elder relationship in society, as well as the senior high school’s unique nature of “teachers directing and students obeying”. In response to the popularity of constructivism in science education in the 1990s, Taylor et al. (1997) developed the CLES to assist researchers and teachers to assess the degree to which a classroom’s environment is consistent with a constructivist epistemology. For the CLES’s 35 items, Taylor and colleagues (1997) reported sound validity with both Australian and American students. Later,

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the CLES was cross-validated in science classes with 1,079 students in 59 classes in Texas (Nix et al., 2005), 739 grade K–3 students in Miami (Peiro & Fraser, 2009), 1,081 students in 50 classes in Australia and 1,879 students in 50 classes in Taiwan (Aldridge et al., 2000), and 1,864 South African students in 43 classes (Aldridge et al., 2004). Collaboration between Korean and Australian science education researchers involved the translation, validation, and use of the CLES (Kim et al., 1999) with 1,083 science students in 12 schools, as well as the use of the What Is Happening In this Class? questionnaire and QTI (Kim et al., 2000) with a sample of 543 Grade 8 science students in the same 12 schools. This study paved the way for later learning environment research in Korea by cross-validating a Korean-language version of three well-established questionnaires, as well as replicating past studies of associations between student outcomes and the nature of Korean science learning environments. Kwan (2020) developed a shorter 25-item version of the CLES in the Chinese language (C-CLES) and validated it using both exploratory and confrmatory factor analyses with a convenience sample of 967 grade 9 students in Hong Kong. Wan and Cheng (2018) developed a modifed Chinese-language version of the CLES to assess the seven scales of personal relevance, uncertainty, skeptical voice, shared control, student negotiation, challenging task, and multiple perspectives. When Wan (2022) used this classroom environment instrument together with a parallel family environment instrument with 2,189 secondary students in Hong Kong, the classroom learning environment was more strongly related to student critical thinking than was the family learning environment.

What Is Happening In This Class? (WIHIC) The WIHIC is the most widely used learning environment questionnaire in the world today. Analysis of data from 1,081 Australian students in 50 classes and 1,879 Taiwanese students in 50 classes (Aldridge & Fraser, 2000; Aldridge et al., 1999) led to a fnal form of the WIHIC containing the following seven eight-item scales: student cohesiveness (e.g., “I make friendships among students in this class”), teacher support (e.g., “The teacher talks with me”), involvement (e.g., “I give my opinions during class discussions”), investigation (e.g., “I explain the meaning of statements, diagrams and graphs”), task orientation (e.g., “I know the goals of this class”), cooperation (e.g., “I work with other students on projects in this class”) and equity (e.g., “I get the same opportunity to answer questions as other students”). For a sample of 3,980 high school students from Australia, Britain, and Canada, confrmatory factor analysis supported the seven-scale a priori structure of the WIHIC (Dorman, 2003). All items loaded strongly on their own scale, although model ft indices revealed a degree of scale overlap. The factor structure was invariant for country, grade level and gender. Overall, this study strongly supported the international applicability of the WIHIC as a valid measure of psychosocial learning environment. Subsequently, in another study using multitrait–multimethod modeling, Dorman (2008) reported further evidence supporting the validity of actual and preferred forms of the WIHIC. Fraser (2019) listed 22 studies involving the cross-validation and use of the WIHIC in 13 countries and 12 languages. Two studies are examples of cross-national research involving Taiwan and Australia (Aldridge & Fraser, 2000; Aldridge et al., 1999) and Indonesia and Australia (Fraser et al., 2010). Four studies involved the use of WIHIC in English in Singapore (Chionh & Fraser, 2009; S. F. Goh & Fraser, 2016; Lim & Fraser, 2018) and India (Koul & D. L. Fisher, 2005). Nine studies involved the use of translations of the WIHIC into other languages, namely, Arabic (Afari et al., 2013; Alzubaida et al., 2016), Chinese (Bi, 2015; Liu & Fraser, 2013), Korean (Baek & Choi, 2002; Kim et al., 2000), the IsiZulu language of South Africa (Aldridge et al., 2009), Myanmar (Khine et al., 2018), and Greek (Charalampous & Kokkinos, 2017). The last six entries in Fraser (2019) are studies that involved the use of the WIHIC in the United States, including two studies in New York

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(Cohn & Fraser, 2016; Wolf & Fraser, 2008), two studies in California (Martin-Dunlop & Fraser, 2008; Skordi & Fraser, 2019), and two studies in Florida (Helding & Fraser, 2013; Zaragoza & Fraser, 2017). Although the six studies in the United States all involved the use of an English-language version of the WIHIC, Helding and Fraser (2013) ofered students the option of responding in either Spanish or English. For each study involving the WIHIC in Fraser (2019), details are provided not only of the country and language involved, but also the size of and nature of the sample, the observation that every study reported evidence to support the WIHIC’s factorial validity and internal consistency reliability, and identifcation of the unique contributions of each study. For the frst time in any study, parents’ perceptions were utilized in conjunction with students’ perceptions in investigating science learning environments among grade 4 and 5 science students in south Florida (Allen & Fraser, 2007). The WIHIC was modifed for young students and their parents and administered to 520 students and 120 parents. Both students and parents preferred a more positive learning environment than the one perceived to be actually present, but efect sizes for actual–preferred diferences were appreciably larger for parents than for students. With a sample of 78 parents and their 172 kindergarten science students in Miami, Robinson and Fraser (2013) used a simplifed Spanish version of the WIHIC to reveal that, relative to students, parents perceived a more favorable learning environment but preferred a less favorable environment. Aldridge and McChesney (2021) developed a questionnaire that can be used to assess and investigate parents’ and caregivers’ perceptions of school environment and validated it with a sample of 1,276 respondents. In the Greek language, the How Chemistry Class is Working (HCCW) questionnaire includes selected WIHIC scales and was used to identify diferences between Greek and Cypriot students (Giallousi et al., 2010). More recently in Greece, Charalampous and Kokkinos (2017) reported a particularly thorough attempt to develop and validate the new elementary-school G-EWIHIC in the Greek language. The WIHIC has been modifed to form the NWIHIC (New What Is Happening In this Class?) and validated among 2,556 grade 5–9 students from nine schools in six districts of mainland China (Cai et al., 2021). The NWIHIC consists of six scales from the original WIHIC (student cohesiveness, teacher support, involvement, task orientation, cooperation, and equity) plus the two new scales of diferentiated instruction and ongoing assessment. The validity of the NWIHIC was established using principal component analysis (PCA), confrmatory factor analysis (CFA), and reliability analysis. The seven scales of the WIHIC have been included along with three new scales (namely, differentiation, computer usage, and young adult ethos) in the Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI) (Aldridge et  al., 2004; Aldridge  & Fraser, 2008). Based on an Australian sample of 2,317 students in 166 classes, Aldridge and Fraser (2008) reported strong factorial validity and internal consistency reliability for the TROFLEI. Aldridge et al. (2004) used multitrait–multimethod modeling with TROFLEI responses from a sample of 1,249 students (772 from Western Australia and 477 from Tasmania). When the ten TROFLEI scales were used as traits and the actual and preferred forms of the instrument as methods, results supported the TROFLEI’s construct validity and sound psychometric properties, as well as indicating that the actual and preferred forms share a common structure. The TROFLEI recently was translated, validated, and used in Turkey with a sample of 980 grade 9–12 students. The English-language version was used with 130 grade 9–12 students in the United States (A. G. Welch et al., 2012). For both actual and preferred forms and for both Turkey and the United States, the TROFLEI exhibited sound reliability and factorial validity when CFA was used.

Constructivist-Oriented Learning Environment Survey (COLES) The Constructivist-Oriented Learning Environment Survey (COLES) incorporates numerous WIHIC scales into an instrument designed to provide feedback as a basis for refection in teacher

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action research. In constructing the COLES, Aldridge et  al. (2012) were especially conscious of the omission in all existing learning environment questionnaires of important aspects related to the assessment of student learning. The COLES incorporates six WIHIC scales (student cohesiveness, teacher support, involvement, task orientation, cooperation, equity). Like the TROFLEI, the COLES also includes diferentiation and young adult ethos. In addition, the COLES includes the personal relevance scale from the CLES. The two new COLES scales related to assessment are formative assessment (extent to which students feel that their assessment tasks make a positive contribution to their learning) and assessment criteria (extent to which assessment criteria are explicit so that the basis for judgments is clear and public). For a sample of 2,043 grade 11 and 12 students from 147 classes in 9 schools in Western Australia, data analysis supported the sound factorial validity and internal consistency reliability of both actual and preferred versions of the COLES. A noteworthy methodological feature of this study is that the Rasch model was used to convert data collected using a frequency response scale into interval data suitable for parametric analyses. Interestingly, when analyses were performed separately for raw scores and Rasch scores, Aldridge et al. (2012) reported that diferences between the validity results (e.g., reliability, discriminant validity and ability to diferentiate between classrooms) were negligible. Aldridge et al. (2012) also used the COLES successfully with teachers in action research aimed at improving their classroom environments. When the COLES was used in a three-year collaborative action research study involving 2,673 students in 171 classes at a school in South Australia (Aldridge et al., 2021), statistically signifcant improvements in the learning environment occurred over the three years. Henderson and Loh (2019) used the COLES in conjunction with classroom observations and teacher collaboration in a whole-school professional learning initiative.

Established, Emerging, and Future Lines of Research on Learning Environments The following subsections identify numerous established, emerging, and future lines of learning environment research: (1) evaluation of educational innovations, (2) associations with student outcomes, (3) secondary analysis, (4) links between multiple environments, (5) cross-national studies, (6) typologies of learning environments, (7) classroom emotional climate, (8) developments in analyzing learning environment data, (9) physical environments, and (10) improving learning environments.

Evaluation of Educational Innovations Walberg’s historic evaluation of Harvard Project Physics (HPP) revealed that HPP and traditional physics curricula could be distinguished in terms of students’ learning environment perceptions (using the LEI) when a range of student outcome measures showed little diferentiation (W. W. Welch  & Walberg, 1972). When learning environment dimensions were used as process criteria in an evaluation of ASEP (Australian Science Education Project), ASEP students perceived their learning environments as being more satisfying and individualized and having a better material environment relative to a comparison group (Fraser, 1979). In evaluating computer-assisted learning in Singapore (Fraser & Teh, 1994), a group of students using micro-PROLOG-based computerassisted learning had much higher scores for achievement (3.5 standard deviations), attitudes (1.4 standard deviations), and the learning environment (1.0–1.9 standard deviations) than a comparison group. Other examples of this application of learning environment assessments include outcomesfocused education (Aldridge & Fraser, 2008) and feld-study settings (Zaragoza & Fraser, 2017). Many studies in the United States provide good examples of the use of learning environment scales as criteria of efectiveness in the evaluation of educational programs and teaching methods, including the use of anthropometric activities (Lightburn  & Fraser, 2007), an innovative science course for prospective primary teachers (Martin-Dunlop  & Fraser, 2008), an innovative science

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teacher development program (Nix et al., 2005), an instructional intervention for early-childhood children based on constructivist principles (Peiro & Fraser, 2009), and inquiry-based activities for middle school science students (Wolf & Fraser, 2008). Although the evaluation of teacher professional development programs often involves surveying participant teachers’ opinions and satisfaction related to various aspects of a program, Pickett and Fraser (2009) argue that the litmus test of the success of any professional development program is the extent of changes in teaching behaviors and ultimately student outcomes in the participating teachers’ school classrooms. This study of a two-year mentoring program in science for beginning elementary school teachers drew on the feld of learning environments in evaluating this program in terms of participants’ teaching behavior as assessed by their school students’ perceptions of their learning environments. Changes over a school year were monitored for a sample of seven beginning grade 3–5 teachers in the southeast United States and their 573 elementary school students. Data analyses supported the efcacy of the mentoring program in terms of some improvements over time in the learning environment, as well as in students’ attitudes and achievement (Pickett & Fraser, 2009). In South Africa, a national curriculum reform involving outcomes-based education (OBE) stimulated the development and use of new learning environment instruments for monitoring outcomesbased learning environments. At the classroom level, Aldridge et al. (2006) developed the OBLEQ (Outcomes-Based Learning Environment Questionnaire) in the North Soto language and validated it with a sample of 2,638 grade 8 science students from 50 schools in the Limpopo Province. At the school level, Aldridge et al. (2006) developed a South African version of the School-Level Environment Questionnaire and validated it with a sample of 403 teachers. Generally, teachers perceived the actual and preferred school environment diferently and also teachers who had been involved in OBE perceived their school environments diferently (in terms of signifcantly more familiarity with OBE and work pressure) compared with teachers who had not been involved. Learning environment research has been sparse in the Arabic-speaking world, perhaps partly because of the limited availability of validated questionnaires in that language. MacLeod and Fraser (2010) pioneered the painstaking translation and comprehensive validation of an Arabic version of the WIHIC with a sample of 763 college students in 82 classes in the United Arab Emirates. In an evaluation study involving a translation of the WIHIC into Arabic, the use of mathematics games promoted a positive learning environment among a sample of 352 college students in 33 classes (Afari et al., 2013). Polat and Karabatak (2022) reviewed past studies of the efectiveness of fipped instruction, as well as reporting their own evaluation of fipped instruction among 94 university students in Turkey. Relative to other models, fipped instruction was associated with higher student satisfaction, achievement, and general belongingness. By using the CUCEI together with feld notes, interviews, and focus groups, Strayer (2012) found that inverted instruction involved greater cooperation and innovation, but less task orientation, among statistics students at a university in the United States. As the COVID-19 pandemic heralded unprecedented changes to online learning worldwide, learning environment researchers began to study these changes. Rahayu et  al. (2022) developed and validated the Online Classroom Learning Environment (OCLEI) to assess access, interaction, lecturer support, equity, and investigation among 669 Indonesian university students. Long et  al. (2022) traced changes in numerous WIHIC learning environment dimensions (student cohesiveness, teacher support, involvement, task orientation, and equity) during the change to remote learning during the pandemic among 230 preservice teacher education students in Texas. Students perceived a statistically signifcant decline during the change to online learning in student cohesiveness, teacher support, involvement, task orientation, and equity, with the largest decline of 0.56 standard deviations occurring for student cohesiveness. At a West Australian university, 194 students in diferent disciplines provided their perceptions of classroom emotional climate during and after COVID-19 lockdown. Although diferences were relatively small (ranging from 0.17 to 0.40 standard deviations),

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students perceived signifcantly more control, clarity, challenge, motivation, consolidation, and collaboration after lockdown than before (McLure et al., in press). Mogas et al. (2022) describe the Fourth Industrial Revolution (IR 4.0) in industry and its potential future impact on education. IR 4.0 includes the Internet of Things (IoT), artifcial intelligence (AI), cloud computing, etc. and has potential for incorporation into “smart schools” (Huang et al., 2013). After interviewing 37 school principals in Catalonia, Mogas and colleagues (2022) concluded that schools are not yet prepared to cope with IR 4.0, made recommendations for embedding IR 4.0 into smart schools, and especially stressed the need for teacher professional development. Although the aforementioned authors identify considerable potential for applying IR 4.0 technologies in education, its implementation in education and the evaluation of this implementation have scarcely begun. Therefore, learning environment researchers will have a crucial role in applying learning environment constructs and methods in evaluating the efectiveness of IR 4.0 in education. In a scoping review of teacher education and technology integration in the context of IR 4.0, Teo et al. (2021) identifed profound potential impacts on teacher education, as well as suggesting future actions for teacher training and research.

Associations With Student Outcomes A strong tradition in past learning environment research has been investigation of associations between a variety of students’ cognitive and afective learning outcomes and their perceptions of psychosocial characteristics of their learning environments. An early meta-analysis involving 17,805 students revealed more-favorable student outcomes in learning environments with more cohesiveness, satisfaction, and goal direction and less disorganization and friction (Haertel et al., 1981). When McRobbie and Fraser (1993) used the SLEI in an investigation of associations between student outcomes and the learning environment among 1,594 senior high school chemistry students in 92 classes, simple, multiple, and canonical correlation analyses were conducted separately for two units of analysis (student scores and class means) and separately with and without control for general ability. Past research was replicated in that the nature of the science laboratory environment (especially integration between theory and laboratory work) accounted for appreciable proportions of the variance in both cognitive and afective outcomes beyond that attributable to general ability. Science educators wishing to enhance student outcomes in science laboratory settings are likely to fnd useful the result that both cognitive and attitude outcomes were enhanced when laboratory activities were integrated with work in non-laboratory settings. For a large sample of 6,120 secondary students in South and Western Australia, Aldridge et al. (2018) used bullying and delinquent behaviors as student outcomes (dependent variables) in studying the infuence of school climate. School connectedness and rule clarity were negatively associated with both bully victimization and delinquency, teacher support was negatively associated with bully victimization, afrming diversity and reporting/seeking help were positively linked with bully victimization, and bully victimization was a mediator of the infuence of school climate on delinquent behaviors. For a sample of 1,592 fnal-year chemistry students in 28 classes in Singapore, Wong and Fraser (1996) reported the frst major study in Singapore into associations between students’ attitudes to science and the perceived learning environment. When the original SLEI was modifed to form the Chemistry Laboratory Environment Inventory (CLEI), strong and statistically signifcant attitude–environment associations (especially for integration and rule clarity) were confrmed through multilevel analysis (Wong et al., 1997). In a later study involving 497 chemistry students responding to both the CLEI and the QTI, Quek et al. (2005) not only replicated the existence of attitude–environment associations, but also identifed signifcant diferences in learning environment perceptions according to the student’s sex and stream (i.e., gifted vs. non-gifted). In other research in Singapore,

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relationships between psychosocial climate and student achievement and attitudes were established for 1,512 primary-school students using both multiple regression analysis (S. C. Goh & Fraser, 1998) and multilevel analysis (S. C. Goh et al., 1995). When Chionh and Fraser (2009) used the WIHIC among 2,310 grade 10 students in 75 classes, better examination scores were found in classrooms with more student cohesiveness, whereas self-esteem and attitudes were more favorable in classrooms with more teacher support, task orientation, and equity. A study of associations between children’s ICT self-efcacy and various aspects of the home digital learning environment (stimulation, instructions, interactions, modeling) involved 651 grade 5 and 6 students in Germany (Bonanati & Buhl, 2021). Digital home learning environment variables accounted for 14% of the variance in ICT self-efcacy, with the number of books, positive attitudes, and shared internet activities having positive relationships with self-efcacy and parental instructions having negative relationships.

Secondary Analysis Researchers in neither the felds of science education nor learning environments have yet realized the potential of secondary analysis, which involves analyzing for new purposes existing data that previously were gathered for diferent purposes. According to Smith (2008), advantages of secondary analysis include access to data sets that are larger and of higher technical quality than normally is possible in individual studies, as well as a reduction in the cost of data collection and the response burden on students and teachers. However, a key disadvantage is that a given data base might contain limited or no measures of key variables of interest. For example, secondary analysis of science data from the National Association of Educational Progress involved 1,995 17-year-olds, 2,025 13-year-olds, and 1,960 9-year-olds (Fraser et al., 1986; Walberg et  al., 1986). Classroom and school environment was a strong independent predictor of both achievement and attitudes when a comprehensive set of other factors was mutually controlled. Khine et al. (2020) illustrate how they conducted secondary analysis of PISA (Programme for International Student Assessment) data from 14,167 students in the United Arab Emirates. Because these researchers were interested in exploring associations between students’ perceptions of the learning environment and their noncognitive outcomes, they examined the wording of PISA questionnaires to identify 22 items assessing learning environment and 16 items assessing noncognitive outcomes. Data for these 22 learning environment items were used to form three scales assessing cooperation (8 items), teacher support (5 items), and investigation (9 items). Similarly, data for the 16 items assessing noncognitive outcomes were used to form three scales assessing epistemological beliefs (6 items), self-efcacy (5 items), and science attitudes (5 items). Structural equation modeling (SEM) revealed a statistically signifcant association between each learning environment scale and each noncognitive outcome scale.

Links Between Multiple Environments Although most individual studies of educational environments focus on a single environment, there is potential in simultaneously considering the links between, and the joint infuence of, two or more environments. Several researchers have investigated whether the nature of the school-level environment infuences or transmits to what goes on in classrooms (i.e., the classroom-level environment). In South Africa, Aldridge et al. (2006) used a school environment instrument with 50 secondaryschool science teachers from 50 diferent schools, together with a classroom environment questionnaire based on the WIHIC with the 2,638 grade 8 students in the 50 classes of these 50 teachers. Although there emerged a small number of specifc relationships (e.g., schools encouraging teachers to be innovative was related to the extent to which students perceived more outcomes-based

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pedagogy in their classrooms), overall, the school environment had limited infuence on what happens in classrooms. When Dorman et al. (1995) administered a classroom environment instrument to 2,211 Australian students in 104 classes and a school environment instrument to the 208 teachers of these classes, only weak associations between classroom environment and school environment emerged. For example, classroom individualization was greater in school environments with more afliation and less work pressure. Although school rhetoric often suggests that the school ethos would be transmitted to the classroom level, it appears that classrooms are somewhat insulated from the school as a whole. For a sample of 2,189 secondary-school students in Hong Kong, Wan (2022) investigated the joint infuence of classroom and family learning environments on students’ critical thinking disposition. Classroom environment was assessed with seven Chinese-language scales based on the Constructivist Learning Environment Survey (personal relevance, uncertainty, skeptical voice, shared control, student negotiation, challenging task, and multiple perspectives), whereas family environment was assessed with fve Chinese-language scales based on the classroom environment instrument (uncertainty, skeptical voice, shared control, challenging task, and multiple perspectives). Overall, the classroom environment more strongly predicted critical thinking than did the family environment. Using secondary analysis (see discussion in the previous subsection) of a large database from a Statewide Systemic Initiative (SSI) in the United States, Fraser and Kahle (2007) examined the efects of several types of environments on student outcomes. Over three years, nearly 7,000 students in 392 middle school science and mathematics classes in 200 diferent schools responded to a questionnaire that assessed class, home, and peer environments, as well as student attitudes and achievement. Rasch analyses allowed comparison across student cohorts and across schools. This study confrmed the importance of extending research on classroom learning environments to include the learning environments of the home and peer group. Although all three environments accounted for statistically signifcant amounts of unique variance in student attitudes, only the class environment (defned in terms of the frequency of use of standards-based teaching practices) accounted for statistically signifcant amounts of unique variance in student achievement (Fraser & Kahle, 2007).

Cross-National Studies Science education research that crosses national boundaries ofers promise for generating new insights. Aldridge et al. (1999) involved six Australian and seven Taiwanese science education researchers in administering the WIHIC to 50 junior high school science classes in each of Taiwan (1,879 students) and Australia (1,081 students). An English version of the questionnaire was translated into Chinese, followed by an independent back translation of the Chinese version into English by team members not involved in the original translation. Qualitative data, involving interviews with teachers and students and classroom observations, complemented the quantitative information and clarifed reasons for patterns and diferences in the means in each country. The largest diferences between the two countries were for involvement and equity, with Australian students perceiving these environment scales more positively than students from Taiwan. Data from the questionnaires in the Taiwan–Australia study were used to guide the collection of qualitative data, with student responses to individual items being used to form an interview schedule to clarify whether items had been interpreted consistently by students and to help to explain diferences in questionnaire scale means between countries. Classrooms were selected for observations based on the questionnaire data, and specifc scales formed the focus for observations in these classrooms. Qualitative data helped in interpreting diferences between the questionnaire results from two countries with cultural diferences (Aldridge & Fraser, 2000). Similar cross-national research involving the use of the CLES in Taiwan and Australia was reported by Aldridge et al. (2000), whereas cross-national research with the WIHIC in Indonesia and Australia was reported by Fraser et al. (2010).

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Typologies of Learning Environments Using the CES in the United States among 200 junior high and high school classrooms, Moos (1978, 1979) identifed fve clusters that describe fve learning environment orientations: control, innovation, afliation, task completion, and competition. Use of the QTI with students in both the Netherlands and the United States led to the identifcation of eight distinct interpersonal profles: directive, authoritative, tolerant–authoritative, tolerant, uncertain–tolerant, uncertain–aggressive, repressive, and drudging (Brekelmans et al., 1993; Wubbels et al., 2006). Based on a large-scale administration of the QTI to 6,148 grade 8–10 science students from four Australian states and their 283 teachers, Rickards et al. (2005) reported that the eight types found for Dutch and American teachers only partly applied to the Australian context. Two new types (namely, fexible and cooperative–supportive) were unique to the Australian sample. Working in Turkey with a Turkish translation of the WIHIC, den Brok et al. (2010) created learning environment profles for a sample of 1,474 high school biology students in 52 classes. Six distinct classroom profles emerged: self-directed learning, task-orientated cooperative learning, mainstream, task-orientated individualized, low-efective learning, and high-efective learning. The most common profle was the mainstream classroom for which all WIHIC scales had medium-high scores. Based on a sample of 4,146 Australian students from 286 grade 8–13 classes, Dorman et al. (2006) used the ten-scale TROFLEI to develop a classroom typology with fve relatively homogeneous groups of classes: exemplary, safe and conservative, non-technological teacher-centered, contested technological, and contested non-technological. For a sample of 1,394 grade 10 chemistry students in 49 classes in Greece, Giallousi et al. (2013) identifed four groups of classes.

Classroom Emotional Climate In a recent instrument-development initiative, Fraser et al. (2021) developed a 41-item 6-scale questionnaire to assess six dimensions (collaboration, control, motivation, care, challenge, and clarity) of the novel construct of classroom emotional climate. With a sample of 698 grade 7–10 students in 57 STEM classes in 20 Australian schools, the Classroom Emotional Climate (CEC) questionnaire was validated using exploratory and confrmatory factor analyses, as well as in terms of internal-consistency reliability, concurrent validity, discriminant validity, and predictive validity. As well, Rasch analysis for each dimension revealed good model ft and unidimensionality for the items describing each latent construct. Table 7.1 clarifes the meaning of each of the six CEC scales by providing a scale description and sample item. There is both contemporary and long-standing interest in gender diferences in science education and STEM education (Koch et al., 2014; Parker et al., 1996; Scantlebury, 2012). Although achievement has been the most common dependent variable in gender studies, learning environment criteria also have been used (e.g., A. G. Welch et al., 2014), with females generally reporting more-favorable perceptions than males. Recently, when classroom emotional climate was used in investigating gender diferences among 256 grade 7–9 students in 24 integrated STEM classes in 8 coeducational government schools (Koul et al., 2021), there emerged statistically signifcant diferences of modest magnitude (0.25–0.50 standard deviations) favoring males for Rasch-scaled scores for clarity, motivation, and consolidation. However, in more-nuanced analyses also involving 157 grade 7–10 students in 12 integrated STEM classes in 6 coeducational nongovernment schools, the gender diferences in emotional climate perceptions in government schools disappeared in nongovernment schools even after controlling for socioeconomic status (Koul et al., 2021). Students of both genders in government schools were signifcantly more positive about all aspects of classroom emotional climate than students of both genders in nongovernment schools (after controlling for socioeconomic status). Also, females in government schools were slightly more positive about classroom emotional climate in

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Barry J. Fraser Table 7.1 Scale Description and Sample Item From Classroom Emotional Climate (CEC) Questionnaire Scale

Scale Description Extent to which students perceive that . . .

Sample Item

Consultation

. . . their teacher encourages them to share their thoughts or opinions

I speak up and share my ideas about the class work.

Consolidation

. . . their teacher consolidates understanding and indicates how to improve

My teacher takes time to summarize what I learn each day.

Collaboration

. . . they collaborate efectively with peers to complete tasks

I learn from other students in my STEM group.

Control

. . . they behave well in class and remain focused on tasks

My teacher makes sure that I stay busy and don’t waste time.

Motivation

. . . their STEM project and the classroom environment motivate them to be engaged in learning and seeking answers to problems

The questions in this class make me want to fnd out the answers.

Care

. . . their teacher cares about their learning needs and treats them with respect

My teacher makes me feel that s/he really cares about me.

Challenge

. . . their teacher encourages them to think deeply and persist in the face of difculties and provides challenging STEM projects

My teacher asks questions that make me think hard.

Clarity

. . . their teacher gives clear directions and explanations

My teacher breaks up the work into easy steps.

multidisciplinary STEM classes (S, T, E, and M) than in unidisciplinary STEM classes (S, T, E, or M) (McLure et al., 2022).

Developments in Analyzing Learning Environment Data Den Brok et al.’s (2019) review of developments in methods for the statistical analysis of learning environment data revealed that “methods used in learning environments research have undergone impressive changes from more descriptive and evaluative approaches to more explanatory, predictive and model-testing ones, and that researchers have employed a variety of methods to investigate issues related to learning environments” (p. 42). These authors contrast the earlier years of learning environments research (1965–2000), involving description and correlation, with the second period (from 2000 onwards), involving methodological diversifcation. Some important types of analyses identifed by den Brok and colleagues include multilevel analysis; structural equation modeling (SEM), including confrmatory factor analysis; and Rasch modeling. (It is noteworthy that, at the time of the development and validation of Walberg’s historic Learning Environment Inventory and Moos’s historic Classroom Environment Scale, not even exploratory factor analysis was readily available and used.) Multilevel analysis takes cognizance of the hierarchical nature of classroom settings (e.g., students within intact classes are more homogeneous than a random sample of students). When Wong et al. (1997) used a sample of 1,592 grade 10 students in 56 chemistry classes in Singapore to investigate associations between three student attitude measures and a modifed version of the SLEI, most of the statistically signifcant results from multiple regression analyses were replicated in the HLM analyses, as well as being consistent in direction. A similar study employing multilevel analysis in investigating outcome–environment associations in Singapore was reported by S.C. Goh et al. (1995). For an

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Indonesian sample, Maulana et al. (2012) used multilevel analysis to investigate student and teacher perceptions of interpersonal relationships. Structural equation modeling (SEM) allows linking questionnaire items to their latent constructs/ dimensions and investigating associations between latent constructs, as well as testing direct and indirect efects. For a sample of 14,167 science students from the United Arab Emirates, Khine et al. (2020) used SEM to confrm the structure of a learning environment questionnaire (i.e., confrmatory factor analysis) and to investigate associations between learning environment constructs and students’ noncognitive outcomes. Sun et al. (2017) used SEM in confrmatory factor analysis to test whether items in a Chinese version of the QTI conformed to the intended circumplex structure. Whereas the parametric IRT (item response theory) model of Rasch scaling is reasonably well known in science education (Boone & Staver, 2020; Khine, 2020; Noben et al., 2021), little use has been made of the nonparametric IRT model, namely, Mokken scaling (Telli et al., 2021). IRT models allow testing of whether items or scales can be ordered under higher-order dimensions, as well as the conversion of ordinal questionnaire data to interval scales suitable for parametric statistical tests. Rasch analyses were used by Maulana and Helms-Lorenz (2016) in the Netherlands in investigating the quality of preservice teachers’ teaching behaviors, as well as by Koul et al. (2021) in Australia in investigating gender diferences in classroom emotional climate and student attitudes in integrated STEM classes.

Physical Learning Environments Recently, attention to psychosocial characteristics of learning environments has been supplemented by interest in the physical environment of educational buildings and learning spaces. This trend is refected in several books in Brill Sense Publishers’ Advances in Learning Environments Research series (Alterator & Deed, 2018; K. Fisher, 2016, 2019; Imms et al., 2016). Martin-Dunlop et al.’s (2019) transdisciplinary team evaluated the impact of an enriched and active learning space for university architecture students using questionnaire surveys, lesson videotapes, interviews, and achievement data. Although new designs or redesigns for learning–teaching spaces often aim to transform psychosocial learning environments in specifc ways, it is rare for researchers to evaluate their efectiveness in terms of changes in psychosocial characteristics. For example, Prain (2018) reported that 2,500 students’ perceptions of personalized learning did not improve over three years in new schools that aimed to promote personalized learning. Therefore, Skordi and Fraser (2019) attempted to show how methods, conceptual frameworks, and research traditions from the feld of learning environment can potentially be applied in evaluating the success of changes in physical educational spaces in promoting positive changes in psychosocial and pedagogical characteristics of learning environments. Baars et al. (2021) used a thematic literature review to develop a conceptual framework to structure the psychosocial learning environment into the dimensions of personal development, relationships and system maintenance/change, and structure the physical learning environment into the dimensions of naturalness, individualization, and stimulation. In Attai et al.’s (2021) study in the southwestern United States involving biweekly observations of ten elementary classrooms with fexible furniture and ten classrooms with traditional furniture, students experiencing fexible furniture reported greater satisfaction and more opportunities for autonomy. In a primary school in Finland, Reinius et al. (2021) studied the learning environment of a “deskless” school that had been architecturally designed with fexible learning spaces without traditional classrooms or desks to encourage pedagogical renewal. In a study of interior lighting at a pre-K child development laboratory in the United States, early-childhood students with developmental disabilities displayed more engagement in classrooms with LED lighting than in classrooms with fuorescent lighting (Pulay & Williamson, 2019).

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Unique research in Canada focused simultaneously on the psychosocial and physical environments of networked classrooms (Zandvliet & Fraser, 2004, 2005). Whereas the psychosocial environment was assessed with the WIHIC, ergonomic evaluations were made of the physical environment (e.g., workspace and visual environments). For a sample of 1,404 Australian and Canadian students, classroom psychosocial environment was signifcantly and directly associated with students’ satisfaction with their learning, but no direct associations were found between student satisfaction and measures of the physical classroom environment. However, statistically signifcant associations emerged between physical and psychosocial learning environment variables in classrooms using new information technologies. These associations suggested a model of educational productivity for learning environments in technology-rich classrooms.

Improving Learning Environments Teachers have used feedback information based on assessment of students’ actual and preferred learning environments as a basis for guiding practical improvements in their classroom environments using a simple but pioneering fve-step procedure of (1) assessment, (2) feedback, (3) refection and discussion, (4) intervention, and (5) reassessment (Fraser, 1999). Fraser and Aldridge (2017) reviewed the intermittent take-up of this approach during the ensuring decades and described its evolution into more-sophisticated methods for computer-scoring questionnaires and graphically displaying feedback more elegantly. These techniques have been applied successfully in case studies in Australia (Fraser & D. L. Fisher, 1986) and the United States (Sinclair & Fraser, 2002), with the book Student Voice, Teacher Action Research and Classroom Improvement (Bell & Aldridge, 2014) describing and illustrating these methods. In South Africa, Aldridge et al. (2004) adapted the English version of the CLES for use among teachers and their 1,864 students in 43 classes who were attempting to change learning environments to a more-constructivist orientation. Not only did their study yield a validated and widely applicable questionnaire for future use, but it also involved case studies of teachers’ successful and not-sosuccessful attempts to change their learning environments during a 12-week intervention period. Also in South Africa, Aldridge et al. (2009) investigated 31 in-service teachers who were undertaking a distance-education program and who administered a primary-school version of the WIHIC in the IsiZulu language to 1,077 grade 4–7 learners in the KwaZulu-Natal province. Diferent teachers were able to use feedback from the WIHIC, with varying degrees of success, in their attempts to improve their learning environments. In Australia, Aldridge et al. (2012) reported teachers’ use of the COLES in successful attempts to improve learning environments. Aldridge and Fraser (2008) reported some studies of how teachers at a new senior high school used the TROFLEI in attempts to improve their learning environments, while Fraser and Aldridge (2017) reported a detailed case study of one teacher’s attempt to improve her learning environment using the COLES. Using a case-study approach, Aldridge and Bianchet (2021) demonstrated how student feedback based on a learning environment survey (namely, the COLES) provided a valuable starting point for including students in co-construction (Rincon-Gallardo  & Elmore, 2012) and educational improvement. Vignettes were used to portray one teacher’s journey in co-constructing the learning environment of her classes, based mainly on interviews. Henderson and Loh (2019) reported the use of students’ learning environment perceptions on the COLES to guide teachers’ professional learning at one school. This mixed-methods study, which involved about 25 teachers and 500 students each year, revealed how highly teachers valued this kind of feedback in supporting their professional learning. Recently, whole-school principal-led collaborative action research aimed at improving a school’s learning environments involved 2,673 grade 8–12 students and 171 teachers and revealed statistically signifcant improvements in both the learning environment and students’ self-efcacy (Aldridge et al., 2021).

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Based on insights accumulated through teachers’ action research on improving learning environments, Fraser and Aldridge (2017) expanded and enhanced Fraser’s (1999) original fve steps to identify the following enhanced fve-step process: 1. 2. 3.

Assessment and feedback: Assessing actual and preferred learning environment using a questionnaire and providing feedback to teachers based on students’ responses. Refection and focusing: Refection on the feedback and discussion with colleagues to identify which aspects of the learning environment might become the focus for change. Planning: Planning an intervention aimed at improving the area of focus and involving: • • •

4.

5.

Developing a working hypothesis that might help to explain the current issue or challenge related to the focus area. Considering what is needed (in terms of knowledge about the focus area) to develop a working plan (such as new learning). Deciding on a strategy to improve the area of focus and planning its implementation in the classroom.

Implementing and refning: Implementing the intervention and, during the intervention period, both in (during) action and on (after) action, constantly refecting as a basis for refning the intervention. Re-assessment: Re-administering the questionnaire to students at the end of the intervention period to determine whether students perceive their learning environment diferently from before. (p. 103)

Conclusion Science education researchers have contributed much to the feld of learning environments’ growth and international expansion and have generated numerous insights. First, because learning outcomes cannot provide a complete picture of the educational process, researchers, evaluators, and practitioners should pay more attention to learning environments. Second, science educators have developed, validated, and cross-validated a range of robust and economical instruments that are used widely around the world for assessing learning environments. Third, these researchers have contributed to our knowledge base about how to improve student outcomes through creating positive learning environments. Fourth, evaluation studies have frequently demonstrated the value of including learning environment dimensions as process criteria of efectiveness in evaluating a host of educational programs, innovations, and teaching methods. In the period since Volume II of this handbook, much has been achieved, but there still is considerable scope for future expansion of research in this feld. It is hoped that this chapter will stimulate and guide future research agendas through its discussion of the assessment of learning environments, together with its portrayal of ten fruitful lines of established, emerging, and future learning environment research. Mixed-methods research has been advocated in this chapter for research on learning environments. To facilitate mixed-methods studies, this chapter has familiarized readers with a range of economical, convenient, and valid surveys that tap students’ and teachers’ perceptions of learning environments. The questionnaires considered are all “high inference” and encompass the historically signifcant Learning Environment Inventory (LEI) and Classroom Environment Scale (CES), as well as contemporary instruments used widely around the world today (e.g., Science Laboratory Environment Inventory, SLEI; Constructivist Learning Environment Survey, CLES; Questionnaire on Teacher Interactions, QTI; What Is Happening In this Class, WIHIC; and Constructivist-Oriented Learning Environment Survey, COLES).

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The areas of past research reviewed in this chapter include well-established applications, such as evaluating educational innovations and investigating associations between the learning environment and student outcomes. Also, some of the recent and emerging lines of research identifed and discussed focus on physical environments and classroom emotional climate. As well, this chapter identifed and reviewed several lines of past research that have received only limited attention from researchers but have considerable future potential: secondary analysis of existing data bases; crossnational studies; links between diferent environments; and typologies of learning environments. Also, promising developments in analyzing learning environment data were identifed. Although a noteworthy recent trend involves practitioners in using learning environment assessments in action research aimed at improving their teaching environments (Aldridge et  al., 2012), overall, there is considerable scope for greater teacher uptake of learning environment ideas worldwide.

References Afari, E., Aldridge, J. M., Fraser, B. J., & Khine, M. S. (2013). Students’ perceptions of the learning environment and attitudes in game-based mathematics classrooms. Learning Environments Research, 16, 131–150. Alansari, M., & Rubie-Davies, C. (2020). What about tertiary climate: Refecting on fve decades of class climate research. Learning Environments Research, 23(1), 1–25. Aldridge, J. M., & Bianchet, S. (2021). Using feedback about the learning environment as a starting point for coconstruction. Learning Environments Research, 25, 939–955. https://doi.org/10.1007/s10984-021-09403-9 Aldridge, J. M., Dorman, J. P., & Fraser, B. J. (2004). Use of multitrait – multimethod modelling to validate actual and preferred forms of the Technology-Rich Outcomes-Focused Learning Environment Inventory (TROFLEI). Australian Journal of Educational and Development Psychology, 4, 110–125. Aldridge, J. M., & Fraser, B. J. (2000). A cross-cultural study of classroom learning environments in Australia and Taiwan. Learning Environments Research, 3, 101–134. Aldridge, J. M.,  & Fraser, B. J. (2008). Outcomes-focused learning environments: Determinants and efects. Sense Publishers. Aldridge, J. M., Fraser, B. J., Bell, L., & Dorman, J. P. (2012). Using a new learning environment questionnaire for refection in teacher action research. Journal of Science Teacher Education, 23, 259–290. Aldridge, J. M., Fraser, B. J., & Huang, I. T.-C. (1999). Investigating classroom environments in Taiwan and Australia with multiple research methods. Journal of Educational Research, 93, 48–62. Aldridge, J. M., Fraser, B. J., & Ntuli, S. (2009). Utilising learning environment assessments to improve teaching practices among in-service teachers undertaking a distance education programme. South African Journal of Education, 29, 147–170. Aldridge, J. M., Fraser, B. J., & Sebela, M. P. (2004). Using teacher action research to promote constructivist learning environments in South Africa. South African Journal of Education, 24, 245–253. Aldridge, J. M., Fraser, B. J., Taylor, P. C., & Chen, C.-C. (2000). Constructivist learning environments in a cross-national study in Taiwan and Australia. International Journal of Science Education, 22, 37–55. Aldridge, J. M., Laugksch, R. C., & Fraser, B. J. (2006). School-level environment and outcomes-based education in South Africa. Learning Environments Research, 9, 123–147. Aldridge, J. M., Laugksch, R. C., Seopa, M. A., & Fraser, B. J. (2006). Development and validation of an instrument to monitor the implementation of outcomes-based learning environments in science classrooms in South Africa. International Journal of Science Education, 28, 45–70. Aldridge, J. M., & McChesney, K. (2021). Parents’ and caregivers’ perceptions of the school climate: Development and validation of the Parent and Caregiver Survey (PaCS). Learning Environments Research, 24(1), 23–41. Aldridge, J. M., McChesney, K., & Afari, E. (2018). Relationship between school climate, bullying and delinquent behaviours. Learning Environments Research, 21(2), 153–172. Aldridge, J. M., Rijken, P. E., & Fraser, B. J. (2021). Improving learning environments through whole-school collaborative action research. Learning Environments Research, 24(2), 183–205. Allen, D., & Fraser, B. J. (2007). Parent and student perceptions of the classroom learning environment and its association with student outcomes. Learning Environments Research, 10, 67–82. Alterator, S., & Deed, C. (Eds.). (2018). School space and its occupation: The conceptualisation and evaluation of innovative learning environments (Advances in Learning Environments Research series). Sense Publishers. Alzubaidi, E., Aldridge, J. M., & Khine, M. S. (2016). Learning English as a second language at the university level in Jordan: Motivation, self-regulation and learning environment perceptions. Learning Environments Research, 19(1), 133–152.

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Learning Environments Anderson, G. L., & Walberg, H. J. (1968). Classroom climate group learning. International Journal of Educational Sciences, 2, 175–180. Attai, S. L., Reyes, J. C., Davis, J. L., York, J. Ranney, K., & Hyde, T. W. (2021). Investigating the impact of fexible furniture in the elementary classroom. Learning Environments Research, 24(2), 153–167. https://doi. org/10.1007/s10984-020-09322-1 Baars, S., Schellings, G. L. M., Krishnamurthy, S., Moore, J. P., den Brok, P. J., & van Wesemael, P. J. V. (2021). A framework for exploration of relationship between the psychosocial and physical learning environment. Learning Environment Research, 24(1), 43–69. Baek, C. G., & Choi, H. J. (2002). The relationship between students’ perceptions of classroom environment and their academic achievement in Korea. Asia-Pacifc Journal of Education, 3(10), 125–135. Bell, L. M., & Aldridge, J. M. (2014). Student voice, teacher action research and classroom improvement. Sense. Bi, X. (2015). Associations between psychosocial aspects of English classroom environments and motivation types of Chinese tertiary-level English majors. Learning Environments Research, 18(1), 95–110. Bonanati, S., & Buhl, H. M. (2021). The digital home learning environment and its relation to children’s ICT self-efcacy. Learning Environments Research, 25, 485–505. https://doi.org/10.1007/s10984-021-09377-8 Boone, W. J., & Staver, J. R. (2020). Advances in Rasch analysis in human sciences. Springer. Brekelmans, M., Levy, J., & Rodriguez, R. (1993). A typology of teacher communication style. In T. Wubbels & J. Levy (Eds.), Do you know what you look like? Interpersonal relationships in education (pp. 46–55). Falmer Press. Brekelmans, M., Wubbels, T.,  & van Tartwijk, J. (2005). Teacher  – student relationships across the teaching career. International Journal of Educational Research, 43, 55–71. Cai, J., Wen, Q., Lombaerts, K., Jaime, I., & Cai, L. (2021). Assessing students’ perceptions about classroom learning environments: The new what is happening in this class (NWIHIC) instrument. Learning Environments Research, 25, 601–618. https://doi.org/10.1007/s/10984-021-09383-w Charalampous, K.,  & Kokkinos, C. M. (2017). The Greek elementary “what is happening in this class?” (G-EWIHIC): A three-phase multi-sample mixed methods study. Studies in Educational Evaluation, 52, 55–70. Chionh, Y. H., & Fraser, B. J. (2009). Classroom environment, achievement, attitudes and self esteem in geography and mathematics in Singapore. International Research in Geographical and Environmental Education, 18, 29–44. Cho, H. J., Levesque-Bristol, C., & Yough, M. (2021). How autonomy-supportive learning environments promote Asian international students’ academic adjustment: A self-determination theory perspective. Learning Environments Research. https://doi.org/10.1007/s10984-021-09401x Cohn, S. T., & Fraser, B. J. (2016). Efectiveness of student response systems in terms of learning environment, attitudes and achievement. Learning Environments Research, 19, 153–167. Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Sage. Deci, E. L., & Ryan, R. M. (Eds.). (2002). Handbook of self-determination research. Rochester University Press. den Brok, P., Fisher, D. L., & Scott, R. H. (2005). The importance of teacher interpersonal behaviour for student attitudes in Brunei primary science classes. International Journal of Science Education, 27, 765–779. den Brok, P., Mainhard, T.,  & Wubbels, T. (2019). Developments in quantitative methods and analyses for studying learning environments. In D. B. Zandvliet & B. J. Fraser (Eds.), Thirty years of learning environments: Looking back and looking forward (pp. 41–58). Brill Sense. den Brok, P., Telli, S., Cakiroglu, J., Taconis, R., & Tekkaya, C. (2010). Learning environment profles of Turkish secondary biology students. Learning Environments Research, 13, 187–204. Dorman, J. P. (2003). Cross-national validation of the what is happening in this class? (WIHIC) questionnaire using confrmatory factor analysis. Learning Environments Research, 6, 231–245. Dorman, J. P. (2008). Use of multitrait – Multimethod modelling to validate actual and preferred forms of the what is happening in this class? (WIHIC) questionnaire. Learning Environments Research, 11, 179–193. Dorman, J. P., Aldridge, J. M., & Fraser, B. J. (2006). Using students’ assessment of classroom environment to develop a typology of secondary school classrooms. International Education Journal, 7, 909–915. Dorman, J. P., Fraser, B., & McRobbie, C. (1995). Associations between school-level environment and science classroom environment in secondary schools. Research in Science Education, 25, 333–351. Erickson, F. (2012). Qualitative research methods for science education. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 1451–1484). Springer. Fisher, D. L., & Fraser, B. J. (1981). Validity and use of my class inventory. Science Education, 65, 145–156. Fisher, D. L., & Fraser, B. J. (l983a). Validity and use of classroom environment scale. Educational Evaluation and Policy Analysis, 5, 261–271. Fisher, D. L., & Fraser, B. J. (1983b). A comparison of actual and preferred classroom environment as perceived by science teachers and students. Journal of Research in Science Teaching, 20, 55–61. Fisher, D. L., Henderson, D., & Fraser, B. J. (1997). Laboratory environments and student outcomes in senior high school biology. American Biology Teacher, 59, 214–219.

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Barry J. Fraser Fisher, K. (Ed.). (2016). The translational design of schools: An evidence-based approach to aligning pedagogy and learning environments (Advances in Learning Environments Research Series). Sense Publishers. Fisher, K. (Ed.). (2019). The translational design of universities: An evidence based approach (Advances in Learning Environments Research Series). Sense Publishers. Fraser, B. J. (1979). Evaluation of a science-based curriculum. In H. Walberg (Ed.), Educational environments and efects: Evaluation, policy, and productivity (pp. 218–234). McCutchan. Fraser, B. J. (1998). Learning environment instruments: Development, validity and applications. Learning Environment Research, 1(1), 7–33. Fraser, B. J. (1990). Individualised classroom environment questionnaire. Australian Council for Educational Research. Fraser, B. J. (1994). Research on classroom and school climate. In D. Gabel (Ed.), Handbook of science teaching and learning (pp. 493–541). Macmillan. Fraser, B. J. (1999). Using learning environment assessments to improve classroom and school climates. In H. J. Freiberg (Ed.), School climate: Measuring, improving and sustaining healthy learning environments (pp. 65–83). Falmer Press. Fraser, B. J. (2012a). Classroom environment. Routledge (Originally published in 1986 by Croom Helm). Fraser, B. J. (2012b). Classroom learning environments: Retrospect, context and prospect. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 1191–1239). Springer. Fraser, B. J. (2014). Classroom learning environments: Historical and contemporary perspectives. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 104–119). Routledge. Fraser, B. J. (2019). Milestone in the evolution of the learning environments feld over the past three decades. In D. B. Zandvliet & B. J. Fraser (Eds.), Thirty years of learning environments: Looking back and looking forward (pp. 1–19). Brill Sense. Fraser, B. J.,  & Aldridge, J. M. (2017). Improving classrooms through assessment of learning environments. In J. P. Bakken (Ed.), Classrooms volume 1: Assessment practices for teachers and student improvement strategies (pp. 91–107). Nova. Fraser, B. J., Aldridge, J. M., & Adolphe, F. S. G. (2010). A cross-national study of secondary science classroom environments in Australia and Indonesia. Research in Science Education, 40, 551–571. Fraser, B. J., & Fisher, D. L. (1982). Predicting student outcomes from their perceptions of classroom psychosocial environment. American Educational Research Journal, 19, 498–518. Fraser, B. J., & Fisher, D. L. (1983). Use of actual and preferred classroom environment scales in person-environment ft research. Journal of Educational Psychology, 75, 303–313. Fraser, B. J., & Fisher, D. L. (1986). Using short forms of classroom climate instruments to assess and improve classroom psychosocial environment. Journal of Research in Science Teaching, 23, 387–413. Fraser, B. J., Giddings, G. J.,  & McRobbie, C. J. (1995). Evolution and validation of a personal form of an instrument for assessing science laboratory classroom environments. Journal of Research in Science Teaching, 32, 399–422. Fraser, B. J., & Kahle, J. B. (2007). Classroom, home and peer environment infuences on student outcomes in science and mathematics: An analysis of systemic reform data. International Journal of Science Education, 29, 1891–1909. Fraser, B. J., & Lee, S. S. U. (2009). Science laboratory classroom environments in Korean high schools. Learning Environments Research, 12, 67–84. Fraser, B. J., McLure, F. I., & Koul, R. B. (2021). Assessing classroom emotional climate in STEM classrooms: Developing and validating a questionnaire. Learning Environments Research, 24(1), 1–21. Fraser, B. J., & McRobbie, C. (1995). Science laboratory classroom environments at schools and universities: A cross-national study. Educational Research and Evaluation, 1, 289–317. Fraser, B. J., & Teh, G. (1994). Efect sizes associated with micro-PROLOG-based computer-assisted learning. Computers and Education: An International Journal, 23, 187–196. Fraser, B. J., & Treagust, D. F. (1986). Validity and use of an instrument for assessing classroom psychosocial environment in higher education. Higher Education, 15, 37–57. Fraser, B. J., Treagust, D. F., & Dennis, N. C. (1986). Development of an instrument for assessing classroom psychosocial environment at universities and colleges. Studies in Higher Education, 11, 43–54. Fraser, B. J., Welch, W. W., & Walberg, H. J. (1986). Using secondary analysis of national assessment data to identify predictors of junior high school students’ outcomes. Alberta Journal of Educational Research, 32, 37–50. Giallousi, M., Gialamas, V., & Pavlatou, E. A. (2013). A typology of chemistry classroom environments: Exploring the relationships between 10th grade students’ perceptions, attitudes and gender. Learning Environments Research, 16, 349–366. Giallousi, M., Gialamas, V., Spyrellis, N.,  & Pavlatou, E. A. (2010). Development, validation, and use of a Greek-language questionnaire for assessing learning environments in grade 10 chemistry classes. International Journal of Science and Mathematics Education, 8, 761–782.

212

Learning Environments Glassner, A. (2022). Training yough at risk mentors in Paths, an innovative TE program: Promoting resilience, mental well-being and meaningful learning through a self-determined learning environment. Learning Environments Research, 25, 841–857. https://doi.org/10.1007/s10984-021-09391-w Goh, S. C., & Fraser, B. J. (1998). Teacher interpersonal behaviour, classroom environment and student outcomes in primary mathematics in Singapore. Learning Environments Research, 1, 199–229. Goh, S. C., Young, D. J., & Fraser, B. J. (1995). Psychosocial climate and student outcomes in elementary mathematics classrooms: A multilevel analysis. Journal of Experimental Education, 64, 29–40. Goh, S. F., & Fraser, B. J. (2016). Learning environment in Singapore primary school classrooms: The ideal and the real. In K. Wallace (Ed.), Learning environments: Emerging theories, applications and future directions (pp. 125–141). Nova. Haertel, G. D., Walberg, H. J.,  & Haertel, E. H. (1981). Socio-psychological environments and learning: A quantitative synthesis. British Educational Research Journal, 7, 27–36. Hasan, A., & Fraser, B. J. (2015). Efectiveness of teaching strategies for engaging adults who experienced childhood difculties in learning mathematics. Learning Environments Research, 18, 1–13. Helding, K. A., & Fraser, B. J. (2013). Efectiveness of NBC (National Board Certifed) teachers in terms of learning environment, attitudes and achievement among secondary school students. Learning Environments Research, 16, 1–21. Henderson, D., Fisher, D. L., & Fraser, B. J. (2000). Interpersonal behavior, learning environments and student outcomes in senior biology classes. Journal of Research in Science Teaching, 37, 26–43. Henderson, D.,  & Loh, M. (2019). Using classroom environment perceptions to guide teacher professional learning. In D. B. Zandvliet & B. J. Fraser (Eds.), Thirty years of learning environments: Looking back and looking forward (pp. 127–140). Brill Sense. Huang, R., Yang, J., & Zheng, L. (2013). The components and functions of smart learning environments for easy, engaged and efective learning. International Journal for Educational Media and Technology, 7(1), 4–14. Imms, W., Cleveland, B., & Fisher, K. (Eds.). (2016). Evaluating learning environments: Snapshots of emerging issues, methods and knowledge (Advances in Learning Environments Research Series). Sense Publishers. Jessor, R., & Jessor, S. L. (1973). The perceived environment in behavioral science. American Behavioral Scientist, 16, 801–828. Khine, M. S. (Ed.). (2020). Rasch measurement: Applications in quantitative educational research. Springer. Khine, M. S., Fraser, B. J., & Afari, E. (2020). Structural relationships between learning environments and students’ non-cognitive outcomes: Secondary analysis of PISA data. Learning Environments Research, 23(3), 395–412. Khine, M. S., Fraser, B. J., Afari, E., Oo, Z., & Kyaw, T. T. (2018). Students’ perceptions of the learning environment in tertiary science classrooms in Myanmar. Learning Environments Research, 21(1), 135–152. Kim, H. B., Fisher, D. L., & Fraser, B. J. (1999). Assessment and investigation of constructivist science learning environments in Korea. Research in Science and Technological Education, 17, 239–249. Kim, H. B., Fisher, D. L., & Fraser, B. J. (2000). Classroom environment and teacher interpersonal behaviour in secondary science classes in Korea. Evaluation and Research in Education, 14, 3–22. Koch, A., Polnick, B., & Irby, B. (Eds.). (2014). Girls and women in STEM: A never ending story. Information Age Publishing. Koul, R. B., & Fisher, D. L. (2005). Cultural background and students’ perspectives of science classroom learning environment and teacher interpersonal behaviour in Jammu, India. Learning Environments Research, 8, 195–211. Koul, R. B., McLure, F. I., & Fraser, B. J. (2021). Gender diferences in classroom emotional climate and attitudes among students undertaking integrated STEM projects: A Rasch analysis. Research in Science & Technological Education. https://doi.org/10.1080/02635143.2021.1981852 Kwan, Y. W. (2020). Psychometric properties of a Chinese version of the constructivist learning environment survey among secondary-school students in Hong Kong. Learning Environments Research, 23(2), 167–184. Lightburn, M. E., & Fraser, B. J. (2007). Classroom environment and student outcomes among students using anthropometry activities in high-school science. Research in Science and Technological Education, 25, 153–166. Lim, C.-T. D., & Fraser, B. J. (2018). Learning environments research in English classrooms. Learning Environments Research, 21(3), 433–449. Liu, L., & Fraser, B. J. (2013). Development and validation of an English classroom learning environment inventory and its application in China. In M. S. Khine (Ed.), Application of structural equation modeling in educational research and practice (pp. 75–89). Sense Publishers. Long, C. S., Sinclair, B. B., Fraser, B. J., Larson, T. R., & Harrell, P. E. (2022). Preservice teachers’ perceptions of learning environments before and after pandemic-related course disruption. Learning Environments Research, 25(2), 343–357. http://doi.org/10.1007/s10984-021-09376-9 MacLeod, C., & Fraser, B. (2010). Development, validation and application of a modifed Arabic translation of the what is happening in this class? (WIHIC) questionnaire. Learning Environments Research, 13, 105–125.

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Barry J. Fraser Majeed, A., Fraser, B. J.,  & Aldridge, J. M. (2002). Learning environment and its associations with student satisfaction among mathematics students in Brunei Darussalam. Learning Environments Research, 5, 203–226. Martin-Dunlop, C., & Fraser, B. J. (2008). Learning environment and attitudes associated with an innovative course designed for prospective elementary teachers. International Journal of Science and Mathematics Education, 6, 163–190. Martin-Dunlop, C., Hohmann, C., Alabanza Akers, M. A., Determan, J., Lewter, L., & Williams, I. (2019). Evaluating the impact of a purposefully-designed active learning space on outcomes and behaviours in an undergraduate architecture course. In D. B. Zandvliet & B. J. Fraser (Eds.), Thirty years of learning environments: Looking back and looking forward (pp. 72–101). Brill Sense. Maulana, R., & Helms-Lorenz, M. (2016). Observations and student perceptions of the quality of pre-service teachers’ teaching behaviour: Construct representation and predictive quality. Learning Environments Research, 19, 335–357. Maulana, R., Opdenakker, M. C., den Brok, P.,  & Bosker, R. (2012). Teacher-student interpersonal relationships in Indonesian lower secondary education: Teacher and student perceptions. Learning Environment Research, 15, 251–271. McLure, F. I., Koul, R. B., & Fraser, B. J. (2021). University students’ classroom emotional climate and attitudes during and after COVID-19 lockdown. Education Sciences (in press). McLure, F. I., Koul, R. B.,  & Fraser, B. J. (2022). Gender diferences among students undertaking iSTEM projects in multidisciplinary vs unidisciplinary STEM classrooms in government vs nongovernment schools: Classroom emotional climate and attitudes. Learning Environments Research, 25, 917–937. https://doi.org/ 10.1007/s10984-021-09329-9 McRobbie, C. J., & Fraser, B. J. (1993). Associations between student outcomes and psychosocial science environment. Journal of Educational Research, 87, 78–85. Mogas, J., Palau, R., Fuentes, M., & Cebrian, G. (2022). Smart schools on the way: How school principals from Catalonia approach the future of education within the fourth industrial revolution. Learning Environments Research, 25, 875–893. http://doi.org/10.1007/s10984-021-09398-3 Moos, R. H. (1974). The social climate scales: An overview. Consulting Psychologists Press. Moos, R. H. (1978). A typology of junior high and high school classrooms. American Educational Research Journal, 15, 53–66. Moos, R. H. (1979). Evaluating educational environments: Procedures, measures, fndings, and policy implications. Jossey-Bass. Moos, R. H., & Trickett, E. J. (1974). Classroom environment scale manual. Consulting Psychologists Press. Murray, H. A. (1938). Explorations in personality. Oxford University Press. Nix, R. K., Fraser, B. J., & Ledbetter, C. E. (2005). Evaluating an integrated science learning environment using the constructivist learning environment survey. Learning Environments Research, 8, 109–133. Noben, I., Maulana, R., Deinum, J. F., & Hofman, W. H. A. (2021). Measuring university teachers’ teaching quality: A Rasch modelling approach. Learning Environments Research, 24(1), 87–107. OECD (Organisation for Economic Co-operation and Development). (2013). Innovative learning environments. OECD. OECD (Organisation for Economic Co-operation and Development). (2017). The OECD handbook for innovative learning environments. OECD. Parker, L. H., Rennie, L. J., & Fraser, B. J. (Eds.). (1996). Gender, science and mathematics: Shortening the shadow. Kluwer. Peiro, M. M., & Fraser, B. J. (2009). Assessment and investigation of science learning environments in the early childhood grades. In M. Ortiz  & C. Rubio (Eds.), Educational evaluation: 21st century issues and challenges (pp. 349–365). Nova Science Publishers. Pennings, H. J. M., Brekelmans, M., Wubbels, Th., van der Want, A. C., Claessens, L. C. A., & van Tartwijk, J. (2014). A nonlinear dynamical systems approach to real-time teacher behaviour: Diferences between teachers. Nonlinear Dynamics, Psychology and Life Sciences, 18(1), 23–45. Pickett, L. H., & Fraser, B. J. (2009). Evaluation of a mentoring program for beginning teachers in terms of the learning environment and student outcomes in participants’ school classrooms. In A. Selkirk & M. Tichenor (Eds.), Teacher education: Policy, practice and research (pp. 1–15). Nova Science Publishers. Polat, H., & Karabatak, S. (2022). Efect of fipped classroom model on academic achievement, academic satisfaction and general belongingness. Learning Environments Research, 25, 159–182. https://doi.org/10.1007/ s10984-021-09355-0 Prain, V. (2018). Using quantitative methods to evaluate students’ post-occupancy perceptions of personalised learning in an innovative learning environment. In S. Alterator & C. Deed (Eds.), School space and its occupation: The conceptualisation and evaluation of innovative learning environments (pp. 223–241). Sense Publishers.

214

Learning Environments Pulay, A., & Williamson, A. (2019). A case study comparing the infuence of LED and fuorescent lighting on early childhood student engagement in a classroom setting. Learning Environments Research, 22(1), 13–24. Quek, C. L., Wong, A. F. L., & Fraser, B. J. (2005). Student perceptions of chemistry laboratory learning environments, student-teacher interactions and attitudes in secondary school gifted education classes in Singapore. Research in Science Education, 35, 299–321. Rahayu, W., Putra, M. D. K., Faturochman, M., Sulaeman, E.,  & Koul, R. B. (2022). Development and validation of Online Classroom Learning Environment Inventory (OCLEI): The case study of Indonesia during the COVID-19 pandemic. Learning Environments Research, 25, 97–113. https://doi.org/10.1007/ s10984-021-09352-3 Reinius, H., Korhonen, T., & Hakkarainen, K. (2021). The design of learning spaces matters: Perceived impact of the deskless school on learning and teaching. Learning Environments Research, 24, 339–354. https:// doi-org/10.1007/s10984-020-09345-8 Rickards, T., den Brok, P., & Fisher, D. L. (2005). The Australian science teacher: A typology of teacher – Student interpersonal behaviour in Australian science classes. Learning Environments Research, 8(3), 267–287. Rincon-Gallardo, S., & Elmore, R. F. (2012). Transforming teaching and learning through social movement in Mexican public middle-schools. Harvard Educational Review, 82(4), 471–490. Robinson, E., & Fraser, B. J. (2013). Kindergarten students’ and parents’ perceptions of science classroom environments: Achievement and attitudes. Learning Environments Research, 16, 151–161. Rosenshine, B. (1970). Evaluation of classroom interaction. Review of Educational Research, 40, 279–300. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. Scantlebury, K. (2012). Still part of the conversation: Gender issues in science education. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 499–512). Springer. Scott Houston, L., Fraser, B. J., & Ledbetter, C. E. (2008). An evaluation of elementary school science kits in terms of classroom environment and student attitudes. Journal of Elementary Science Education, 20, 29–47. Scott, R., & Fisher, D. L. (2004). Development, validation and application of a Malay translation of the Questionnaire on Teacher Interaction. Research in Science Education, 34(2), 173–194. Sinclair, B. B., & Fraser, B. J. (2002). Changing classroom environments in urban middle schools. Learning Environments Research, 5, 301–328. Sink, C. A., & Spencer, L. R. (2005). My class inventory – Short form as an accountability tool for elementary school counsellors to measure classroom climate. Professional School Counseling, 9, 37–48. Sivan, A.,  & Cohen, A. (2021). The structure of teacher interpersonal behaviour in Hong Kong secondary schools. Learning Environments Research (in press). Skordi, P., & Fraser, B. J. (2019). Assessment of the psychosocial learning environment of university statistics classrooms. In K. Fisher (Ed.), The translational design of higher education learning environments and campuses: An evidence based approach (pp. 131–148). Brill Sense. Smith, E. (2008). Pitfalls and promises: The use of secondary data analysis in educational research. Journal of Educational Studies, 56(3), 323–339. Strayer, J. F. (2012). How learning in an inverted classroom infuences cooperation, innovation and task orientation. Learning Environments Research, 15, 171–193. Sun, X., Mainhard, T., & Wubbels, T. (2017). Development and evaluation of a Chinese version of the Questionnaire on Teacher Interaction (QTI). Learning Environments Research, 21(1), 1–17. Taylor, P. C., Fraser, B. J., & Fisher, D. L. (1997). Monitoring constructivist classroom learning environments. International Journal of Educational Research, 27, 293–302. Telli, S., Maulana, R., Helms-Lorenz, M. (2021). Students’ perceptions of teaching behaviour in Turkish secondary schools: A Mokken scaling of my teacher questionnaire. Learning Environments Research, 24(2), 315– 337. https://doi-org/10.1007/s10984-020-09329-8 Teo, T., Unwin, S., Scherer, R., & Gardiner, V. (2021). Initial teacher training for twenty-frst century skills in the Fourth Industrial Revolution (IR 4.0): A scoping review. Computers and Education, 170, 104223. https:// doi.org/10.1016/j.compedu.2021.104223 Tobin, K., & Fraser, B. (1998). Qualitative and quantitative landscapes of classroom learning environments. In B. J. Fraser & K. G. Tobin (Eds.), The international handbook of science education (pp. 623–640). Kluwer. Walberg, H. J. (Ed.). (1979). Educational environments and efects: Evaluation, policy and productivity. McCutchan. Walberg, H. J., Fraser, B. J., & Welch, W. W. (1986). A test of a model of educational productivity among senior high school students. Journal of Educational Research, 79, 133–139. Wan, Z. H. (2022). What predicts students’ critical thinking disposition? A comparison of the roles of classroom and family environments. Learning Environments Research, 25, 565–580. https://doi.org/10.1007/ s10984-021-09381-y

215

Barry J. Fraser Wan, Z. H., & Cheng, M. H. M. (2018). Classroom learning environment, critical thinking, and achievement in an interdisciplinary subject: A study of Hong Kong secondary school graduates. Educational Studies, 45(3), 285–304. Welch, A. G., Cakir, M., Peterson, C., & Ray, C. M. (2012). A cross-cultural validation of the Technology-Rich Outcomes-Focussed Learning Environment Inventory (TROFLEI) in Turkey and the USA. Research in Science & Technological Education, 30, 49–63. Welch, A. G., Cakir, M., Peterson, C., & Ray, C. M. (2014). The relationship between gender and classroom environment in Turkish science classrooms. Educational Research and Reviews, 9(20), 893–903. Welch, W. W., & Walberg, H. J. (1972). A national experiment in curriculum evaluation. American Educational Research Journal, 9, 373–383. Wolf, S. J., & Fraser, B. J. (2008). Learning environment, attitudes and achievement among middle-school science students using inquiry-based laboratory activities. Research in Science Education, 38, 321–341. Wong, A. F. L., & Fraser, B. J. (1996). Environment-attitude associations in the chemistry laboratory classroom. Research in Science & Technological Education, 14, 91–102. Wong, A. F. L., Young, D. J., & Fraser, B. J. (1997). A multilevel analysis of learning environments and student attitudes. Educational Psychology, 17, 449–468. Wubbels, T., & Brekelmans, M. (2012). Teacher-students relationships in the classroom. In B. J. Fraser, K. G. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 1241–1255). Springer. Wubbels, T., Brekelmans, M., den Brok, P., & van Tartwijk, J. (2006). An interpersonal perspective on classroom management in secondary classrooms in the Netherlands. In C. Evertson & C. Weinstein (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 1161–1191). Lawrence Erlbaum. Wubbels, T., Brekelmans, M., Mainhard, T., den Brok, P., & van Tartwijk, J. (2016). Teacher – student relationships and student achievement. In K. R. Wetzel & G. B. Ramani (Eds.), Handbook of social infuences in school contexts: Social-emotional, motivation and cognitive outcomes (pp. 127–145). Routledge. Wubbels, T., den Brok, P., van Tartwijk, J., & Levy, J. (Eds.). (2012). Interpersonal relationships in education: An overview of research (Advances in Learning Environments Research Series). Sense Publishers. Wubbels, T., & Levy, J. (Eds.). (1993). Do you know what you look like? Interpersonal relationships in education. Falmer Press. Zandvliet, D. B., & Fraser, B. J. (2004). Learning environments in information and communications technology classrooms. Technology, Pedagogy and Education, 13, 97–123. Zandvliet, D. B.,  & Fraser, B. J. (2005). Physical and psychosocial environments associated with networked classrooms. Learning Environments Research, 8, 1–17. Zandvliet, D. B., & Fraser, B. J. (Eds.). (2019). Thirty years of learning environments: Looking back and looking forward (Advances in Learning Environments Research Series). Brill Sense. Zaragoza, J. M., & Fraser, B. J. (2017). Field-study classrooms as positive and enjoyable learning environments. Learning Environments Research, 20(1), 1–20.

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Appendix Overview of scales contained in eight classroom environment instruments (LEI, CES, ICEQ, CUCEI, SLEI, CLES, WIHIC, and COLES) Table 7.2 Overview of Scales Contained in Eight Classroom Environment Instruments (LEI, CES, ICEQ, CUCEI, SLEI, CLES, WIHIC, and COLES) Instrument

Level

Items per Scale

Scales Classifed According to Moos’s Scheme Relationship Dimensions

Personal Development Dimensions

System Maintenance and Change Dimensions

Learning Environment Inventory (LEI)

Secondary

7

Cohesiveness Friction Favoritism Cliqueness Satisfaction Apathy

Speed Difculty Competitiveness

Diversity Formality Material environment Goal direction Disorganization Democracy

Classroom Environment Scale (CES)

Secondary

10

Involvement Afliation Teacher support

Task orientation Competition

Order and organization Rule clarity Teacher control Innovation

Individualized Classroom Environment Questionnaire (ICEQ)

Secondary

10

Personalization Participation

Independence Investigation

Diferentiation

College and University Classroom Environment Inventory (CUCEI)

Higher education

7

Personalization Involvement Student cohesiveness Satisfaction

Task orientation

Innovation Individualization

Science Laboratory Environment Inventory (SLEI)

Upper secondary/ higher education

7

Student cohesiveness

Open-endedness Integration

Rule clarity Material environment

Constructivist Learning Environment Survey (CLES)

Secondary

7

Personal relevance Uncertainty

Critical voice Shared control

Student negotiation

What Is Happening In this Class? (WIHIC)

Secondary

8

Student cohesiveness Teacher support Involvement

Investigation Task orientation Cooperation

Equity

ConstructivistOriented Learning Environment Survey (COLES)

Secondary

11

Student cohesiveness Teacher support Involvement Young adult ethos Personal relevance

Task orientation Cooperation

Equity Diferentiation Formative assessment Assessment criteria

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Diversity and Equity in Science Learning Section Editors: Cory A. Buxton and Okhee Lee

8 UNPACKING AND CRITICALLY SYNTHESIZING THE LITERATURE ON RACE AND ETHNICITY IN SCIENCE EDUCATION How Far Have We Come? Felicia Moore Mensah and Julie A. Bianchini

Historically and contemporarily, in the previous version of this chapter, Parsons (2014) gave an informed account of the evolution of race and ethnicity in the United States, from the Naturalization Act of 1790 to the shift in the language of using “ethnic-signifying labels” for self-identifcation and identifcation from others (p. 169). “Race is understood as real not because it is an essential category but as a historically specifc means of efecting certain forms of social organization, of mediating human relations” (Warmington, 2009, p. 289); it is indeed real and demands our attention for discussion and research. Race and ethnicity are related, yet theoretically they are distinct. For example, in his summary of race, ethnicity, and culture, Fenton (2010) distinguished the construct of race as founded on markers of diference based upon visible, physical characteristics; ethnicity as based upon cultural markers that serve as the reference point for diference; and culture as broadly defned to encompass diverse elements, such as customs, language, religion, and traditions as well as distinctions based upon self-identifcation and self-afliation (Nagel, 1994) or group identity and historical experiences (Omi & Winant, 1994). Thus, race tends to reference biological or physiological diferences, whereas ethnicity tends to reference cultural diferences. However, to bring a bit more context and reason for the complexity in distinguishing race and ethnicity, Kivisto and Croll (2012) proposed three responses for explaining race and ethnicity: The frst suggests that racial groups and ethnic groups are two diferent types of groups. The second position claims that while racial and ethnic groups are usually distinct, in some circumstances they are overlapping. The third views racial groups as a subset of ethnic groups. (p. 4) Even with research acknowledging that race is not a biological phenomenon (Smedley & Smedley, 2005), the hierarchical nature of diference is maintained (Omi & Winant, 1994) because race more than ethnicity is defned in terms of power relations, group hierarchies, assessments of superiority and inferiority, and a “natural category” or “permanent” designation, with ethnicity subject to historical modifcation (Cornell & Hartman, 2007, pp. 26–32). Arguments against race as a social construction

DOI: 10.4324/9780367855758-11

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over the genetic or biological basis of race have been accepted by many theorists who are ready to move onward in the conceptualization of a white racial framing (Feagin, 2020) and the “new racism” explained by colorblindness and race neutrality, which do much harm when these are seen as goals and ends of a post-racial world (Bonilla-Silva, 2017). These notions do not represent racial progress but racial concealment in that a “structural-foundation metaphor” captures well the realities of the United States’ racism, past and present (Feagin, 2020, p. 3). On a global scale, and historically, the use of racial and ethnic terms is based on categorization and hierarchy, including a rationalization for European colonization, slavery, and subjugation of Indigenous Peoples by settlers from Australia, Canada, and the United States (Kivisto & Croll, 2012). Countries make distinctions across race and/or across ethnicity. For example, in Belgium, the Flemish and Wallon ethnic communities are divided along ethnicity rather than race; similarly, in Canada, for British Canadians and French Canadians; and in contrast, in South Africa, four groups comprise their racial system. In these examples and many others, the use of terms and the language of race and ethnicity are contextualized; thus, attention to how people are named and how they name themselves, whether along racial or ethnic lines, requires awareness and understanding of the historical context, place, and how categorizations change. As additional examples, one can look at the US census categories over time, from 1790 to 2020, or the Ofce for National Statistics in the United Kingdom. How people are categorized and what constitutes a racial or ethnic category will continue to shift and change, which helps us to realize that these categories are socially constructed. In her review of unpacking and critically synthesizing the literature on race and ethnicity in science education published in an earlier handbook, Parsons (2014) noted the limited scope and depth of the body of work in science education on race and ethnicity from a critical perspective and proposed recommendations for future research and scholarship for science education. Parsons borrowed from anthropology, history, and sociology to defne race and ethnicity as “context-based sociohistorical constructs that exist across space and time” (Parsons & Bayne, 2012, as cited in Parsons, 2014, p. 167), that are situated in the United States and that have evolved over time. She gave secondary attention to the methods and fndings of the studies identifed. We have taken up these areas as well as highlighted studies across global contexts in this current review. Parsons (2014) ofered four critiques, or limitations, of race and ethnicity in the science education literature: presentism, lack of conceptual clarity, individualism, and methodological myopia. First, presentism is “a view that exclusively situates and circumscribes current conditions to the here and now” (p. 168). Second, a lack of conceptual clarity is “the neglect of the purposeful construction of race and ethnicity over time in the United States, the connection between their historical meanings and contemporary adaptations, and the resemblances of past and present outcomes” (p. 168). Third, the research on race and ethnicity did not explicitly defne these constructs, or proxies were used, and frameworks considered the individual while ignoring the structural and systemic views of race and ethnicity. Finally, the fourth is methodological myopia, which is a focus on a narrow selection of research methods that “severely limited the potential” impact to address equality and equity in science education (p. 168). Finally, Parsons (2014) provided several recommendations and suggestions for research and scholarship on race and ethnicity for the feld of science education to address. These suggestions include using tenets of critical race theory as underpinnings to lessen presentism and provide conceptual clarity to balance race and ethnicity at the individual, group, structural, and systemic levels. In terms of methodological myopia, she recommended taking on a “transformative paradigm” (p. 168) as an alternative to either quantitative or qualitative methods and post-positivistic and constructivist paradigms that reduce and restrict science education research to inform practice and policy. The purpose in conducting this current review is to determine our progress in identifying the ways the structured and systemic construction of race and ethnicity  – or the individual, grouplevel, and systemic construction of race and ethnicity (taken from Parsons, 2014)  – have shaped

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the teaching and learning of science and how this has been taken up in science education research. Like Parsons (2014), we unpack and synthesize the recent literature and ofer recommendations to transform methodological and conceptual work in science education research, policy, and practice. We also examine how and in what ways the feld has realized the recommendations proposed by Parsons in the years since her review, in particular, using critical theories alone or in conjunction with other theories in race- and ethnicity-focused research; abandoning the paradigm wars of research approaches to focus on breadth and depth of research; and not only generating knowledge but also transforming science education to become more equitable and socially just.

Method We conducted a systematic review of the literature on race and ethnicity in science education published during the last decade – from 2010 to 2020. (A few of the articles included here have 2021 or 2022 publication dates but were initially published online in 2019 or 2020.) Although the previous handbook was published in 2014, we decided to begin our review in 2010 for two reasons. First, the previous version of this chapter did not review articles, particularly articles outside the United States, in a systematic way; the review missed some articles that we found to be relevant to this current review. Second, because the previous version of this chapter found the literature on race and ethnicity to be limited in scope and depth from a critical perspective, we wished to provide readers a sense of how the literature has changed over the most recent decade, including part of the time from the previous chapter. We focused our review on relevant science education articles published in the following ten well-respected, peer-reviewed journals: the American Educational Research Journal, Cultural Studies of Science Education, Educational Researcher, the International Journal of Science Education, the Journal of Science Teacher Education, the Journal of Research in Science Teaching, Research in Science Education, School Science and Mathematics, Science Education, and Teachers College Record. We used the advanced search tool for each journal and entered the following search terms: science, race, and ethnicity. Our searches resulted in a total of 833 possible science education articles, which we uploaded into Zotero, a reference management software program. We narrowed this initial pool of articles through two rounds of review. For each round, we used the following additional criteria: Studies needed to be empirical; to focus on some aspect of K–12 education (e.g., K–12 students, informal K–12 science education, preservice or practicing K–12 teachers, and/or K–12 science teacher educators); and to explicitly attend to race and ethnicity in the study’s problem statement, literature review, and/or conceptual framework, not simply in the description of the research context or as a factor in the analysis (for more on this last criterion, see Parsons, 2014, p. 173). In our frst round, we read the abstract of each article to determine its ft with our second set of criteria. In doing so, we reduced our initial pool to 231 articles. In our second round of review, we conducted a more thorough examination of each article, again using the aforementioned criteria in reading not only each article’s abstract but the theoretical framework and/ or literature review, methods, and discussion sections as well. After this second round of review, we determined 169 articles met both sets of criteria – 143 from the United States and 26 from other countries, including Brazil, China, Israel, Nigeria, South Africa, and the United Kingdom. To facilitate the presentation of our fndings, we organized these resulting 169 articles into eight categories: Three are structured by theory (Categories A, B, and C) and fve, by empirical topic (Categories D, E, F, G, and H). We acknowledge from the outset that several articles could have been placed in more than one of our eight categories and that several do not neatly ft into any category. We then used Parsons’s (2014) three recommendations for future research on race and ethnicity in science education to unpack and synthesize the articles in each category. As previously stated, Parsons recommended (1) that critical theories be used alone or with other theories to frame studies of race and ethnicity, (2) that mixed methods be used more regularly to investigate the complex nature

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Total Number of Studies

Category A: Critical Race Theory

11

1

Category B: Critical Race Theory and Intersectionality

11

0

Category C: Sociocultural Perspectives on Theory and Identity

43

8

Category D: Science Curriculum and Pedagogy

53

11

Category E: Language in Intersection with Race and Ethnicity

16

4

Category F: Aspiration and Motivation to Learn and Teach Science

16

1

Category G: Teacher Perceptions and Experiences

14

0

5

0

Category H: Assessments

Number of Studies With Mixed Methods

of race and ethnicity constructs, and (3) that studies seek to transform science education to become more equitable and socially just. For example, within a given category, we identifed studies as qualitative, quantitative, or mixed method (Creswell & Creswell, 2017). Table 8.1 lists our eight categories, the number of articles included in each, and the number of articles that employed mixed methods (see again the second recommendation from Parsons, 2014). The number of studies that employed mixed methods was strikingly small, comprising only 15% (n=25) of the 169 studies we reviewed here. Indeed, only three of our eight categories had more than one study that included mixed methods. In contrast, approximately 60% of the studies reviewed (n=101) used qualitative methods, and a quarter (n=43) used quantitative methods.

Critical Race Theory: The Structured and Systemic Construction of Race and Ethnicity We begin our review of K–12 science education literature by focusing on research that employed a critical race theory lens. As stated earlier, this was the frst of Parsons’s (2014) three recommendations for future research on race and ethnicity. From our pool of 169 articles, we identifed a total of 22 articles that drew from constructs tied to critical race theory to shape their investigation. We divided these articles into two categories: (1) those that employed critical race theory and (2) those that used intersectionality, a construct that connects to critical race theory, Black feminist thought, and critical race feminism (Collins, 2016; Crenshaw, 2016; Wing, 1997). We elaborate on each of these two categories next.

Category A: Critical Race Theory We identifed 11 articles – all situated in the United States – that were categorized as critical race theory. Critical race theory arose from critical legal studies (Delgado & Stefancic, 2017) and has been applied across various felds, including education (Dixson et al., 2016). Nine of these articles were qualitative studies (Kang & Zinger, 2019; Nazar et al., 2019; Ridgeway & Yerrick, 2018; Settlage,

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2011; Sparks & Pole, 2019; Visintainer, 2020; Wallace & Brand, 2012; Yerrick & Johnson, 2011; Zirkel & Pollack, 2016), one was mixed methods (Mutegi et al., 2019), and one was quantitative (Walls, 2016). Six of these studies used critical race theory within the context of K–12 schools with African American students in high school science classes (Visintainer, 2020; Yerrick  & Johnson, 2011), with two middle school science teachers who taught their African American students using culturally responsive teaching (Wallace  & Brand, 2012), and with three out-of-school programs that engaged African American students and their teachers (Mutegi et al., 2019; Nazar et al., 2019; Ridgeway  & Yerrick, 2018). Three studies grouped in this category used critical race theory in teacher education contexts: one is an education course with education majors (Settlage, 2011), one is a master’s-level course on diversity for teachers (Sparks & Pole, 2019), and one is a series of science methods courses for novice teachers (Kang & Zinger, 2019). The remaining two studies used critical race theory to analyze school district data (Zirkel & Pollack, 2016) or empirical studies on nature of science (NOS; Walls, 2016).

Counternarratives In 8 of the 11 studies in this category, scholars gave attention to one of critical race theory’s dominant tenets – counternarratives or counterstorytelling or counterstories. Counterstorytelling, defned by Solórzano and Yosso (2002b), is a method of telling the stories of people whose experiences are on the margins of society and is used as “a tool for exposing, analyzing, and challenging the majoritarian stories of racial privilege” (p. 32). They are shared to dispel the dominant narratives that pervade society and education. Thus, as a tool, counterstorytelling draws explicitly on experiential knowledge and the unique voice of people of color (Matsuda et al., 1993). While counternarratives are used to question what is accepted through stories and narratives from people of color (Zamudio et al., 2010), they may be used as a methodological approach in the collection and analysis of counterstories (Parker & Lynn, 2002; Solórzano & Yosso, 2002b). Settlage (2011) used counternarratives to extinguish defcit thinking about white education majors. In collaboration with his preservice teachers, he revealed a more complex and less “caricatured representation” of the shifting identities of mainstream future teachers who did not ft the “damaged goods” image of working with students from diverse cultural backgrounds. Visintainer (2020) also collected counternarratives with her study participants to examine youths’ accounts of their racialized science experiences. She investigated how these high school students of color made sense of racialized narratives about who can and does science. In their longitudinal research study, Nazar et al. (2019) used the construct of critical epistemologies of place to engage with one 12-year-old African American boy in engineering design with experts and knowledgeable others in his community space. These researchers used counternarratives more in the sense of challenging dominant narratives of place, where “epistemologies of place as ‘embodied knowledge’ arising from one’s relationship with their environment, always oriented towards their future and that of their survival” (p. 642). Nazar and colleagues suggested that engaging in engineering design through a critical epistemology of place involves an iterative and generative process of layering community wisdom and knowledge onto STEM toward (1) acknowledging how epistemologies of place – and their layers – challenge dominant master narratives and (2) reimagining practices in place as well as (3) transforming the dangerous territory of STEM. In brief, these researchers expanded upon current understandings of supporting youth in engaging in engineering through highlighting the vital role of sociohistorically constructed understandings of STEM and community in determining when, how, and why engineering takes place. These latter two studies used counternarratives as a primary method of gathering stories from participants and themselves, like Settlage (2011). Walls (2016) used critical race theory to examine 112 peer-reviewed studies, from 1967 to 2013, that investigated NOS. This was the only quantitative study in this category that used

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critical race theory. Walls reported that Black, Latino/a, Native American, and other people of color were found to be disproportionally excluded as participants in NOS research, thus bringing to attention the silencing of these individuals and making suggestions for NOS research to make it more equitable. In his fndings, Walls identifed four types of inequity based on race operating within NOS research analyzed. In addition to missing or muted counternarratives, he highlighted the existence of a colorblind ideology, white privilege, and structural racism in NOS research. Furthermore, within this subcategory of studies, critical race theory was used in conjunction with other theoretical perspectives. As one example, Sparks and Pole (2019) used critical race theory with social cognitive career theory in their study of 14 science and mathematics teachers. The participants engaged in a series of virtual chats using open-ended questioning that was also facilitated by two university instructors. The topics of ethnic and racial diversity, gender, and stereotypes were discussed with the participants and their students. Sparks and Pole presented three primary themes: understanding of issues related to stereotypes, encouragement of females and minorities to pursue careers in STEM, and the place for diversity discussions in science and mathematics classrooms. The teachers felt burdened by administrative and curricular constraints that inhibited their ability to participate in thought-provoking critical conversations. As a second example of using critical race theory with other theoretical constructs, in this case, LatCrit theory and the social construction of merit and worth, Zirkel and Pollack (2016) presented a case analysis of the controversy and public debate generated from a school district’s eforts to address racial inequities in educational outcomes. The narratives presented to the school district and communicated from the debate and in documents revealed how students were viewed and how funding was allocated. They showed that diverting special funds from the highest-performing students seeking elite college admission to the lowest-performing students who were struggling to graduate from high school was met with great opposition. Zirkel and Pollack identifed a narrative cycle of debate: (1) colorblind rhetoric, (2) academic performance is presumed to emerge solely from talent and efort so that (3) academic performance then becomes a measure of worth, and fnally, (4) eforts to address racial disparities are “unfair”. The narratives presented in documents and debated among stakeholders identifed some students as worthy and others as unworthy, which greatly infuenced funding, educational outcomes, and policies.

Microaggressions One study used critical race theory, but through the process of analysis, ofered extended constructs related to it (Mutegi et al., 2019). In the only mixed-methods study in this category, Mutegi et al. (2019) addressed African American students’ experiences in science with microaggressions, which are subtle, everyday forms of racism (Solórzano & Huber, 2020). Racial microaggressions are brief and commonplace daily verbal, behavioral, or environmental indignities, whether intentional or unintentional. They communicate hostile, derogatory, or negative racial slights and insults toward people of color (Sue et al., 2007). Mutegi et  al. (2019) investigated both secondary students and teachers who participated in a two-week nanotechnology camp. From the pre- and post-survey data, the camp was found to successfully foster increased interest in STEM; however, the ethnographic data revealed diferences in how the participants experienced the camp. More specifcally, the qualitative data revealed that African American students had radically diferent experiences than their non–African American peers and identifed specifc incidents of microaggressions. The microaggressions were pervasive  – they came from students, instructors, and the environment – and in response, African American students adopted detachment-coping strategies. All of these factors collectively worked against the African American students’ success, camp experience, and learning.

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Category B: Critical Race Theory and Intersectionality Intersectionality is also a tenet of critical race theory (Delgado  & Stefancic, 2017). The concept of intersectionality refers to particular suppressive forms that are manifested by the overlapping or intersection of multiple oppressions. For example, race and gender are intersecting oppressions for Black women in a society where full membership is based upon whiteness for feminist thought, maleness for Black social and political thought, and a combination of both for maximum participation in mainstream society (Collins, 2016). Therefore, intersectionality is a way of understanding and analyzing the complexity in the world, in people, and in human experience. Six core ideas provide a guidepost for thinking through intersectionality as an analytic tool: (1) inequality, (2) relationality, (3) power, (4) social context, (5) complexity, and (6) social justice. All of these ideas do not have to be present in a particular study nor do they have to appear in studies in the same ways (Collins, 2016). For this reason, an analysis of studies of critical race theory and intersectionality for this category is divided into subcategories related to some of these analytical tools and within diferent areas of science education. We identifed 11 articles that were categorized as intersectionality. Ten of these articles used different approaches to qualitative research. Four were long-term or longitudinal studies (Calabrese Barton & Tan, 2018; Mark, 2018; Mensah, 2019; Pringle et al., 2012). Three others were conducted in science methods courses (Mensah & Jackson, 2018; Rivera Maulucci, 2013; Sparks, 2018); one was conducted in an advanced placement secondary biology class (Ryu, 2015); one, in a communitybased informal STEM program (King & Pringle, 2019); and one, in and outside science classrooms (Teo, 2015). The one quantitative study in this category used a national cohort of eighth-grade students to consider how diferent gender and racial/ethnic subgroups compared to white males in their aspirations for careers in science or mathematics (Riegle-Crumb et  al., 2011). Only one of these studies (Teo, 2015) was situated in a country (Singapore) other than the United States. The ten qualitative studies in this group are further divided into the two following subcategories.

Part 1: Intersectionality in Science Teacher Education Four studies in science teacher education used critical race theory as both a theoretical and methodological lens (Mensah, 2019; Mensah & Jackson, 2018; Rivera Maulucci, 2013; Sparks, 2018). First, Mensah’s (2019) longitudinal case study utilized critical race theory methodology to chronicle the journey of an African American female in elementary science teacher education. The study looked at her educational history frst as a young Black child and then how she navigated a contested, racialized, predominantly white teacher education program; grew and developed in science education; and secured her frst full-time teaching appointment as an elementary teacher. Mensah noted that intersectionality foregrounds and adds to the complexity of understanding race, racism, and science in teacher education in telling several critical race narratives of Michele, the teacher in the study. Sparks (2018), in his science methods course, worked with a group of three African American female preservice STEM teachers. These preservice teachers participated in semi-structured, faceto-face interviews, where they shared their experiences in STEM, reasons for their choice of major, obstacles and challenges, instances of racism or sexism, and identity development. The results showed that the females were not discouraged by their underrepresentation; were confdent in their abilities; and expressed wide variation in their identity development related to race, gender, and feld of study. Like Mensah (2019) and Sparks (2019), Rivera Maulucci (2013) focused on the development of teacher identity within a science methods course. As a case study of the historical development of an African American, Caribbean preservice teacher’s social justice stance, Rivera Maulucci used emotional genealogy, critical emotional praxis, and positional identity as frameworks. The narratives she shared of Nicole focused on what emotions she expressed and how those emotions helped Nicole

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position herself concerning social justice issues as she navigated becoming a social justice chemistry teacher. Finally, the setting for the last article in this subcategory was a graduate-level preservice elementary science methods course (Mensah & Jackson, 2018). The purpose of this study was to analyze the experiences of preservice Teachers of Color (PTOC) enrolled in the science methods course and how they gained access to science as white property. Mensah and Jackson used themes from critical race theory (CRT), such as the unique voice of people of color, and positionalities to interpret the data from a CRT perspective. Taken together, the four studies in this subcategory used several of the analytical tools of intersectionality, such as a focus on inequality, power, and social justice, to transform science teacher education to make it more equitable and socially just for teacher candidates. Attention to the race and ethnicity of the participants also revealed how their identities assisted or challenged their navigation in science teacher education.

Part 2: Intersectionality in School or Community-Based Settings Six studies employed intersectionality to investigate science teaching and learning in school and/ or community-based settings. The study by Teo (2015) was the only one that focused on teachers; the rest focused on students and are discussed further here. Calabrese Barton and Tan (2018), as one example, conducted a multiyear study focused on 41 team projects involving 48 youth makers within the rich culture of their communities. The interviews with the youth teams covered questions about understanding the artifact they created, engaging in the process of making, sharing STEM knowledge and practices, and expressing their meaning and value of engaging in the project. A central aspect of the youth’s participation in the community maker project was to “reclaim their experiences, lives, and communities in more complex and agentic ways than what dominant narratives imply” (p. 779). A major lesson from the study was supporting youth in co-making in their community and situating knowledge production within their local contexts. By doing so, for the youth and their projects, participation, learning, and sharing were pivotal in decolonizing and disrupting normative power dynamics among the youth, adults, and context. The youth drew from their local knowledge as “oppressed and empowered insiders and forced attention on typically silenced narratives around low-income communities” (p. 798). King and Pringle (2019) also utilized intersectionality, and specifcally critical race feminism, in their work with K–12 Black girls to expose racial and gender essentialism as the girls navigated between formal and informal STEM learning spaces. Critical race feminism, like intersectionality, focuses on the lives of people who face multiple forms of discrimination based on race, gender, and class, for example, and reveals how these factors interact with a system of white male patriarchy and racist oppression (Wing, 1997). Like Calabrese Barton and Tan’s (2018) study of youth community science learning, King and Pringle (2019) used counterspaces, or “sites where defcit notions of people of color can be challenged and where a positive climate can be established and maintained” (Solórzano et al., 2000, p. 70). The researchers provided shelter from the “daily torrent of microaggressions” (Howard-Hamilton, 2003, p. 23) that people of color experience. This community-based program operated in partnership with local organizations and businesses as well as university students and faculty. The program exposed participants to colleges, careers, and other informal science institutions. A total of 73 students participated, 39 of whom were girls and 34 of whom were boys; all 73 participants identifed as Black or biracial, and approximately 74% of the participants qualifed for free or reduced lunch. Using CRT methodology (Solórzano  & Yosso, 2002a), the authors shared counterstories of the girls’ experiences in the I AM STEM program and presented their multidimensional identities across contexts. Illuminating the voices of Black girls informs research methods and ofers some best practices for more inclusive informal STEM learning programs for children of color.

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Though Pringle et al. (2012) did not use intersectionality as the theoretical framework for their study, their work was rooted in the feminist ideology of positionality (Holland et al., 1998), which provides a framework to examine the complex and relational roles of ethnicity, gender, socioeconomics, and other identifers that can infuence their experiences. Pringle and colleagues argued that the persistent underachievement of African American girls from low-income communities remains a challenge and that this phenomenon has not been addressed thoroughly in the literature. In their three-year longitudinal study, they examined how African American girls positioned themselves concerning science and mathematics learning from ffth to seventh grade. Their reporting of fndings from this study looked at the positioning of teachers, counselors, and parents in this process, and the science and mathematics teachers’ actions, perceptions, and positioning of the African American girls. In their fndings, they indicated that the science and mathematics teachers lacked awareness of their roles as advocates for Black girls and that they were also unaware of the deleterious efects of low expectations, which could ultimately afect the girls’ positionalities as science and mathematics learners as they transitioned into secondary school. Further, standardized tests utilized as a measurement tool of accountability also afected teachers’ beliefs and behaviors in positioning the girls in science and mathematics. Similar to the work by Pringle et al. (2012), Ryu (2015) did not apply an intersectionality lens, yet she examined six Korean transnational girls enrolled in two advanced placement (AP) biology classes to understand their experiences in science classrooms at the intersection of race, language, and gender. She confronted the model minority stereotype for Asian students, which is particularly salient in science-, technology-, engineering-, and mathematics-related disciplines. The premise of the study was to inquire why the six girls chose to take advanced science and mathematics courses. Ryu noted that the girls’ decision to take such courses was their way of negotiating their positions as members of a racial minority, as English learners, and as Koreans with stereotyped characteristics. Though they were challenged due to modes of language, unfamiliarity with science terminology, complex texts, and various knowledge beyond the texts, as well as social linguistic skills and discursive practices, they did not feel empowered to pursue academic support in the gendered settings of their advanced courses. Most of the studies we identifed on intersectionality focused on girls or women. However, Mark (2018), in her long-term qualitative case study, focused on one African American male, Randy, who expressed high-achieving STEM career goals in computer science and engineering. Randy developed a STEM identity during an informal STEM-for-social-justice community of practice program where he also used an “economics” lens and integrated STEM, economics, and community engagement. This study communicated the importance of recognizing and supporting the development of holistic and nontraditional STEM identities, especially for diverse populations in STEM, such as Black boys, and exploring long-term STEM career options.

Additional Constructs Used to Shape Investigations of Race and Ethnicity We grouped the remaining 147 articles into six additional constructs or categories that facilitated our unpacking and critically synthesizing the literature on race and ethnicity in science education. We begin this section with our third conceptual category – sociocultural perspectives on theory and identity – and then present the remaining topical categories by size – from the category with the largest number of articles identifed to the smallest. Similar to the previous sections, we found common themes to divide and discuss the articles by paying attention to the recommendations from Parsons (2014).

Category C: Sociocultural Perspectives on Theory and Identity We categorized 43 articles as sociocultural perspectives on theory and identity. The articles we reviewed in this category drew from sociocultural perspectives in their investigations of science

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education tied to race and ethnicity. We divided the articles into two major parts: sociocultural theoretical frameworks and sociocultural identity. The latter category was further divided into mixed methods and quantitative designs, school contexts, and teacher education for ease of presenting the studies.

Part 1: Sociocultural Theoretical Frameworks There are 13 articles in this subcategory on sociocultural perspectives as they relate to theoretical frameworks used by researchers to guide their work. All of these studies took a qualitative approach, although they were situated in diferent contexts, such as science teacher education (Alexakos et al., 2016; McNewBirren et al., 2018); K–12 science classrooms with a focus on students (Carlone et al., 2015; Kang et al., 2018; Parker, 2014; Varelas et al., 2015; Zhang & Barnett, 2015); K–12 science classrooms with a focus on interactions between teachers and students (Pitts, 2011; Richardson Bruna, 2010); students’ lived experiences across academic, professional, psychosocial, and emotional spaces (Gallard Martínez et al., 2019); American [Native] Indian students’ performance on standardized tests (Dupuis & Abrams, 2017); cross-cultural exploration of children’s everyday ideas across the United States, Singapore, and China (Wee, 2012); and families’ experiences in science museums in London (Archer et al., 2016). Within this collection of studies, researchers emphasized the importance of student voice or research with participants from diverse positionalities and experiences in science, mathematics, and teacher education. Findings from the studies revealed how the educational system was vulnerable to society’s cultural values and norms and thus infuenced Latina students’ STEM trajectories and interests in science (Gallard Martínez et al., 2019; Parker, 2014), African American students’ behaviors and attitudes in their science classes (Kang et al., 2018), and Native American students’ performance on standardized tests (Dupuis & Abrams, 2017). What these studies have in common in their use of sociocultural frameworks is the visible interplay of race, class, and gender primarily in students’ and teachers’ interactions within larger societal structures and the ways they interrogate and navigate meanings of science. Individual case studies make evident these experiences and challenges for participants (Pitts, 2011; Richardson Bruna, 2010; Varelas et al., 2015). The theoretical perspectives used by the researchers juxtapose sociocultural perspectives that allow race and ethnicity to be mediating factors in understanding science teaching, learning, and experiences. We would not say these perspectives are inherent to the participants’ social identities or the theoretical frameworks used, yet the positionalities of the participants make explicit reference to their experiences due to racial and ethnic identity as well as gender and class. For example, two studies specifcally, one that introduced a sociocultural analytical framework (Gallard Martínez et al., 2019) and another that used multiple sociocultural lenses (Richardson Bruna, 2010), revealed the complex interaction of race and ethnicity with class. First, Gallard Martínez and colleagues (2019) introduced the concept of contextual mitigating factors (CMFs) as a theoretical construct and found it helpful in understanding how Latinas who demonstrated success in the STEM pipeline navigated in shifting socio-historical-political contexts. Using CMFs as both theoretical and methodological analyses, the authors discussed that fuid social felds are essential to understanding the factors Latinas experience and create in their social interactions. The development of CMFs is discussed within the constructs of social place (Bourdieu  & Wacquant, 1992), social feld (Swartz, 1997), and dynamic space (Tobin, 2009). The researchers presented two case studies focusing on Latinas’ successes in STEM felds using an intrinsic case study method. They emphasized the benefts associated with CMF analysis, namely, the provision of an additive framework in understanding the lived experiences of minoritized groups. By attending to the role macro-, meso-, and microgenic CMFs play in ethnic minority students’ educational experiences, educators at all levels may play a substantively larger role in helping sustain their agency as learners.

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Second, building upon previous work, Richardson Bruna (2010) continued her investigation of the experiences of a Mexican immigrant transnational student, Augusto, and employed a class-frst perspective to examine Augusto’s science education experience coming from a subsistence farming community in rural Mexico to an industrialized meatpacking community in semirural Iowa. Augusto underwent a class transformation, or a change in his class identity, that was a product of his science class. Augusto worked to resist the processes of disciplinary production as he reshaped his teacher’s instruction through specifc transnational social capital using peer mediation. The attention to a sociohistorical, situated perspective to science teaching and learning contributed to how not only race and ethnicity but also “class-cognizant” analysis in science education was informed by Augusto’s transnational social capital. Finally, there has been little research from both an international perspective and a critical foundation of science learning perspective of children’s everyday ideas. Using social constructivism as a theoretical framework, Wee (2012) conducted a study with 210 children, mainly Asians and Asian Americans, from urban settings. The participants ranged in age from elementary to middle school. This paper explored children’s everyday ideas and drawings about the environment across the United States, Singapore, and China to understand what they reveal about children’s relationship to the environment. The fndings implied the need for (1) a change in the role of science teachers from knowledge providers to social developers, (2) a science curriculum that is specifc to learners’ experiences in diferent sociocultural settings, and (3) a shift away from inter-country comparisons using international science test scores. Though several categories supported existing literature on children’s ideas about the environment, Wee acknowledged that there were novel categories that also emerged, giving new insight into the role that language, sociocultural norms, and ethnicity play in shaping children’s everyday ideas.

Part 2: Sociocultural Identity For this second subcategory on sociocultural perspectives related to identity, we identifed 30 articles and further divided them into three subcategories. In most of these studies, the researchers identifed the race, ethnicity, and gender of the participants, and in a few cases, additional identity markers, such as socioeconomic status or immigration status. However, these studies varied along several other dimensions, including methods employed. Of these studies, one (Chapman & Feldman, 2017) explicitly discussed their rationale for using the terms “race” and “ethnicity”. To elaborate, Chapman and Feldman (2017) used the construct “race/ethnicity” to denote their understanding of race as “the physical characteristics of an individual that arise genetically, while ethnicity is the norms, customs, and rituals of a particular group in a specifc region” (p. 470). They continued, “It is not always possible to diferentiate between the students’ perceptions concerning race or ethnicity. For this reason, we use the phrase race/ethnicity throughout the study” (p. 470). Later in the study, in their description of the Identify-A-Scientist instrument they used to examine students’ perceptions of scientists, Chapman and Feldman explained why they asked students to state the ethnicity, rather than the race, of the individuals they selected as most likely to be scientists: However, our decision to use the term ethnicity rather than race was based in part on the considerable diversity in the community in which the study took place. For example, some participants and their families might identify as African American, Spanish speaking African Caribbean, English speaking African Caribbean, Kreyol speaking African Caribbean, or from any of many African countries. Those not identifying with one of these ethnicities might see these individuals as belonging to a single racial group. Similar consideration emerges from the use of Hispanic or Latino. Outsiders might see the Latino participants as a single ethnic group, while the participants and their families might identify as being

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from any of many Latin American countries, including Puerto Rico, Cuba, the Dominican Republic, Honduras, and Mexico. (p. 478) Thus, this quote demonstrates explicit attention to naming, identifying, and not essentializing the racial and ethnic identity of individuals and groups (Rivera Maulucci & Mensah, 2015). We note this exception in that most of the studies in this review did not diferentiate and make this distinction for the participants, or an individual, in their study.

Part 2a: Sociocultural Identity and Mixed-Methods or Quantitative Designs Ten studies used either mixed-methods or quantitative approaches to investigating sociocultural identity. Three mixed-methods studies were conducted in London (Archer et al., 2010, 2014, 2015), four were situated in the United States (Chapman & Feldman, 2017; Habig et al., 2020; Hughes et al., 2013, 2021), and one used data drawn from multiple countries (Ferguson & Lezotte, 2020). Two studies also conducted in the United States employed quantitative approaches (Kang et  al., 2019; Vincent-Ruz & Schunn, 2021). In one study, Habig et al. (2020) used triangulation methods in collecting and synthesizing qualitative and quantitative data to examine how participation in a longitudinal ISE out-of-school time (OST) program facilitated by the American Museum of Natural History (AMNH) impacted the STEM trajectories of 66 alumni. The majority of the participants were females and/or members of historically underrepresented racial and ethnic groups. Of 62 respondents (data from four participants were missing), 64.5% were females and 35.5% were male. Further, 32.3% self-identifed as African American; 29.0% as Asian; 22.6% as Latino/a; 12.9% as white/Non-Hispanic; and 3.2% as Other. A purposeful sample of 21 of these participating alumni was selected for interviews. Of the 21 participants in this purposeful sample, 71.4% were female and 28.6% were male. Further, 33.3% self-identifed as African American; 23.8% as Asian; 19.0% as Latino/a; and 23.8% as white/NonHispanic. The authors used a community of practice and social capital framework to discuss the alumni’s STEM career trajectories or persistence in STEM. Using latent class analysis, Vincent-Ruz and Schunn (2021) collected survey responses from over 1,200 urban public school students in the sixth, seventh, and ninth grades from two diferent regions in the United States. The surveys asked about students’ topical identities, choice preferences, and optional science experiences. Data were collected from students attending schools that varied widely by race/ethnicity (i.e., underrepresented groups in science, 23–99%) and socioeconomic status (i.e., students from low-income families eligible for free/reduced lunch at school, 26–84%). In total, data were collected from 20 sixth-grade, 45 seventh-grade, and 37 ninth-grade classes drawn from 23 public schools. Participants were from both genders, and the racial and ethnic groups consisted of white, Black, Asian, and Latinx. For three studies in this subgroup, the race and/or ethnicity in intersection with the gender of the participants were acknowledged. First, Hughes et al. (2013) added to the policy debate about singlesex schooling for girls in science, with data collected from African American, Asian American, and white girls as well as African American and white boys. They used pre- and post-surveys (with Likert scale and open-ended questions), post-interviews with teachers, and select post-interviews with students to derive their fndings. The researchers found that the single-sex learning environment was not as important as the pedagogy used in the two programs for the experiences and science identity development for girls in an all-girls STEM camp and a coeducational STEM camp. Second, Hughes et al. (2021) looked further into the coding identity development of three girls they studied as cases – one Latina, one African American, and one white. The study was driven by understanding how the girls performed their coding identity work (competence performance and

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identity performance), and how their work was recognized by peers and educators with the ultimate purpose of creating a coding identity framework for future researchers. The authors wondered, however, what role gender and racial stereotypes have on leadership and competence, especially in how Black girls and women are viewed. They suggested more research is needed in looking at gender and race, and the impact of stereotypes on how girls shape their coding identities. In the third study, Kang et al. (2019) looked at STEM identity development for girls of color. They used social practice theory, identity, and sense of self as theoretical constructs. The researchers analyzed a large data set of survey responses (n=1,821) collected at fve middle schools in lowincome communities across four states in the United States. Analyses focused on key constructs that inform girls’ development of sense of self; relations among those indicators of STEM identities varied by their race/ethnicity. In the fnal version of the modifed Is Science Me? (ISME) survey, the race/ethnicity item included six categories (e.g., African American/Black African; white/ Caucasian/European/European American) as well as an “Other” category. The survey prompted students to “check all that apply”. A total of seven racial/ethnic groups emerged from students’ responses, including three groups with small sample sizes: white (n= 357), African American (n= 306), Latinx (n= 378), Asian American (n= 322), Multiracial (n= 366), Hawaiian (n= 34), American Indian (n= 9), and Other (n= 56). Due to their small numbers, the researchers dropped adolescents who only identifed as Hawaiian, American Indian, and Other for their analysis using structural equation modeling (SEM). The fnal fve race/ethnicity groupings allowed the researchers to conduct robust statistical analyses, in particular focusing on group diferences by gender and by race/ethnicity. For specifc examples of identity work and mixed-methods design, there was a series of three studies conducted in the United Kingdom (UK) from a fve-year longitudinal survey that investigated minority ethnic students’ science and career aspirations (Archer et  al., 2010, 2014, 2015). The ASPIRES project was funded by the UK’s Economic and Social Research Council as part of its Targeted Initiative on Science and Mathematics Education. This fve-year, longitudinal study was conducted among 10–14-year-olds, and data were collected from a quantitative online survey of more than 9,000 10–11-year-old pupils from a range of backgrounds, socioeconomic statuses, and ethnic diversities, and qualitative interviews with a subset of students and parents. The studies have some overlap with the next category, identity and school contexts; still, the children’s responses were analyzed through the lens of identity as an embodied and performed construction. Specifcally, Archer et al. (2015) utilized survey data from nationally representative student cohorts and longitudinal interview data collected over four years. Ten Black African/Caribbean students, who were tracked from ages 10 to 14 (Years 6–9), and their parents were participants in this study. The researchers used an intersectional analysis of the qualitative data to examine why science careers are less “thinkable” for Black students. They also conducted a case study of two young Black women who “bucked the trend” and aspired to science careers. Their theoretical approach draws on several constructs: frst, feminist, postcolonial, and “intersectional” poststructuralist theorizations of identity as a tool for understanding students’ identifcation with science and, second, a Bourdieusian-inspired conceptualization of “science capital”. Finally, Ferguson and Lezotte’s (2020) systematic review and meta-analysis of articles that used the 1995 Draw-A-Scientist Checklist (DAST-C) ofered recommendations for revisions to the DAST-C that could assist in capturing more modern scientist stereotypes and culturally bound perceptions of scientists. The researchers recommended that culturally defned concepts, such as Caucasian and facial hair, should be given fexibility for diferent uses, either expanding the category (e.g., facial hair could become mustache, beard, etc., and be adjusted based on cultural norms) or redefning the category to be a comparison against the dominant culture (e.g., Caucasian could become dominant race/ethnicity vs. minority). Along with more specifc instructions, these changes would strengthen this drawing assessment.

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Part 2b: Sociocultural Identity and School Contexts The remaining articles that investigated sociocultural identity employed a range of diferent qualitative approaches. The three primary qualitative approaches used were: (1) six ethnographies, including a general ethnography in an urban high school (Brotman & Mensah, 2013; Gamez & Parker, 2018), a longitudinal ethnographic case study (Calabrese Barton et al., 2013), a comparative ethnography (Carlone et al., 2011), a critical ethnographic case study of 16 middle school girls (Tan et al., 2013), and a three-year ethnographic study of an urban high school Latina teacher (Denerof, 2016); (2) two case studies, including a collective case study with three elementary preservice teachers (Chen & Mensah, 2018) and a case study of the historical development of an African American, Caribbean preservice teacher’s social justice stance (Rivera Maulucci, 2013); and (3) three narratives, including personal stories and refections from a Caribbean teacher (Grimes, 2013), three African American teachers in a science methods course (Seiler, 2011), and an arts-based ethnodance as embodied narrative methodology (Chappell & Varelas, 2020). We also categorized nine studies as using general qualitative methods with the collection and analysis of multiple data sources from African American children in elementary classrooms (Kane, 2012; Varelas et al., 2011), of interviews collected from minority ethnic 10–14-year-old students and their parents in London schools (Archer et al., 2012b, 2017, 2019; Wong, 2012, 2015), and of drawings from preservice teachers (Sharma & Honan, 2020; Mensah, 2011). Those studies that focused on school contexts are discussed in this section; those studies that focused on teacher education are examined in the next section. Several studies used sociocultural frameworks of identity that did not necessarily foreground race and ethnicity; instead, these researchers used race and ethnicity to describe the setting and participants of their studies. As an example, Calabrese Barton et al. (2013) conducted a longitudinal ethnographic case study with 36 girls from two small Midwestern cities, one large East Coast city, and one Pacifc Ocean city. Each of the girls attended schools that served a large nondominant population (i.e., students from underrepresented racial, ethnic, or linguistic backgrounds and lowerincome homes). They identifed the girls by socioeconomic status (low, middle), ethnicity (African American, Latina, Asian, Native Hawaiian, white), and science interest (high, low). The authors built a conceptual argument for identity trajectories and discussed the ongoing, cumulative, and contentious nature of identity work and the mechanisms that foster critical shifts in trajectories for possible future selves in science. In their comparative ethnography, Carlone et al. (2011) conducted a study in two fourth-grade elementary school classrooms. Though both classrooms of students developed similar levels of scientifc understanding and expressed positive attitudes about learning science, a group of African American and Latina girls in one classroom expressed disafliation with the identity of a “smart science person” even though most of them knew the science equally well or, in one case, better than their classmates. Through a yearlong ethnographic investigation of a health-focused New York City public high school’s HIV/AIDS and sex education program, Brotman and Mensah (2013) illustrated a case in which 20 12th-grade African American students responded positively to their education on these topics and largely asserted that school signifcantly infuenced their perspectives and actions related to sexual health decision-making. The 20 students who consented to participate in the study included 18 females and 2 males, and the participants identifed their ethnicities in the following way: seven Latino(a), seven African American, three African, and three West Indian. Eight students were born outside the United States and had lived in the United States between 4 and 17 years. All but one female had attended the school since the ninth grade. All participants were 17 or 18 years of age, except one female who was 16. Two additional studies of identity situated in classrooms employed ethnographic methods as well. Tan et  al. (2013) used a critical ethnographic approach to examine the narrated and embodied

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identities-in-practice of 16 non-white, middle school girls who articulated future career goals in STEM-related felds. For these girls who desired a STEM-related career, researchers examined the relationships between their narrated and embodied identities in practice. The four schools across four research sites located in urban areas had large populations of “students from underrepresented racial, ethnic or linguistic backgrounds, and lower-income households” (p. 1149). Second, Gamez and Parker (2018) conducted an ethnographic case study of two newcomer students learning a reform-based science curriculum taught in English. While the school used the word “newcomer” to categorize students who had attended an English-speaking school for less than a year, the researchers conducted interviews in each student’s preferred language choice, which sometimes meant interviews were conducted entirely in Spanish or English or a mix of the two. The authors discussed the relevancy of peer networks and how they can inform both theory and practice around how newcomers, and emergent bilinguals more broadly, engage in science. The researchers also noted the importance of being able to combat defcit views of emergent bilingual students as “good science students” and “good English language learners” within the classroom. Using arts-based practices, and specifcally ethnodance, Chappell and Varelas (2020) presented a theoretical argument for ethnodance as a medium for Black students to narrate their evolving science identities, communicating meanings, interactions, and emotions as well as to construct identities and artifacts of participating in science classroom communities. The authors focused on Black students in an urban high school choreographing a dance performance to capture their science identity construction. Using ballet, lyrical, and contemporary dances to represent their challenging position within science, students were ofered a sense of cultural solidarity and joy of rising above the struggle. Varelas et al. (2011) focused on 25 young, low-income, African American children in frst- to third-grade classrooms where they experienced varied forms of an interactive, participatory, and dialogic pedagogy in the context of a yearlong, integrated science-literacy program. The idea of ideological becoming centered on the ways the children talked about doing school and doing science (see also Kane, 2012). Five studies in the UK by Archer, Wong, and colleagues (Archer et  al., 2012b, 2017, 2019; Wong, 2012, 2015) that were connected to the larger ASPIRES project used identity constructions and work with teachers, students, and parents. In Archer et al. (2017), for example, researchers investigated identity performance and intelligibility in a nine-month research and development program conducted with nine teachers from six inner London schools. Except for students in one of the schools, students came predominantly from working-class backgrounds and a range of ethnic backgrounds. For example, Urdu/Bengali, Turkish, Polish, and Portuguese were the most frequently spoken languages among the students. In the Archer et al. (2019) study, researchers drew on Judith Butler’s concepts of intelligibility and identity as performance to make sense of enactments of subaltern (that is, subordinated) urban students within secondary school science. Wong (2015), a member of the larger research team, conducted an exploratory study using 46 semi-structured interviews with British young people (aged 11–14) from Black Caribbean, Pakistani, Bangladeshi, Indian, and Chinese ethnic backgrounds. The study explored why careers in science are not popular aspirations among ethnic minority students, while careers from science are highly sought after. Wong found that being a scientist was constructed by students as a highly gendered and racialized profession, which may refect popular discourse of being a scientist as typically for “white men.” Careers from science, particularly in medicine, appeared popular among some, but not all, ethnic minority groups, as being a member of the medical staf was considered intrinsically and extrinsically rewarding. What these studies ofered is a conceptualization of identity across varied racial, ethnic, and socioeconomic statuses and across children in diferent grade levels. The ASPIRES project and other studies mentioned in this category communicated the importance of looking at the unique experiences of children in science. For instance, the students and parents who were interviewed

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in the Archer et al. (2012b) study were recruited from 11 elementary schools in England. Students came from a broad range of socioeconomic classes and ethnic backgrounds, although the majority were white British (19 boys and 30 girls) followed by South Asian (6 boys and 7 girls), and Black African/Caribbean (3 boys and 6 girls). The researchers were deliberate in describing not only the ethnic, racial, and socioeconomic status of the participants but also members of the research team. They stated that the “interviews were conducted by four of the paper authors, with the majority of the interviews being conducted by the second author. Of the interviewers, three (LA, JdW, BW) are white middle-class women (with English, American, and French national backgrounds) and one (BWg) is a British-Chinese male Ph.D. student” (Archer et al., 2012b, p. 972).

Part 2c: Sociocultural Identity, Teacher Education, and Teacher Development For the seven qualitative, teacher-focused studies in this subcategory, fve highlighted the infuence of both race and ethnicity and other social markers of the participants and discussed the implications of race and ethnicity within sociocultural frameworks of identity (Chen & Mensah, 2018; Denerof, 2016; Grimes, 2013; Rivera Maulucci, 2011; Seiler, 2011). Two studies utilized the collection and analysis of drawings from preservice teachers (Sharma & Honan, 2020; Mensah, 2011). For example, in their collective case study, Chen and Mensah (2018) examined how the teacher and science teacher identities of three elementary preservice teachers striving to become social justice educators developed during their frst semester of student teaching after taking a one-semester science methods course. The three participants self-identifed as Jamaican/Black, Indian American, and white/Hispanic. The preservice teachers’ identities and histories, university coursework, positioning in their student teaching classrooms, and opportunities to authentically teach were identifed as mediating infuences on the development of their teacher and science teacher identities as well as their perceived ability to teach science for social justice. Their positioning in the classroom dictated the kinds of actions they took toward becoming social justice teachers. For Gabriela, positioned as an observer, she identifed how the dominant racial narrative of good and bad children and defcit views of students of color were reinforced and perpetuated through teacher and student actions and class activities. She questioned her cooperating teacher’s lack of action to address racist acts and refected on what steps she would take as a teacher when confronted with these situations in her future classroom. Seiler (2011) explored identity hybridization among nondominant science teachers, or two African American females and one African American male, as they merged identity narratives around science and science teaching with who they were out of school. The teachers’ experiences of disidentifcation with science in terms of diaspora, or the sense of being taken away from what one knows and values, generated a “creolized” approach to science teaching in that the teachers created possibilities for greater student identifcation with science in school, which in turn has the potential for changing the face of who does science and science itself. A creolized science provides new opportunities for communication and participation for those who contribute to and employ science (Elmesky & Seiler, 2007). Grimes (2013) noted the importance of an educator’s gender, nationality, language, and interests among other social markers as all permeating the classroom feld and coexisting alongside the professional role identity. Using several sociocultural theoretical perspectives and identity constructs, Grimes explored her Black female Caribbean identity, which she also identifed as a frstgeneration Trinidadian immigrant, and how her identity transformed the science classroom and created positive resonance for some of her privileged white students who had Caribbean caretakers at home. As a refexive dialogue with science students situated both within and outside the science

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classroom, the conversations with students who were raised through the hired help of Caribbean nannies revealed a strong resemblance to the way they perceived their caretakers and Grimes as their instructor. Both Sharma and Honan (2020) and Mensah (2011) used drawing tasks with preservice teachers. Sharma and Honan (2020) administered the Draw-A-Scientist Test (DAST) and collected written descriptions of scientists from 88 Fijian preservice teachers about their perceptions of science and scientists. While the fndings of the study resonated with similar DAST studies, not much about race and ethnicity was mentioned in the analysis of the drawings; still, the authors noted a particular signifcance in a Pacifc context where little research on Fijian education and curriculum is done. In contrast, Mensah (2011) used several social identity markers to analyze 99 drawings created by elementary preservice teacher candidates. The self-reported gender and racial/ethnic backgrounds of 48 of the 54 preservice teachers were females: 29 white/Caucasian/European American, 8 Asian/Southeast Asian/Korean/Chinese American, 2 Middle Eastern American, 2 African American, 1 French American, 1 Native American, 1 Latina American, 1 African American Latina, 1 Indian-South Asian, 1 Italian Lithuanian American Indian, and 1 Caucasian Croatian. There were also six males: four white/Caucasian/European American and two Asian American. Two preservice teachers were registered with the Ofce of Disabilities, and all students were fuent and communicated in Standard American English. Findings from this study of preservice teacher candidates’ drawings indicated that, while many elements of the stereotypical scientist image were prevalent, such an activity can make more explicit teachers’ views and prior experiences to promote discussions about teacher identity, science teaching, and the construction of new images and practices for teaching elementary science. The preservice teachers drew images showing their racial and ethnic identities.

Category D: Science Curriculum and Pedagogy We identified 53 studies that examined science curriculum and pedagogy in intersection with race and ethnicity. We divided these studies into three subcategories: culturally and linguistically relevant instruction, STEM-focused programs, and curriculum materials for ease of presentation.

Part 1: Culturally and Linguistically Relevant Instruction Culturally and linguistically relevant instruction was our largest subcategory, with 24 articles. These studies described science pedagogy as culturally relevant and/or responsive (Brown & Crippen, 2016; Brown et al., 2021; Charity Hudley & Mallinson, 2017; Grimberg & Gummer, 2013; Hernandez & Shroyer, 2017; Madkins & McKinney de Royston, 2019; McCollough & Ramirez, 2012; Mensah et al., 2018; Ramirez et al., 2016; Underwood & Mensah, 2018; Upadhyay et al., 2017; Xu et al., 2012; Yoon & Martin, 2019), multicultural (Atwater et al., 2013; Charity Hudley  & Mallinson, 2017; Mensah et  al., 2018), place-based (Brkich, 2014; Harper, 2017; Schindel Dimick, 2016; Sedawi et  al., 2020), justice-centered (Atwater et  al., 2013; Dimick, 2012; Morales-Doyle, 2017), culturally adaptive (Shady, 2014), culturally sustaining (Weiland, 2015), critical constructivist (Wild, 2015), or equity in instruction (Ramnarain, 2011). Although we recognize these constructs are not synonymous, for the sake of brevity, we refer to them here as culturally and linguistically relevant instruction. We use this term to encompass asset-based approaches to instruction that place students’ individual, racial, ethnic, linguistic, and community resources at their center. All but two studies in this subcategory (Ramnarain, 2011, in South Africa; Sedawi et al., 2020, in Israel) were situated in the United States. Still, researchers focused their investigations on a wide

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range of participants. One study investigated Hispanic mothers and their elementary school children (Weiland, 2015). Six studies foregrounded students (Brkich, 2014; Brown et al., 2021; Harper, 2017; Sedawi et al., 2020; Upadhyay et al., 2017; Wild, 2015); six focused on students and their teachers (Dimick, 2012; Grimberg  & Gummer, 2013; Morales-Doyle, 2017; Ramnarain, 2011; Schindel Dimick, 2016; Shady, 2014); and one addressed students, their teachers, administrators, and families (Madkins & McKinney de Royston, 2019). Eight investigated teachers, either preservice teachers (Hernandez & Shroyer, 2017; McCollough & Ramirez, 2012; Yoon & Martin, 2019), preservice teachers and the families they served (Ramirez et al., 2016), practicing teachers (Brown & Crippen, 2016; Charity Hudley & Mallinson, 2017; Xu et al., 2012), or both preservice and practicing teachers (Mensah et al., 2018). And two explored science teacher educators (Atwater et al., 2013; Underwood & Mensah, 2018). Eighteen of these 24 studies were qualitative (Atwater et  al., 2013; Brkich, 2014; Brown  & Crippen, 2016; Brown et  al., 2021; Charity Hudley  & Mallinson, 2017; Dimick, 2012; Harper, 2017; Hernandez & Shroyer, 2017; Madkins & McKinney de Royston, 2019; Mensah et al., 2018; Morales-Doyle, 2017; Ramnarain, 2011; Schindel Dimick, 2016; Shady, 2014; Underwood & Mensah, 2018; Upadhyay et al., 2017; Weiland, 2015; Xu et al., 2012); three were quantitative (Grimberg & Gummer, 2013; Wild, 2015; Yoon & Martin, 2019), and three employed mixed methods (McCollough & Ramirez, 2012; Ramirez et al., 2016; Sedawi et al., 2020). All included the race and ethnicity of their participants. Eighteen also provided information about participants’ gender, language, and/or socioeconomic status (Atwater et  al., 2013; Brkich, 2014; Brown et  al., 2021; Dimick, 2012; Grimberg & Gummer, 2013; Hernandez & Shroyer, 2017; Madkins & McKinney de Royston, 2019; Morales-Doyle, 2017; Ramirez et al., 2016; Ramnarain, 2011; Schindel Dimick, 2016; Sedawi et al., 2020; Shady, 2014; Underwood & Mensah, 2018; Weiland, 2015; Wild, 2015; Xu et al., 2012; Yoon & Martin, 2019). As introduced, all 24 studies included a theoretical framework tied at least peripherally to culturally and linguistically relevant instruction. Atwater et al. (2013), as one example, used the theory of social constructivism to investigate 20 Black science teacher educators’ experiences, especially those about their Blackness and their eforts to include multicultural education, equity, and social justice in their teaching. As a second example, Weiland (2015) employed the theory of culturally sustaining pedagogy – a pedagogy that sustains learners’ linguistic, literate, and cultural diversity while extending their repertoires to include dominant language, literacies, and other cultural practices – to investigate the experiences of Hispanic mothers and their children in an informal science center. Weiland focused on the ways science centers provide inclusive and afrming contexts to support parents’ eforts to engage their children in STEM learning. As a third example, Madkins and McKinney de Royston (2019) used culturally relevant pedagogy to examine one African American science teacher at a middle school that served primarily African American students. They focused their investigation on the third tenet of culturally relevant pedagogy – developing students’ sociopolitical consciousness – which is often overlooked and can present the greatest challenges for teachers. In particular, they examined their teacher participants’ political clarity, that is, the clarity that represents a teacher’s deep understanding of how school, society, and science itself operate to reproduce inequalities and diferentially privilege the knowledge and experiences of white, middle-class students over those of racially and economically minoritized students. Madkins and McKinney de Royston chose to study political clarity not as an in-the-head phenomenon, but as enacted through instruction (see also Morales-Doyle, 2017). Many of these studies’ implications addressed teachers and teacher educators. Researchers found that their preservice and/or practicing teacher participants were able to implement instruction that was culturally and linguistically relevant (Charity Hudley & Mallinson, 2017; Grimberg & Gummer, 2013; Madkins & McKinney de Royston, 2019; McCollough & Ramirez, 2012; Morales-Doyle, 2017; Ramirez et al., 2016; Shady, 2014; Xu et al., 2012; Yoon & Martin, 2019). Indeed, as teachers

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developed robust cultural and linguistic competence, they learned to view diverse students and their families’ rich and varied identities not as defcits but as resources (Charity Hudley & Mallinson, 2017; Hernandez & Shroyer, 2017; McCollough & Ramirez, 2012; Ramirez et al., 2016; Shady, 2014; Xu et al., 2012). Still, in four studies, researchers found that teachers struggled to implement all three tenets of culturally relevant pedagogy, particularly the tenet of sociopolitical or critical consciousness (Brown & Crippen, 2016; Dimick, 2012; Hernandez & Shroyer, 2017; Mensah et al., 2018). In a ffth study, researchers found that teacher educators struggled to understand culturally relevant pedagogy and how it can be implemented in science classrooms (Underwood & Mensah, 2018). Also related to teachers and teaching, the authors of 15 studies called for preservice and/or practicing teachers to be provided additional opportunities to learn about multicultural education, equity, and social justice so that they could better address the needs of historically underserved and marginalized students in science classrooms (Atwater et al., 2013; Brkich, 2014; Brown & Crippen, 2016; Charity Hudley & Mallinson, 2017; Hernandez & Shroyer, 2017; Madkins & McKinney de Royston, 2019; McCollough & Ramirez, 2012; Mensah et al., 2018; Morales-Doyle, 2017; Ramirez et al., 2016; Schindel Dimick, 2016; Shady, 2014; Underwood & Mensah, 2018; Upadhyay et al., 2017; Yoon & Martin, 2019). Hernandez and Shroyer (2017) highlighted the need for greater racial and ethnic diversity among science teachers. Likewise, Atwater et al. (2013) emphasized the need for greater racial and ethnic diversity among science teacher educators. Further, several implications focused on culturally and linguistically diverse students and their families. Instruction that was culturally and linguistically relevant was argued to position learners, their families, and/or the larger community as science people – as people who know, do, and produce science (Brown et al., 2021; Harper, 2017; Madkins & McKinney de Royston, 2019; Morales-Doyle, 2017; Schindel Dimick, 2016; Upadhyay et al., 2017; Weiland, 2015). Several researchers called for students to have more experiences learning science grounded in their place, community, and lived experiences (Brkich, 2014; Brown et al., 2021; Grimberg & Gummer, 2013; Harper, 2017; Schindel Dimick, 2016; Xu et al., 2012). Dimick (2012), Madkins and McKinney de Royston (2019), Morales-Doyle (2017), Upadhyay et al. (2017), and Wild (2015), in particular, emphasized that such opportunities would enable students to use science more easily as a catalyst for social transformation. Finally, Morales-Doyle (2017) provided a compelling example of the strengths of culturally and linguistically relevant instruction. He situated his qualitative study in an advanced chemistry class of African American and Latinx students in an urban high school, where he served as both teacher and researcher. He used a justice-centered science pedagogy as his conceptual frame, a framework built on the traditions of critical pedagogy and culturally relevant pedagogy, to address longstanding oppressions and inequities in science education across race and class lines. The teacher and students investigated an environmental social justice issue identifed by their community – the lasting impact of two recently closed coal power plants on the community’s physical environment – by measuring the concentrations of lead and mercury in neighborhood soil samples. Morales-Doyle found that the project both supported students’ academic success and helped to position them as transformative intellectuals who were knowledgeable about complex science and social justice issues that impacted their community. In his implications, he elaborated on the theory of justice-centered science education as a catalyst for social change. He also emphasized the importance of teachers engaging deeply and demonstrating solidarity with the students and communities where they teach.

Part 2: STEM-Focused Programs We found 17 articles to examine STEM-focused programs, broadly defned to include STEMfocused schools, school-based STEM programs, and informal or afterschool programs centered on STEM. Fifteen of these studies were situated in the United States (Burgin et al., 2015; Carrier et al., 2014; Cone, 2012; Dickerson et al., 2014; Jackson & Ash, 2012; Means et al., 2017; Parker et al.,

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2020; Pruitt & Wallace, 2012; Scogin et al., 2018; Sondergeld et al., 2020; Sprague Martinez et al., 2016; Terzian & Rury, 2014; Tong et al., 2014; Wallace, 2013; Weis et al., 2015); one, in South Africa (Ramnarain & de Beer, 2013); and one, in the United Kingdom (Archer et al., 2021). Eleven of these studies investigated students’ views and experiences engaging in such programs (Archer et al., 2021; Burgin et al., 2015; Dickerson et al., 2014; Means et al., 2017; Parker et al., 2020; Pruitt & Wallace, 2012; Ramnarain & de Beer, 2013; Sondergeld et al., 2020; Sprague Martinez et al., 2016; Terzian & Rury, 2014; Tong et al., 2014); three, students and their teachers (Carrier et al., 2014; Jackson & Ash, 2012; Weis et al., 2015); two, preservice elementary teachers (Cone, 2012; Wallace, 2013); and one, high school science program themselves (Scogin et  al., 2018). The 17, relatively speaking, were evenly distributed across methodologies: Six employed qualitative methods (Archer et al., 2021; Burgin et al., 2015; Cone, 2012; Ramnarain & de Beer, 2013; Wallace, 2013; Weis et al., 2015); six, quantitative methods (Means et al., 2017; Pruitt & Wallace, 2012; Sondergeld et al., 2020; Sprague Martinez et al., 2016; Terzian & Rury, 2014; Tong et al., 2014); and fve, mixed methods (Carrier et al., 2014; Dickerson et al., 2014; Jackson & Ash, 2012; Parker et al., 2020; Scogin et al., 2018). Also in their methods, all but one study (Scogin et al., 2018) included the race and ethnicity of their student and/or teacher participants in addition to at least one other demographic marker (i.e., gender, socioeconomic status, language, and/or disability status). The theoretical frameworks of studies in this subcategory, as a collective, were not as clearly tied to race and ethnicity as the other two subcategories under curriculum and instruction. Although not necessarily foregrounded, all but two studies (Jackson & Ash, 2012; Wallace, 2013) included attention to race and ethnicity in their framing. As one example, Cone (2012) used the lens of efective science instruction for students from diverse racial, ethnic, socioeconomic, language, and cultural backgrounds to investigate diferences in perceptions between preservice elementary teachers who completed a community-based service learning feld experience and those who did not. As a second example, Carrier et al. (2014) investigated two elementary schools’ science programs that included outdoor instruction. Their mixed-methods study was framed by three constructs: teachers’ beliefs about science teaching and environmental education; school culture; and diferences in environmental concern, connection, and power tied to gender and ethnicity. In their discussions and implications, the majority of studies emphasized the importance of inquiry, authentic, and/or outdoor experiences in facilitating learning for diverse students (Archer et al., 2021; Burgin et al., 2015; Carrier et al., 2014; Cone, 2012; Dickerson et al., 2014; Jackson & Ash, 2012; Means et al., 2017; Pruitt & Wallace, 2012; Ramnarain & de Beer, 2013; Scogin et al., 2018; Sondergeld et al., 2020; Sprague Martinez et al., 2016). Five studies pointed to the need to attend to racial and ethnic minority students’ science attitudes, identities, and/or sense of belonging (Archer et al., 2021; Burgin et al., 2015; Carrier et al., 2014; Sondergeld et al., 2020; Sprague Martinez et al., 2016) and four, to their families and communities (Cone, 2012; Ramnarain & de Beer, 2013; Sondergeld et al., 2020; Sprague Martinez et al., 2016). Six emphasized that teacher education programs and professional development eforts must better help teachers acquire the knowledge, dispositions, and/or skills needed to teach science in equitable ways (Carrier et  al., 2014; Cone, 2012; Jackson & Ash, 2012; Pruitt & Wallace, 2012; Scogin et al., 2018; Wallace, 2013). Five studies acknowledged that the teaching and learning of science are shaped by structural and systemic inequities, including programs ofered, standardized testing required, and/or educational policies put in place (Carrier et al., 2014; Cone, 2012; Means et al., 2017; Terzian & Rury, 2014; Weis et al., 2015). As one example of a study on STEM-focused programs, Weis et al. (2015) used the construct of opportunity structures to frame their three-year study of students, teachers, and counselors at inclusive STEM-focused high schools in two cities. They defned these structures as the institutional arrangements  – including mathematics and science tracks, course oferings, and course requirements – that organize the trajectories of (un)successful educational futures in STEM for low-income,

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underrepresented ethnic minority students. From their qualitative analysis of interviews, classroom observations, and school and district documents, researchers found that the enhanced opportunities to learn STEM (e.g., advanced STEM courses, STEM academies) put in place when the STEMfocused schools frst opened were gradually eroded. Over time, then, because low-income, ethnic minority students lost access to high-level STEM courses and initiatives relative to those with privilege, they also lost access to opportunities to pursue STEM majors and STEM careers. Weis and colleagues called for future work to more carefully investigate when and under what circumstances STEM-focused schools can ofer sustained and authentic high-level opportunities for low-income and minority students.

Part 3: Curriculum Materials We identifed 12 studies that focused on the design and/or implementation of curriculum materials. All 12 were conducted in the United States. The majority of these studies, seven, used quantitative methods (Chesnutt et al., 2018; Donovan, 2017; Donovan et al., 2019, 2021; Kanter & Konstantopoulos, 2010; Rawson & McCool, 2014; Taylor et al., 2015). Three employed mixed methods (Brown & Livstrom, 2020; Donovan, 2014, 2016), and two used qualitative methods (Matuk et al., 2021; Suriel & Atwater, 2012). Eleven were situated at the secondary level: fve examined students (Chesnutt et al., 2018; Donovan, 2014, 2016, 2017; Donovan et al., 2021); three, practicing teachers (Brown & Livstrom, 2020; Matuk et al., 2021; Suriel & Atwater, 2012); two, students and teachers in science classrooms (Kanter & Konstantopoulos, 2010; Taylor et al., 2015); and one, students and adults (Donovan et al., 2019). These 11 identifed their student and/or teacher participants by their race and ethnicity, and included their gender, socioeconomic status, language, and/or disability status as well. The fnal study in this subcategory examined images of scientists in children’s nonfction trade books (Rawson & McCool, 2014). As with methods employed, a range of theoretical frameworks informed these investigations of the curriculum. Eleven of these 12 studies explicitly included a focus on race and ethnicity in their framing (Brown & Livstrom, 2020; Chesnutt et al., 2018; Donovan, 2014, 2016, 2017; Donovan et al., 2019, 2021; Kanter & Konstantopoulos, 2010; Matuk et al., 2021; Rawson & McCool, 2014; Suriel & Atwater, 2012). As examples, Brown and Livstrom (2020) used Banks’s (2016) four levels of multicultural curriculum, which included exploring content from the perspectives of ethnically and racially diverse groups and making decisions on social justice issues at its upper levels to inform their study. They connected Banks’s multicultural curriculum typology to the construct of pedagogical design capacity to investigate preservice science teachers’ eforts to develop curriculum to meet the needs of ethnically, culturally, and linguistically diverse students. Matuk et al. (2021) also foregrounded pedagogical design capacity  – in connection to comic books as a tool for equity, diversity, and engagement – in framing their study. Suriel and Atwater (2012) used the theory of social constructivism, with attention to cultural experiences, to ground their investigation of fve white teachers’ eforts to develop a multicultural science curriculum. Similar to Brown and Livstrom (2020), in their analysis, they employed an earlier edition of Banks’s levels of multicultural curriculum in addition to his dominant and desirable characteristics of multicultural studies (Banks, 1995, 2001). In their discussions and implications, almost all researchers ofered suggestions to improve the structure and/or substance of curriculum materials to better meet the needs of racially, ethnically, culturally, and linguistically diverse students (Brown & Livstrom, 2020; Donovan, 2014, 2016, 2017; Donovan et  al., 2019, 2021; Kanter  & Konstantopoulos, 2010; Matuk et  al., 2021; Rawson  & McCool, 2014). Five emphasized how science curriculum materials impact students’ views and actions regarding perceived diferences across racial groups because of genetic variation (Donovan, 2014, 2016, 2017; Donovan et al., 2019, 2021). Five discussed the importance of teachers’ knowledge, beliefs, and/or experiences in shaping multicultural curricular choices (Brown  & Livstrom,

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2020; Kanter & Konstantopoulos, 2010; Matuk et al., 2021; Suriel & Atwater, 2012; Taylor et al., 2015). Four ofered recommendations for ways to improve classroom instruction to better support racially and ethnically diverse students’ learning of science (Chesnutt et al., 2018; Kanter & Konstantopoulos, 2010; Matuk et al., 2021; Taylor et al., 2015). As a concrete example of a curriculum study tied to race and ethnicity, Donovan (2017) examined how the use of racial terminology in a secondary biology curriculum impacted students’ beliefs about racial diferences and their development of racial prejudice. The research was situated in the framework of bio-behavioral essentialism (i.e., the beliefs that people of the same race are biologically uniform; that races are biologically discrete categories; and that the cause of uniformity within groups and discreteness across groups has to do with the underlying genetic essence of each group) and its role in racial prejudice. Students from two secondary schools were randomly assigned within their classroom to learn about the diferences in human skeletal structure and the prevalence of genetic diseases from four text-based lessons that either discussed race or lacked racial terminology. Through quantitative analysis of student surveys, Donovan found that students from the racial terminology curricular group developed beliefs about racial diferences based on genetic thinking. These students also became less interested in socializing across racial lines and less supportive of policies that reduce racial inequality in education. Donovan argued that the biology curriculum should be redesigned to teach students both that racial inequality is not the inevitable product of genes and that racial inequality is perpetuated when people mistakenly believe that races difer cognitively and behaviorally for genetic reasons.

Category E: Language in Intersection With Race and Ethnicity We identifed 16 studies that investigated race and ethnicity through the conceptual lens of language. We understood these articles to span three areas of research related to language in science education. Ten investigated multilingual learners or students who speak one or more home languages in addition to or instead of English (Bravo et al., 2014; Mavuru & Ramnarain, 2020; Okebukola et al., 2013; Ryu, 2013, 2019; Ryu & Sikorski, 2019; Shaw et al., 2014; Stevenson et al., 2019; Swanson et al., 2014; Tolbert & Knox, 2016). Related constructs include English language learners, a term that has been recently criticized as defcit-oriented (Gonzalez-Howard & Suarez, 2021); emergent bilingual students; and bilingual students. Three explored the connections between science learning and English language and literacy learning (Clark et al., 2020; Greenleaf et al., 2011; Huerta et al., 2014). And three examined the use of diferent types of discourse (e.g., everyday, inclusionary/ exclusionary, disciplinary) in the teaching and learning of science (Brown, 2011; Brown et al., 2010; Gomes et al., 2011). In terms of research contexts, 13 studies were situated in the United States (Bravo et al., 2014; Brown, 2011; Brown et al., 2010; Clark et al., 2020; Greenleaf et al., 2011; Huerta et al., 2014; Ryu, 2013, 2019; Ryu & Sikorski, 2019; Shaw et al., 2014; Stevenson et al., 2019; Swanson et al., 2014; Tolbert & Knox, 2016); one in Brazil (Gomes et al., 2011); one in Nigeria (Okebukola et al., 2013); and one in South Africa (Mavuru & Ramnarain, 2020). Seven of the 16 studies focused on students in K–12 schools (Brown, 2011; Brown et al., 2010; Clark et al., 2020; Gomes et al., 2011; Huerta et al., 2014; Shaw et al., 2014; Stevenson et al., 2019). Six others also included a focus on students – either students in interaction with their teacher in K–12 classrooms (Greenleaf et al., 2011; Okebukola et al., 2013; Swanson et al., 2014) or students in informal education contexts (Ryu, 2013, 2019; Ryu & Sikorski, 2019). The fnal three studies examined preservice (Bravo et al., 2014; Tolbert & Knox, 2016) or practicing (Mavuru & Ramnarain, 2020) teachers. The studies were almost evenly distributed across research methods: Seven employed qualitative methods (Gomes et al., 2011; Mavuru & Ramnarain, 2020; Ryu, 2013, 2019; Stevenson et al., 2019; Swanson et al., 2014; Tolbert & Knox, 2016); fve, quantitative methods (Bravo et al., 2014; Clark et al., 2020; Greenleaf et al., 2011;

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Huerta et al., 2014; Shaw et al., 2014); and four, mixed methods (Brown, 2011; Brown et al., 2010; Okebukola et  al., 2013; Ryu  & Sikorski, 2019). In all but one study (Brown, 2011), researchers identifed their participants by their race and ethnicity and included their gender, home language(s), and/or socioeconomic status as well. The 16 studies used diverse theoretical frameworks to shape their investigations. Given their placement in this category, a common thread across the frameworks was attention to language. Twelve explicitly included attention to race and ethnicity in their conceptual framing (Bravo et al., 2014; Brown, 2011; Brown et al., 2010; Gomes et al., 2011; Greenleaf et al., 2011; Mavuru & Ramnarain, 2020; Okebukola et al., 2013; Ryu, 2013, 2019; Ryu & Sikorski, 2019; Stevenson et al., 2019; Tolbert & Knox, 2016). As one example, Brown et al. (2010) used the constructs of cultural confict, cultural continuity, and discursive identity to argue for the implementation of disaggregate instruction with ethnically and linguistically diverse students in science classrooms. Disaggregate instruction was defned as separating science teaching and learning into conceptual and linguistic components – beginning by teaching content using everyday language and then following with intensive science language instruction. As a second example, Stevenson et al. (2019) conceptualized resiliency as a strategy developed by their Latina participants using contextual mitigating factors to achieve success in STEM education. Contextual mitigating factors include enduring positive or negative macro issues related to equity and diversity, such as gender, socioeconomic status, and race, that contribute to or inhibit learning opportunities, access to school resources, and engagement in meaningful educational experiences (see again Gallard Martínez et al., 2019). Positive contextual mitigating factors are associated with resiliency and can be used as tools of liberation. As a fnal example, Okebukola et al. (2013) drew from constructs of science language and mother tongue (i.e., frst, or home, language) to investigate the mis/alignment between Nigerian policy and practice in teachers’ use of the mother tongue to teach science in primary classrooms. In their discussions and implications, researchers again underscored the importance of language, explaining how language helps to shape science learning, an emphasis to be expected given the studies’ common focus on language. Twelve studies suggested ways science instruction can be revised both to increase diverse students’ access to opportunities to produce language and to increase the likelihood that students’ ideas, once articulated, will be valued by themselves, their peers, and their teachers (Bravo et al., 2014; Brown, 2011; Brown et al., 2010; Clark et al., 2020; Gomes et al., 2011; Ryu, 2013, 2019; Ryu & Sikorski, 2019; Shaw et al., 2014; Stevenson et al., 2019; Swanson et al., 2014; Tolbert & Knox, 2016). Seven emphasized how race and ethnicity in interaction with language and sometimes gender shape students’ understanding of who counts as competent science people (Brown, 2011; Clark et  al., 2020; Greenleaf et  al., 2011; Ryu, 2013, 2019; Ryu & Sikorski, 2019; Stevenson et al., 2019). Two studies called for additional research on the successes and challenges of encouraging teacher and student use of home languages in science classrooms (Mavuru & Ramnarain, 2020; Okebukola et al., 2013). As one example of a study in this category, Ryu and Sikorski (2019) provided insight into the ways language intersects with race – and gender – to shape the teaching and learning of science. Researchers focused their investigation on an informal science program that was designed for Korean immigrant students and that encouraged them to participate in sensemaking activities using their funds of knowledge, including the full range of their linguistic resources and preferences. Ryu and Skiorski used the construct of a talk repertoire to provide a fexible way of characterizing how students talk and act in diferent contexts, across time and space, and within and across diferent racial and ethnic groups. They employed mixed methods to study one student’s verbal participation across program activities. Through a quantitative analysis of video records, researchers found that Selena talked more often and for longer periods over time. However, through a qualitative analysis of these data, they also found that Selena’s use of hedging devices and preference for mixing Korean with English limited her opportunities to engage in

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collaborative sense-making. Because institutional labels (i.e., girl and English learner), social hierarchies, and power relationships went unchallenged by both the instructors and the larger program, Selena’s ideas were not taken up by her peers. This was the case even though the learning context was for a single ethnic group and the ideas expressed by Selena carried scientifc merit. Ryu and Skiorski underscored the need for teachers and researchers to attend to students’ race more closely in interaction with gender and language profciency in teaching toward equity – to attempt to address rather than perpetuate the marginalization of females and language minority students in talking and doing science.

Category F: Aspiration and Motivation to Learn and Teach Science We placed 16 articles in our category of aspiration, motivation, and related constructs. Ten of these articles were situated in the United States (Andersen & Ward, 2014; Bolshakova et al., 2011; Bonnette et al., 2019; Chapman et al., 2019; Dorph et al., 2018; Ganchorre & Tomanek, 2012; Lofgran et  al., 2015; Maltese  & Tai, 2011; Moseley  & Taylor, 2011; Snodgrass Rangel et  al., 2020); the other articles were part of a longitudinal study conducted in the United Kingdom (Archer et al., 2012a; Dewitt  & Archer, 2015; DeWitt et  al., 2016; Dewitt, Archer et  al., 2013; DeWitt et  al., 2014; Dewitt, Osborne et al., 2013). The majority of articles, 13, focused on students: fve examined secondary school students (Andersen & Ward, 2014; Bonnette et al., 2019; Dorph et al., 2018; Maltese & Tai, 2011; Snodgrass Rangel et al., 2020); three, primary, or elementary, students (Archer et  al., 2012a; Dewitt, Archer et  al., 2013; Dewitt, Osborne et  al., 2013); and fve, both primary and secondary students (Chapman et al., 2019; Dewitt & Archer, 2015; DeWitt et al., 2014, 2016; Lofgran et al., 2015). A smaller number included teachers as participants: Bolshakova et al. (2011) investigated middle school teachers and their students; Moseley and Taylor (2011), secondary school teachers; and Ganchorre and Tomanek (2012), prospective secondary school teachers. The majority of these studies, 11, were quantitative (Andersen & Ward, 2014; Bonnette et al., 2019; Dewitt & Archer, 2015; DeWitt et al., 2014, 2016; Dewitt, Osborne et al., 2013; Dorph et al., 2018; Lofgran et al., 2015; Maltese & Tai, 2011; Moseley & Taylor, 2011; Snodgrass Rangel et al., 2020). Of the other fve, four were qualitative (Archer et al., 2012a; Bolshakova et al., 2011; Dewitt, Archer et al., 2013; Ganchorre & Tomanek, 2012), and one employed mixed methods (Chapman et al., 2019). All explicitly attended to participants’ race and ethnicity – in addition to gender and sometimes language status and/or socioeconomic status – in their methods. In examining their theoretical frames on aspiration or motivation broadly conceived, all 16 studies were found to include race and ethnicity in some aspect of their conceptual framing and/or literature review. As one example, Archer, Dewitt, and colleagues used the construct of aspirations in science – as connected to science attitudes, identities, family habitus, and/or science capital – to investigate student science and career aspirations and to identify factors that contribute to or hinder the development of aspirations in science (Archer et  al., 2012a; Dewitt  & Archer, 2015; DeWitt et al., 2014, 2016; Dewitt, Archer et al., 2013; Dewitt, Osborne et al., 2013). As a second example, four sets of researchers included students’ and/or teachers’ sense of self-efcacy to inform their studies (Bolshakova et al., 2011; Chapman et al., 2019; Lofgran et al., 2015; Moseley & Taylor, 2011). As a third example, two sets of researchers used expectancy-value theory to ground their investigations of students: Andersen and Ward (2014) studied Black, Hispanic, and white students, and Snodgrass Rangel et al. (2020) examined underrepresented students in STEM, including students from racially minoritized populations. As a fnal example, Dorph et al. (2018) drew from both self-efcacy and expectancy-value theory, in addition to the construct of fascination, to organize their study. They examined middle school student science learning activation as consisting of competency beliefs, valuing science, and fascination with science in addition to scientifc sense-making and as predicting STEM career preferences.

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On a related note, Snodgrass Rangel et al. (2020) cautioned readers that motivational theories, like expectancy-value theory (EVT), may be better suited to explaining the choices and achievement of white and Asian students than racially minoritized students. They elaborated, “Expecting that EVT as a theory should perform similarly across racial and ethnic groups is colorblind because it assumes that students have similar experiences with and beliefs about science and math when research suggests this is not the case” (p. 1061). This was one of the few instances where researchers in this category explicitly stated their theoretical framework might be limited in its attention to and ability to make sense of race and ethnicity in their investigation. The implications of these studies can be divided between recommendations to enhance student aspiration and motivation and recommendations to strengthen teacher motivation and self-efcacy. Concerning students, Bolshakova et al. (2011) emphasized the positive impact efective science teachers can have on Hispanic students’ self-efcacy and achievement. Andersen and Ward (2014) encouraged teachers to work to strengthen Black and Hispanic students’ sense of congruence between their identities and STEM identities, their awareness of how science and mathematics courses connect to their future goals for career and college, and their interests in STEM subjects more generally. Eight studies called for educators, policymakers, and researchers to better highlight the diversity and range of individuals who are scientists and the diversity and range of science-related careers to ofer more opportunities for students from poor backgrounds to fnd a place for themselves within science and a place for science within their own developing identities (Archer et al., 2012a; Dewitt & Archer, 2015; DeWitt et al., 2014, 2016; Dewitt, Archer et al., 2013; Dewitt, Osborne et al., 2013; Dorph et al., 2018; Maltese & Tai, 2011). Two other studies emphasized the importance of providing more opportunities for students from underrepresented groups to participate in informal science experiences to increase their motivation to engage in STEM learning (Bonnette et al., 2019; Chapman et  al., 2019). Further, two studies called for additional research on ethnically and racially diverse students to identify unexplored diferences and nuances in their STEM beliefs, afnities, and aspirations (Dorph et al., 2018; Snodgrass Rangel et al., 2020). Finally, Chapman et al. (2019) emphasized the importance of considering the holistic and cumulative efects of social, cultural, historical, and political factors that have led to the marginalization of students from underrepresented groups, such as Hispanic females. Concerning teachers, two studies recommended practicing teachers in urban schools or with high minority class ethnicity distribution (CED) be better supported to prevent feelings of low science teaching efcacy, helplessness, and demoralization (Bolshakova et al., 2011; Moseley & Taylor, 2011). Ganchorre and Tomanek (2012) encouraged teacher educators to begin by cultivating preservice teachers’ dispositions of care and compassion as starting points to promote their success in working with diverse students, regardless of the preservice teachers’ ethnic background and experiences. A substantive example of an article in this category is the mixed-methods study by Chapman et al. (2019). In their study, Chapman et al. investigated the efects of a STEM summer camp on the learning outcomes of 434 K–12 students, approximately 90% of whom identifed as Hispanic, Mexican American, or Latinx. Researchers drew together several constructs to frame their investigation of why there are so few Hispanic females in STEM. These constructs included the achievement gap; the efects of stereotypes and gender bias; the leaky STEM pipeline; and student self-efcacy, motivation, and self-determination toward STEM. Data analyzed included pre-and post-test scores of all students and interviews of randomly selected Hispanic female students. Researchers found that Hispanic middle school girls had signifcantly higher achievement scores than Hispanic middle school boys, even though a gap in camp participation by gender had begun to emerge. By high school, however, females were less likely than their male counterparts to participate in the STEM summer camp. They also had signifcantly lower pretest scores than males. Researchers concluded that informal STEM opportunities, such as this STEM summer camp, could help to mitigate decreases in Hispanic females’ interest, participation, and academic achievement from elementary to high school.

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Category G: Teacher Perceptions and Experiences We identifed 14 studies that attended to race and ethnicity in their examination of teacher perceptions and experiences teaching science. All of these studies focused on preservice and/or practicing teachers in the United States: Six investigated practicing secondary teachers (Bianchini et al., 2015; Brenner et al., 2016; Marco-Bujosa et al., 2021; Nehmeh & Kelly, 2018; Rivera Maulucci, 2010; Titu et al., 2018); one, practicing elementary teachers (Lee et al., 2016); two, preservice secondary teachers (Marco-Bujosa et al., 2020; Mark et al., 2020); one, preservice elementary teachers (Subramaniam, 2013); and four, both practicing and preservice teachers (Liou et al., 2010; Liou & Lawrenz, 2011; Southerland et al., 2011; Zapata, 2013). All but three of these studies employed qualitative methods (Bianchini et al., 2015; Brenner et al., 2016; Marco-Bujosa et al., 2020, 2021; Mark et al., 2020; Nehmeh  & Kelly, 2018; Rivera Maulucci, 2010; Southerland et  al., 2011; Subramaniam, 2013; Titu et al., 2018; Zapata, 2013); the remaining studies were quantitative (Lee et al., 2016; Liou et al., 2010; Liou & Lawrenz, 2011). All but one study (Titu et al., 2018) identifed participants by their race and ethnicity. Twelve included at least one other demographic characteristic, such as gender, socioeconomic status, or language status (Bianchini et al., 2015; Brenner et al., 2016; Lee et al., 2016; Liou et al., 2010; Liou & Lawrenz, 2011; Marco-Bujosa et al., 2020, 2021; Mark et al., 2020; Nehmeh & Kelly, 2018; Rivera Maulucci, 2010; Subramaniam, 2013; Zapata, 2013). With the type of teachers investigated, theoretical lenses employed to frame teachers’ perceptions and experiences varied. Although all but one (Nehmeh & Kelly, 2018) integrated race and ethnicity into their conceptual frame, few studies centered their framework on race and ethnicity. As one example of these latter studies, Mark et al. (2020) used critical discourse analysis, focusing on the use of language to create and sustain power hierarchies, to investigate preservice secondary science teachers’ experiences in a culturally diverse, urban high school. In particular, these researchers investigated how preservice secondary science teachers positioned themselves in relation to their clinical experiences in a culturally diverse context and research-based defcit beliefs about culturally diverse students and their families. Rivera Maulucci (2010), as a second example, used the dialectical relationship between authentic caring – focused on the individual and collective needs, interests, linguistic resources, and cultures of youth and teachers – and aesthetic caring – focused on institutional programming, rules, policies, procedures, and accountability mechanisms – within a larger social justice framework to guide her study. She employed these constructs in tracing the developmental trajectory of a beginning science teacher across her frst two years at an urban middle school. In their discussions and implications, several researchers emphasized that teacher participants grew in their understanding of racially and ethnically diverse students and/or culturally and linguistically relevant instruction as a result of their teacher education or professional development experiences (Bianchini et al., 2015; Brenner et al., 2016; Lee et al., 2016; Marco-Bujosa et al., 2020; Rivera Maulucci, 2010; Titu et al., 2018). Two studies found teacher participants grew to view the parents of their culturally and linguistically diverse students in a more positive light (Lee et al., 2016; Mark et al., 2020), while one noted that teachers experienced more growth in their understanding of themselves and their students than of their students’ families and communities (Brenner et al., 2016). Three studies recommended closer examination of non-white or ethnic minority preservice and practicing teachers to better understand their needs, perspectives, and strengths (Liou et al., 2010; Liou  & Lawrenz, 2011; Subramaniam, 2013). Several studies called for teachers to work to adopt a more critical stance: Mark et al. (2020) recommended that teachers critically examine the beliefs they have about science teaching and learning; Marco-Bujosa et al. (2020, 2021) suggested that teachers align their professional identity with teaching science for social justice, with an emphasis on identifying structural injustices in schools; Rivera Maulucci (2010) recommended that teachers broaden their scope of caring to include a critical awareness of and commitment to transform structural and institutional sources of inequality in schools; and Zapata (2013) advised

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that teachers help their students interrogate implicit and problematic sociocultural and gendered perspectives about science in classrooms. Bianchini et  al. (2015) and Southerland et  al. (2011) encouraged teacher educators, professional developers, and researchers to move away from blaming teacher participants for their reluctance to engage in explorations of equity and diversity, and toward more careful consideration of the opportunities and constraints aforded teachers by the professional learning contexts themselves. As a substantive example of research in this category, Subramaniam (2013) merged conceptions of teaching science with role constructs used to describe and distinguish minority preservice teachers from their white counterparts (i.e., role model, social transformer, and cultural mediator) to frame the investigation of fve ethnic minority preservice elementary teachers’ conceptions and enactments of teaching science. The researcher qualitatively analyzed diferent types of data, including participants’ drawings of a teacher teaching science, narratives, and semi-structured interviews as well as observations of microteaching sessions and their self-reviews of these sessions. Participants’ conceptions of teaching were found to be similar – tied to similar past educational experiences situated within similar educational contexts. Participants in this study were also found to conceptualize teaching content to their students in constructivist ways, with science content linked to home experiences, students’ ideas, hands-on activities, and group work. Subramaniam called for teacher educators to better support ethnic minority preservice teachers in sharing their K–12 experiences. They also recommend better support for both ethnic minority and white preservice teachers on how these experiences can productively inform conceptions of teaching science.

Category H: Assessments We identifed fve articles that ft the category of assessments; this was our smallest category of articles. All of the studies were conducted in the United States. Four of the fve focused on students: elementary school students (Maerten-Rivera et al., 2010; Noble et al., 2012), elementary and middle school students (Quinn & Cooc, 2015), or high school students (You et al., 2021). The ffth examined the construct validity of an assessment itself (You et al., 2022). Four of the fve also employed quantitative analyses (Maerten-Rivera et al., 2010; Quinn & Cooc, 2015; You et al., 2021, 2022). The ffth used qualitative methods to investigate a small number of students’ understanding of test items (Noble et al., 2012). Further, four of the fve examined students’ race and ethnicity in addition to at least one other factor, such as gender, language status, socioeconomic status, or disability status (Maerten-Rivera et al., 2010; Quinn & Cooc, 2015; You et al., 2021, 2022). The ffth study examined students from historically nondominant communities, without specifying students’ race and ethnicity (Noble et al., 2012). In their conceptual framing, authors presented possible explanations for diferences in assessment scores by racial and ethnic groups. Maerten-Rivera et al. (2010), for example, employed three constructs as their frame: student background factors (e.g., race or ethnicity, gender) that infuence science achievement; school characteristics that infuence science achievement; and the relationship across reading, mathematics, and science achievement. Noble et al. (2012) investigated assessments using a sociocultural lens, viewing science tests as grounded in the language and cultural norms of students who are European American, native English-speaking, and from the middle class. They explained how the linguistic mismatch between the features of language included in standardized tests and the features of language that students from historically nondominant communities use interferes with the performance of these students. As a third example, Quinn and Cooc (2015) framed their study using the construct of a science achievement gap between racial/ethnic groups and by gender. They identifed diferences in socioeconomic status, school quality, and mathematics and reading achievement as contributing to gaps by race/ethnicity, and cultural norms and mathematics achievement as contributing to gaps by gender.

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Although small in number, these studies drew a wide range of implications. Maerten-Rivera et al. (2010) emphasized that the efects of ethnicity, gender, and socioeconomic status on science achievement were smaller than disability status and language status; language status had the largest efect on science achievement. Quinn and Cooc (2015) noted that science achievement gaps by race/ ethnicity, as well as by gender, remained stable or narrowed as students moved through elementary and middle school. However, when prior mathematics and reading achievement, socioeconomic status, and classroom fxed efects were taken into account, racial/ethnic gaps in science achievement at the middle school level were not statistically signifcant. Noble et al. (2012) and You et al. (2022) suggested that test items are biased against students from historically nondominant communities. An important goal for the research community, Noble et al. (2012) underscored, should be to develop alternative measures of science knowledge that are responsive to students’ backgrounds and experiences and that allow them multiple ways to demonstrate what they know. Quinn and Cooc (2015) reminded readers that eliminating science achievement gaps to improve the rates of STEM entry and persistence for ethnic minorities and girls cannot be the sole goal; eforts to correct inequities experienced by ethnic minorities and women in the STEM workforce must be implemented as well.

Discussion and Implications: Moving Ahead As stated in the Introduction, this review is an extension of the work done by Parsons (2014) in an earlier handbook. The purpose of conducting this current review was to determine our progress in identifying the ways the structured and systemic construction of race and ethnicity has been taken up in science education research. We systematically unpacked and synthesized the literature, using Parsons’s recommendations for future research on race and ethnicity in science education as the organizing structure for this review. Parsons recommended that (1) critical theories be used alone or with other theories to frame studies of race and ethnicity, (2) mixed methods be used more regularly to investigate the complex nature of race and ethnicity constructs, and (3) studies seek to transform science education to become more equitable and socially just. We take those recommendations now as discussion points for the fndings of this current review and address implications and suggestions for future research.

Recommendations for Mixed Methods Referring again to Table 8.1, there were markedly few studies that employed mixed methods, the second of Parsons’s (2014) three recommendations for future research in the feld. With so few mixed-methods studies, except for the team of researchers from the UK (see Archer, Osborne, DeWitt, and colleagues), it is difcult to identify the strengths or the added benefts such studies yield to understand the structured and systemic construction of race and ethnicity more thoroughly in the United States and other countries. This is important to know, as explained in the Introduction, because countries conceptualize race and ethnicity diferently and it is a moving construct. As a collective, these studies more often focused their investigations on student participants than on teachers and/or their families. For the quantitative component, almost all used surveys and/or tests; only a handful analyzed data from observations, video records, or written work. For the qualitative component, researchers most often analyzed interviews; some examined written work, video records, and/or observations. Therefore, one recommendation to move the feld forward is to conduct more mixed-methods studies in science education that focus on race and ethnicity. This could better determine what kinds of questions about race and ethnicity mixed-methods research may ofer for deeper analysis and better determine the benefts of using mixed methods to understand race and ethnicity in science education. A second, related recommendation is to broaden both the participants invited to

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participate in studies (more on this next) and the kinds of data analyzed in mixed-methods research toward the goal of more deeply understanding race and ethnicity in science education. At the same time, we understand the challenges of conducting quality mixed-methods studies, particularly for large-scale studies. There are challenges to data collection and data analysis alone as well as the need to have researchers with expertise in both qualitative and quantitative design and integrating the two (Creswell & Plano Clark, 2011). Conducting mixed-methods studies may also require extra time, resources, and funding. Thus, as a third recommendation, we encourage science educators to collaborate and work with other researchers across diverse areas of expertise and apply for funding to conduct mixed-methods studies of race and ethnicity in science education. Opportunities for data sharing may support research teams coming together to understand race and ethnicity across and within varying contexts, settings, countries, individuals, and groups. We think it would be transformative for scholars across countries, contexts, and expertise to develop research projects that will contribute to research designs that will transform science education.

Recommendations for Theory and Transformation We next turn to Parsons’s (2014) two other recommendations: What does it mean to theorize about race and ethnicity, and what does it mean to transform science? Phinney (1990) discussed that ethnicity is often used to describe group members who share a common set of cultural traditions, attitudes, and values, whereas race often refers to biological and physical traits that unite a group (such as skin color or hair type; Quintana, 1998). Furthermore, certain groups are more likely to be described in terms of their ethnic group (e.g., Latinx, Asian Americans), whereas others are more commonly referred to as a racial group (e.g., African Americans). It is these distinctions that are not made to a large degree in the studies we reviewed. This indicates that more theorization about race and ethnicity in science education research is needed. In some cases, the race and ethnicity of participants were not mentioned, thus reinforcing the assumption that race and ethnicity are colorblind (Bonilla-Silva, 2017; Delgado & Stefancic, 2017), or invisible and irrelevant, to the study. Critical race theory and intersectionality ft the recommendations for critical theories and outcomes to transform science education to become more equitable and socially just. We note the use of critical race theory as a critical theory was used alone and with other theories to frame race and ethnicity in the 22 studies we reviewed in two categories. Many of the studies employed tenets of critical race theory, focusing on counternarratives that challenge master narratives and highlight the experiences of people of color (Solórzano & Yosso, 2002a; Zamudio et al., 2010). In particular, some of the studies in this group used counternarratives in telling the experiences of African American youth and adults. The studies also promoted the importance of centering race and racism when creating learning experiences across K–12 and teacher education contexts and for people of color. In critical race theory and intersectionality studies, researchers expanded critical race theory to focus on intersectionality, and some who did not explicitly use intersectionality still discussed how the race and ethnicity of their participants intersected with other identifers to infuence their experiences. The implications for this collection of studies in critical race theory as well as critical race theory and intersectionality emphasize the importance of engaging in research that addresses larger systemic issues in science education and being critically conscious and responsive to how race and racism impact science education. The studies in these two categories used a variety of research methods and approaches, with critical race theory as a theoretical framework, and sometimes in collaboration with other theoretical frameworks. However, as introduced earlier, the explicit theorization of race and ethnicity was not as well developed or theorized as expected. Though critical race theory emphasizes the social construction of race, both analyses of race and ethnicity were included tangentially in the discussion and implications of the fndings of most of these studies.

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In the subcategories connected to sociocultural identity, studies reviewed identifed how the race and ethnicity of the participants also infuenced their teaching or learning of science, their development as science teachers or learners, and thus their identity development. With a few exceptions (e.g., Chapman & Feldman, 2017), the sociocultural frameworks the researchers used were not specifcally focused on race and ethnicity. Unlike the previous categories, race and ethnicity were not central to the theoretical and methodological bases of these studies. We note that language and terms such as “nondominant”, “nondominant backgrounds”, “nonwhite”, and “minority” were used in several studies – both in the sociocultural identity subcategories and other categories – to describe the racial and ethnic backgrounds of the participants (e.g., Bonnette et al., 2019; Calabrese Barton et al., 2013; Seiler, 2011; Tan et al., 2013). These descriptors were used in the abstract and/or throughout the other sections of the studies; however, in the methods, specifc racial and ethnic identity markers were included. Employing the language of “nondominant” or “nonwhite” calls attention to the implicit bias of inferiority of racial and ethnic groups as compared to a white dominant racial frame of white supremacy (see Rivera Maulucci & Mensah, 2015). Furthermore, in Omi and Winant’s (1994) diferential-racialization hypothesis, they asserted that each group of color is racialized in diferent ways from others. Thus, to be racially and ethnically specifc to the racial and ethnic identity of the participants or groups in research elevates them and, not in comparison to another group, distinguishes them based upon their ethnic and racial identity. These distinctions are important as language, naming, and representation have difering meanings in specifc contexts (Kivisto & Croll, 2012). Even within community-based research, the broader community’s understanding of how race and ethnicity impact relationships within and among members also has to consider power dynamics in developing relationships while also understanding science and its role in the community. On a related note, for those studies that employed frameworks often used where race and ethnicity are not mentioned, such as attitudes and beliefs, language as central to learning, or gaps in science achievement, few recognized the potential limitations of their frames. In other words, few researchers acknowledged the theories or concepts they employed might limit what they were able to see and understand about race and ethnicity given the constructs’ origins and purposes. As a rare example, Snodgrass Rangel et al. (2020) noted that motivational theories, like the theory of expectancy-value they employed, might better explain the views, experiences, and actions of some racial and ethnic groups than others. From these indications, there is still much work to do in theorizing race and ethnicity in science education. We acknowledge that there has been advancement regarding race more than ethnicity, and this is due mainly to authors’ use of critical race theory and intersectionality; there were two categories dedicated to critical race theory in this review. Still, even with critical race theory and intersectionality used as theoretical frameworks, more attention to intersectional analysis beyond the naming of racial and ethnic groups and the inclusion of gender, socioeconomic status, frst language, or nationality in identifying the participants is suggested. Many of the studies reported racial and ethnic backgrounds of participants, as we stated, but did not go further in acknowledging how racial and ethnic markers intersect to reveal deeper power and systemic issues in science education, even in how groups are excluded from science. In particular, research studies are needed to see how intersectional identities reveal themselves in transforming science teacher or science learner identities for both men and women, or boys and girls (see again Mark, 2018), or people who identify as queer, nonbinary, or nonconforming. Research studies on intersectionality or identity could also beneft from mixedmethods and quantitative designs, which were largely absent from both categories of study. Science educators may learn from scholars in higher education who use QuantCrit methods (Covarrubias, 2011; López et al., 2018). QuantCrit quantitative methodologies anchored in the understandings of critical race theory (Solórzano et al., 2005). Without essentializing groups, we can learn more about racial and ethnic group identities, along with multiple and intersecting identities that overlap in complex ways (Crenshaw, 2016).

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We also recommend that researchers acknowledge that issues related to race and ethnicity, as well as language and other social markers, are highly contentious in our society. Thus, we must also contextualize research by including the perspectives of who is doing the research, in addition to who the participants are, with the description of the context in which the work is conducted. This allows the reader to become cognizant of the culturally situated meanings that we bring to our research endeavors and to appreciate the complexity in the lives of those who participate in our studies, including the researchers. In this way, these acknowledgments raise our level of sociopolitical consciousness that must be understood as we transform the feld. Further, to move toward transforming science education research, we adopt the defnition of transformative learning theory explicated by Mezirow (2000) in adult education to apply to science education: Transformative learning is the process by which we transform our taken-for-granted frames of reference (meaning perspectives, habits of mind, mind-sets) to make them more inclusive, discriminating, open, emotionally capable of change, and refective so that they may generate beliefs and opinions that will prove more true or justifed to guide action. (pp. 7–8) Though Mezirow’s work has been critiqued for missing the social aspect of transformative learning, this dimension must be part of science education research without exception. Therefore, to move ahead and transform science education so that it is more equitable and socially just, the idea of transformative learning for the collective mindset of the feld is for science educators to uncover those taken-for-granted frames of reference, theoretical notions, and ideologies, and question them. It involves being more open about why we conduct research and what the fndings of our research reveal, or do not reveal, for the participants, communities, and contexts in which we do our work, particularly for and within communities of color, multilingual communities, and communities where issues of race and ethnicity and other intersecting variables are most salient. It also requires diferent approaches and questions to our research – how we present the fndings, what the implications of these fndings are, and what they mean for all involved in the research process. In other words, through our research, what additional truths are made evident and for whom? How does our research allow us to engage in discourse and action more intently, purposefully, and justly? How do our research and fndings impact others? How is our work the catalyst for transformation?

Limitations and Conclusion We acknowledge the tremendous work of scholars and researchers to investigate race and ethnicity in science education. The feld has grown and continues to grow in its attention to the wide range of questions that impact teaching, learning, and policy in science education. We close our tracking of this growth in research by noting two limitations to our review. One limitation in writing this chapter was its scope: We considered work from ten journals published from 2010 to 2020, with focused attention on the terms “race” and “ethnicity”. Thus, we looked at only a subset of articles published in science education on race and ethnicity over time, and only from the ten journals we selected for analysis. We recognize that there are relevant studies in other journals as well as relevant studies that employed other terms besides race and ethnicity that could have been included in this review. In particular, we recognize that our selection of these ten journals limited our examination of studies conducted in countries other than the United States. We also recognize that our selection of these journals resulted in the omission of alternative and transformational studies that were published in other venues.1 A second limitation that also sets a basis for future work is that we did not distinguish between those studies that included a theoretical or conceptual framework and those that only ofered a literature

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review. Distinguishing between these two types of studies – providing a more detailed examination of the presence or absence of clearly articulated frameworks – could enhance the conceptualization of studies and provide greater insight into the strengths such frameworks add to research on race and ethnicity. We were able to capture some of the theoretical frameworks, such as critical race theory, feminist theory, and sociocultural theory, in categorizing studies; still, by more accurately indicating the range of theoretical perspectives used across our categories, we would have provided yet another vantage point to determine how far and in what additional ways we may move the feld forward to introduce theories that resonate well with race and ethnicity, in addition to language. Despite these limitations, we intend this review to be a source of information and inspiration to current researchers committed to furthering eforts to transform science, science teaching, science learning, and science policy. We have ofered several suggestions on ways to foreground more clearly the construction of race and ethnicity in science education research. These recommendations include conducting more mixed-methods studies across contexts; acknowledging the intersectionality of researchers, participants, and contexts for looking at race and ethnicity; and working more intentionally to transform science education as currently envisioned and enacted. These recommendations build on those ofered by Parsons (2014) in her unpacking and critically synthesizing the literature on race and ethnicity in science education. Although we have made strides since her review, her closing call to action remains salient today: Now is the time to engage and generate science education research and scholarship on race and ethnicity that informs and impacts future research, policy, and practice. The science education community has the capacity to engage race and ethnicity. Does the community have the will and the courage to engage in such a high-risk endeavor? (p. 183) As a feld, we have made progress since the last handbook was published in attending to race and ethnicity as a feld. Now, what more can we do as a community to further this work? What will the next chapter on unpacking race and ethnicity in science education say about the feld’s movement and transformation in this regard?

Note 1

A list of other journals to consider for future review include the following: Science Education International (SEI) by the International Council of Association for Science Education (ICASE); the Canadian Journal of Science, Mathematics and Technology Education; and the African Journal of Research in Mathematics, Science and Technology Education (AJRMSTE). We also suggest journals such as Urban Education, Race and Ethnicity, and The Urban Review.

References Alexakos, K., Pride, L. D., Amat, A., Tsetsakos, P., Lee, K. J., Paylor-Smith, C., Zapata, C., Wright, S., & Smith, T. (2016). Mindfulness and discussing “thorny” issues in the classroom. Cultural Studies of Science Education, 11(3), 741–769. https://doi.org/10.1007/s11422-015-9718-0 Andersen, L., & Ward, T. J. (2014). Expectancy-value models for the stem persistence plans of ninth-grade, high-ability students: A comparison between Black, Hispanic, and white students. Science Education, 98(2), 216–242. https://doi.org/10.1002/sce.21092 Archer, L., Dawson, E., DeWitt, J., Godec, S., King, H., Mau, A., Nomikou, E., & Seakins, A. (2017). Killing curiosity? An analysis of celebrated identity performances among teachers and students in nine London secondary science classrooms. Science Education, 101(5), 741–764. https://doi.org/10.1002/sce.21291 Archer, L., Dawson, E., Seakins, A.,  & Wong, B. (2016). Disorientating, fun or meaningful? Disadvantaged families’ experiences of a science museum visit. Cultural Studies of Science Education, 11(4), 917–939. https:// doi.org/10.1007/s11422-015-9667-7

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Literature on Race and Ethnicity in Science Education Archer, L., Dewitt, J., & Osborne, J. (2015). Is science for us? Black students’ and parents’ views of science and science careers. Science Education, 99(2), 199–237. https://doi.org/10.1002/sce.21146 Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2010). “Doing” science versus “being” a scientist: Examining 10/11-year-old schoolchildren’s constructions of science through the lens of identity. Science Education, 94(4), 617–639. https://doi.org/10.1002/sce.20399 Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012a). Science aspirations, capital, and family habitus: How families shape children’s engagement and identifcation with science. American Educational Research Journal, 49(5), 881–908. https://doi.org/10.3102/0002831211433290 Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012b). “Balancing acts’’: Elementary school girls’ negotiations of femininity, achievement, and science. Science Education, 96(6), 967–989. https:// doi.org/10.1002/sce.21031 Archer, L., DeWitt, J., & Willis, B. (2014). Adolescent boys’ science aspirations: Masculinity, capital, and power. Journal of Research in Science Teaching, 51(1), 1–30. https://doi.org/10.1002/tea.21122 Archer, L., Godec, S., Calabrese Barton, A., Dawson, E., Mau, A., & Patel, U. (2021). Changing the feld: A Bourdieusian analysis of educational practices that support equitable outcomes among minoritized youth on two informal science learning programs. Science Education, 105(1), 166–203. https://doi.org/10.1002/sce.21602 Archer, L., Nomikou, E., Mau, A., King, H., Godec, S., DeWitt, J., & Dawson, E. (2019). Can the subaltern “speak” science? An intersectional analysis of performances of “talking science through muscular intellect” by “subaltern” students in UK urban secondary science classrooms. Cultural Studies of Science Education, 14(3), 723–751. https://doi.org/10.1007/s11422-018-9870-4 Atwater, M. M., Butler, M. B., Freeman, T. B., & Carlton Parsons, E. R. (2013). An examination of Black science teacher educators’ experiences with multicultural education, equity, and social justice. Journal of Science Teacher Education, 24(8), 1293–1313. https://doi.org/10.1007/s10972-013-9358-8 Banks, J. A. (1995). Multicultural education: Historical development, dimensions, and practice. In J. A. Banks & C. A. M. Banks (Eds.), Handbook of research on multicultural education (pp. 3–24). Macmillan. Banks, J. A. (2001). Cultural diversity and education: Foundations, curriculum, and teaching (4th ed.). Allyn and Bacon. Banks, J. A. (2016). Cultural diversity and education: Foundations, curriculum and teaching (6th ed.). Routledge. Bianchini, J. A., Dwyer, H. A., Brenner, M. E., & Wearly, A. J. (2015). Facilitating science and mathematics teachers’ talk about equity: What are the strengths and limitations of four strategies for professional learning? Science Education, 99(3), 577–610. https://doi.org/10.1002/sce.21160 Bolshakova, V. L. J., Johnson, C. C.,  & Czerniak, C. M. (2011). “It depends on what science teacher you got”: Urban science self-efcacy from teacher and student voices. Cultural Studies of Science Education, 6(4), 961–997. https://doi.org/10.1007/s11422-011-9346-2 Bonilla-Silva, E. (2017). Racism without racists: Color-blind racism and the persistence of racial inequality in America (5th ed.). Rowman & Littlefeld. Bonnette, R. N., Crowley, K., & Schunn, C. D. (2019). Falling in love and staying in love with science: Ongoing informal science experiences support fascination for all children. International Journal of Science Education, 41(12), 1626–1643. https://doi.org/10.1080/09500693.2019.1623431 Bourdieu, P., & Wacquant, L. (1992). An invitation to refexive sociology. University of Chicago Press. Bravo, M. A., Mosqueda, E., Solís, J. L., & Stoddart, T. (2014). Possibilities and limits of integrating science and diversity education in preservice elementary teacher preparation. Journal of Science Teacher Education, 25(5), 601–619. https://doi.org/10.1007/s10972-013-9374-8 Brenner, M. E., Bianchini, J. A., & Dwyer, H. A. (2016). Science and mathematics teachers working toward equity through teacher research: Tracing changes across their research process and equity views. Journal of Science Teacher Education, 27(8), 819–845. https://doi.org/10.1007/s10972-016-9490-3 Brkich, K. L. (2014). Urban ffth graders’ connections-making between formal earth science content and their lived experiences. Cultural Studies of Science Education, 9(1), 141–164. https://doi.org/10.1007/ s11422-013-9505-8 Brotman, J. S., & Mensah, F. M. (2013). Urban high school students’ perspectives about sexual health decisionmaking: The role of school culture and identity. Cultural Studies of Science Education, 8(2), 403–431. https:// doi.org/10.1007/s11422-012-9451-x Brown, B. A. (2011). Isn’t that just good teaching? Disaggregate instruction and the language identity dilemma. Journal of Science Teacher Education, 22(8), 679–704. https://doi.org/10.1007/s10972-011-9256-x Brown, B. A., Pérez, G., Ribay, K., Boda, P. A., & Wilsey, M. (2021). Teaching culturally relevant science in virtual reality: “When a problem comes, you can solve it with science”. Journal of Science Teacher Education, 32(1), 7–38. https://doi.org/10.1080/1046560X.2020.1778248 Brown, B. A., Ryoo, K.,  & Rodriguez, J. (2010). Pathway towards fuency: Using “disaggregate instruction” to promote science literacy. International Journal of Science Education, 32(11), 1465–1493. https://doi. org/10.1080/09500690903117921

253

Felicia Moore Mensah and Julie A. Bianchini Brown, J. C., & Crippen, K. J. (2016). The knowledge and practices of high school science teachers in pursuit of cultural responsiveness. Science Education, 101(1), 99–133. https://doi.org/10.1002/sce.21250 Brown, J. C., & Livstrom, I. C. (2020). Secondary science teachers’ pedagogical design capacities for multicultural curriculum design. Journal of Science Teacher Education, 31(8), 821–840. https://doi.org/10.1080/1046 560X.2020.1756588 Burgin, S. R., McConnell, W. J., & Flowers, A. M. (2015). ‘I actually contributed to their research’: The infuence of an abbreviated summer apprenticeship program in science and engineering for diverse high-school learners. International Journal of Science Education, 37(3), 411–445. https://doi.org/10.1080/09500693.2014 .989292 Calabrese Barton, A., Kang, H., Tan, E., O’Neill, T. B., Bautista-Guerra, J., & Brecklin, C. (2013). Crafting a future in science: Tracing middle school girls’ identity work over time and space. American Educational Research Journal, 50(1), 37–75. https://doi.org/10.3102/0002831212458142 Calabrese Barton, A., & Tan, E. (2018). A longitudinal study of equity-oriented STEM-rich making among youth from historically marginalized communities. American Educational Research Journal, 55(4), 761–800. https://doi.org/10.3102/0002831218758668 Carlone, H. B., Haun-Frank, J., & Webb, A. (2011). Assessing equity beyond knowledge-and skills-based outcomes: A comparative ethnography of two fourth-grade reform-based science classrooms. Journal of Research in Science Teaching, 48(5), 459–485. https://doi.org/10.1002/tea.20413 Carlone, H. B., Johnson, A., & Scott, C. M. (2015). Agency amidst formidable structures: How girls perform gender in science class. Journal of Research in Science Teaching, 52(4), 474–488. https://doi.org/10.1002/ tea.21224 Carrier, S. J., Thomson, M. M., Tugurian, L. P., & Stevenson, K. T. (2014). Elementary science education in classrooms and outdoors: Stakeholder views, gender, ethnicity, and testing. International Journal of Science Education, 36(13), 2195–2220. https://doi.org/10.1080/09500693.2014.917342 Chapman, A.,  & Feldman, A. (2017). Cultivation of science identity through authentic science in an urban high school classroom. Cultural Studies of Science Education, 12(2), 469–491. https://doi.org/10.1007/ s11422-015-9723-3 Chapman, A., Rodriguez, F. D., Pena, C., Hinojosa, E., Morales, L., Del Bosque, V., Tijerina, Y., & Tarawneh, C. (2019). “Nothing is impossible”: Characteristics of Hispanic females participating in an informal STEM setting. Cultural Studies of Science Education, 15(3), 723–737. https://doi.org/10.1007/s11422-019-09947-6 Chappell, M. J., & Varelas, M. (2020). Ethnodance and identity: Black students representing science identities in the making. Science Education, 104(2), 193–221. https://doi.org/10.1002/sce.21558 Charity Hudley, A. H., & Mallinson, C. (2017). “It’s worth our time”: A model of culturally and linguistically supportive professional development for K-12 STEM educators. Cultural Studies of Science Education, 12(3), 637–660. https://doi.org/10.1007/s11422-016-9743-7 Chen, J. L., & Mensah, F. M. (2018). Teaching contexts that infuence elementary preservice teachers’ teacher and science teacher identity development. Journal of Science Teacher Education, 29(5), 420–439. http://doi.org /10.1080/1046560X.2018.1469187 Chesnutt, K., Jones, M. G., Hite, R., Cayton, E., Ennes, M., Corin, E. N., & Childers, G. (2018). Next generation crosscutting themes: Factors that contribute to students’ understandings of size and scale. Journal of Research in Science Teaching, 55(6), 876–900. https://doi.org/10.1002/tea.21443 Clark, S. K., Lott, K., Larese-Casanova, M., Taggart, A. M., & Judd, E. (2020). Leveraging integrated science and disciplinary literacy instruction to teach frst graders to write like scientists and to explore their perceptions of scientists. Research in Science Education, 51(4), 1153–1175. https://doi.org/10.1007/s11165-020-09927-9 Collins, P. H. (2016). Intersectionality (Key Concepts) [Kindle Android version]. Cone, N. (2012). The efects of community-based service learning on preservice teachers’ beliefs about the characteristics of efective science teachers of diverse students. Journal of Science Teacher Education, 23(8), 889–907. https://doi.org/10.1007/s10972-012-93050 Cornell, S., & Hartmann, D. (2007). Ethnicity and race: Making identities in a changing world. Pine Forge Press. Covarrubias, A. (2011). Quantitative intersectionality: A critical race analysis of the Chicana/o educational pipeline. Journal of Latinos and Education, 10(2), 86–105. https://doi.org/10.1080/15348431.2011.556519 Crenshaw, K. W. (2016). Mapping the margins: Intersectionality, identity politics, and violence against women of color. In E. Taylor, D. Gillborn, & G. Ladson-Billings (Eds.), Foundations of critical race theory in education (2nd ed., pp. 223–250). Routledge. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage. Creswell, J. W., & Plano Clark, V. L. (2011). Designing and conducting mixed methods research (2nd ed.). Sage. Delgado, R., & Stefancic, J. (2017). Critical race theory: An introduction (3rd ed.). New York University Press.

254

Literature on Race and Ethnicity in Science Education Denerof, V. (2016). Professional development in person: Identity and the construction of teaching within a high school science department. Cultural Studies of Science Education, 11(2), 213–233. https://doi.org/10.1007/ s11422-013-9546-z DeWitt, J., & Archer, L. (2015). Who aspires to a science career? A comparison of survey responses from primary and secondary school students. International Journal of Science Education, 37(13), 2170–2192. https://doi. org/10.1080/09500693.2015.1071899 DeWitt, J., Archer, L., & Mau, A. (2016). Dimensions of science capital: Exploring its potential for understanding students’ science participation. International Journal of Science Education, 38(16), 2431–2449. https://doi. org/10.1080/09500693.2016.1248520 DeWitt, J., Archer, L., & Osborne, J. (2013). Nerdy, brainy and normal: Children’s and parents’ constructions of those who are highly engaged with science. Research in Science Education, 43(4), 1455–1476. https://doi. org/10.1007/s11165-012-9315-0 DeWitt, J., Archer, L., & Osborne, J. (2014). Science-related aspirations across the primary-secondary divide: Evidence from two surveys in England. International Journal of Science Education, 36(10), 1609–1629. https:// doi.org/10.1080/09500693.2013.871659 DeWitt, J., Osborne, J., Archer, L., Dillon, J., Willis, B., & Wong, B. (2013). Young children’s aspirations in science: The unequivocal, the uncertain and the unthinkable. International Journal of Science Education, 35(6), 1037–1063. https://doi.org/10.1080/09500693.2011.608197 Dickerson, D. L., Eckhof, A., Stewart, C. O., Chappell, S.,  & Hathcock, S. (2014). The examination of a pullout STEM program for urban upper elementary students. Research in Science Education, 44(3), 483–506. https://doi.org/10.1007/s11165-013-9387-5 Dimick, A. S. (2012). Student empowerment in an environmental science classroom: Toward a framework for social justice science education. Science Education, 96(6), 990–1012. https://doi.org/10.1002/sce.21035 Dixson, A. D., Rousseau Anderson, C. K., & Donner, J. K. (2016). Critical race theory in education: All God’s children got a song. Taylor & Francis. Donovan, B. M. (2014). Playing with fre? The impact of the hidden curriculum in school genetics on essentialist conceptions of race. Journal of Research in Science Teaching, 51(4), 462–496. https://doi.org/10.1002/ tea.21138 Donovan, B. M. (2016). Framing the genetics curriculum for social justice: An experimental exploration of how the biology curriculum infuences beliefs about racial diference. Science Education, 100(3), 586–616. https:// doi.org/10.1002/sce.21221 Donovan, B. M. (2017). Learned inequality: Racial labels in the biology curriculum can afect the development of racial prejudice. Journal of Research in Science Teaching, 54(3), 379–411. https://doi.org/10.1002/tea.21370 Donovan, B. M., Semmens, R., Keck, P., Brimhall, E., Busch, K. C., Weindling, M., Duncan, A., Stuhlsatz, M., Bracey, Z. B., Bloom, M., Kowalski, S.,  & Salazar, B. (2019). Toward a more humane genetics education: Learning about the social and quantitative complexities of human genetic variation research could reduce racial bias in adolescent and adult populations. Science Education, 103(3), 529–560. https://doi.org/10.1002/sce.21506 Donovan, B. M., Weindling, M., Salazar, B., Duncan, A., Stuhlsatz, M.,  & Keck, P. (2021). Genomics literacy matters: Supporting the development of genomics literacy through genetics education could reduce the prevalence of genetic essentialism. Journal of Research in Science Teaching, 58(4), 520–550. https://doi. org/10.1002/tea.21670 Dorph, R., Bathgate, M. E., Schunn, C. D., & Cannady, M. A. (2018). When I grow up: The relationship of science learning activation to STEM career preferences. International Journal of Science Education, 40(9), 1034–1057. https://doi.org/10.1080/09500693.2017.1360532 Dupuis, J.,  & Abrams, E. (2017). Student science achievement and the integration of Indigenous knowledge on standardized tests. Cultural Studies of Science Education, 12(3), 581–604. https://doi.org/10.1007/ s11422-016-9728-6 Elmesky, R., & Seiler, G. (2007). Movement expressiveness, solidarity and the (re)shaping of African American students’ scientifc identities. Cultural Studies of Science Education, 2(1), 73–103. https://doi.org/ 10.1007/ s11422-007-9050-4 Feagin, J. R. (2020). The white racial frame: Centuries of racial framing and counter-framing (3rd ed.). Routledge. Fenton, S. (2010). Ethnicity (2nd ed.). Polity Press. Ferguson, S. L.,  & Lezotte, S. M. (2020). Exploring the state of science stereotypes: Systematic review and meta-analysis of the Draw-A-Scientist Checklist. School Science and Mathematics, 120(1), 55–65. https://doi. org/10.1111/ssm.12382 Gallard Martínez, A. J., Pitts, W., Ramos de Robles, S. L., Milton Brkich, K. L., Flores Bustos, B., & Claeys, L. (2019). Discerning contextual complexities in STEM career pathways: Insights from successful Latinas. Cultural Studies of Science Education, 14(4), 1079–1103. https://doi.org/10.1007/s11422-018-9900-2

255

Felicia Moore Mensah and Julie A. Bianchini Gamez, R., & Parker, C. A. (2018). Becoming science learners: A study of newcomers’ identity work in elementary school science. Science Education, 102(2), 377–413. https://doi.org/10.1002/sce.21323 Ganchorre, A. R., & Tomanek, D. (2012). Commitment to teach in under-resourced schools: Prospective science and mathematics teachers’ dispositions. Journal of Science Teacher Education, 23(1), 87–110. https://doi. org/10.1007/s10972-011-9263-y Gomes, M. de F. C., Mortimer, E. F., & Kelly, G. J. (2011). Contrasting stories of inclusion/exclusion in the chemistry classroom. International Journal of Science Education, 33(6), 747–772. https://doi.org/10.1080/095 00693.2010.506665 Gonzalez-Howard, M., & Suarez, E. (2021). Retiring the term English language learners: Moving toward linguistic justice through asset-oriented framing. Journal of Research in Science Teaching, 58(5), 749–752. https:// doi.org/10.1002/tea.21684 Greenleaf, C. L., Litman, C., Hanson, T. L., Rosen, R., Boscardin, C. K., Herman, J., Schneider, S. A., Madden, S.,  & Jones, B. (2011). Integrating literacy and science in biology: Teaching and learning impacts of reading apprenticeship professional development. American Educational Research Journal, 48(3), 647–717. https://doi.org/10.3102/0002831210384839 Grimberg, B. I., & Gummer, E. (2013). Teaching science from cultural points of intersection. Journal of Research in Science Teaching, 50(1), 12–32. https://doi.org/10.1002/tea.21066 Grimes, N. K. (2013). The nanny in the schoolhouse: The role of femme-Caribbean identity in attaining success in urban science classrooms. Cultural Studies of Science Education, 8(2), 333–353. https://doi.org/10.1007/ s11422-012-9476-1 Habig, B., Gupta, P., Levine, B., & Adams, J. (2020). An informal science education program’s impact on STEM major and STEM career outcomes. Research in Science Education, 50(3), 1051–1074. https://doi.org/10.1007/ s11165-018-9722-y Harper, S. G. (2017). Engaging Karen refugee students in science learning through a cross-cultural learning community. International Journal of Science Education, 39(3), 358–376. https://doi.org/10.1080/09500693.20 17.1283547 Hernandez, C., & Shroyer, M. G. (2017). The use of culturally responsive teaching strategies among Latina/o student teaching interns during science and mathematics instruction of CLD students. Journal of Science Teacher Education, 28(4), 367–387. https://doi.org/10.1080/1046560X.2017.1343605 Holland, D., Lachiotte, W., Skinner, D., & Cain, C. (1998). Identity and agency in cultural worlds. Harvard University Press. Howard-Hamilton, M. F. (2003). Theoretical frameworks for African American women. New Directions for Student Services, 104, 19–27. https://doi.org/10.1002/ss.104 Huerta, M., Lara-Alecio, R., Tong, F., & Irby, B. J. (2014). Developing and validating a science notebook rubric for ffth-grade non-mainstream students. International Journal of Science Education, 36(11), 1849–1870. https:// doi.org/10.1080/09500693.2013.879623. Hughes, R. M., Nzekwe, B., & Molyneaux, K. J. (2013). The single-sex debate for girls in science: A comparison between two informal science programs on middle school students’ STEM identity formation. Research in Science Education, 43(5), 1979–2007. https://doi.org/10.1007/s11165-012-9345-7 Hughes, R. M., Schellinger, J., & Roberts, K. (2021). The role of recognition in disciplinary identity for girls. Journal of Research in Science Teaching, 58(3), 420–455. https://doi.org/10.1002/tea.21665 Jackson, J. K., & Ash, G. (2012). Science achievement for all: Improving science performance and closing achievement gaps. Journal of Science Teacher Education, 23(7), 723–744. https://doi.org/10.1007/s10972-011-9238-z Kane, J. M. (2012). Young African American children constructing academic and disciplinary identities in an urban science classroom. Science Education, 96(3), 457–487. https://doi.org/10.1002/sce.20483 Kang, H., Calabrese Barton, A., Tan, E., Simpkins, D., Rhee, H., & Turner, C. (2019). How do middle school girls of color develop STEM identities? Middle school girls’ participation in science activities and identifcation with STEM careers. Science Education, 103(2), 418–439. https://doi.org/10.1002/sce.21492 Kang, H., & Zinger, D. (2019). What do core practices ofer in preparing novice science teachers for equitable instruction? Science Education, 103(4), 823–853. https://doi.org/10.1002/sce.21507 Kang, R., Skinner, F., & Hyatt, C. (2018). An activity theory analysis of African American students’ experiences in advanced placement science courses. School Science and Mathematics, 118(8), 358–369. https://doi. org/10.1111/ssm.12305 Kanter, D. E., & Konstantopoulos, S. (2010). The impact of a project-based science curriculum on minority student achievement, attitudes, and careers: The efects of teacher content and pedagogical content knowledge and inquiry-based practices. Science Education, 94(5), 855–887. https://doi.org/10.1002/sce.20391 King, N. S., & Pringle, R. M. (2019). Black girls speak STEM: Counter stories of informal and formal learning experiences. Journal of Research in Science Teaching, 56(5), 539–569. https://doi.org/10.1002/tea.21513

256

Literature on Race and Ethnicity in Science Education Kivisto, P., & Croll, P. R. (2012). Race and ethnicity: The basics. Routledge. Lee, O., Llosa, L., Jiang, F., O’Connor, C., Haas, A. (2016). School resources in teaching science to diverse student groups: An intervention’s efect on elementary teachers’ perceptions. Journal of Science Teacher Education, 27(7), 769–794. https://doi.org/10.1007/s10972-016-9487-y Liou, P.-Y., Desjardins, C. D.,  & Lawrenz, F. (2010). Infuence of scholarships on STEM teachers: Cluster analysis and characteristics. School Science and Mathematics, 110(3), 128–143. https://doi.org/ 10.1111/j.1949-8594.2010.00016.x Liou, P.-Y., & Lawrenz, F. (2011). Optimizing teacher preparation loan forgiveness programs: Variables related to perceived infuence. Science Education, 95(1), 121–144. https://doi.org/10.1002/sce.20409 Lofgran, B. B., Smith, L. K., & Whiting, E. F. (2015). Science self-efcacy and school transitions: Elementary school to middle school, middle school to high school. School Science and Mathematics, 115(7), 366–376. https://doi.org/10.1111/ssm.12139 López, N., Erwin, C., Binder, M., & Chavez, M. J. (2018). Making the invisible visible: Advancing quantitative methods in higher education using critical race theory and intersectionality. Race Ethnicity and Education, 21(2), 180–207. https://doi.org/10.1080/13613324.2017.1375185 Madkins, T. C., & McKinney de Royston, M. (2019). Illuminating political clarity in culturally relevant science instruction. Science Education, 103(6), 1319–1346. https://doi.org/10.1002/sce.21542 Maerten-Rivera, J., Myers, N., Lee, O., & Penfeld, R. (2010). Student and school predictors of high-stakes assessment in science. Science Education, 94(6), 937–962. https://doi.org/10.1002/sce.20408 Maltese, A. V.,  & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among U.S. students. Science Education, 95(5), 877–907. https://doi. org/10.1002/sce.20441 Marco-Bujosa, L. M., Friedman, A. A., & Kramer, A. (2021). Learning to teach science in urban schools: Context as content. School Science and Mathematics, 121(1), 46–57. https://doi.org/10.1111/ssm.12441 Marco-Bujosa, L. M., McNeill, K. L., & Friedman, A. A. (2020). Becoming an urban science teacher: How beginning teachers negotiate contradictory school contexts. Journal of Research in Science Teaching, 57(1), 3–32. https://doi.org/10.1002/tea.21583 Mark, S. L. (2018). A bit of both science and economics: A non-traditional STEM identity narrative. Cultural Studies of Science Education, 13(4), 983–1003. https://doi.org/10.1007/s11422-017-9832-2 Mark, S. L., Id-Deen, L., & Thomas, S. (2020). Getting to the root of the matter: Pre-service teachers’ experiences and positionalities with learning to teach in culturally diverse contexts. Cultural Studies of Science Education, 15(2), 453–483. https://doi.org/10.1007/s11422-019-09956-5 Matsuda, M. J., Lawrence, C., Delgado, R., & Crenshaw, K. (1993). Words that wound: Critical race theory, assaultive speech, and the frst amendment. Westview. Matuk, C., Hurwich, T., Spiegel, A., & Diamond, J. (2021). How do teachers use comics to promote engagement, equity, and diversity in science classrooms? Research in Science Education, 51(3), 685–732. https://doi.org/ 10.1007/s11165-018-9814-8 Mavuru, L., & Ramnarain, U. D. (2020). Language afordances and pedagogical challenges in multilingual grade 9 natural sciences classrooms in South Africa. International Journal of Science Education, 2472–2492. https://doi. org/10.1080/09500693.2019.1655177 McCollough, C.,  & Ramirez, O. (2012). Cultivating culture: Preparing future teachers for diversity through family science learning events. School Science and Mathematics, 112(7), 443–451. https://doi. org/10.1111/j.1949-8594.2012.00158.x McNew-Birren, J., Hildebrand, T., & Belknap, G. (2018). Strange bedfellows in science teacher preparation: Conficting perspectives on social justice presented in a Teach for America-university partnership. Cultural Studies of Science Education, 13(2), 437–462. https://doi.org/10.1007/s11422-016-9791-z Means, B., Wang, H., Wei, X., Lynch, S., Peters, V., Young, V., & Allen, C. (2017). Expanding STEM opportunities through inclusive STEM-focused high schools. Science Education, 101(5), 681–715. https://doi. org/10.1002/sce.21281 Mensah, F. M. (2011). The DESTIN: Preservice teachers’ drawings of the ideal elementary science teacher. School Science and Mathematics, 111(8), 379–388. https://doi.org/10.1111/j.1949-8594.2011.00103.x Mensah, F. M. (2019). Finding voice and passion: Critical race theory methodology in science teacher education. American Educational Research Journal, 56(4), 1412–1456. https://doi.org/10.3102/0002831218818093 Mensah, F. M., Brown, J. C., Titu, P., Rozowa, P., Sivaraj, R., & Heydari, R. (2018). Preservice and inservice teachers’ ideas of multiculturalism: Explorations across two science methods courses in two diferent contexts. Journal of Science Teacher Education, 29(2), 12–147. https://doi.org/10.1080/1046560X.2018.1425820 Mensah, F. M., & Jackson, I. (2018). Whiteness as property in science teacher education. Teachers College Record, 120(1), 1–38. www.tcrecord.org ID Number: 21958.

257

Felicia Moore Mensah and Julie A. Bianchini Mezirow, J. (2000). Learning to think like an adult: Core concepts of transformation theory. In J. Mezirow & Associates (Eds.), Learning as transformation: Critical perspectives on a theory in progress (pp. 3–34). Jossey-Bass. Morales-Doyle, D. (2017). Justice-centered science pedagogy: A catalyst for academic achievement and social transformation. Science Education, 101(6), 1034–1060. https://doi.org/10.1002/sce.21305 Moseley, C., & Taylor, B. (2011). Analysis of environmental and general science teaching efcacy among instructors with contrasting class ethnicity distributions: A four-dimensional assessment. School Science and Mathematics, 111(5), 199–208. https://doi.org/10.1111/j.1949-8594.2011.00079.x Mutegi, J. W., Sorge, B., Fore, G. A., & Gibau, G. S. (2019). A tale of two camps: A mixed methods investigation into racially disparate outcomes in a nanotechnology research experience. Science Education, 103(6), 1456–1477. https://doi.org/10.1002/sce.21548 Nagel, J. (1994). Constructing ethnicity: Creating and recreating ethnic identity and culture.  Social Problems, 41(1), 152–176. https://www.jstor.org/stable/3096847 Nazar, C. R., Calabrese Barton, A., Morris, C., & Tan, E. (2019). Critically engaging engineering in place by localizing counternarratives in engineering design. Science Education, 103(3), 638–664. https://doi. org/10.1002/sce.21500 Nehmeh, G., & Kelly, A. M. (2018). Urban science teachers in isolation: Challenges, resilience, and adaptive action. Journal of Science Teacher Education, 29(6), 527–549. https://doi.org/10.1080/1046560X.2018.1474425 Noble, T., Suarez, C., Rosebery, A., O’Connor, M. C., Warren, B., & Hudicourt-Barnes, J. (2012). “I never thought of it as freezing”: How students answer questions on large-scale science tests and what they know about science. Journal of Research in Science Teaching, 49(6), 778–803. https://doi.org/10.1002/tea.21026 Okebukola, P. A., Owolabi, O., & Okebukola, F. O. (2013). Mother tongue as default language of instruction in lower primary science classes: Tension between policy prescription and practice in Nigeria. Journal of Research in Science Teaching, 50(1), 62–81. https://doi.org/10.1002/tea.21070 Omi, M.,  & Winant, H. (1994). Racial formation in the United States: From the 1960s to the 1990s (2nd ed.). Routledge. Parker, C. (2014). Multiple infuences: Latinas, middle school science, and school. Cultural Studies of Science Education, 9(2), 317–334. https://doi.org/10.1007/s11422-014-9573-4 Parker, C., Grigg, J., D’Souza, S., Mitchell, C., & Smith, E. (2020). Informed aspirations in science and engineering with upper elementary students after 1 year of a STEM intensive university-school district partnership. School Science and Mathematics, 120(6), 364–374. https://doi.org/10.1111/ssm.12428 Parker, L., & Lynn, M. (2002). What’s race got to do with it? Critical race theory’s conficts with and connections to qualitative research methodology and epistemology. Qualitative Inquiry, 8(1), 7–22. https://doi. org/10.1177/107780040200800102 Parsons, E. C. (2014). Unpacking and critically synthesizing the literature on race and ethnicity in science education. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. 2, pp. 167–186). Taylor & Francis. Parsons, E. C., & Bayne, G. U. (2012). Conceptualizations of context in science education research: Implications for equity. In J. A. Bianchini, V. L. Akerson, A. Calabrese Barton, O. Lee, & A. J. Rodriguez (Eds.), Moving the equity agenda forward: Equity research, practice, and policy in science education (pp. 153–172). Springer. Phinney, J. S. (1990). Ethnic identity in adolescents and adults: Review of research. Psychological Bulletin, 108, 499–514. https://doi.org/10.1037/0033-2909.108.3.499 Pitts, W. (2011). Potentialities beyond defcit perspectives: Globalization, culture and urban science education in the Bronx. Cultural Studies of Science Education, 6(1), 89–112. https://doi.org/10.1007/s11422-010-9301-7 Pringle, R. M., Brkich, K. M., Adams, T. L., West-Olatunii, C., & Archer-Banks, D. A. (2012). Factors infuencing elementary teachers’ positioning of African American girls as science and mathematics learners. School Science and Mathematics, 112(4), 217–229. https://doi.org/10.1111/j.1949-8594.2012.00137.x Pruitt, S. L.,  & Wallace, C. S. (2012). The efect of a state department of education teacher mentor initiative on science achievement. Journal of Science Teacher Education, 23(4), 367–385. https://doi.org/10.1007/ s10972-012-9280-5 Quinn, D. M.,  & Cooc, N. (2015). Science achievement gaps by gender and race/ethnicity in elementary and middle school: Trends and predictors. Educational Researcher, 44(6), 336–346. https://doi. org/10.3102/0013189X15598539 Quintana, S. M. (1998). Children’s developmental understanding of ethnicity and race. Applied & Preventative Psychology, 7, 27–45. https://doi.org/10.1016/S0962-1849(98)80020-6 Ramirez, O., McCollough, C. A., & Diaz, Z. (2016). Creating a model of acceptance: Preservice teachers interact with non-English-speaking Latino parents using culturally relevant mathematics and science activities at family learning events. School Science and Mathematics, 116(1), 43–54. https://doi.org/10.1111/ssm.12150 Ramnarain, U. D. (2011). Equity in science at South African schools: A pious platitude or an achievable goal? International Journal of Science Education, 33(10), 1353–1371. https://doi.org/10.1080/09500693.2010.510855

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Literature on Race and Ethnicity in Science Education Ramnarain, U. D., & de Beer, J. (2013). Science students creating hybrid spaces when engaging in an expo investigation project. Research in Science Education, 43(1), 99–116. https://doi.org/10.1007/s11165-011-9246-1 Rawson, C. H., & McCool, M. A. (2014). Just like all the other humans? Analyzing images of scientists in children’s trade books. School Science and Mathematics, 114(1), 10–18. https://doi.org/10.1111/ssm.12046 Richardson Bruna, K. (2010). Mexican immigrant transnational social capital and class transformation: Examining the role of peer mediation in insurgent science. Cultural Studies of Science Education, 5(2), 383–422. https://doi.org/10.1007/s11422-009-9232-3 Ridgeway, M. L.,  & Yerrick, R. K. (2018). Whose banner are we waving? Exploring STEM partnerships for marginalized urban youth. Cultural Studies of Science Education, 13(1), 59–84. https://doi.org/10.1007/ s11422-016-9773-1 Riegle-Crumb, C., Moore, C., & Ramos-Wada, A. (2011). Who wants to have a career in science or math? Exploring adolescents’ future aspirations by gender and race/ethnicity. Science Education, 95(3), 458–476. https://doi.org/10.1002/sce.20431 Rivera Maulucci, M. S. (2010). Navigating role forces and the aesthetic|authentic caring dialectic: A novice urban science teacher’s developmental trajectory. Cultural Studies of Science Education, 5(3), 625–647. https:// doi.org/10.1007/s11422-009-9221-6 Rivera Maulucci, M. S. (2011). Language experience narratives and the role of autobiographical reasoning in becoming an urban science teacher. Cultural Studies of Science Education, 6(2), 413–434. https://doi. org/10.1007/s11422-011-9323-9 Rivera Maulucci, M. S. (2013). Emotions and positional identity in becoming a social justice science teacher: Nicole’s story. Journal of Research in Science Teaching, 50(4), 453–478. https://doi.org/10.1002/tea.21081 Rivera Maulucci, M. S., & Mensah, F. M. (2015). Naming ourselves and others. Journal of Research in Science Teaching, 52(1), 1–5. https://doi.org/10.1002/tea.21196 Ryu, M. (2013). “But at school . . . I became a bit shy”: Korean immigrant adolescents’ discursive participation in science classrooms. Cultural Studies of Science Education, 8(3), 649–671. https://doi.org/10.1007/ s11422-012-9406-2 Ryu, M. (2015). Understanding Korean transnational girls in high school science classes: Beyond the model minority stereotype. Science Education, 99(2), 350–377. https://doi.org/10.1002/sce.21142 Ryu, M. (2019). Mixing languages for science learning and participation: An examination of Korean-English bilingual learners in an after-school science learning programme. International Journal of Science Education, 41(10), 1303–1323. https://doi.org/10.1080/09500693.2019.1605229 Ryu, M., & Sikorski, T. R. (2019). Tracking a learner’s verbal participation in science over time: Analysis of talk features within a social context. Science Education, 103(3), 561–589. https://doi.org/10.1002/sce.21493 Schindel Dimick, A. (2016). Exploring the potential and complexity of a critical pedagogy of place in urban science education. Science Education, 100(5), 814–836. https://doi.org/10.1002/sce.21233 Scogin, S. C., Cavlazoglu, B., LeBlanc, J.,  & Stuessy, C. L. (2018). Inspiring science achievement: A mixed methods examination of the practices and characteristics of successful science programs in diverse high schools. Cultural Studies of Science Education, 13(3), 649–670. https://doi.org/10.1007/s11422-016-9796-7 Sedawi, W., Ben Zvi Assaraf, O., & Reiss, M. J. (2020). Indigenous children’s connectedness to nature: The potential infuence of culture, gender and exposure to a contaminated environment. Cultural Studies of Science Education, 15(4), 955–989. https://doi.org/10.1007/s11422-020-09982-8 Seiler, G. (2011). Becoming a science teacher: Moving toward creolized science and an ethic of cosmopolitanism. Cultural Studies of Science Education, 6(1), 13–32. https://doi.org/10.1007/s11422-009-9240-3 Settlage, J. (2011). Counter stories from white mainstream preservice teachers: Resisting the master narrative of deficit by default. Cultural Studies of Science Education, 6(4), 803–836. https://doi.org/10.1007/s11422-011-9324-8 Shady, A. (2014). Negotiating cultural diferences in urban science education: An overview of teacher’s frsthand experience refection of cogen journey. Cultural Studies of Science Education, 9(1), 31–51. https://doi. org/10.1007/s11422-013-9486-7 Sharma, R. A., & Honan, E. (2020). Fijian pre-service teachers’ ideas about science and scientists. Journal of Science Teacher Education, 31(3), 335–357. https://doi.org/10.1080/1046560X.2019.1706904 Shaw, J. M., Lyon, E. G., Stoddart, T., Mosqueda, E., & Menon, P. (2014). Improving science and literacy learning for English language learners: Evidence from a pre-service teacher preparation intervention. Journal of Science Teacher Education, 25(5), 621–643. https://doi.org/10.1007/s10972-013-9376-6 Smedley, A., & Smedley, B. D. (2005). Race as biology is fction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race. American Psychologist, 60(1), 16. https://doi. org/10.1037/0003-066X.60.1.16 Snodgrass Rangel, V., Vaval, L., & Bowers, A. (2020). Investigating underrepresented and frst-generation college students’ science and math motivational beliefs: A nationally representative study using latent profle analysis. Science Education, 104(6), 1041–1070. https://doi.org/10.1002/sce.21593

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Felicia Moore Mensah and Julie A. Bianchini Solórzano, D. G., & Huber, L. P. (2020). Racial microaggressions: Using critical race theory to respond to everyday racism. Teachers College Press. Solórzano, D. G., Ceja, M., & Yosso, T. (2000). Critical race theory, racial microaggressions, and campus racial climate: The experiences of African American college students. Journal of Negro Education, 69(1–2), 60–73. https://www.jstor.org/stable/2696265 Solórzano, D. G., Villalpando, O., & Oseguera, L. (2005). Educational inequities and Latina/o undergraduate students in the United States: A critical race analysis of their educational progress. Journal of Hispanic Higher Education, 4(3), 272–293. http://doi.org/10.1177/1538192705276550 Solórzano, D. G., & Yosso, T. J. (2002a). A critical race counterstory of race, racism, and afrmative action. Equity & Excellence in Education, 35(2), 155–168. https://doi.org/10.1080/713845284 Solórzano, D. G., & Yosso, T. J. (2002b). Critical race methodology: Counter-storytelling as an analytical framework for education research. Qualitative Inquiry, 8(1), 23–44. https://doi.org/10.1177/107780040200800103 Sondergeld, T. A., Provinzano, K., & Johnson, C. C. (2020). Investigating the impact of an urban community school efort on middle school STEM-related student outcomes over time through propensity score matched methods. School Science and Mathematics, 120(2), 90–103. https://doi.org/10.1111/ssm.12387 Southerland, S., Gallard, A., & Callihan, L. (2011). Examining teachers’ hurdles to “science for all”. International Journal of Science Education, 33(16), 2183–2213. https://doi.org/10.1080/09500693.2010.530698 Sparks, D. M. (2018). The process of becoming: Identity development of African American female science and mathematics preservice teachers. Journal of Science Teacher Education, 29(3), 243–261. https://doi.org/10.108 0/1046560X.2018.1436359 Sparks, D. M.,  & Pole, K. (2019). “Do we teach subjects or students?” Analyzing science and mathematics teacher conversations about issues of equity in the classroom. School Science and Mathematics, 119(7), 405–416. https://doi.org/10.1111/ssm.12361 Sprague Martinez, L., Bowers, E., Reich, A. J., Ndulue, U. J., Le, A. A., & Peréa, F. C. (2016). Engaging youth of color in applied science education and public health promotion. International Journal of Science Education, 38(4), 688–699. https://doi.org/10.1080/09500693.2015.1134850 Stevenson, A. D., Gallard Martínez, A. J., Brkich, K. L., Flores, B. B., Claeys, L., & Pitts, W. (2019). Latinas’ heritage language as a source of resiliency: Impact on academic achievement in STEM felds. Cultural Studies of Science Education, 14(1), 1–13. https://doi.org/10.1007/s11422-016-9789-6 Subramaniam, K. (2013). Minority preservice teachers’ conceptions of teaching science: Sources of science teaching strategies. Research in Science Education, 43(2), 687–709. https://doi.org/10.1007/s11165-012-9284-3 Sue, D. W., Capodilupo, C. M., Torino, G. C., Bucceri, J. M., Holder, A., Nadal, K. L., & Esquilin, M. (2007). Racial microaggressions in everyday life: Implications for clinical practice. American Psychologist, 62(4), 271– 286. https://doi.org/10.1037/0003-066X.62.4.271 Suriel, R. L., & Atwater, M. M. (2012). From the contribution to the action approach: White teachers’ experiences infuencing the development of multicultural science curricula. Journal of Research in Science Teaching, 49(10), 1271–1295. https://doi.org/10.1002/tea.21057 Swanson, L. H., Bianchini, J. A., & Lee, J. S. (2014). Engaging in argument and communicating information: A case study of English language learners and their science teacher in an urban high school. Journal of Research in Science Teaching, 51(1), 31–64. https://doi.org/10.1002/tea.21124 Swartz, D. (1997). Culture and power: The sociology of Pierre Bourdieu. University of Chicago Press. Tan, E., Calabrese Barton, A., Kang, H., & O’Neill, T. (2013). Desiring a career in STEM-related felds: How middle school girls articulate and negotiate identities-in-practice in science. Journal of Research in Science Teaching, 50(10), 1143–1179. https://doi.org/10.1002/tea.21123 Taylor, J. A., Getty, S. R., Kowalski, S. M., Wilson, C. D., Carlson, J., & Van Scotter, P. (2015). An efcacy trial of research-based curriculum materials with curriculum-based professional development. American Educational Research Journal, 52(5), 984–1017. https://doi.org/10.3102/0002831215585962 Teo, T. W. (2015). Inside versus outside the science classroom: Examining the positionality of two female science teachers at the boundaries of science education. Cultural Studies of Science Education, 10(2), 381–402. https:// doi.org/10.1007/s11422-014-9581-4 Terzian, S. G., & Rury, J. L. (2014). “A highly selected strain of guinea pigs”: The Westinghouse science talent search and educational meritocracy, 1942–1958. Teachers College Record, 116(5), 1–33. Titu, P., Ring-Whalen, E. A., Brown, J. C., & Roehrig, G. H. (2018). Exploring changes in science teachers’ attitudes toward culturally diverse students during an equity-focused course. Journal of Science Teacher Education, 29(5), 378–396. https://doi.org/10.1080/1046560X.2018.1461006 Tobin, K. (2009). Sociocultural perspectives on science and science education [keynote address]. 8th International Congress on Science Teaching and Learning, Barcelona, Spain. Tolbert, S., & Knox, C. (2016). “They might know a lot of things that I don’t know”: Investigating diferences in preservice teachers’ ideas about contextualizing science instruction in multilingual classrooms. International Journal of Science Education, 38(7), 1133–1149. https://doi.org/10.1080/09500693.2016.1183266

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Literature on Race and Ethnicity in Science Education Tong, F., Irby, B. J., Lara-Alecio, R., Guerrero, C., Fan, Y., & Huerta, M. (2014). A randomized study of a literacy-integrated science intervention for low-socio-economic status middle school students: Findings from frst-year implementation. International Journal of Science Education, 36(12), 2083–2109. https://doi.org/10. 1080/09500693.2014.883107 Underwood, J. B., & Mensah, F. M. (2018). An investigation of science teacher educators’ perceptions of culturally relevant pedagogy. Journal of Science Teacher Education, 29(1), 46–64. https://doi.org/10.1080/10465 60X.2017.1423457 Upadhyay, B., Maruyama, G., & Albrecht, N. (2017). Taking an active stance: How urban elementary students connect sociocultural experiences in learning science. International Journal of Science Education, 39(18), 2528– 2547. https://doi.org/10.1080/09500693.2017.1392645 Varelas, M., Kane, J. M.,  & Wylie, C. D. (2011). Young African American children’s representations of self, science, and school: Making sense of diference. Science Education, 95(5), 824–851. http://doi.org/10.1002/ sce.20447 Varelas, M., Tucker-Raymond, E., & Richards, K. (2015). A structure-agency perspective on young children’s engagement in school science: Carlos’s performance and narrative. Journal of Research in Science Teaching, 52(4), 516–529. https://doi.org/10.1002/tea.21211 Vincent-Ruz, P., & Schunn, C. D. (2021). Identity complexes and science identity in early secondary: Monotopical or in combination with other topical identities. Research in Science Education, 51(Suppl1), S369–S390. https://doi.org/10.1007/s11165-019-09882-0 Visintainer, T. (2020). “I think at frst glance people would not expect me to be interested in science”: Exploring the racialized science experiences of high school students of color. Journal of Research in Science Teaching, 57(3), 393–422. https://doi.org/10.1002/tea.21597 Wallace, C. S. (2013). Promoting shifts in preservice science teachers’ thinking through teaching and action research in informal science settings. Journal of Science Teacher Education, 24(5), 811–832. https://doi. org/10.1007/s10972-013-9337-0 Wallace, T.,  & Brand, B. R. (2012). Using critical race theory to analyze science teachers’ culturally responsive practices. Cultural Studies of Science Education, 7(2), 341–374. https://doi.org/10.1007/ s11422-012-9380-8 Walls, L. (2016). Awakening a dialogue: A critical race theory analysis of U. S. nature of science research from 1967 to 2013. Journal of Research in Science Teaching, 53(10), 1546–1570. https://doi.org/10.1002/ tea.21266 Warmington, P. (2009). Taking race out of scare quotes: Race-conscious social analysis in an ostensibly postracial world. Race Ethnicity & Education, 12(3), 281–296. https://doi.org/10.1080/13613320903178253 Wee, B. (2012). A cross-cultural exploration of children’s everyday ideas: Implications for science teaching and learning. International Journal of Science Education, 34(4), 609–627. https://doi.org/10.1080/09500693.2011. 579193 Weiland, I. (2015). An exploration of Hispanic mothers’ culturally sustaining experiences at an informal science center. Journal of Research in Science Teaching, 52(1), 84–106. https://doi.org/10.1002/tea.21190 Weis, L., Eisenhart, M., Cipollone, K., Stich, A. E., Nikischer, A. B., Hanson, J., Ohle Leibrandt, S., Allen, C. D.,  & Dominguez, R. (2015). In the guise of STEM education reform: Opportunity structures and outcomes in inclusive STEM-focused high schools. American Educational Research Journal, 52(6), 1024–1059. https://doi.org/10.3102/0002831215604045 Wild, A. (2015). Relationships between high school chemistry students’ perceptions of a constructivist learning environment and their STEM career expectations. International Journal of Science Education, 37(14), 2284– 2305. https://doi.org/10.1080/09500693.2015.1076951 Wing, A. K. (1997). Critical race feminism: A reader. New York University Press. Wong, B. (2012). Identifying with science: A case study of two 13-year-old “high achieving working class” British Asian girls. International Journal of Science Education, 34(1), 43–65. https://doi.org/10.1080/0950069 3.2010.551671 Wong, B. (2015). Careers “from” but not “in” science: Why are aspirations to be a scientist challenging for minority ethnic students? Journal of Research in Science Teaching, 52(7), 979–1002. https://doi.org/10.1002/ tea.21231 Xu, J., Coats, L. T.,  & Davidson, M. L. (2012). Promoting student interest in science: The perspectives of exemplary African American teachers. American Educational Research Journal, 49(1), 124–154. https://doi. org/10.3102/0002831211426200 Yerrick, R., & Johnson, J. (2011). Negotiating white science in rural Black America: A case for navigating the landscape of teacher knowledge domains. Cultural Studies of Science Education, 6(4), 915–939. https://doi. org/10.1007/s11422-011-9350-6 Yoon, J., & Martin, L. A. (2019). Infusing culturally responsive science curriculum into early childhood teacher preparation. Research in Science Education, 49(3), 697–710. https://doi.org/10.1007/s11165-017-9647-x

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Felicia Moore Mensah and Julie A. Bianchini You, H. S., Park, S., & Delgado, C. (2021). A closer look at US schools: What characteristics are associated with scientifc literacy? A multivariate multilevel analysis using PISA 2015. Science Education, 105(2), 406–437. https://doi.org/10.1002/sce.21609 You, H. S., Park, S., Marshall, J. A.,  & Delgado, C. (2022). Interdisciplinary science assessment of carbon cycling: Construct validity evidence based on internal structure. Research in Science Education, 52(2), 473–492. https://doi.org/10.1007/s11165-020-09943-9 Zamudio, M., Russell, C., Rios, F., & Bridgeman, J. L. (2010). Critical race theory matters: Education and ideology. Taylor & Francis. Zapata, M. (2013). Substantiating the need to apply a sociocultural lens to the preparation of teachers in an efort to achieve science reform. Cultural Studies of Science Education, 8(4), 777–801. https://doi.org/10.1007/ s11422-013-9513-8 Zhang, L., & Barnett, M. (2015). How high school students envision their STEM career pathways. Cultural Studies of Science Education, 10(3), 637–656. https://doi.org/10.1007/s11422-013-9557-9 Zirkel, S.,  & Pollack, T. M. (2016). “Just let the worst students go”: A critical case analysis of public discourse about race, merit, and worth. American Educational Research Journal, 53(6), 1522–1555. https://doi. org/10.3102/0002831216676568

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9 GENDER MATTERS Building on the Past, Recognizing the Present, and Looking Toward the Future Anna Danielsson, Lucy Avraamidou, and Allison Gonsalves

Introduction: Building on the Past In this chapter, as per the title, we build on the past, we recognize the present, and we look toward the future as we explore gender matters in science education research. Similarly to other chapters in this handbook, a departure point for this chapter is Kathryn Scantlebury’s chapter with the same title, which was published in 2014 in the Handbook of Research on Science Education. We use Scantlebury’s chapter as a departure point because until today it remains the most recent published work on gender and science education ofering a rich and comprehensive historical overview on how research on gender and science education has evolved over the years. In summarizing the fndings of key review studies in school education as well as women scientists, Scantlebury argues that the fndings of these review studies “suggest little has changed in the daily teaching of science in school education, and for many women, the sociocultural climate in science and science education remains chilly” (p. 189). Following on this, Scantlebury goes on to summarize the fndings of two international studies (TIMSS and PISA) carried out in 2008 and 2009 and examined gender diferences in students’ science achievement and attitudes toward science. For most countries there were no gender diferences on science achievement; however, more than three times the number of boys indicated interested in computing, engineering, or mathematics than girls, while more girls indicated a preference for a biology, agricultural, or health career. These diferences have been examined through research studies that followed these two international studies and focused on students’ attitudes toward science. The outcomes of these studies, as Scantlebury summarized, point to structural issues and gender essentialism as impacting students’ participation within the sciences. Next, Scantlebury critically synthesizes the fndings of several gender studies, ranging on purpose from an examination of learning science in diferent spaces and teachers’ gendered perspectives. In discussing the fndings of these studies, Scantlebury highlights the important role that teachers play in supporting girls to engage with science as well as the possible impact that spaces outside of a formal classroom might have on positively impacting attitudes toward science. Following on this section, Scantlebury summarizes studies focused on individuals’ engagement with learning science and/or as a career pathway. In doing so, she draws upon feminist theory and reviews studies in the area of science identity and physics masculinity to argue that the culture of physics needs to change its practices and image if we want more students to identify with the subject. The chapter ends with a set of recommendations for future directions based on identifed gaps in existing knowledge base and questions that remained unanswered. Some of these include the

DOI: 10.4324/9780367855758-12

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following: (1) at a structural level, gender has been approached through a binary approach; (2) data on gender are rarely reported through an intersectional lens to include race, socioeconomic status, frst language acquisition, and immigration status; (3) how gender or other social categories infuence research, the context, or the theoretical framework remains unexplored; and (4) the knowledge base on the intersection of gender and subjects besides physics remains scarce. In responding to these questions, as Scantlebury argues, calls for the adoption of intersectionality, material feminism, and queer theory as theoretical frames for the purpose of expanding gender research and including other social categories as well. In this chapter, we critically synthesize studies on gender matters in science education from 2014 onwards. We have included studies where the authors use “gender” as a descriptor of their work. This means that the conceptualizations of gender in the included work vary considerably; from gender as a way to denote studies of women and men to studies utilizing, for example, poststructuralist or posthumanist theories of gender. As such, not all included studies have an explicitly stated theoretical approach to gender, but all studies make gender a relevant category of analysis. In addition, we have included studies concerning LGBT+/queer, even when such studies do not explicitly deal with gender. The included studies are published in international, peer-reviewed journals. It should be noted that we have only reviewed English-language articles, and while we have strived to include research from diferent national contexts, the limitation in terms of language skews the selection. The chapter is organized in three main sections, where the frst includes research focused on understanding gender gaps, the second includes research that utilizes identity-based approaches to gender, and the third brings emerging perspectives to the fore. Each main section is followed by a short summary and synthesis. The chapter is concluded by a discussion and recommendations for future research directions.

Part I: Understanding Gender Gaps Gender gaps in science participation and performance have received considerable attention from researchers since at least the 1980s and continue to do so (Jacobs, 2005; Kanny et al., 2014). Research about “gender gaps” typically assumes a taken-for-granted view of gender as representing either social and/or biological sex, by, for example, dividing research participants into men and women, without further problematizing this. Consequently, studies within this line of research do not necessarily defne or theorize gender. Instead, theoretical constructs such as self-efcacy or sense of belonging are used to explain gender diferences in performance and participation.

Performances and Participation Although female students overall outperform male students in school (Voyer & Voyer, 2014), gender gaps in performance favoring male students have been found in a variety of physics learning contexts (Day et al., 2016; Gok, 2014; Henderson et al., 2017). However, researchers have also stressed that fndings concerning gender gaps in performance need to be treated with caution and examined in relation to contextual factors. For example, Day et al. (2016) found gender diferences in performance among physics students on the Concise Data Processing Assessment (CDPA), but also observed compelling gender diferences in how students divide their time in the lab. On a similar note, Traxler et al. (2018) found that when items on the Force Concept Inventory that appear to be substantially unfair to either women (six items) or men (two items) were removed, the gender gap in performance was halved. They analyzed three samples (N [pre-test] = 5391, N [post-test] = 5,769) and looked for gender asymmetries using classical test theory, item response theory, and diferential item functioning. Andersson and Johansson (2016) analyzed a gender gap in course grades favoring male students during a six-year period for a university course in electromagnetism (N=1,139) and

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found that the grade diference between female and male students on the same program was in most cases not statistically signifcant. The gender gap for the student group as a whole was predominantly related to diferent achievements on diferent programs. Women continue to be underrepresented in mathematics-intensive science felds (Schwab et al., 2017; Skibba, 2019). However, a numerical parity in a science feld is not the same in that there are no gender disparities in participation. In a study of 23 large introductory university biology classes, Eddy et al. (2014) examined two measures of gender disparity in biology: academic achievement and participation in whole-class discussions. They found that even though women on average made up 60% of the students in the studied courses they only made up 40% of responses to instructor-posed questions in the classes. Females also consistently underperformed on exams, compared to males with similar college grade point averages. An interaction analysis of video data recorded in Swedish high schools demonstrates that boys still occupy more space in science classrooms, taking up more teacher–student interaction time in classroom discussions (Eliasson et al., 2016), although girls do seem to have extended the time they take up in science discussions. The researchers cautiously suggest that this may in part explain why Swedish girls today perform better than boys; however, boys still occupy the majority of interaction space in the classroom. The researchers suggest that an implication could be that teachers more often address their interactions to boys, which may negatively impact girls’ attitudes in science. A follow-up study to this one (Eliasson et al., 2017) investigates the nature of those interactions and found that closed (lower cognitive demand) questioning tends to predominate classroom discussion, which limits interactions between students and teachers. Taken in light of the limited classroom interactions that girls already have with teachers, the researchers suggest that this may further disadvantage girls, who already have limited time to engage in productive science talk.

International and National Tests One recurring theme in the research on gender gaps is gender diferences in international and national tests. In a study of gender diferences in science achievements on the 2011 Trends in Mathematics and Science Survey among 45 participating nations (N=261,738) Reilly et al. (2019) found small to medium diferences, with varied directions, and no global gender diferences overall. In a meta-analysis of data from the US National Assessment of Educational Progress, Reilly et al. (2015) found small, but stable mean gender diferences in mathematics and science achievement and that at the higher levels of achievement boys outnumber girls by a ratio of 2:1. In order to extend studies of achievement gaps to the early school years, several researchers have utilized the US Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K), a nationally representative cohort of children who entered kindergarten in 1998. Quinn and Cooc (2015) examined gender (and race) achievement gaps in science in third grade in ECLS-K and found that boys score approximately one quarter of a standard deviation higher than the girls. Quinn and Cooc (2015) also extended their study to eighth grade and found that controlling for prior mathematics achievement explained the entire eighth-grade science gender gap. Curran and Kellogg (2016) analyzed the ECLS-K 2010– 2011 and did not fnd a gender gap in science achievement in kindergarten and only a small gap by the end of frst grade, indicating that the gender gap found by Quinn and Cooc (2015) develops during the frst years of schooling.

Cognitive Abilities In a review of studies of observed gender diferences in cognitive and motivational factors that afect women’s decisions to opt out of mathematics-intensive STEM felds, Wang and Degol (2017) distinguish between biological and sociocultural explanations for observed gender diferences. Studies

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investigating cognitive ability as linked to women’s and men’s diferential participation in science suggest that such gender diferences are not a result of diferences in absolute cognitive ability, but rather linked to diferences in the breadth of cognitive ability (Valla & Ceci, 2014). Individuals with similar cognitive abilities in mathematics and verbal skills are more likely to pursue non-STEM careers and since ability patterns are divided by gender, with women typically having more evenly distributed cognitive abilities across diferent felds, Wang et al. (2013) argue that this could be an important explanatory factor for the lack of women in mathematics-intensive STEM felds. However, the fndings of studies examining the link between biological factors (such as brain lateralization) and the diferent cognitive profles of men and women are inconclusive (Miller & Halpern, 2014). Research on cognitive abilities have also focused on ability beliefs, bringing this forward as potentially contributing to the underrepresentation of women in mathematics-intensive STEM-felds. Studies show that individuals are more likely to rate male-dominated felds than female-dominated felds as requiring raw intellectual talent or brilliance (Leslie et al., 2015; Meyer et al., 2015). Meyer et al. (2015) suggest that adults’ feld-specifc ability beliefs, together with the stereotype that females are less likely than males to be brilliant, could lead to diferences in how adults encourage girls’ and boys’ interests and provide them with opportunities to develop skills in diferent felds.

Stereotypes and Bias The impact of stereotypes and bias is another area of research seeking to understand gender gaps. In the Global North, children as young as six years old subscribe to the stereotype of mathematics and science as male domains (Miller et al., 2015). Carli et al. (2016) examined the stereotypes about men, women, and scientists and found greater similarity between stereotypes about men and stereotypes about scientists than between stereotypes about women and scientists. They also found that in felds with a higher proportion of women, the stereotypes about scientists in that feld was closer to the stereotype about women. Similarly, Ramsey (2017) found that agentic traits, which are typically associated with men, are considered more important for success in science than communal traits, which are typically associated with women. There is substantial evidence suggesting that stereotype threat can impact both the retention and performance of women in science (Smith et al., 2015) as well as women’s career choices (Deemer et al., 2014). This has been conceptualized in the phenomenon stereotype threat. In this phenomenon, stereotypes held about a particular group (women; women of color) create psychologically threatening scenarios wherein individuals fear that they may be judged based on their membership in that group. This, in turn, inhibits their learning and can impact their performance (most often assessed on tests or exams). Smith et al. (2015) found that female students perceived less stereotype threat in female-dominated biology courses. Similarly, Taasoobshirazi et al. (2019) found that stereotype threat did not impact students’ performances in biology and concluded that the negative efects of stereotype threat are found predominantly in physics, engineering, and mathematics felds. There is also evidence that agreeing with a gender stereotype correlates negatively with the performance of female students in physics (Maries et al., 2018). In addition, Makarova et al. (2019) found that women who held a strongly masculine image of mathematics and science were less likely to choose a science major at university. The extent to which the stereotype that science is a masculine profession is endorsed also varies between diferent national contexts, and in nations with a higher proportion of women employed in science such stereotypes are less likely to be explicitly endorsed (Miller et al., 2015). There is also research indicating that stereotypes and biases lead to discriminatory practices. For example, Reuben et al. (2014) found that when female and male applicants who performed equally on a mathematical task applied for a hypothetical job, male candidates were twice as likely to be recommended for the position as females. Research has demonstrated that gender bias exists in hiring practices (Eaton et al., 2020), granting (Fox & Paine, 2019; Witteman et al., 2019), grading (Hofer,

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2015), and in students’ evaluations of teachers and professors (Graves et al., 2017). Regarding the latter, research has shown that male students underrate female high school teachers in biology and chemistry, while all students underrate female teachers of physics (Potvin et al., 2009). Following up on this work, Potvin and Hazari (2016) found that students who report a strong physics identity show a larger bias in favor of male teachers than those who report less strong physics identities. These results suggest a mechanism by which the physics community upholds the gender status quo, as younger members of the community move through inbound trajectories into physics membership. This is not a phenomenon that is isolated to physics. A recent social network analysis demonstrated that students in university biology classrooms over-nominate their male peers as knowledgeable about course content (Grunspan et al., 2016). This was predominantly due to male students overnominating their male peers where female students nominated knowledgeable others based on student performance rather than gender. The researchers suggest that the favoring of male students by their peers can result in over-confdence, thus contributing to their persistence in biology.

Imposter Syndrome The imposter phenomenon was frst described in the late 1970s (Clance & Imes, 1978) to understand why highly successful women had difculty recognizing their own achievements, and described feeling as imposters in their career felds. Since then, science education researchers have investigated the impact of the imposter phenomenon on gendered participation in the sciences. Generally, imposter syndrome is defned by attributing one’s success in a feld to luck or being in the right place at the right time. Imposter syndrome can also attribute success to hard work rather than natural ability. Ivie et al. (2016) suggest that women in astrophysics are more likely to have imposter syndrome than men, and that women who felt like imposters were more likely to take a path that led out of the feld.

Self-effcacy, Self-determination, Interest, and Sense of Belonging A set of studies have shown that male and female students have diferent interests toward science studies and careers and that those diferences are attributed to various factors ranging from cognitive to sociocognitive ones, such as self-efcacy, self-determination, and sense of belonging in science. In a study with Finnish students, Kang et al. (2019) examined to what extent relationships between factors of students’ science interest and career perspectives difer between male and female. With the use of a sample of 13-year-old students (N=401), the researchers found that there were strong gender diferences regarding interest and preferences of science subjects as well as their relationship toward future careers. With regard to future careers, female students’ science interest was positively correlated with time- and motivation-oriented career perspectives, while male students’ science interest was positively correlated with outcome-oriented career expectations. Biology was preferred by females and physics and chemistry were preferred by males. Similar results have also been found in the South African context, where Grade 7 boys preferred chemistry and physics and Grade 7 girls biology and astronomy (Reddy, 2017). Gender diferences have also been found in relation to self-efcacy, where studies have found that female students have lower self-efcacy than male students in physics, while fndings concerning other STEM disciplines are mixed (Cheryan et al., 2017; Kalender et al., 2020; Nissen, 2019; Nissen & Shemwell, 2016; Verdín et al., 2020). This is important since self-efcacy is highly correlated with performance, student persistence, and career aspirations, especially in physics (Henderson et al., 2020). In a longitudinal study carried out in the United States, Kalender et al. (2020) surveyed about 1,400 students in an introductory physics course to examine female and male students’ self-efcacy scores and the extent to which self-efcacy is related to learning outcomes and gender diferences in conceptual post-test scores. The fndings showed that initial self-efcacy diferences showed a

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direct efect on outcomes and that self-efcacy had the strongest total gender efect on conceptual learning. Marshman et al. (2018) examined the self-efcacy of male and female students with similar performance in introductory physics courses. They found that female students had signifcantly lower self-efcacy compared to their male students in all grade groups. In fact, female students receiving A’s had similar self-efcacy to male students receiving C’s. Besides cognitive factors, sociocognitive factors were found to explain women’s underrepresentation in science (Kelly, 2016). For example, a recent study applied the sociocognitive construct of selfdetermination to analyze six undergraduate female students’ experiences leading to their choice of physics study, along with factors afecting their persistence in the context of an undergraduate physics program in the United States (Nehmeh & Kelly, 2020). The fndings of the study revealed that the support of faculty, research opportunities, and peer socialization contributed to the development of self-determination. Hindrances to the participants’ undergraduate experiences included negative gender stereotypes, persistent self-doubt, minority status, and unwelcoming classroom cultures. Similar fndings were produced in a study carried out by Tellhed et al. (2017), who tested self-efcacy and social belongingness expectations as mediators of gender diferences in interest in STEM in a representative sample of 1,327 Swedish high school students. The fndings of this study showed that gender diferences in interest in STEM majors strongly related to women’s lower self-efcacy for STEM careers and, to a lesser degree, to women’s lower social belongingness. These results imply that more attention is needed toward counteracting gender stereotypical competence beliefs while interventions need to focus on the social belongingness of students. Social and cultural factors have been examined in chemistry as well. For example, Rüschenpöhler and Markic (2020) examined gender relations, the impact of secondary school students’ cultural backgrounds and the impact of chemistry self-concept on learning processes in Germany. The fndings of this mixed-methods study with 48 students showed that chemistry self-concept is strongly related to learning-goal orientations. Contrary to existing research evidence, the results of this study showed that the gender gap in relation to self-concept traditionally described in the literature was not found. Instead, the study provided evidence of how the interaction of gender and cultural background might infuence chemistry self-concepts. Culture was central in a study situated in Kazakhstan, in Central Asia, exploring the experiences of female university students enrolled in STEM majors (Almukhambetova & Kuzhabekova, 2021). In carrying out this study the researchers were interested in how diferent cultures existing within the university and outside the university infuence the girls pursuing education in STEM majors and how they deal with the conficting ideological discourses in this unique context. With data collected through interviews with 14 purposefully selected women, the researchers provided evidence of the infuence that three conficting discourses had on the participants’ experiences: the Western discourse emphasizing progressive norms and equal opportunities; the Soviet discourse that expects women to be educated and combine professional duties with family responsibilities; and the traditional discourse, which expects women to be attractive and prioritize family life and sees working in STEM as socially awkward.

Gendered/Sexual Harassment and Microaggressions Several studies have identifed sources of gendered discrimination in science, particularly in physics, astrophysics, and planetary science felds. Aycock et al. (2019) surveyed undergraduate women at a physics conference in the United States and found that three quarters of them had experienced at least one form of sexual harassment. They determined that experiences of sexual harassment can predict negative sense of belonging and imposter syndrome among women physicists. Similarly, Clancy et al. (2017) found that women of color frequently reported feeling unsafe in their workplaces in astronomy and planetary science felds, and this sometimes led to women skipping professional events and reporting a loss of career opportunities.

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Repeated acts of sexual or gendered harassment in the form of “subtle and unintentional expressions” of sexism (Sue, 2010) are known as gender microaggressions. Barthelemy et al. (2016) detail the various types of microaggressions women experience in physics, reported from interviews with women in physics and astronomy. They found several microaggressive themes in women’s narratives. For instance, women reported sexual objectifcation and detailed how this impacts possibilities for being viewed as professionals in the feld. Women reported the use of sexist language and sexist jokes. Assumptions of inferiority were reported frequently along with restrictive gender roles where women were assumed to not have the physics strength or the spatial cognition skills to conduct physics. Similarly, women reported invisibility wherein participants reported not being heard or listened to by their peers. Finally, a form of microaggression women in this study reported is the denial of sexism, wherein peers refute the need for support for women in physics, denying that gender is an issue for them.

Part I: Summary and Synthesis Research on gender gaps in science participation and achievement has long been an important part of research on gender and science education – and will probably continue to be so as long as such gaps exist. One strand of research has been predominantly focused on documenting diferences in performance and participation, without dwelling deeper into the underlying causes to such diferences. The preferred research methodology is large-scale quantitative studies. This research has been essential for revealing inequalities, and a strength is that this research can be generalized and generates easily communicable results. However, if the problem is represented as limited to numbers, the solution is likely to focus on, in Londa Schiebinger’s words, “to fx the numbers” (Schiebinger, 2014). The research we have reviewed suggests that numerical parity is no guarantee that there will not be gender diferences in participation. Hence, it is crucial for studies to take a wider perspective on participation (or achievement, for that matter). We suggest that studies that examine classroom interactions and the role that gender plays in structuring these continues to be an area that is understudied and useful to advancing the feld. Even as science classrooms reach gender parity both in secondary schools and in post-secondary contexts, these studies can illuminate the gender inequities that are still produced in science learning contexts, showing that the problem is not one of numbers that can be easily fxed. Studies that seek to understand gender gaps have also uncovered a range of discriminatory practices ranging from unconscious bias to gender-based harassment. While this research frmly confrms that sociocultural factors (such as societal expectations on female/male diferences in ability) are far more likely to explain gender gaps in science than biological diferences (see also discussion in Wang and Degol [2017)], the quest for cognitive diferences between men and women continues. We do not fnd evidence that such research contributes considerably to the understanding of gender inequities in science teaching and learning. We wish to highlight that the studies reviewed in Part I largely rely on a taken-for-granted understanding of gender as another way to say “women and men”, or even “women”. The theorization of gender is limited and there is the risk that notions of gender as something binary and static are reinforced. To some extent, gender is problematized in terms of characteristics typically associated with men and women, but gender is mostly treated as categories. Consequently, research participants who do not ft neatly into the categories are at the risk of being made invisible. Further, such work has been criticized for reinforcing diferences and not being able to count beyond two, with little room for investigating nuances in how gender is performed. The lack of clear defnitions of gender and/or an explicit theoretical foundation is also problematic in that it potentially contributes to a lack of precision in fndings. Partly as a response to perceived limitations with the scholarship focused on understanding gender gaps, science education researchers have sought theoretical inspiration from gender studies and cultural anthropology, conceptualizing gender and identity as performative. This is the scholarship we turn to next.

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Part II: Identity-Based Approaches In the past two decades there has been a growing interest in identity-based approaches to exploring how gender matters in science education and examining girls’ and women’s engagement with science. Quite a few researchers have used an identity lens to explore girls and women’s self-identifcation with science as well as recognition by others (Brickhouse & Potter, 2001; Gonsalves & Danielsson, 2020; Scantlebury & Baker, 2007). For our discussion on identity, we take as a departure point the section in Kathryn Scantlebury’s (2014) chapter “Gender and individuals”. Whereas Scantlebury suggests that identity studies focus on individuals’ engagement with learning science and career pathways, we suggest that science identity (Carlone & Johnson, 2007) as a broad concept now frames a range of research focused on gender and its intersections with other social identities. As such, we argue that the construct of science identity is of great importance when studying engagement with science because identity ofers itself as a tool for examining the ways in which various cognitive and afective experiences infuence the ways in which individuals might see themselves as science persons and also recognized by others (Avraamidou, 2020). Next, we review key studies in identity-based research that examined how gender identity might shape science participation across contexts and age levels. We begin with studies focused on performances of femininities and masculinities and then move on to studies that seek to scale up identity-based approaches, either by doing large-scale quantitative studies or by the development of teaching and learning interventions.

Femininity and Science Femininity as a unit of analysis and investigations that seek to understand how cultural understandings of femininity in relation to science have consequences for students’ identity work has been a consistent focus of study in science education research. In recent years, studies have begun to examine the cultural image of certain sciences, like physics, that appear to be particularly hostile to women (Archer et al., 2017; Archer et al., 2020a; Francis et al., 2017; Gonsalves, 2014). The cultural image of physics as “hard” (Whitten et al., 2003) or a “culture of no culture” (Traweek, 1988) has contributed to its persistent exclusion of femininity, which is deemed incompatible with physics (e.g., Francis et al., 2017). Research that explores this supposed incompatibility draws predominantly from Butler (1999), who understands gender as performative, and performativity as salient to understanding the production of social identities (e.g., Archer et al., 2012b). The focus on performativity, and particularly the regulatory practices that govern intelligible notions of identity, permit an investigation into the various strategies or positions that girls especially must perform to be recognized as “intelligible” in science (Archer et al., 2017; Carlone, Johnson et al., 2015). An exception to this is the work of Simon et al. (2017), who investigated STEM majors’ scores on masculine or feminine personality scales and the correlations with odds of majoring in a STEM feld, and perceptions of a chilly climate in that feld. The results of this study show that women who scored highly on the femininity scale were less likely to go into STEM careers, but this was not true for men who had a positive correlation between scoring highly on the femininity scale and going into STEM. The authors argue that this points to the diferent meanings of femininity and masculinity when embodied in women and men – it appears that men and women are rewarded diferently for their feminine and masculine personality dimensions. For example, men who had more abundant feminine personality characteristics were associated with more positive perceptions of academic climate. They also reported that they received fairer treatment from their professors, more attention in class, and had more friends. Women who scored highly on the femininity index, on the other hand, had fewer friends in STEM than those who scored lower. The authors point to a “femininity penalty” for female STEM majors, that is not present in male counterparts.

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This fnding echoes qualitative work that has identifed the various ways that femininity has been constructed as incompatible with science in its female embodied form. Francis et al. (2017) identifed constructions of femininity as “superfcial” and associated with an overall denigration of girly/super-feminine girls. Girly girls were deemed to be focused on their friends, lacking “strength of character”, and would be dissuaded from science because of its association with “manual and/ or dirty work” (p. 1104). The contradiction between girly girls and science has been documented elsewhere (Gonsalves, 2014), where girliness is regarded in contradiction with science. Gonsalves (2014) found that women doctoral students in physics were positioned as “Other” because of gender norms, while some women were found to be compromising their femininities and performing gender neutrality or “androgynous” performance in order to ft into the dominant culture of their department. In a recent study, Godec (2020) described hyper-femininity as involving an investment in personal appearance, firtatiousness, and popularity. Godec notes that hyper-femininity is not always (hetero)sexual and takes care to note that it is distinct from emphasized femininity (Connell, 2013). Emphasized femininity (discussed next) can include more restrained performances of “good girls” or “nice girls” and relates more concretely to a middle-class femininity. Godec (2020) argues that hyper-femininity is reprimanded and positioned at odds with science. Thus, in contexts where hyper-femininity is rewarded with popularity and friendship, “cool girls” will reject science to embrace hyper-femininity.

Balancing Performances of Femininity Researchers have identifed multiple constructions of femininity in relation to science, some that are not entirely regarded as incompatible with science identities. In a UK study of with working-class girls between 11 and 13 from diverse ethnic backgrounds, Godec (2018) found that fve science-identifying girls negotiated their identifcation and engagement with science through several diferent discursive strategies: (1) rendering gender invisible, (2) drawing attention to the presence of women in science, (3) reframing “science people” as caring and nurturing, and (4) cultural discourses of desirability of science. Reframing “science people” as nurturing and caring makes identifying with science more “intelligible” but simultaneously reifes the desirable femininity as contradictory to “dirty” felds, like engineering. A similar formulation of science identity in relation to femininity can be seen in the “pleasers” identifed by Carlone et al. (2015). In a fourth-grade class, girls performed diferent versions of femininity and identifed a proper femininity (e.g., the proper way of being a girl in a school science class) as pleasing. Pleasers performed well scientifcally and were recognized for their scientifc performances. However, as girls moved along in their schooling trajectories, they continued to perform as pleasers but did not make further bids to be recognized scientifcally. Pleasing was a dominant theme in research investigating emphasized femininity (Connell, 2013) in science. For example, Dawson et al. (2019) identifed the “good girl student” identity performance among secondary students, which focuses on politeness and completion of tasks. However, these girls were largely concerned with maintaining a good student identity rather than connecting to science in any meaningful way. These middle school girls engaging with learning at a science museum seemed to resist meanings of science or scientists that were threatening to their “good girl student” performances. In the museum, there appeared to be limited available discourses of appropriate feminine behavior; as a result, girls risked compromising their good girl behavior to carry out learning tasks in the exhibits. These kinds of performances of femininity have also been noted in workplace contexts. Mattsson (2015) describes a similar balancing of pleasing forms of femininity with engagement in science practices in the health sciences. Mattson notes that women researchers used the expression “good girls” to describe how hardworking they are. Hard work was celebrated (e.g., when publications or awards were received), but women also noted that they needed to balance achievements in order to

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not be seen as problematic by male colleagues. Thus, women took on roles as “responsible caregiver” in the department to dampen their efects of being bold intellectuals: According to this logic, the women in the Unit – successful and highly productive researchers – risked becoming problematic in the eyes of their male peers, but by being caring and responsible they established a form of middle-class femininity that made their subordinate feminine position clear. (p. 692) It is worth noting that constructions of femininity should be regarded as local, as well as culturally and context specifc. For example, research in British contexts suggests that working-class femininities are constructed in confict with science (Dawson et al., 2019; Godec, 2020), while some cultural constructions of femininity (e.g., among frst-generation youth from South Asian contexts) are seen as compatible with science identities (Godec, 2018). Moshfeghyeganeh and Hazari (2021) suggest that expressions of femininity in Muslim majority countries can have constructive intersections with physics identities and may in fact promote participation and persistence in physics. All local constructions of femininities, however, require strategies to navigate science, and build science identities, which we elaborate on next.

Strategies Used to Navigate Femininity and Science (Especially Physics) Predominant in the literature investigating femininity and science were various strategies women and girls took to navigate discourses that denigrate femininity and position it outside of science. In an early study, Archer et al. (2012a) describe students positioning themselves as “feminine scientists” in attempt to balance identifcation with science with “appropriate” heteronormative femininity. The success of these performances depended on whether the girls were able to draw on aesthetic resources, such as being fashionable, sporty, or good-looking. Often, girls accomplished this by fnding ways to render their science performances as “cool”, so their identities as science people could be balanced out with positioning themselves as “normal girls”. For girls invested in hyper-femininity in ways that position them outside of science, Godec (2020) found that their brief engagements with science can be supported by “popularity capital”. Resources related to popular culture (and usually not available in school contexts) were mobilized to support engagement in science in ways that aligned with hyper-femininity. In these brief engagements, girls could hybridize science with their non-science interests and create possibilities shifts toward insiderness. On the contrary, Archer and colleagues also identifed strategies of “bluestocking scientists” – girls who were unable to draw on aesthetic embodied resources, and as such positioned themselves as “non-girly” and focused instead on academic success. These girls positioned themselves as diferent to other girls, thus aligning with common strategies to position femininity as “other” to science. Similarly, in a 2017 study, Archer and colleagues identifed girls doing “geek chic” as a strategy to reconcile their “non-girly” gender performances with socially acceptable ways of being good at science. In this way, girls described being comfortable working in male-dominated environments and did not anticipate being put of by this in further education. Gender performances in relation to science that lean toward the masculine or position girls as “non-girly” do so while subjugating other gender performances; in particular, they denigrate hyper-femininity. However, Dawson and colleagues (2019) suggest that some masculine performances in science can also be transgressive rather than hegemonic. Their analysis of girls doing gender and science in a museum suggests that assertive performances interpreted as masculine can be strategies to challenge the limited identity positions available to them. Finally, among faculty, Mattsson (2015) found that “sameness” operates as a protective strategy to cement women’s presence in faculty. In this study, women faculty members in medicine were

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thought to adopt strategies of aesthetic sameness in which “They were united in a femininity that rendered them alike as white, middle-class, heterosexual women, that made them well adapted to the faculty of medicine, and that secured their position as women researchers” (p. 695). Mattson suggests that women in this unit “cloned” a collective femininity that strengthened their presence in the context of academia and as researchers in medicine.

Theorizing and Investigating Masculinities in Science Education In some senses, investigating connections between masculinity and science/science education is not a novel research area. Feminist philosophers of science have long theorized and explored the masculine connotations of science (Harding, 1986; Schiebinger, 1991). Likewise, it is well-known that young people tend to see science as “for boys” (Archer et al., 2012b; Calabrese Barton & Tan, 2009), and it has been argued that physics education embodies an understanding of physics as a masculine activity (Hasse, 2002). Recent research studies have also confrmed how pupils view physics as a subject is strongly associated with masculinity (Archer et al., 2020a; Francis et al., 2017). The association between physics and masculinity is also entangled with notions of nerdiness (Johansson, 2018), connected to an interest in particular forms of science fction (Hasse, 2015) and ways of using humor in physics teaching (Johansson & Berge, 2020). Research has also shown that the association of masculinity with science requires female science teachers to navigate students’ and colleagues’ stereotypical perceptions of women’s incompatibility with science (Mim, 2020). Yet, studies of how men and boys relate to the teaching and learning of science is surprisingly sparse and the application of theories from masculinity studies unusual within science education. However, science education scholars are increasingly starting to utilize empirical studies to scrutinize constructions of masculinity within science, but in order to analyze how students from nondominant backgrounds may be marginalized, but also how to analyze the norms of particular science teaching and learning contexts.

Masculinity and Outsideness In this section we focus on studies that in various ways highlight how masculinity performances are not always unproblematic in relation to science. Archer et al. (2014) has explored the role of masculinity within boys’ negotiations of science aspirations, from a theoretical perspective of gender as performative (Butler, 1999) and masculinities as a “doing” (Connell, 2005). They identifed fve discursive performances of masculinity related to the boys’ aspirations, two of which directly concern their relationship to science (termed “young professors” and “cool/footballer scientists”). Similarly, Archer et al. (2016) analyzed performances of masculinity, in this case in the context of school trips to science museums. Mark (2018) does not explicitly theorize masculinity, but the examination of how one African American male youth engaged in an informal STEM program intervention from a perspective of identity development shares an interest with Archer et al. (2014) and Archer et al. (2016), highlighting how male students from nondominant student populations relate to science. This line of scholarship thereby challenges the taken-for-granted association between masculinity and science by highlighting a range of masculinity performances, which to diferent extents are possible to combine with science participation. Further, the studies foreground the importance of considering the intersections of masculinity with class and race/ethnicity. For example, Archer et al. (2014) stress the importance of disrupting the association between science and middle-class “brainy” masculinity in order to make science more accessible to students from working-class backgrounds. In a forum paper written in response to Mark (2018), Rosa (2018) further stresses the complex dynamics of potentially utilizing the masculine connotations of science to attract men from non-hegemonic backgrounds to the discipline “while simultaneously working to deconstruct patriarchal views that seem to reinforce this very STEM image”. A commonality across these studies is that they focus on

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increasing and widening participation in science by illuminating the identity work of students from nondominant backgrounds.

Masculinity and Insideness One strand of research focuses on individuals from dominant backgrounds within science communities, who often are positioned as the invisible norm (Archer et al., 2020b; Carlone, Webb et al., 2015; Gonsalves et al., 2016), with the aim of scrutinizing norms and investigating how insideness is produced. Carlone et al. (2015) examined the school science trajectories of four scientifcally talented and interested boys from fourth to sixth grades, asking the question “What kind of boy does science?” Theoretically, the article utilizes feminist and critical men’s studies literature (Connell, 2005; Letts, 2001), conceptualizing masculinity performances as varying across time and context. They are also interested in how diferent masculinity performances are valued over others, and what constitutes a hegemonic masculinity (Connell, 2005) is a particular context. As such, the study seeks to trouble taken-for-granted notions of the link between science and masculinity. The analysis of the four boys’ identity work shows that “being smart” is necessary but not sufcient in order to be positioned as scientifc. The analysis also shows how science subject positions are related to class and ethnic positionings. Gonsalves et al. (2016) also have a similar research agenda, in that they focus on how diferent masculinities are produced and valued in particular contexts, in their case various experimental practices in physics higher education and research. They argue that: Men and cultures dominated by men within academic disciplines and research communities should also be analyzed as political categories and political subjects. In order to understand why physics in particular is still dominated by men, the cultures and actions that are associated with masculinity are analyzed. (p. 2) Empirically, the article draws on case studies of three diferent physics contexts, and an important fnding across all three case studies is a strong emphasis on physical skill, including the capability to engage with instruments designed for larger (male) bodies. By conceptualizing masculinity as performative, across both men and women, they are able to consider how masculine ideals are also negotiated and taken up by female physicists. Another study seeking to unpack the relationship between science and masculinity is Ottemo et  al. (2021), who drawing on poststructural gender theory, explore how notions of corporeality, style, and aesthetics are articulated within two diferent computer engineering and physics higher-education settings. The authors investigate the coproduction of disciplines and gendered forms of subjectivity, and while most informants understand their respective disciplines as gender neutral, they also acknowledge that being a student in the discipline is highly gendered. The analysis brings to the fore how notions of corporeality and style are central to such gendering. Archer et al. (2020b) take a diferent theoretical perspective by utilizing a Bourdieusian lens to explore how physics identity is shaped by habitus, capital, and feld. The chapter investigates how and why White, middle-class boys are more likely than many other students to end up in physics, through a longitudinal case study of Victor, from age 10 to 18. The authors show how Victor’s trajectory is strongly shaped by the cultivation of a particular kind of embodied masculine habitus, which also structures what is possible and desirable for boys like him.

Large-Scale Studies of Identity and Gender As a means to extending the use of identity-based approaches beyond small-scale qualitative studies, researchers have begun to operationalize the concept of identity in ways that make up-scaling possible.

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Drawing on social identity theory, Seyranian et al. (2018) examined the longitudinal efects of STEM identity and gender on fourishing and achievement in college physics in the United States. Data were collected from 160 undergraduate students enrolled in an introductory physics course who completed a baseline survey with self-report measures on course belonging, physics identifcation, and fourishing as the beginning of the course and a post-survey at the end of the academic term. Additional data were collected through force concept inventories and the use of physics course grades. The fndings of this study showed that women reported less course belonging and less physics identifcation than men even though no gender disparities emerged for course grades. In addition, students with higher physics identifcation were more likely to earn higher grades, and students with higher grades reported more physics identifcation at the end of the term. For women, higher physics identifcation was associated with more positive changes in fourishing over the course of the term. These fndings point to gender disparities in physics, especially in terms of belonging, and suggest that strong STEM identity may be associated with academic performance. Similar fndings were produced in Kalender et al.’s (2019) study, which examined physics identity alongside other motivational constructs of male and female students (N=559) by administering a survey in introductory calculus-based physics courses at a large research university in the United States. The fndings of this study showed that female students reported signifcantly lower identity scores than male students. The analysis revealed a statistically signifcant gender diference (lower for female students) for both physics identity items in the survey related to students’ perceptions of both being a physics person and being recognized by others as a physics person. Another large-scale study in the context of university physics in the United States is the one by Hazari et al. (2017), who examined when girls (N > 900) became interested in physics careers through a survey. The fndings showed that the highest percentage of participants became interested in physics careers during high school and sources of recognition included the following: selfrecognition, a perceived recognition from others, and a perceived recognition for other students around them. Interestingly, the most important source of recognition appeared to be the students’ high school teacher. These fndings point to the crucial role of high school teachers in supporting students, and especially girls, to develop strong physics identities. These fndings conquer with the fndings of a systematic review of empirical research, mostly large case studies (N=47) in the United States, published in the period of 2006–2017, that focused on the experiences of female students in STEM during middle school and high school, drawing on social identity theory (Kim et al., 2018). In these studies, identity and/or identifcation with science is theorized in ways that are concomitant with the studies previously reviewed in Part II of the chapter, but gender is nonetheless treated in a categorical way, with a focus on (statistically signifcant) diferences between male and female students.

Interventions Aimed at Supporting Science Identity and Belonging In addition to up-scaling through large-scale quantitative studies, there is also a line of research seeking to extend small-scale explorative studies by the development of interventions that aim to support girls’ science identity and belonging. The motivation for such studies is typically to come to terms with the underrepresentation of women in science felds and has targeted interventions toward girls and women in both informal and formal learning contexts. Levine et al. (2015) studied the impact that a camp aimed at providing hands-on chemistry learning opportunities and featuring female chemistry role models and feld trips had on middle school girls’ excitement and appreciation for science. They found that short-term efects were positive, but they were unable to ascertain any longer-term impacts or shifts in identity work and career interest. Todd and Zvoch (2019) sought to measure the impact of an informal science intervention on middle school girls’ science afnities. This study suggests that role messages, peer learning, and hands-on

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science experiments, which were featured in the program, are critical for girls’ science identity and self-efcacy development. The emphasis on these features, rather than content knowledge, is thus understood to be critical to the program’s success in improving girls’ self-efcacy and science attitudes. While these interventions suggest short-term gains in terms of interest and attitudes, we do not know what the long-term impact is on how girls view science in their lives and whether science becomes a part of their career paths. In addition, we cannot know from these kinds of studies what the impact might be on girls’ identity work in science. Adopting sociocultural frameworks highlighting the importance of attending to identity, some recent research in physics education has aimed to construct formal learning environments for girls that focuses on strategies to develop a sense of self as a physicist. Following a 2013 study by Hazari et al., suggesting that talking about underrepresentation can be critical to supporting girls’ identity development in physics, Lock and Hazari (2016) have investigated the impact that classroom conversations about women’s minoritization in physics can have on girls’ physics identities. This study reports that opportunities to gather and talk about underrepresentation can be an efective strategy to “bufer” against the ill efects of minoritization that women feel in male-dominated felds like physics. They found that explicit opportunities to discuss underrepresentation can shift young women’s fgured worlds about the norms in physics and open up possibilities for identity shifts to insiderness in physics. Similarly, Wulf et al. (2018) demonstrate that constructing deliberate environments for young women to engage in physics learning in small, single-sex groups at a Physics Olympiad appeared to create possibilities for them to access increased opportunities for recognition and thus to develop their physics identities. Despite the emphasis on identity work in formal and informal learning contexts, these studies do not interrogate binary constructions of gender, or what Traxler et al. (2016) call the “gender-binary defcit model”, and the constructions of masculinities and femininities in science learning contexts.

Part II: Summary and Synthesis Unlike the studies reviewed in the previous part that have treated gender as a category, the studies reviewed in Part II have treated gender as “performance” and hence researchers engaged with the constructs of “femininity” and “masculinity” to examine science participation through the lens of science identity. Over the past 20 years, science education research has seen a large increase in studies adopting identity-based approaches (Danielsson et al., in review). Theoretically, many studies are inspired by cultural anthropology (Gee, 2000; Holland et al., 2001; Lave, 1996), and in the studies reviewed in Part II such a theoretical vantage point is often combined with a poststructuralist perspective of gender (Butler, 1999). This allows for detailed and nuanced investigations of how gender is performed in particular contexts. Embarking from the fact that science has historically been constructed as masculine, researchers have examined how women navigate their presence in science environments or how they author their science identities. The fndings of this set of studies, largely qualitative, point to the fact that femininity has been constructed as incompatible with science identity, and hence those performing more feminine identities have been constructed as “other” in science. However, as another set of studies showed, femininity is not a binary construct. Instead, femininity, being culture-dependent, is enacted through diferent kinds of performances with unique characteristics, and each of those shape how those performing such identities are recognized (or not) in science contexts. These studies have been very successful in showcasing the great variety in how students engage with science in various contexts. Theoretically situated in similar underpinnings, another set of studies have adopted a large-scale, quantitative approach to examining women’s science identity. Despite their usefulness in producing more generalizable claims, these studies have treated gender as a category instead of performance, and hence fail to provide insights about the nuance, complexity, and specifcity of gender performances

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and how those might cause misrecognition and make certain bodies vulnerable. Moreover, when utilizing a performative view of gender, there is scope for decentering gender performances from sexed bodies, but among the reviewed studies, those that center femininity tend to focus on girls and studies that center masculinity tend to focus on boys. As such, there is a risk that gender binaries are reproduced by the very studies that seek to critique them, in that girls’ doings are equated with femininities and boys’ with masculinities. This risk might be even greater in studies that operationalize gender and identity in ways that make the concepts possible to adapt to large-scale quantitative studies – at the same time that such studies are pivotal for extending fndings beyond the scope of small-scale qualitative case studies.

Part III: Emerging Perspectives In studies of gender and science education we can discern two main approaches; the treatment of gender as something categorical (basically equated to “men and women”) and the conceptualization of gender as performative and, as such, interrelated with identity performances in a broader sense. Both these approaches have been present in science education research for at least 20 years and can be considered consolidated felds of research. Getting sight of emerging perspectives is more challenging, but in this third part of the review we would like to highlight three contemporary trends: intersectional perspectives, queer perspectives, and posthumanism. Identity-based approaches to studies of gender in science education have since the beginning to some extent attended to intersections between gender and race/ethnicity (see, for example, Brickhouse & Potter, 2001), but we have chosen to include intersectionality among the emerging perspectives, as intersectionality recently has been more theoretically pronounced in science education and also empirically extended beyond studies of gender and race/ethnicity.

Intersectional Approaches In the past couple of years, we witness more and more studies adopting intersectional approaches to examining gender in science participation. As a term, coined by Kimberlé Crenshaw in 1989 to counter the disembodiment of Black women from law, intersectionality captured the inadequacy of legal frameworks to address inequality and discrimination resulting from the ways race and gender intersected to shape the employment experiences of Black women (Crenshaw, 1989). Since then, intersectionality theory has transcended the boundaries of legal research and the US context and found application in various other geographical contexts and disciplines. Ringrose and Renold (2010) argued that feminist researchers invested in understanding women’s experiences must continue to develop intersectional approaches that challenge “regulative gender and (hetero)sexual discourses, as these are cross-cut by race, class, cultural and other specifcities” (p. 591). Charleston et al.’s (2014) study examined the role of race and gender in the academic pursuits of 15 African American women in STEM. The fndings of the study showed that the participants faced a series of racial and gender challenges related to their educational trajectories, felt marginalized as persons of color, and shared a sense of cultural isolation in departments heavily populated by White males, which essentially points to the double bind: the simultaneously experienced sexism and racism in STEM careers. Similar fndings were produced in Rosa and Mensah’s (2016) study, which explored the life histories of six African American women in physics. The analysis of the interview data revealed specifc commonalities in their experiences. The frst one is that all participants felt isolated in the academy, especially as members of study groups in which they felt excluded. The second one is that they all participated in after-school or summer school programs where they were exposed to a science environment at an early age. Lastly, all participants had opportunities to engage in summer research programs along with their academic training and to be members of a community

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of practice. Collectively, what these three studies show, is the sexism and racism that women of color face throughout their STEM career trajectories, and at the same time they highlight the importance of examining gender in conjunction with race, especially for women of color, when examining STEM career trajectories. In order to examine the role of safe social places or counterspaces that women seek during their STEM trajectories, Ong et al. (2018) analyzed interview data collected from 39 women of color in the United States. These women were purposefully selected to comprise a group of varying racial/ethnic groups, career stages, and STEM disciplines. In dealing with negative experiences in STEM, the participants looked for counterspaces that provided critical support for their persistence in STEM, however. These counterspaces occurred in a variety of settings and served diferent functions: (1) counterspaces in peer-to-peer relationships, (2) counterspaces in mentoring relationships, (3) counterspaces in national STEM diversity conferences, (4) counterspaces in STEM and nonSTEM campus student groups, and (5) STEM departments as counterspaces. These fndings ofer useful insights, especially for STEM university departments seeking to be counterspaces for women of color. Similar fndings are found in other levels of education, such as teacher education, STEM university education, as well as school science. A key study in teacher preparation is the one carried out by Moore-Mensah (2019), who examined the journey of an African American female (Michelle) in science teacher education by looking at her educational history from childhood to teacher education and professional life as an elementary teacher with a focus on how she viewed herself as a science learner and as a science teacher. The fndings of this study exemplifed issues related to underrepresentation of both Black preservice teachers as well as instructors and the emotional impact that this underrepresentation had on Black preservice teachers. The fndings also showcased how specifc courses on teacher preparation might serve as transformative experiences. One such example is provided in this work, which is a course taught by the author, who is an African American woman, and which provided opportunities for discussions about the intersections of race, ethnicity, gender, and class in their role on the development of science teachers. In contrast with Moore-Mensah’s study, Wade-Jaimes and Schwartz’s (2019) ethnographic study with a group of seventh-grade African American girls illustrated how dominant discourses of education, science, race, and gender led to the exclusion of these girls. The fndings of the study showed that although the girls tried to engage in scientifc practices, they did not receive positive recognition from their teacher. In fact, the students were recognized for copying from sources and memorizing facts, which encouraged a passive and noncreative participation in science, which favored specifc types of students. Most of the girls, however, did not ft within that type of student, and hence did not receive positive recognition from their teacher. This fnding illustrates how narrow, limiting, and exclusionary the dominant discourse of school science is. As evidenced in this brief review of key studies that adopted intersectional approaches to examining girls’ and women’s participation in science, these have predominantly focused on the experiences of Black women and women of color. This points to a gap in knowledge when it comes to other types of identity intersections, for example, ethnic identity, religious identity, social class, disability, and motherhood. A couple of studies aiming to address these types of identity intersections provide evidence of a diferent set of barriers that women in science face. Using science identity as a unit of analysis, Avraamidou (2020) explored the barriers, difculties, and conficts that Amina, a young Muslim immigrant woman in Western Europe, confronted throughout her trajectory in physics and the ways in which her multiple identities intersected. The main sources of data consisted of three long biographical interviews, which were analyzed through a constant comparative method. The fndings of the study illustrated that Amina was confronted with various barriers across her journey in physics, with the intersection of religion and gender being the major barrier to her perceived recognition due to cultural expectations, sociopolitical factors, and

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negative stereotypes. Moreover, Amina’s social class, religion, gender performance, and ethnic status positioned her as Other in various places throughout her trajectory in physics, and consequently hindered her sense of belonging. Two more recent publications also consider the impact that religion has on women’s experiences, especially in physics. Avraamidou (2021) continues the exploration of women’s experiences in physics, using explicitly intersectional frameworks to consider the “politics of recognition” and the local and contextual ways that bodies, identities, and associated cultural objects can contribute to misrecognition in physics. For example, Amina’s story suggests that her Muslim identity (and associated cultural artifacts, like the hijab) contributed to her misrecognition in Western physics contexts. In contrast, in a study conducted with women in physics in Muslim majority countries, Moshfeghyeganeh and Hazari (2021) revealed a relative absence of such gender identity conficts and negotiations. Taken together, these fndings complicate notions that femininity is incongruent with physics and suggest that, rather, femininity needs to be understood in relation to science within the cultural contexts in which it is done. In another understudied population, Castro and Collins (2021) investigated the experiences of Asian American women in STEM. In this study, the researchers interviewed 23 women who selfidentifed as Asian Americans and were either in a doctoral program or within fve years of earning their degrees in STEM felds at the time of the study. The study is one of the very few studies that examine Asian Americans experiences with science in the US contexts where Asian Americans are commonly portrayed as a monolithic group and as incapable of assimilating into American society. Similarly with the fndings of the studies reviewed earlier, the fndings of the study provided evidence of how Asian American women are not validated in STEM, are perceived as outsiders, and experience microaggressions and harassment because of not ftting the “White male logic systems”.

Queer Theory and LGBTQ+ Issues in Science Education The teaching and learning of science intersect with issues of sexuality in several diferent ways, most directly as related to in the representation and experience of LGBTQ+ individuals in science disciplines (e.g., Barthelemy, 2020) and sex and sexuality as a teaching content in biology (e.g., Reiss, 1998). In addition, queer theory is used to explore the entanglement of sex, gender, and sexuality with science education. Queer theory seeks to deconstruct sexuality and gender, and to destabilize binary constructs, such as gay/straight. In studies of science education queer theory frst appeared in the early 2000s. Early work includes Letts’s (2001) analysis of heteronormativity as part of the hidden curriculum in primary school science. Later, Bazzul and Sykes (2011) used queer theories to analyze how gender and sexuality were addressed in biology textbooks.

LGBTQ+ Students and Teachers in Science Education Sansone and Carpenter (2020) use survey data to analyze the representation of LGB individuals in STEM felds. They found that men in same-sex couples were less likely to have completed a bachelor’s degree in a STEM feld compared to men in diferent-sex couples, but found no diference for women in same-sex and diferent-sex couples. They also found that the representation of gay men in STEM felds were positively associated with female representation in those STEM felds. Sansone and Carpenter conclude that: Taken together, these patterns are highly suggestive that the mechanisms underlying the very large gender gap in STEM felds such as heteropatriarchy, implicit and explicit bias, sexual harassment, unequal access to funding, and fewer speaking invitations are related to the factors driving the associated gap in STEM felds between gay men and heterosexual men. (p. 12)

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There are also studies of the experiences of LBTQ+ individuals in STEM. In a national survey of LGBTQA individuals in STEM careers in the United States, Yoder and Mattheis (2016) found that participants who described their workplace as welcoming and safe reported greater openness to colleagues and students. They also found that participants who worked in STEM felds with a higher proportion of women were more likely to be out to colleagues. Consequently, in line with Sansone and Carpenter (2020), they also make a connection between the gender gap and the experiences of LGBTQA individuals, hypothesizing that a better gender parity in a workplace also fosters a more inclusive climate for LGBTQA individuals. In a qualitative survey study of the feld of physics specifcally 71 out of 324 respondents reported some form of exclusionary behavior or harassment, most often based on their gender expression or being a woman (Barthelemy, 2020). A survey of biology college instructors in the United States found that over half of the biology instructors surveyed were out to their work colleagues, but less than 20% were out to their students (Cooper et al., 2019). In interviews following the survey, instructors reported that reasons for being out in class included providing students with LGBQ role models in science, but some were also worried about students developing negative views of the instructor if they were out in class. Studies of students are more unusual, but a study of student retention among students who identify as a sexual minority (for example, lesbian, gay, bisexual, or queer) using US national longitudinal survey data showed that sexual minority students were less likely to be retained in STEM compared to their heterosexual peers. To summarize, the studies of LGBTQ+ teachers and professionals, and to a lesser extent students, in STEM felds are mostly quantitative, sometimes with qualitative components. The purpose of the studies is typically to uncover inequalities, within an agenda of inclusion. Sexuality is problematized in the sense that the use and defnition of acronyms (e.g., LGBQ, LGBTQA) are discussed, but mostly sexuality is operationalized as a variable that can be neatly captured by multiple-choice questions. It can also be noted that all studies reviewed have been carried out in the US context.

Queering Science Teaching and Learning In the handbook chapter from 2014, Scantlebury called for an increase in the use of queer theory within science education research. The development of this area has been slow, but a major contribution was made recently by an anthology collecting work that queers STEM education (Letts & Fifeld, 2019). The chapters concern a variety of disciplinary areas, such as environmental education and higher-education physics, and both informal and formal education, from elementary to higher education. An important objective of the volume is also to move the discussion beyond a project of equal rights for LGBTQ people, to use queer theory and allied perspectives as a way to question what is perceived as normal and release new possibilities for reimagining the world. Gunckel (2019b) argues that “queering the constructions that legitimize the discrimination in the frst place, and thus insists that we (re)imagine and (re)construct our world to eliminate the normal/queer binary altogether” (p. 150). Further, Knaier (2019) stresses that problems caused by restricting two-category systems of boys/girls or masculine/feminine cannot be solved by adding more categories, but that the categorizing in itself is problematic. Instead, she proposes that we move beyond gender, not by ignoring or erasing gender identities, but by using queer theory to challenge the idea of normative gender and sex identities. Götschel (2019) brings queer theory into the higher-education physics classroom, leveraging its potential to scrutinize what is supposed to be normal and what is invisible or silenced and thereby making the familiar strange. In particular, she is interested in its potential for refecting on the discursive production of physical knowledge and questioning hegemonic narratives of physics and physicists. In the context of biology teaching, Reiss (2019) and Gunckel (2019a) both argue that a consideration of queer theory can provide both a richer understanding of the content at hand and a more inclusive teaching. Reiss (2019) argues that conventional teaching about sex and sexuality in biology tend to represent poor science, and queering the way sex and sexuality is

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taught in school science will allow for a richer understanding of (human) sexuality and what it is to be a sexual person. Such a teaching could have implications both for students’ views of science and of themselves and their sexuality. In summary, Reiss concludes that queer approaches to teaching about sex and sexuality within the science curriculum can “aid human fourishing, enable students to gain more powerful knowledge, and address social injustices’ (p. 265). Gunckel (2019a) analyzes an elementary school science lesson about crayfsh and shows how a focus on dichotomies and categorization hides the diversity of sexual morphologies and reproductive processes, something that not only is poor science but, she argues, can also be harmful to students who have non-normative bodies and identities. A related strand of research unpacks how gender is discursively produced in classrooms, and studies have highlighted the ways that classroom talk and interactions discursively produce masculinity and femininity in science contexts. Orlander (2016) investigates how the ways teachers communicate invoke masculinity and femininity in examples from biology and how this constructs notions of “natural” sexual behavior in humans. Orlander (2020) examines how masculinities and femininities are mobilized in argumentation in science classrooms, in the context of debates around sustainable development. These studies suggest that disciplinary content is imbued with masculinities and femininities, in which masculinity and heteronormativity is privileged, and send normative messages about gender roles through science.

Posthumanism Scantlebury’s (2014) chapter identifed material feminism as a new direction for gender and science education research. Scantlebury provides an overview of the manner in which language and discourse have been granted “too much power”, according to Barad (2003), and highlights calls for research to engage with matter/material. In the years since the publication of Scantlebury’s chapter, “new materialist” approaches to studying gendered participation in science education have begun to emerge. Other approaches relying on video ethnographic data and participant observation instead focus on embodied performances of gender and identity. Barad (2007), whose theoretical work has infuenced new materialist orientations to research in science education, argues that we must also investigate the entanglement of discourse, language, embodiment, and matter. New materialist approaches to research in science education thus give possibilities to understand the identity performances of participants who do not possess language resources to narrate identity work. In very recent years, we have begun to see research taking new materialist perspectives to understand the gendered identity work of youth and very young children in relation to science and technology. Godec et  al. (2020) have investigated the role that physical and digital materiality play in the identity performances of young people engaged in STEM learning in informal settings. While this research does not seek to understand gendered interactions with materials, the research draws on aspects of gender theory to understand how materials shape tech identity performances. Particularly, this group mobilizes the concepts of identity performativity (Butler, 1993; Butler, 1999) and intra-activity (Barad, 2007) to understand the ubiquitous presence and importance of materiality in the contexts of young people’s STEM identifcations. The fndings suggest that reading materialdiscursive entanglements as identity performances can yield insights into the importance of intraactions with matter in contexts where these might yield new forms of recognition that contribute to identity performances. These researchers additionally described intra-actions with the digital, thereby raising the possibility for nondiscursive and nonmaterial intra-actions to contribute to identity recognition. This work also raised equity issues when it came to gendered intra-actions with technological matter and the digital. The researchers noticed that even though youth were ofered access to technology (e.g., via coding events), young women participating in the club tended toward

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verbal, pen-and-paper, and photographic modes of intra-actions, suggesting that these young people tended to engage with technology in ways that reproduce dominant gender relations. Importantly, recognition emerges as a salient concept for investigating gendered intra-actions with materiality. Günther-Hanssen and colleagues (Günther-Hanssen, 2020; Günther-Hanssen et  al., 2020) describes the intra-action between a preschool girl and a swing in ways that produce embodied understandings of physical phenomena, but also provide opportunities for the girl to be recognized as an insider to science phenomena (at least to the researcher and to the teacher observing her movements). These intra-actions, however, also create outsiderness when the girl’s movements and intraactions with the swing are usurped by a boy who uses the swing to gain attention from his peers. Gonsalves (2020) describes similar possibilities that intra-actions with materials in physics laboratories provide for recognition as an insider or expert in new ways. Investigating diferent forms of tinkering both in and outside of the lab, Gonsalves suggests that viewing tinkering through the lens of materialdiscursive intra-actions can yield new and unexpected possibilities to learn how students may become recognized as competent in physics on instruments “built with gender in mind” (Berg & Lie, 1995). Scantlebury et al. (2019) use the concept of “material moments” (Taylor, 2013) to investigate how intra-actions produce space and time in classroom through moments of intra-action. By asking questions about space (how students are included and excluded in classroom spaces) and time (how students’ movements through school are marked by time) in the intra-actions with matter (blackboards and textbooks) they explore how iterative difractive readings of text can yield understandings about why students “get bored” with science. These readings highlight material-discursive practices in the classroom that send messages to students about the (gendered) forms of engagement in science that are welcomed (e.g., note taking) at the expense of their interest.

Part III: Summary and Synthesis The studies reviewed in this section urge researchers to expand our understanding of issues related to gender and science learning beyond experiences that foreground binary formulations of gender and even performances of gender as central organizing concepts. This work challenges the primacy placed on gender in research and invites us to consider more broadly how the intersections of race, class, sexuality, identity, and gender interact to create environments where recognition is aforded or constrained in various ways. Additionally, this work challenges some of the primary constructs researchers use to investigate experiences of recognition. For example, Avraamidou (2021) suggests that current conceptualizations of recognition are themselves limited by a binary formulation where research focuses on opportunities where one is recognized or not. This approach masks complex interactions that can result in diferent forms of recognition, notably, misrecognition. These fndings demand approaches to identity research that investigate these complexities and move beyond binary formulations both in how we treat gender and in how we seek to understand experience. Thus, we suggest that recent trends in research invite us to move beyond gender as a primary organizing concept, and rather to advance frameworks that seek to unpick the complexities of experiences, events, and interactions that shape individuals’ modes of becoming in science learning environments. In this section, we have reviewed three emerging perspectives that contribute richness and complexity to our understanding of minoritized learners’ experiences in science. While the integrated treatment of gender and race/ethnicity is not new to science education research, a more explicit consideration of intersectionality provides depth to our understanding of the kinds of identities, bodies, and cultural objects are considered “in place” or “out of place” (e.g., Avraamidou, 2021) in science. There is also an emerging scholarship around gender and queer/LGBTQ+ perspectives that concerns both the identities of students/teachers and the science content as such. Still, studies of how gender intersects with other markers of identity, such as social class, age, and dis/ability, are largely missing. The material turn has slowly started to make its way into science education research, but

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considering how materiality and (human and animal) bodies are at the heart of science as a discipline, there is plenty of room for additional explorations of the entanglement of science learning, gender, identities, and bodies. In particular, questions around what it means for a body to be sexed/gendered in relation to materials in science learning spaces calls for increased attention, and posthumanism here provides a powerful theoretical vantage point.

Discussion and Future Directions In the years since Scantlebury’s (2014) chapter, there has been a sustained interest in gender issues within science education research. Studies that map and seek to understand gender disparities continue to form a substantial part of this research, but we have also witnessed a consolidation of identity-based approaches. Still, the conversation between studies in Part I and Part II of this chapter have been limited. This is perhaps not surprising given that the division between the two frst parts of the review largely follows the divide between cognitive and sociocultural perspectives on (science) learning. But given the complexity of the issue of understanding (gendered) students’ relationship to science as not only a (potentially gendered) body of knowledge but also a (gendered) cultural enterprise, the creation of silos of research that do not communicate with one another is unfortunate. While studies within the diferent traditions may be theoretically incompatible – for example, the implicit assumption about gender as binary and static that underlie some studies in Part I does not sit comfortably with a performative perspective of gender – work on stereotype threat, unconscious bias, and sense of belonging can be informative to studies using identity-based approaches. In synthesizing empirical evidence on gender and science education, one thing becomes clear: women’s underrepresentation in science cannot be explained by cognitive diferences or simply a lack of interest, but it is an issue of women being constructed as outsiders in science. This is illustrated through both international study results (e.g., PISA), which show that girls are less confdent than boys in their science abilities even though they score higher in science tests, as well as small-scale studies that show that women are not recognized as competent science persons and that they face a series of barriers and constraints throughout their studies/careers in science. In terms of disciplinary contexts, the majority of studies reviewed are found in the context of physics. This is perhaps not surprising given the fact that physics remains the most male-dominated scientifc feld. But this focus of research carries the assumption that gender perspectives are only useful in areas where there are inequalities in gender ratio. In terms of geographical contexts, the majority of the studies are situated in the Global North, specifcally the United States, Canada, and Western Europe. As noted earlier, while intersectional studies are gaining increased traction, such studies still predominantly concern the intersection between gender and race/ethnicity. Further, studies almost exclusively deal with gender and science in terms of gender as something relevant to individuals and their relationship to science. Apart from a few notable exceptions, gender in the disciplinary content is not problematized. Empirical research gaps identifed concerning gender and science education thus include: • • • •

•

Studies of science disciplines other than physics, including boys’ and men’s gender performances in the female-dominated discipline of biology as well as studies of interdisciplinary areas Studies situated in a broader variety of national contexts Studies concerning the intersection of gender and social class, age, dis/ability, and religious afliation that operationalize intersectionality to move beyond additive conceptions Studies of gender in the disciplinary content in biology, both in terms of assumptions carried by this content (such as companion meanings concerning heterosexuality) and in terms of the meeting between the content and students’ sexed and gendered bodies (with a particular relevance for transgender and nonbinary students) Studies in the context of science teacher education and professional development

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Methodologically, the feld reviewed in this chapter is strikingly heterogeneous. Very broadly speaking we can discern large-scale quantitative studies (often operationalizing gender in terms of men/ women), small-scale qualitative studies (often drawing on identity-based approaches), and intervention studies. However, few studies cross these methodological divides, and large-scale quantitative studies drawing on identity-based approaches are just starting to emerge. We also note that study designs often fall back on binary defnitions of gender, even when made explicit that this is something the researchers are seeking to avoid. As intervention studies based in identity-based approaches to gender become more common it will be important to involve teachers in collaborative studies, to ensure that models for practice are based in systematic and refected development that bridge across theory and practice (for example, by utilizing the idea of didactic modeling, see Sjöström, 2019). Thus, for a methodological point of view, we identify the need for: • • • • •

Mixed-methods studies that combine qualitative and quantitative elements Large-scale studies informed by identity-based approaches Study designs (both in quantitative and qualitative studies) that allow for the inclusion of transgender and nonbinary participants Collaborative studies involving researchers and teachers, including such studies set in the context of science teacher education and professional development An increased attention to how fndings can be made transferable, both in terms of how largescale quantitative studies can be made relevant for teachers and in terms of how small-scale qualitative studies can be made relevant for policy and for practitioners in other contexts

So far, we have discussed empirical and methodological gaps identifed, with recommendations for future research. But how far will this take us? Filling the gaps outlined earlie will create a more solid and complete research basis for gender and science education as the feld is currently understood and practiced. It is, however, notable that our synthesis of emerging perspectives remains similar to those outlined by Kathryn Scantlebury in her 2014 chapter in this handbook. For instance, we have found that emerging trends in gender research have focused on eforts to ensure that the “gender-binary defcit model” is not reproduced, and researchers have focused on identity negotiations in gender performances. However, science itself (and its practices) has been left untroubled and unchallenged by this identity turn in gender research. Furthermore, in our synthesis we notice an increasing distance from the critical feminist theories that laid the groundwork for research into gender issues in science. For example, in the late 1990s and early 2000s it was common to fnd gender research grounded in the work of Evelyn Fox-Keller (Keller, 1982, 1987), who challenged the masculine underpinnings of science, or Sandra Harding’s work (e.g. Harding, 1991), which went further to critique science as an enterprise. Harding’s criticism of science-as-usual challenges the ethics, goals, and functions of science and sees adding more women (by promoting equitable pedagogical and employment practices) as complicit with our culture’s failure to confront the status quo in science. From a theoretical point of view, our recommendation is therefore to turn the critical gaze back at science. Feminist philosophers of science here provide a theoretical grounding for doing so; it is worth both returning to and building on the past (Harding, 1991; Keller, 1982) and looking into more contemporary developments in this feld (Archer & Kohler, 2020; Barad, 2007). In this context, it is worth repeating that “what the problem is represented to be” (Bacchi, 2012) is a key question to consider, both when research problems are justifed and when implications for practice are presented. For example, if researchers  – ourselves included  – keep justifying gender studies in science education by existence of various forms of gender gaps, the problem is assumed to concern the number of men and women in a discipline. Much research today is still grounded in this goal of gender mainstreaming, asking questions about how to get more women into science or how to retain them once there. Maybe it is the narratives about the “need” to bring more students

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into science – which in efect contributes to solidifying the position of science as a superior – that ought to be disrupted? While critical of social practices that continue to discourage women in science or pedagogical practices that make them feel like science is “not for me”, this kind of gender research has lost sight of the critique of science’s history as an enterprise that is rooted in racism and the oppression of women and gender-diverse people (e.g., Carter et al., 2019), and settler colonialism (Bang & Marin, 2015). While the efect of bringing gender studies in science education research into the mainstream is overall very positive, we caution that losing sight of these critical perspectives may lead to practices aiming to promote gender equality (e.g., interventions that reproduce stereotypical ideas about gender roles) without changing the culture of science that women and other minoritized groups are entering. If we were to conclude this chapter with just one recommendation for studies of gender in science education, it would be: Bring feminism back!

References Almukhambetova, A., & Kuzhabekova, A. (2021). Negotiating conficting discourses. Female students’ experiences in STEM majors in an international university in Central Asia. International Journal of Science Education, 1–24. Andersson, S., & Johansson, A. (2016). Gender gap or program gap? Students’ negotiations of study practice in a course in electromagnetism. Physical Review Physics Education Research, 12(2), 020112. Archer, L., Dawson, E., Seakins, A., DeWitt, J., Godec, S., & Whitby, C. (2016). “I’m being a man here”: Urban boys’ performances of masculinity and engagement with science during a science museum visit. Journal of the Learning Sciences, 25(3), 438–485. Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012a). “Balancing acts”: Elementary school girls’ negotiations of femininity, achievement, and science. Science Education, 96(6), 967–989. Archer, L., DeWitt, J., Osborne, J., Dillon, J., Willis, B., & Wong, B. (2012b). Science aspirations, capital, and family habitus how families shape children’s engagement and identifcation with science. American Educational Research Journal, 49(5), 881–908. Archer, L., DeWitt, J., & Willis, B. (2014). Adolescent boys’ science aspirations: Masculinity, capital, and power. Journal of Research in Science Teaching, 51(1), 1–30. Archer, L., Moote, J., Francis, B., DeWitt, J., & Yeomans, L. (2017). The “exceptional” physics girl: A sociological analysis of multimethod data from young women aged 10–16 to explore gendered patterns of post-16 participation. American Educational Research Journal, 54(1), 88–126. https://doi.org/10.3102/0002831216678379 Archer, L., Moote, J., & MacLeod, E. (2020a). Learning that physics is “not for me”: Pedagogic work and the cultivation of habitus among advanced level physics students. Journal of the Learning Sciences, 1–38. Archer, L., Moote, J., & MacLeod, E. (2020b). Lighting the Fuse: Cultivating the masculine physics habitus – A case study of victor aged 10–18. In Physics Education and Gender (pp. 29–51). Springer. Archer, S. M., & Kohler, A. (2020). Feminist science studies. Companion to Feminist Studies, 247–263. Avraamidou, L. (2020). “I am a young immigrant woman doing physics and on top of that I am Muslim”: Identities, intersections, and negotiations. Journal of Research in Science Teaching, 57(3), 311–341. Avraamidou, L. (2021). Identities in/out of physics and the politics of recognition. Journal of Research in Science Teaching (online frst). Aycock, L. M., Hazari, Z., Brewe, E., Clancy, K. B., Hodapp, T., & Goertzen, R. M. (2019). Sexual harassment reported by undergraduate female physicists. Physical Review Physics Education Research, 15(1), 010121. Bacchi, C. (2012). Introducing the “what’s the problem represented to be?” approach. In Engaging with Carol Bacchi: Strategic interventions and exchanges (pp. 21–24). The University of Adelaide Press. Bang, M., & Marin, A. (2015). Nature – Culture constructs in science learning: Human/non-human agency and intentionality. Journal of Research in Science Teaching, 52(4), 530–544. Barad, K. (2003). Posthumanist performativity: Toward an understanding of how matter comes to matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. Barad, K. (2007). Meeting the universe halfways. Quantum physics and the entanglement of matter and meaning. Duke University Press. Barthelemy, R. S. (2020). LGBT+ physicists qualitative experiences of exclusionary behavior and harassment. European Journal of Physics, 41(6), 065703. Barthelemy, R. S., McCormick, M., & Henderson, C. (2016). Gender discrimination in physics and astronomy: Graduate student experiences of sexism and gender microaggressions. Physical Review Physics Education Research, 12(2), 020119.

285

Anna Danielsson, Lucy Avraamidou, and Allison Gonsalves Bazzul, J., & Sykes, H. (2011). The secret identity of a biology textbook: Straight and naturally sexed. Cultural Studies of Science Education, 6(2), 265–286. Berg, A.-J., & Lie, M. (1995). Feminism and constructivism: Do artifacts have gender? Science, Technology, & Human Values, 20(3), 332–351. Brickhouse, N. W., & Potter, J. T. (2001). Young women’s scientifc identity formation in an urban context. Journal of Research in Science Teaching, 38, 965–980. Butler, J. (1993). Critically queer. GLQ: A Journal of Lesbian and Gay Studies, 1(1), 17–32. Butler, J. (1999). Gender Trouble. Feminism and the subversion of identity. Routledge. Calabrese Barton, A., & Tan, E. (2009). Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching: The Ofcial Journal of the National Association for Research in Science Teaching, 46(1), 50–73. Carli, L. L., Alawa, L., Lee, Y., Zhao, B., & Kim, E. (2016). Stereotypes about gender and science: Women ≠ scientists. Psychology of Women Quarterly, 40(2), 244–260. Carlone, H. B., & Johnson, A. (2007). Understanding the science experiences of successful women of color: Science identity as an analytic lens. Journal of Research in Science Teaching, 44(8), 1187–1218. Carlone, H. B., Johnson, A., & Scott, C. M. (2015). Agency amidst formidable structures: How girls perform gender in science class. Journal of Research in Science Teaching, 52(4), 474–488. Carlone, H. B., Webb, A. W., Archer, L.,  & Taylor, M. (2015). What kind of boy does science? A critical perspective on the science trajectories of four scientifcally talented boys. Science Education, 99(3), 438–464. https://doi.org/10.1002/sce.21155 Carter, D. F., Dueñas, J. E. R., & Mendoza, R. (2019). Critical examination of the role of STEM in propagating and maintaining race and gender disparities. Higher Education: Handbook of Theory and Research, 39–97. Castro, A. R., & Collins, C. S. (2021). Asian American women in STEM in the lab with “White men named John”. Science Education, 105(1), 33–61. Charleston, L., Adserias, R. P., Lang, N. M., & Jackson, J. F. (2014). Intersectionality and STEM: The role of race and gender in the academic pursuits of African American women in STEM. Journal of Progressive Policy & Practice, 2(3), 273–293. Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM felds more gender balanced than others? Psychological Bulletin, 143(1), 1. Clance, P. R., & Imes, S. A. (1978). The imposter phenomenon in high achieving women: Dynamics and therapeutic intervention. Psychotherapy: Theory, Research & Practice, 15(3), 241. Clancy, K. B., Lee, K. M., Rodgers, E. M., & Richey, C. (2017). Double jeopardy in astronomy and planetary science: Women of color face greater risks of gendered and racial harassment. Journal of Geophysical Research: Planets, 122(7), 1610–1623. Connell, R. W. (2005). Masculinities. Polity. Connell, R. W. (2013). Gender and power: Society, the person and sexual politics. John Wiley & Sons. Cooper, K. M., Brownell, S. E., & Gormally, C. (2019). Coming out to the class: Identifying factors that infuence college biology instructor decisions about revealing their LGBQ identities in class. Journal of Women and Minorities in Science and Engineering, 25(3). Crenshaw, K. (1989). Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 139. Curran, F. C., & Kellogg, A. T. (2016). Understanding science achievement gaps by race/ethnicity and gender in kindergarten and frst grade. Educational Researcher, 45(5), 273–282. https://doi.org/10.3102/ 0013189x16656611 Danielsson, A., King, H., Godec, S.,  & Nyström, A.-S. (accepted). The identity turn in science education research: A review of a consolidating feld. Accepted for publication in Cultural Studies in Science Education. Dawson, E., Archer, L., Seakins, A., Godec, S., DeWitt, J., King, H., Mau, A., & Nomikou, E. (2019). Selfes at the science museum: Exploring girls’ identity performances in a science learning space. Gender and Education, 1–18. Day, J., Stang, J. B., Holmes, N., Kumar, D., & Bonn, D. (2016). Gender gaps and gendered action in a frst-year physics laboratory. Physical Review Physics Education Research, 12(2), 020104. Deemer, E. D., Thoman, D. B., Chase, J. P., & Smith, J. L. (2014). Feeling the threat: Stereotype threat as a contextual barrier to women’s science career choice intentions. Journal of Career Development, 41(2), 141–158. Eaton, A. A., Saunders, J. F., Jacobson, R. K., & West, K. (2020). How gender and race stereotypes impact the advancement of scholars in STEM: Professors’ biased evaluations of physics and biology post-doctoral candidates. Sex Roles, 82(3), 127–141. Eddy, S. L., Brownell, S. E., & Wenderoth, M. P. (2014). Gender gaps in achievement and participation in multiple introductory biology classrooms. CBE – Life Sciences Education, 13(3), 478–492.

286

Gender Matters Eliasson, N., Karlsson, K. G., & Sørensen, H. (2017). The role of questions in the science classroom – How girls and boys respond to teachers’ questions. International Journal of Science Education, 39(4), 433–452. Eliasson, N., Sørensen, H.,  & Karlsson, K. G. (2016). Teacher  – student interaction in contemporary science classrooms: Is participation still a question of gender? International Journal of Science Education, 38(10), 1655–1672. Fox, C. W., & Paine, C. T. (2019). Gender diferences in peer review outcomes and manuscript impact at six journals of ecology and evolution. Ecology and Evolution, 9(6), 3599–3619. Francis, B., Archer, L., Moote, J., DeWitt, J., MacLeod, E., & Yeomans, L. (2017). The construction of physics as a quintessentially masculine subject: Young people’s perceptions of gender issues in access to physics. Sex Roles, 76(3), 156–174. https://doi.org/10.1007/s11199-016-0669-z Gee, J. P. (2000). Identity as an analytic lens for research in education. Review of Research in Education, 99–125. Godec, S. (2018). Sciencey girls: Discourses supporting working-class girls to identify with science. Education Sciences, 8(1), 19. Godec, S. (2020). Home, school and the museum: Shifting gender performances and engagement with science. British Journal of Sociology of Education, 41(2), 147–159. Godec, S., Patel, U., Archer, L., & Dawson, E. (2020). Young peoples’ tech identity performances: Why materiality matters. International Journal of STEM Education, 7(1), 1–12. Gok, T. (2014). Peer instruction in the physics classroom: Efects on gender diference performance, conceptual learning, and problem solving. Journal of Baltic Science Education, 13(6), 776. Gonsalves, A. J. (2014). “Physics and the girly girl – There is a contradiction somewhere”: Doctoral students’ positioning around discourses of gender and competence in physics. Cultural Studies of Science Education, 2(9), 503–521. Gonsalves, A. J. (2020). Constructing inside-ness to physics: How matter comes to matter in physics identity work. Cultural Studies of Science Education, 15(4), 911–921. Gonsalves, A. J., & Danielsson, A. (2020). Physics education and gender: Identity as an analytic lens for research. Springer. Gonsalves, A. J., Danielsson, A., & Pettersson, H. (2016). Masculinities and experimental practices in physics: The view from three case studies. Physical Review Physics Education Research, 12(2), 020120. Götschel, H. (2019). Teaching queering physics: An agenda for research and practice. In STEM of desire (pp. 125–145). Brill Sense. Graves, A. L., Hoshino-Browne, E., & Lui, K. P. (2017). Swimming against the tide: Gender bias in the physics classroom. Journal of Women and Minorities in Science and Engineering, 23(1). Grunspan, D. Z., Eddy, S. L., Brownell, S. E., Wiggins, B. L., Crowe, A. J., & Goodreau, S. M. (2016). Males under-estimate academic performance of their female peers in undergraduate biology classrooms. PLoS ONE, 11(2), e0148405. Gunckel, K. L. (2019a). Repairing elementary school science. Theory Into Practice, 58(1), 71–79. Gunckel, K. L. (2019b). What does queer theory have to do with teaching science in elementary schools? In STEM of desire (pp. 147–159). Brill Sense. Günther-Hanssen, A. (2020). A swing and a child: How scientifc phenomena can come to matter for preschool children’s emergent science identities. Cultural Studies of Science Education, 15(4), 885–910. Günther-Hanssen, A., Danielsson, A. T., & Andersson, K. (2020). How does gendering matter in preschool science: Emergent science,‘neutral’environments and gendering processes in preschool. Gender and Education, 32(5), 608–625. Harding, S. (1986). The science question in feminism. Open University Press. Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Cornell University Press. Hasse, C. (2002). Gender diversity in play with physics: The problem of premises for participation in activities. Mind, Culture, and Activity, 9(4), 250–269. Hasse, C. (2015). The material co-construction of hard science fction and physics. Cultural Studies of Science Education, 10(4), 921–940. Hazari, Z., Brewe, E., Goertzen, R. M., & Hodapp, T. (2017). The importance of high school physics teachers for female students’ physics identity and persistence. The Physics Teacher, 55(2), 96–99. Henderson, R., Sawtelle, V., & Nissen, J. M. (2020). Gender & self-efcacy: A call to physics educators. The Physics Teacher, 58(5), 345–348. Henderson, R., Stewart, G., Stewart, J., Michaluk, L., & Traxler, A. (2017). Exploring the gender gap in the conceptual survey of electricity and magnetism. Physical Review Physics Education Research, 13(2), 020114. Hofer, S. I. (2015). Studying gender bias in physics grading: The role of teaching experience and country. International Journal of Science Education, 37(17), 2879–2905. https://doi.org/10.1080/09500693.2015.1114190 Holland, D., Lachicotte Jr, W., Skinner, D., & Cain, C. (2001). Identity and agency in cultural worlds. Harvard University Press.

287

Anna Danielsson, Lucy Avraamidou, and Allison Gonsalves Ivie, R., White, S., & Chu, R. Y. (2016). Women’s and men’s career choices in astronomy and astrophysics. Physical Review Physics Education Research, 12(2), 020109. Jacobs, J. E. (2005). Twenty-fve years of research on gender and ethnic diferences in math and science career choices: What have we learned? New Directions for Child and Adolescent Development, 2005(110), 85–94. Johansson, A. (2018). Negotiating intelligence, nerdiness, and status in physics master’s studies. Research in Science Education, 1–22. Johansson, A., & Berge, M. (2020). Lecture Jokes: Mocking and reproducing celebrated subject positions in physics. In Physics education and gender (pp. 97–113). Springer. Kalender, Z. Y., Marshman, E., Schunn, C. D., Nokes-Malach, T. J., & Singh, C. (2019). Gendered patterns in the construction of physics identity from motivational factors. Physical Review Physics Education Research, 15(2), 020119. Kalender, Z. Y., Marshman, E., Schunn, C. D., Nokes-Malach, T. J., & Singh, C. (2020). Damage caused by women’s lower self-efcacy on physics learning. Physical Review Physics Education Research, 16(1), 010118. Kang, J., Hense, J., Scheersoi, A., & Keinonen, T. (2019). Gender study on the relationships between science interest and future career perspectives. International Journal of Science Education, 41(1), 80–101. Kanny, M. A., Sax, L. J., & Riggers-Piehl, T. A. (2014). Investigating forty years of STEM research: How explanations for the gender gap have evolved over time. Journal of Women and Minorities in Science and Engineering, 20(2). Keller, E. F. (1982). Feminism and science. Signs: Journal of Women in Culture and Society, 7(3), 589–602. Keller, E. F. (1987). On the need to count past two in our thinking about gender and science. New Ideas in Psychology, 5(2), 275–287. Kelly, A. M. (2016). Social cognitive perspective of gender disparities in undergraduate physics. Physical Review Physics Education Research, 12(2), 020116. Kim, A. Y., Sinatra, G. M., & Seyranian, V. (2018). Developing a STEM identity among young women: A social identity perspective. Review of Educational Research, 88(4), 589–625. Knaier, M. L. (2019). What makes girls and boys so desirable?: STEM education beyond gender binaries. In STEM of desire (pp. 209–221). Brill Sense. Lave, J. (1996). Teaching, as learning, in practice. Mind, Culture, and Activity, 3(3), 149–164. Leslie, S.-J., Cimpian, A., Meyer, M., & Freeland, E. (2015). Expectations of brilliance underlie gender distributions across academic disciplines. Science, 347(6219), 262–265. Letts, W. (2001). When science is strangely alluring: Interrogating the masculinist and heteronormative nature of primary school science. Gender and Education, 13(3), 261–274. Letts, W., & Fifeld, S. (2019). STEM of desire: Queer theories and science education. Brill| Sense. Levine, M., Serio, N., Radaram, B., Chaudhuri, S., & Talbert, W. (2015). Addressing the STEM gender gap by designing and implementing an educational outreach chemistry camp for middle school girls. Journal of Chemical Education, 92(10), 1639–1644. Lock, R. M., & Hazari, Z. (2016). Discussing underrepresentation as a means to facilitating female students’ physics identity development. Physical Review Physics Education Research, 12(2), 020101. Makarova, E., Aeschlimann, B.,  & Herzog, W. (2019). The gender gap in STEM felds: The impact of the gender stereotype of math and science on secondary students’ career aspirations. Frontiers in Education, 4, Article 60. Maries, A., Karim, N. I., & Singh, C. (2018). Is agreeing with a gender stereotype correlated with the performance of female students in introductory physics? Physical Review Physics Education Research, 14(2), 020119. Mark, S. L. (2018). A bit of both science and economics: A non-traditional STEM identity narrative. Cultural Studies of Science Education, 13(4), 983–1003. Marshman, E. M., Kalender, Z. Y., Nokes-Malach, T., Schunn, C., & Singh, C. (2018). Female students with A’s have similar physics self-efcacy as male students with C’s in introductory courses: A cause for alarm? Physical Review Physics Education Research, 14(2), 020123. Mattsson, T. (2015). “Good girls”: Emphasised femininity as cloning culture in academia. Gender and Education, 27(6), 685–699. Mensah, F. M. (2019). Finding voice and passion: Critical race theory methodology in science teacher education. American Educational Research Journal, 56(4), 1412–1456. Meyer, M., Cimpian, A., & Leslie, S.-J. (2015). Women are underrepresented in felds where success is believed to require brilliance. Frontiers in Psychology, 6, 235. Miller, D. I., Eagly, A. H., & Linn, M. C. (2015). Women’s representation in science predicts national genderscience stereotypes: Evidence from 66 nations. Journal of Educational Psychology, 107(3), 631. Miller, D. I., & Halpern, D. F. (2014). The new science of cognitive sex diferences. Trends in Cognitive Sciences, 18(1), 37–45.

288

Gender Matters Mim, S. A. (2020). Masculinity of science: Unveiling gendered challenges of female science teachers in Bangladesh. Gender and Education, 1–16. Moshfeghyeganeh, S., & Hazari, Z. (2021). Efect of culture on women physicists’ career choice: A comparison of Muslim majority countries and the West. Physical Review Physics Education Research, 17(1), 010114. Nehmeh, G., & Kelly, A. M. (2020). Facilitating the self-determination of undergraduate women in physics: The role of external validation. Research in Science & Technological Education, 1–22. Nissen, J. M. (2019). Gender diferences in self-efcacy states in high school physics. Physical Review Physics Education Research, 15(1), 013102. Nissen, J. M., & Shemwell, J. T. (2016). Gender, experience, and self-efcacy in introductory physics. Physical Review Physics Education Research, 12(2), 020105. Ong, M., Smith, J. M.,  & Ko, L. T. (2018). Counterspaces for women of color in STEM higher education: Marginal and central spaces for persistence and success. Journal of Research in Science Teaching, 55(2), 206–245. Orlander, A. A. (2016). “So, what do men and women want? Is it any diferent from what animals want?” Sex education in an upper secondary school. Research in Science Education, 46(6), 811–829. Orlander, A. A. (2020). When “scary” science “just feels wrong”: How the facts in a masculine fact-based debate couldn’t stop science students’ feminine feelings. Cultural Studies of Science Education, 15(1), 265–285. Ottemo, A., Gonsalves, A. J., & Danielsson, A. T. (2021). (Dis) embodied masculinity and the meaning of (non) style in physics and computer engineering education. Gender and Education, 1–16. Potvin, G., & Hazari, Z. (2016). Student evaluations of physics teachers: On the stability and persistence of gender bias. Physical Review Physics Education Research, 12(2), 020107. Potvin, G., Hazari, Z., Tai, R. H., & Sadler, P. M. (2009). Unraveling bias from student evaluations of their high school science teachers. Science Education, 93(5), 827–845. Quinn, D. M.,  & Cooc, N. (2015). Science achievement gaps by gender and race/ethnicity in elementary and middle school: Trends and predictors. Educational Researcher, 44(6), 336–346. https://doi. org/10.3102/0013189x15598539 Ramsey, L. R. (2017). Agentic traits are associated with success in science more than communal traits. Personality and Individual Diferences, 106, 6–9. Reddy, L. (2017). Gender diferences in attitudes to learning science in grade 7. African Journal of Research in Mathematics, Science and Technology Education, 21(1), 26–36. Reilly, D., Neumann, D. L., & Andrews, G. (2015). Sex diferences in mathematics and science achievement: A meta-analysis of National Assessment of Educational Progress assessments. Journal of Educational Psychology, 107(3), 645. Reilly, D., Neumann, D. L., & Andrews, G. (2019). Investigating gender diferences in mathematics and science: Results from the 2011 Trends in Mathematics and Science Survey. Research in Science Education, 49(1), 25–50. Reiss, M. J. (1998). The representation of human sexuality in some science textbooks for 14–16 year olds. Research in Science & Technological Education, 16(2), 137–149. Reiss, M. J. (2019). Thinking like a fox: Queering the science classroom when teaching about sex and sexuality. In STEM of desire (pp. 255–267). Brill Sense. Reuben, E., Sapienza, P., & Zingales, L. (2014). How stereotypes impair women’s careers in science. Proceedings of the National Academy of Sciences, 111(12), 4403–4408. Ringrose, J., & Renold, E. (2010). Normative cruelties and gender deviants: The performative efects of bully discourses for girls and boys in school. British Educational Research Journal, 36(4), 573–596. Rosa, K. (2018). Science identity possibilities: A look into Blackness, masculinities, and economic power relations. Cultural Studies of Science Education, 13(4), 1005–1013. Rosa, K., & Mensah, F. M. (2016). Educational pathways of Black women physicists: Stories of experiencing and overcoming obstacles in life. Physical Review Physics Education Research, 12(2), 020113. Rüschenpöhler, L., & Markic, S. (2020). Secondary school students’ chemistry self-concepts: Gender and culture, and the impact of chemistry self-concept on learning behaviour. Chemistry Education Research and Practice, 21(1), 209–219. Sansone, D., & Carpenter, C. S. (2020). Turing’s children: Representation of sexual minorities in STEM. PLoS ONE, 15(11), e0241596. Scantlebury, K. (2014). Gender matters: Building on the past, recognizing the present, and looking towards the future. In N. Lederman & S. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 187–203). Routledge. Scantlebury, K., & Baker, D. (2007). Gender issues in science education research: Remembering where the difference lies. Handbook of Research on Science Education, 1, 257–285.

289

Anna Danielsson, Lucy Avraamidou, and Allison Gonsalves Scantlebury, K., Danielsson, A. T., Hussénius, A., Gullberg, A., & Andersson, K. (2019). Using spacetimemattering to engage science education with matter and material feminism. In Material practice and materiality: Too long ignored in science education (pp. 39–50). Springer. Schiebinger, L. (1991). The mind has no sex? Women in the origins of modern science. Harvard University Press. Schiebinger, L. (2014). Gendered innovations: Harnessing the creative power of sex and gender analysis to discover new ideas and develop new technologies. Triple Helix, 1(1), 9. https://doi.org/10.1186/s40604-014-0009-7 Schwab, K., Samans, R., Zahidi, S., Leopold, T. A., Ratcheva, V., Hausmann, R., & Tyson, L. D. (2017). The global gender gap report 2017. World Economic Forum. Seyranian, V., Madva, A., Duong, N., Abramzon, N., Tibbetts, Y., & Harackiewicz, J. M. (2018). The longitudinal efects of STEM identity and gender on fourishing and achievement in college physics. International Journal of STEM Education, 5(1), 1–14. Simon, R. M., Wagner, A., & Killion, B. (2017). Gender and choosing a STEM major in college: Femininity, masculinity, chilly climate, and occupational values. Journal of Research in Science Teaching, 54(3), 299–323. Sjöström, J. (2019). Didactic modelling for socio-ecojustice. Journal for Activist Science and Technology Education, 10(1). Skibba, R. (2019). Women in physics. Nature Reviews Physics, 1(5), 298–300. Smith, J. L., Brown, E. R., Thoman, D. B., & Deemer, E. D. (2015). Losing its expected communal value: How stereotype threat undermines women’s identity as research scientists. Social Psychology of Education, 18(3), 443–466. Sue, D. W. (2010). Microaggressions and marginality: Manifestation, dynamics, and impact. John Wiley & Sons. Taasoobshirazi, G., Puckett, C., & Marchand, G. (2019). Stereotype threat and gender diferences in biology. International Journal of Science and Mathematics Education, 17(7), 1267–1282. Taylor, C. A. (2013). Objects, bodies and space: Gender and embodied practices of mattering in the classroom. Gender and Education, 25(6), 688–703. Tellhed, U., Bäckström, M., & Björklund, F. (2017). Will I ft in and do well? The importance of social belongingness and self-efcacy for explaining gender diferences in interest in STEM and HEED majors. Sex Roles, 77(1), 86–96. Todd, B. L., & Zvoch, K. (2019). The efect of an informal science intervention on middle school girls’ science afnities. International Journal of Science Education, 41(1), 102–122. Traweek, S. (1988). Beamtimes and lifetimes. The world of high energy physicists. Harvard University Press. Traxler, A. L., Cid, X. C., Blue, J., & Barthelemy, R. (2016). Enriching gender in physics education research: A binary past and a complex future. Physical Review Physics Education Research, 12(2), 020114. Traxler, A. L., Henderson, R., Stewart, J., Stewart, G., Papak, A., & Lindell, R. (2018). Gender fairness within the force concept inventory. Physical Review Physics Education Research, 14(1), 010103. Valla, J. M., & Ceci, S. J. (2014). Breadth-based models of women’s underrepresentation in STEM felds: An integrative commentary on schmidt (2011) and Nye et al. (2012). Perspectives on Psychological Science, 9(2), 219–224. https://doi.org/10.1177/1745691614522067 Verdín, D., Godwin, A.,  & Benedict, B. (2020). Exploring frst-year engineering students’ innovation selfefcacy beliefs by gender and discipline. Journal of Civil Engineering Education, 146(4), 04020006. Voyer, D., & Voyer, S. D. (2014). Gender diferences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174. Wade-Jaimes, K., & Schwartz, R. (2019). “I don’t think it’s science:” African American girls and the fgured world of school science. Journal of Research in Science Teaching, 56(6), 679–706. Wang, M.-T.,  & Degol, J. L. (2017). Gender gap in Science, Technology, Engineering, and Mathematics (STEM): Current knowledge, implications for practice, policy, and future directions. Educational Psychology Review, 29(1), 119–140. https://doi.org/10.1007/s10648-015-9355-x Wang, M.-T., Eccles, J. S.,  & Kenny, S. (2013). Not lack of ability but more choice: Individual and gender diferences in choice of careers in science, technology, engineering, and mathematics. Psychological Science, 24(5), 770–775. Whitten, B. L., Foster, S. R., Duncombe, M. L., Allen, P. E., Heron, P., McCullough, L., Shaw, K. A., Taylor, B. A. P., & Zorn, H. M. (2003). What works? Increasing the participation of women in undergraduate physics. Journal of Women and Minorities in Science and Engineering, 9(3&4), 239–258. Witteman, H. O., Hendricks, M., Straus, S., & Tannenbaum, C. (2019). Are gender gaps due to evaluations of the applicant or the science? A natural experiment at a national funding agency. The Lancet, 393(10171), 531–540. Wulf, P., Hazari, Z., Petersen, S., & Neumann, K. (2018). Engaging young women in physics: An intervention to support young women’s physics identity development. Physical Review Physics Education Research, 14(2), 020113. Yoder, J. B., & Mattheis, A. (2016). Queer in STEM: Workplace experiences reported in a national survey of LGBTQA individuals in science, technology, engineering, and mathematics careers. Journal of Homosexuality, 63(1), 1–27.

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10 MULTILINGUAL LEARNERS IN SCIENCE EDUCATION Cory A. Buxton and Okhee Lee

Since the publication of our chapter on English learners in science education in the last edition of the handbook (Buxton & Lee, 2014), major developments have occurred in science education and language education, leading both felds to embrace new perspectives. The subfeld of science and language with multilingual learners has responded to both shifts, transitioning from what we call traditional perspectives to what we call contemporary perspectives on both science learning and language learning. Moreover, the profound social infuences of the COVID-19 pandemic and of growing attention to systemic racism are accelerating educational changes that have implications for the role of science education with multilingual learners moving forward. This chapter is written in the context of these educational and societal changes that have occurred since the publication of the last volume of this handbook. This chapter addresses publications from 2014 through 2020. In science education, we characterize this period as covering the shift from traditional perspectives to contemporary perspectives of science based on A Framework for K–12 Science Education (National Research Council [NRC], 2012; shortened to the Framework hereafter) and the Next Generation Science Standards (NGSS; NGSS Lead States, 2013a). Bringing these new science standards in line with the new content standards in English language arts and mathematics from the early 2010s necessitated collaborations across content area education and language education (Lee, 2019). Further, rapidly shifting demographics in our school-aged population, including substantial increases in students for whom English is not their mother tongue, began to receive increased attention during this period through national and international eforts, including the Understanding Language Initiative (https://ell.stanford.edu) and the consensus report on English learners in STEM subjects (National Academies of Sciences, Engineering, and Medicine [NASEM], 2018). The two authors of this chapter were both involved in the aforementioned eforts that helped frame contemporary perspectives on language in the STEM content areas. For the NASEM consensus report, the two authors were the only panel members from science education whose research focused on multilingual learners. Table 10.1 highlights key components of our working defnitions of traditional and contemporary perspectives on both science and language learning. To refect the shifts in both science education and language education with multilingual learners, this chapter provides an interpretive review of the literature surrounding these shifts (2014–2020) and focuses on publications that helped move both felds toward contemporary perspectives on science and language with multilingual learners. The chapter also acknowledges the contributions of publications that continued to apply more traditional perspectives on science and/or language. As

DOI: 10.4324/9780367855758-13

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Cory A. Buxton and Okhee Lee Table 10.1 Shifts From Traditional to Contemporary Views of Science and Language Learning  

Traditional Perspectives

Contemporary Perspectives

Science

• Central focus is on canonical science knowledge defned by scientists and science educators • Science is represented through authoritative texts; students are expected to absorb science primarily through reading and listening • Science inquiry, “habits of mind”, and “process skills” are separate from and secondary to canonical science knowledge • Students’ prior knowledge is explored to identify “alternative conceptions” of science concepts

• Central focus is on making sense of phenomena and problems in similar ways to how scientists and engineers engage in their professional work (focus on what knowledge does, or knowledge-in-use) • Science is represented as three-dimensional learning, blending disciplinary core ideas, science and engineering practices, and crosscutting concepts • Students develop coherent understandings or storylines over time • All students are encouraged to leverage their cultural and linguistic resources, interests, and identities toward learning science

Language • Content area language is learned through focus on discrete elements of vocabulary (lexicon) and grammar (syntax) – focus on “what language is” • Level of profciency in English determines level of access to grade-level content standards • Languages and language registers are seen as dichotomous: “academic” vs. “everyday” language, “general academic” vs. “discipline specifc”, “home language” vs. “second language” • Focus is on linguistic modalities (listening, speaking, reading, and writing) • Sheltered instruction is ofered to support English language development through content learning • Pre-teaching of vocabulary and language supports (e.g., sentence frames) are done to “build the feld” (background knowledge) needed to engage with disciplinary texts

• Content area language is learned through participation in social contexts (focus on what language does, or language-in-use) • Language profciency standards are aligned with academic content standards • Language registers are seen as fexible choices from a continuum between everyday and disciplinary language • Multimodality (e.g., gestures, visual representations) extends beyond linguistic modalities • Translanguaging is ofered to support multilingual language development through content learning • Language profciency (including specialized disciplinary language) is not a prerequisite for content learning but an outcome of efective content learning

we will see, most studies that maintained traditional perspectives from one feld (science or language) applied a contemporary perspective to the other feld, which indicates how both felds continue to progress. By highlighting the current shifts, we intend to lay a foundation that others can use to continue conceptualizing the integration of science and language in ways that can better support and challenge not only multilingual learners but all learners. We use the term “multilingual learners” to designate students who are educated in predominantly English-language instructional settings but whose home languages are other than or in addition to English (González-Howard & Suárez, 2021; Grapin, 2021; Lee, 2021). Changes in the terms used over the years to describe this population refect conceptual shifts from defcit to asset orientations as well as policy shifts, as described next. The federal legislation of the No Child Left Behind Act (NCLB) of 2001 used the terms “limited English profcient students” and “LEP students”, which have been criticized for the defcit view explicit in centering limited profciency in English language without recognition of other language profciencies. Alternative terms emerged from the feld but were also criticized. For example, “non-English language backgrounds” was found to be faulty because it centered English profciency,

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and “culturally and linguistically diverse students” was criticized for being too broad and for using “diverse students” as code for racially and ethnically minoritized student groups. To replace the term “limited English profcient” in the NCLB, “English language learners” (or ELLs) and then “English learners” (or ELs) were subsequently adopted as less objectionable terms and have been widely used in the feld. The federally legislated Every Student Succeeds Act (ESSA) of 2015 adopted the term “English learners”, indicating a conscious shift away from a defcit orientation at the federal policy level. The academic feld continued to point out that the term English learners, while less defcit oriented, maintains the exclusive focus on English language and fails to recognize and value other languages spoken by students. The alternative term “emerging (or emergent) bilinguals” gained popularity but was also viewed as problematic, as “emerging” indicates lower levels of English language profciency (ELP); “bilinguals” fails to recognize students who are multilingual; and “bilinguals” is associated with bilingual education, which has its own controversial history. Most recently, the term “multilingual learners” has gained favor as being descriptive and indicative of an asset view of students. We anticipate this term will be the most widely adopted in the coming years. For example, WIDA English-language development standards in US K–12 education uses the term “multilingual learners” (see https://wida.wisc.edu/sites/default/fles/resource/WIDA-ELDStandards-Framework-2020.pdf; note that WIDA is not an acronym but rather the organization name). In this chapter, we use the term “multilingual learners” to highlight that all students use the full range of meaning-making resources available to them for engaging in and communicating about science, both in and out of academic settings. We use the full term, multilingual learners, rather than the acronym of MLs, as we believe that the education feld’s consistent overuse of acronyms to describe students is potentially dehumanizing to individuals and groups who can come to be seen as little more than such labels. The chapter is organized into the following sections. We begin with a discussion of the shifting policies, theoretical grounding, and instructional approaches that have led the science education feld to begin moving from traditional to contemporary perspectives on both science learning and language learning. Second, we describe the rationale and methods used in our review and organization of the literature in this chapter. Third, the fndings section is organized thematically based on prominent topic areas in the research literature for the period under review. The four major topic areas include science learning, science instruction, science assessment, and science teacher education. For each topic area, shifts from traditional to contemporary perspectives on science and language are addressed. Finally, after a summary of key fndings, the chapter concludes with implications for the feld of science education, with attention to the aftermath of the COVID-19 pandemic and the call for racial and linguistic justice in science education.

Policy, Theory, and Practice This section describes new developments since the early 2010s in science education and language education with multilingual learners in terms of policy legislation, theoretical perspectives, and instructional approaches.

Policy Legislation Science education policy has been shaped by ongoing shifts in our understanding of what counts as science and how children learn science. Reform eforts in the 1990s, infuenced by the documents Science for All Americans (American Association for the Advancement of Science [AAAS], 1989), Benchmarks for Science Literacy (AAAS, 1993), and National Science Education Standards (NRC, 1996), promoted the central role of science inquiry and a focus on science content that advocated for depth

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of ideas over breadth of coverage. In the current reform since the early 2010s, the Framework (NRC, 2012) and the NGSS have led to three instructional shifts as students (1) make sense of phenomena and problems in similar ways to how scientists and engineers engage in their professional work; (2) engage in three-dimensional learning by blending disciplinary core ideas, science and engineering practices, and crosscutting concepts; and (3) develop coherent understandings or storylines over time. The Framework and the NGSS have also been used to position issues of equity more centrally in the current reform movement (NGSS Lead States, 2013b; see also Lee et al., 2015). Prior to the Framework, the central focus was on canonical science knowledge defned by scientists and science teachers and often represented through authoritative texts that students were expected to absorb through reading and writing. In the previous reform eforts in the 1990s, while substantial attention was also given to science inquiry and scientifc “habits of mind”, these “science process skills” were both separate from and secondary to canonical science knowledge. Some students succeeded in learning science in this way, but for many others, science neither made sense nor seemed meaningful or relevant. In the contemporary approach developed from the Framework, all students are asked to make sense of phenomena and design solutions to problems as they engage in the practices that scientists and engineers use in their professional work. In addition, all students are encouraged to leverage their cultural and linguistic resources, interests, and identities toward learning science, which is further promoted through the inclusion of phenomena and problems situated in students’ homes and communities. At the state level, since the release of the Framework in 2012 and the NGSS in 2013, 20 states and the District of Columbia have adopted the NGSS, and 24 additional states have developed their own standards adapted from the Framework. This broad adoption, adaptation, and implementation of the NGSS highlight that most multilingual learners in the nation are now expected to engage in science according to contemporary perspectives on science learning. Turning to the shifts in language education, the ESSA of 2015 mandates that “[each] State has adopted English language profciency standards that . . . are aligned with the challenging State academic standards” (Every Student Succeeds Act, 20 U.S.C. § 6301, 2015, p. 24). This mandate has several implications. First, ESSA does not say that ELP standards are expected to be met prior to academic standards. This is a break from traditional instructional assumptions that often highlighted the need for students to have developed a certain level of literacy in the language of instruction as a prerequisite to engaging in content learning in the disciplines. This traditional view often led to pull-out programs for multilingual learners to teach English with a focus on basic communication skills, vocabulary, and grammar instead of push-in models with disciplinary language supports. In contrast, the contemporary view is supported by federal legislation that clarifes “language profciency is not a prerequisite for content instruction, but an outcome of efective content instruction” (NASEM, 2018, p. 10). Second, the nature of this relationship between language and content calls for “language profciency standards [to] align to content standards and not the other way around” (NASEM, 2018, p. 10). This means that, for science, “the language to be learned needs to focus on the important STEM content and what is known about how children learn STEM content” (NASEM, 2018, p. 10). Thus, multilingual learners are expected to engage in science regardless of their English language profciency and are expected to learn content and language in parallel, not in series. At the state level, the WIDA English Language Development Standards Framework, 2020 Edition (WIDA Consortium, 2020) provides a clear picture of the shift from traditional to contemporary perspectives on how ELP standards can be aligned with content standards in English language arts, mathematics, science, and social studies (Lee et al., 2013; Lee & Stephens, 2020). Compared to the 2012 edition of WIDA based on traditional perspectives on the relationship between content and language with multilingual learners (Lee, 2018), the 2020 edition represents more contemporary perspectives (Grapin & Lee, 2022; Lee, 2019). For example, the focus on multimodality goes beyond

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the traditional linguistic modalities (listening, speaking, reading, and writing) in English language arts to include a range of visual, multimedia, and kinesthetic modalities that are central to the science and engineering practices. In addition, the more expansive conception of meaning-making extends to multiple languages, including translanguaging practices, expanding the vision of language learning beyond English language development. Further, the adoption of the term “multilingual learner” in these standards is notable considering that ESSA continues to use “English learner”. In the coming years, the WIDA 2020 edition will be implemented across 35 US states, the District of Columbia, and 4 territories and agencies that, collectively, make up the WIDA Consortium (https://wida.wisc. edu/memberships/consortium). The science education community now has a broad consensus across policy, research, and practice on what counts as science and how children learn science. Despite the promise of the latest WIDA standards, the multilingual learner education community has not yet developed a consensus on what language is and does and how language is learned (Lee, 2019). Currently, there are multiple sets of ELP standards, multiple theoretical perspectives, and multiple instructional approaches advocated in multilingual learner education (Lee, 2019). This lack of consensus presents challenges for collaboration with science education and other content area education. Despite these challenges, the science education research community, frmly grounded in its own consensus, is in a good position to forge meaningful collaborations with other felds, including language education.

Theoretical Perspectives Much like the shifts in instructional approaches driven by contemporary perspectives on science learning and language learning, there have been parallel shifts in the theoretical perspectives that underlie the teaching of science with multilingual learners. In science learning, traditional perspectives have focused on how learners gain mastery of discrete science concepts, whereas contemporary perspectives emphasize students making sense of phenomena and problems much as scientists and engineers do in their professional work (NRC, 2012). Because contemporary perspectives involve using and applying knowledge for a particular purpose, they have been referred to as knowledge-in-use (Harris et al., 2016). In language learning, traditional perspectives on supporting disciplinary knowledge building have focused on discrete elements of vocabulary (lexicon) and grammar (syntax) to be internalized by learners, whereas contemporary perspectives emphasize that language is a set of meaning-making practices learned through participation in social contexts (Larsen-Freeman, 2007; Valdés, 2015). Because contemporary perspectives involve using language for a particular purpose, they have been referred to as language-in-use (Lee et  al., 2013). Thus, the contemporary perspectives of language learning promote multilingual learners’ engaging in rigorous science experiences and rich language use through the mutually supportive nature of science and language (Lee et  al., 2019; NASEM, 2018). As multilingual learners engage in science and engineering practices (e.g., developing models, arguing from evidence, constructing explanations), they use language for the purpose of making sense of phenomena and problems through interactions with peers and their teachers. In this way, multilingual learners use language and other meaning-making resources purposefully while engaging in science and communicating science ideas (Lee et al., 2013). The focus is on “what language does” (i.e., the functional use of language for a purpose), in addition to “what language is” (i.e., structural elements of language, including vocabulary and grammar; Grapin et al., 2019). This shift in theoretical orientation supports the call for multilingual learners to use the widest possible range of meaning-making resources to communicate their increasingly sophisticated science ideas with growing precision over the course of instruction (for an example, see NASEM, 2018, Box 3–1 on pp. 64–65).

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Instructional Approaches The instructional shifts called for in the NGSS are expectations for all learners, and not just for high-achieving or English-profcient students, resulting in a goal of “all standards, all students” (Lee et al., 2015; NGSS Lead States, 2013b). Considering both the needs and the assets of multilingual learners specifcally, a consortium of language and content area communities came together under the auspices of the Understanding Language Initiative to provide an initial framework for integrating science and language with multilingual learners (Lee et al., 2013). Policy frameworks, such as the NGSS and the Understanding Language Initiative, provide guidance for shaping instructional approaches. More recently, the NASEM convened a panel of experts across language and STEM subjects and released the English Learners in STEM Subjects consensus report (NASEM, 2018). The report helps clarify what we have defned in Table 10.1 as contemporary perspectives on science education, with teachers expected to • • • • • •

Identify compelling phenomena and problems for their students to fgure out Engage students in disciplinary practices in STEM subjects Engage students in productive discourse and interactions with peers as well as the teacher Encourage students to use multiple modalities, including both linguistic and nonlinguistic modalities, and multiple registers as they move toward the use of specialized registers Leverage multiple meaning-making resources for communication, including physical objects, gestures, everyday language, home language, and translanguaging Provide some explicit focus on how language functions in the discipline (i.e., metalanguage or language about language)

These contemporary instructional approaches highlight several key features for how science learning and language learning can be mutually supportive (Lee & Stephens, 2020). First, these instructional approaches begin with attention to science learning and then integrate language learning as needed for learning science. Using language for the functional goal of doing and learning science in contemporary instructional approaches difers from building (pre-teaching) vocabulary and grammar as a prerequisite to learning about STEM subjects, as is common in traditional instructional approaches. This instructional approach is mandated by the ESSA as described earlier. Second, student engagement in varied forms of classroom discourse, interactions, multimodalities, and registers while doing and learning science points to the sweet spot where science and language intersect in mutually supportive ways. Finally, as multilingual learners engage in science and use language for science learning, they develop disciplinary language over time, as an outcome of doing science. This view of language for science further paves the way for deeper engagement in scientifc thinking, doing, and communicating. Over time, as multilingual learners develop more sophisticated understanding of science, they develop more disciplinary language to communicate science ideas with precision.

Methodology The framing of this chapter around the shifts from traditional to contemporary perspectives on both science learning and language learning (see Table 10.1) has led us to certain methodological choices for how to search the literature and how to represent it. Unlike a conventional systematic review based on keywords, we consider the methodology for this chapter to be an interpretive synthesis review of the literature. By this, we mean that the review and analysis are based on a conceptual framework, a strategically selected set of journals, and a set time period.

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Our intention is to highlight the shifts from traditional to contemporary perspectives on science learning and language learning that have occurred since the 2014 publication of the previous volume of this handbook. Our categorization is an initial attempt to operationalize this distinction both for science and for language. Integrating these perspectives is challenging in part because, while the science education community has reached a consensus about traditional and contemporary perspectives on science and science learning, the second language learning education community does not have a similar consensus for language and language learning. As science educators, we are not in a position to fully resolve the question of traditional and contemporary perspectives on language education; thus, we wish to err on the side of simplicity in our conceptualization of language perspectives. We ofer our characterization of contemporary perspectives on language education in hopes of extending a conversation between language educators and science educators. As the two science educators who were panelists on the NASEM consensus report, we feel the responsibility to push the feld toward agreed-upon contemporary perspectives on science and language education.

Selection of High-Visibility Journals Rather than conducting a comprehensive database search of the literature around science education and multilingual learners, we purposefully selected key journals and then conducted a manual search of all issues of those journals for the period 2014–2020. In identifying the journals to include, we selected journals that met at least one of the following two criteria. First, we focused on journals that represent the major professional associations that infuence our feld (e.g., the Journal of Science Teacher Education is the ofcial journal of the Association of Science Teacher Education). Second, we considered journals with a high impact on the feld of science education, operationalized as journals with an average impact factor above 3.0 during the time frame of our review. We reviewed a total of 11 journals, organized into 3 groups. We describe them here in terms of fve elements: journal name, criteria met for inclusion, number of issues per year, purpose or focus of the journal, and types of articles the journal publishes. We note as a limitation of this approach that these journals obviously do not fully capture what has been published on this topic during the time frame in question. They do, however, capture the most visible and impactful work on the topic that continues to shape the feld. The frst group of journals are specifc to the feld of science education. In this group we included four journals. The International Journal of Science Education (IJSE) is the journal of the European Science Education Research Association. IJSE publishes 18 issues per year, focuses on international contexts and issues in science education, and publishes a mix of empirical studies and conceptual/ theoretical articles. The Journal of Research on Science Teaching (JRST) is the journal of the NARST organization. JRST publishes ten issues per year, mostly addresses US issues in science education, and almost exclusively publishes empirical studies. The Journal of Science Teacher Education (JSTE) is the journal of the Association of Science Teacher Education. JSTE publishes eight issues per year; focuses on the initial preparation and ongoing professional development of science teachers, primarily in the US context; and publishes primarily empirical studies. Science Education (SE) is not associated with a specifc professional organization but has a high impact factor both currently and historically. SE publishes six issues per year; addresses a broad range of issues in science education, mostly within the US context; and includes a range of empirical and conceptual studies, essays, reviews, and commentaries. We currently regard these four journals to currently be the most infuential in science education. Second, we considered a group of journals we conceptualized as high-visibility journals in topical areas in which the connection to science and multilingual learners should be evident. We included four journals in this group. The Journal of the Learning Sciences (JLS) is the journal of the International Society of the Learning Sciences. JLS publishes four issues per year; focuses on topics and issues

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related to learning from across the disciplines, including science; and publishes mostly empirical studies, with occasional conceptual/theoretical articles. The Journal of Teacher Education (JTE) is the journal of the American Association for Colleges of Teacher Education. JTE publishes fve issues per year; focuses on the initial preparation and ongoing professional development of teachers broadly, primarily in the US context; and publishes a mix of empirical studies, theoretical papers, and editorials. The TESOL Quarterly (TQ) is the journal of the TESOL international association. TQ publishes four issues per year; addresses issues central to teaching English to speakers of other languages, from across international contexts; and includes a range of empirical and conceptual studies, essays, reviews, and commentaries. The Journal of Science Education and Technology (JOST) is not associated with a specifc professional organization but has a high impact factor and addresses the critical and fast-growing topic of technology in science education. JOST publishes six issues per year, addresses applications of technology in the teaching and learning of science, is international in scope, and publishes predominantly empirical studies. Third, we considered the three major journals of the American Educational Research Association (AERA) as high-visibility journals that address the full range of topics in educational research and have an infuence on all education felds, including science education and language education. This group of journals included the American Educational Research Journal (AERJ), Educational Researcher (ER), and Review of Educational Research (RER). AERJ publishes six issues per year, addresses topics of broad interest to the education research community, and publishes predominantly empirical studies. ER publishes nine issues per year; addresses timely topics of broad educational relevance, primarily in the US context; and publishes a mix of conceptual studies, essays, reviews, and commentaries. RER publishes six issues per year, addresses topics of broad interest to the education research community, and publishes only literature reviews. When taken together, these 11 journals provide a robust look at how the topic of science education with multilingual learners has been represented in the research literature, what subtopics and themes have been most prominent, and how the shift from traditional to contemporary perspectives on science learning and language learning has appeared in the literature.

Review of Journal Articles For the 11 journals we identifed, both authors conducted a manual search of every volume and issue from 2014 through 2020. We chose this time frame both because the last volume of the handbook ended with literature in 2013 and because 2013 was when the NGSS was newly published, representing a turning point in the feld of science education. We completed our search with the end of 2020 to include the last full year possible for this chapter, given that only a fraction of 2021 publications are available at the time of this writing. Manual searching allowed us to see the full landscape of research over this period and the major ideas in these felds as represented in high-visibility journals. In this way, we got a clear perspective on how the topic of science education with multilingual learners ft within science education research in this time frame, as well as how the new generation of policies and framing documents has infuenced the relevant research literature. A total of 104 articles were identifed as relevant to our topic, including editorials, conceptual pieces, and empirical studies in contexts that spanned the globe. Table 10.2 shows a summary of these articles by journal and across years. For each journal and year, we report the number of included articles in the format T/C, where T indicates the number of articles that take a traditional perspective on either science learning or language learning or both, and C indicates the number of articles that take a contemporary perspective on both science and language learning. As described earlier, and operationalized in Table 10.1, we defne the contemporary and traditional perspectives based on the NASEM (2018) consensus report and subsequent elaboration in Lee et al. (2019). We classify publications that adopted a mixed approach (e.g., contemporary science with traditional language)

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Multilingual Learners in Science Education Table 10.2 Included Publications by Journal, Year, and Perspective  

2014

2015

2016

2017

2018

2019

2020

Total

Traditional (T)/ Contemporary (C) (Yearly total)

16/4 (20)

6/3 (9)

6/4 (10)

6/3 (9)

5/5 (10)

7/10 (17)

13/16 (29)

59/45 (104)

Journal of Research in Science Teaching

2/1

2/2

2/1

0/0

2/0

0/1

2/2

10/7 (17)

Science Education

1/0

1/1

0/0

1/0

0/4

1/3

1/3

5/11 (16)

International Journal of Science Education

5/1

0/0

1/1

1/2

1/0

4/2

7/5

19/11 (30)

Journal of Science Teacher Education

4/1

0/0

1/1

3/0

0/0

1/2

0/1

9/5 (14)

Science education journals

43/34 (77)

7/4 (11)

Related topical area journals Journal of the Learning Sciences

0/0

0/0

0/0

0/0

0/0

0/0

0/0

0/0 (0)

Journal of Teacher Education

0/0

0/0

0/0

0/0

0/0

1/0

2/1

3/1 (4)

TESOL Quarterly

1/1

0/0

0/0

0/0

1/0

0/1

0/1

2/3 (5)

Journal of Science Education & Technology

0/0

1/0

0/0

0/0

1/0

0/0

0/0

2/0 (2)

Educational Researcher

0/0

1/0

0/0

0/1

0/1

0/1

0/1

1/4 (5)

Review of Educational Research

2/0

0/0

1/0

1/0

0/0

0/0

0/0

4/0 (4)

American Educational Research Journal

1/0

1/0

1/1

0/0

0/0

0/0

1/2

4/3 (7)

9/7 (16)

AERA Journals

as traditional due to our understanding that contemporary perspectives on science and language are mutually supportive, with both necessary to push the feld toward the integrated vision of science and language learning advocated in this chapter. In the fndings sections, we disaggregate and discuss the diferences between studies that take a traditional perspective on language but a contemporary perspective on science from those that take a contemporary perspective on language but a traditional perspective on science. In Table 10.2, however, we have collapsed these for simplicity such that a traditional perspective on science, on language, or on both are grouped together in the traditional perspective. The two authors reviewed all studies, categorizing each study as taking a contemporary or traditional perspective for science and language separately. Initial disagreements in classifcation were reconciled through discussion.

Themes and Organization of Findings In examining Table 10.2, four themes are evident. First, when considering the total number of articles by year across all journals, a peak in 2014 is followed by a decline in 2015, a low plateau from 2016 to 2018, and then a substantial upswing in the fnal two years of 2019 and 2020. We see the peak in 2014 as representing interest in this topic of linguistic diversity in science learning that had been on the rise due to policy shifts and reports on changing demographics in the early 2010s. Many studies from this period were reviewed in the chapter from the prior edition of the handbook. After a decreased focus from 2015 to 2018, attention to multilingual learners, and equity issues in science education more broadly, began increasing again in 2019 and surged to new highs in 2020. Second, when looking across the three groups of journals (science education, related topical areas, and broad AERA journals), the vast majority of the research on science education and multilingual learners has consistently been published in the science education journals. Little of this work has spilled over into either the related topical area journals or the broad interest AERA journals.

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Third, an overall pattern can be seen in the separation of articles into traditional and contemporary perspectives on science and language. While both contemporary and traditional perspectives can be found across the years, there has been an overall shift from predominantly traditional perspectives in the earlier part of this time frame to predominantly contemporary perspectives in the past few years. Finally, a pattern that cannot be seen in the table based on frequency counts is the variation in topics that are the focus of the publications on science education with multilingual learners. We initially sorted the publications into the following topics: science learning, science instruction, science assessment, science curriculum, science teacher initial preparation, science teacher professional development, science standards, family engagement through science, and access to science learning. After taking the quantity of studies into account for each topic, we focused on the four most frequent topics: science learning, science instruction, science assessment, and science teacher education (combining initial preparation and continued professional learning). In the fndings section, for each of these four topics, we highlight what the shift from traditional to contemporary perspectives looks like. We briefy describe the less frequently addressed topics at the beginning of the fndings section, with all relevant references included. We begin with these less frequent topics because we see them as emerging themes that require increased research attention moving forward. As we noted in the earlier discussion of the evolving terms used to describe multilingual learners, the word choices used in this area of scholarship continue to evolve in ways that refect changes in the feld. In the fndings section, we have applied two conventions in our use of terms. First, when we describe studies, we generally use the terms that are used by the authors of those studies. When we interpret studies, however, we use the terms that represent contemporary perspectives. For example, the term “academic language” is highlighted in multiple studies and continues to be used in documents that guide policy and practice, such as ESSA and edTPA (a common teacher performance assessment). However, we see the rigid dichotomy that often defnes academic language in opposition to everyday language as a traditional perspective. Similarly for science, the term “science inquiry” continues to be a central construct in some current studies. While a focus on science inquiry was a valuable improvement for science learning in the prior wave of reforms, we now see inquiry as a traditional perspective that is being replaced by a focus on science and engineering practices. Thus, we avoid the terms academic language and science inquiry in our interpretations of the studies, preferring the terms “disciplinary registers” and “science and engineering practices”. Second, when key terms in the literature on multilingual learners (e.g., translanguaging, multimodality, register) are frst used in descriptions of studies, we explain these terms in the context of science education. Our intent is to establish common language and shared understanding of contemporary perspectives on multilingual learners in science education. For each of the four major topics of fndings we began by characterizing themes in the studies that were identifed as using a traditional perspective. If either science or language was conceptualized from a traditional perspective, we believe that this limited the efectiveness of the other feld to build contemporary understanding, and thus we categorized the study as traditional. For traditional perspectives, we highlighted one study within each theme and then provided references for the other studies, as these perspectives are well established in the feld and do not require as much elaboration. In contrast, when studies took a contemporary perspective on both science and language, these perspectives were mutually reinforcing. We provided a more detailed discussion of the studies that worked from contemporary perspectives on both science and language. As these perspectives are still emerging, a broader range of examples can highlight the research directions that the feld is currently taking. In describing each study, we highlighted those aspects that resonated with contemporary perspectives on science and language rather than describing the research methods or results, as might have been done in a comprehensive literature review.

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Findings About Less Frequent Topics Several topics in our corpus of literature did not include enough publications to categorize thematically. For each of these topics we provide a summary of the research in that topic area. In some cases, these are emerging topics that we expect will gain greater prominence moving forward. In other cases, these topics may remain peripheral. We note that, as for all the topics covered in this review, there are additional relevant publications in journals that are outside the bounds of our search for this chapter. Readers wishing to explore these topics further will fnd relevant literature beyond what is referenced here. Because of the limited number of studies and the limited treatment they are given in this chapter, we do not separate these publications by contemporary and traditional perspectives. We briefy describe these less frequent topics before moving on to the four major topics with greater representation in the literature. The frst theme involves science curriculum with multilingual learners. These publications included conceptual papers framing characteristics of curriculum materials to support multilingual learners in science as well as empirical studies that tested specifc curriculum materials. Publications that focused on the role of curriculum with multilingual learners included Lee et al. (2019), Smith et al. (2017), and Kibler et al. (2014). Publications that focused on the use of particular curriculum materials with multilingual learners included Meyerhöfer and Dreesmann (2019a), TerrazasArellanes et al. (2018), Llosa et al. (2016), and Lee et al. (2016). We note that the research by Lee and colleagues illustrates the transition from traditional perspectives (Lee et al., 2016; Llosa et al., 2016) to contemporary perspectives (Lee et al., 2019) in work by the same research team. A second theme involves science standards with multilingual learners. This body of work was represented by a series of essays published by Lee in Educational Researcher. Taken together, these publications argue for the need to clarify and align contemporary perspectives on both science and language through standards that can guide policy, research, and practice. The series of essays included Lee (2017, 2018, 2019) and Lee and Stephens (2020). A third theme involves family engagement in science with multilingual learners. While there is ample research in the broader literature on family engagement to support science learning with students from marginalized groups, the topic is poorly represented in the high-visibility journals that are the focus of this chapter. The two studies we identifed both address the role of informal science learning spaces for supporting multilingual families in engaging with science, in one case through an informal science center (Weiland, 2015) and in the other case in a science museum (Dawson, 2014). A fnal theme involves access to science learning for multilingual learners. Studies in this theme fell largely within the area of policy analysis and typically used large data sets to look at broad questions of access resulting from policies and practices, such as tracking, types of supports provided, and assessments of readiness for grade-level content. Studies of the efects of tracking on access for multilingual learners included Kangas and Cook (2020), Umansky (2016), and Kanno and Kangas (2014). Studies that looked at other aspects of access to science learning for multilingual students included Johnson (2020), which considered the role of summer credit recovery programs, and AdamutiTrache and Sweet (2014), which considered assessments of readiness for grade-level content. We now turn to the four topics that accounted for most of the studies we identifed in this review, starting with the topic of science learning.

Findings About Science Learning The most common topic of the articles reviewed was a focus on science learning, with 30 of the 104 total publications attending primarily to student learning. We start from traditional perspectives, organized into studies that were traditional for science but contemporary for language, those that were traditional for language but contemporary for science, and those that were traditional for both.

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We discuss one sample study from each group and then identify the other studies within that group. Following that, we organize the studies that evidenced contemporary perspectives on both science and language into themes, discussing each study specifcally in terms of how it represents contemporary perspectives.

Traditional Perspectives Of the 30 publications focused on science learning, 15 refected traditional perspectives on science or language or both. Seven studies presented contemporary perspectives on language learning but indicate traditional perspectives on science learning. As one example, Charamba (2020) examined eighth-grade students’ use of their multiple languages in a South African science classroom. The author used a contemporary translanguaging perspective on multilingualism to show how middle school students studying natural sciences used both their home language resources and English language resources fuidly and purposefully as they engaged in peer group discussions and teacher-led discussions to support multilingual language development during science learning. The concept of translanguaging was proposed by García and Wei (2014) as a theoretical and pedagogical lens for seeing the ways in which multilingual learners draw from all their available semiotic resources to make meaning. Otheguy et al. (2015) defned translanguaging as “the deployment of a speaker’s full linguistic repertoire without regard for watchful adherence to the socially and politically defned boundaries of named languages” (p. 281). In his study, Charamba adopted this contemporary perspective on language-in-use, but science was still presented as a body of extant canonical knowledge that should be discussed to build understanding rather than experienced. As a result, the learning opportunities fostered by the contemporary approach to language were limited by the traditional approach to science. Other studies that likewise utilized a contemporary perspective on language but a traditional perspective on science included Meyerhöfer and Dreesmann (2019b), Ünsal et al. (2018), Dafouz et al. (2018), Zhang (2016), Seah et al. (2014), and Lo and Lo (2014). Four of the studies on science learning conceptualized students’ language learning in traditional ways while considering science learning in contemporary ways. These studies tended to make a distinction between everyday language and academic language in science, conceptualizing these as being two distinct language registers. Register refers to the various ways people use language while engaging in diferent kinds of activities for diferent purposes. While a traditional perspective on registers views academic language and everyday language as both distinct and hierarchical, a contemporary perspective on language registers sees everyday language and academic language as a continuum and recognizes all language as being useful for doing science in diferent contexts and at diferent points in the process of science learning. For example, Tretter et al. (2019) created extended science supports, such as planetarium-based visualization activities, and extended literacy supports, such as learning from science-focused graphic novels and trade books. The planetarium-based visualization activities focused students on making sense of phenomena in the night sky, like how astronomers engage in their work. However, the linguistic focus was on building academic language as a privileged register for expressing science learning that was distinct from everyday language. Because the literacy supports were conceptualized and utilized separately from science supports, with a focus on explicit structured vocabulary and reading instruction to build academic language, learners may come to see these two sets of resources as supporting separate learning goals, and teachers may come to see pre-teaching of vocabulary and language supports as necessary for students to engage in science. Other publications that highlighted contemporary perspectives on science learning but failed to connect this with contemporary views of language included Lan and de Oliveira (2019), Jackson et al. (2019), and Van Laere et al. (2014). The fnal theme included four studies that conceptualized both science and language from traditional perspectives. For example, Ryoo (2015) conducted a quasi-experiment in which ffth-grade

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multilingual learners were assigned to either an everyday English approach or a textbook approach as they engaged in technology-enhanced instruction about photosynthesis and respiration. The students who were taught the concepts frst in everyday, conversational English before learning contentspecifc scientifc terms developed a more coherent understanding of abstract concepts related to photosynthesis and respiration than the students who were taught the same concepts and vocabulary simultaneously using a textbook approach. This study highlights that there can still be valuable lessons learned from research that has taken traditional perspectives while also pointing to the limitations of traditional perspectives on science and language. Other studies that ft into this theme included Ryoo et al. (2018) and Ryu (2015a, 2015b) that focused on how multilingual learners were positioned based on their content knowledge and discursive practices. These studies maintained rigid distinctions between everyday language and disciplinary language for science learning, refecting traditional perspectives on language and science.

Contemporary Perspectives We turn to studies that demonstrated contemporary perspectives on both science and language learning, accounting for the other 15 of the 30 publications in this topic, which are categorized into three themes: centering science and engineering practices to guide language use by engaging in those practices, linguistic and nonlinguistic forms of multimodal communication for doing science together, and translanguaging approaches to support multilingual language development through content learning.

Centering Science and Engineering Practices to Guide Language Use by Engaging in Those Practices Several studies centered on one or more of the science and engineering practices to guide how language develops by engaging in those practices. In one example, a study by Harper (2017) focused on the practice of constructing explanations and designing solutions to promote bilingual language development toward both cultural sustenance and mastery of this practice. Karen (a minority ethnic population from Burma) elementary students in an afterschool club co-taught with a Karen community member engaged in science and engineering practices and cultural explorations grounded in their community. Photovoice methodology was used to engage students in science and engineering in their lives and communities, as they used the photos to construct scientifc explanations based on evidence. The Karen co-teacher led students in weekly Karen language lessons that were tied both to cultural knowledge and scientifc explanations. The study revealed a pattern of emerging agency by Karen students regarding both constructing explanations and maintaining and strengthening their profciency in the Karen language. The author suggested that place-based science embedded within a cross-cultural learning community can empower refugee students to construct their own hybrid knowledge that can support both language learning and science learning. Three studies used the practice of engaging in argument from evidence to support students’ language use. In one example, González-Howard and McNeill (2016) maintained that despite expectations that NGSS will result in all students engaging in language-rich opportunities that science and engineering practices can support, most science educators are not prepared and do not have the needed resources to create such opportunities with multilingual learners. The authors presented a case study of multilingual students’ argumentation practices in sheltered English immersion in a middle school science classroom. They found that intentional student grouping played a critical role for multilingual students to have robust opportunities to engage in science argumentation. When students worked in pairs with partners who shared their home language and were supported in utilizing

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both their home language and second language (i.e., English) as linguistic resources for engaging in science discourse, their engagement in science argumentation was enhanced in ways that supported both conceptual development and language development. In another study with a focus on argumentation, Wilson-Lopez et al. (2018) provided examples of how engineering can serve to validate multilingual students’ experiences as valuable assets for doing science. By focusing on the role of engineering in solving social problems, the researchers found that participants’ multilingualism became an important asset for accomplishing the real-world goals of engineering. For example, participants’ ability to communicate in Spanish with community stakeholders was a valued form of intellectual and social capital because this communication enabled the students to create designs that met actual community needs rather than focus on designs solely based on scientifc thinking. Using engineering as an example of a discipline that requires responsiveness to clients, multilingual students came to see the ability to communicate orally and in writing with linguistically diverse clients as an asset that empowered them to consider scienceand engineering-related aspirations more seriously. The researchers concluded that as a vehicle for learning science and language, engaging in engineering practices provided opportunities for multilingual youths’ science- and language-related resources to be recognized, legitimized, and put to good use. Hand et al. (2018) also centered the practice of arguing from evidence in a study of the impact of an argument-based approach to teaching science on elementary students’ science learning and critical thinking. In a large-scale, cluster-randomized study involving 48 schools and approximately 10,000 students, annual standardized tests assessing science content knowledge plus a supplemental assessment of critical thinking were used to assess the argument-based intervention. While not focused explicitly on multilingual learners, they were one of two student subgroups who benefted from the intervention. While the researchers found no statistically signifcant gains for science content knowledge, they did fnd statistically signifcant evidence that the intervention was associated with improved critical thinking, with the strongest gains found for multilingual learners and students with special needs.

Linguistic and Nonlinguistic Forms of Multimodal Communication for Doing Science Together A second theme within the contemporary perspective involves students leveraging linguistic and nonlinguistic modes of communication as they engage in science learning, with particular attention to the underappreciated importance of nonlinguistic modalities. Multimodality indicates that in addition to linguistic modalities, we use nonlinguistic resources for communication, including gestures, visual displays (e.g., diagrams, graphs, symbols), and other representations (e.g., artifacts, formulas) that ofer afordances for meaning making (Bezemer & Kress, 2015). The most progressive studies within this theme considered nonlinguistic modalities that stood in for or existed beyond language as they supported disciplinary practices. In a case study of one multilingual newcomer student, Siry and Gorges (2020) intentionally looked beyond written and spoken language to describe the diverse range of communicative resources that this student used to express her understandings during an investigation of sound. The authors highlighted the multiple modalities this student used to make her understandings visible to her peers and teacher, including gestures, facial expressions, and drawings, both in whole-class discussions and small-group interactions. This study built on an earlier publication (Wilmes & Siry, 2018) that used a conceptual framing of interaction rituals to describe another newcomer student’s interactions with peers during small group science investigations. The authors found that this student’s persistent use of nonverbal participation strategies resulted in higher levels of group inclusion and participation with his classmates. Together, these two studies highlight the often-overlooked value of silent, embodied participation in

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inquiry-oriented instruction that is possible for multilingual students who are new to the language of instruction. Similarly, Grapin (2019) argued that despite seemingly widespread agreement in science education on the value and importance of multimodal engagement for supporting science learning, there is considerable variability in how multimodality is conceptualized. Specifcally, Grapin pointed to distinctions between understandings of multimodality in multilingual education and in disciplinary areas, such as science. He proposed to clarify these distinctions through a framing of weak and strong versions of multimodality. Weak multimodality is conceptualized as privileging language while relegating nonlinguistic modalities to the role of scafolds for language development with multilingual learners. This is similar to the role of home language in transitional models of bilingual education, where the goal is to transition students from the home language to English as quickly and efciently as possible. In contrast, Grapin’s strong version of multimodality positions multiple modes of communication as essential for engaging in disciplinary practices, with nonlinguistic modalities having inherent value beyond serving as a transitional scafold toward language. Providing an example of strong multimodality, the author analyzed fourth-grade science work samples to illustrate how students utilized multimodality during scientifc modeling. Grapin concluded that science educators must embrace the strong version of multimodality as part of a contemporary perspective on disciplinary learning, as all students are now expected to leverage their full meaning-making repertoires while engaging in disciplinary practices. Other studies in this theme looked at linguistic and nonlinguistic modalities working together in support of science learning. Wu et al. (2019) described students communicating through hybrid language, by which they mean communication through multiple modalities, including natural language, visual representations, mathematical expressions, and manual operations. The purpose of this study was to investigate how eighth-grade multilingual students used hybrid language both to demonstrate their knowledge of science concepts related to soil erosion and to engage in the science practice of argumentation. The researchers found that multilingual students who more fully integrated multiple modalities into their hybrid language, such as by clearly connecting written claims with visual representations, tended to construct stronger arguments than those students who did not regularly integrate multiple modalities in their science meaning making. Similarly, Britsch (2020) examined how linguistic and nonlinguistic communication were mutually supportive as semiotic resources for both science and language learning when intentionally scaffolded for eighth-grade multilingual learners during a unit on surface tension. However, this study also pointed to challenges that arise when the teacher and students have diferent ideas about the role of nonlinguistic communication. Britsch found that the teacher saw nonlinguistic communication primarily as a transitional tool for keeping students engaged and on task (Grapin’s weak multimodality), while the students were engaging in nonlinguistic communication in attempts to build conceptual understanding (Grapin’s strong multimodality). The researcher concluded that whether focusing on linguistic or nonlinguistic modalities, students need communicative scafolding that explicitly helps them link what they are doing with science to the conceptual understanding they are meant to gain. Finally, Williams and Tang (2020) conducted a review of 40 empirical studies published between 1995 and 2019 that seemed to rely at least partly on the afordances of nonlinguistic modalities to support multilingual students’ science learning but that did not prominently feature this role of nonlinguistic modalities in the reported results. The review sought to reframe these research fndings, frst by calling into question the traditional language assumptions that guided the interpretations of nonlinguistic modalities in these studies and then by providing an updated interpretation through a contemporary perspective on language. The authors ofer educators new ways to think about the role of nonlinguistic modalities in their teaching while presenting multilingual learners with alternative avenues for sense-making.

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Translanguaging Approaches to Support Multilingual Language Development Through Content Learning Translanguaging pedagogies, as described earlier, are generally associated with contemporary perspectives on language learning but do not necessarily imply contemporary perspectives on science learning. Several recent publications combine contemporary perspectives on science with a contemporary translanguaging perspective on language development. Suárez (2020) used translanguaging both theoretically and pedagogically to frame how multilingual elementary students in an afterschool program used a broad range of semiotic moves that leveraged ideas expressed in both English and Spanish to engage with science practices as they investigated phenomena involving electricity. By attending to these multilingual students’ disciplinary engagement, the author challenged assumptions about what educators should expect from multilingual elementary students in terms of their science sense-making and communication. Studies of this kind show how to move beyond binary views of named languages that too often result in pedagogical models that promote rigid linguistic boundaries and toward a fuid and holistic conception of linguistic resources. A similar study by Karlsson et al. (2019) considered how a translanguaging science classroom for elementary students in Sweden supported their use of frst and second languages as they built semantic relationships among science understandings over time and across science topics. Using what the authors referred to as linguistic loops between multiple languages and discourses, the study found that consistent support over time for translanguaging in the science classroom created resources and opportunities for students to engage in joint negotiations about varied scientifc content. Translanguaging allowed students to relate and contextualize these science concepts to their prior lived experiences. Karlsson and colleagues argued that translanguaging science classrooms can provide opportunities for students’ diverse cultural and linguistic resources and experiences to interweave with school science when multilingual students are encouraged and guided to use all available language resources. Msimanga and Lelliott (2014) described how tenth-grade chemistry students in South Africa engaged with science content during small-group work in which they were encouraged to use their home languages as well as English. Students had robust discussions as they experienced discrepant events in chemistry and were found to increasingly rely on their home language in these peer discussions as the science content became more complex. The researchers also examined the common concern expressed by teachers who do not speak the home language of their students, that if students are allowed to speak in their home language, they will not remain focused on the topic of study or will not engage meaningfully with the academic content. In fact, the researchers found the opposite to be true, with over 90% of student talk in their home language being on task and science focused. Two other publications that we grouped in this theme referred to supporting students in mixing their linguistic resources without explicitly adopting or mentioning a translanguaging framework. For example, Ryu (2019) described how Korean–English bilingual students mixed languages as they negotiated their participant positions and their understanding of science during a community-based after-school science program. Similarly, Gamez and Parker (2018) discussed the role that student language brokering (bilingual youth translating for less bilingual adults) and code-switching (speakers alternating between diferent languages or registers) played in a reform-oriented science classroom as mediators of the science identities and practices that newcomer students were able to enact.

Findings About Science Instruction The second most prevalent topic was science instruction with multilingual learners, with 23 of the 104 total publications attending to this topic. We begin by briefy characterizing themes in the publications that were identifed as using a traditional framing of either science, language, or both. These

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accounted for 17 of the 23 publications for this topic. We then provide a more detailed discussion of themes from the six publications from contemporary perspectives on both science and language.

Traditional Perspectives Four studies demonstrated contemporary perspectives about science while maintaining a traditional perspective on language. As one example, Enderle et al. (2020) considered how teachers of deaf and hard-of-hearing students who used American Sign Language (ASL) for communication were supported in developing strategies for engaging in the science and engineering practices of the NGSS. While the researchers pointed to the importance of all students, including those with special needs, engaging in the science and engineering practices as an expectation of science learning, the study focused on the lack of current disciplinary vocabulary in ASL as the biggest limitation and most important resource for supporting signing students in using the practices. While consideration of ASL and other sign languages as examples of multimodal language provides a generative context for thinking in new ways about language for science and multimodal communication, this study also shows that vocabulary continues to exert an oversized infuence on eforts to support multilingual students’ science sense-making. Other studies of science instruction that similarly presented a contemporary perspective on science but a traditional perspective on language included Block (2020), Meyer and Crawford (2015), and Weinburgh et al. (2014). Seven studies on the topic of science instruction highlighted a contemporary perspective on language but a traditional perspective on science. For example, Mavuru and Ramnarain (2020) examined science teachers’ experiences as they attempted to use students’ multiple home languages during instruction with ninth-grade students in natural sciences classes in three South African high schools. Using classroom observations and teacher interviews, the authors pointed to both afordances and challenges of integrating learners’ home languages with the language of instruction (in this case, English). Teachers successfully made use of learners’ home languages to facilitate conceptual understanding, while also encouraging their students to engage in translanguaging practices to increase confdence needed to express personal views about science concepts and to challenge the ideas of their peers. Teachers experienced pedagogical difculties, however, as they struggled with their own limited scientifc language in students’ home languages. These nuanced refections on instructional interactions around language were, however, paired with a traditional representation of science, with instruction centering teacher-led discussions of concepts rather than engagement with natural science phenomena through science practices. Other studies that similarly presented a traditional perspective on science blended with a contemporary perspective on language included Amin and Badreddine (2020), Salloum and BouJaoude (2020), Bonello (2020), Seah and Silver (2020), Ryoo and Bedell (2019), and Seah and Yore (2017). Finally, we identifed six studies focused on science instruction that refect traditional perspectives both on science and on language. We note that while some of these publications were empirical studies, most were thematic reviews that, while published in the time frame of 2014–2020, predominantly reviewed studies from before this time frame and thus prior to the clarifcation of contemporary perspectives. Such review studies included Pyle et al. (2017), Barrett and Liu (2016), Frantz et al. (2015), and DiCerbo et al. (2014). The empirical studies in this category were similarly published at the beginning of the NGSS era, when contemporary perspectives were still emerging. For example, a study by Varelas et al. (2014) examined how a third-grade teacher used read-alouds of informational science books combined with related hands-on explorations to provide Latinx students with multiple opportunities to learn about earthworms over a weeklong exploration. The researchers analyzed student writing, drawing, and classroom discourse about earthworms and their features and behaviors after the read-aloud sessions, observations, and experiments with earthworms. The study highlights the instructional value of integrating informational texts and hands-on explorations to ofer students

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multiple points of access to science. While this study contributes useful insights into the integration of textual and experiential instructional resources, the uses of language and of science in this study both align with the traditional perspective. Similarly, Glass and Oliveira (2014) focused on teachers’ use of science read-alouds to support oral discussion of science concepts.

Contemporary Perspectives We turn now to publications that combined contemporary perspectives on science and language to support science instruction with multilingual learners. Our reading of the six studies in this category highlighted two themes: studies focusing on pedagogical tools that support contemporary instruction and studies focusing on teachers’ fexible decision-making to guide contemporary instruction.

Pedagogical Tools That Support Contemporary Instruction The studies focusing on pedagogical tools embedded contemporary perspectives on science and language into instructional tools that were used to support the work of both teachers and students. For example, Wilmes and Siry (2020) described the use of an open-ended science notebook format as a pedagogical tool for supporting multimodal interactions as fourth-grade multilingual students constructed explanations of evaporation and condensation while attempting to solve a science mystery. The researchers highlighted multiple ways in which the science notebook format served as a semiotic social space in which students worked together to elaborate both their language and their conceptual understanding of how condensation formed inside a camping tent. Most relevant to our discussion of instructional supports is how diferent student groups used the semiotic social space of the notebooks to plot their own unique pathways of understanding (what the researchers called paths of resemiotization) as students employed multiple communicative resources to build an explanatory storyline. The study pointed to the benefts of using open-ended science notebooks with plurilingual students as they investigate science phenomena. Similarly, González-Howard et al. (2017) examined one middle school science teacher’s instructional strategies as she engaged her class of predominantly multilingual learners in argumentationfocused instruction through investigations of microbiomes and metabolism. This focal teacher adopted multiple tools to provide students with language supports for both the structural and dialogic components of argumentation as students considered how changes in the microbiomes inside their bodies could afect their health in positive and negative ways. More specifcally, this teacher included language supports, such as peer-produced exemplars, argumentation templates, and rubrics, that explicitly focused on building students’ use of argument structures as they applied those tools in their dialogic interactions. The researchers suggested the need for new instructional tools with embedded language supports that make the rationale for argumentation and other science practices more explicit. Although the use of language and literacy supports is typically associated with a traditional perspective on language, in this study, these supports were used to specifcally engage in the science and engineering practice of arguing from evidence. Pedagogical tools that support contemporary science instruction for multilingual learners include the use of physical artifacts as tools for science sense-making. A common traditional English for speakers of other languages (ESOL) instructional strategy involves teachers demonstrating concepts or ideas using realia, or real-world artifacts. Extending this strategy in a contemporary way involves having students use physical artifacts to explain science phenomena, using manipulation of the objects in combination with limited oral language to explain their observations. Ünsal et al. (2020) demonstrated the utility of such an approach in a study of third-grade multilingual students in Sweden studying phenomena involving electricity (such as static electricity causing black pepper to jump). They concluded that when students’ profciency in the language of instruction limited their possibilities for

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making and expressing meaning, manipulating physical artifacts enabled them to engage in practices that both built science understanding and facilitated explanation of that understanding.

Teachers’ Flexible Decision-Making That Guides Contemporary Instruction The second theme within contemporary perspectives on science instruction focused on the need for teachers to take ownership of instructional strategies and adapt them in ways that recognize their expertise in knowing their own students and school communities as well as the resulting resources needed for constructing scientifc sense-making. A study by Buxton and Caswell (2020) used data from teacher implementation logs and interviews to examine how middle and high school science teachers took resources from an ongoing professional learning project and used them to make nuanced and strategic adaptations to their district model for meeting the needs of multilingual learners in science. The study found that teachers moved away from implementing existing instructional practices based on generalized principles or formal student classifcations, such as matching the reading levels of classroom resources with students’ English-language profciency levels. Instead, teachers in the project began taking a more nuanced approach to adopting, adapting, and rejecting practices, such as by using strategic and fexible groupings of students based on actual needs, assets, and personal knowledge about individual students in the specifc disciplinary context of their science teaching. In an earlier study from the same project, Buxton et al. (2015) outlined a conceptual framework of teacher engagement and enactment grounded in teacher agency and multiplicities of enactment as an alternative to framing implementation research in terms of program adherence and fdelity of implementation. Using longitudinal data from their professional learning project for science and ESOL teachers, this study highlighted how individual teachers negotiated power structures in their schools and exerted personal and collective agency in ways that pushed for integrating contemporary perspectives on both science and language. Teachers worked with members of the research team to adapt science investigations they already taught, to better integrate an emphasis on science practices and new linguistic and multimodal supports that built on their students’ existing linguistic and cultural assets. Pushing this idea further in a subsequent paper, Buxton et al. (2017) examined the limitations of improvement science for supporting teachers’ science instruction with multilingual students. Taking a postmodern turn, this study followed Latour’s (2005) thinking about the role of fexible actor-networks in shaping social constructs, such as professional learning communities. Buxton et al. proposed a new framing they called the mediation of associations to describe how teachers’ and researchers’ eforts to guide students’ exploration of science phenomena often resulted in unanticipated restructurings, with linguistic and material resources being repurposed and realigned through student-driven agency as well as through the material agency of nonhuman actors. In a diferent enactment of the theme of teacher agency to support science instruction with multilingual students, Swanson et al. (2014) examined how one high school science teacher engaged her multilingual students in the science and engineering practices of arguing from evidence and obtaining, evaluating, and communicating information during an integrated science course for frst-year high school students. The researchers found that through multiple years of teaching this course to multilingual students, this teacher had shifted away from viewing the teaching of language in science as a discrete curricular target (academic language). Instead, using the agency she felt as school department chair, she developed three distinct types of instructional supports to help her multilingual students develop and apply language through engagement in the practices: primary language support in Spanish, deliberate scafolds, and small-group instruction. The authors provided the example of these supports being used during investigations of sound waves as students reasoned about the claim that vibrations cause sound.

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Findings About Science Assessment While less prevalent than studies focused on science learning or science instruction, the topic of science assessment for multilingual learners represents a critical issue given the role that assessments play in driving the nature of teaching and learning in today’s schools. A total of ten publications attended primarily to assessment issues, with half adopting a contemporary perspective on both science and language. As with the prior sections, we begin with a brief characterization of the publications that were identifed as using a traditional framing, followed by a more detailed discussion of themes from the publications that took a contemporary perspective on both science and language.

Traditional Perspectives Studies that took a traditional perspective on language and/or science when considering the role of science assessments with multilingual students tended to focus on binary conceptualizations of academic language (as distinct from everyday language) and their role as a barrier to assessment performance for multilingual learners, needing to be addressed either by modifcations to the assessments or by pre-teaching of the language perceived as needed for success on the assessments. As one example, a study by Huerta et al. (2014) examined the use of a structured science notebook format and rubric as an assessment to measure academic language and conceptual understanding of ffth-grade multilingual students. Grounded in older concepts from second-language acquisition theory, such as the distinction between basic interpersonal communication skills (BICS) and cognitive academic language profciency (CALP; Cummins, 2008), the science notebook rubric in this study was based on two constructs that we categorized as traditional: a static view of academic language and a science focus on learning to restate disciplinary knowledge. Other studies that applied aspects of these traditional conceptions either of science or of language to the use of science assessments with multilingual learners included Noble et al. (2020), Aftska and Heaton (2019), Lyon (2017), and Siegel et al. (2014).

Contemporary Perspectives We identifed fve publications that combined contemporary perspectives on science and language to support science assessment with multilingual learners. Our reading of the studies in this category highlighted two themes around use of assessments: studies that focused on using the Framework and NGSS to guide assessment design and studies that applied critical language ideologies to argue for more equitable multilingual and multimodal science assessments.

Using the Framework and NGSS to Guide Assessment Design As discussed earlier, the Framework laid the foundation for our contemporary thinking about science, including the need to rethink science assessments to align with three-dimensional learning rather than just focusing on conceptual understanding. As Fine and Furtak (2020) pointed out, science teachers currently have few assessment resources for evaluating student learning that are aligned with contemporary perspectives on science. This challenge is even greater when contemporary perspectives on language are integrated as well. The researchers argued that new tools are needed to shape the design of assessments to account for contemporary perspectives rather than creating accommodations for multilingual learners as an afterthought. They proposed a literature-based framework for science assessment design for multilingual students that included a set of design principles to highlight the imperative of a new generation of science assessments aligned with the NGSS. Key components of their framework included attention to culture and language, alignment and rigor, tasks that focus on phenomena, clear objectives, and the integration of supportive scafolds.

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In a publication soon after the release of the NGSS, Mislevy and Durán (2014) provided one of the earliest analyses of how science assessments must evolve based on the goals of the NGSS for all students, including multilingual learners. They argued for closer attention to the design of tasks for both learning and assessment that are consistent with the vision of the NGSS for engaging all students in three-dimensional learning through use of phenomena while also attending to the differing communicative capabilities of multilingual learners. They argued that all assessment designers, whether teachers designing formative assessments for their own students or developers of large-scale standardized tests, need to consider how the novel features of the NGSS require increased attention to supportive structures, such as contextualization of assessment items and ways that technology can support new assessment tasks.

Language Ideologies and Science Assessments A second theme within studies using contemporary perspectives on science and language for science assessments involved critiques of assessment practices that attend to how language ideologies guide assumptions about multilingual learners. Lemmi et al. (2019) explained language ideologies as “assumptions, conceptions, beliefs, and rationalizations about language that we use to make meaning of the world” (p. 857). They went on to describe and apply two notions of language ideology with implications for the formative assessment practices that secondary school science teachers enacted with their multilingual students. Specifcally, the authors distinguished between language-exclusive ideologies that presumed only certain forms of language are expected and appropriate for use in science classrooms while other forms are not seen as appropriate and language-inclusive ideologies that presumed multiple forms of language use are useful and acceptable in science classrooms. They found that teachers who held language-exclusive ideologies focused their concerns about student language use on appropriateness, use of terminology, and formality of language. As applied to their formative assessments, these teachers expressed concerns with what they considered to be students’ lack of clear and appropriate communication. In contrast, teachers who held more language-inclusive ideologies expressed greater attention to the students’ ideas and to their use of audience-specifc communication and creative use of colloquialisms. Cardozo-Gaibisso et al. (2020) made a similar argument for the need to look beyond assessment scores as the primary way of communicating information to teachers about their students’ learning. The authors argued that the assessment feld has become trapped in a defcit view of multilingual students’ performance in part because our assessment reporting systems strip away evidence of students’ emergent science understandings and emergent language use by reducing the information to scores. The study used a multimodal and bilingual (Spanish–English) constructed response assessment of science and language for middle and high school students to show how a multilayered analysis of student responses can provide a more asset-based view of students’ emergent learning. The authors concluded that science educators would beneft from multidimensional ways of interpreting student performance on assessments. Taking a diferent approach to language ideologies in developing science assessments, Curran and Kitchin (2019) examined standardized test scores of science performance in the early elementary grades and corresponding science test score gaps by race and ethnicity. In many cases, they found the racial and ethnic test score gaps in science to be signifcantly larger than the corresponding gaps in reading and mathematics. The researchers tested a number of factors (such as reading activities, home activities, time spent in nature) that could possibly explain the diferences in the magnitudes of racial and ethnic disparities in performance on science standardized tests as compared to those in reading and mathematics (referred to as “gaps-in-gaps”). They found that students’ home language and immigration status explained the largest portions of the gap-in-gaps for Hispanic and Asian students. The researchers concluded that these fndings should push educators, policymakers, and

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assessment developers to consider both stronger early intervention eforts to enrich all children’s science experiences and more innovative science assessments that align with asset-oriented, rather than defcit-based, language ideologies.

Findings About Teacher Preparation and Professional Learning The fourth and fnal major topic in our review is science teacher education through both initial teacher preparation and ongoing teacher professional learning. While studies with preservice teachers and in-service teachers are often treated separately in the literature, we found much overlap in terms of the topics and approaches addressed, and thus we combined studies involving teacher education into one topic. Twenty of the 104 studies fell within this topic, with 13 studies aligned with traditional perspectives on science, language, or both, and 7 studies represented contemporary perspectives on science and language.

Traditional Perspectives Two studies of teacher learning took a contemporary perspective of language but a traditional perspective on science. A study by Ollerhead (2020) used cases of two preservice science teachers in Australia to examine the ways in which both the coursework and feld experiences in their teacher preparation program contributed to their developing thinking about the role of language in their science teaching. The approach highlighted an asset orientation to multilingual students’ science learning. A feld experience in a newcomer center for migrant students played a central role in the focal teachers’ evolving identities “as ‘embedders of literacy’ in their content teaching” (p. 2510). While the perspective on science in this study had some features of the contemporary approach, such as having students work toward an evidence-based explanation of a real-world event (a volcano erupting), much of the science learning was text based and focused on supporting students in gaining background knowledge from scientifc texts and then expressing those same ideas back in writing. That is, the focus was on how scientifc content could provide opportunities for targeted language and literacy support to facilitate multilingual students’ access to scientifc content knowledge. The study by Bacon (2020) likewise combined a contemporary perspective on language with a traditional perspective on science. Two studies of teacher education took a contemporary perspective on science but a traditional perspective on language. These studies positioned some form of academic language, defned structurally in opposition to everyday language, as a prerequisite for engaging in science learning. Jung and Brown (2016) highlighted the need for teachers to make academic language a key part of their lesson planning, arguing that students’ development of a strong command of academic language was a prerequisite to engage in the practices of science. To support preservice elementary science teachers in this goal, the researchers created the Academic Language Planning Organizer to support teachers in identifying academic language features, demands, and supports in alignment with the view of academic language as presented in the edTPA teacher performance assessment. At the same time, the study represented science in a contemporary way, aligned with the vision of the NGSS, as the preservice teachers planned lessons that engaged their students in using the science and engineering practices to explain phenomena, such as diferences in behaviors of worms and nightcrawlers or goldfsh and guppies. Another study that refected a traditional perspective on language combined with a contemporary perspective on science was Meier et al. (2020). Nine other studies within the topic of teacher preparation and professional learning were classifed as traditional for both science and language. For example, Leckie and Wall (2017) focused on supporting preservice teachers in learning to integrate a model of academic language into middle grades science units. The researchers operationalized academic language as having three domains (i.e.,

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vocabulary, syntax, and discourse), and they highlighted strategies for how teacher educators could support preservice teachers in integrating these three aspects of academic language into their lesson planning. Specifcally, the researchers studied how preservice teachers incorporated each aspect into science units they planned around topics of thermal energy, Newton’s laws, and the classifcation of living organisms. Building on the requirements of edTPA, the focus was on how preservice teachers selected language functions, demands, and supports to develop students’ understanding of scientifc concepts. There was no focus on students learning to make strategic language choices; instead, teachers were asked to guide students toward the appropriate academic language based on the science topic. To clarify, our point is not that leveraging the construct of academic language automatically makes a study traditional for language or that explicitly planning to support language related to disciplinary learning objectives is unnecessary. Rather, the contemporary perspective considers language registers to be fuid continua instead of dichotomous points, highlighting how students need to learn to make language choices that support their communicative needs rather than being told by others what the “appropriate” language is that they need to use to make meaning in science. In the realm of teacher education, academic language is often operationalized through rigid framings of vocabulary, syntax, and discourse in what we take to be a structural defnition of what language is rather than a functional defnition of what language does. This pushes teachers, and by extension their students, toward dichotomous views of language that privilege academic language as the desired register for expressing meaning in science rather than emphasizing the moment-to-moment linguistic choices that we all learn to make to communicate efectively with changing topics, purposes, and audiences. Put another way, a pedagogical focus on academic language can either support multilingual learners or alienate and marginalize them, depending on how it is operationalized and applied. Other studies of teacher learning that took traditional perspectives on both science and language included Heineke and Giatsou (2020); Lavery et al. (2019); Hernandez and Shroyer (2017); Rivard and Gueye (2016); Hopkins et al. (2015); Bravo et al. (2014); Shaw et al. (2014); and Siegel (2014).

Contemporary Perspectives We identifed seven publications that combined contemporary perspectives on both science and language to support science teacher education with multilingual learners. Our reading of the studies in this category highlighted two themes around teacher education: preparing teachers with models for integrating science and language and contextualizing science for linguistically and culturally responsive teaching.

Preparing Teachers With Models for Integrating Science and Language Rutt and Mumba (2020) examined how secondary science preservice teachers who engaged in a model of language- and literacy-integrated science instruction grounded in science and engineering practices changed their instructional planning over time. Applying the Teaching English Learners Language- and Literacy-Integrated Science instructional framework, preservice teachers in a sequence of two science methods courses practiced planning lessons to engage all students, including multilingual learners, in rigorous and age-appropriate applications of science and engineering practices. The language-integrated model included components of contextualized learning, multilingualism as an instructional support, opportunities and goals for literacy development embedded in each lesson, tools for supporting students’ science discourse, and language use for engaging in the practices. The researchers concluded that engagement with this framework over time led preservice science teachers to seek additional opportunities to integrate language and literacy supports into science and engineering practices in their lesson plans.

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Valdés-Sánchez and Espinet (2020) described a model for science teacher education that highlighted development of teacher professional identity as teachers of language as well as science. Using a model of collaborative co-teaching in the context of primary grades Content and Language Integrated Learning classrooms in Catalonia, Spain, the study examined how one elementary teacher’s professional identity evolved through engaging in co-teaching experiences with science teachers. The authors described three teaching identities that emerged as this focal teacher adopted collaborative teaching practices for integrating science and language teaching: a new science teaching identity that allowed her to confdently interact with science teachers, a new multilingual language teaching identity that replaced her belief in target language–only instruction during second-language (English) instruction, and an integrated content and language teacher identity that allowed her to prioritize a balanced view of teaching language through content as she supported both English and Spanish language. The researchers found that the third identity especially allowed the focal teacher to adopt contemporary perspectives on language use in the discipline as she worked to integrate semantic patterns, participation patterns, and language patterns in students’ science learning. Heineke et al. (2019) argued for the need to prepare teachers with an integrated view of science and language learning despite the separation that typically exists between these topics in teacher preparation programs. In traditional teacher preparation, frameworks for teaching science are addressed in science methods courses, while frameworks for content area language support tend to be addressed in content area literacy courses and general ESOL methods courses, placing the onus on new teachers to do the challenging work of integrating these frameworks. This single case study provided a nuanced analysis of one preservice high school science teacher’s eforts to support multilingual learners’ language development through their engagement in rigorous, authentic, science experiences. Changes in this teacher’s practices over two years were attributed to an intentionally designed combination of site visits, courses, feld experiences, and assignments. Of particular importance in learning to integrate contemporary perspectives on science and language were feld experiences in classrooms with mentor teachers who embraced these perspectives, feld supervisors with expertise in bilingual education, and feld-based assignments that required teacher candidates to articulate their evolving understanding of the integration of science and language. In another single case study, Capitelli et al. (2016) examined how one second-/third-grade elementary teacher came to integrate her professional development experiences in an NGSS-aligned teacher learning project with her classroom experience. Over time, this teacher created a hybrid model of instruction for integrating science and language. The study highlighted the importance of ongoing professional learning experiences grounded in contemporary perspectives about language development that immersed teachers in practices-based approaches to instruction. The focal teacher came to see language acquisition as emergent from discussions based on phenomena and facilitated by scafolds that enabled multilingual learners to work together on constructing evidence-based explanations. Finally, Solano-Campos et  al. (2020) provided a review of literature on preparing preservice teachers of multilingual students to use the linguistically responsive teaching (LRT) framework proposed by Lucas and Villegas (2011). The LRT framework highlights three teaching orientations (i.e., sociolinguistic consciousness, valuing of language diversity, and advocacy for multilingual learners) and a corresponding set of pedagogical knowledge and skills (e.g., a repertoire of strategies for learning about the linguistic and academic backgrounds of multilingual students). The authors used this framework to analyze 64 studies of preservice teacher education that specifcally attended to preparing teachers for working with multilingual learners. We note that the reviewed studies were not limited to science education. The authors concluded that most teacher preparation programs described in these studies (n = 36) attended to the teaching orientations of the LRT, while only 12 focused on the pedagogical knowledge and skills of the LRT, and 16 studies described programs that included both the teaching orientations and the pedagogical knowledge and skills. Like other studies in this

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topic area, the authors concluded that teacher preparation programs often seem to lack programwide coherence for supporting teachers in applying linguistically responsive pedagogies to content area instruction. While teacher preparation programs may include aspects of models for integrating language and science, such approaches are often partial or only addressed in certain courses within these programs.

Contextualizing Science for Linguistically and Culturally Responsive Teaching Two studies highlighted the importance of contextualizing science learning experiences by connecting science content to contexts outside of the classroom, with the aim of making science learning more authentic and meaningful for students. Contextualization improves science learning generally (Giamellaro, 2014) and is particularly helpful for multilingual learners. Tolbert and Knox (2016) examined the ideas that preservice teachers developed for contextualizing science instruction in ways that would support multilingual students’ science learning. The researchers found that these teachers primarily used two categories of contexts: local ecological contexts and multicultural contexts. These preservice teachers often held an implicit or explicit defcit framing of the science-related experiences that multilingual students had outside of school. In contrast, preservice teachers who were simultaneously participating in a dual-language certifcation program produced more assetoriented and less stereotypical ideas for contextualization than those teachers who were not pursuing dual-language certifcation. Contextualization helped to surface contemporary perspectives on both language and science in these preservice teachers’ science lessons. A second study within the theme of contextualization involved a multi-institutional project to redesign science methods courses (Lyon et al., 2018). This was done through creating contextualized spaces for language and literacy development while supporting preservice teachers in gaining the vision of science learning advocated by the Framework. The methods courses contextualized science by framing lessons with a driving question and a problem or scenario that was connected to multilingual learners’ lived experiences (e.g., local ecologies, home activities, community events, global contexts, socioscientifc issues). Contextualized science was aligned with a contemporary perspective on science sense-making in which students asked questions and shared fndings, and a contemporary perspective on language that highlighted linguistic and nonlinguistic supports for making student thinking visible. The authors concluded that science methods courses can prepare future science teachers to attend to language as a tool for thinking and learning within disciplinary contexts, and that contextualized instruction can support this goal.

Summarizing and Moving Forward As described in the opening sections of this chapter, an emerging consensus on the need to better integrate science and language education for multilingual learners and all students has grown in recent years. First, research, policy, and practice are being aligned with a common vision across the felds of content and language education. Second, this common vision is based on contemporary perspectives on multilingual learners’ engagement in academically rigorous content and rich language use. Finally, an asset-oriented framing of multilingual learners is replacing traditional defcit-based views. The need to clarify a common vision across research, policy, and practice was the motivation to undertake an interpretive review of the literature published since the prior edition of this handbook. In this discussion, we provide a summary of key fndings about major science topics, including science learning, science instruction, science assessment, and science teacher education. Then, we discuss implications of our interpretive literature review for future research, as it seems premature to draw implications from this nascent literature for policy and practice. We close the

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chapter with our thoughts on advancing the feld for multilingual learners in science education in the context of national policies based on current federal priorities that highlight science and equity at the core.

Summary of Findings About Major Science Topics This chapter focused on the research directions that the feld of science education took during the 2014–2020 period regarding evolving perspectives on science and language with multilingual learners. We summarize key fndings of studies grounded in contemporary perspectives on both science and language for each of four major science topics. We highlight studies conducted by early career scholars who are likely to carry out research and expand the literature for years to come. With regard to science learning, contemporary perspectives highlight the mutually supportive nature of science learning and language learning. Making sense of phenomena and problems with connections to multilingual learners’ experiences in home and community serves as resources for science learning and language learning simultaneously. As science and engineering practices are language intensive, they provide opportunities to communicate science ideas, while also valuing multilingualism in authentic problem solving (e.g., Wilson-Lopez et al., 2018). Rather than viewing vocabulary and disciplinary language as prerequisites for engaging in the practices, contemporary perspectives highlight that these practices provide contexts for rich language use (e.g., Harper, 2017). Likewise, the intentional deployment of a wide range of meaning-making resources, including multimodality (e.g., Grapin, 2019) and translanguaging (e.g., Suárez, 2020), promotes communication of science ideas. Thus, taking contemporary perspectives on science and language learning require a focus on “doing science, using language”. Regarding science instruction, contemporary perspectives point to the critical role of teachers adopting instructional strategies that make explicit and intentionally build on multilingual learners’ assets and resources for learning science and language. Several studies developed and tested pedagogical tools that embed explicit goals for supporting contemporary perspectives. Instructional tools (e.g., science notebooks, peer-generated exemplars) on their own do not ensure a contemporary instructional approach. However, when teachers understand the functional purpose of such tools to scafold communication while doing science, these tools do support contemporary perspectives (González-Howard et al., 2017). Further, if instructional innovations are needed to bring about shifts to contemporary perspectives, teachers must take ownership of instructional strategies and adapt the strategies based on their knowledge of their students’ homes and communities. Findings (e.g., Swanson et al., 2014) highlight the importance of teachers’ agency in making decisions that shift instruction from traditional to contemporary perspectives. With regard to science assessment, contemporary perspectives emphasize three-dimensional assessment of science and engineering practices, crosscutting concepts, and disciplinary core ideas simultaneously. While three-dimensional science assessment is new and challenging for everyone, these challenges are greater for multilingual learners. Most signifcantly, the feld has few examples of what three-dimensional science assessment designed to leverage the assets of multilingual learners looks like. While Fine and Furtak (2020) highlighted design principles, such as the use of culturally relevant phenomena, and Cardozo-Gaibisso et al. (2020) argued for multidimensional interpretations of students’ performance beyond a numeric score, models of assessments that showcase contemporary perspectives on both science and language are lacking. Regarding science teacher education, contemporary perspectives focus on developing and testing models and tools for teacher learning while also changing teachers’ existing beliefs and conceptions of multilingual learners as science learners. One strategy for guiding teachers toward contemporary perspectives involves developing teachers’ identity as a language teacher and a science teacher simultaneously (e.g., Valdés-Sánchez & Espinet, 2020). Another strategy involves contextualizing science

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topics in real-world problems and challenges facing today’s youth, compelling them to do the hard work of communicating science ideas in a second language (Giamellaro, 2017). We note that one recurring feature in this fnal topic of science teacher education that was absent from the other major topics in our review was the connection to the edTPA teacher performance assessment. The infuence of edTPA on science teacher education is one example of why it is important to refect on contemporary and traditional perspectives for both science and language and to consider how they interact. Because edTPA highlights a structural (rather than functional) view of language, as discussed earlier, teacher candidates completing the edTPA need additional instruction and support around functional views of language for science learning. As with our earlier discussions of academic language and science inquiry, our point is not that these constructs have nothing left to teach us about the work of science teaching and learning, but rather that they need to be more fully contextualized within discussions of the functional roles that content and language play in science learning.

Implications for Science Education Research The shifts from traditional to contemporary approaches for integrating science and language with multilingual learners refect a broader shift from a defcit-oriented view to an asset-oriented view of multilingual learners (Lee, 2021; Lee & Stephens, 2020; NASEM, 2018). Traditional perspectives focused on what multilingual learners were lacking (i.e., English-language profciency, experiences with science) and how to fx this problem, for example, by pre-teaching and frontloading vocabulary as a precursor or prerequisite to multilingual learners’ participation in content area learning. Contemporary perspectives have shifted to focus on leveraging the meaning-making resources that multilingual learners bring to the classroom, for example, by engaging them in disciplinary practices and providing them with opportunities to use language and communicate disciplinary meaning regardless of their English-language profciency. From an equity perspective, traditional approaches based on defcit-oriented views are forms of linguistically marginalizing pedagogy, whereas contemporary approaches based on asset-oriented views are linguistically sustaining pedagogies (Harman et al., 2020; Lee, 2021; Paris, 2012). This interpretative literature review of 11 high-visibility journals during the period of 2014–2020 ofers implications for the role of science education researchers specifcally and science educators more generally in the coming years. First, we are aware of trade-ofs in our decision about the search process, especially conducting the literature review with a limited number of selected journals in science education, topical areas, and AERA journals. As noted earlier, we focused on journals representing the major professional associations and/or having a high impact factor. While acknowledging that these selection criteria omitted studies that are relevant to the feld of science education with multilingual learners, we argue that when taken together, these journals ofer a robust look at how this feld has been represented in the research literature while helping us understand the emerging contemporary perspectives. As this feld develops in the coming years, a more comprehensive literature review will be needed, which could be expected for the next volume of the handbook. Second, the vast majority of the research on science education with multilingual learners has consistently been published in science education journals, whereas little of this work has spilled over into either the related topical area journals or the broad interest AERA journals (see Table 10.1). Thus, a clear implication of this review is that researchers publishing in the feld of science education with multilingual learners should diversify publication venues. Seeking to publish in the prominent topical area journals, such as the JLS and JTE, would invite researchers who have related interests and insights more fully into this conversation. Perhaps more importantly, publishing in high-visibility language journals, such as TESOL Quarterly, would engage language education researchers more fully as well. Given that there is currently less agreement among language educators about contemporary

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perspectives on language than there is among science educators about contemporary perspectives on science (Lee, 2018, 2019), increased dialog in language journals seems especially fruitful (e.g., Grapin, 2019; Grapin & Lee, 2022). Overall, the upswing in publications on this topic in 2019 and 2020 speaks to an increased interest in the role of language among science educators. The framing of this chapter is meant to help push related felds to expand and refne our understandings of contemporary perspectives on science and language with multilingual learners. Finally, research on integration of science and language with multilingual learners has often involved collaborations among science and language researchers. In any such academic collaboration, there is typically one person who takes the intellectual lead for framing the approach that is used. In this review we found that studies we categorized as traditional for language but contemporary for science were often led by science educators. Similarly, studies we categorized as traditional for science but contemporary for language were often led by language educators. While perhaps not surprising, the relative strengths and limitations of each knowledge base underscore the critical importance for science educators and language educators to seek opportunities to share their most current thinking on contemporary perspectives. Working together to create a contemporary framework (see Table 10.1) will require each of the felds to learn about the shifts occurring in the other feld and to engage in honest discourse about these shifts.

Advancing the Field for Multilingual Learners in Science Education Since the release of the last edition of the handbook in 2014, the shifts from traditional to contemporary approaches in science learning and language learning along with the shift from defcit-oriented views to asset-oriented views on multilingual learners have prepared the feld of science education with multilingual learners for the educational landscape that is likely to emerge in the aftermath of the COVID-19 pandemic and the increased attention to systemic racism. At the time of this writing in mid-2021, the federal government in the United States has identifed seven immediate priorities (www.whitehouse.gov/priorities/). Three priorities  – COVID-19, climate, and health care – are rooted in science. Two priorities – racial equity and immigration – focus on justice. These fve priorities collectively infuence the remaining two priorities – economic recovery and restoring America’s global standing. With the exception of the fnal priority, each of these can be seen as global rather than national priorities, and aligned with the need to integrate science and language learning for all students, as refected in the global reach of the studies reviewed in this chapter. A national or global call to action to address these priorities in unison will require dramatic changes in society and involve science disciplines, science education, and various movements for racial and linguistic equity to be at the forefront of this vision of economic and social progress for all. In concert with science disciplines, science education is an essential partner, as the necessary changes will not be possible unless all students, including multilingual learners, receive the kind of science education needed to make sense of phenomena and problems, make informed decisions, and take responsible actions. By addressing these complex and pressing societal issues, students can recognize the roles that both science and justice play in broader policies as we seek to educate informed and responsible individuals who can harness new knowledge and practices to promote a more equitable global society.

References Adamuti-Trache, M.,  & Sweet, R. (2014). Science, technology, engineering and math readiness: Ethnolinguistic and gender diferences in high-school course selection patterns. International Journal of Science Education, 36(4), 610–634. Aftska, O., & Heaton, T. J. (2019). Mitigating the efect of language in the assessment of science: A study of English-language learners in primary classrooms in the United Kingdom. Science Education, 103(6), 1396–1422.

318

Multilingual Learners in Science Education American Association for the Advancement of Science. (1989). Science for all Americans. Oxford University Press. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. Oxford University Press. Amin, T., & Badreddine, D. (2020). Teaching science in Arabic: Diglossia and discourse patterns in the elementary classroom. International Journal of Science Education, 42(14), 2290–2330. Bacon, C. K. (2020). “It’s not really my job”: A mixed methods framework for language ideologies, monolingualism, and teaching emergent bilingual learners. Journal of Teacher Education, 71(2), 172–187. Barrett, N. E., & Liu, G. Z. (2016). Global trends and research aims for English academic oral presentations: Changes, challenges, and opportunities for learning technology. Review of Educational Research, 86(4), 1227–1271. Bezemer, J., & Kress, G. (2015). Multimodality, learning and communication: A social semiotic frame. Routledge. Block, N. C. (2020). Evaluating the efcacy of using sentence frames for learning new vocabulary in science. Journal of Research in Science Teaching, 57(3), 454–478. Bonello, C. (2020). Teaching science in multilingual contexts: An exploratory study into the experiences and insights of Maltese science teachers as they learn about teaching in multilingual scenarios. International Journal of Science Education, 42(14), 2407–2425. Bravo, M. A., Mosqueda, E., Solís, J. L., & Stoddart, T. (2014). Possibilities and limits of integrating science and diversity education in preservice elementary teacher preparation. Journal of Science Teacher Education, 25(5), 601–619. Britsch, S. (2020). “Nosotras no empezamos a hacer eso”: A social semiotic view of a sheltered science investigation. TESOL Quarterly, 54(2), 310–347. Buxton, C. A., Allexsaht-Snider, M., Kayumova, S., Aghasaleh, R., Choi, Y. J., & Cohen, A. (2015). Teacher agency and professional learning: Rethinking fdelity of implementation as multiplicities of enactment. Journal of Research in Science Teaching, 52(4), 489–502. Buxton, C. A., & Caswell, L. (2020). Next generation sheltered instruction to support multilingual learners in secondary science classrooms. Science Education, 104(3), 555–580. Buxton, C. A., Harper, S., Payne, D., & Allexsaht-Snider, M. (2017). Using the sociology of associations to rethink STEM education. Educational Studies, 53(6), 587–600. Buxton, C. A., & Lee, O. (2014). English learners in science education. In Handbook of research on science education (Vol. II, pp. 218–236). Routledge. Capitelli, S., Hooper, P., Rankin, L., Austin, M., & Caven, G. (2016). Understanding the development of a hybrid practice of inquiry-based science instruction and language development: A case study of one teacher’s journey through refections on classroom practice. Journal of Science Teacher Education, 27(3), 283–302. Cardozo-Gaibisso, L., Kim, S., Buxton, C., & Cohen, A. (2020). Thinking beyond the score: Multidimensional analysis of student performance to inform the next generation of science assessments. Journal of Research in Science Teaching, 57(6), 856–878. Charamba, E. (2020). Translanguaging in a multilingual class: A study of the relation between students’ languages and epistemological access in science. International Journal of Science Education, 42(11), 1779–1798. Cummins, J. (2008). BICS and CALP: Empirical and theoretical status of the distinction. Encyclopedia of Language and Education, 2(2), 71–83. Curran, F. C., & Kitchin, J. (2019). Why are the early elementary race/ethnicity test score gaps in science larger than those in reading or mathematics? National evidence on the importance of language and immigration context in explaining the gap-in-gaps. Science Education, 103(3), 477–502. Dafouz, E., Hüttner, J., & Smit, U. (2018). New contexts, new challenges for TESOL: Understanding disciplinary reasoning in oral interactions in English-medium instruction. TESOL Quarterly, 52(3), 540–563. Dawson, E. (2014). “Not designed for us”: How science museums and science centers socially exclude lowincome, minority ethnic groups. Science Education, 98(6), 981–1008. DiCerbo, P. A., Anstrom, K. A., Baker, L. L., & Rivera, C. (2014). A review of the literature on teaching academic English to English language learners. Review of Educational Research, 84(3), 446–482. Enderle, P., Cohen, S., & Scott, J. (2020). Communicating about science and engineering practices and the nature of science: An exploration of American Sign Language resources. Journal of Research in Science Teaching, 57(6), 968–995. Every Student Succeeds Act, 20 U.S.C. § 6301 (2015). www.congress.gov/114/plaws/publ95/PLAW114publ95.pdf Fine, C. G. M., & Furtak, E. M. (2020). A framework for science classroom assessment task design for emergent bilingual learners. Science Education, 104(3), 393–420. Frantz, R. S., Starr, L. E., & Bailey, A. L. (2015). Syntactic complexity as an aspect of text complexity. Educational Researcher, 44(7), 387–393. Gamez, R., & Parker, C. A. (2018). Becoming science learners: A study of newcomers’ identity work in elementary school science. Science Education, 102(2), 377–413.

319

Cory A. Buxton and Okhee Lee García, O., & Wei, L. (2014). Translanguaging: Language, bilingualism and education. Palgrave Macmillan UK. Giamellaro, M. (2014). Primary contextualization of science learning through immersion in content-rich settings. International Journal of Science Education, 36(17), 2848–2871. Giamellaro, M. (2017). Dewey’s yardstick: Contextualization as a crosscutting measure of experience in education and learning. SAGE Open, 7(1). Glass, R., & Oliveira, A. W. (2014). Science language accommodation in elementary school read-alouds. International Journal of Science Education, 36(4), 577–609. González-Howard, M.,  & McNeill, K. L. (2016). Learning in a community of practice: Factors impacting English-learning students’ engagement in scientifc argumentation. Journal of Research in Science Teaching, 53(4), 527–553. González-Howard, M., McNeill, K. L., Marco-Bujosa, L. M., & Proctor, C. P. (2017). “Does it answer the question or is it French fries?”: An exploration of language supports for scientifc argumentation. International Journal of Science Education, 39(5), 528–547. González-Howard, M., & Suárez, E. (2021). Retiring the term English language learners: Moving toward linguistic justice through asset-oriented framing. Journal of Research in Science Teaching, 58(5), 749–752. https:// doi.org/10.1002/tea.21684 Grapin, S. E. (2019). Multimodality in the new content standards era: Implications for English learners. TESOL Quarterly, 53(1), 30–55. Grapin, S. E. (2021). Toward asset-oriented and defnitionally clear terminology: A comment on GonzálezHoward and Suárez (2021). Journal of Research in Science Teaching, 58(5), 753–755. Grapin, S. E., & Lee, O. (2022). WIDA English language development standards framework, 2020 edition: Key shifts and emerging tensions. TESOL Quarterly, 56(2), 827–839. Grapin, S. E., Llosa, L., Haas, A., Goggins, M., & Lee, O. (2019). Precision: Toward a meaning-centered view of language use with English learners in the content areas. Linguistics and Education, 50(1), 71–83. Hand, B., Shelley, M. C., Laugerman, M., Fostvedt, L., & Therrien, W. (2018). Improving critical thinking growth for disadvantaged groups within elementary school science: A randomized controlled trial using the science writing heuristic approach. Science Education, 102(4), 693–710. Harman, R., Buxton, C., Cardozo-Gaibisso, L., Jiang, L., & Bui, K. (2020). Culturally sustaining systemic functional linguistics praxis in science classrooms. Language and Education, 35(2), 106–122. Harper, S. G. (2017). Engaging Karen refugee students in science learning through a cross-cultural learning community. International Journal of Science Education, 39(3), 358–376. Harris, C. J., Krajcik, J. S., Pellegrino, J. W., & McElhaney, K. W. (2016). Constructing assessment tasks that blend disciplinary core ideas, crosscutting concepts, and science practice for classroom formative applications. SRI International. Heineke, A. J., & Giatsou, E. (2020). Learning from students, teachers, and schools: Field-based teacher education for emergent bilingual learners. Journal of Teacher Education, 71(1), 148–161. Heineke, A. J., Smetana, L., & Carlson Sanei, J. (2019). A qualitative case study of feld-based teacher education: One candidate’s evolving expertise of science teaching for emergent bilinguals. Journal of Science Teacher Education, 30(1), 80–100. Hernandez, C., & Shroyer, M. G. (2017). The use of culturally responsive teaching strategies among Latina/o student teaching interns during science and mathematics instruction of CLD students. Journal of Science Teacher Education, 28(4), 367–387. Hopkins, M., Lowenhaupt, R., & Sweet, T. M. (2015). Organizing English learner instruction in new immigrant destinations: District infrastructure and subject-specifc school practice. American Educational Research Journal, 52(3), 408–439. Huerta, M., Lara-Alecio, R., Tong, F., & Irby, B. J. (2014). Developing and validating a science notebook rubric for ffth-grade non-mainstream students. International Journal of Science Education, 36(11), 1849–1870. Jackson, J. K., Huerta, M., Garza, T., & Narvaez, R. (2019). Examining the efects of a professional development initiative on English learning and economically disadvantaged adolescents’ scores on a high-stakes science test. Journal of Science Teacher Education, 30(2), 122–143. Johnson, A. (2020). Summer credit recovery impact on newcomer English learners. American Educational Research Journal, 57(4), 1757–1790. Jung, K. G., & Brown, J. C. (2016). Examining the efectiveness of an academic language planning organizer as a tool for planning science academic language instruction and supports. Journal of Science Teacher Education, 27(8), 847–872. Kangas, S. E., & Cook, M. (2020). Academic tracking of English learners with disabilities in middle school. American Educational Research Journal, 57(6), 2415–2449. Kanno, Y., & Kangas, S. E. (2014). “I’m not going to be, like, for the AP” English language learners’ limited access to advanced college-preparatory courses in high school. American Educational Research Journal, 51(5), 848–878.

320

Multilingual Learners in Science Education Karlsson, A., Nygård Larsson, P., & Jakobsson, A. (2019). Multilingual students’ use of translanguaging in science classrooms. International Journal of Science Education, 41(15), 2049–2069. Kibler, A., Valdés, G., & Walqui, A. (2014). What does standards-based educational reform mean for English language learner populations in primary and secondary schools? TESOL Quarterly, 48(4), 873–874. Lan, S. W., & de Oliveira, L. C. (2019). English language learners’ participation in the discourse of a multilingual science classroom. International Journal of Science Education, 41(9), 1246–1270. Larsen-Freeman, D. (2007). Refecting on the cognitive-social debate in second language acquisition. The Modern Language Journal, 7, 773–787. Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press. Lavery, M. R., Nutta, J., & Youngblood, A. (2019). Analyzing student learning gains to evaluate diferentiated teacher preparation for fostering English learners’ achievement in linguistically diverse classrooms. Journal of Teacher Education, 70(4), 372–387. Leckie, A., & Wall, A. (2017). Language for science: Preservice teachers develop science concepts through language study. Journal of Science Teacher Education, 28(5), 468–484. Lee, O. (2017). Common core state standards for ELA/literacy and Next Generation Science Standards: Convergences and discrepancies using argument as an example. Educational Researcher, 46(2), 90–102. Lee, O. (2018). English language profciency standards aligned with content standards. Educational Researcher, 47(5), 317–327. Lee, O. (2019). Aligning English language profciency standards with content standards: Shared opportunity and responsibility across English learner education and content areas. Educational Researcher, 48(8), 534–542. Lee, O. (2021). Asset-oriented framing of science and language learning with multilingual learners. Journal of Research in Science Teaching, 58(7). https://doi.org/10.1002/tea.21694 Lee, O., Llosa, L., Grapin, S., Haas, A., & Goggins, M. (2019). Science and language integration with English learners: A conceptual framework guiding instructional materials development. Science Education, 103(2), 317–337. Lee, O., Llosa, L., Jiang, F., Haas, A., O’Connor, C., & Van Booven, C. D. (2016). Elementary teachers’ science knowledge and instructional practices: Impact of an intervention focused on English language learners. Journal of Research in Science Teaching, 53(4), 579–597. Lee, O., Miller, E., & Januszyk, R. (2015). NGSS for all students. NSTA Press. Lee, O., Quinn, H., & Valdés, G. (2013). Science and language for English language learners in relation to Next Generation Science Standards and with implications for Common Core State Standards for English language arts and mathematics. Educational Researcher, 42(4), 223–233. Lee, O., & Stephens, A. (2020). English learners in STEM subjects: Contemporary perspectives on STEM subjects and language with English learners. Educational Researcher, 49(6), 426–432. Lemmi, C., Brown, B. A., Wild, A., Zummo, L., & Sedlacek, Q. (2019). Language ideologies in science education. Science Education, 103(4), 854–874. Llosa, L., Lee, O., Jiang, F., Haas, A., O’Connor, C., Van Booven, C. D., & Kiefer, M. J. (2016). Impact of a large-scale science intervention focused on English language learners. American Educational Research Journal, 53(2), 395–424. Lo, Y. Y., & Lo, E. S. C. (2014). A meta-analysis of the efectiveness of English-medium education in Hong Kong. Review of Educational Research, 84(1), 47–73. Lucas, T., & Villegas, A. (2011). A framework for preparing linguistically responsive teachers. In T. Lucas (Ed.), Teacher preparation for linguistically diverse classrooms: A resource for teacher educators (pp. 55–73). Routledge. Lyon, E. G. (2017). Exploring secondary science teachers’ enactment of assessment practices to refect responsive science teaching for English learners. Journal of Science Teacher Education, 28(8), 674–698. Lyon, E. G., Stoddart, T., Bunch, G. C., Tolbert, S., Salinas, I., & Solis, J. (2018). Improving the preparation of novice secondary science teachers for English learners: A proof of concept study. Science Education, 102(6), 1288–1318. Mavuru, L., & Ramnarain, U. D. (2020). Language afordances and pedagogical challenges in multilingual grade 9 natural sciences classrooms in South Africa. International Journal of Science Education, 42(14), 2472–2492. Meier, V., Aminger, W., McLean, M., Carpenter, S. L., Moon, S., Hough, S., & Bianchini, J. A. (2020). Preservice secondary science teachers’ understanding of academic language: Moving beyond “just the vocabulary”. Science Education, 104(2), 222–251. Meyer, X. S., & Crawford, B. A. (2015). Multicultural inquiry toward demystifying scientifc culture and learning science. Science Education, 99(4), 617–637. Meyerhöfer, N.,  & Dreesmann, D. C. (2019a). English-bilingual biology for standard classes development, implementation and evaluation of an English-bilingual teaching unit in standard German high school classes. International Journal of Science Education, 41(10), 1366–1386.

321

Cory A. Buxton and Okhee Lee Meyerhöfer, N., & Dreesmann, D. C. (2019b). The exclusive language of science? Comparing knowledge gains and motivation in English-bilingual biology lessons between non-selected and preselected classes. International Journal of Science Education, 41(1), 1–20. Mislevy, R. J., & Durán, R. P. (2014). A sociocognitive perspective on assessing EL students in the age of Common Core and Next Generation Science Standards. TESOL Quarterly, 48(3), 560–585. Msimanga, A., & Lelliott, A. (2014). Talking science in multilingual contexts in South Africa: Possibilities and challenges for engagement in learners home languages in high school classrooms. International Journal of Science Education, 36(7), 1159–1183. National Academies of Sciences, Engineering, and Medicine. (2018). English learners in STEM subjects: Transforming classrooms, schools, and lives. National Academies Press. National Research Council. (1996). National science education standards. National Academies Press. National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. Next Generation Science Standards Lead States. (2013b). Appendix D “All standards, all students”: Making Next Generation Science Standards accessible to all students. National Academies Press. NGSS Lead States. (2013a). Next generation science standards: For states, by states. The National Academies Press. Noble, T., Sireci, S. G., Wells, C. S., Kachchaf, R. R., Rosebery, A. S., & Wang, Y. C. (2020). Targeted linguistic simplifcation of science test items for English learners. American Educational Research Journal, 57(5), 2175–2209. No Child Left Behind Act of 2001, 20 U.S.C. § 6319 (2008). Ollerhead, S. (2020). “The pre-service teacher tango”: Pairing literacy and science in multilingual Australian classrooms. International Journal of Science Education, 42(14), 2493–2512. Otheguy, R., García, O., & Reid, W. (2015). Clarifying translanguaging and deconstructing named languages: A perspective from linguistics. Applied Linguistics Review, 6(3), 281–307. Paris, D. (2012). Culturally sustaining pedagogy: A needed change in stance, terminology, and practice. Educational Researcher, 41(3), 93–97. Pyle, D., Pyle, N., Lignugaris Kraft, B., Duran, L.,  & Akers, J. (2017). Academic efects of peer-mediated interventions with English language learners: A research synthesis. Review of Educational Research, 87(1), 103–133. Rivard, L. P., & Gueye, N. R. (2016). Enhancing literacy practices in science classrooms through a professional development program for Canadian minority-language teachers. International Journal of Science Education, 38(7), 1150–1173. Rutt, A. A., & Mumba, F. M. (2020). Developing secondary pre-service science teachers’ instructional planning abilities for language-and-literacy-integrated science instruction in linguistically diverse classrooms. Journal of Science Teacher Education, 31(8), 841–868. Ryoo, K. (2015). Teaching science through the language of students in technology-enhanced instruction. Journal of Science Education and Technology, 24(1), 29–42. Ryoo, K., & Bedell, K. (2019). Supporting linguistically diverse students’ science learning with dynamic visualizations through discourse-rich practices. Journal of Research in Science Teaching, 56(3), 270–301. Ryoo, K., Bedell, K., & Swearingen, A. (2018). Promoting linguistically diverse students’ short-term and longterm understanding of chemical phenomena using visualizations. Journal of Science Education and Technology, 27(6), 508–522. Ryu, M. (2015a). Positionings of racial, ethnic, and linguistic minority students in high school biology class: Implications for science education in diverse classrooms. Journal of Research in Science Teaching, 52(3), 347–370. Ryu, M. (2015b). Understanding Korean transnational girls in high school science classes: Beyond the model minority stereotype. Science Education, 99(2), 350–377. Ryu, M. (2019). Mixing languages for science learning and participation: An examination of Korean-English bilingual learners in an after-school science-learning programme. International Journal of Science Education, 41(10), 1303–1323. Salloum, S., & BouJaoude, S. (2020). Language in teaching and learning science in diverse Lebanese multilingual classrooms: Interactions and perspectives. International Journal of Science Education, 42(14), 2331–2363. Seah, L. H., Clarke, D. J., & Hart, C. E. (2014). Understanding the language demands on science students from an integrated science and language perspective. International Journal of Science Education, 36(6), 952–973. Seah, L. H., & Silver, R. E. (2020). Attending to science language demands in multilingual classrooms: A case study. International Journal of Science Education, 42(14), 2453–2471. Seah, L. H., & Yore, L. D. (2017). The roles of teachers’ science talk in revealing language demands within diverse elementary school classrooms: A study of teaching heat and temperature in Singapore. International Journal of Science Education, 39(2), 135–157.

322

Multilingual Learners in Science Education Shaw, J. M., Lyon, E. G., Stoddart, T., Mosqueda, E., & Menon, P. (2014). Improving science and literacy learning for English language learners: Evidence from a pre-service teacher preparation intervention. Journal of Science Teacher Education, 25(5), 621–643. Siegel, M. A. (2014). Developing preservice teachers’ expertise in equitable assessment for English learners. Journal of Science Teacher Education, 25(3), 289–308. Siegel, M. A., Menon, D., Sinha, S., Promyod, N., Wissehr, C., & Halverson, K. L. (2014). Equitable written assessments for English language learners: How scafolding helps. Journal of Science Teacher Education, 25(6), 681–708. Siry, C., & Gorges, A. (2020). Young students’ diverse resources for meaning making in science: Learning from multilingual contexts. International Journal of Science Education, 42(14), 2364–2386. Smith, L. K., Hanks, J. H., & Erickson, L. B. (2017). Secondary biology textbooks and national standards for English learners. Science Education, 101(2), 302–332. Solano-Campos, A., Hopkins, M.,  & Quaynor, L. (2020). Linguistically responsive teaching in preservice teacher education: A review of the literature through the lens of cultural-historical activity theory. Journal of Teacher Education, 71(2), 203–217. Suárez, E. (2020). “Estoy explorando science”: Emergent bilingual students problematizing electrical phenomena through translanguaging. Science Education, 104(5), 791–826. Swanson, L. H., Bianchini, J. A., & Lee, J. S. (2014). Engaging in argument and communicating information: A case study of English language learners and their science teacher in an urban high school. Journal of Research in Science Teaching, 51(1), 31–64. Terrazas-Arellanes, F. E., Gallard, M. A. J., Strycker, L. A., & Walden, E. D. (2018). Impact of interactive online units on learning science among students with learning disabilities and English learners. International Journal of Science Education, 40(5), 498–518. Tolbert, S., & Knox, C. (2016). “They might know a lot of things that I don’t know”: Investigating diferences in preservice teachers’ ideas about contextualizing science instruction in multilingual classrooms. International Journal of Science Education, 38(7), 1133–1149. Tretter, T. R., Ardasheva, Y., Morrison, J. A.,  & Karin Roo, A. (2019). Strengthening science attitudes for newcomer middle school English learners: Visually enriched integrated science and language instruction. International Journal of Science Education, 41(8), 1015–1037. Umansky, I. M. (2016). Leveled and exclusionary tracking: English learners’ access to academic content in middle school. American Educational Research Journal, 53(6), 1792–1833. Ünsal, Z., Jakobson, B., Wickman, P. O., & Molander, B. O. (2018). Gesticulating science: Emergent bilingual students’ use of gestures. Journal of Research in Science Teaching, 55(1), 121–144. Ünsal, Z., Jakobson, B., Wickman, P. O.,  & Molander, B. O. (2020). Jumping pepper and electrons in the shoe: Using physical artefacts in a multilingual science class. International Journal of Science Education, 42(14), 2387–2406. Valdés, G. (2015). Latin@s and the intergenerational continuity of Spanish: The challenges of curricularizing language. International Multilingual Research Journal, 9(4), 253–273. Valdés-Sánchez, L.,  & Espinet, M. (2020). Coteaching in a science-CLIL classroom: Changes in discursive interaction as evidence of an English teacher’s science-CLIL professional identity development. International Journal of Science Education, 42(14), 2426–2452. Van Laere, E., Aesaert, K., & van Braak, J. (2014). The role of students’ home language in science achievement: A multilevel approach. International Journal of Science Education, 36(16), 2772–2794. Varelas, M., Pieper, L., Arsenault, A., Pappas, C. C., & Keblawe-Shamah, N. (2014). How science texts and hands-on explorations facilitate meaning making: Learning from Latina/o third graders. Journal of Research in Science Teaching, 51(10), 1246–1274. Weiland, I. (2015). An exploration of Hispanic mothers’ culturally sustaining experiences at an informal science center. Journal of Research in Science Teaching, 52(1), 84–106. Weinburgh, M., Silva, C., Smith, K. H., Groulx, J., & Nettles, J. (2014). The intersection of inquiry-based science and language: Preparing teachers for ELL classrooms. Journal of Science Teacher Education, 25(5), 519–541. WIDA Consortium. (2020). WIDA English language development standards framework, 2020 edition. Board of Regents of the University of Wisconsin System. https://wida.wisc.edu/sites/default/fles/resource/WIDAELD-Standards-Framework-2020.pdf Williams, M., & Tang, K. S. (2020). The implications of the non-linguistic modes of meaning for language learners in science: A review. International Journal of Science Education, 42(7), 1041–1067. Wilmes, S. E., & Siry, C. (2018). Interaction rituals and inquiry-based science instruction: Analysis of student participation in small-group investigations in a multilingual classroom. Science Education, 102(5), 1107–1128. Wilmes, S. E., & Siry, C. (2020). Science notebooks as interactional spaces in a multilingual classroom: Not just ideas on paper. Journal of Research in Science Teaching, 57(7), 999–1027.

323

Cory A. Buxton and Okhee Lee Wilson-Lopez, A., Sias, C., Smithee, A., & Hasbún, I. M. (2018). Forms of science capital mobilized in adolescents’ engineering projects. Journal of Research in Science Teaching, 55(2), 246–270. Wu, S. C., Silveus, A., Vasquez, S., Bif, D., Silva, C., & Weinburgh, M. (2019). Supporting ELLs’ use of hybrid language and argumentation during science instruction. Journal of Science Teacher Education, 30(1), 24–43. Zhang, Y. (2016). Multimodal teacher input and science learning in a middle school sheltered classroom. Journal of Research in Science Teaching, 53(1), 7–30.

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11 SPECIAL NEEDS AND TALENTS IN SCIENCE LEARNING Sami Kahn

Special. Exceptional. Twice Exceptional. Gifted. Disabled. Diferently-abled. Talented. Hyper-performing. High Ability. Impaired. Struggling. Neurodiverse. In our quest to understand and advance human learning, education researchers apply a panoply of terms to describe the wide range of abilities and ways of accessing the world that is inherent in human existence. As is often the case in social sciences, the various constructs amount to little more than a shorthanded means of describing the complexity and, arguably, the beauty of individual learners. Perhaps most interesting is that each of the constructs imply diference from “the norm,” for there is always a bell curve beckoning education researchers’ attention not only to the populous mean but to the tails of human achievement. But one must ask, as did Davis (2016), what exactly is normal? Arguably, normality is context dependent. One can easily envision the high school valedictorian who attends a highly competitive university shifting on the academic bell curve from right-tailed exceptional to average overnight. Of course, when one drills down further, individuals who are considered average learners may well prove exceptional in certain domains, such as creativity, analytical and quantitative abilities, and so on, while learners who are identifed by their high or low performance on certain criteria may well be quite average in most others (Van Tassel-Baska, 1995). How, then, do we reconcile the desire to support all learners to reach their potential in science without oversimplifying the human experience? A reasonable starting place is to assume that human diversity is normal, an assumption made by researchers ascribing to disability studies in education (Connor et al., 2008). This is not to say that students do not have diferent learning needs, but rather that all students have areas of relative strength and weakness. Furthermore, this paradigm assumes that students’ inability to access

DOI: 10.4324/9780367855758-14

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and engage with science curriculum is not a defect within the student, but rather a defect in the learner’s educational environment. This social rather than medical model of diference emphasizes the limitations of environments – educational, physical, and societal – rather than individuals (Connor, 2012). Relating back to the idea of context dependency: A student with a visual impairment who is provided with accessible learning materials and is taught in an accessible way still has a visual impairment, but they are no longer “adversely afected” (IDEA, 2004) by this diference in the classroom, this latter phrase forming the defnition of “disability” for purposes of educational services in the United States. When diference is assumed to be a natural part of the human condition, the language that is used becomes less about who is normal and who is diferent, but rather what supports and interventions can be implemented to improve the learning environment and outcomes for all learners (Koomen et al., 2018). With this backdrop in mind, this chapter seeks to provide science education researchers with a contemporary snapshot of the state of scholarly discourse around special needs and talents in science learning; that is, addressing the needs of students whose physical, sensory, cognitive, or socialemotional needs are not met in traditional science classrooms. For those interested in a historical perspective on the topic, the parallel chapters in the prior editions of this handbook (McGinnis & Kahn, 2014; McGinnis & Stefanich, 2007) serve as formidable resources. The earlier chapters also delve more deeply into the philosophical and epistemological diferences between science education and special education that have historically contributed to disagreement as to where and how students should be educated. In contrast, the present chapter is situated within a 21st-century context that defnes inclusive science education not by where a student is educated (e.g., special education classroom vs. general education classroom) but by how the student is educated in order to ensure that every student is provided with the tools, opportunities, and experiences to thrive in science learning. Such an approach is critical, as most students with disabilities spend at least 80% of their day in general education (IDEA Part B Child Count and Educational Environments Collection, 2019), while students identifed as gifted spend most of their day in general education (NAGC, 2020). Just in the last two decades, the movement toward inclusive education has prompted the realization that collaboration and communication across teaching disciplines is not only recommended but necessary, particularly if contemporary visions of scientifc literacy and educational equity are to be met. The Next Generation Science Standards (NGSS) call for equitable and excellent science opportunities in “All Standards, All Students” (NGSS Lead States, 2013, Appendix D). Meanwhile, the Individuals with Disabilities Education Act (IDEA, 2004) guarantees a free appropriate public education that is commensurate with students’ abilities across subjects (including science) in the least restrictive environment. Taken together, these documents present a contemporary vision of scientifc literacy that supports the vision of all people to be able to apply science in their everyday lives, in their work, and in society to their fullest extent. Yet this vision remains elusive, as students with special needs and talents are underserved as evidenced by underrepresentation of people with disabilities in STEM (National Science Foundation, 2019), lack of programmatic opportunities for gifted students in STEM (Taber & Sumida, 2016), and practical and attitudinal barriers to equitable and excellent science teaching (Kahn & Lewis, 2014). To explore these and other issues further, this chapter is organized around the following questions: 1. 2. 3. 4.

Who are learners with special needs and talents in science? How are (or aren’t) they served? What key themes and pedagogical approaches characterize contemporary inclusive science education? What constitutes “assessment for all” in science? What is the state of science teacher education with respect to students with special needs and talents?

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Throughout the chapter, contemporary research will be discussed for students who are considered to have physical or sensory learning needs, intellectual or emotional learning needs, and gifted learning needs. It is worth noting that this schema diverges from the prior handbook chapters, which addressed “special needs” and “giftedness” in separate sections. The present chapter intentionally considers these terms to be contextually fuid and assumes that students can have both special needs and gifts in science. This assumption is supported by the concept of neurodiversity (Armstrong, 2012) and the twice exceptional (2e) educational framework (Reis et al., 2014), both of which posit that students who are considered to have developmental diferences, such as those with learning disabilities, ADHD, and autism, can also have particular gifts. Perhaps surprisingly, this chapter also subdivides some sections based on legislative (US) categories of disability and giftedness, a decision made in order to create a heuristic to support researchers’ efciency. However, this pragmatic approach undermines the philosophical as well as the practical educational implications of the complexity and depth of learners’ identities. Contemporary science education research recognizes the importance of intersectionality (Warner, 2008), which is the manner in which learners’ cultural, racial, gender, and disability identities interact and form new, more complex manifestations of identity. This richer understanding of the layers and intersections of identity must be attended to in science education, and therefore, science education research, in order to understand how to meet the needs of students for whom disability and/or giftedness contributes to their whole selves. To balance the need for research expediency while challenging the impetus of oversimplifcation, empirical studies that address learner identities in an intersectional manner are woven into the sections on disability or giftedness most closely aligned with the study population.

What Research Is Included/Excluded in This Chapter? The research studies in this chapter represent recent (within ten years) scholarship in the feld of inclusive science education, with the exception of some foundational works that are included in order to provide context. They represent a mix of methodologies and perspectives, and those that were deemed particularly robust or unique are given slightly greater coverage. The studies were identifed by keyword searches in databases such as Proquest, Education Resources Information Center (ERIC), and PsychINFO using terms such as those listed in the introduction. Ancestral searches then followed, with emphasis placed on PK–12 education, although particularly signifcant higher-education works are also included. Research represented comes from international science education, special education, gifted education, as well as sources with more general and teacher education foci, all peer-reviewed and written in English. Almost all of the included articles are framed in literature on special education and giftedness in science, although a few articles that did not focus on this topic but nonetheless had signifcant fndings for these areas were included. For readers wishing to delve more deeply into inclusive education trends beyond the feld of science, many excellent resources are available (see, for example, Dovigo’s [2017] review on the state of inclusive education across six countries).

Who Are Learners With Special Needs and Talents and How Are (or Aren’t) They Served? Learners With Special Educational Needs Approximately, 7.2 million students ages 3–21, or 15% of all public school students, received special education services in 2020–2021 under the Individuals with Disabilities Education Act (IDEA, 2004), which guarantees a free appropriate education in the least restrictive environment for all eligible 3–21-year-old students in the United States (Note: the IDEA also covers birth to two

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years old under a separate section not discussed here). To be eligible, students must demonstrate an impairment within at least one of the following categories that adversely afects academic performance: autism, deaf-blindness, developmental delay, emotional disturbance, hearing impairment (including deafness), intellectual disability, multiple disabilities, orthopedic impairment, other health impairment (including ADD/ADHD), specifc learning disability, speech or language impairment, traumatic brain injury, visual impairment (including blindness). Figure 11.1 presents the percentage distribution of children served under the IDEA by disability (National Center for Educational Statistics; NCES, 2022). While the percentage of students served under the IDEA spending most of their day (> 80%) in general education classes in regular (i.e., non-special education) schools has increased markedly over the last two decades, the most current being 66% in fall 2020, the percentage varies widely across disabilities. For example, students with speech language impairments spend 88% of their day in general education classes, compared to only 19% of students with intellectual disabilities (NCES, 2022). This disparity illuminates the necessity of researchers to look beyond aggregate statistics and examine precisely who is included in what classes, and what kind of services and opportunities are available to them. Similar vigilance is warranted when reviewing the percentage of students served under the IDEA by race/ethnicity, which is displayed in Table 11.1 (NCES, 2022). These statistics appear to suggest that there are disproportionately high American Indian/Alaskan Native and Black children in special education. The overrepresentation of Black children in special education has historically been attributed to teacher bias and other discriminatory identifcation practices, leading to the “Equity in IDEA” rule, which requires federal monitoring of minority overrepresentation in special education (IDEA regulations, 2016). However, the story of overrepresentation in special education is a bit more complicated. Farkas et al. (2020) suggests that children of color are actually underrepresented in special education, since the percent of population statistic does not control for factors that are associated with higher risks for disability, including poverty,

Percentage Distribution of Students Ages 3–21 Served Under the Individuals with Disabilities Education Act (IDEA), by selected disability type: School year 2020–21

33

Specific Learning Disability

19

Speech or Language Impairment

15

Other Health Impairment

12

Autism

7

Developmental Delay

6

Intellectual Disability

5

Emotional Disturbance

2

Multiple Disabilities

1 0a 0a 0a 0a

Hearing Impairment Deaf-Blindness Orthopedic Impairment Traumatic Brain Injury Visual Impairment 0

5

10

15

20

25

30

35

Figure 11.1 Percentage Distribution of Students Aged 3–21 Served Under the Individuals With Disabilities Education Act (IDEA), by Disability: School Year 2020–2021 a

Deaf-Blindness, Orthopedic Impairment, Traumatic Brain Injury, and Visual Impairment each account for less than 0.5% of students served under the IDEA.

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Special Needs and Talents in Science Learning Table 11.1 Percentage of Students Ages 3–21 Served Under the Individuals With Disabilities Education Act (IDEA), by Race/Ethnicity: School Year 2020–2021  

Percentage of Students Served Under the IDEA by Race/Ethnicity: 2020–2021

Total

15

Black

17

White

15

Hispanic

14

Asian

8

Pacifc Islander

12

American Indian/Alaskan Native

19

Two or more races

15

food insecurity, elevated lead levels in their blood, and low birth weight, to which racial and ethnic minorities are more likely to be exposed. The authors argue that, when children with similar family incomes, past academic performance on tests, and other school and SES factors, such as access to health care, are compared, students of color are actually less likely to receive special education services than White children. Relatedly, researchers note that doctors are less likely to inquire about a child’s development when working with parents of children of color, a situation that likely leads to under-identifcation of developmental disabilities at an early age and potentially precipitating longterm developmental consequences (Guerrero et al., 2011). Language can also impact the likelihood of identifcation for special education services as failure to provide assessments in students’ native languages likely leads to overdiagnosis of learning disabilities in second language learners even when SES is accounted for (Shifrer et al., 2014). The vexing question of under/overrepresentation in special education is one that is currently being debated in the literature, not only due to the challenges of objectively parsing out variables, but also due to varying subjective perceptions of special education as a whole; in other words, upon whether one views diagnosis and provision of special education services as an asset (i.e., proving needed services) or a liability (i.e., increasing stigma and reducing access to general education). Other researchers note that attempts to answer questions about underor overrepresentation in special education based solely on statistical analyses of labels and placements undermines necessary consideration of the myriad societal and historical manifestations of race and disability bias that historically, and currently, plague educational systems in the United States (Boda, 2019). More research that integrates multifarious historical and sociological facets and amplifes the voices of those impacted by the provision or withholding of special educational services is certainly warranted. One fnal note regarding inequitable representation: It is clear from the data that Native Americans’ experiences are sorely understudied in research, as they have the highest probability of receiving special education services among American students (National Center for Educational Statistics, 2022) yet little research on their experiences exists, particularly when compared to the strong emphasis on Indigenous groups in other countries (Cooc & Kiro, 2018). A persistent frustration among advocates for inclusive education is that, despite reform movements to ensure quality educational opportunities for all students, students with disabilities continue to underperform on standardized assessments. In the most recent administration of the National Assessment of Educational Progress (NAEP) (eighth grade), only 8% of students with disabilities achieved a profcient level or higher, while 37% of their typically achieving peers did so (NAEP, 2018). While some might be tempted to attribute this disparity to presence of a disability, which, by IDEA defnition adversely afects a student’s academic performance, it should be noted that the

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vast majority of students who receive special education services do not do so due to lower cognitive functioning or IQ. Even students who are considered to have a “specifc learning disability” (reading, writing, or math) do not have markedly diferent cognitive abilities or IQs from students without this label (Kamhi & Catts, 2014). And it should go without saying (yet often still needs to be said) that physical and sensory impairments do not diminish intelligence or cognition. For good reason, the assumption undergirding the IDEA and, indeed, the philosophy of inclusion is that most students labeled as having disabilities should perform comparably to their peers if accommodated efectively. The challenge for science educators, and arguably, all educators, is that we are currently not reducing barriers to learning or providing students with special educational needs with the tools or strategies to reach their potential. The lack of quality science education for students with disabilities has staggering consequences for the STEM felds and society as a whole. Persons with disabilities remain underrepresented in STEM felds. Although 14% of K–12 students and 19.5% of undergraduates report to have disabilities, only approximately 10% of employed scientists and engineers have one or more disabilities (National Science Foundation, 2019). Students with disabilities are lost as they traverse the STEM pipeline, due to myriad reasons, including tracking of students into nonacademic pathways, exclusion from advanced courses and laboratory experiences, expectancy efect (i.e., low expectations of others leading to low expectations of self), and bias and stereotyping (Thurston et al., 2017). Sadly, this fltering out of talent represents a loss to a feld that benefts from diverse perspectives (Kingsbury et al., 2020), a loss to individuals insofar as informed personal decision-making and career options, and a practical and moral loss to society that must reckon with the knowledge that we have failed. We can and must do better through science education research and human understanding.

Learners With Gifts and Talents The language related to students with gifts and talents is perhaps even more variable than that of disability since there is currently no federal mandate for or defnition of giftedness in the United States. (For an international comparison of gifted and talented education policies and practices in 38 countries, see Frantz & McClarty, 2016.) However, most states in the United States do have requirements for identifying and serving gifted students, leading to approximately 3 million students receiving gifted education services (Rinn et  al., 2020). The leading organization in the United States, the National Association for Gifted Children (NAGC), defnes students with gifts and talents as those that “have the capability to perform – at higher levels compared to others of the same age, experience, and environment in one or more domains. They require modifcation(s) to their educational experience(s) to learn and realize their potential” (NAGC, 2019). That said, defnitions vary widely at state, district, and even school levels. Researchers have noted that most defnitions ft into one of four categories: psychometric defnitions (scoring well on tests for intelligence or creativity), performance defnitions (demonstrating high achievements in school), labeling defnitions (socially accorded by an expert), and specifc giftedness/talent defnitions (strength in a particular domain, such as music or sports) (Stoeger et al., 2018). Some general characteristics of gifted students include strong curiosity about how things work, preference for independence and autonomy, ability to link seemingly disparate ideas and information, creativity in thinking, and nonconformity (Tzuriel et al., 2011). While earlier conceptions viewed gifts and talents as inherent personality traits, the most prevalent of which was “intelligence,” more recent notions have recognized the complex interaction of learner contexts and processes that promote high achievement, as well as the range of resources that learners can apply to be successful at various learning tasks (Sternberg, 1985; Stoeger et al., 2018). Part of this broader interpretation comes from the underrepresentation of students of color and students of low SES in gifted education (de Brey et al., 2021). Regrettably, high potential students from lower-SES

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groups who do not receive early education or gifted education are more likely to underperform or drop out of school than their high-SES peers (Olszewski-Kubilius & Corwith, 2018). Calls for universal screening, that is, using tests that are administered to entire populations, such as an entire grade or age group, rather than to a select group of students based on earlier screenings or nomination procedures, attempt to overcome disproportionality in gifted education (Plucker & Peters, 2016). Not unlike the situation with disability education, misconceptions permeate gifted education. Gifted education is often seen as elitist, expensive, and unnecessary due to the belief that students with exceptional abilities will achieve regardless of their education or experiences (Subotnik et al., 2011). To the contrary, gifted students may sufer if their potential is not nurtured and their socialemotional needs not met. Gifted students often experience uneven development between their domain of giftedness and their social-emotional development, leading to perfectionism; sensitivity to criticism; feeling diferent from others; and experiencing anxiety, boredom, and ostracism in school (Dixson et al., 2016; Jarrell & Lajoie, 2017). For these and other reasons, it is critical to recognize that comprehensive, holistic programs are needed to address the multifaceted needs of gifted students, much in the way that they are needed by students with disabilities. Failure to identify and nurture the giftedness in students, particularly in science, misses untold opportunities for the feld of STEM and for society, and perhaps most importantly, for the students themselves whose capabilities and possibilities are never realized.

Infuential Standards, Policies, and Practices for Students With Special Needs and Talents In order to provide all students with education that is commensurate to their abilities, several policies, practices, and standards are applied. When students are identifed as needing special education services under the IDEA, diferent accommodations and modifcations, as well as supplemental services such as speech or occupational therapy, are incorporated into the child’s Individualized Education Program, or IEP (IDEA, 2004). Accommodations are adjustments in the learning environment, curricular format, or presentation of instruction that attempt to level the playing feld for all students without altering the learning expectations. Some examples include providing large print or braille handouts for students with visual impairments, allowing extended time on activities on assessments for students with learning disabilities, or providing speech-to-text software for students with orthopedic impairments that makes writing or keyboarding difcult (McGinnis & Kahn, 2014). Modifcations, on the other hand, are changes to curriculum that alter the learning expectations and are used when accommodations alone do not allow a student to progress in the standard curriculum. For example, a student with a specifc learning disability in decoding text might be given a lower-level book to read while a student with an intellectual disability might be given fewer test questions to complete. Gifted students are not covered under the IDEA, and there is currently no federal requirement for a gifted IEP in the United States. However, some states do require gifted IEPs that lay out adjustments and supplements to the child’s program (Rinn et al., 2020). Unlike accommodations and modifcations that address learners’ needs individually, Universal Design for Learning (UDL; Rose & Meyer, 2002) is a framework grounded in research from cognitive and learning sciences that seeks to reduce the barriers for all students in a classroom, rather than just those who are identifed as having disabilities. While accommodations and modifcations are more closely aligned with diferentiated instruction (i.e., teaching learners specially according to their needs) (Maeng & Bell, 2015), UDL attempts to minimize the needs for specifc accommodations in favor of generalized practices that increase accessibility for all students by providing multiple means of presenting information to students, allowing for a range of modalities through which students can share what they have learned, and providing students with a variety of ways to interact and engage with the curriculum (CAST, 2018; Izzo & Bauer, 2015).

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Relatedly, an emphasis on improving teacher education for students with special needs has led to the development of high-leverage practices (HLPs; McLeskey et al., 2017) which are 22 efective, research-based instructional practices that are commonly used in classrooms and have been shown to improve outcomes for students. An example of an HLP in assessment is: Use multiple sources of information to develop a comprehensive understanding of a student’s strengths and needs. In gifted education, the 2019 NAGC Pre-K to Grade 12 Gifted Programming Standards (NAGC, 2019) defne benchmarks for student outcomes and for using evidence-based practices that are the most efective for students with gifts and talents. Teacher preparation standards in gifted education also exist (NAGC, 2013), and the Every Student Succeeds Act (ESSA, 2015) requires states applying for teacher professional development funds to include plans for helping teachers and administrators identify and teach gifted learners. To meet the needs of gifted students in the classroom, several research-based strategies have been identifed, including talent development (Olszewski-Kubilius & Thomson, 2015), enrichment (Renzulli, 2021), acceleration and curriculum compacting (Worrell et al., 2019), diferentiated instruction (Tomlinson, 2014), and diferentiated curriculum and assessment (Van Tassel-Baska et  al., 2021). Each of these approaches seeks to meet the unique needs of learners with gifts and talents by ensuring appropriate challenge, depth, and complexity of learning while allowing for fexibility and openendedness in assignments and assessments to foster creativity and problem solving.

Twice Exceptional (2e) Learners It is not uncommon for learners to be gifted in one or more domains while also having one or more disabilities. Researchers estimate that 9.1% of children who have disabilities may be gifted, yet only a small percent of them participate in gifted programs (Barnard-Brak et al., 2015). These twice exceptional, or 2e, learners (Reis et al., 2014) may face special educational challenges, as it is not uncommon for educators to focus on accommodating the student’s disability at the expense of promoting the student’s gifts. This situation can lead to depression, low motivation, and low self-esteem (Wang & Neihart, 2015). While few empirically validated interventions have been identifed for 2e students (Foley-Nicpon et al., 2018), strengths-based approaches (Armstrong, 2012), talent-focused assessments and interventions, and other strategies gleaned from special and gifted education literature are promising practices. With some general context for learners with special needs and gifts in place, we will now turn our attention to science-specifc approaches for teaching and learning.

What Key Themes and Pedagogical Approaches Characterize Contemporary Inclusive Science Education? Supporting Inquiry-Based Science Inquiry-based science has emerged as the gold standard of pedagogical approaches in contemporary science. At its core, inquiry-based instruction aims to position students as the generators rather than passive receivers of knowledge, thereby emulating the work of scientists in an authentic way. While there is no clear consensus on the precise defnition of inquiry-based science (Cairns & Areepattamannil, 2019), most scholars view inquiry as a spectrum that moves from teacher to studentdirected decision-making about the question being investigated, the procedure to be followed, and the materials to be used. The goal is to develop students who ask questions about the natural world and seek to derive explanations for phenomena based on empirical evidence (NGSS, 2013). In doing so, nature of science tenets, such as the tentativeness of scientifc knowledge and the empirical nature of evidence, are imbued (Lederman & Lederman, 2014). Much research supports the notion that inquiry-based learning is more efective than text-based learning for students with disabilities

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in science (Aydeniz et al., 2012; McCarthy, 2005). However, there appears to be some divergence in the literature regarding the recommended level of inquiry when implemented with students with learning disabilities. In their literature review of 12 studies related to inquiry-based science instruction, Rizzo and Taylor (2016) concluded that students with disabilities require scafolds to experience beneft and that explicit instruction is required. Experts defne explicit instruction as a systematic instructional approach that utilizes empirically validated principles of instruction to overtly teach fundamental concepts and skills that students might not otherwise acquire on their own (Archer & Hughes, 2010). Such principles include (1) overt demonstrations and explanations of science concepts, procedures, and vocabulary; (2) frequent practice opportunities that allow students to convey their scientifc thinking; and (3) timely, academic feedback to address potential misconceptions and gaps in understanding. In this study, the authors identify strategies such as pre-teaching essential vocabulary, using explicit scafolds during inquiry instruction, and providing formative feedback as being particularly efective for students with a range of disabilities. The authors speculate that the success of structured inquiry stems from delivering explicit instruction from the outset and fading that support over time, while other inquiry models, they suggest, begin with student exploration and then have teachers support and clarify based on conceptual change theories. Cairns and Areepattamannil (2019) similarly concluded, via hierarchical linear modeling of data from more than 170,000 15-year-old students from 4,780 schools in 54 countries, that inquiry is not necessarily better for students’ science achievement, as measured on the Program for International Student Assessment (PISA), but is better for student interest and enjoyment in science, which may support long-term retention. The predictor measure, science inquiry, used six student-answered questions from the PISA related to inquiry-based teaching/learning. The fndings in this study are limited by lack of knowledge on the quality of inquiry teaching, the classroom environments, and issues with student selfreporting. Most recently, Doabler et al. (2021) examined the impacts of a second-grade NGSS-aligned curriculum on Earth’s systems that utilized explicit instruction, which the authors equate with “guided inquiry” in the context of science, on student learning. Specifcally, the authors point to providing empirically validated instructional methods being explicitly taught or modeled, including demonstrations or explanations of science concepts and vocabulary, frequent verbal or written responses to share scientifc thinking, and timely feedback to address misconceptions and formatively assess student learning. In the study, 18 second-grade classrooms were randomly assigned to treatment (the “Sci2”program) or control conditions. The researchers implemented a randomized controlled trial with 294 students (43 of whom were eligible for special education) nested within classrooms and classrooms nested within condition. Pre/post-tests were administered to all students using four researcher-developed measures and found that students in the treatment condition signifcantly outperformed their control-group peers on three of the four measures. While the fndings are preliminary, the authors argue that the guided inquiry is preferable to less scafolded levels of inquiry (e.g., open inquiry). Appropriate scaffolding of inquiry in inclusive science classrooms has been cited as an area of concern by other scholars (Mumba et al., 2015). Given that teachers lack training and confdence in inclusive science teaching (Kahn & Lewis, 2014), teacher training may be needed to ensure that teachers can provide appropriate supports within structured inquiry (McGrath & Hughes, 2018). In sum, when looking across these studies, it appears that there is consensus around support for inquiry-based science for all students yet disagreement regarding the level and types of support that may be needed for implementation, and perhaps some confation between levels of inquiry (e.g., whether teacher or student drives question, method, materials, etc.) and the choice of instructional models.

Supporting Disciplinary Literacy Science instruction frequently relies heavily on expository (informational) text, which can present challenges for learners with and without disabilities. Expository text typically contains new

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information, vocabulary, and data (i.e., is “conceptually dense”), requires students to make inferences based on prior knowledge, and is unpredictable in comparison to narrative text, which tells a story (Mason & Hedin, 2011). Reading ability is key to learning science concepts and demonstrating learning of science knowledge on tests (Reed et al., 2017a). Disciplinary literacy (DL) refers to learning how to read, think about, write, communicate, and use information as a discipline’s expert does (Koomen, 2018b; Zygouris-Coe, 2015). The discipline-specifc skills science educators wish to foster include developing and interpreting scientifc arguments, making evidence-based claims, describing scientifc phenomena, integrating text and visual representations, and using scientifc language, among others (Reed et al., 2017b). DL can prove particularly challenging for students with learning disabilities (Kaldenberg et al., 2015), autism (Carnahan et al., 2016), emotional and behavioral disorders (EBD) (Taylor, 2016; Therrien et al., 2014), and speech language impairments (Boyle et al., 2020). Many researchers suggest that literacy strategies should be embedded within content instruction due to the high literacy demands in science for all learners (Kaldenberg et  al., 2015; Zygouris-Coe, 2015). Although the type and extent of language challenges may vary across learners, certain interventions have proven successful for a range of learners, many of which are considered mainstays in UDL due to their success across learning environments. Graphic organizers, for example, support vocabulary and comprehension for learners with autism spectrum disorders and have the added beneft of supporting individualized learning (Knight et al., 2013). Therrien and colleagues’ (2011) metaanalysis pointed to the validity of mnemonics, structured inquiry, and supplemental interventions, including peer learning and explicit instruction, as having medium to large efect sizes on supporting DL-related aspects of science for students with learning disabilities. In a similar meta-analysis for students with emotional behavioral disorders, Therrien et al. (2014) found many of the same interventions to have medium to large efect sizes, with mnemonics again showing the greatest impact and science response/review cards being added as a noteworthy scafold for supplemental intervention. Kaldenberg et al. (2015) found that explicitly teaching vocabulary defnitions, semantic mapping, and mnemonics to be highly efective with students with learning disabilities as well. Koomen (2018b) found that read-alouds, shared reading, text structures (e.g., breaking up large amounts of text with charts, maps, headings, etc.), graphic organizers, question the author (QTA), self-monitoring strategies, and summarizing pre-teaching vocabulary to be most helpful. Robust literacy instruction may be challenging to integrate into science, as teachers tend to use simplistic strategies such as providing students with vocabulary words and defnitions rather than teaching comprehension strategies (e.g., interventions that focus on main idea instruction, selfquestioning, the use of graphic organizers, etc.) (Wexler et  al., 2017). Barriers to quality literacy instruction included time, lack of training, as well as misperceptions teachers held about the level of evidence-based practices that they implement in their classes. To summarize, several research-based strategies can support DL in science, including use of mnemonics, graphic organizers, semantic mapping, and explicit instruction of vocabulary, yet helping students to develop the metacognitive strategies to independently select and apply appropriate strategies is an area of challenge.

Students With Learning Disabilities Learning disabilities (LD) comprise the most common form of learning diference in science classrooms, both because they are the most prevalent and because students with LD are most likely to be included in general education classrooms (NCES, 2020). LDs impact one or more of the basic psychological processes underpinning a students’ ability to understand or use language, written or verbal, and may specifcally manifest as challenges with reading, writing, speaking, listening, reasoning, or doing math calculations (Individuals with Disabilities Education Act [IDEA], 2004). In science, this means that students with LD may have challenges with reading or comprehending text,

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lectures, class discussions, working with data, writing or orally presenting assignments, and so on. Consequently, science teachers may severely underestimate students with LDs’ abilities (Marino et al., 2013). Some common terms utilized in relation to LD are: (1) dyslexia – difculty with reading; (2) dysgraphia – difculty with writing; (3) dysphagia – difculty speaking; and (4) dyscalculia – difculty with math (Centers for Disease Control and Prevention [CDC], 2021). As students with LD are increasingly included in general education science classes, the importance of supporting their educational needs cannot be overstated. Therrien and colleagues’ (2011) meta-analysis of 12 studies published between 1985 and 2006 suggested that an emphasis on “big ideas,” hands-on concrete experiences, formative feedback, behavioral supports, and review of core concepts were most helpful for encouraging science learning for students with LD. To address the challenges that LD students might encounter in inquiry-based science settings, McGrath and Hughes (2018) conducted a cross-case analysis of six middle school students with LD taught in an inclusive general education setting using inquiry-based instruction. Analysis of student portfolios, observations, and interviews yielded results suggesting that students with LD had difculty with inquiry, specifcally due to challenges with abstract concepts and phenomena that are difcult to teach concretely, challenging vocabulary and other literacy skills, and group work. The authors surmise that, while inquiry reduces reading compared to text-based lessons, authentic inquiry still requires reading, diagrams, tables, etc., which necessitates signifcant support of students with LD. King-Sears and Johnson (2020) explored the efcacy of a UDL intervention for students with LD in high school chemistry classrooms. In this two-part study, the authors examined whether students with and without LD in co-taught general education chemistry classes, and students with LD in a self-contained classroom, calculated molar conversions more accurately after receiving instruction through a UDL approach (consisting of demonstration via videos, guided practice, and scafolded independent practice) versus a traditionally taught comparison group. The authors found that, while the UDL treatment was efective at increasing all students’ ability to accurately calculate the molar conversions, further research is needed to parse out which components of the UDL package contributed most signifcantly to student success. An increasing body of literature suggests that video games are a promising instructional tool for students with LD in science. Israel et al. (2016) applied multilevel modeling to explore the impacts of three video game–enhanced life science units on the science performance and attitudes of 366 US middle school students, representing 18 life science classrooms, over a six-week period. The researchers examined the impacts and interactions of students’ reading levels (based on National Assessment of Educational Progress reading scores), presence of a learning disability, gender, and student perceptions of science and video games in order to better understand the interplay of disability challenges (e.g., lack of accessible materials) and gender challenges (e.g., women’s lack of confdence and lower sense of connectedness to science). All students, with and without LD, showed signifcant learning gains. While reading ability and self-perceptions of their skills in science were predictive of comparatively lower performance, LD status and gender were not. These results suggest that video games may serve to mediate the challenges faced by students with LD and/or gender. The need to better support students whose identities reside at the intersection of disability and gender is critical in addressing inequitable representation in STEM felds (Grifths et al., 2020). In order to better understand the science learning experiences of a 13-year-old African American male in seventh grade with both learning and behavioral disabilities, Koomen (2016) undertook a 13-week case study during which time the class was learning about ecology. Data included interviews with the student, whose pseudonym was Wizard, as well as his science and special education teachers, videotaped classroom observations, and feld notes. Findings suggested that Wizard’s experiences were (1) characterized by both autonomy and dependence and (2) shaped by fragmented learning experiences due to challenges with disciplinary literacy. An additional fnding was that students can appear to

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be “included” but can in fact be academically excluded and underserved. The author provides several strategies for attending to students’ emergent disciplinary literacy while also supporting their agency and voice. The overrepresentation in special education and underrepresentation in science of bilingual children prompted Martínez-Álvarez (2017) to explore the impact of language on bilingual students with LDs’ science achievement during implementation of a place-based and text-based geoscience unit. Six bilingual fourth-grade students with LD, whose parental language was Spanish, participated in the study in which the researcher analyzed audiotapes, feld notes, pre- and post-assessments, and interviews with the participants using a cultural historical activity theory (CHAT) perspective. Findings suggest that the students’ understanding was undervalued when interpreted within traditional Western scientifc perspectives, but this could be mediated by using instructional tools such as questions, visuals, and texts that allowed for linguistic and conceptual advancement. Audiobooks and podcasts may play a key role in supporting students with LD. Gomes and Mensah (2016) found that audiobooks supported students with language-based learning disabilities when the content was unfamiliar to students. Relatedly, podcasts may be a viable alternative to teacherread test accommodations for students with learning disabilities. Examining 47 sixth-grade students with reading difculties in an urban middle school, McMahon et al. (2015) found that there was no signifcant diference in scores between the teacher read-aloud and podcast conditions, but medium positive efect of both over standard (no read-aloud) condition. The podcast allows students to take tests at their own pace and enables teachers to have more time for planning and instructing rather than reading tests. To summarize, students with LD may encounter challenges in science due to language and organizational demands, as well as an emphasis on abstract concepts. Many research-based practices can support students with LDs, with new technologies, including audiobooks and podcasts, showing great promise.

Students With Intellectual and Developmental Disabilities Students with intellectual (cognitive) disabilities (ID) demonstrate difculties in cognitive functioning as well as social, communication, or adaptive skills. IDs can range from mild (i.e., “high incidence”) to moderate, severe, and profound (i.e., “low incidence”). One common challenge in science teaching for students with ID is that teachers often adapt science content by aiming it toward much younger students and below grade level according to science standards (Taylor et al., 2020). In addition, much of the research on students with ID in science focuses on life skills (e.g., cooking, watering plants) or behavioral indicators (e.g., following directions) rather than science learning goals (Andersen & Nash, 2016). The term “developmental disabilities” (DD) is a blanket term that refers to impairments in physical, learning, language, or behavior areas that begin during a child’s developmental period, often impacting the reaching of developmental milestones (Centers for Disease Control and Prevention, 2022). DD includes diagnoses such as autism, ID, cerebral palsy, and a range of other conditions that may require learning accommodations. DD is not a category under the IDEA, but autism, or autism spectrum disorder (ASD), is generally considered a developmental disability that may or may not impact intellectual abilities. Under the IDEA, autism is distinct from ID and is recognized as a spectrum that includes many individuals of very high intellect. In fact, students with autism are more likely than other students with or without disabilities to gravitate toward STEM felds (Wei et al., 2017). Although autism and ID may be completely distinct, many studies combine participants, particularly when students demonstrate cognitive impairment. Some studies also refer to “developmental disabilities” to mean intellectual disabilities, which may or may not include participants with autism, primarily because the interventions overlap. For these reasons, this section will include studies on teaching science to students with ID and/or developmental disabilities (IDD) and/or autism with full recognition that this is a woefully imperfect approach.

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A series of recent reviews attempt to evaluate and encapsulate the research on students with ID and/or ASD in science learning. In a meta-analysis, Taylor, Hwang et  al. (2020) analyzed 18 studies published between 2000 and 2018, fnding low efect sizes for elementary classroom interventions, perhaps due to elementary teachers’ reluctance to teach science. Students taught in general education classrooms or self-contained special education had higher efect sizes than when students were taught in resource room (i.e., “pullout”) classes. Self-management strategies had the highest efect size of all strategies, with task analysis (breaking down a complex task into smaller steps) also proving to be efective, while explicit instruction indicated low efects, the latter being surprising given prior research touting explicit instruction. The authors surmise that this might be due to students with IDD language challenges, which would be particular barriers to explicit/ scripted instruction. In their meta-analysis on science for students with ASD, Taylor, Rizzo et al. (2020) included 11 single case studies from 2000 to 2018, concluding that most interventions utilized an array of strategies, with explicit/scripted instruction being most prevalent. Most of the studies occurred in special education resource rooms, with very few occurring in inclusive general education science classes. Graphic organizers, self-management strategies, and text-based strategies emerged as having some of the largest efect sizes, yet almost all of the strategies would likely contribute to enhanced learning, suggesting that students with ASD beneft from combinations of approaches to maximize learning. Knight and colleagues’ (2020) review on teaching science practices (e.g., asking questions, developing and using models, constructing explanations) as opposed to the more commonly studied area of teaching science content, to students with ID or ID/ASD covered studies from 2009 to 2018. With 12 single case studies included, the authors identifed the following three strategies as evidencebased practices (EBPs; having positive efects in at least fve studies): multiple exemplar training (i.e., giving students a variety of opportunities in a variety of contexts to apply the science practice); task analytic instruction (i.e., teaching practices via a series of step-by-step tasks); and time delay (i.e., using increasing amounts of time between instruction from the teacher and a prompt to elicit practice response). Particularly noteworthy is that the authors concluded that these three strategies were also efective at teaching content. Ehsan et al. (2018) conducted a systematic review of STEM instruction with students with ASD, ages 5–25. While the authors examined all STEM disciplines, specifcally in science, they found that most interventions were multicomponent, with successful strategies including explicit instruction, self-directed instruction, educational technology, and graphic organizers. The authors opine that most of the studies refect behaviorist approaches and that more “student-centered” approaches would be benefcial in research. In perhaps one of the most comprehensive reviews, Apanasionok et  al. (2019) systematically reviewed interventions for teaching science to students with DD, defned as having ID and/or ASD. Of the 30 included studies between 2003 and 2017, 29 were from the United States and one from the United Kingdom, and together they represented 118 students. Not surprisingly, 23 studies utilized systematic instruction, including task analysis, embedded instruction (providing instruction for target skills during ongoing activities), constant time delay (procedure involving delivery of the prompt after a specifc amount of time after the instruction), simultaneous prompting (the prompt is delivered straight after the instruction and then gradually faded out), system of least to most prompts (hierarchy of prompts used to help the students, starting from the least intrusive), and scripted lessons (provides teachers with scripts with exact information on how to teach each target and deliver the instruction). Of 90 participants taught by systematic instruction, only three students did not make progress in their target skills as a result of the intervention, and participants mostly felt that the approaches were valuable and efective. The authors conclude that, while systematic instruction may be an efective (and preferred) strategy by special education researchers, there is limited evidence supporting practices that support learners’ scientifc reasoning abilities, including generating predictions

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and evaluating evidence, again raising the specter of the behaviorist/constructivist schism in science education research. While conclusions about best practices for students with IDD can be drawn from these reviews, attention to some specifc studies merits our attention. Heinrich and colleagues (2016) found embedded simultaneous prompting (SP) to be an efective intervention for teaching STEM content about heredity to three secondary students with moderate ID in an inclusive general education classroom. SP involves giving students an instruction and immediately providing a response during the teaching phase, and then removing the instructor’s response during the probe or test phase. Using a multipleprobe-across-participants design, the researchers found that all participants were able to reach their target goals in two to eight sessions, maintain their skills for one month following the intervention, and generalize their learning to novel situations. Collins et al. (2017) also applied a multiple-probeacross-participants design to examine the efects of SP through the steps of a task analysis to teach four secondary students with mild intellectual disabilities to care for plants, a potential employment skill, while also learning science content on photosynthesis. The intervention consisted of asking a question (e.g., “What’s the frst step to take care of the plant?”) followed immediately by a verbal prompt (e.g., “Rotate the plant so it gets sunlight evenly”), after which the student would rotate the plant and be praised. The teacher then included information on photosynthesis (e.g., “You rotate the plant because . . .”). The results indicated that all four participants were able to meet their employment goals for plant care, increase their science content knowledge, and retain skills for up to eight weeks following intervention. Supporting students with ID’s autonomy is a key concern, particularly when trying to support inquiry. Miller and Taber-Doughty (2014) explored the use of self-monitoring checklists with three middle school students with intellectual disabilities in order to increase independence in science inquiry. The curriculum included instruction and hands-on activities on topics such as testing sunscreens using UV-sensitive beads and creating anemometers using a variety of materials. Relatedly, Miller et al. (2015) experienced similar results when investigating self-monitoring checklists with high school students with ID as well. The use of peers as interventionists is a well-supported strategy for students with ID. Jimenez and colleagues (2012) investigated the use of peer-mediated instruction (PMI) to teach inquiry science and use of a K-W-H-L chart to students with moderate ID in an inclusive setting. Six general education peers were trained in embedded constant time-delay procedure and then implemented it during three science units with fve students with moderate ID. The fndings suggested that all of the students with moderate ID increased correct responses, while all six peers implemented the intervention with high fdelity, maintained their grades, and wanted to continue in the role, as they felt that the experience was enjoyable. Similarly, Wu et  al. (2020) examined the impact of PMI when used with augmentative and alternative communication (AAC) (speech-generating devices) on science learning. Three Taiwanese students with signifcant cognitive disabilities participated in the study. Nine students without disabilities were trained on the intervention-taught scripted lessons on buoyancy and electricity. Using a multiple-baseline-across-participants design, the researchers found that PMI, along with the AAC intervention, was efective in improving participants’ science knowledge as well as their communication interactions with peers when compared to the general teaching strategy. The use of technology may be particularly helpful for students with IDD, including electronic science notebooks (Miller et al., 2013) and computer-assisted instruction (CAI) to teach vocabulary (Smith et al., 2013), including in rural settings (McKissick et al., 2018); multicomponent multimedia shared story (MSS) intervention via an iPad to teach science vocabulary (Rivera et al., 2017); electronic text (eText) (Knight et al., 2018); augmented reality (AR) for teaching science vocabulary (McMahon et al., 2016); and robotics and coding as emerging content areas for students with ASD (Knight et al., 2019).

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In a marked departure from the majority of studies, Madden et al. (2018) represents a collaborative research efort between a science education faculty member, a program administrator, and three young women with ID who share their personal science learning experiences. Their stories helped to identify trends, including overreliance on text in science classrooms, a strong preference for hands-on authentic learning, and a desire to have teachers who wanted to teach students similar to themselves. Similarly, work by Kingsbury et al. (2020) presents three autistic geoscience authors’ framework for students with autism in geosciences, which may well be applicable across all science felds. The three points of their framework are: (1) develop efective communication pathways with autistic students; (2) presume competence, recognizing that autistic individuals are experts in their autistic existence; and (3) employ strategies for expectation management. In sum, a signifcant body of empirical evidence supports the use of systematic instruction, graphic organizers, approaches that support self-monitoring (e.g., checklists), embedded instruction, and emerging technologies as efective supports for students with ID and/or autism. That said, the research overwhelmingly refects studies with similar approaches and methodologies that rarely include or lift the voice(s) of the participants. Perhaps greater use of qualitative and mixed methods within this area of research would illuminate a wider range of interventions.

Students With Emotional and Behavioral Disabilities Students with emotional behavioral disabilities (EBD) often have difculty achieving in science due to several barriers, including student behavior, teacher training and confdence, and instructional format (Taylor, 2016). Students with EBD may struggle in their interpersonal relationships with peers and teachers, which in turn may manifest as inattention, noncompliance, aggression, anxiety, and depression, leading to academic challenges (Sanders et al., 2018). Instruction through lectures and textbooks is particularly inefective for students with EBD, as these methods rely on high literacy skills and prior knowledge (Therrien et al., 2014). Theoretically, science should be a key area for students with EBD, as it presents an opportunity for students to connect to topics of personal or societal relevance, daily living, and independence (Taylor, 2016). Therrien et al. (2014) conducted a meta-analysis representing 11 studies including 72 students with EBD over a 30-year time period. The interventions as a whole had small to medium impacts on students with EBD’s science achievement; most of the assessments measured factual recall rather than understanding, and only two studies looked at whether students were able to demonstrate their learning on generalized achievement measures (e.g., standardized tests). Mnemonics again proved to be a successful strategy for promoting factual recall and retention; inquiry may be promising, but more research is needed to explore scafolds for student learning and behavior as other researchers have noted that of-task behavior can be particularly problematic in hands-on, inquiry-based lessons, which are typically less structured from the outset (Watt et al., 2014). One emerging strategy that may increase reading comprehension while attending to students with EBD’s emotional needs is self-regulated strategy development (SRSD), the focus of a study by Sanders et al. (2018). The purpose of this study was to examine the efectiveness of the TWA (Think before reading, think While reading, and think After reading) (Mason & Hedin, 2011). TWA is a form of SRSD where the student is taught to self-monitor and self-evaluate while reading. This study specifcally focused on increasing reading comprehension of science texts in 25 middle and high school students with EBD. Results of a piecewise hierarchical linear model suggest signifcant gains in reading comprehension. SRSD was further studied by Garwood and colleagues (2019), who applied a multi-probe, multiple-baseline-across-groups-of-students study involved a science teacher in a residential treatment facility who implemented SRSD to teach persuasive writing with 11 secondary students experiencing complex trauma. The researchers found large efects for persuasive parts, word count, and holistic quality, and all students felt that this intervention improved their writing.

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Taylor and Hwang (2021) present a novel framework for teaching inquiry-based science to students with LD and/or EBD in an online environment. Based on fndings from prior research, the authors’ proposed framework involves inquiry-based instruction, which uses many of the alreadynoted scafolds (e.g., structured inquiry, use of big ideas, multimodal representations, including moving from concrete to abstract) as well as arts integration (e.g., visual, performance, writing, digital), teaching technology, and integrating engineering design. The authors contend that such a framework would provide necessary supports for students’ organizational and executive functioning while presenting a real-world context or authentic problem that students can address through arts, math, and engineering design. In sum, the research on students with EBD in science is rather limited; the research that exists reiterates many of the same scafolds for literacy, inquiry, and autonomy that were previously mentioned. However, the question of whether the use of inquiry and/or real-world problem solving through integration with arts and engineering remains open, as there appears to be tension between researchers as to how much autonomy is best for students with EBD.

Students With Physical Disabilities/Mobility and Orthopedic Impairments Mobility and orthopedic impairments refer to a broad range of functional abilities ranging from the inability to stand or walk far or at all without support to balance challenges and dexterity limitations, among others (Moon et al., 2012). Students’ mobility or orthopedic impairments may be related to medical conditions such as muscular dystrophy, cerebral palsy, or spina bifda, or temporary circumstances such as broken bones or chemotherapy. Some students with lower-body impairments may use wheelchairs, walkers, or canes/crutches, while those with upper-body impairments may have limited use of their hands or arms (DO-IT, n.d.). A wide range of assistive technologies are available to support students’ full participation in science, such as beakers with handles, lever-controlled rather than knob-controlled laboratory fxtures (Moon et al., 2012), push-button adjustable height tables in labs and maker spaces (Love et al., 2020), and specialized devices for computer and internet use that allow input from eye movement or speech (Lang et al., 2014). While little current research exists on the experiences of students with physical disabilities in the K–12 classroom, Jeannis et al. (2018) set out to understand the barriers and facilitators to full participation of students with physical disabilities in postsecondary STEM through a literature review of publications from 1991 to 2015. From the 22 articles analyzed, the authors found that little empirical evidence exists on this subject, as most of the studies were anecdotal. The authors were able to group the study fndings into three categories: (1) learning environment of the physical science and engineering laboratory, (2) physical built environment of the laboratory, and (3) the tasks needed to be executed in the laboratory space. Carabajal et al. (2017) similarly point to the range of barriers that impede full participation for people with physical disabilities in science, including physical barriers (e.g., inaccessible classroom or laboratory equipment and spaces, feld site terrains that are difcult to navigate) and nonphysical barriers (e.g., attitudinal and institutional including bias and stereotyping). The authors describe UDL approaches that support inclusive instructional design for geoscience feld work, such as tactile feld maps, audiorecorded feld guides, and alternative feld access, that allow all students, including those with hearing, visual, and mobility disabilities, to participate. Virtual learning environments (VLE)s, particularly virtual feld trips (VFT)s, may provide access for students for whom feld trips would be inaccessible otherwise but if used as replacements for traditional feld work may be seen as inferior. In a related study, Atchison et al. (2019) utilized a multiple case study applying a lens of inclusive learning communities to analyze three inclusive geoscience feld trip projects. The authors determined that providing physical and social access to learning were the most pressing. Additionally, strong feelings of social inclusion and belonging, along with individual ownership in the learning activities were also found to be essential. Similarly, Stokes and colleagues (2019) suggest that multisensory engagement,

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consideration for pace and timing, fexibility of access and delivery, and a focus on shared tasks are key factors in inclusive feldwork design. Koomen (2018a) use a framework of narrative inquiry to share the retrospective experiences in K–12 science learning from Alejandro, a college graduate born with cerebral palsy. Some of the themes that arose from this study were that science teachers need to pay more attention to physical accessibility, read IEPs, and create a positive and supportive classroom environment in which it is acceptable to make mistakes. Refecting on this section, it appears that there is little recent research on supporting students with physical disabilities in K–12 science classrooms. While much of the research done at the postsecondary level likely applies to K–12, more can be done to understand and address barriers to accessibility, both physical and attitudinal.

Students With Visual Impairments Making materials accessible for students with sensory impairments is a critical focus within current science education research, particularly for students with visual impairments (VI). Science concepts are often taught using two-dimensional models, graphs, and images, which makes it difcult for students with visual impairments to form mental models for scientifc representations. Wild and Koehler (2017) stress the importance of providing adaptive materials and addressing misconceptions that may emerge from interpretations of models/graphics. Moreover, science educators must be mindful of the fact that students with VI may require targeted support for concepts and skills that are learned incidentally with vision, such as orientation/mobility and social interaction. Chiu and Wild (2021)’s analysis of fve popular science curriculum books shows that many of these skills, which form the basis of the Expanded Core Curriculum for students with VI, can be taught through quality science lessons. Kizilaslan et  al. (2020) applied exploratory and evaluative case study methodologies to examine the efect of using adaptive materials and pedagogical accommodations on Turkish sixth-grade students with VI’s conceptual understanding of matter and energy. Results of pre/post-academic achievement tests and semi-structured interviews revealed that practices such as developing braille diagrams, using multisensory materials, applying color contrast for handouts, and simplifying complex concepts increased students’ conceptual understanding. Relatedly, Koehler et al. (2018) found 3D printed models superior to tactile graphics for communicating scientifc concepts to students with VI. However, the authors note that the novelty of the 3D models may have impacted their fndings. Data on the prevalence of adaptive equipment and pedagogical accommodations for students with VI are somewhat mixed. In their survey of 92 teachers of elementary students with VI, Rosenblum et al. (2019) found that teachers reported accommodating students with tactile or large-print graphics, 3D models, auditory instruction, talking science equipment, and experiential and kinesthetic learning. Pre-teaching diagrams and models, prior to instruction, were also widely used. In the same year, however, Bell and Silverman (2019) found, in their survey of 49 blind and visually impaired (BVI) students, that 85% of the students reported lacking access to information that was presented on blackboards, and many noted receiving accessible materials later than their peers without VI. These studies may suggest the importance of examining both teacher and student perceptions of accommodations within the same study in gaining deeper understanding of the barriers and facilitators to teaching and learning of science for students with VI. (For a review of technology and adaptive equipment for students with visual impairments in science, see Reynaga-Peña & del Carmen LópezSuero, 2020.) Field-based, informal learning experiences have also been shown to be efective in promoting conceptual understanding in science for students with VI. Wild et al. (2013) investigated the experiences of 18 middle and high school students with VI who participated in a weeklong feld-based

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geology summer camp. The researchers found that after a week of intensive experiences, students gained more accurate conceptions of scientifc phenomena but not all misconceptions were eliminated. Perhaps most interesting was that students communicated some misconceptions that were previously unreported in the literature, including people contributing to Earth’s processes, water pressure causing tectonic plates to move, and using tree rings to date planetary history. The researchers suggest that additional informal experiences paired with formal classroom instruction will be necessary to clarify concepts. In another feld study, Tsinajinie et al. (2021) examined the impacts of an outdoor project-based learning (PBL) program for middle and high school students with VI. The authors qualitatively analyzed photographs, camp associate intern notes, and researcher memos, fnding that the combination of PBL, multisensory experiences, and assistive technology to support students with VI proved to be successful at promoting inclusion. To summarize, the research on science for students with VI suggests that full inclusion is feasible with appropriate adaptive equipment and instructional materials. Special attention to physical models and experiential learning provides students with BVI multisensory opportunities and can aid in forming mental models.

Deaf and Hard-of-Hearing Students Deaf and hard-of-hearing (DHH) learners have been sorely understudied in science education. Given that teachers report lack of preparation (Raven & Whitman, 2019), it is perhaps not surprising that DHH students underperform their peers in science, are less likely to be enrolled in secondary science courses, and are underrepresented in STEM careers (Nagle et al., 2016; National Science Foundation, National Center for Science and Engineering Statistics, 2019). Although auditory deprivation is commonly thought to negatively impact DHH students’ executive functioning, it is likely language deprivation that is the key threat (Hall et al., 2017), prompting many researchers to emphasize literacy and language acquisition in science learning for DHH students (Im & Kim, 2014). Ensuring that DHH students have rich educational experiences that tap students’ creative, problem-solving, and collaborative skills, as well as linguistic and literacy skills, is critical to ensuring equitable STEM opportunities in schooling and beyond (Enderle et al., 2020). In order to gain an understanding of how DHH students are educated in the science classroom, Raven and Whitman (2019) used an exploratory mixed-methods design with data including classroom observations, standardized test scores, an online survey, and interviews with 16 teachers of DHH students. The authors found that most of the teachers lacked science content background and used simplistic instructional strategies, such as drawing pictures, rather than engaging students in scientifc investigation. The authors suggest that reducing vocabulary-heavy teaching may be preferable to trying to make instruction visual and that more science content profciency, as well as accommodations for DHH students, is warranted. The technical nature of scientifc language can prove particularly challenging for DHH students. Many vocabulary words need to be specially interpreted/translated, as there may not be an equivalent in American Sign Language (ASL) (Enderle et al., 2020) or British Sign Language (BSL) (Cameron et al., 2016). To counteract this latter challenge, Cameron et al. (2016) describe the manner in which the BSL online glossary of science technical terms, based at the Scottish Sensory Centre at the University of Edinburgh and designed with input from deaf scientists and experts in BSL, supports both students and teachers. Enderle and colleagues (2020) further illuminated the limitations of sciencespecifc ASL vocabulary, particularly insofar as conveying key aspects of the NGSS, including nature of science (NOS) and science and engineering practices (SEP). The authors examined eight publicly available resources for ASL for key words pertaining to NOS and SEP to understand how these words communicate scientifc meanings. Out of the 74 terms searched, the researchers determined that only 21 terms that had a consensus sign available across at least three of the eight resources. Moreover, even those signs conveyed misconceptions. For example, it was noted that the sign for “science” involves

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rotating both hands in a shape to appear as though they were holding fasks; the authors noted that this image of science privileges chemistry but fails to convey the breadth of scientifc investigatory endeavors. Hufing et al. (2018) also recognized the limitations of ASL for conveying scientifc terminology when implementing a weeklong residential herpetological research experience for high school students. Barriers for inclusion of two DHH students included the auditory nature of some of the aspects of the project (“frog calls”) and the lack of ASL signs for the various species, among other terms. Applying a DSE and UDL framework and case study methodology, the authors utilized assistive apps to convert the frog calls to visual sonograms and developed new signs for animals (e.g., “salamander” was signed as “wet lizard”). The fndings suggest that these types of curricular adaptations had positive impacts on both DHH and hearing students involved in the project. Reinforcing the critical nature of scientifc language acquisition for DHH learners, Im and Kim (2014) examined the efects of combining inquiry-based science lessons, which encouraged interactive communication, with guided manual and report writing, on the language fuency and inquiry skills of 13 DHH students in South Korea. The authors found that the emphasis on structured language use, particularly written language, signifcantly increased students’ fuency and inquiry skills, suggesting that science can serve as a critical context for DHH students’ language acquisition, while conversely, language acquisition can support science learning. Renken et  al. (2021) examined the relationship between the intersectionality of DHH racial/ ethnic minority high school students’ identities and their connection to STEM during a summer program and throughout the following school year. The authors examined data including surveys assessing scientifc sense-making, afnity toward STEM, and reading ability, as well as focus groups, interviews, and photographs taken during camp, and applying cross-case analysis, identifed a number of incidences where inclusion and exclusion occurred. The authors recommend approaches to overcome some of the language barriers that confront ASL users, while also recommending STEM mentorship and connections to real-world and career applications of STEM. In another study aimed at supporting the persistence of DHH students in STEM felds, Kahn et al. (2013) examined the role of teacher facilitation of inquiry on DHH students’ autonomy and ability to negotiate scientifc problem solving. Applying a general inductive approach, the authors utilized an instrumental cross-case study to analyze videos of instructional periods in three earth science classrooms located in three high schools for DHH students. Findings suggest that teachers who utilize highly directed instruction (e.g., pointing and describing phenomena, answering questions posed) may impede the development of independence and autonomy in DHH students, whereas teachers who allow DHH students to explore and “fail” may foster greater autonomy, a key factor in DHH students’ success in higher education. To summarize, it may surprise some to learn that language, not auditory deprivation, may lead many DHH learners to lag in academics. Teachers of DHH students who lack science content knowledge and/or underestimate students’ abilities, in combination with the limits of ASL or BSL in conveying scientifc concepts accurately, all contribute to DHH students’ underrepresentation in science. Greater emphasis on conveying NOS tenets, particularly insofar as science is a human endeavor, may expand DHH students’ persistence in STEM.

Gifted Learners in Science As noted earlier, there are many instructional practices that have been found to be efective with gifted learners across disciplines. Specifcally in science, three recent books (Sumida & Taber, 2017; Taber & Sumida, 2016; Taber et  al., 2017) can be referenced for international perspectives on policy and practice. These books broaden the discussion of gifted science education to include promotion of creativity, incorporation of the arts into STEM, and research apprenticeships, among other approaches. To better understand gifted students’ learning, Gubbels and colleagues (2014) investigated the cognitive, socioemotional, and attitudinal efects of a triarchic (i.e., analytical, creative, and practical)

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enrichment program on 66 upper-elementary school students in the Netherlands. Pre- and post-test control group design revealed that the program had positive impact on practical intelligence, motivation, self-concept, and enjoyment of science, supporting earlier research suggesting that challenging educational experiences and opportunities to socialize within similar ability groupings through enrichment opportunities are important to gifted students’ motivation in STEM. Gifted students’ retention in STEM was the focus of Mullet and colleagues’ (2018) qualitative study to examine gifted college students’ conceptions of their high school STEM educations. Seven frst-year students enrolled in an honors program were interviewed, and data were analyzed inductively using a phenomenographic analysis framework. The authors found that intellectually challenging environments, teachers who took a personal interest in students and held them to a high standard, active learning, and ability grouping all contributed to gifted students’ positive conceptions and achievement in STEM. Gifted students in rural areas face particular challenges, as enrichment programs and other resources may be lacking. To overcome this challenge, Morris et al. (2021) examined the impact of a STEM program focused on local knowledge on 26 rural lower-secondary Australian students’ engagement and experience in STEM. Mixed methods were used, analyzing students’ general science class experiences and their experiences with program in which students worked with an ecologist to rehabilitate plots of damaged land near the school. The fndings suggested that the local rural knowledge program increased students’ engagement in STEM, enhanced retention, and translated STEM understanding more broadly in the community. The implications of the study support the use of real-world problems that activate social justice orientations within gifted students, while also encouraging the development of positive bonds between teachers and students. Addressing the underrepresentation of ethnic minority students in gifted and talented programs, Yoon et al. (2020) examined the impact of an enrichment program on gifted and talented students’ (GTS) leadership, attitude, and motivation. Ten gifted Korean American students in grades 9–12 who attended the Youth Science and Technology Leadership Camp experienced signifcant growth in self-awareness, self-confdence, and learning. The results suggest that similar enrichment programs that draw upon student voice, the Science Technology and Society (STS) learning model, and culturally inclusive instruction can foster ethnic minority GTSs’ success in STEM. The importance of enrichment programs for gifted minority students in STEM was underscored by Fraleigh-Lohrfnk and colleagues’ (2013) comparison of 29 minority (Hispanic or Latino, African American, and other minorities) gifted high school students who attended the Centers Scholars Program and a comparison group of 37 gifted minority students who did not attend. Results suggested that Center Scholars showed higher and better-defned career and academic aspirations and scientifc interests. This study supports early intervention for gifted minority students and contradicts the perception that gifted students will pursue science careers regardless of enrichment. Māori boys, part of the Indigenous population of New Zealand, are often overrepresented in special education programs and underrepresented in gifted programs. To investigate gifted Māori boys’ engagement and potential in science, Riley et al. (2017) undertook a case study of the REAPS model, which integrates real-world problem solving, active learning, and cultural relevance, with 90 secondary school students ages 12 or 13 in a rural, low-socioeconomic school in New Zealand. The program challenged students to brainstorm ways to address a local waterway issue as they learned about ecology and engaged with local stakeholders. Interviews, observations, documents, and surveys revealed that students displayed greater engagement, academic achievement, and collaboration, and demonstrated talents that had previously been overlooked. The fndings lend credence to the importance of diferentiating curriculum to ensure that it is meaningful, collaborative, and appropriately challenging for gifted underrepresented students. In sum, there is a tremendous body of research on frameworks for educating gifted students, yet there is little recent research specifcally supporting science learning. Many of the studies in science

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contexts focus on student attitudes, which is certainly critical for persistence in STEM but does not address the question of how to ensure that gifted students are challenged and nurtured to their potential in science. Perhaps sociocultural frameworks like socioscientifc sssues (SSI; Zeidler, 2014) that position STEM in meaningful real-world contexts would extend the fndings of the Morris et al. (2021) and Riley et al. (2017) studies.

What Constitutes “Assessment for All” in Science? Assessment is a necessary component of teaching and learning that allows educators to plan instruction, evaluate the efects of the instruction, and attend to those areas that require additional attention. Taylor (2018) proposed conceptualizing assessment by type (what is being assessed?), time (when is assessment occurring?), and tools (how is assessment occurring?). The tools of assessment are perhaps the most controversial when considering the assessment of learners with special needs and talents in science, as many authors warn that, too often, traditional assessments may refect students’ aptitude in reading and writing rather than their understanding of science (Seifert & Espin, 2012). Moreover, large-scale standardized assessments carry particular limitations in understanding achievement gaps among underserved groups in science, including overgeneralizing achievement of highly diverse groups (i.e., combining students with diferent disabilities into one demographic group); reinforcing stereotypes (i.e., low expectations for students with disabilities, high expectations for gifted students); and ignoring intersectionality (NGSS, 2013, Appendix D). While laws such as the Every Student Succeeds Act (ESSA, 2015), which mandates the inclusion of most students with disabilities in large-scale, standardized assessments, have been aimed at creating an equitable playing feld and illuminating gaps in marginalized students’ educations, the overemphasis on such assessments has created challenges for teachers attempting to balance accountability measures against the individualized educational approach promised by the IDEA (McGinnis & Kahn, 2014). It is, therefore, particularly critical for science educators to consider a range of tools to assess all students, including those with special needs and talents. Espin and colleagues’ (2013) study analyzed the efcacy of vocabulary-matching probes to predict secondary students’ progress in science. Over a period of 14 weeks, fve-minute vocabulary-matching probes were administered on a weekly basis to 198 seventh-grade students, 17 of which had LD. The researchers found signifcant correlation between performance on the probes and criterion measures, suggesting that the vocabulary-matching probes show promise as progress-monitoring assessments, that is, as long as teachers do not simply focus on pre-teaching the vocabulary words. Lederman and Bartels (2018) considered the ways in which nature of science (NOS) and scientifc inquiry (SI) could be assessed in students who have difculty with traditional written assessments due to physical and/or learning disabilities. The authors discuss the use and development of an orally administered protocol, the Young Children’s Views of Science (YCVS) (Lederman et al., 2014) to assess students’ understanding of science, NOS, and SI. When implemented in 6 elementary schools with 340 students, 41 of whom were identifed as having special needs, the students were found to have comparable scores. However, students with disabilities were found to be better informed on two aspects of NOS (subjectivity and use of multiple methods) than their peers. The authors surmise that students with disabilities’ experience with medical professionals may infuence their understanding of scientifc endeavors. Brendel et  al. (2019) examined the role of narrative assessments in understanding elementary students’ science learning. Through action research, the authors collaborated with a teacher who implemented the use of portfolios along with discourse around students’ products to support metacognition and assess learning. The authors suggest that narrative assessments are a powerful tool in understanding students’ conceptual understandings of science, scafolding students’ metacognitive processes, and positioning all children as capable learners.

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An emerging feld is the assessment of twice exceptional (2e) students in science. Sumida (2016) developed a gifted checklist for adults in science, building upon an earlier study by the author, which resulted in a checklist for identifying gifted characteristics of science learning in elementary students. The adult checklist is premised upon the notion that understanding the nature of giftedness in the general society can inform screening and assessment of children, including those with disabilities, at early ages. Emphasizing assessment of all students’ strengths in science has been advocated by a number of authors (Armstrong, 2012; Hwang & Taylor, 2016; Kahn, 2018). Ensuring that students with signifcant cognitive impairments, who comprise approximately 1% of the total student population, can participate in valid and equitable alternate standardized assessments was the focus of two related studies (Andersen & Nash, 2016; Andersen et al., 2018). The frst study describes the development of a set of 43 alternate science content standards, called Essential Elements (EE), along with an alternate assessment at each of three grade spans. Results from a pilot test consisting of 251 items and implemented with 1,606 students presented evidence of validity and accessibility of the assessment for participants. The second study extended the frst by implementing and evaluating the validity of the alternate assessment across an eight-state consortium and administered to over 21,000 students in 2015–2016. Findings suggest that the alternate assessment is a valid measure of students with signifcant cognitive impairments’ science knowledge while also achieving the precarious goal of balancing the need for standardization with the need for fexibility when developing alternate assessments. In sum, while accountability measures remain a focal point of discussion and research in science education, ensuring a range of assessments that value students’ strengths, accommodate diference, and represent the full breadth of students’ learning is warranted.

What Is the State of Science Teacher Education With Respect to Students With Special Needs and Talents? Given the many years of emphasis on equity in science education, one would think that teachers would be well prepared to teach the increasingly diverse students in their classrooms. This is simply not the case. According to a national (US) survey of 1,088 K–12 science teachers, nearly one third of participants received no training in teaching students with disabilities, and of those who did, the most commonly cited source of training was on the job (Kahn & Lewis, 2014). Some scholars point to practical obstacles to teachers’ implementation of inclusive science practices, including lack of co-planning time, challenges with establishing roles and responsibilities, and simply lack of familiarity with discipline-specifc accommodations (Moin et al., 2009), while others paint a more critical portrait, concluding that “ontological erasure” allows educators and teacher educators to deny the very existence of people with disabilities (Nusbaum  & Steinborn, 2019). This latter perspective shares some alignment with other researchers who position the preparation of science teachers who are prepared and positively inclined to teach students with disabilities as a moral imperative (Kahn, 2015; McGinnis, 2003). Koomen and colleagues’ (2022) review of scholarship in science teacher education from approximately 2010 to 2020 yielded fndings suggesting that very few empirical studies actually focus on the professional learning of educators; most studies emphasize student learning. In addition, the authors found that preservice teachers have tremendous difculties translating their classroom learning into practice in inclusive classrooms. While this latter point is not unique to inclusive science education, it is arguably the most critical context. Finally, the authors amplify the earlier-mentioned situation of disability being minimized even within courses on equity in science teacher education, a fnding that resonates deeply with the conclusions of Bancroft and Nyirenda (2020), whose literature review of equity-focused science teacher professional development found that only ten included studies (27.8%) embedded practices specifcally designed to prepare science teachers to support special education students. Even when disability is embedded within equityfocused “science for all” courses, defcit conceptualizations can persist (Boda, 2021).

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Spektor-Levy and Yifrach (2019) ask a poignant question: If science teachers are positively inclined toward inclusive education, why is it so difcult? The authors applied the theory of planned behavior, which links behavioral intentions to attitudes, subjective norms, and perceived control, to a mixed-methods investigation of 215 Israeli junior high school science teachers. Questionnaires and interviews were analyzed and led the authors to conclude teachers’ positive intentions to employ inclusive instruction was linked to their perception of control, but lack of support and guidance, which was linked to subjective norms, creates a fairly intractable barrier. The authors conclude that ongoing professional development, instructional materials, and support for collaboration between science and special education teachers are critical to successful inclusive science education. Adu-Boateng and Goodnough’s (2021) qualitative study of a Canadian high school science teacher with a special education background’s instructional practice in an inclusive classroom also illuminated signifcant practical challenges, even though many facets of the UDL framework were present. The phenomenon of science teachers being philosophically inclined toward inclusion yet simultaneously experiencing attitudinal, institutional, and practical barriers to its implementation reiterate the fndings of Kahn and Lewis (2014), who similarly pointed to administrative support, ongoing training, and opportunities for planning and collaboration between science and special educators as key. This latter point, however, also appears to be fraught with challenges. While joint professional development between science and special education (SPED) teachers is strongly supported by research (Brusca-Vega et al., 2014), co-planning can be a particularly complex endeavor. Swanson and Bianchini (2015) examined co-planning among two school teams of science and SPED teachers participating in a two-week professional development summer institute. The authors found that, although all teachers were able to contribute ideas to the co-planning process, science was privileged within their discussions. These fndings suggest that further studies and eforts are needed to ensure parity within professional development and school contexts. Mulvey and colleagues (2016) also noted that SPED teachers may need additional support to ensure that their voices are heard during the planning of science lessons. In their study, the researchers applied a cross-case analysis of four elementary special education teachers’ semester-long professional development experiences focused on NOS and inquiry. While fndings suggest that this type of professional development can enhance special educators’ understanding and implementation of NOS and inquiry, the authors noted little discussion of students’ IEPs during the co-planning process, emphasizing the importance of building upon SPEDs’ expertise to support their confdence as they negotiate the co-planning process. Three additional promising professional development interventions include: (1) collaborative lesson study (Mutch-Jones et  al., 2012), which involves the systematic examination of practice and student learning; (2) development of heuristics for implementing diferentiated instruction (de Graaf et al., 2019); and (3) increasing teachers’ knowledge of evidence-based morphological awareness practices in order to improve students’ science vocabulary and literacy needs (Lauterbach et al., 2020). On the preservice side, Librea-Carden et  al. (2021) examined 18 preservice SPED teachers’ plans, refections, interviews, and microteaching episodes in relation to NOS. The authors found that preservice SPED teachers planned and implemented NOS and also viewed it as potentially relevant to the unique ways of thinking associated with students with special needs. These results suggest that NOS may be a critical tool in shaping future educators’ views of disability as an asset rather than defcit. The fndings in this study complement those found by Kahn et al. (2017) when examining the teaching plans of preservice elementary teachers as they progressed through their science and SPED methods courses. The authors found that preservice elementary teachers increased their use of UDL in science lessons and attempted to scafold their students’ implementation of inquiry. However, the preservice teachers also expressed more “behavior-oriented” concerns in their planning as their methods courses progressed, suggesting that science teacher educators should

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identify key junctures for intervening to ensure that assets rather than defcit perspectives of students with special needs develop. The benefts of co-taught preservice science courses were illuminated by Zimmer et al. (2018), who found that students taught in a co-taught course had signifcantly increased scores on a modifed Attitudes Toward Inclusion Survey. Providing preservice teachers the opportunity to teach and coplan through informal science experiences also shows promise in bolstering future teachers’ inclusive science preparation. Kang and Martin (2018) explored preservice science teachers’ experiences with developing and implementing a science fair for students in a school for DHH students in Korea. The authors found that the preservice teachers became more open to the idea of teaching in an inclusive manner and also became more aware of students with special needs as being capable individuals. Similarly, Kahn et al. (2018) found that having science and SPED preservice teachers co-plan and co-teach an inclusive science day for the community led to positive views of collaboration as a powerful learning experience and recognition of a shared desire to make positive diferences in the lives of students while also recognizing diferences in their philosophical perspectives. Regarding the professional development of science teachers of gifted students, Benny and Blonder (2016) examined the factors that promote and hinder chemistry teachers in teaching gifted students. The authors collected 84 photonarratives from 14 Israeli chemistry teachers that participated in a professional development focused on teaching gifted students in regular classrooms. Findings suggest that an inquiry-based chemistry laboratory and teachers’ interactions with gifted students were the strongest facilitators of learning for gifted students, while time and the mixed ability of their classes were seen as the greatest inhibitors. The fndings suggest that supporting teachers’ eforts to garner support for gifted education with school administrators could strongly enhance teachers’ professional development. In reviewing the literature on science teacher education, while there appear to be a substantial number of studies related to preparing teachers for inclusive science teaching, those studies do not necessarily indicate that teachers are particularly well prepared to teach all students or well supported through the process of navigating co-teaching or co-planning. Just as troubling is the fact that there is a dearth of empirical research on science teacher preparation or professional development for teachers of gifted students. Since most gifted students are educated within general education classrooms, all teachers should receive ample training in research-based practices that ensure adequate challenges, socio-emotional support, and autonomy for gifted learners in science.

Future Research Those interested in pursuing research in science for students with special needs and talents can rest assured that there is a great deal of work to be done. Some questions that emerge from the present review are: Can high-leverage practices (HLPs) support scientifc inquiry, and if so, might this be a vehicle for closing the behaviorist/constructivist gap? What supports are needed to ensure that persons with disabilities’ voices are included in preservice and in-service science teacher education and research? How can science educators use virtual and remote technologies for inclusive labs and feld trips while concurrently attending to students’ social needs and sense of belonging? Which of the strategies embedded within multicomponent interventions are primarily responsible for positive outcomes? What evidence is there for the validity of strength-based, critical thinking, and other contributory assessments? What constitutes a UDL science classroom? Does UDL require modifcations to meet the needs of gifted learners? What are the best practices in gifted science teacher education? How can emerging learning spaces, such as maker spaces and fab labs, support science learning for students with special needs and talents? What novel research approaches can be gleaned from work in intersectionality? How can science education advance from understanding the unique ways of knowing of neurodiverse individuals?

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Concluding Thoughts To clarify the concept of “teaching,” educational philosopher Thomas Green (1964) used the context of teaching a dog tricks to parse out fne distinctions between the terms, “teaching,” “training,” and “instructing.” By simply considering the everyday uses of the words (e.g., we might teach a dog to do a trick but train a dog to heel, etc.), Green identifed the nuances between the closely related terms and ultimately derived an analytic topography of teaching. It is, perhaps, this type of subconscious linguistic callisthenic that perpetuates schisms between the science and special education research communities. To science education researchers’ ears, implementing “explicit” or “systematic” instruction may sound like the antithesis of inquiry, while special education researchers may interpret “inquiry” and “discovery” as unstructured and chaotic. Of course, diferences in the disciplines’ historical and philosophical lenses are partially to blame for what appears to be minimal communication between researchers, but one must wonder, in an age of instant translation and as comrades in a united quest for scientifc literacy for all, shouldn’t we be speaking to each other? The “truth” of inclusive science lies somewhere between the extremes of paradigmatic perceptions. Nothing in the present review suggests that pre-teaching vocabulary words, providing graphic organizers, or using mnemonic strategies and the like inhibits inquiry for students with disabilities, yet little research written by science educators mentions these practices, which are widely supported in special education literature. Similarly, special education literature afrms that most students are capable of and intrigued by inquiry, yet the vast majority of the studies from special education research groups focus on vocabulary acquisition and factual and procedural memorization, rather than higher-order inquiry skills, even when “hands-on” activities are integrated. Methodological approaches are almost completely distinctive, with special education research focused strictly quantitatively, in some cases to the exclusion of any descriptors of students, and with science education research running the gamut. In their bibliometric and descriptive analysis of inclusive science, Comarú et al. (2021) mapped the scholarly contributions and communications between science and special education, noting that, not only is there little crossover between the felds but that a relatively small number of labs, mostly located in the United States, contribute quite heavily to the literature. This fnding was quite evident in the present review of research, pointing to the need for greater participation of researchers globally and cross-disciplinarily in this feld. Meanwhile, as the felds of science and special education research battle for the soul of how to best educate students with disabilities, gifted education in science emerges as perhaps the most precarious guest at the equity table as it struggles not only for a defnition, legislation, and funding, but remains suspended in an existential battle to convince the educational community and the public at large that those who are highly capable do require support. If science education is fundamentally about ensuring that every student can access, engage with, and apply science in their everyday lives, in their careers, and in society to the fullest extent of their abilities, then it is clear that science education, special education, and gifted education are three sides of the same coin. Researchers in each of these disciplines separately and similarly contend with the daunting yet critical task of transcending labels in favor of attending to individuals – beautifully complex individuals – who defy simplistic silos. Some may suggest seeking refuge in science teacher education; co-taught courses, dual (or tri-) certifcations, and the like are arguably valid starting points. But the impetus for those and other innovations must be quality evidence-based research, preferably conducted collaboratively with novel intersectional approaches that, like any good teacher, maintain fdelity to historical, philosophical, and epistemological perspectives, continuously learn from theirs and others’ experiences, and are frst and foremost, faithful to the learner.

Acknowledgments I would like to thank my undergraduate assistants Tifany Agyarko, Grace Lanouette, Sean Lee, and Courteney Wiredu for their help with the initial literature search.

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References Adu-Boateng, S., & Goodnough, K. (2021). Examining a science teacher’s instructional practices in the adoption of inclusive pedagogy: A qualitative case study. Journal of Science Teacher Education, 1–23. https://doi.org/ 10.1080/1046560X.2021.1915605 Andersen, L., & Nash, B. L. (2016). Making science accessible to students with signifcant cognitive disabilities. Journal of Science Education for Students with Disabilities, 19(1), 17–38. http://doi.org/10.14448/jsesd.09.0002 Andersen, L., Nash, B. L., & Bechard, S. (2018). Articulating the validity evidence for a science alternate assessment. Journal of Research in Science Teaching, 55(6), 826–848. https://doi.org/10.1002/tea.21441 Apanasionok, M. M., Hastings, R. P., Grindle, C. F., Watkins, R. C., & Paris, A. (2019). Teaching science skills and knowledge to students with developmental disabilities: A systematic review. Journal of Research in Science Teaching, 56(7), 847–880. https://doi.org/10.1002/tea.21531 Archer, A. L., & Hughes, C. A. (2010). Explicit instruction: Efective and efcient teaching. Guilford Publications. Armstrong, T. (2012). Neurodiversity in the classroom: Strength-based strategies to help students with special needs succeed in school and life. ASCD. Atchison, C. L., Marshall, A. M., & Collins, T. D. (2019). A multiple case study of inclusive learning communities enabling active participation in geoscience feld courses for students with physical disabilities. Journal of Geoscience Education, 67(4), 472–486. https://doi.org/10.1080/10899995.2019.1600962 Aydeniz, M., Cihak, D. F., Graham, S. C., & Retinger, L. (2012). Using inquiry-based instruction for teaching science to students with learning disabilities. International Journal of Special Education, 27(2), 189–206. Bancroft, S. F., & Nyirenda, E. M. (2020). Equity-focused K-12 science teacher professional development: A review of the literature 2001–2017. Journal of Science Teacher Education, 31(2), 151–207. https://doi.org/10. 1080/1046560X.2019.1685629 Barnard-Brak, L., Johnsen, S. K., Hannig, A. P., & Wei, T. (2015). The incidence of potentially gifted students within a special education population. Roeper Review, 37(2), 74–83. https://doi.org/10.1080/02783193. 2015.1008661 Bell, E. C., & Silverman, A. M. (2019). Access to math and science content for youth who are blind or visually impaired. Journal of Blindness Innovation and Research, 9(1). http://doi.org/10.5241/9-152 Benny, N., & Blonder, R. (2016). Factors that promote/inhibit teaching gifted students in a regular class: Results from a professional development program for chemistry teachers. Education Research International, 2016, 1–11. https://doi.org/10.1155/2016/2742905 Boda, P. A. (2019). Conceptualizing the margins in science education: The limits of multicultural analyses. Cultural Studies of Science Education, 14(2), 493–514. https://doi.org/10.1007/s11422-019-09926-x Boda, P. A. (2021). The conceptual and disciplinary segregation of disability: A phenomenography of science education graduate student learning. Research in Science Education, 51, 1725–1758. https://doi.org/10.1007/ s11165-019-9828-x Boyle, S., Rizzo, K. L., & Taylor, J. C. (2020). Reducing language barriers in science for students with special educational needs. Asia-Pacifc Science Education, 6(2), 364–387. https://doi.org/10.1163/23641177-BJA10006 Brendel, M., Siry, C., Haus, J. M., & Breedijk-Goedert, F. (2019). Transforming praxis in science through dialogue towards inclusive approaches. Research in Science Education, 49(3), 767–786. https://doi.org/10.1007/ s11165-017-9642-2 Brusca-Vega, R., Alexander, J., & Kamin, C. (2014). In support of access and inclusion: Joint professional development for science and special educators. Global Education Review, 1(4), Article 4. https://ger.mercy.edu/ index.php/ger/article/view/42 Cairns, D., & Areepattamannil, S. (2019). Exploring the relations of inquiry-based teaching to science achievement and dispositions in 54 countries. Research in Science Education, 49(1), 1–23. https://doi.org/10.1007/ s11165-017-9639-x Cameron, A., O’Neill, R., & Quinn, G. (2016). Deaf students using sign language in mainstream science classrooms. In A. W. Oliveira & M. Weinburgh (Eds.), Science teacher preparation in content-based second language acquisition (pp. 341–362). Springer. https://doi.org/10.1007/978-3-319-43516-9_19 Carabajal, I. G., Marshall, A. M., & Atchison, C. L. (2017). A synthesis of instructional strategies in geoscience education literature that address barriers to inclusion for students with disabilities. Journal of Geoscience Education, 65(4), 531–541. https://doi.org/10.5408/16-211.1 Carnahan, C. R., Williamson, P., Birri, N., Swoboda, C., & Snyder, K. K. (2016). Increasing comprehension of expository science text for students with autism spectrum disorder. Focus on Autism and Other Developmental Disabilities, 31(3), 208–220. http://doi.org/10.1177/10883 57615 610539 CAST (2018). Universal design for learning guidelines version 2.2. CAST. http://udlguidelines.cast.org Centers for Disease Control and Prevention. (2021). Learning disorders in children. www.cdc.gov/ncbddd/childdevelopment/learning-disorder.html

350

Special Needs and Talents in Science Learning Centers for Disease Control and Prevention. (2022). Developmental disabilities. www.cdc.gov/ncbddd/developmentaldisabilities/index.html Chiu, Y. T., & Wild, T. (2021). Incorporating the expanded core curriculum into science. British Journal of Visual Impairment, 1–7. https://doi.org/10.1177/02646196211029341 Collins, B. C., Terrell, M., & Test, D. W. (2017). Using a simultaneous prompting procedure to embed core content when teaching a potential employment skill. Career Development and Transition for Exceptional Individuals, 40(1), 36–44. https://doi.org/10.1177/2165143416680347 Comarú, M. W., Lopes, R. M., Braga, L. A. M., Batista Mota, F., & Galvão, C. (2021). A bibliometric and descriptive analysis of inclusive education in science education. Studies in Science Education, 1–24. https://doi. org/10.1080/03057267.2021.1897930 Connor, D. J. (2012). Does dis/ability now sit at the table(s) of social justice and multicultural education? – A descriptive survey of three recent anthologies. Disability Studies Quarterly, 32(3), Article 3. https://doi. org/10.18061/dsq.v32i3.1770 Connor, D. J., Gabel, S. L., Gallagher, D. J., & Morton, M. (2008). Disability studies and inclusive education – Implications for theory, research, and practice. International Journal of Inclusive Education, 12(5–6), 441–457. https://doi.org/10.1080/13603110802377482 Cooc, N., & Kiru, E. W. (2018). Disproportionality in special education: A synthesis of international research and trends. The Journal of Special Education, 52(3), 163–173. https://doi.org/10.1177%2F0022466918772300 Davis, L. J. (Ed.). (2016). The disability studies reader (5th ed.). Routledge. https://doi-org/10.4324/9781315680668 de Brey, C., Snyder, T. D., Zhang, A., & Dillow, S. A. (2021). Digest of education statistics 2019 (NCES 2021–009). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. de Graaf, A., Westbroek, H., & Janssen, F. (2019). A practical approach to diferentiated instruction: How biology teachers redesigned their genetics and ecology lessons. Journal of Science Teacher Education, 30(1), 6–23. https://doi.org/10.1080/1046560X.2018.1523646 Disabilities, Opportunities, Internetworking, and Technology (DO-IT). (n.d.). Mobility impairments. www. washington.edu/doit/mobility-impairments Dixson, D. D., Worrell, F. C., Olszewski-Kubilius, P., & Subotnik, R. F. (2016). Beyond perceived ability: The contribution of psychosocial factors to academic performance. Annals of the New York Academy of Sciences, 1377(1), 67–77. https://doi.org/10.1111/nyas.13210 Doabler, C. T., Therrien, W. J., Longhi, M. A., Roberts, G., Hess, K. E., Maddox, S.A., Uy, J., Lovette, G.E., Fall, A-M., Kimmel, G.L., Benson, S., VanUitert, V.J., Wilson, S.E., Powell, S.R., Sampson, V., & Toprac, P. (2021). Efcacy of a second-grade science program: Increasing science outcomes for all students. Remedial and Special Education, 42(3), 140–154. https://doi.org/10.1177%2F0741932521989091 Dovigo, F. (2017). Special educational needs and inclusive practices: An international perspective. Sense Publishers. http://doi.org/10.1007/978-94-6300-857-0 Ehsan, H., Rispoli, M., Lory, C., & Gregori, E. (2018). A systematic review of STEM instruction with students with autism spectrum disorders. Review Journal of Autism and Developmental Disorders, 5(4), 327–348. http:// doi.org/10.1007/s40489-018-0142-8 Enderle, P., Cohen, S., & Scott, J. (2020). Communicating about science and engineering practices and the nature of science: An exploration of American Sign Language resources. Journal of Research in Science Teaching, 57(6), 968–995. https://doi.org/10.1002/tea.21619 Espin, C. A., Busch, T. W., Lembke, E. S., Hampton, D. D., Seo, K., & Zukowski, B. A. (2013). Curriculumbased measurement in science learning: Vocabulary-matching as an indicator of performance and progress. Assessment for Efective Intervention, 38(4), 203–213. https://doi.org/10.1177/1534508413489724 Every Student Succeeds Act, 20 U.S.C. § 6301 (2015). www.congress.gov/bill/114th-congress/senate-bill/1177 Farkas, G., Morgan, P. L., Hillemeier, M. M., Mitchell, C., & Woods, A. D. (2020). District-level achievement gaps explain black and hispanic overrepresentation in special education. Exceptional Children, 86(4), 374–392. https://doi.org/10.1177/0014402919893695 Foley-Nicpon, M., & Candler, M. M. (2018). Psychological interventions for twice-exceptional youth. In S. I. Pfeifer, E. Shaunessy-Dedrick, & M. Foley-Nicpon (Eds.), APA handbook of giftedness and talent (pp. 545– 558). American Psychological Association. https://doi.org/10.1037/0000038-035 Fraleigh-Lohrfnk, K. J., Schneider, M. V., Whittington, D.,  & Feinberg, A. P. (2013). Increase in science research commitment in a didactic and laboratory-based program targeted to gifted minority high-school students. Roeper Review, 35(1), 18–26. https://doi.org/10.1080/02783193.2013.740599 Frantz, R. S., & McClarty, K. L. (2016). Gifted education’s refection of country-specifc cultural, political, and economic features. Gifted and Talented International, 31(1), 46–58. https://doi.org/10.1080/15332276.2016. 1220794 Garwood, J. D., Werts, M. G., Mason, L. H., Harris, B., Austin, M. B., Ciullo, S. Magner, K., Koppenhaver, D.A., & Shin, M. (2019). Improving persuasive science writing for secondary students with emotional and

351

Sami Kahn behavioral disorders educated in residential treatment facilities. Behavioral Disorders, 44(4), 227–240. https:// doi.org/10.1177%2F0198742918809341 Gomes, C. V.,  & Mensah, F. M. (2016). Sounding out science: Using assistive technology for students with learning diferences in middle school science classes. In M. J. Urban & D. A. Falvo (Eds.), Improving K-12 STEM education outcomes through technological integration (pp. 44–67). IGI Global. Green, T. F. (1964). A topology of the teaching concept. Studies in Philosophy and Education, 3(4), 284–319. https://doi.org/10.1007/BF00375980 Grifths, A. J., Nash, A. M., Maupin, Z., & Mathur, S. K. (2020). Her voice: Engaging and preparing girls with disabilities for science, technology, engineering, and math careers. International Electronic Journal of Elementary Education, 12(3), 293–301. http://doi.org/10.26822/iejee.2020358223 Gubbels, J., Segers, E., & Verhoeven, L. (2014). Cognitive, socioemotional, and attitudinal efects of a triarchic enrichment program for gifted children. Journal for the Education of the Gifted, 37(4), 378–397. https://doi. org/10.1177/0162353214552565 Guerrero, A. D., Rodriguez, M. A., & Flores, G. (2011). Disparities in provider elicitation of parents’ developmental concerns for US children. Pediatrics, 128(5), 901–909. https://doi.org/10.1542/peds.2011-0030 Hall, M. L., Eigsti, I.-M., Bortfeld, H., & Lillo-Martin, D. (2017). Auditory deprivation does not impair executive function, but language deprivation might: Evidence from a parent-report measure in deaf native signing children. Journal of Deaf Studies and Deaf Education, 22(1), 9–21. https://doi.org/10.1093/deafed/enw054 Heinrich, S., Collins, B. C., Knight, V., & Spriggs, A. D. (2016). Embedded simultaneous prompting procedure to teach STEM content to high school students with moderate disabilities in an inclusive setting. Education and Training in Autism and Developmental Disabilities, 51(1), 41–54. www.jstor.org/stable/26420363 Hufing, L., Benavides, A., Matthews, C. E., Compton, M. V., Kurtts, S., & Carlone, H. B. (2018). Learning frog calls when you can’t hear: Fieldwork with high school students who are deaf and hard-of-hearing. In M. Koomen, S. Kahn, C. L. Atchison, & T. A. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 165–174). Brill Sense. https://doi.org/10.1163/9789004368422_018 Hwang, J., & Taylor, J. (2016). Stemming on STEM: A STEM education gramework for students with disabilities. Journal of Science Education for Students with Disabilities, 19(1). https://doi.org/10.14448/jsesd.09.0003 IDEA Part B Child Count and Educational Environments Collection (2019). https://www2-ed-gov.programs/ osepidea/618-data/static-tables/index.html IDEA Regulations, 34 C.F.R. § 300 (2016). Im, S., & Kim, O.-J. (2014). An approach to teach science to students with limited language profciency: In the case of students with hearing impairment. International Journal of Science and Mathematics Education, 12(6), 1393–1406. http://doi.org/10.1007/s10763-013-9465-1 Individuals with Disabilities Education Act [IDEA], 20 U.S.C. § 1400 (2004). Israel, M., Wang, S., & Marino, M. T. (2016). A multilevel analysis of diverse learners playing life science video games: Interactions between game content, learning disability status, reading profciency, and gender. Journal of Research in Science Teaching, 53(2), 324–345. https://doi.org/10.1002/tea.21273 Izzo, M. V., & Bauer, W. M. (2015). Universal design for learning: Enhancing achievement and employment of STEM students with disabilities. Universal Access in the Information Society, 14(1), 17–27. http://doi. org/10.1007/s10209-013-0332-1 Jarrell, A.,  & Lajoie, S. P. (2017). The regulation of achievements emotions: Implications for research and practice. Canadian Psychology/Psychologie Canadienne, 58(3), 276–287. https://doi.org/10.1037/cap0000119 Jeannis, H., Joseph, J., Goldberg, M., Seelman, K., Schmeler, M., & Cooper, R. A. (2018). Full-participation of students with physical disabilities in science and engineering laboratories. Disability and Rehabilitation: Assistive Technology, 13(2), 186–193. https://doi.org/10.1080/17483107.2017.1300348 Jimenez, B. A., Browder, D. M., Spooner, F.,  & Dibiase, W. (2012). Inclusive inquiry science using peermediated embedded instruction for students with moderate intellectual disability. Exceptional Children, 78(3), 301–317. https://doi.org/10.1177/001440291207800303 Kahn, S. (2015). Another “M” for STEM? Moral considerations for advancing STEM literacy. K-12 STEM Education, 1(4), 149–156. www.learntechlib.org/p/209561/ Kahn, S. (2018). From access to assets: Strength-based visions for inclusive science education. In M. Koomen, S. Kahn, C. L. Atchison, & T. A. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 105–114). Brill. https://doi.org/10.1163/9789004368422_012 Kahn, S., Feldman, A., & Cooke, M. L. (2013). Signs of autonomy: Facilitating independence and inquiry in deaf science classrooms. Journal of Science Education for Students with Disabilities, 17(2), 13–35. https://doi. org/10.14448/jsesd.06.0001 Kahn, S., Hartman, S., & Samblanet, M. (2018). Promoting “science for all” through teacher candidate collaboration and community engagement. Innovations in Science Teacher Education, 3(2). https://innovations.theaste. org/promoting-science-for- all-through-teacher-candidate-collaboration-and-community-engagement/

352

Special Needs and Talents in Science Learning Kahn, S., & Lewis, A. R. (2014). Survey on teaching science to K-12 students with disabilities: Teacher preparedness and attitudes. Journal of Science Teacher Education, 25(8), 885–910. https://doi.org/10.1007/s10972-014-9406-z Kahn, S., Pigman, R., & Ottley, J. (2017). A tale of two courses: Exploring teacher candidates’ translation of science and special education methods instruction into inclusive science practices. Journal of Science Education for Students with Disabilities, 20(1), 50–68. https://doi.org/10.14448/jsesd.08.0004 Kaldenberg, E. R., Watt, S. J.,  & Therrien, W. J. (2015). Reading instruction in science for students with learning disabilities: A meta-analysis. Learning Disability Quarterly, 38(3), 160–173. https://doi.org/10.1177 %2F0731948714550204 Kamhi, A. G., & Catts, H. W. (2014). Language and reading disabilities. Pearson. Kang, D. Y., & Martin, S. N. (2018). Improving learning opportunities for special education needs (SEN) students by engaging pre-service science teachers in an informal experiential learning course. Asia Pacifc Journal of Education, 1–29. https://doi.org/10.1080/02188791.2018.1505599 Kingsbury, C. G., Sibert, E. C., Killingback, Z., & Atchison, C. L. (2020). “Nothing about us without us:” The perspectives of autistic geoscientists on inclusive instructional practices in geoscience education. Journal of Geoscience Education, 68(4), 302–310. https://doi.org/10.1080/10899995.2020.1768017 King-Sears, M. E., & Johnson, T. M. (2020). Universal design for learning chemistry instruction for students with and without learning disabilities. Remedial and Special Education, 41(4), 207–218. https://doi.org/10.1 177%2F0741932519862608 Kizilaslan, A., Zorluoglu, S. L., & Sozbilir, M. (2020). Improve learning with hands-on classroom activities: Science instruction for students with visual impairments. European Journal of Special Needs Education, 1–22. https://doi.org/10.1080/08856257.2020.1732110 Knight, V. F., Creech-Galloway, C. E., Karl, J. M., & Collins, B. C. (2018). Evaluating supported eText to teach science to high school students with moderate intellectual sisability. Focus on Autism and Other Developmental Disabilities, 33(4), 227–236. https://doi.org/10.1177/1088357617696273 Knight, V. F., Spooner, F., Browder, D. M., Smith, B. R., & Wood, C. L. (2013). Using systematic instruction and graphic organizers to teach science concepts to students with autism spectrum disorders and intellectual disability. Focus on Autism and Other Developmental Disabilities, 28(2), 115–126. https://doi.org/ 10.1177/1088357612475301 Knight, V. F., Wood, L., McKissick, B. R., & Kuntz, E. M. (2020). Teaching science content and practices to students with intellectual disability and autism. Remedial and Special Education, 41(6), 327–340. https://doi. org/10.1177/0741932519843998 Knight, V. F., Wright, J., Wilson, K., & Hooper, A. (2019). Teaching digital, block-based coding of robots to high school students with autism spectrum disorder and challenging behavior. Journal of Autism and Developmental Disorders, 49(8), 3113–3126. http://doi.org/10.1007/s10803-019-04033-w Koehler, K., Wild, T., & Tikkun, S. (2018). Implications of 3-D printing for teaching Geoscience concepts to students with visual impairments. Journal of Science Education for Students with Disabilities, 21(1), 49–81. https://doi.org/10.14448/jsesd.10.0004 Koomen, M. H. (2016). Inclusive science education: Learning from Wizard. Cultural Studies of Science Education, 11(2), 293–325. https://doi.org/10.1007/s11422-015-9668-6 Koomen, M. H. (2018a). A good teacher makes science lighthearted: Experiences in learning science from Alejandro. In M. Koomen, S. Kahn, C. L. Atchison, & T. A. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 43–51). Brill Sense. https://doi.org/10.1163/9789004368422_005 Koomen, M. H. (2018b). Some of them have problems, too, like me: Content and disciplinary literacy for all. In M. Koomen, S. Kahn, C. L. Atchison, & T. A. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 251–262). Brill Sense. https://doi.org/10.1163/9789004368422_027 Koomen, M. H., Kahn, S., Atchison, C. L., & Wild, T. A. (Eds.). (2018). Towards inclusion of all learners through science teacher education. Brill Sense. Koomen, M. H., Kahn, S., & Shume, T. (2022). Including all learners through science teacher education. In J. Luft & G. Jones (Eds.), Handbook of research on science teacher education (pp. 363–375). Routledge. https://doi. org/10.4324/9781003098478-32 Lang, R., Ramdoss, S., Sigafoos, J., Green, V. A., van der Meer, L., Tostanoski, A., Lee, A., & O’Reilly, M. F. (2014). Assistive technology for postsecondary students with disabilities. In G. Lancioni & N. Singh (Eds.), Assistive technologies for people with diverse abilities (pp. 53–76). Springer. https://doi-org/10.1007/978-1-4899-8029-8_3 Lauterbach, A. A., Benedict, A. E., Yakut, A. D., & Garcias, A. A. (2020). Improving vocabulary outcomes in inclusive secondary science classrooms through professional development. Journal of Science Teacher Education, 31(1), 56–74. https://doi.org/10.1080/1046560X.2019.1661738 Lederman, J. S., & Bartels, S. (2018). Assessing the ultimate goal of science education: Scientifc literacy for all! In M. Koomen, S. Kahn, C. L. Atchison, & T. A. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 277–285). Brill Sense. https://doi.org/10.1163/9789004368422_030

353

Sami Kahn Lederman, J. S., Bartels, S., Lederman, N., & Gnanakkan, D. (2014). Demystifying nature of science. Science and Children, 52(1), 40. Lederman, N. G., & Lederman, J. S. (2014). Research on teaching and learning of Nature of Science. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II, pp. 600–620). Routledge. https://doi-org/10.4324/9780203097267 Librea-Carden, M. R., Mulvey, B. K., Borgerding, L. A., Wiley, A. L., & Ferdous, T. (2021). “Science is accessible for everyone”: Preservice special education teachers’ nature of science perceptions and instructional practices. International Journal of Science Education, 1–20. https://doi.org/10.1080/09500693.2021.1893857 Love, T. S., Roy, K. R., & Marino, M. T. (2020). Inclusive makerspaces, fab labs, and STEM labs: How can instructors make appropriate accommodations and modifcations while maintaining a safer teaching and learning environment for ALL students and themselves? Technology & Engineering Teacher, 79(5), 23–27. Madden, L., Schuler, A., Friedman, M., Panday, S., & Koehler, D. (2018). Looking through the window to help teach others: Science stories of three young women. In M. Koomen, S. Kahn, C. L. Atchison, & T. Wild (Eds.), Towards inclusion of all learners through science teacher education (pp. 59–67). Brill Sense. https://doi. org/10.1163/9789004368422_007 Maeng, J. L.,  & Bell, R. L. (2015). Diferentiating science instruction: Secondary science teachers’ practices. International Journal of Science Education, 37(13), 2065–2090. https://doi.org/10.1080/09500693.2015. 1064553 Marino, M. T., Israel, M., Beecher, C. C., & Basham, J. D. (2013). Students’ and teachers’ perceptions of using videogames to enhance science instruction. Journal of Science Education and Technology, 22, 667–680. http:// doi.org/10.1007/s10956-012-9421-9 Martínez-Álvarez, P. (2017). Special ways of knowing in science: Expansive learning opportunities with bilingual children with learning disabilities. Cultural Studies of Science Education, 12(3), 521–553. https://doi. org/10.1007/s11422-016-9732-x Mason, L. H., & Hedin, L. R. (2011). Reading science text: Challenges for students with learning disabilities and considerations for teachers. Learning Disabilities Research  & Practice, 26(4), 214–222. https://doi. org/10.1111/j.1540-5826.2011.00342.x McCarthy, C. B. (2005). Efect of thematic-based, hands-on science teaching versus a textbook approach for students with disabilities. Journal of Research in Science Teaching, 42(3), 245–263. https://doi.org/10.1002/ tea.20057 McGinnis, J. R. (2003). The morality of inclusive verses exclusive settings. In D. L. Zeidler (Ed.), The role of moral reasoning on socioscientifc issues and discourse in science education (pp.  195–216). Springer. https://doi. org/10.1007/1-4020-4996-X_11 McGinnis, J. R.,  & Kahn, S. (2014). Special needs and talents in science learning. In S. K. Abell  & N. G. Lederman (Eds.), Handbook of research on science education (Vol. II, pp.  237–259). Routledge. https:// doi-org/10.4324/9780203097267 McGinnis, J. R., & Stefanich, G. P. (2007). Special needs and talents in science learning. In S. K. Abell & N. G. Lederman (Eds.), The handbook of research in science education (pp. 287–318). Lawrence Erlbaum Press. McGrath, A. L., & Hughes, M. T. (2018). Students with learning disabilities in inquiry-based science classrooms: A cross-case analysis. Learning Disability Quarterly, 41(3), 131–143. https://doi.org/10.1177/0731948717736007 McKissick, B. R., Davis, L. L., Spooner, F., Fisher, L. B., & Graves, C. (2018). Using computer-assisted instruction to teach science vocabulary to students with autism spectrum disorder and intellectual disability. Rural Special Education Quarterly, 37(4), 207–218. https://doi.org/10.1177/8756870518784270 McLeskey, J., Barringer, M.-D., Billingsley, B., Brownell, M., Jackson, D., Kennedy, M., Lewis, T., Maheady, L., Rodriguez, J., Scheeler, M. C., Winn, J., & Ziegler, D. (2017, January). High-leverage practices in special education. Council for Exceptional Children & CEEDAR Center. McMahon, D. D., Cihak, D. F., Wright, R. E., & Bell, S. M. (2016). Augmented reality for teaching science vocabulary to postsecondary education students with intellectual disabilities and autism. Journal of Research on Technology in Education, 48(1), 38–56. https://doi.org/10.1080/15391523.2015.1103149 Miller, B. T., Doughty, T., & Krockover, G. (2015). Using science inquiry methods to promote self-determination and problem-solving skills for students with moderate intellectual disability. Education and Training in Autism and Developmental Disabilities, 50(3), 356–368. www.jstor.org/stable/24827516 Miller, B. T., Krockover, G. H., & Doughty, T. (2013). Using iPads to teach inquiry science to students with a moderate to severe intellectual disability: A pilot study. Journal of Research in Science Teaching, 50(8), 887–911. https://doi.org/10.1002/tea.21091 Miller, B. T., & Taber-Doughty, T. (2014). Self-monitoring checklists for inquiry problem-solving: Functional problem-solving methods for students with intellectual disability. Education and Training in Autism and Developmental Disabilities, 555–567. www.jstor.org/stable/24582351

354

Special Needs and Talents in Science Learning Moin, L. J., Magiera, K., & Zigmond, N. (2009). Instructional activities and group work in the US inclusive high school co-taught science class. International Journal of Science and Mathematics Education, 7(4), 677–697. https://doi.org/10.1007/s10763-008-9133-z Moon, N. W., Todd, R. L., Morton, D. L., & Ivey, E. (2012). Accommodating students with disabilities in science, technology, engineering, and mathematics (STEM). Center for Assistive Technology and Environmental Access, Georgia Institute of Technology. Morris, J., Slater, E., Fitzgerald, M. T., Lummis, G. W., & van Etten, E. (2021). Using local rural knowledge to enhance STEM learning for gifted and talented students in Australia. Research in Science Education, 51(1), 61–79. https://doi.org/10.1007/s11165-019-9823-2 Mullet, D. R., Kettler, T., & Sabatini, A. (2018). Gifted students’ conceptions of their high school STEM education. Journal for the Education of the Gifted, 41(1), 60–92. https://doi.org/10.1177/0162353217745156 Mulvey, B. K., Chiu, J. L., Ghosh, R., & Bell, R. L. (2016). Special education teachers’ nature of science instructional experiences. Journal of Research in Science Teaching, 53(4), 554–578. https://doi.org/10.1002/tea.21311 Mumba, F., Banda, A., & Chabalengula, V. M. (2015). Chemistry teachers’ perceived benefts and challenges of inquiry-based instruction in inclusive chemistry classrooms. Science Education International, 26(1), 180–194. https://eric.ed.gov/?id=EJ1064034 Mutch-Jones, K., Puttick, G., & Minner, D. (2012). Lesson study for accessible science: Building expertise to improve practice in inclusive science classrooms. Journal of Research in Science Teaching, 49(8), 1012–1034. https://doi.org/10.1002/tea.21034 NAGC. (2013). NAGC  – CEC teacher preparation standards in gifted and talented education. www.nagc.org/ resources-publications/resources/national-standards-gifted-and-talented-education NAGC. (2019). What is giftedness? NAGC. www.nagc.org/resources-publications/resources/what-giftedness Nagle, K., Newman, L. A., Shaver, D. M., & Marschark, M. (2016). College and career readiness: Course taking of deaf and hard of hearing secondary school students. American Annals of the Deaf, 160(5), 467–482. www. jstor.org/stable/26235239 National Assessment of Educational Progress. (2018). About NAEP. https://nces.ed.gov/nationsreportcard/about/ National Center for Educational Statistics (NCES). (2022). Students with disabilities. Condition of Education. U.S. Department of Education, Institute of Education Sciences. https://nces.ed.gov/programs/coe/indicator/cgg. National Science Foundation, National Center for Science and Engineering Statistics. (2019). Women, minorities, and persons with disabilities in science and engineering: 2019. Special Report NSF 19–304. Alexandria, VA. www. nsf.gov/statistics/wmpd/ NGSS Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press. Nusbaum, E. A., & Steinborn, M. L. (2019). A “visibilizing” project:“Seeing” the ontological erasure of disability in teacher education and social studies curricula. Journal of Curriculum Theorizing, 34(1). Olszewski-Kubilius, P.,  & Corwith, S. (2018). Poverty, academic achievement, and giftedness: A literature review. Gifted Child Quarterly, 62(1), 37–55. https://doi.org/10.1177/0016986217738015 Olszewski-Kubilius, P., & Thomson, D. (2015). Talent development as a framework for gifted education. Gifted Child Today, 38(1), 49–59. https://doi.org/10.1177/1076217514556531 Plucker, J. A., & Peters, S. J. (2016). Excellence gaps in education. Harvard Education Press. Raven, S., & Whitman, G. M. (2019). Science in silence: How educators of the deaf and hard-of-hearing teach science. Research in Science Education, 49(4), 1001–1012. http://doi.org/10.1007/s11165-019-9847-7 Reed, D. K., Petscher, Y., & Truckenmiller, A. J. (2017a). The contribution of general reading ability to science achievement. Reading Research Quarterly, 52(2), 253–266. www.jstor.org/stable/26622542 Reed, D. K., Whalon, K., Lynn, D., Miller, N., & Smith, K. (2017b). A comparison of general and contentspecifc literacy strategies for learning science content. Exceptionality, 25(2), 77–96. https://doi.org/10.108 0/09362835.2016.1196441 Reis, S. M., Baum, S. M., & Burke, E. (2014). An operational defnition of twice-exceptional learners: Implications and applications. Gifted Child Quarterly, 58(3), 217–230. https://doi.org/10.1177/0016986214534976 Renken, M., Scott, J., Enderle, P., & Cohen, S. (2021). “It’s not a deaf thing, it’s not a black thing; it’s a deaf black thing”: A study of the intersection of adolescents’ deaf, race, and STEM identities. Cultural Studies of Science Education, 16(4), 1105–1136. https://doi.org/10.1007/s11422-021-10023-1 Renzulli, J. S. (2021). The enrichment triad model: A guide for developing defensible programs for the gifted and talented. In  J. Renzulli & S.M. Reis (Eds.), Refections on gifted education  (pp. 193–210). Routledge. https://doi.org/10.4324/9781003237693-11 Reynaga-Peña, C. G.,  & López-Suero, C. D. (2020). Strategies and technology aids for teaching science to blind and visually impaired students. In T. D. Álvarez Robles, F. Álvarez Rodríguez, & E. Benítez-Guerrero (Ed.), User-centered software development for the blind and visually impaired: Emerging research and opportunities (pp. 26–37). IGI Global. http://doi:10.4018/978-1-5225-8539-8.ch002

355

Sami Kahn Riley, T., Webber, M., & Sylva, K. (2017). Real engagement in active problem solving for Māori boys: A case study in a New Zealand secondary school. Gifted and Talented International, 32(2), 75–86. https://doi.org/1 0.1080/15332276.2018.1522240 Rinn, A. N., Mun, R. U., & Hodges, J. (2020). 2018–2019 state of the states in gifted education. National Association for Gifted Children and the Council of State Directors of Programs for the Gifted. www.nagc. org/2018-2019-state-states-gifted-education Rivera, C. J., Hudson, M. E., Weiss, S. L., & Zambone, A. (2017). Using a multicomponent multimedia shared story intervention with an iPad to teach content picture vocabulary to students with developmental disabilities. Education and Treatment of Children, 40(3), 327–352. https://doi.org/10.1353/etc.2017.0014 Rizzo, K. L., & Taylor, J. C. (2016). Efects of inquiry-based instruction on science achievement for students with disabilities: An analysis of the literature. Journal of Science Education for Students with Disabilities, 19(1), 16. https://doi.org/10.14448/jsesd.09.0001 Rose, D. H., & Meyer, A. (2002). Teaching every student in the digital age: Universal design for learning. Association for Supervision and Curriculum Development. Rosenblum, L. P., Ristvey, J.,  & Hospitál, L. (2019). Supporting elementary school students with visual impairments in science classes. Journal of Visual Impairment  & Blindness, 113(1), 81–88. https://doi.org/ 10.1177/0145482X19833801 Sanders, S., Ennis, R. P., & Losinski, M. (2018). Efects of TWA on science text comprehension of students with emotional and behavior disorders in a special day school. Education and Treatment of Children, 41(4), 483–505. http://doi.org/10.1353/etc.2018.0026 Seifert, K., & Espin, C. (2012). Improving reading of science text for secondary students with learning disabilities: Efects of text reading, vocabulary learning, and combined approaches to instruction. Learning Disability Quarterly, 35(4), 236–247. https://doi.org/10.1177/0731948712444275 Shifrer, D., Muller, C., & Callahan, R. (2014). Disproportionality and learning disabilities: Parsing apart race, socioeconomic status, and language. Journal of Learning Disabilities, 44(3), 246–257. www.ncbi.nlm.nih.gov/ pmc/articles/PMC4133990/ Smith, B. R., Spooner, F., & Wood, C. L. (2013). Using embedded computer-assisted explicit instruction to teach science to students with autism spectrum disorder. Research in Autism Spectrum Disorders, 7(3), 433–443. https://doi.org/10.1016/j.rasd.2012.10.010 Spektor-Levy, O., & Yifrach, M. (2019). If science teachers are positively inclined toward inclusive education, why is it so difcult? Research in Science Education, 49(3), 737–766. https://doi.org/10.1007/s11165-017-9636-0 Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press. Stoeger, H., Balestrini, D. P., & Ziegler, A. (2018). International perspectives and trends in research on giftedness and talent development. In S. Pfeifer, M. Foley-Nicpon,  & E. Shaunessy-Dedrick (Eds.), APA handbook of giftedness and talent (pp. 25–39). American Psychological Association. https://psycnet.apa.org/ doi/10.1037/0000038-002 Stokes, A., Feig, A. D., Atchison, C. L., & Gilley, B. (2019). Making geoscience feldwork inclusive and accessible for students with disabilities. Geosphere, 15(6), 1809–1825. https://doi.org/10.1130/GES02006.1 Subotnik, R. F., Olszewski-Kubilius, P., & Worrell, F. C. (2011). Rethinking giftedness and gifted education: A proposed direction forward based on psychological science. Psychological Science in the Public Interest, 12(1), 3–54. https://doi.org/10.1177/1529100611418056 Sumida, M. (2016). Scientifc giftedness in Japanese society. In K. Taber  & M. Sumida (Eds.), International perspectives on science education for the gifted: Key issues and challenges (pp. 126–139). Routledge. https://doi. org/10.4324/9781315814247 Sumida, M., & Taber, K. S. (Eds.). (2017). Policy and practice in science education for the gifted: Approaches from diverse national contexts. Taylor & Francis. https://doi.org/10.4324/9781315814155 Swanson, L. H., & Bianchini, J. A. (2015). Co-planning among science and special education teachers: How do diferent conceptual lenses help to make sense of the process? Cultural Studies of Science Education, 10(4), 1123–1153. https://doi.org/10.1007/s11422-014-9582-3 Taber, K. S., & Sumida, M. (Eds.). (2016). International perspectives on science education for the gifted: Key issues and challenges. Routledge. ISBN 9780815360872 Taber, K. S., Sumida, M., & McClure, L. (Eds.). (2017). Teaching gifted learners in STEM subjects: Developing talent in science, technology, engineering and mathematics. Taylor & Francis. Taylor, J. (2016). Research-based instructional strategies in science for students with EBD. Beyond Behavior, 25(3), 34–39. https://doi.org/10.1177/107429561602500306 Taylor, J. (2018, November 15). Assessment for all science students: Meeting science and SPED goals [Conference Presentation]. Virginia Association of Science Teachers (VAST) Donna Sterling Pre-Conference.

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Special Needs and Talents in Science Learning Taylor, J.,  & Hwang, J. (2021). Science, technology, engineering, arts, and mathematics remote instruction for students with disabilities. Intervention in School and Clinic, 57(2), 111–118. https://doi.org/ 10.1177/10534512211001858 Taylor, J., Hwang, J., Rizzo, K. L., & Hill, D. A. (2020). Supporting science-related instruction for students with intellectual and developmental disabilities: A review and analysis of research studies. Science Educator, 27(2), 102–113. Taylor, J., Rizzo, K. L., Hwang, J., & Hill, D. (2020). A review of research on science instruction for students with autism spectrum disorder. School Science and Mathematics, 120(2), 116–125. https://doi.org/10.1111/ ssm.12388 Therrien, W. J., Taylor, J. C., Hosp, J. L., Kaldenberg, E. R., & Gorsh, J. (2011). Science instruction for students with learning disabilities: A meta-analysis. Learning Disabilities Research & Practice, 26(4), 188–203. Therrien, W. J., Taylor, J. C., Watt, S.,  & Kaldenberg, E. R. (2014). Science instruction for students with emotional and behavioral disorders. Remedial and Special Education, 35(1), 15–27. https://doi.org/10.1177/ 0741932513503557 Thurston, L. P., Shuman, C., Middendorf, B. J., & Johnson, C. (2017). Postsecondary STEM education for students with disabilities: Lessons learned from a decade of NSF funding. Journal of Postsecondary Education and Disability, 30(1), 49–60. https://eric.ed.gov/?id=EJ1144615 Tomlinson, C. A. (2014). Diferentiated instruction. In. J. A. Plucker & C. M. Callahan (Eds.), Critical issues and practices in gifted education: What the research says (2nd ed., pp. 197–210). Prufrock Press. Tsinajinie, G., Kirboyun, S., & Hong, S. (2021). An outdoor project-based learning program: Strategic support and the roles of students with visual impairments interested in STEM. Journal of Science Education and Technology, 30(1), 74–86. https://doi.org/10.1007/s10956-020-09874-0 Tzuriel, D., Bengio, E., & Kashy-Rosenbaum, G. (2011). Cognitive modifability, emotional-motivational factors, and behavioral characteristics among gifted versus nongifted children. Journal of Cognitive Education and Psychology, 10(3), 253–279. https://doi.org/10.1891/1945–8959.10.3.253 Van Tassel-Baska, J. B. (1995). The development of talent through curriculum. Roeper Review, 18(2), 98–102. http://doi.org/10.1080/02783199509553708 Van Tassel-Baska, J. B., Hubbard, G. F., & Robbins, J. I. (2021). Diferentiation of instruction for gifted learners: Collated evaluative studies of teacher classroom practices. In S. R. Smith (Ed.), Handbook of giftedness and talent development in the Asia-Pacifc. Springer International Handbooks of Education. Springer. https://doi. org/10.1007/978-981-13-3041-4_45 Wang, C. W., & Neihart, M. (2015). Academic self-concept and academic self-efcacy: Self-beliefs enable academic achievement of twice-exceptional students. Roeper Review, 37(2), 63–73. https://doi.org/10.1080/0 2783193.2015.1008660 Warner, L. R. (2008). A best practices guide to intersectional approaches in psychological research. Sex Roles, 59(5), 454–463. http://doi.org/10.1007/s11199-008-9504-5 Watt, S. J., Therrien, W. J.,  & Kaldenberg, E. R. (2014). Meeting the diverse needs of students with EBD in inclusive science classrooms. Beyond Behavior, 23(2), 14–19. https://doi.org/10.1177/ 107429561402300203 Wei, X., Yu, J. W., Shattuck, P., & Blackorby, J. (2017). High school math and science preparation and postsecondary STEM participation for students with an autism spectrum disorder. Focus on Autism and Other Developmental Disabilities, 32(2), 83–92. http://doi.org/10.1177/10883 57615 588489 Wexler, J., Mitchell, M. A., Clancy, E. E., & Silverman, R. D. (2017). An investigation of literacy practices in high school science classrooms. Reading & Writing Quarterly, 33(3), 258–277. Wild, T., Hilson, M., & Farrand, K. (2013). Conceptual understanding of geological concepts by students with visual impairments. Journal of Geoscience Education, 61(2), 222–230. https://doi.org/10.5408/12-379.1 Wild, T., & Koehler, K. (2017). Science education. In C. Holbrook, C. Kamei-Hannan, & T. McCarthy (Eds.), Foundations of education volume II (2nd ed.). AFB Press. Worrell, F. C., Subotnik, R. F., Olszewski-Kubilius, P., & Dixson, D. D. (2019). Gifted students. Annual Review of Psychology, 70(1), 551–576. https://doi.org/10.1146/annurev-psych-010418-102846 Wu, Y., Chen, M., Lo, Y.,  & Chiang, C. (2020). Efects of peer-mediated instruction with AAC on science learning and communitive responses of students with signifcant cognitive disabilities in taiwan. Research and Practice for Persons with Severe Disabilities, 45(3), 178–195. https://doi.org/10.1177/ 1540796919900955 Yoon, J., Kim, K. J., & Koo, K. (2020). Enrichment program for the ethnic minority of gifted and talented students in science and engineering. International Journal of Science Education, Part B, 10(1), 36–50. https:// doi.org/10.1080/21548455.2020.1714092

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Sami Kahn Zeidler, D. L. (2014). Socioscientifc issues as a curriculum emphasis: Theory, research and practice. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 697–726). Routledge. https:// doi-org/10.4324/9780203097267 Zimmer, K. E., McHatton, P. A., Driver, M. K., Datubo-Brown, C. A., & Stefen, C. (2018). Innovative communities: Embedding special education faculty in science methods courses. Teacher Education Quarterly, 45(4), 73–92. www.jstor.org/stable/26762170 Zygouris-Coe, V. I. (2015). Teaching discipline-specifc literacies in grades 6–12: Preparing students for college, career, and workforce demands. Routledge. https://doi.org/10.4324/9780203073162

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12 SCIENCE EDUCATION IN URBAN AND RURAL CONTEXTS Expanding on Conceptual Tools for Urban-Centric Research Gayle A. Buck, Pauline W. U. Chinn, and Bhaskar Upadhyay Education researchers are increasingly exploring urban-centric science education. “Urban-centric” is being used to describe schools’ geographical locations. There are multiple urban-centric classifcation systems, with most including degrees of urban and rural (e.g., National Center for Educational Statistics, US Census Bureau). Science education research eforts are supplying new understandings and positive changes for students in underserved locations. However, problems unique to certain urban-centric contexts continue to exist worldwide, new problems are emerging, and there is still much more to discover about science education situated in these complex environments. The purpose of this chapter is to explore contemporary science education research on urban and rural science education in a manner that expands on the conceptual tools being used to consider the impact of geographical location and context on science education. Science education researchers are focusing their eforts in rural and urban settings for a variety of reasons. Among the reasons noted by researchers are: high teacher attrition/teacher shortages (e.g., Marco-Bujosa et al., 2020), lower test scores (e.g., Quansah et al., 2019), limited access to new curricula and professional development opportunities (e.g., Ramnarain, 2016), diminishing status and priority of science teaching in middle schools (Rivera Maulucci, 2010), underprepared or uncertifed science teachers (Nixon et al., 2017), and students that have been marginalized in science classrooms (e.g., Chapman & Feldman, 2017). Although the characteristics of urban and rural locales, and the associated educational problems, difer across the globe, most countries report educational discrepancies specifc to at least one of these locales. In the United States, urban students are performing signifcantly lower on standardized tests than students from other locales (NAEP, 2019; OECD, 2015). In China, the economic gap between urban and rural is expanding, leaving rural schools increasingly under-resourced (Wang et al., 2012). In India, rural families are moving to urban locations in search of better educational opportunities for upward mobility (Sundararaman, 2020), which impacts both the rural and urban schools. In Bulgaria, Beijing-Shanghai-JiangsuGuangdong, Hungary, Portugal, the Slovak Republic, and Turkey, urban students outperform rural students. However, in Belgium, the United Kingdom, and the United States, students in rural schools outperform those in city schools. Worldwide, of the countries represented in the OECD data, approximately 50% of urban students expect to attain a college degree, compared to only 30% of rural students (OECD, 2015), and the COVID-19 pandemic spotlighted the fact that many students that live in rural communities do not have access to the internet. Clearly, this is a worldwide

DOI: 10.4324/9780367855758-15

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discussion, and more work needs to be done if we are to understand science education considering geographical location and context. Urban and rural educational inequities are a signifcant problem. Out of 100,000 public schools in the United States, 32,000 were located in rural areas, 27,000 in suburban areas, and approximately 40,000 in cities and towns (OECD, 2015). Among the US students represented in the PISA 2015 data, approximately half were from rural or urban locations (OECD, 2015). Thailand had a larger number than the United States of rural students represented, and a smaller percentage were from urban schools. In comparison, Poland and Indonesia had more representation from rural than from urban schools, while Chile and Singapore had no/few students in rural schools (OECD, 2015). Overall, although the demographic makeup of countries widely difers, the number of students in schools in urban or rural schools worldwide is signifcant, and the contextualized factors that impact their learning experiences must be understood and addressed. Local context matters in science education. Social constructivist theory explains how social and cultural interactions infuence individuals’ creation of understanding. A sociocultural approach to learning is not solely focused on the individual learner, but rather on the learner and learning process as participation in experiences in a socially constructed world. Learning occurs within a social context, an interpsychological plane, and then within the learner on an intrapsychological plane (Vygotsky, 1978a). Learners negotiate understandings with their peers and community through cogenerative situations (Lave & Wenger, 1991). This learning process is grounded in the notion that understandings are mediated within, and cannot be separate from, the milieu in which they are carried out (Wertsch, 1991). The milieu includes school, home, community, and science. Sociocultural theorists argue that social context signifcantly afects the learning process for youth. Many education gaps associated with urban or rural students stem not from a lack of ability but from factors associated with local contexts that difer from the mainstream. In his analysis, Williams (1995) noted three components of culture: (1) the ideal component is a state or process of perfection in terms of absolutes or values, (2) the documentary component is the body of intellectual and imaginative work in which human thoughts and experiences are recorded, and (3) the social component of culture is a description of a particular way of life that expresses certain meanings and values not only in art and learning but in institutions and behaviors (page 48). The local community, in this discussion whether it is urban or rural, has an impact on all of these components. Several scholars have written about aspects of urban or rural cultures and schooling (e.g., Stance, 2012; Stovall, 2006; Reed, 2010). Defning an urban or rural culture, however, is contingent upon an understanding of history, power, meaning, and acknowledgement (Brooks, 1997). The culture includes a complex relationship between memory and history, and culture and power. The cultures of cities and towns are rooted in particular spaces and include spatial characteristics of the specifc context (Boizonella, 2016). Although these cultures are not totally decontextualized or exempt from the infuences of more general shaping cultural forces, they do include tendencies. Some of these include power, contentions between meaning, and control of institutionalization. Thus, there are multiple urban and rural cultures. An increasing number of science education scholars are calling attention to ways in which science identity formation and learning are infuenced by these local cultures. In sum, there are specifc aspects of urban and rural locations that continue to impact the majority of students worldwide. The contexts, as well as the classifcation systems for these contexts, are complex. It is imperative that science education continues to advance our understandings of the impacts of and necessary strategies within urban-centric education.

Past Chapters and the Path Forward This chapter continues the discussions initiated in previous chapters in The Handbook of Research on Science Education (Abell & Lederman, 2007; Lederman & Abell, 2014). The handbook chapter

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“Science Learning in Urban Settings” (Calabrese Barton, 2007) looks across the research literature to explore (1) who is learning science in urban schools, (2) what are the conditions that mediate student achievement and learning, (3) what are the primary tools that urban science education researchers employ to understand teaching and learning in science, and (4) what else are students learning in science? The conceptual tools explored in the chapter include appropriate frameworks, congruence, and legitimate participation. Calabrese Barton et al. (2014) follow up on this chapter by taking an anti-defcit stance on urban education and further exploring conceptual tools for critically unpacking how urban contexts shape science education in the second volume of the handbook. They noted, “Urban centers are characterized by a complex ecology of population density, economic and cultural resources, politics, and geography” (p. 247). Literature that explicitly looked at the ways in which the urban context matters in education was explored, and understandings of certain conceptual tool sets established to go beyond fxing the problems and identifying educational approaches to practices that work were ofered. The conceptual tool sets included place, funds of knowledge, and identityin-practice. The authors of this chapter pointed out that these tool sets provide the professional community with certain afordances, such as an understanding of the movement across boundaries between school, home, community, and peer groups. The chapter “Rural Science Education” (Oliver, 2007) in the frst volume of the handbook provided the history of rural science education research and noted concerns. Suggestions for future research were made, with a fnal question posed about the potential of “technology and its potential to bring universal access to knowledge to all persons regardless of location” (p.  366). Oliver and Hodges (2014) followed up on this fnal question in the second volume of the handbook. Overall, their review addressed the (1) nature of the defnition of rural schools, (2) how the scholarship has move forward to evoke an emphasis on social justice and diversity, (3) technology and its impact as a force of change, (4) recruitment of science teachers, and (5) implications for research on rural and small schools. They note the study of rural science education has been hampered by the difculty in establishing a valid defnition of what rural is and is not. They further note the problem with using numbers to categorize a place and suggest a possible resolution being a multidimensional approach to defning rural. In their review, they address funds of knowledge, cultural relevance, school– community linkage, and strong consideration of technology and how advances in technology shape the discussion of isolation and remoteness as an educational variable. Final questions on high-stakes testing and standardization and how this is impacted by resources and subsequently impacts teachers’ abilities to relate science learning to the rural context and environment, as well as the future of rural schooling given the transformative power of technology to transform rural schooling, are raised. There were many common discussion points in these two previous handbook chapters given they both addressed urban-centric considerations in science teaching and learning. Three of these are discussed here. First, they both point out naive, inaccurate, or diverse conceptions of these contexts and address the need to go beyond population density and include the local context. Overall, the approach of separating urban and rural schools into one of two distinct categories allows researchers the beneft of focusing on a more narrowly defned context. There are, however, costs associated with this approach, as it leads to an understanding of rural schools or all urban schools as being functionally one context. The simplistic distinction ignores the complexity of place given a wide range of population density, economic and cultural resources, politics, and geography of urban and rural centers (Isserman, 2005; Wang et  al., 2012). Second, both previous chapters addressed common conceptual tools. They addressed place, community, economics, identity, and funds of knowledge. These conceptual tools open up our inquiries into these local contexts in a manner that addresses the complexity and provides the necessary foundation for discussions on teaching and learning. Much of the science education research in urban and rural contexts is grounded in sociocultural perspectives of the learning environment. A third point made in both chapters is that the students in the urbancentric locales are often underserved and/or misunderstood by current educational scholarship and

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practice, although the ways in which they are underserved or misunderstood difers across the various contexts, such as country and demographics. These previous chapters, as well as the information noted earlier in this chapter, establish the continued and urgent need to further consider urban and rural contexts in science education. This current chapter combines what was previously two chapters – urban and rural science education. The goal is to continue the discussions highlighted in earlier chapters of this handbook (e.g., place, funds of knowledge, identity) by extending them across sociocultural research where the focus includes both urban and rural locales. The purpose of the following discussion is to explore the strengths and weaknesses of the existing research base on urban-centric science education and suggest future research directions. The binary classifcation of urban and rural is both embraced and challenged. The following section is a broad exploration of contemporary empirical studies on urban and rural teaching, learning, and teacher education. This section is concluded with a comparison of the research occurring in these two locales; however, the review mostly refects the traditional approach of categorizing urban-centric science education research as either urban or rural. This traditional approach is transcended in section two as conceptual tools that show promise across various locales are highlighted and explored. Overall, this chapter: (1) considers the ways in which the contemporary research in urban and rural communities is looking at the community’s relationship to science teaching, learning, and teacher education; (2) picks up on the stories of possibilities and explores them across urban and rural contexts; and (3) explores potential future directions for science education research involving place and context.

Contemporary Science Education Research in Urban and Rural Contexts One of the goals of this chapter is to explore contemporary science education research occurring in urban and rural contexts. Such an exploration could involve many aspects of the research process, and reviewing all of them would require much more space than is available or necessary for this chapter. Given all aspects of studies fow from the purpose (Creswell, 2003), this exploration and subsequent discussion focuses on the purposes researchers are providing for studying science teaching, learning, and teacher education in/for these contexts. For this section, a substantial sampling of contemporary empirical studies (185) were read, categorized, and cumulatively summarized in the following narrative. The quality of research providing the foundation for this discussion was controlled by focusing on top peer-reviewed education journals in science education. These included: Journal of Research in Science Teaching, Science Education, International Journal of Science Education, Cultural Studies in Science Education and Journal of Science Teacher Education. The parameters for the search of research literature for this section included: (1) an empirical study; (2) research done fully within either an urban or local locale, as defned by the researcher given the lack of an agreed-upon defnition of such in the feld; (3) a focus on science education; and (4) published between 2015 and 2021. The starting date, 2015, was selected as the last handbook was printed in late 2014. The readings and subsequent discussions were used to develop and guide a narrative on how science education researchers are currently approaching inquiries in these locales. Examples from the research base are used to further illustrate statements made throughout the narrative. Science education researchers are actively seeking to enhance our understanding of teaching and learning in urban and rural locales. There is signifcantly more research focused on urban than there is on rural science education. The degree to which these studies focused on context difered greatly. For some, the urban-centric context was the primary focus. These researchers were specifcally focused on science education for the specifc locale. For others, the locale was the context in which the professional preparation or development was occurring. The degree to which this context infuenced the main goal (e.g., teaching by inquiry) difered. Some researchers prioritized the locale (e.g., there are urban or rural contextual factors that infuence how to teach by inquiry), while others

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merely noted it as a context in which a study occurred. A discussion of what was learned from the set of studies follows.

Urban Science Education Teacher preparation and development for urban schools: There is a cultural gap between teachers and students in high-needs urban schools. The student population in many urban schools is increasingly diverse, while those preparing to be teachers remain, for the most part, homogeneous. Research has shown that this gap negatively impacts students of color (Ahmed & Boser, 2014), and it serves as the underlying rationale for much of the contemporary research on urban preservice and in-service teacher education. This research focuses on both the need to prepare a homogeneous population of teachers to teach a heterogeneous population of students and attracting and preparing a more diverse teacher workforce. Within this set of research, the urban locale often includes representation from multiple racial and ethnic populations, as well as low-socioeconomic situations. In terms of initial teacher preparation, researchers are investigating eforts to prepare future teachers in a manner that addresses this cultural gap. For example, Rosebery et al. (2016) and Mark et al. (2020) explored diferent approaches to preparing preservice teachers from predominantly White populations to teach a diverse student population. Rosebery et al. (2016) investigated a professional development seminar for students in an urban residency program for teacher preparation and induction. The focus of the program was on developing early career teachers’ attunement to the sensemaking of students from historically nondominant communities; specifcally, student populations represented in the Boston Public School system. Mark et al. (2020) investigated the experiences of preservice teachers completing an internship program focused on learning to teach in a large urban, culturally diverse public high school. Their focus was on exploring positionality while teaching in this context. Wendell et al. (2018) explored this gap through a focus on student-centered engineering design experiences for their future classrooms. The learning experiences followed a communitybased engineering approach with a focus on solving problems in the local community. This approach was established in a manner that it provides an avenue for the teachers to learn to see the cultural and linguistic diversity as a resource rather than a challenge. In comparison to these examples, researchers such as Varelas et al. (2017) further our understanding of addressing this cultural gap by achieving a more diverse population of teachers that are prepared to partner with community organizations that focus on issues connecting science to larger social issues in marginalized communities. The participants in their study, preservice teachers in a Robert Noyce Teacher Scholarship program, were preparing to teach in high-needs schools in Chicago. As part of this program, they were prepared to address environmental racism and connected science in an economically dispossessed Mexican community in Chicago. Taken together, research on professional preparation for urban schools is often pragmatic in nature with a focus on addressing cultural diversity in large urban school systems. Like the purpose of the research on urban preservice professional preparation for urban, culturally diverse communities, the primary purpose of most research on urban in-service teacher professional development in these contexts is cultural diversity. For example, Marco-Bujosa et al. (2020) studied a science teacher working alongside a special education teacher in an urban, dual-language classroom. The school was a small urban school that serves a culturally diverse, mostly low-income, group of students. A signifcant portion of the students were classifed as English language learners. The researchers sought to understand how physical, social, and knowledge structures constrain teacher agency in such a high-needs school. Similarly, Mangiante (2018) studied two classroom teachers from a high-poverty urban school in order to understand how their reform-based beliefs, reform-based knowledge base, and sense of agency shaped their planning and subsequent success at transforming their science teaching. In addition to high levels of students living below the poverty level, both teachers taught a student body that was diverse in terms of culture, language, and learning

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ability. Olitsky (2021) investigated the processes by which two recipients of Robert Noyce Teaching Scholarships developed a sense of professional identity, agency, and group membership in the context of educational reform as they completed their teaching experiences in high-need urban schools. The study supported the use of self-talk to help urban teachers generate internal solidarity that fosters the maintenance of a positive identity even in the face of obstacles. This process, however, was not independent of contextual factors such as supportive colleagues and administrators. Although the majority of teacher education research being completed in urban districts is focused on cultural diversity, there is a signifcant number that does not have this purpose. Within this set of research, the urban locale is central to the inquiry and may include cultural diversity; but such diversity is not the primary focus. For example, Paprzycki et al. (2017) studied a professional development program focused, in part, on partnering with the community and parents to provide urban youth in a science/reading- and science/math-integrated curriculum. The locale was a vital component of the study, and racial diversity within the community was noted, but the primary purpose regarding the community was on preparing teachers to use it to support the curriculum, such as sending family packets home. In comparison, Yang et al. (2019) focused on the urban context in terms of the school alone. Noting the urban school context and how teacher- and student-level factors impact teacher development, these authors sought to enhance urban teachers’ eforts regarding teachers’ pedagogical content knowledge and inquiry instruction. Also within this category of studies, a number focus on teachers fully within urban schools, but the study nor the intervention address the locale to any degree. These are not a substantial part of this discussion, except to note the existence of many studies focused on teacher preparation or development in urban schools that do not explore the impact of the context on teaching in urban settings. Teaching and learning in urban classrooms: The classroom-level contexts in most of the urban science education research literature includes large metropolitan areas, low-socioeconomic conditions, and diversity in terms of race, culture, and language. Often, districts in these locales are seen as unresponsive bureaucracies that do not meet the education needs of most of their students (Katz, 1995). Traditionally, science education curricula do not foreground such contexts within students’ experiences in science. The term “curriculum” is being broadly used to include the ofcial, operational, hidden, null, and extra curricula (Posner, 2004). This gap, and the inherent impacts on teaching and learning, often serves as the foundation for research on urban teaching and learning. This research has foci such as student identity, voice, funds of knowledge, and critical pedagogy. The ways in which this research addresses urban locales is explored next. Emdin (2010) notes that urban science educators provide students of all racial, ethnic, and socioeconomic backgrounds equal access to science in a manner that addresses the historical inequities in science education and the complexities of the existence of urban youth. Many science education researchers that view urban science education with this lens are exploring how urban students negotiate a science identity as they take part in science courses or programs (e.g., Mark, 2018; Gamez & Parker, 2018; Chapman & Feldman, 2017). Identity is the composition of self-views that involve being recognized as a certain kind of person in a given context (Gee, 2000). Science identity is a topical identity related to seeing oneself as someone that participates in science or science learning. A science identity is important regarding learning science; however, many urban students do not recognize themselves as someone that does science. Contemporary studies in urban science education explore, in part, the science identities of urban youth from underrepresented populations in science, how science identities are negotiated or fostered in nontraditional pedagogical approaches, such as ethnodance (Chappel & Varelas, 2018), or through science-focused communities of practice specifcally for youth that may not see themselves as part of a traditional science community (e.g., Chapman & Feldman, 2017; Mark, 2018; Kang et al., 2018; Rahm & Moore, 2016). Other sociocultural researchers exploring the gap between urban students’ experiences and the science curriculum in urban science education do so with a social justice lens (e.g., Morales-Doyle,

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2016; Ridgeway  & Yerrick, 2018). Educational inequities are inextricably entangled with border issues of social justice (Duncan-Andrade & Morrell, 2008), and science educators are challenging these inequities and prioritizing social transformation. For example, Morales-Doyle (2016) explored the impact of a justice-centered AP chemistry course at an urban high school. Their justice-centered inquiry positioned urban students as curriculum critics who ask what, how, and why in regard to science and science education. Similarly, Ridgeway and Yerrick (2018) critically explored the nuances of after-school citizen science programming. They explored the role of race and ethnicity and the ways in which marginalization can manifest with Black urban youth and teachers. Their fndings challenge the science education community to actively expand its understanding of working with community partners, particularly regarding defcit perspectives of urban youth. A sizable portion of the urban science education research base focuses on the impact of this complex context in terms of the inherent complications to meeting standard educational goals in an initiative and/or learning across a diverse set of learners. For example, Harris et al. (2015) studied a large inquiry-based curriculum for urban middle school students. The researchers studied a well-developed curriculum that prior studies had indicated showed promise for improving student learning. The urban context was central to their study given their desire to produce understandings about employing this curriculum as an instrument for reform in large, diverse urban districts. Similarly, Boda and Brown (2019) studied the use of virtual reality to foster student understanding of the relevancy of science to their local community and subsequently improve their attitudes toward science. The urban setting was selected because urban elementary teachers are often ill prepared to make such connections in science due, in part, to an inability to develop science understanding as having relevant application to students’ locales, the people, and communities. Other researchers in this category situated their inquiry in an urban setting due to the ability to sample student learning across diverse student populations from underperforming schools, thus enhancing their study on student learning. For example, Lucero et al. (2017) explored the relationship between science teachers’ subject matter knowledge and their knowledge of students’ conceptions while teaching evolution by natural selection. Their study took place in a large urban high school with a sizable percentage of students from a Latino community. Most of the students were classifed as at-risk and economically disadvantaged. The school district failed to meet the federal government’s adequate yearly progress. The author selected this site for their study, as they felt it represented many schools across the United States. The working defnition of “urban” in international research is much broader than that in the United States, but the purposes are like those studying US schools. This research may explore teaching and learning in urban schools characterized, in part, as large and diverse (e.g., Gandolf, 2020), poorly resourced (e.g., Akuma  & Callaghan, 2019), or homogeneous and highly resourced (e.g., Huang et al., 2017). Like the US-based research described earlier, this research base includes studies focused on student identity, the complex structure of a large urban district, or was simply based in an urban setting due to the opportunity to have a large diverse sample population for a study. For example, Sundararaman (2020) explored the role of textbooks, pedagogy, families, and socioeconomic context in shaping girls’ science experiences. The urban school was a private English school in Bangalore, India, that caters to families’ socioeconomic backgrounds. In comparison, the urban schools that provided the context for the study completed by Al-Balushi and Martin-Hansen (2019) had a student population from middle-class backgrounds. The students had access to resources, such as the internet, and were above average in terms of achievement. These researchers studied these high school students’ ideas regarding two theoretical scientifc models in the context of active learning in small groups. This urban context was quite diferent from the resource-constrained South African urban school where Akuma and Callaghan (2019) explored inquiry-based practical work strategies or the ethnically diverse schools of London, England (Wong, 2015; Archer et al., 2017), which served as the context for these studies on student identity.

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Rural Science Education Teacher preparation and development for rural schools: There is a notable lack of research focused on preparing new teachers for rural schools. Science teacher education researchers are, however, inquiring into in-service teacher development for rural schools, although to a much lesser extent than for urban schools. In contrast to research on teacher development for urban schools, the primary focus of rural teacher development is on challenges to the traditional approaches to professional development. Specifcally, geographical distance and limited resources are unique challenges for the development of rural teachers (Johnson et al., 2014). For example, Lee et al. (2018) and Nugent et al. (2018) both explored distance-delivered instructional coaching. Noting the evidence for a need to go beyond short-term, disconnected teacher development and provide follow-up support and the realities of geographical distance of rural schools, these researchers studied the benefts of providing distance-delivered coaching following a summer professional development experience. Sandhotz and Ringstaf (2016) moved beyond the factors associated with distance from the teacher educators and studied contextual factors within the rural schools and communities. They found that rural science teachers from diferent schools within the same district and geographical region were afected by diferent contextual factors. These factors included, but were not limited to, administrative support and grade level. Their fndings further complicate the notion of rural teacher development. The lack of opportunities to keep up with reform eforts and new pedagogies is another motivating force to study professional development for rural teachers. These studies use evidence-based approaches to teacher development to bring rural teachers’ skills and knowledge up to date. For example, several teacher education researchers studied National Science Foundation–funded mathematics and science partnership programs. Pringle et al. (2020) studied a model that was designed to change the practices of middle school teachers from traditional to that which is recommended in reform documents. The knowledge and subsequent instructional practices of 18 middle school science teachers from small rural and low-performing school districts were improved because of taking part in the frst two years of a fve-year teacher institute. Similarly, Shemwell et al.’s (2015) model was organized around rural communities with a focus on a continuous and gradual process of long-term growth in students’ capacity to think and act scientifcally. Although cultural and language diversity was not a focus of a sizable number of studies in rural teacher education, there are some studies in this area. For example, Rivard and Gueye (2016) examine the impact of a professional development program for secondary science teachers in minority-language rural schools in Canada. The study focused on language-enhanced instruction and the impact on learners. Teaching and learning in rural classrooms: The K–12 school contexts in the rural science education research vary greatly. Rural science education is often seen as defcit in terms of modern resources, but rich in terms of environmental context and Indigenous science knowledge. Like urban education researchers, many rural science education researchers view educational curricula as lacking in terms of a connection to the students’ experiences in science. This gap, and the inherent impacts on teaching and learning, often serves as the foundation for research on rural teaching and learning. Diversity within the rural education research base is more homogeneous or focused on one underserved population. A portion of this research base focuses on educating Indigenous populations. These studies tend to approach their inquiry from a place of possibility (Calabrese Barton et al., 2014). Indigenous science knowledge is viewed as homogeneous populations often overlooked by traditional science education curricula but rich in science knowledge. For example, Kassam et al. (2017) describe the eforts of Lakota and Dakota communities in rural New York to revitalize the ecological knowledge at the core of their food systems. These authors explore this knowledge as a possibility to transform perspectives on educational practices and policies in these communities, as well as worldwide. Wilson-Lopez et al. (2018) explored the social capital Latinx youth from families that immigrated to the rural Western

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United States utilized as they completed self-selected engineering projects. Social capital is defned, in brief, as a form of capital that is applied, exchanged, or converted as people pursue science-related felds. The engineering projects focused on solving problems in their local community. Similarly, other researchers look to rural communities that serve non-Indigenous students as places that hold a unique knowledge often overlooked by traditional science education curricula. For example, Kohut (2019) and Borgerding (2017) sought understandings that advance eforts to teach evolution in rural classrooms. Borgerding (2017) noted that teaching evolution in rural contexts is noted to be particularly complicated because rural students tend to be more religious and less accepting of evolution. For this reason, she completed a case study to examine how a teacher may serve as a cultural border guide and tap into students’ evolution funds of knowledge at a rural high school. Kohut extended this work by challenging the defcit view of rural students’ views of science, particularly about evolution. By completing a cultural consensus analysis, this researcher concluded that rural students draw on cynical and celebratory ideas about science irrespective of their position on evolution. There is a sizable portion of research completed in rural schools that focus on the impact of this context in terms of the inherent resource complications to meeting standard educational goals in an initiative and/or learning across this set of learners. For example, Zimmerman and Weible (2017) worked with 14- and 15-year-old students in a rural poverty-impacted school. They explored watersheds and place-based learning. They had the students explore the health and importance of watersheds in their local community. The students’ everyday experiences were valued in the educational process. Scogin and Stuessy (2015) noted the logistical and geographical barriers limiting science educators’ ability to unite students with scientists in research apprenticeships. They sought to address these barriers by investigating online scientists’ motivational support of seventh-grade rural students, most of whom were from low-socioeconomic households, and evaluate the potential impact of this support on inquiry engagement. The rural students they worked with were noted to rarely venture outside of their rural community or interact with a scientist face to face. Saleh et  al. (2020) also sought to enhance sixth-grade rural students’ science experiences in a way that accommodates distance and isolation by coordinating hard and soft scafolds for collaborative inquiry in game-based science learning. Taken together, the working defnition of “rural” in international research mirrors that of the USbased research. Rural science education is seen as defcit in terms of qualifed teachers and monetary resources, but also as rich in terms of environmental context and a source of Indigenous science knowledge. Upadhyay et al. (2020) note that Indigenous students are often less engaged in critically examining science knowledge, practices, curriculum, and activities. In light of this, they explored ninth-grade rural students in a mostly Indigenous school. Focusing on sociopolitical discourses and action, these researchers studied Tharus, an Indigenous group of Western Nepal, as they took actions for social change and personal transformation as part of a sociopolitical consciousness-oriented science classroom. In Israel, Sedawi et al. (2020) also studied rural Indigenous students, Bodouin, in terms of their connectedness to nature. The authors noted that it is often children from disadvantaged socioeconomic backgrounds that are afected by environmental toxins; these include rural Indigenous communities. Considering this, they sought to understand how the cultural, social, and environmental factors that shape the children in the Bodouin villages impact their connectedness to nature. Murphy (2020) notes that rural students in Australia, like those in schools across the globe, underperform in science education due, in part, to small school size, isolation, difculties retaining quality staf, and low-socioeconomic levels found within the community. There are, however, exceptions such as the rural school they studied. The school at the center of their case study attracted high enrollment and attained high scores on national assessments. Their fndings reveal how this one rural school capitalizes on the local resources, small size, and community relationships to enhance science learning.

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Summary The underlying purposes of the research being conducted in urban and rural science teacher education difered. Urban teacher preparation and development is, for the most part, well studied and often focused on cultural diversity either directly or indirectly. This research often focuses on closing the cultural gap between teachers and students, although there are many studies that focus on teaching in a complex setting that includes cultural diversity, among other factors. In comparison, the research on rural teacher education was much more limited, particularly preservice teacher preparation, and most often focused on adjusting professional development models to address a physical distance between teachers and teacher educators. Specifcally, geographical distance, lack of professional development opportunities, and limited resources are unique challenges for the development of rural teachers. The research base on teaching and learning in urban classrooms has foci such as student identity, voice, funds of knowledge, and critical pedagogy. A sizable portion of this research focuses on the impact of this complex context in terms of the inherent complications to meeting standard educational goals in an initiative and/or learning across a diverse set of learners. Like the research being conducted in urban settings, a sizable portion of the research completed in rural schools focuses on the impact of this context in terms of the inherent resource/physical complications to meeting standard educational goals in an initiative and/or learning across this particular set of learners. Common across both urban and rural US classroom-centered research is a limited working defnition of locale. Despite many degrees of urban and rural in the classifcation systems, as well as diferences found within these categories, the working defnition of “urban” across the research is large, poor, and culturally diverse communities, while the defnition of “rural” is small population and geographically isolated. Whereas diversity within urban settings is often heterogeneous in terms of race, culture, and language, diversity within the rural education research base is more homogeneous or focused on one underserved population. Rural science education is often seen as lacking in terms of modern resources and rich in terms of environmental context and Indigenous science knowledge. Common across both sets of research is an understanding of a science education lacking in terms of a connection to the students’ lives. At the international level, the working defnition of urban is much broader, but the purposes are similar to those studying US schools. This research may explore teaching and learning in urban schools characterized, in part, as large and diverse, homogeneous, poorly resourced, or highly resourced. Like the US-based research described earlier, this research base includes studies focused on student identity, the complex structure of a large urban district, or simply based in an urban setting due to the opportunity to have a large diverse sample population for a study. Taken together, the working defnition of “rural” in international research mirrors that of domestic research. Rural science education is seen as defcit in terms of qualifed teachers and monetary resources but also as rich in terms of environmental context and a source of Indigenous knowledge science knowledge.

Exploring Conceptual Tools That Prioritize the Locale The purpose of this section is to expand on the discussion of several conceptual tools for urban or rural science education and further explore their use across both contexts. Calabrese Barton et al. (2014) argued that science education needs such tools “for critically unpacking the ways in which urban contexts shape science education and repositioning our collective view of urban science education as spaces of possibility” (p. 247). This approach allows for transcending the binary classifcation of urban and rural in a way that fosters a deeper discussion of the urban-centric infuences and afordances in the teaching and learning process. The list of conceptual tools explored here is not exhausted. It includes socioeconomic status, science identities, funds of knowledge, and place-based

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education. These tools were selected as broad concepts that encompass various aspects of physical locale, student learning, and teaching. They are defned, and their current and potential uses in urban-centric research are explored. In this section, the literature reviewed followed a conceptual framework stance that guided both the selection and deselection of published works, empirical and theoretical, to allow for the building of anticipated relationships among various concepts and evidence that supported them (Lester, 2005). There was a greater emphasis on empirical studies than on theoretical/conceptual papers. Yet, the value of conceptual works was not ignored since it provided the basis for deeper and more nuanced understandings and potentially new directions to science education research. In this section we present critique, analysis, and scholarly engagement, with the majority of scholarly works published in high-quality science education and broad education peer-reviewed journals, such as the Journal of Research in Science Teaching, Science Education, Journal of Science Teacher Education, Cultural Studies of Science Education, International Journal of Science Education, The Urban Review, Equity & Excellence in Education, and American Educational Research Journal. Some books and book chapters were also included as they became valuable sources for works carried out in rural and non-Western communities. For this section, mostly research published between 2010 and 2020 were read. However, some earlier publications were included for their foundational work and value. The publications were chosen based on their focus on K–12 teaching and learning and the specifc conceptual tool.

Conceptual Tool: Socioeconomic Status/Resources Although the criteria for identifying a locale as either rural or urban difers across the contemporary research, one common characteristic is socioeconomic status (SES). In addition, SES is one of the primary factors associated with the diference in the urban condition on an international level given that urban is associated with high SES in some countries and low SES in others. The impact of SES as a conceptual tool for rural and urban science education is explored by analyzing the nature of the infuence of SES in science education, why SES is a social justice issue, and why SES needs more layered attention. SES is not viewed as an isolated feature of science education. It is complex and intertwined with social, cultural, historical, racial, gender, wellness, and many other social issues. Therefore, understanding the challenges and opportunities of SES on science teaching, learning, and succeeding needs us to think more critically about the intersectional factors (Collins, 2010; Crenshaw, 1991). Science and science education has historically sufered from the urban–rural and afuent–poor divide, BIPOC–White, male–female, and English (dominant)–non-English (nondominant language), among many others. In many countries, such as the United States, Canada, Europe, Australia, New Zealand, African nations, Central and South American nations, race and racism have played a vital role in who participates in science and who does not. In other countries, ethnic groups and castes have played a signifcant role in discriminating against those who have access to excellent science education and greater socioeconomic mobility (Upadhyay et al., 2021). In science education research and policy works, SES is largely understood as the level of earnings. This simplistic defnition ignores the social aspects of socioeconomic status. SES needs to be more thoroughly conceptualized in order to better understand the impact of SES on science education. SES includes family income, level of parental education, and type of occupation parents held in much of the literature (e.g., Betancur et al., 2018; Bradley & Corwyn, 2002; Breen & Jonsson, 2005; Duncan et al., 2011; Harwell & LeBeau, 2010; Morgan et al., 2016; NCES, 2017; Van Ewijk & Sleegers, 2010). In public health and well-being studies, SES is understood as comprising a subjective dimension of well-being (afective, psychological experiences, culture, human capital, etc.) and objective dimensions (wealth, income, parental education, etc.) (e.g., Diener, 2000, 2002; Fuentes & Rojas, 2001). Furthermore, as SES is intricately linked to social capital, Coleman (1988) and Bourdieu and Passeron (1990) consider social capital and its connection to human capital development as

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part of SES. Villalba (2014) cautions researchers and policymakers to take great care when defning SES because it could infuence both measurement and classifcation of households in appropriate categories afecting policy measures designed to improve quality of life. Sociocultural and sociopolitical factors infuence how SES is measured in diferent geographies, creating challenges in interpreting the outcomes more contextually and less universal. For example, SES is hard to measure reliably in the Global South countries; thus, alternative measures such as material consumption of goods and services and in-kind income (free schooling, food stamps, free/low-cost bus passes, etc.) could be a better and more reliable measure of SES (Vyas & Kumaranayake, 2006). This brief review of what SES means in research shows the complexities of measuring it in a meaningful way. The following defnition of SES from the National Forum on Education Statistics (NFES) (2015) is more inclusive in that it recognizes the broad factors that make up a composite defnition of SES: SES can be defned broadly as one’s access to fnancial, social, cultural, and human capital resources. Traditionally, a student’s SES has included, as components, parental educational attainment, parental occupational status, and household or family income, with appropriate adjustment for household or family composition. An expanded SES measure could include measures of additional household, neighborhood, and school resources. (p. 4) Similarly, the defnition of “socioeconomic status” by Mueller and Parcel (1981) recognizes the hierarchical nature of SES and tends to encompass greater variety in social stratifcation (Sirin, 2005, p. 418): “Socio-economic status is the relative position of a family or individual in a social system in which individuals are ranked according to their access to or control over wealth, power and status” (Muller & Parcel, 1981, p. 14; Sirin, 2005, p. 418). Science education researchers, educators, and policymakers are encouraged to consider SES not just from the lens of household income but much broader geographical, cultural, social, human, and other factors. American society has an intractable wealth gap because of racial disparities. In the United States, race is the most consequential and visible factor determining an individual’s economic trajectory (Chetty et al., 2018). Schleicher (2018) reported that the 2015 Programme for International Student Assessment (PISA) showed SES infuences the education level of rural and urban students in science. Educators, researchers, and policymakers concerned about the racial income-gap can fnd several potential reasons for the persistence of the disparities. The myth that BIPOC people, specifcally Black people, have lower cognitive abilities (Nisbet, 2005) does not help when this same racial stereotype threat is enhanced during test-taking infuencing Black students’ performance (Beasley & Fischer, 2012; Jencks & Phillips, 1998; Maerten-Rivera et al., 2010; Steele & Aronson, 1995). The stereotype threat is even more heightened when racial minorities take tests in science and mathematics (Cheryan et al., 2009; Steele & Aronson, 1995). Exposure to poor environments and signifcant racial discrimination (Orr, 2003; Walton & Cohen, 2011) during childhood shows a more substantial efect on the future income of racial minorities, particularly Black males (Berkowitz et al., 2017). Science education that provides authentic science teaching and learning experiences to all students is a highly resource-demanding enterprise. Authentic science learning experiences, the way most scientists practice science in their research, demands so much material and pedagogical resources that a school’s economy heavily dictates that experience (Aikens & Barbarin, 2008; Lee & Burkam, 2002). Research in science education continuously shows that poor schools are always at a disadvantage when it comes to high-quality science teaching and learning (Krueger & Sutton, 2001; Schleicher, 2018). Students from high-poverty families are continuously disadvantaged because they attend poor schools, have poorly prepared science teachers, fewer science resources, and more signifcant students from underrepresented groups (Rutkowskia et al., 2018). Unfortunately, lower school funding (Schaft  & Jackson, 2010), 20% of rural children living in poverty (United States Department of

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Agriculture [USDA], 2019), and low education level (less than high school) have exasperated rural schools to provide high-quality science education. The Trends in International Mathematics and Scientifc Studies (TIMMS) and National Assessment of Educational Progress (NAEP) data on US students’ performance in science show that students who attend resource-deprived schools tend to score low in science (TIMMS, 2019). A study of an Australian rural high school showed that students thrive in a science learning environment built on local resources, strong student–teacher and staf relationships, and high academic expectations despite being geographically distant from many other informal science resources (Murphy, 2020). One of the signifcant casualties of SES is the kind of science instruction and curriculum students receive. Many scholars of science education who have focused on urban schools in high-poverty schools persistently show that these students come from underrepresented groups and receive the least engaging and highly rote learning instructions (Calabrese Barton & Upadhyay, 2010; Upadhyay et al., 2017). In a study by McNeill et al. (2017), researchers showed that high- and mid-SES students believed argumentation helped students engage with complex discourses such as questioning. Teachers for low-SES students felt that the purpose of argumentation was to encourage participation rather than building higher-order thinking skills. In a study of rural–urban comparison in middle school student participation in informal science activities, fndings revealed that students from rural and urban schools benefted and desired informal and after-school science-related activities and engagements (Hill et al., 2018). The study showed a correlation between SES and rural students’ engagement in after-school activities and museum visits. Geographical proximity to informal science settings and racial minority status decreased rural students’ engagements with science museums. Recognition of activities and resources such as agriculture, animal husbandry, 4-H, and other engagements could improve rural students’ science learning and engagement. These resources are no cost and low cost for rural communities. In another study (Tan et  al., 2020), researchers looked at the relationship between schools’ SES and students’ science achievement in PISA 2015 and their enjoyment in science learning. Their fndings suggested that inquiry-based pedagogy and teachers’ teaching quality positively infuence student learning and achievement for low-SES schools (Tan et al., 2020). In summary, SES has a tremendous impact on urban and rural science education worldwide. SES is rooted in historical, social, educational, cultural, and political capitals that a family or an individual has accumulated. Issues of race and gender further complicate these factors in hindering or supporting mobility into higher SES, thus uncovering further complexity by expanding on diferences within the urban-centric categorization system. Thus, science education researchers and educators are encouraged to broaden their conceptualization of SES to include cultural and social capitals as they explore urban and rural students’ science experiences and identities. The latter is further explored in the next section.

Conceptual Tool: Science Identities Science education curricula is often thought of as lacking in terms of a connection to the urban and rural students’ identities and experiences. In addition, teachers’ and students’ perceptions of the relationship between their urban-centric identities and science have a strong impact on the teaching and learning process. This section outlines the historical development of Western science and focuses on the sociocultural contexts that welcomed certain categories of people while excluding others. The work of sociologists, sociolinguists, and philosophers of science and technology, including Thomas Kuhn (1962), Sandra Harding (1991), Bruno Latour and Steven Woolgar (1979), and James Gee (2000), shows that science and those who practice science and consider themselves scientists cannot be isolated from their sociocultural, historical, and interpersonal contexts. In 2012, the Framework for K–12 Science Education: Practices, Crosscutting Concepts, and Core Ideas was released (NRC, 2013). The Framework served as a foundation for developing the science standards

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of 24 states and developing the Next Generation Science Standards (NGSS), adopted by 20 states (https://ngss.nsta.org/about.aspx). By explicitly stating that the science “community and its culture exist in the larger social and economic context of their place and time and are infuenced by events, needs, and norms from outside science” (p.  27), the Framework frmly situates and contextualizes science in historical and sociocultural contexts. This contrasts with the legacy efects of contentspecifc approaches in science textbooks, majors, and science teacher preparation narrowly focused on universally applicable (therefore placeless) content and confrmatory vs. exploratory laboratorybased experiments. The trope of “scientist”, a word conveying meaning beyond its literal defnition, evokes the stereotype of an older White male in a lab coat, the 20th-century stereotype of the scientist-discoverer. The historical development of STEM in Europe and the United States as White and male contributes to an identity of privilege, power, and exclusion refected in the underrepresentation of women, 48% of the workforce but 27% of STEM workers (Martinez  & Christnacht, 2021), and ethnic minorities, 30% of the US population but 22% of bachelor’s and 12% of PhD degrees in 2016 (Stockard et al., 2021) in STEM felds. Narrative research by Chinn (2007) found that undergraduate Asian and Pacifc Islander women in physics and engineering were often actively discouraged from entering felds perceived as masculine by parents or stereotyped by peers and teachers as being from groups thought incapable of succeeding in math and science. Female physics and engineering students presented themselves as physically ft to be seen by peers and instructors as strong enough to use tools and heavy equipment. A woman who is now a prominent engineer mentioned her technician father playing math games with her while they stood in line and her boyfriend’s gift of a see-through engine, making up for inexperience working on car engines, as leading up to her institutional identity, a degree in engineering. Sociolinguist James P. Gee (2000) considers identity as a multifaceted construct developing over time into “being recognized as a certain ‘kind of person’ in a given context” (p. 99). Gee considers identity to be socially constructed on four dimensions: a Nature N-identity, e.g., gender and ethnicity; Institutional I-identity e.g., a special education or English language learner; Discourse D-identity, e.g., personal trait such as clever, talkative, or determined; and Afnity A-identity, e.g., surfer, biker. In the United States, a female, ethnic minority student begins her journey toward a STEM I-identity with two mismatched N-identities and potentially a mismatched I-identity if she selects non-college preparatory courses to maintain her A-identity with non–college bound peers. Chinn (2007) interviewed a Polynesian female who had a draftsman uncle and nurse mother. She entered and remained in engineering despite being placed with unruly peers as a role model by her elementary teacher. Later she heard her male physics classmate refer to their classmate as a “dumb H_____” knowing that she was also of that ethnicity. She commented that academic grouping by teachers in early grades supported formation of ethnic stereotypes and cliques, afnity groups held together by common views. She maintained her friendship with her “unruly” peers who tended to enroll together in non–college bound courses. Though she was still friendly with both college-bound and non–college bound peers, she could not bring them together to socialize. Her wistful comment about how being in both groups still left her feeling as an outsider suggests the power of everyday school and outsideof-school relationships to support or undermine fragile STEM identities. This vignette suggests how an act of separation and categorization to address a classroom management problem can lead to enduring I-, D-, and A-identities that impact educational outcomes of students whose appearance and behaviors do not match those expected by the United States’ predominantly White, middle-class female teachers. This underscores the importance of preparing all teachers, especially those of young children, with strategies for inclusionary, welcoming instruction of culturally, linguistically, ethnically, and socioeconomically diverse learners. The Framework for K–12 Science Education (National Research Council, 2012) recognizes the social contexts of practices and discourses that afect identity development. Alignment with Gee’s

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(2000) and Lave and Wenger’s (1991) views of learning as socially situated is revealed in the statement “Learning science depends not only on the accumulation of facts and concepts but also on the development of an identity as a competent learner of science with the motivation and interest to learn more” (National Research Council, 2012, p. 286). In this context, both rural and urban learners of science would be more likely to develop identities as competent learners of science if their experiences are engaging, personally meaningful, and connected to the knowledge and home identities they bring to the science classroom. However, rural young people may fnd their teachers and administrators who are not from their communities and cultures bring to the shared learning space other lenses and stereotypical identities that may infuence the way they view, interact, and hold expectations of students. The Online Slang Dictionary lists 22 words for rural dwellers, including hayseed, hillbilly, and whiskey tango, a discreet way of saying white trash. In the category of words related to urban dwellers are ghetto, hood, and turf. Sorhagen (2013) reports that teachers’ inaccurate expectations predicted frst graders’ math, reading comprehension, vocabulary knowledge, and verbal reasoning standardized test scores at age 15. Family income and teachers’ over- and underestimation of students’ abilities had a stronger impact on students from lower income than higher income. Chapman and Feldman (2017) examined how the science identities of marginalized urban high school students were afected by participation in a contextually based authentic science experience. Their empirical data on urban students’ perceptions provide insights into the efectiveness of using authentic science experiences to foster the science identities of urban students. Chappel and Varelas (2017) narrowed their study to focus on Black urban students and the possibilities of artistic representations of a broader social life, specifcally ethnodance, for science education. These researchers used empirical data to support a theoretical argument regarding the possibilities of enthodance for strengthening the science identities of Black urban youth. Kang et al. (2018) narrowed their exploration of science identities by focusing on urban middle school girls. The schools these girls attended were public, located in urban communities, served a diverse population of students, and provided youth with informal science learning opportunities. Their data revealed that multiple contexts (i.e., home, school, extracurricular activities) play a signifcant role in shaping middle school girls’ STEM identities. The nuances of forging a science identity were further explored by Kane (2015). As part of a grant-funded initiative that integrated science and literacy, the ways contested spaces provide opportunities that shape urban third-grade students’ agentic selves and how their agentic expressions shape classroom spaces were explored. In summary, STEM identity begins to be shaped in a child’s earliest years. Teachers, parents, peers, community, and cultural expectations all play important roles in shaping the social contexts of STEM identity from the frst days of school through postsecondary education. This identity is dynamic and intertwined with gender, geography, class, ethnicity, and language. In both urban and rural classrooms, teachers as evaluators act as gatekeepers or onramps into STEM networks and play critical roles in addressing equity in educational environments.

Conceptual Tool: Funds of Knowledge Funds are viewed as an accumulation of wisdom (ideas or thoughts), artifacts, skills, and relationships that an individual can access to better understand and engage with the sociopolitical and sociocultural environments. Thus, funds of knowledge (FoK) are viewed as storage of highly valuable skills, ideas, practices, cultures, languages, tools that are built through everyday personally lived experiences, history, politics, and place (González, 1995; González et al., 2005; Moll, 2005; Moll et al., 1992). Knowledge includes deeply rooted values, beliefs, skills, and ways of knowing ingrained in the local sociohistorical and sociocultural environment. A goal of FoK is to aid members of a community in critically examining issues of discrimination and social justice for personal transformation and social change. This kind of dynamic utilization of these FoK allows for a robust and culturally relevant

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science learning in our conception. In this section, we frst explore the theory of FoK, how it is used in science education, and how this idea could enhance teaching and learning science in urban and rural communities. The theory of FoK was frst explored by literacy scholars in Arizona who were eager to support the learning of mostly children from Hispanic families who lived in the borderlands between Mexico and the United States (Vélez-Ibáñez & Greenberg, 1992). Since many Hispanic families and their children struggled to succeed and participate in the mainstream schools (Riojas-Cortez et al., 2008), this connection to home students’ home experiences and language seemed appropriate tools to leverage. However, fnding ways to infuse home experiences into classroom teaching and learning required deep cultural location information. Therefore, FoK relies on ethnographic (González, 1995) approaches to learning about students and their homes, giving teachers a better understanding of students’ true skills, knowledge, history, and sociocultural practices and values. There are fve goals of FoK. The frst goal of the FoK framework is securing academic success for students from underrepresented groups (frst-generation, immigrant, poor, racial and ethnic minorities, Indigenous, English language learners) (Rios-Aguilar et al., 2011), specifcally for racially and economically marginalized groups (Upadhyay et al., 2017). The second goal is to infuse students’ FoK in everyday school curriculum and pedagogies (McIntyre et al., 2001; Upadhyay, 2006) and critical engagement in content learning (Upadhyay et al., 2017). The third goal is to create opportunities and environments that seek to build relationships between families (caretakers of children) and teachers (school) where families are valuable assets (Albrecht  & Upadhyay, 2018). The fourth is to provide cultural relevancy and sociocultural spaces to learning for social and personal transformation (Albrecht & Upadhyay, 2018; Basu & Calabrese Barton, 2007). The ffth is to consider FoK originating from poor households and racial minorities as assets (Moll & González, 1994) for learning, rather than “defcit thinking” (Valencia, 2010, p. xvi). To attain these goals in classrooms across rural and urban schools, science teachers, specifcally mainstream White teachers, need to fnd ways to recognize, learn, and imbed diverse students’ experiences gained at home as legitimate sources of knowledge for efective teaching. The idea of FoK is based on recognizing and celebrating diverse cultures and cultural practices in classroom instructions. Cultures are viewed as dynamic and varied (Gutiérrez & Rogof, 2003) within and across people’s homes. Culture, in general, represents language, music, artifacts, traditions, beliefs, and values that are mediated by geography, history, politics, and ontology of a community (e.g., Collins, 2004; DiMaggio, 1997; Klett, 2014; Schilke  & Rossman, 2018; Shepherd, 2011). Therefore, as home experiences evolve for the students, they bring diferent knowledge, skills, and practices into science classrooms. This rich and dynamic experience creates opportunities for better and relevant science learning and challenges to provide supportive learning spaces and materials in rural and urban science classrooms. Unfortunately, science education has struggled to situate science as a relevant knowledge for all students irrespective of their educational and professional trajectories. Scholars, educators, policymakers, teachers, and other individuals and institutions have looked at various frameworks and ideas to give science more relevance to people’s lives. Many diferent theories and frameworks, such as culturally relevant pedagogy (Ladson-Billings, 1995), culturally responsive teaching (Gay, 2000), multicultural education (Banks, 1995), sociocultural and sociohistorical nature of learning (Rogof, 1990; Vygotsky, 1978a), culturally sustaining pedagogy (Paris & Alim, 2014), critical (race) theories (hooks, 1984; Delgado  & Stefancic, 2012), and many others, have been explored to both understand and enhance underrepresented rural and urban students’ science engagement and learning. In science education, FoK has been a useful framework to understand and support science teaching and learning in urban and rural schools. Since the focus of FoK has been making learning relevant and connected to students’ home experiences, there are key areas where FoK has been utilized in the context of science teaching and learning research and development. Research in education and science education has looked at FoK to help answer the challenges and opportunities that have plagued broader participation in science from underrepresented groups

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in urban and rural schools. Equity and social justice studies in science education that have invoked FoK as their framework and analysis have shown that teachers and students both beneft from more engaged and meaningful science learning (Calabrese Barton, 2003; Calabrese Barton & Tan, 2009; Carlone & Johnson, 2012; Upadhyay, 2010; Upadhyay et al., 2017). In one study (Upadhyay, 2010), immigrant students from Hmong communities drew from their cultural experiences to question the process of observing and documenting their science activities and learning. With the support from the teacher, the students established the value of Hmong FoK for better learning and culturally just science space. Calabrese Barton and Tan (2009) study showed FoK as a suitable framework that could bring a critical lens to articulate and challenge the status quo and power that sidelines diverse experiences that students bring into class. In this case, students got encouraged to pursue what they wanted rather than what the school science curriculum imposed on them. FoK has a tremendous power to transform student and teacher experiences, whereby science teaching and learning is more about social and personal change (Albrecht & Upadhyay, 2018; Upadhyay et  al., 2017). In a study of urban elementary students, Upadhyay et  al. (2017) saw benefts of FoK when infused with the recent experiences of economic turmoil in a family that students experienced. For immigrant families, recognition and infusion of FoK, both positive and negative, in science teaching and learning plays a signifcant role in their children’s success. Somali families, especially mothers, viewed the value of their FoK in their children’s success because Somali families have a remarkably close kinship with other members (Albrecht & Upadhyay, 2018). Similar kinship is observed among Hmong immigrants and their children (Upadhyay, 2010). In the studies of Somali families and in a study of after-school programs, girls drew upon their FoK to engage in science that mattered to them and their community (Gonsalves, 2014). The digital engagement related to local issues created emotional interactions with a more positive view of the self and the science. Therefore, FoK can provide valuable space for families, students, and teachers to connect with science that is more personal, equitable, socially and culturally just, and transformative. Most research in FoK has been focused on enhancing teacher consciousness on cultural diferences that students bring form their home and how to manage them in short-term science interventions (Bouillion & Gomez, 2001; Brown et al., 2018; Hammond, 2001) rather than “sustain” them over time (Calabrese Barton & Tan, 2009, p. 4). In a Māori science classroom study, the researchers found that teachers utilized Māori stories of mountains to teach science ideas about landforms (Cowie et  al., 2011). The inclusion of Māori knowledge provided teachers the opportunity to encourage students to draw from other funds of knowledge they had gained at home. Similarly, in another instance, an upper-elementary science class, the teacher encouraged a Hispanic immigrant student to show and tell the hand-pollination process that he had learned back home to grow strawberries in the class during winter (Upadhyay et al., 2017). In a rural US study, teachers designed lessons around the agriculture theme, utilizing students’ lives and what they knew about animal life, food, fertilizers, animal husbandry and health, and many other rural farm-related activities (McIntyre et al., 2001). Teachers successfully connected school science, math, literacy, and writing with what students already knew a lot about. Teachers improved and were able to adjust their science pedagogy because they found value in FoK as an essential link to make science more connected and useful to students (Johnson & Fargo, 2010) and mostly benefted those from underrepresented groups or who struggled academically (Sikoyo & Jacklin, 2009). FoK is based on the knowledge a community has and how that knowledge could be brought into science pedagogy and learning. Thus, there is a strong bond between the FoK idea and the community to which students belong. The notion of community and the relationship that it forges in people’s minds is much more robust in Indigenous groups, rural places, and many underrepresented groups. Therefore, FoK as “community cultural wealth” (Yosso, 2005) can support a robust connection between what happens in science and what the community has in store as cultural capital. In one study, students and the local community came together to fnd a solution for river pollution, which

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tackled community interest. They provided students a lived context to learn science (Bouillion & Gomez, 2001). In another study, Hammond (2001) leveraged Hmong community knowledge to build a community garden that provided space for science engagement. In another study of a rural sixth-grade STEAM classroom, students drew from peers’ FoK as immigrants, local rural context of a windmill-driven partial energy source, and a historical fction book about Chinese immigrant life in San Francisco in the early part of the 20th century to critically examine science, engineering, and social implications of science and engineering learning (Upadhyay et al., 2021, in press). FoK is not only about accepting cultural diferences among student experiences from diferent households. It is also about critical sociocultural, sociohistorical, and sociopolitical awareness. Therefore, it is about utilizing science to challenge dominant narratives in science practices, pedagogy, curriculum, and learning. FoK allows students and teachers to draw from local and personal lived issues to foster a critical lens into connecting science to social change and personal transformation. For example, in a study, urban students discussed the science behind vaccines. They brought that knowledge home to infuence adults who are steeped into cultural beliefs about the disease and the process of curing it or preventing it (Upadhyay  & Giford, 2011). Science learned in an FoK-supported environment allowed students to be activists in their own homes without jeopardizing cultural values, respect, and community coherency. Similarly, in rural and Indigenous communities, FoK could be leveraged to explore local social and historical issues to engage students in critical social change and personal transformation activities through science. Many Indigenous communities are in rural locations across the United States, and globally they have rich FoK that can support highly dynamic and socially conscious interactions in science classrooms. The FoK inherent in these communities could be used to question science’s epistemology and promote Indigenous pedagogies that are more engaging and meaningful to Indigenous students (Archibald, 2008; Cajete, 1994; Chinn, 2007; Iseke, 2013; Iseke & Brennus, 2011). In summary, FoK in science teaching and learning can provide personally transformative experiences in teaching and learning. The value of FoK in teaching and learning is that it places great emphasis on the importance of the history, language, culture, and race experiences (Zipin, 2009) of a community to which students belong. Since FoK is a collective experience of individuals and communities in social, cultural, historical, and linguistic experiences in those spaces, science teaching and learning could be more meaningful and equitable when FoK is integrated. A rural school science class could beneft from integrating rusting phenomenon in farm tools while teaching oxidation in chemistry. Therefore, FoKs like these are intimately and meaningfully connected to a local place, thus providing an enriching learning experience to students. Thus, place-based science education would provide a relevant theoretical and conceptual framework.

Conceptual Tool: Place-Based Education Place-based education is a pedagogical approach that is only a few decades old in the education literature but has an exceptionally long history grounded in subsistence lifestyles and systems-oriented Indigenous knowledge. A short literature review of place-based science education leading up to evidence from urban and rural settings in the continental United States, Hawai‘i, and American Samoa suggests it may be an instructional approach that disrupts the binary of rural and urban science education. Place-based education provides a way of thinking about pedagogy and curriculum grounded in sociocultural theories of learning. As awareness grew in the 1970s of the impacts of human activities on supposedly inexhaustible natural systems of water, air, and wild resources, educators began to turn their attention to resources for science curriculum oriented to sustainable, resilient social ecosystems in their places. It has been theorized and enacted in several ways in science educational settings. These include service learning, environmental education (Ardoin et al., 2018), citizen science (Dickinson

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et al., 2012), and critical pedagogy of place (Gruenewald, 2003 a, 2003b). Place-based education has ancestral roots in the systems-oriented knowledge and practices of cultural groups across the world who live subsistence lifestyles, relying upon historical, community, and personal knowledge of complex linkages among humans, seasons, climate, and biological and abiotic resources. For example, the place- and time-based knowledge of when and where to harvest seasonal fsh, birds, and mammals and access abiotic resources such as clean water and minerals are place-based knowledge. They allow for developed systems of place-based practices and values that moderate human behavior in order to conserve and possibly augment resources for future generations through Indigenous engineering projects, such as fshponds, terracing, and water management. Until the innovation of writing and recent development of institutions devoted to studying natural phenomena and applying that knowledge toward economic and political ends, knowledge about nature was transmitted across the generations through sayings, stories, and myths. Martha Beckwith, the frst person to hold a chair in Folklore in the United States, co-authored Hawaiian Mythology (1940) with Hawaiian scholar Mary Kawena Pukui, a native speaker of Hawaiian. With Pukui’s access to ancestral stories, they noted that stories that contained genealogies could be traced to historical events and places in Hawai‘i and other Pacifc islands. Unfortunately, in categorizing this book as myth, libraries following the Dewey Decimal System placed it outside of the STEM categories, meaning that these stories would not be recognized as a resource for STEM education. As Western scientists and science educators have historically been drawn from the upper and middle classes of dominant cultures not typically represented in underserved urban and rural schools, relatively little place-based knowledge enters either mainstream science or science education. The divisions of biology, chemistry, physics, and earth science that are found in US teacher certifcation programs and K–12 science programs refect the historical partitioning of knowledge into increasingly specialized science felds that build knowledge in laboratories divorced from the lived experiences of people in today’s rural and urban settings. Rural and Indigenous peoples’ place-based, sustainability, and systems-oriented ways of living in and understanding the world, developed over many generations, became increasingly divorced and marginalized from the knowledge-building processes of Western science: one that portrayed science as the masculine struggle to uncover the secrets of mother nature. John Dewey (1920) considers Francis Bacon (1561–1626) to be the “great forerunner of modern life” (p. 28), based on his conceptualization of scientifc methods and linking of knowledge with power. Though often considered a proponent of progressive child-centered schooling, Dewey also conveyed Western science’s metaphor of power and domination when he wrote, “Scientifc principles and laws do not lie on the surface of nature. They are hidden and must be wrested from nature by an active and elaborate technique of inquiry” (p.  32). Linda Tuhiwai Smith (1999) notes that the association of Eurocentric science research with “the worst excesses of colonialism remain a powerful remembered history for many of the world’s colonized peoples” (p. 1). In Hawai‘i, where rural communities rely upon gardening and wild marine and terrestrial resources for much of their food, culture still shapes how children learn, how people relate to their place, and how they relate to each other and their community. Nearly 3,000 Hawaiian sayings, ‘ōlelo no‘eau, collected by Mary Kawena Pukui (1983) in the early 20th century, convey ecological relationships as well as the cultural values and practices that guide people’s behavior and shape their identity. The saying “Pua ka wiliwili, nanahu ka manō” translates to “when the wiliwili tree blooms, the shark bites”. This has been confrmed by research showing an estimated 25% of female tiger sharks in the Northwestern Hawaiian Islands migrate to the main Hawaiian islands to give birth in the fall when the wiliwili (Erythrina sandwicensis) blooms (Maui News, 2017). Hawaiian children still learn to observe closely and silently in order to build their observational, listening, and analytical skills as conveyed in the saying “Nana ka maka, ho‘olohe ka pepeiao, pa’a ka waha”. This translates to “observe with the eyes; listen with the ears; shut the mouth”. Such sayings guide behaviors of observing and monitoring closely, kilo, a practice that underlies stewardship as a collective act. Taken together, ongoing monitoring of

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both environmental phenomena and human activities in the place that sustains a people constantly updates a working model of a complex world that people must respect and care for as individuals and as a community. In Hawai‘i, coalitions of resource scientists, resource managers, cultural practitioners, and educators are intersecting deep place-based knowledge, Western science, and Native Hawaiian understanding of ritual as relationship to create a shared biocultural space where identities, practices, and values coalesce around the shared vision of sustainable, resilient social ecosystems (Kealiikanakaoleohaililani et al., 2018). Current technologies can bring the makahiki, a precontact knowledge-gathering system into the present in both urban and rural settings. At the island level, the annual makahiki, a 2–3-monthslong circuit of each major island during the winter months by the mo‘i, reigning chief, served as an information-gathering system for resource management (Abbott, 1992). At the moku district level and ahupua‘a largely sustainable geopolitical level, the annual site visit assessed resources and collected tribute based on prior visits. A konohiki, a knowledgeable lower chief, was empowered to order people in the ahupua‘a to collective action, e.g., repairing waterways or fshponds, or halting actions, e.g., a kapu forbidding fshing when fsh were spawning and populations most vulnerable. In Hawai‘i and other ecologically vulnerable Pacifc islands, being aware of surroundings, monitoring changes, fnding patterns, building new knowledge of the interlinked social ecosystem, valorizing hard work and cooperation, and controlling people’s behavior supported sustainable, resilient societies. Since frst contact with the West in 1788, environmental degradation from introduced hoofed animals, sugar and pineapple plantations, urbanization, and invasive species have altered even the mountainous forested zones. Now community groups, informed by a culturally grounded, place-based vision of stewardship, collaborative work, and reciprocity, are active in many ahupua‘a. The proverb I ka wa mamua, ka wa mahope, the future is in the past, reinforces a cultural vision to look to the lessons of a more sustainable and resilient past to guide the path forward. The boundaries of ahupua‘a are marked with signage, restoring a Hawaiian sense of place, and school and urban and rural community groups are sharing the responsibility, kuleana, of cleaning up streams, roadsides, and coastlines, restoring native species and planting staple crops brought by Polynesians, disrupting the urban–rural binary. Chinn (2012, 2014, 2015) applied a place-based, FoK lens in the service of critical professional development of teacher leaders who utilize strategies of community mapping, curricular mapping, and place-based pedagogies to develop interdisciplinary STEM curricula for their specifc communities. Knowledge of how a place has changed from frst human contact to the present develops through archival research and interviews of elders, enabling urban teachers to write place-based curricula that help students reenvision and engage with their familiar places. Teachers’ personally engaged research in the communities in which their students live supports school–community partnerships that enable community-engaged, problem-fnding, and problem-solving curricula. Semken et al. (2017) view place-based education as a “situated, context-rich, transdisciplinary teaching and learning modality distinguished by its unequivocal relationship to place, which is any locality that people have imbued with meanings and personal attachments through actual or vicarious experiences” (p. 542). Establishing personal connections to place through the critical lens of community-engaged problem fnding and problem solving is a present-day version of adaptive resource management that may be applied in both urban and rural settings. In the United States, urban and rural K–12 schools both have issues of insufcient economic resources and underperformance by marginalized, minoritized youth. Every place, however, has both physical and human resources that can be understood through a critical historical lens. Strategies of community mapping can reveal Indigenous place names for springs, waterways, and woodlands that are renamed or no longer exist, allowing insights into past ecosystems and a critical perspective on the changes leading up to the present. As teachers develop their historical place and culture-based expertise and pedagogy through community partnerships, they engage their students in learning communities that develop experiential, personally meaningful, systems-oriented knowledge of their

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places and an appreciation of the way values, beliefs, and culture interact with STEM knowledge and practices to shape urban and rural landscapes. Place-based education is a critical pedagogy employed by Freire (1970), who wrote: “A deepened consciousness of their situation leads people to apprehend that situation as an historical reality susceptible of transformation” (p. 21). As an inquiring and empowering pedagogy, place-based education has become a science education approach to address issues of academic disparities (Promise of Place, n.d.). Reviews of the literature on rural STEM education by Harris and Hodges (2018) and research with 200 K–12 urban students by Flanagan et al. (2019) have led them to independently conclude that K–12 students show increased engagement and academic achievement when learning is place based. Harris and Hodges’s (2018) review of articles on rural STEM education found that when teachers understood local culture, taught place-based STEM lessons drawn from their students’ community, and engaged local STEM professionals as partners, they gained parental support and students were more academically engaged and likely to become interested in STEM and higher education. Flanagan et al. (2019) analyzed the refections of 205 urban, mostly ethnic/racial minority, 4th–12th graders on what they learned from participating in place-based stewardship education involving hands-on collective learning/action by teams of students, teachers, and community partners in the communities where students attended school. They found the great majority of students recognized that humans played both positive and negative roles in ecosystems. A third of the students felt collective action was necessary to solve environmental problems, half experienced collective efcacy, and more than a third felt more attachment and identifcation with their community. Similar to what researchers have reported in relation to inner-city, high-density neighborhoods in general (Permentier et al., 2011), Bronx students’ place attachments were weak, perhaps due to stigmatization of this area as ecologically degraded, akin to other types of stigmatizations of inner-city places (Wacquant, 2007). This result may be explained by research that suggests that place attachment develops over long or frequent experiences of places (Tuan, 1977; Hay, 1998). The environmental education programs in this study were only 5–6 weeks long, which may be too brief to increase attachment to a place where most participants already reside. In summary, conventional STEM schooling may be critiqued as accumulation of decontextualized, place-less content knowledge leading to pseudo expertise, inequitable academic outcomes, and inability to solve real-world problems (Sternberg, 2003). In contrast, culturally and historically informed place-based approaches that include Indigenous perspectives, community voices, and partnerships disrupt the rural–urban binary and provide critical narratives that connect teachers and students, whether urban or rural, to familiar real-world places with authentic histories that students are able to enter as agentic actors to problem fnd, problem solve, and create their own futures.

Implications and Future Approaches to Disrupting the Urban/Rural Binary Social contexts infuence learners’ creation of understanding; thus, local contexts are important to consider as the science education community seeks to enhance its understandings and practices associated with science teacher education, teaching, and learning. Such contexts, however, are complex and include tendencies associated with many aspects of society, such as history, culture, and power of people and institutions. Education research continues to demonstrate that within this complexity are specifc factors, or a combination of factors, that are contributing to education gaps identifed in both urban and rural locations. The purposes driving current urban-centric science education are, for the most part, based on cultural diferences both within these schools and with their communities. Rural and urban residents may be similar in valuing their community, feeling vested in its growth and prosperity, and perceiving that others look down on them, but depending on their cultural history, they difer in their thinking about community values and politics (Bialik, 2018).

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The complexity inherent in urban and rural school contexts, and its impact on the science teaching and learning process, cannot be captured by an approach based solely on population density. Possessing a limited understanding of the complexity and the factors impacting educational goals is concerning for many reasons, not the least of which is the fact that the majority of the world’s students are in these geographical locations. Although learning gaps in areas of certain population densities suggest a focus on these areas is warranted, there are other factors beyond the density of the population that are impacting education. Education researchers need to consider how social, economic, and other contextual factors within these locales interact with educational processes and outcomes within the schools (Burdick-Will & Logan, 2017). Some of this complexity is inherent in the existing research base, albeit often implicitly. However, this exploration revealed that many studies provide very limited information on the contexts and complexities of STEM teaching and learning despite emphasizing their importance. For example, most of the urban research focuses on schools in megacities that have large student bodies that include many diferent ethnic and racial populations. However, research is also needed on urban schools outside the megacity and/or schools with students mostly from one underserved population. Likewise, most rural science education research in the United States focuses on students who are part of the dominant population attending small and geographically isolated schools. Again, understanding these contexts through qualitative and quantitative research is critical; however, research is also needed on rural schools that enroll non-majority refugee or Indigenous students. These rural schools often draw from populations that possess rich culture and practices (Avery, 2013), i.e., funds of knowledge that difer from the mainstream, but lack a voice in political and economic decision-making, participate in limited ways in mainstream economies, and receive limited social services. These characteristics afect science teaching and learning in quite diverse ways not typically identifed or explored in urban and rural science education. Emdin (2010) notes the problem with assuming that practices used in non-urban settings are suitable for urban settings. Educators are also being cautioned not to lump all rural or urban communities together (e.g., Abrams & Middleton, 2017). A growing number of researchers are providing new conceptions of urban or rural education (e.g., Welsh & Swain, 2020; Echazarra & Radinger, 2019). In their 2020 article, Welsh and Swain ofer a diferent conceptualization and operationalization of urban education. This (re)defnition (1) considers whether the community is dynamic and complex, often being shaped by the vestiges of discrimination and oppression, rather than static and monolithic; (2) is based on the degree of conditions based on characteristics, challenges, and context; (3) includes the presence of educational inequality; and (4) is not rooted in a defcit perspective. Echazarra and Radinger (2019) provide a list of traits that characterize an area as rural. These traits include (1) geographical distance, (2) small and sparse population, (3) dwindling share of the population, (4) low socioeconomic status, and (5) ethnically homogeneous and socially cohesive. Science educational research can no longer be categorized in a binary manner of urban and rural or considered as a continuum, as urban and rural are not opposite ends of a line. Researchers are encouraged to explore the educational gaps in urban and rural schools by considering the intersectionality of identities associated with the geographical and historical contexts. Intersectionality considers the realities of multiple inequalities and the interactions of them (Collins, 2016; Crenshaw, 1991). Science students in urban and rural underperforming schools have multiple identities that are infuenced not only by community characteristics but also racial and cultural diversity, socioeconomic status, academic resources, status/quality of science teaching, social dynamics, and institutional identities that difer from those traditionally assumed in science and science education. By approaching research in a manner that embraces the sociocultural and historical complexities of rural and urban students, the feld can achieve more complete understandings. This chapter explored four broad conceptual tools that encompass various aspects of urban or rural students’ identities. SES involves not only family income, as is commonly used, but also parental educational levels, parental occupations, and the way these impact a students’ cultural capital in the

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institution and science. Identity formation and learning are infuenced by the interaction of dominant, Indigenous, and local cultures. The disconnects between students’ urban-centric identities and science identities have an impact on teaching and learning. Understanding the students’ FoK not only provides meaning to what students engage in science, but it also connects local places, cultures, values, and ideas to science. FoK generated in communities would look diferent because of the historical, social, and cultural nature of these places and their inhabitants. Place-based education ofers a way to foster such a connection. These are examples of important conceptual approaches, but the list is certainly not exhaustive. The ability to address inequities within urban or rural locations with conceptual tools that better capture the sociocultural aspects of a student within an institutional setting location, as well as the impacts on a student’s development of a STEM identity, allows for a more holistic understanding of science teaching and learning in urban and rural locales as a complex and dynamic process. In conclusion, across the globe, many rural and urban community schools are requiring urgent attention as science educators work to improve opportunities for greater human capital growth among all students. Human capital is directly tied to future economic success of the students (Maarseveen, 2020). Science education has been touted as the path to good-paying jobs, community and national technological growth, and a democratic society. It is imperative that science educators and researchers move beyond simplistic binaries of rural and urban education and pay attention to historical and geographic contexts, as well as the sociocultural complexities. The science education professional community needs to ask questions, challenge understandings, and take action to support teachers as critical curriculum developers able to incorporate students’ FoK, deep historical knowledge of place and resources, including community, into a science education ecosystem that engages the identities students bring with them to forge science identities that empower them to be agentic actors.

References Abbott, I. (1992). La’au Hawai’i: Traditional Hawaiian uses of plants. Bishop Museum Press. Abell, S. K., & Lederman, N. (Eds). (2007). Handbook of research on science education. Routledge. Abrams, E., & Middleton, M. (2017). Towards multidimensional approaches to research on rural science education. Cultural Studies in Science Education, 12, 167–176. Ahmed, F. Z., & Boser, U. (2014). America’s leaking pipeline for teachers of color: Getting more teachers of color into the classroom. https://eric.ed.gov/?id=ED561065 Aikens, N. L., & Barbarin, O. (2008). Socioeconomic diferences in reading trajectories: The contribution of family, neighborhood, and school contexts. Journal of Educational Psychology, 100(2), 235–251. https://doi. org/10.1037/0022-0663.100.2.235 Akuma, F. V., & Callaghan, R. (2019). Teaching practices linked to the implementation of inquiry-based practical work in certain science classrooms. Journal of Research in Science Teaching, 56, 64–90. Al-Balushi, S. M., & Martin-Hansen, L. (2019). The development of students’ justifcations for their positions regarding two theoretical models: Electron cloud or sodium chloride crystal – After engaging in diferent learning activities. Journal of Research in Science Teaching, 56, 1011–1036. Albrecht, N., & Upadhyay, B. (2018). What do refugee mothers want in U.S. school science: Learning from the perceptions of science of three Somali mothers. The Urban Review, 50, 604–629. https://doi.org/10.1007/ s11256-018-0458-9 Archer, L., Dawson, E., DeWitt, J., Godec, S., King, H., Mau, A., Nomikou, E., & Seakins, A. (2017). Killing curiosity? An analysis of celebrated identity performances among teachers and students in nine London secondary science classrooms. Science Education, 101, 741–764. Archibald, J.-A. (2008). Indigenous storywork: Educating the heart, mind, body and spirit. UBC Press. Ardoin, N. M., Bowers, A. W., Roth, N. W., & Holthuis, N. (2018). Environmental education and K-12 student outcomes: A review and analysis of research, The Journal of Environmental Education, 49(1), 1–17. http:// doi.org/10.1080/00958964.2017.1366155 Avery, L. M. (2013). Rural science education: Valuing local knowledge. Theory Into Practice, 52(1), 28–35. http://doi.org/10.1080/07351690.2013.743769

381

Gayle A. Buck, Pauline W. U. Chinn and Bhaskar Upadhyay Banks, J. A. (1995). Multicultural education: Historical development, dimensions, and practice. In J. A. Banks & C. A. M. Banks (Eds.), Handbook of research on multicultural education (pp. 3–24). Macmillan. Basu, S. J., & Barton, A. C. (2007). Developing a sustained interest in science among urban minority youth. Journal of Research in Science Teaching, 44, 466–489. Beasley, M. A., & Fischer, M. J. (2012). Why they leave: The impact of stereotype threat on the attrition of women and minorities from science, math and engineering majors. Social Psychology of Education, 15(4), 427–448. Beckwith, M. W. (1940). Hawaiian mythology: By Martha Beckwith. Publ. for the Folklore Foundation of Vassar College. Yale University Press, Humphrey Milford Oxford University Press. Berkowitz, R., Moore, H., Astor, R. A., & Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Review Educational Research, 87, 425–469. http://doi.org/10.3102/0034654316669821 Betancur, L., Votruba-Drzal, E., & Schunn, C. (2018). Socioeconomic gaps in science achievement. International Journal of STEM Education, 5(38). http://doi.org/10.1186/s40594-018-0132-5 Bialik, K. (2018). Key fndings in American life in urban, suburban and rural areas. https://pewrsr.ch/2IAXvTP Boda, P. A., & Brown, B. (2019). Priming urban learners’ attitudes toward the relevancy of science: A mixedmethods study testing the importance of context. Journal of Research in Science Teaching, 57, 567–596. Boizonella, F. (2016). Urban culture: Defnition and contextualization. www.researchgate.net/publication/ 305936766_Urban_Culture_Defnition_and_Contextualization Borgerding, L. A. (2017). High school biology evaluation learning experiences in a rural context: A case of and for cultural border crossing. Cultural Studies in Science Education, 12, 53–79. Bouillion, L. M.,  & Gomez, L. M. (2001). Connecting school and community with science learning: Real world problems and school – Community partnerships as contextual scafolds. Journal of Research in Science Teaching, 38, 878–898. Bourdieu, P., & Passeron, J.-C. (1990). Reproduction in education, society and culture (2nd ed.) (R. Nice, Trans.). Sage Publications. Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399. Breen, R., & Jonsson, J. O. (2005). Inequality of opportunity in comparative perspective: Recent research on educational attainment and social mobility. Annual Review of Sociology, 31, 223–243. Brooks, A. (1997). Postfeminisms: Feminism, cultural theory and cultural forms. Routledge. Brown, J., Ring-Whalen, E., Roehrig, G., & Ellis, J. (2018). Advancing culturally responsive science education in secondary classrooms through an induction course. International Journal of Designs for Learning, 9(1), 14–33. Burdick-Will, J., & Logan, J. (2017). Schools at the rural-urban boundary: Blurring the divide? The ANNALS of the American Academy of Political and Social Science, 672(1), 185–201. Cajete, G. (1994). Look to the mountain: An ecology of Indigenous education. Kivaki Press. Calabrese Barton, A. (2003). Teaching science for social justice. Teachers College Press. Calabrese Barton, A. (2007). Science learning in urban settings. In S. K. Abell & N. Lederman (Eds.), Handbook of research on science education (Vol. I). Routledge. Calabrese Barton, A. & Tan, E. (2009). Funds of knowledge and discourses and hybrid space. Journal of Research in Science Teaching, 46, 50–73. https://doi.org/10.1002/tea.20269 Calabrese Barton, A., Tan, E., & O’Neill, T. (2014). Science education in urban contexts: New conceptual tools and stories of possibilities. In N. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II). Routledge. Calabrese Barton, A., & Upadhyay, B. (2010). Teaching and learning science for social justice: Introduction to the special issue. Equity and Excellence in Education, 43, 1–5. Carlone, H., & Johnson, A. (2012). Unpacking “culture” in cultural studies of science education: Cultural difference versus cultural production. Ethnography and Education, 7, 151–173. Chapman, A., & Feldman, A. (2017). Cultivation of science identity through authentic science in an urban high school classroom. Cultural Studies in Science Education, 12, 469–491. Chappell, M. J., & Varelas, M. (2018). Ethnodance and identity: Black students representing science identities in the making. Science Education, 104, 193–221. Cheryan, S., Plaut, V. C., Davies, P. G., & Steele, C. M. (2009). Ambient belonging: How stereotypical cues impact gender participation in computer science. Journal of Personality and Social Psychology, 97(6), 1045–1060. Chetty, R., Hendren, N., Jones, M. R., & Porter, S. R. (2018). Race and economic opportunity in the United States: An intergenerational perspective. www.equality-of-opportunity.org/assets/documents/race_paper.pdf Chinn, P. W. U. (2007). Decolonizing methodologies and indigenous knowledge: The role of culture, place and personal experience in professional development. Journal of Research in Science Teaching, 44, 1247–1268.

382

Science Education in Urban and Rural Contexts Chinn, P. W. U. (2012). Developing teachers’ place-based and culture-based pedagogical content knowledge and agency. In B. J. Fraser et al. (Eds.), Second international handbook of science education. Springer. http://doi. org/10.1007/978-1-4020-9041-7_23 Chinn, P. W. U. (2014). Educating for scientifc literacy, citizenship, and sustainability: Learning from Native Hawaiian perspectives. In M. P. Mueller, D. J. Tippins, & A. J. Stewart (Eds.), Assessing schools for generation R (responsibility): A guide to legislation and school policy in science education. Springer. Chinn, P. W. U. (2015). Place and culture-based professional development: Cross-hybrid learning and the construction of ecological mindfulness. Cultural Studies of Science Education, 10(1), 121–134. Coleman, J. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95–S120. www.jstor.org/stable/2780243 Collins, P. H. (2010). The new politics of community. American Sociological Review, 75(7), 1–30. Collins, P. H. (2016). Intersectionality. Polity Press. Collins, R. (2004). Interaction ritual chains. Princeton University Press. Cowie, B., Jones, A., & Otrel-Cass, K. (2011). Re-engaging students in science: Issues of assessment, funds of knowledge and sites for learning. International Journal of Science and Mathematics Education, 9, 347–366. Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299. Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications. Delgado, R., & Stefancic, J. (2012). Critical race theory: An introduction (2nd ed.). New York University Press. Dewey, J. (1920). Reconstruction in philosophy. H. Holt and Company. Dickinson, J., Shirk, J., Bonter, D., Bonney, R., Crain, Martin, Phillips, T., & Purcell (2012). The current state of citizen science as a tool for ecological research and public engagement. Frontiers in Ecology and the Environment, 10, 291–297. http://doi.org/10.1890/110236 Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. American Psychologist, 55(1), 34–43. https://doi.org/10.1037/0003-066X.55.1.34 Diener, E. (2002). Will money increase subjective well-being? Social Indicators Research, 57, 119–169. DiMaggio, P. (1997). Culture and cognition. Annual Review of Sociology, 23, 263–287. Duncan, G. J., Morris, P. A., & Rodrigues, C. (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random-assignment experiments. Development Psychology, 47, 1263–1279. Duncan-Andrade, J., & Morrell, E. (2008). The art of critical pedagogy: Possibilities of moving from theory to practice in urban schools. Peter Lang Publishers. Echazarra, A., & Radinger, T. (2019). Learning in rural schools: Insights from PISA, TALIS and the literature. OECD Education Working Papers, No. 196. OECD Publishing. https://doi.org/10.1787/8b1a5cb9-en Emdin, C. (2010). Chapter seven: What is urban education? Counterpoints, 215, 101–111. www.jstor.org/ stable/42980440 Flanagan, C., Gallay, E., Pykett, A., & Smallwood, M. (2019). The environmental commons in urban communities: The potential of place-based education. Frontiers in Psychology, 10, 226. http://doi.org/10.3389/ fpsyg.2019.00226. https://research.childrenandnature.org/research/place-based-stewardship-education-inurban-communities-promotes-knowledge-skills-and-motivation-needed-for-sustaining-common-poolresources/ Freire, P. (1970, 1993). Pedagogy of the oppressed (M. B. Ramos, Trans.; D. Macedo, Intro., 30th anniversary ed.). The Continuum International Publishing Group Inc. Fuentes, N., & Rojas, M. (2001). Economic theory and subjective well-being: Mexico. Social Indicators Research, 53, 289–314. Gamez, R., & Parker, C. A. (2018). Becoming science learners: A study of newcomers’ identity work in elementary school science. Science Education, 102, 377–413. Gandolf, H. E. (2020). “It’s a lot of people in diferent places working on many ideas”: Possibilities from global history of science to learning about nature of science. Journal of Research on Science Teaching, 58(4), 551–588. Gay, G. (2000). Culturally responsive teaching: Theory, research, and practice. Teachers College Press. Gee, J. P. (2000). Identity as an analytic lens for research in education. Review of Research in Education, 25, 99–125. Gonsalves, A. (2014). “Science isn’t just what we learn in school”: Interaction rituals that value youth voice in out-of-school-time science. Canadian Journal of Education, 37, 185–208. González, N. (1995). The funds of knowledge for teaching project. Practicing Anthropology, 17, 3–6. González, N., Moll, L. C., & Amanti, K. (2005). Funds of knowledge: Theorizing practices in households, communities, and classrooms. Lawrence Erlbaum Associates. Gruenewald, D. A. (2003a). The best of both worlds: A critical pedagogy of place. Educational Researcher, 32(4), 3–12. https://doi.org/10.3102/0013189X032004003

383

Gayle A. Buck, Pauline W. U. Chinn and Bhaskar Upadhyay Gruenewald, D. A. (2003b). Foundations of place: A multidisciplinary framework for place-conscious education. American Educational Research Journal, 40(3), 619–654. https://doi.org/10.3102/00028312040003619 Gutiérrez, K., & Rogof, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher, 32, 19–25. Hammond, L. (2001). Notes from California: An anthropological approach to urban science education for language minority families. Journal of Research in Science Teaching, 38, 983–999.  https://doi.org/10.1002/ tea.1043 Harding, S. (1991). Whose science? Whose knowledge? Thinking from women’s lives. Cornell University Press. Harris, C. J., Penuel, W. R., D’Angelo, C. M., DeBarger, A. H., Gallagher, L. P., Kennedy, C. A., Cheng, G. H., & Krajcik, J. S. (2015). Impact of project-based curriculum materials on student learning in science: Results of a randomized controlled trial. Journal of Research in Science Teaching, 52(10), 1362–1385. Harris, R. S., & Hodges, C. B. (2018). STEM education in rural schools: Implications of untapped potential. National Youth-At-Risk Journal, 3(1). https://doi.org/10.20429/nyarj.2018.030102 Harwell, M., & LeBeau, B. (2010). Student eligibility for a free lunch as an SES measure in education research. Educational Researcher, 39, 120–131. Hay, R. (1998). Sense of place in developmental context. Journal of Environmental Psychology, 18, 5–29. ISSN 0272–4944. https://doi.org/10.1006/jevp.1997.0060 Hill, P. W., McQuillan, J., Hebets, E. A., Spiegel, A. N., & Diamond, J. (2018). Informal science experiences among Urban and Rural youth: Exploring diferences at the intersections of socioeconomic status, gender and ethnicity. Journal of STEM Outreach, 1, 1–12. hooks, b. (1984). Feminist theory: From the margins to the center. South End Press. Huang, X., Lederman, N. G., & Cai, C. (2017). Improving Chinese junior high school students’ ability to ask critical questions. Journal of Research in Science Teaching, 54(8), 963–987. Iseke, J. (2013). Indigenous storytelling as research. International Review of Qualitative Research, 6(4), 559–577. Iseke, J., & Brennus, B. (2011). Learning life lessons from Indigenous storytelling with Tom McCallum. In G. J. S. Dei (Ed.), Indigenous philosophies and critical education (pp. 245–261). Peter Lang. Isserman, A. M. (2005). In the national interest: Defning rural and urban correctly in research and public policy. International Regional Science Review, 28(4), 465–499. Jencks, C., & Phillips, M. (1998). The Black–White test score gap. Brookings Institution Press. Johnson, C. C., & Fargo, J. D. (2010). Urban school reform enabled by transformative professional development: Impact on teacher change and student learning of science. Urban Education, 45, 4–29. Johnson, J., Showalter, D., Klein, R., & Lester, C. (2014). Why rural matters 2013–2014: The condition of rural education in the 50 states. Rural Schools and Community Trust. www.ruraledu.org/user_uploads/fle/201314-Why-Rural-Matters.pdf Kane, J. M. (2015). The structure-agency dialectic in contested science spaces: “Do earthworms eat apples?” Journal of Research in Science Teaching, 52(4), 461–473. Kang, H., Barton, A. C., Tan, E., Simpkins, S. D., Rhee, H., & Turner, C. (2018). How do middle school girls of color develop STEM identities? Middle school girls’ participation in science activities and identifcation with STEM careers. Science Education, 103, 418–439. Kassam, K. S., Avery, L. M., & Ruelle, M. J. (2017). The cognitive relevance of Indigeneous and rural: Why is it critical to survival? Cultural Studies in Science Education, 12, 97–118. Katz, M. B. (1995). Improving poor people: The welfare state, the ‘underclass’, and urban schools as history. Princeton University Press. Kealiikanakaoleohaililani, K., Kurashima, N., Francisco, K., Giardina, C., Louis, R., McMillen, H., Asing, C., Asing, K., Block, T., Browning, M., Camara, K., Camara, L., Dudley, M., Frazier, M., Gomes, N., Gordon, A., Gordon, M., Heu, L., Irvine, A., Kaawa, N., Kirkpatrick, S., Leucht, E., Perry, C., Replogle, J., Salbosa, L.-L., Sato, A., Schubert, L., Sterling, A., Uowolo, A., Uowolo, J., Walker, B., Whitehead, A., & Yogi, D. (2018). Ritual + sustainability science? A portal into the science of aloha. Sustainability, 10: 3478. https:// doi.org/10.3390/su10103478. www.fs.usda.gov/treesearch/pubs/57782 Klett, J. (2014). Sound on sound: Situating interaction in sonic object settings. Sociological Theory, 32, 147–161. Kohut, M. (2019). Changing minds or rhetoric? How students use their many natures of science to talk about evolution. Cultural Studies of Science Education, 14, 839–862. Krueger, A., & Sutton, J. (2001). EDThoughts: What we know about science teaching and learning. Ofce of Educational Research and Improvement (ED). Kuhn, T. (1962). The structure of scientifc revolutions. University of Chicago Press. Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491. Latour, B., & Woolgar, S. (1979). Laboratory life: The construction of scientifc facts. Sage Publications.

384

Science Education in Urban and Rural Contexts Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press. Lederman, N. G., & Abell, S. K. (2014). Handbook of research on science education. Routledge. Lee, S. C., Nugent, G., Kunz, G. M., Houston, J., & DeChenne-Peters, S. (2018). Case study: Value-added beneft of distance-based instructional coaching on science teachers’ inquiry instruction in rural schools. Journal of Science Teacher Education, 29(3), 179–199. http://doi.org/10.1080/1046560X.2018.1432226 Lee, V. E., & Burkam, D. T. (2002). Inequality at the starting gate: Social background diferences in achievement as children begin school. Economic Policy Institute. Lester, F. (2005). On the theoretical, conceptual, and philosophical foundations for research in mathematics education. International Reviews on Mathematical Education, 37(6), 457–467. Lucero, M. M., Petrosino, A. J., & Delgado, C. (2017). Exploring the relationship between secondary science teachers’ subject matter knowledge and knowledge of student conceptions while teaching evolution by natural selection. Journal of Research in Science Teaching, 54(2), 219–246. Maarseveen, R. V. (2020). The urban rural-education gap: Do cities indeed make us smarter? www.cpb.nl/sites/ default/fles/omnidownload/CPB-Discussion-Paper-412-The-urban-rural-education-gap.pdf Maerten-Rivera, J., Myers, N., Lee, O., & Penfeld, R. (2010), Student and school predictors of high-stakes assessment in science. Science Education, 94, 937–962. Mangiante, E. S. (2018). Planning for reform-based science: Case studies of two urban elementary teachers. Research in Science Education, 48, 207–232. Marco-Bujosa, L. M., McNeill, K. L., & Friedman, A. A. (2020). Becoming an urban science teacher: How beginning teachers negotiate contradictory school contexts. Journal of Research on Science Teaching, 57(1), 3–32. Mark, S. (2018). A bit of both science and economics: A non-traditional STEM identity narrative. Cultural Studies in Science Education, 13, 983–1003. Mark, S., Id-Deen, L., & Thomas, S. (2020). Getting to the root of the matter: Pre-service teachers’ experiences and positionalities with learning to teach in culturally diverse contexts. Cultural Studies of Science Education, 15, 453–483. Martinez, A.,  & Christnacht, C. (2021, January 26). Women are nearly half of U.S. workforce but only 27% of STEM workers. www.census.gov/library/stories/2021/01/women-making-gains-in-stem-occupations-butstill-underrepresented.html Maui News (2017, October 6). DAR: October sees greatest number of shark bites. https://mauinow.com/2017/10/06/ dar-october-sees-greatest-number-of-shark-bites/ McIntyre, E., Swazy, R. A., & Greer, S. (2001). Agricultural feld day. Linking rural cultures to school lessons. In E. McIntyre, A. Rosebery, & N. González (Eds.), Classroom diversity. Connecting curriculum to students’ lives (pp. 76–84). Heinemann. McNeill, K. L., Gonzalez-Howard, M., Katsh-Singer, R., & Loper, S. (2017). Moving beyond pseudoargumentation: Teachers’ enactments of an educative science curriculum focused on argumentation. Science Education, 101, 426–457. Moll, L. (2005). Refections and possibilities. In N. González, L. Moll, & C. Amanti (Eds.), Funds of knowledge: Theorizing practices in households, communities, and classrooms (pp. 278–287). Lawrence Erlbaum Associates. Moll, L., Amanti, C., Nef, D., & González, N. (1992). Funds of knowledge for teaching: Using a qualitative approach to connect homes and classrooms. Theory into Practice, 31, 132–141. Moll, L.,  & González, N. (1994). Lessons from research with language minority children. Journal of Reading Behavior, 25, 439–56. Morales-Doyle, D. (2016). Students as curriculum critics: Standpoints with respect to relevance, goals, and science. Journal of Research in Science Teaching, 55, 749–773. Morgan, P. L., Farkas, G., Hillemeier, M. M., & Maczuga, S. (2016). Science achievement gaps begin very early, persist, and are largely explained by modifable factors. Educational Researcher, 45, 18–35. Mueller, C. W.,  & Parcel, T. L. (1981). Measures of socioeconomic status: Alternatives and recommendations. Child Development, 52(1), 13–20. https://doi.org/10.2307/1129211 Murphy, S. (2020). Science education success in a rural Australian school: Practices and arrangements contributing to high senior science enrolments and achievement in an isolated rural school. Research in Science Education, 1–13. https://doi.org/10.1007/s11165-020-09947-5 National Assessment of Educational Progress [NAEP]. (2019). The nations report card. www.nationsreportcard. gov/science/nation/achievement/?grade=4 National Center for Education Statistics [NCES]. (2017). Digest of education statistics. http://nces.ed.gov/ programs/digest National Forum of Educational Statistics. (2015). Improving the measurement of socioeconomic status for the National Assessment of Educational Progress: A theoretical foundation. https://nces.ed.gov/nationsreportcard/pdf/researchcenter/socioeconomic_factors.pdf

385

Gayle A. Buck, Pauline W. U. Chinn and Bhaskar Upadhyay National Research Council. (2012). A framework for K-12 education: Practices, crosscutting concepts, and core ideas. The National Academies Press. National Research Council [NRC]. (2013). Next generation science standards: For states, by states. The National Academies Press. https://doi.org/10.17226/18290 Nisbet, J. (2005). What is educational research? Changing perspectives through the 20th century. Research Papers in Education, 20(1), 25–44. doi:10.1080/0267152052000341327 Nixon, R. S., Luft, J. A., & Ross, R. J. (2017). Prevalence and predictors of out-of-feld teaching in the frst year. Journal of Research in Science Teaching, 54(9), 1197–1218. Nugent, G., Kunz, G., Houston, J., Wu, C., Patwardhan, I., Lee, S., DeChenne-Peters, S. E., & Luo, L. (2018). The efectiveness of a summer institute and remotely delivered science instructional coaching in middle and high school. Journal of Science Teacher Education, 29(8), 760–784. OECD. (2015). PISA 2015 technical report. www.oecd.org/pisa/data/2015-technical-report/ Olitsky, S. (2021). Identity, agency, and the internal conversation of science and math teachers implementing instructional reforms in high-need urban schools. Cultural Studies in Science Education, 16, 19–45. Oliver, J. S. (2007). Rural science education. In S. K. Abell & N. Lederman (Eds.), Handbook of research on science education (Vol. I). Routledge. Oliver, J. S., & Hodges, G. W. (2014). Rural science education: New ideas, redirections, and broadened defnitions. In N. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. II). Routledge. Orr, A. J. (2003). Black-white diferences in achievement: The importance of wealth. Sociology of Education, 76, 281–304. Paprzycki, P., Tuttle, N., Czerniak, C. M., Molitor, S., Kadervaek, J., & Mendnhall, R. (2017). The impact of a framework-aligned science professional development program on literacy and mathematics achievement of K-3 students. Journal of Research in Science Teaching, 54(9), 1174–1196. Paris, D., & Alim, S. H. (2014). What are we seeking to sustain through culturally sustaining pedagogy? A loving critique forward. Harvard Educational Review, 84, 85–100. Permentier, M., Bolt, G., & van Ham, M. (2011). Determinants of neighbourhood satisfaction and perception of neighbourhood reputation. Urban Studies, 48, 977–996. http://doi.org/10.1177/0042098010367860 Posner, G. J. (2004). Analyzing the curriculum. McGraw Hill. Pringle, R. M., Mesa, J., & Hayes, L. (2020). Meeting the demands of science reforms: A comprehensive professional development for practicing middle school teachers. Research in Science Education, 50, 709–737. Pukui, M. K. (1983). ‘Ōlelo No’eau: Hawaiian proverbs & poetical sayings. Bishop Museum Press. Quansah, R. E., Sakyi-Hagan, N. A., & Essiam, C. (2019). Challenges afecting the teaching and learning of integrated science in rural junior high schools in Ghana. Science Education International, 30(4), 329–333. Rahm, J., & Moore, J. C. (2016). A case study of long-term engagement and identity-in-practice: Insights into the STEM pathways of four underrepresented youths. Journal of Research in Science Teaching, 51(5), 768–801. Ramnarain, U. (2016). Understanding the infuence of intrinsic and extrinsic factors on inquiry-based science education at township schools in South African. Journal of Research in Science Teaching, 53(4), 598–619. Reed, K. (2010). Multicultural education for rural schools: Creating relevancy in rural America. The Rural Educator, 31(2), 15–20. Ridgeway, M. L., & Yerrick, R. K. (2018). Whose banner are we waving? Exploring STEM partnerships for marginalized urban youth. Cultural Studies of Science Education, 13, 59–84. Riojas-Cortez, M., Huerta, M. E., Flores, B. B., Perez, B.,  & Clark, E. R. (2008). Using cultural tools to develop scientifc literacy of young Mexican American preschoolers. Early Childhood Development and Care, 178, 527–536. Rios-Aguilar, C., Kiyama, J. M., Gravitt, M., & Moll, L. (2011). Funds of knowledge for the poor and forms of capital for the rich? A capital approach to examining funds of knowledge. Theory and Research in Education, 9, 163–184. Rivard, L. P., & Gueye, N. R. (2016). Enhancing literacy practices in science classrooms through a professional development program for Canadian minority language teachers. International Journal of Science Education, 38(7), 1150–1173. http://doi.org/10.1080/09500693.2016.1183267 Rivera Maulucci, M. S. (2010). Resisting the marginalization of science in an urban school: Coactivating social, cultural, materials, and strategic resources. Journal of Research in Science Teaching, 47, 840–860. Rogof, B. (1990). Apprenticeship in thinking. Cognitive development in social context. Oxford University Press. Rosebery, A. A., Warren, B., & Raymond, E. T. (2016). Developing interpretive power in science teaching. Journal of Research in Science Teaching, 53(10), 1571–1600. Rutkowskia, D., Rutkowskia, L., Wild, J., & Burroughs, N. (2018). Poverty and educational achievement in the U.S.: A less-biased estimate using PISA 2012 data. Journal of Children and Poverty, 24, 47–67. Saleh, A., Yuxin, C., Hmelo-Silver, C. E., Glazewski, K., Mott, B. W., & Lester, J. C. (2020). Coordinating scafolds for collaborative inquiry in a game-based learning environment. Journal of Research in Science Teaching, 57, 1490–1518.

386

Science Education in Urban and Rural Contexts Sandholtz, J. H., & Ringstaf, C. (2016). The infuence of contextual factors on the sustainability of professional development outcomes. Journal of Science Teacher Education, 27(2), 205–226. Schaft, K. A., & Jackson, A. Y. (2010). Rural education for the twenty-frst century: Identity, place, and community in a globalizing world. The Pennsylvania State University Press. Schilke, O., & Rossman, G. (2018). It’s only wrong if it’s transactional: Moral perceptions of obfuscated exchange. American Sociological Review, 83, 1079–1107. Schleicher, A. (2018). World class: How to build a 21st-century school system, strong performers and successful reformers in education. OECD Publishing. https://dx.doi.org/10.1787/9789264300002-en Scogin, S. C., & Stuessy, C. L. (2015). Encouraging greater student inquiry engagement in science through motivational support by online scientist-mentors. Science Education, 99(2), 312–349. Sedawi, W., Assaraf, O. B. Z.,  & Reiss, M. J. (2020). Indigeneous children’s connectedness to nature: The potential infuence of culture, gender and exposure to a contaminated environment. Cultural Studies in Science Education, 15, 955–989. Semken, S., Ward, E. G., Moosavi, S., & Chinn, P. W. U. (2017). Place-based education in geoscience: Theory, research, practice, and assessment. Journal of Geoscience Education, 65(4), 542–562. http://doi.org/ 10.5408/17-276.1 Shemwell, J. T., Avargil, S., & Capps, D. (2015). Grappling with long-term learning in science: A qualitative study of teachers’ views of developmentally oriented instruction. Journal of Research in Science Teaching, 52(8), 1163–1187. Shepherd, H. (2011), The cultural context of cognition: What the implicit association test tells us about how culture works. Sociological Forum, 26, 121–143. Sikoyo, L. N., & Jacklin, H. (2009). Exploring the boundary between school science and everyday knowledge in primary school pedagogic practices. British Journal of Sociology of Education, 30, 713–726. Sirin, S. R. (2005). Socioeconomic status and academic achievement: A meta-analytic review of research. Review of Educational Research, 75(3), 417–453. https://doi.org/10.3102/00346543075003417 Smith, L. T. (1999). Decolonizing methodologies: Research and indigenous peoples. University of Oxford Press. Sorhagen, N. S. (2013). Early teacher expectations disproportionately afect poor children’s high school performance. Journal of Educational Psychology, 105(2), 465–477. https://doi.org/10.1037/a0031754 Stance, L. J. (2012). Tough fronts: The impact of street culture on schooling. Routledge. Steele, C. M.,  & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans.  Journal of Personality and Social Psychology, 69(5), 797–811.  https://doi.org/10.1037/00223514.69.5.797 Sternberg, T. (2003). What is an “expert student?” Educational Researcher, 32, 5–9. Stockard, J., Rohlfng, C. M., & Richment, G. L. (2021, January 26). Equity for women and underrepresented minorities in STEM: Graduate experiences and career plans in chemistry. PNAS, 118(4), e2020508118. https://doi.org/10.1073/pnas.2020508118 Stovall, D. (2006). We can relate: Hip-hop culture, critical pedagogy, and the secondary classroom. Urban Education, 41, 585–602. Sundararaman, I. (2020). “World would move ahead!”: Exploring the learning of science and aspirations in the urban context through a case study. Cultural Studies in Science Education, 15, 775–792. Tan, C. Y., Liu, P., & Wong, W. L. V. (2020). Diferent patterns of relationships between principal leadership and 15-year-old students’ science learning: How school resources, teacher quality, and school socioeconomic status make a diference. Frontiers of Psychology, 11, 1–19. Trends in International Mathematics and Scientifc Studies [TIMMS]. (2019). TIMMS 2019 international results in mathematics and science. https://timss2019.org/reports/ Tuan, Y.-F. (1977). Space and place: The perspective of experience. University of Minnesota Press. United States Department of Agriculture [USDA]. (2019). Rural poverty and well-being. www.ers.usda.gov/ topics/rural-economy-population/rural-poverty-well-being/ Upadhyay, B. (2006). Using students’ lived experiences in an urban science classroom: An elementary school teacher’s thinking. Science Education, 90, 94–110. Upadhyay, B. (2010). Elementary school science teachers’ perceptions of social justice: A study of two elementary teachers. Equity and Excellence in Education, 43, 56–71. Upadhyay, B., Atwood, E., &Tharu, B. (2020). Actions for sociopolitical consciousness in a high school science class: A case study of ninth grade class with predominantly indigenous students. Journal of Research in Science Teaching, 57(7), 1119–1147. https://doi.org/10.1002/tea.21626 Upadhyay, B., Atwood, E., & Tharu, B. (2021). Antiracist pedagogy in a high school science class: A case of high school science teacher in an Indigenous high school. Journal of Science Teacher Education, 32(5), 518–536. http://doi.org/10.1080/1046560X.2020.1869886

387

Gayle A. Buck, Pauline W. U. Chinn and Bhaskar Upadhyay Upadhyay, B., & Gifford, A. (2011). Changing lives: Coteaching immigrant students in a middle school science classroom. In C. Murphy & K. Scantlebury (Eds.), Coteaching in international contexts: Research and practice (pp. 267–283). Springer. Upadhyay, B., Maruyama, G., & Albrecht, N. (2017). Taking an active stance: How urban elementary students connect sociocultural experiences in learning science. International Journal of Science Education, 39, 2528–2547. Valencia, R. (2010). Dismantling contemporary deficit thinking: Educational thought and practice. Taylor and Francis. van Ewijk, R., & Sleegers, P. (2010). Peer ethnicity and achievement: A meta-analysis into the compositional effect. School Effectiveness and School Improvement, 21, 237–265. Varelas, M., Morales-Doyle, D., Raza, S., Segura, D., Canales, K., & Mitchener, C. (2017). Community organizations’ programming and the development of community science teachers. Science Education, 102, 60–84. Vélez-Ibáñez, C. G., & Greenberg, J. B. (1992). Formation and transformation of funds of knowledge among US-Mexican households. Anthropology & Education Quarterly, 23, 313–335. Villalba, C. M. H. (2014). Socioeconomic status (SES). In A. C. Michalos (Ed.), Encyclopedia of quality of life and well-being research (pp. 267–311). Springer. Vyas, S., & Kumaranayake, L. (2006). Constructing socio-economic status indices: How to use principal components analysis. Health Policy and Planning, 21, 459–468. Vygotsky, L. S. (1978a). Socio-cultural theory. Mind in Society, 6, 52–58. Vygotsky, L. S. (1978b). Speech and thinking. In R. Rieber & A. Carton (Eds.), Collected works (Vol. 1, N. Minick, Trans., pp. 39–285). Plenum. Wacquant, L. (2007). Territorial stigmatization in the age of advanced marginality. Thesis Eleven, 91(1), 66–77. https://doi.org/10.1177/0725513607082003 Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331, 1447–1451. Wang, M., Kleit, R. G., Cover, J., & Fowler, C. S. (2012). Spatial variation in the US poverty: Beyond metropolitan and non-metropolitan. Urban Studies, 49(3), 563–585. Welsh, R. O., & Swain, W. A. (2020). (Re)defining urban education: A conceptual review and empirical exploration of the definition of urban education. Educational Researcher, 49(2), 90–100. Wendell, K. B., Swenson, J. E. S., & Dalvi, T. S. (2018). Epistemological framing and novice elementary teachers’ approaches to learning and teaching engineering design. Journal of Research in Science Teaching, 56, 956–982. Wertsch, J. (1991). Voices in the mind: A sociocultural approach to mediated action. Harvard University Press. Williams, R. (1995). The sociology of culture. University of Chicago Press. Wilson-Lopez, A., Sias, C., Smithee, A., & Hasbun, I. M. (2018). Forms of science capital mobilized in adolescents’ engineering practices. Journal of Research in Science Teaching, 55(2), 246–270. Wong, B. (2015). Careers “from” but not “in” science: Why are aspirations to be a scientist challenging for minority ethnic students? Journal of Research in Science Teaching, 52(7). 979–1002. Yang, Y., Liu, X., & Gardella Jr., J. A. (2019). Effects of a professional development program on science teacher knowledge and practice, and student understanding of interdisciplinary science concepts. Journal of Research in Science Teaching, 57, 1028–1057. Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8, 69–91. Zimmerman, H. T.,  & Weible, J. L. (2017). Learning in and about rural places: Connections and tensions between students’ everyday experiences and environmental quality issues in their community. Cultural Studies in Science Education, 12, 7–31. Zipin, L. (2009). Dark funds of knowledge, deep funds of pedagogy: Exploring boundaries between lifeworlds and schools. Discourse: Studies in the Cultural Politics of Education, 30(3), 317–331. http://doi.org/ 10.1080/01596300903037044

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13 CULTURALLY RESPONSIVE SCIENCE EDUCATION FOR INDIGENOUS AND ETHNIC MINORITY STUDENTS Meshach Mobolaji Ogunniyi

Introduction An important goal of science education in the developing world (i.e., countries with a less developed industrial base and human development index compared to the more scientifcally and technologically advanced countries), has been to rid the curriculum materials of its colonial legacies. Although the situation of Indigenous/ethnic minorities in the Western world is slightly diferent from what occurs among the predominantly Indigenous students in the developing countries, their needs and aspirations for socioeconomic emancipation are similar. According to Rodney (2005), Indigenous communities in both societies still sufer directly or indirectly from the efects of slave trade, colonization, economic exploitation, and other sociopolitical ills. I am in agreement with this view because as little children, we used to listen with rapt attention to my grandfather telling us stories about how he and a few others survived several raids by the slave dealers. According to him, the white scars on his legs and other parts of his body were bullet wounds while hiding among the fronds of a tall palm tree. After the slave trade came colonization and further subjugation, exploitation, and mass dislocation of the Indigenous communities for economic and administrative purposes. An example of this is that my ethnic group, the Yoruba in southwestern Nigeria, are now located in three West African countries either speaking English or French, while those carried away as slaves can be found in Brazil, West Africa, the West Indies, and the United States. The aftermath of these human tragedies coupled with racialist policies justifed by scientifc knowledge at the time have resulted in creating generations of traumatized communities that even today still struggle with the problem of sociocultural identity. In light of all this it should not be surprising why anything from the colonialists (including Western science) has been held suspect by Indigenous students. Besides, these students often fnd a mismatch between school science and their tenaciously held Indigenous beliefs. As several studies (e.g., Aikenhead & Elliot, 2010; Iwuanyanwu, 2020; McKinley & Gan, 2014) have indicated, many science classrooms as a result of historical and current globalization factors are now occupied by ethnically diverse classrooms. Therefore, science teachers attempting to implement new science curricula tend to encounter a number of challenges. Some of the challenges mentioned in the extant literature include (1) the disparity between school science and Indigenous knowledge, (2) large classes, (3) the use of foreign language of instruction, (4) inadequate instructional materials,

DOI: 10.4324/9780367855758-16

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(5) frequent curriculum reforms, and (6) the stranglehold efects of assessments on the whole education system. However, since a lot has been written about this subject, the focus of the chapter is to show how innovative instructional strategies have been used to mitigate the negative impact of these challenges. For example, it is hard to fnd any studies in the science education literature exploring any connection between the slave trade, the Industrial Revolution, colonization, racism, or other remote socioeconomic factors on Indigenous students’ underperformance in school science. But as I have pointed out elsewhere (see Yacoubian & Hansson, 2020), these historical factors cannot be ignored without being intellectually dishonest. Although not all the ethnic communities have experienced the direct efects of the slave trade or colonialism, it is rare to fnd any that has not sufered some form of exploitation or the other. Research studies on science and Indigenous or local knowledge are relatively new and still few relative to other areas of science education. The emergence of the former incidentally coincides with the waning infuence studies underpinned on behaviorist and positivist research paradigms in the mid-eighties. I need to state upfront though that the focus of this chapter is not to review such studies but to examine some substantive issues that have emanated in the study of Indigenous studies, especially the underperformance of Indigenous/ethnic students in school science. For the same reason, only a passing remark will be made. Despite the new interest in sociocultural issues in science education research, I am not aware of any comprehensive reviews or evaluations of studies expressly concerned with eforts to integrate Indigenous knowledge with school science in the classroom. Though the study by Sotero et al. (2020) is not a review, it has created awareness among science educators about the eforts that have been made so far to include science in the science curriculum. In this regard, they have identifed some formerly colonized countries, namely, the United States, South Africa, Brazil, Canada, and Australia, as being at the forefront of such attempts. They indicated that the frst authors in these fve countries have collaborators in diferent regions of the world. They further claim that a considerable number of these authors focus mainly on the inclusion of Indigenous knowledge in school science as a way to: (1) to make the Indigenous students value their Indigenous heritage; (2) help students to see the relevance of school science in their daily lives; (3) help the teachers to produce innovative and contextually relevant instruction for a multicultural classroom; (4) expose all students regardless of their ethnic backgrounds to perceive science, technology, society, and the environment in a more robust manner that would have otherwise been the case; (5) encourage Indigenous students to participate more actively in classroom discourses; (6) expose students to argumentation instruction; (7) expose both Indigenous and mainstream students to other cultural ways of thinking and interpreting phenomena; (8) explore local issues alongside socioscientifc issues with the aim to learn multiple forms of interpretation and problem solving; (9) increase students’ awareness of the need to conserve their environment; (10) encourage students to see their environment as a formal learning tool; (11) increase teachers’ awareness about how Indigenous knowledge can become a pedagogical and communicative instrument in their classrooms; (12) encourage teachers to teach science in a contextually and culturally sensitive way; and (13) reinforce teachers’ and researchers’ sensitivity to the specifc sociocultural contexts of their students. Though a commendable efort, it is worth noting that the study by Sotero et al. (2020) is limited only to journal articles that were revealed through the use of search engines. However, my view is that there are much more published articles in other less popular journals, conference proceedings, and doctoral dissertations than those cited by these authors. The most probable reason for this is that: (1) research studies on science and Indigenous knowledge are still relatively new; (2) the gate-keeping phenomenon that limits what articles are published in high-impact science education journals seems to persist; (3) the foremost journals in science education are still dominated largely by reviewers and editors that have been groomed in the positivist and structuralist traditions and forms of research; (4)  it takes a minimum of 18 months to 2 years to get an article published in the high-impact (a concept based on the quantity of citations) science education journals, e.g., the Journal of Research

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in Science Teaching, Science Education, and the International Journal of Science Education; (5) science and Indigenous knowledge systems of thought are based on complex and distinctly diferent paradigms; (6) very few hypotheses or theories have been posited in the area; (7) most of the researchers publishing in the area do not have Indigenous backgrounds; (8) Indigenous researchers lack articulation, i.e., they lack language skills to communicate their experiences or fndings in the “standard account” way; (9) science and Indigenous knowledge are incommensurable systems of thought based on distinctly diferent assumptions and as such are difcult to compare; and (10) science educators undertaking studies on science and Indigenous knowledge systems are concerned mainly with sociocultural issues, such as teaching in a multicultural classroom, racism, social justice, equity, and so on. Over the years, I have noticed that a large percentage of science educators show little or a lack of interest on issues relating to Indigenous knowledge systems probably because they do not construe it as an authentic way of knowing or describing human experience with nature simply because it does not appeal to their intellectual interests. For the same reason, I have had the experience of having my articles rejected in recent years by reputed journals who have hardly published in the area. Sometimes some editors have had the courtesy to suggest that my articles did not coincide with the journal’s current interest. Recently, I had an article stemmed in a complex historical/political debate rejected on account that it did not conform to a certain standard positivist way of reporting. A reviewer complained that the paper lacked a clear-cut hypothesis, research design, sufciently large sample, strong statistical analysis, and so on. I wonder if that reviewer has ever published a single article in the murky and complex terrain of scientifc and Indigenous cosmological inquiries. Certainly the study carried out by Sotero et al. (2020) is a good start, but much still needs to be done. Another remark deserving close attention is that the networks existing among researchers on science and Indigenous knowledge systems are much more than these authors have noted. For instance, as a lead author in South Africa mentioned by Sotero et al. (2020), my collaborators reside in many areas of the world rather than a few countries in Africa. I have worked with collaborators in the United States, Australia, New Zealand, United Arab Republic, Egypt, Italy, Brazil, Norway, the Philippines, Canada, West Indies, India, and several Southern, Western, Central, Northern, and Eastern African countries, just to mention a few. My collaborators have similar multiple links. After the fourth conference associated with the Science and Indigenous Knowledge Systems Project (SIKSP), which I have coordinated for over a decade, I notifed the participants of that conference that I was fnally retiring from active academic life. After much discussion, the participants decided to launch the African Association for the Study of Indigenous Knowledge Systems. Members of that association are now located in over 30 countries. If not for COVID-19, the membership should have increased considerably. The inaugural conference of the association was launched in Namibia in 2015 and since its inception it has produced several conference proceedings with hundreds of articles already published.

The Impact of the Slave Trade, Industrial Revolution, and Colonialism on Indigenous Students’ Disposition Toward School Science During the colonial era, most of the so-called developing countries have merely replicated the education systems of the colonial countries. However, it is noteworthy that since independence many of these countries have been making concerted eforts to improve their socioeconomic development. Indeed, since the independent era many of these countries have embarked on curriculum reforms to eradicate the ills of colonial education. For example, since South Africa gained political independence in 1994, the frst democratic government led by the African National Congress saw curriculum reform as its priority. The new government formulated education policies underpinned by an inclusivist principle of Ubuntu. The term Ubuntu is a central African philosophy that stands for unity of purpose, interdependence, communality, collaborative consensus, and collective responsibility. In

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this regard, education (including science education), unlike the erstwhile apartheid education, was construed as a liberatory and collective force to authenticate true freedom for all. But curriculum reforms per se are no panacea to all the ills of the past education systems unless all the stakeholders are well aware of the potential benefts of such a reform for them. That implies they are well informed about their role in the curriculum development process. Instead of being compelled to implement a fnished product, the teachers should be part and parcel of any curriculum reform. In the same vein, curriculum reform in science education, or any feld, should follow as much as possible the current wisdom of educational innovation and change strategy. It should have a clear goal in view. It must be based on a sound educational paradigm. It should pay close attention to the stakeholders’ needs, contexts, aspirations, sociocultural beliefs, and values, as well as current discourses in the area. Besides, the planners must be well aware that curriculum reform is a complex endeavor that goes far beyond the mere political or technical rationalities. As such, they must be aware of the need to plan carefully. In addition, they must allow for sufcient time to conceptualize, plan, and monitor the reform documents and its implementation process. Finally, they must evaluate to see that what is being achieved is compatible with the intended goal. As several studies have shown (e.g., Aikenhead & Elliot, 2010; Iwuanyanwu & Ogunniyi, 2018) curriculum reforms have been anything but systematically done. Instead, teachers have been exposed to a number of crash professional development programs with the hope that somehow positive outcomes would emerge. As a result, teachers have often found themselves opposing the curriculum they were expected to implement. For instance, before long there was a public outcry against the new curriculum and the government was forced to review the new curriculum. Before the new curriculum was implemented in South Africa, the classrooms were built for the diferent monocultures with little or no interactions with children of other cultural groups. Besides, the new curricular demand for the integration of Indigenous knowledge with school science proved very challenging for most science teachers considering that most of them had been schooled to teach their subjects largely for the Western education system. The poignant question of course is: What sort of science education has prevailed in the formerly colonized countries? What many developing countries did not seem to realize perhaps was the hidden agenda of Western education policies, which were aimed at extending Western culture and way of life to the subjugated Indigenous communities (e.g., Bishop, 1990; Gill & Levidow, 1989). But as it has now been realized by most of these countries, the education system they had adopted enthusiastically has turned out to be a sort of poison ivy for them in the long run. Many Indigenous communities have now realized that as much as Western education has some merit in terms of improved socioeconomic development, it has also created among them a sense of inferiority complexity and loss of their sense of identity. Talking of wholesale education systems without any Indigenous content was tantamount to voluntary self-abnegation. The consequence of this, according to Khuzwayo and Ogunniyi (2016), Rodney (2005), and McKinley and Gan (2014), is the enslavement of their minds, beliefs, and cultural practices. Rodney (2005) claims further that colonial policies have succeeded in blocking the emergence of an intellectual tradition even in societies with such a tradition before they were colonized. It is as if those societies never had any education system with which to transmit their knowledge, skills, or cultural values to the younger generation. Hardly a generation has passed since many developing countries gained their independence, but the efects of colonialism still persist even today. Rodney (2005) and Ogunniyi (2020) also claim that the goal of colonial education is cultural assimilation of the Indigenous communities through the schooling system which was forced on the Indigenous communities. Talking about the British colonial education, for instance, Gill and Levidow (1989) assert that science teaching in Britain: (1) embodies a subtle form of racist propaganda difcult to detect because of the misguided belief that science is politically neutral; (2) masks the real political and economic agenda of science education; (3) hides its appropriation of non-Western Indigenous sciences; (4) is

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based on a racist ideology; and (5) is based on an exploitative economic system. They contend further that historically the Western powers have always enslaved the Indigenous communities and reduced them to subservient roles. As a result, millions of Indigenous peoples around the world have migrated to Western countries where they now provide the needed cheap labor to be dispensed with when no longer needed. Aikenhead and Elliot (2010), Bishop (1990), and others have made similar claims about Western education. With few exceptions (e.g., the United States, Canada, Australia, and New Zealand) colonial education was used to under-develop the Indigenous communities. Dei et  al. (2000) puts this quite succinctly by asserting that: Knowledge forms are usually privileged to construct dominance, and can be “fetished” so as to produce and sustain power inequalities. Fetished knowledges are assigned or come to acquire an objectifed, normal status, the status of truth. Thus they become embedded in social practices and identities, as well as in institutional structures, policies, and relationships. (p. 4) It should also be remembered that globalization has its root in the centuries of the slave trade, Industrial Revolution (1760–1840), colonization, and the mass exploitation of human and material resources, especially by European invaders searching for freedom or better quality of life. The maladies of despotic European imperial governments and the atrocities they committed in the countries they occupied have left in its wake a vicious cycle of unmitigated poverty and “crisis of knowledge” among the Indigenous peoples. These evil practices that have contributed to the wealth of the Western world have led to the underdevelopment of the colonized countries. Rodney (2005) claims that the European slave trade coupled with colonization were basic factors in African underdevelopment. He adds that: the process by which captives were obtained on African soil was not trade at all. It was through warfare, trickery, banditry and kidnaping. . . . When one tries to measure the efect of European slave trading on the African continent, it is very essential to realise that one is measuring the efect of social violence rather than trade in any normal sense of the word. (pp. 108–109) It is also apposite to say that while many developing countries have gained their political independence, they have not gained economic independence. The destruction of the ecosystems, environmental pollution, global warming, the greenhouse efect, and other environmental disasters caused currently by multinational oil companies and others in many countries since the Industrial Revolution era have by no means abated. Today, many of these once-vibrant countries can no longer feed their populations, and joblessness among their youth has reached an alarming rate.

Curriculum Reforms Targeting Indigenous/Ethnic Minority Students Curriculum reforms targeting Indigenous/ethnic minority students have been carried out for about three decades. While some progress has been made, the impact has been minimal. The frst challenge that a newly independent country is likely to face is how curriculum reform could help bring about rapid change. Although most formerly colonized countries in Africa have implanted several curriculum reforms, such eforts have failed to fulfll the postulates of their hard-won independence and the aspirations of their peoples. For instance, South Africa attained independence in 1994 and embarked immediately on curriculum reform as early as 1997. Although the new curriculum, known as the Curriculum 2005, and subsequent ones aimed at indigenizing school science and the eradication of inequality created during the colonial/apartheid era, this goal has proved to be rather

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elusive (Ogunniyi, 2004, 2007a). Similar experiences have been reported in other countries where reforms have targeted Indigenous/ethnic students (e.g., Aikenhead & Elliot, 2010; Govender, 2011; Kim, 2017; Reddy et al., 2016). Some of the reasons cited in the literature include poor conceptualization of the curricula, conficting or lack of clear-cut curriculum policies, mismatch between the adopted curricula and the needs and the contextual realities of the students, poor preparation or non-involvement of teachers in curricular reforms, lack of instructional materials, the use of inefective instructional strategies, strangle efect of national examinations on the education system, teachers’ inadequate understanding of the nature of science and Indigenous knowledge systems, residual efects of inherited colonial education, and so on (Ogunniyi, 2004, 2007a, 2011). Many of the new curricula are learner centered and inclusive to refect the realities associated with multicultural classrooms in many countries today. These are in sharp contrast to the older colonial, teacher-centered, exclusivist, and examinationdriven curricula that have been merely tinkered with over and over again, all in the name of curriculum reform. However, considering the yawning gap between the goals of the new curricula and the old curricula, it should be no surprise why not much success has been attained even after substantial investments have been directed toward the improvement of Indigenous students’ performance in school science (Reddy et al., 2016).

Decolonizing the Science Curriculum for Indigenous Students The term “decolonization” is a loaded term that has come to mean diferent things for the people who have used it. But despite the diferent defnitions given to the term, there is a general consensus among the advocates that it is signifes attempts to eradicate unhealthy colonial legacies that are still prevalent in every facet of life, be it in the arts and sciences, humanistic and social sciences (including education), cultural practices, beliefs, value systems, or other areas. In short, it is an attempt to replace the dominant colonial intellectual tradition with what is considered to be authentically Indigenous in theory and practice. Decolonizing the science curriculum, therefore, might entail modifying or rewriting the science curriculum in a way that appeals to the intellectual interests of students without resulting in the loss of their sociocultural identity. It might also involve integrating science with the Indigenous sciences, translating certain key terms into the local languages, saturating diferent scientifc concepts with local examples, teaching science using innovative instructional strategies well nuanced in traditional ways of solving problems, and so on. As I have indicated elsewhere (Ogunniyi, 2017), the critical role of language in communication and facilitating understanding in any subject matter, not least science, is unequivocal. Because when a person’s language is suppressed or marginalized, he/she is deprived of his/her means to think and to communicate his/her view efectively to others. In an attempt to make science more relevant to the life worlds of Indigenous students, some attempts have been made to decolonize the science curriculum. The new curriculum statement published by the Department of Education (DOE) in South Africa recognizes the fact that: Diferent world-views are usually present in the science classroom. .  .  . People tend to use diferent ways of thinking for diferent situations, and even scientists in their private lives may have religious frameworks or other ways of giving values to life and making choices. . . . One can assume that learners in the Natural Sciences Learning Area think of more than one world-view. Several times a week they cross from the culture at home, over the border into school science, and then back again. (DOE, 2002, pp. 12–13) Similarly, the Department of Basic Education (DBE) in its Curriculum Assessment Policy Statement (CAPS), explicitly states students should be encouraged to value their Indigenous heritage. It states

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further that that school science should be decolonized to make it more culturally sensitive to all students (DBE, 2011) regardless of their sociocultural diferences. Similar views have been expressed worldwide where multicultural classrooms have emerged (e.g., Aikenhead, 2006; Aikenhead & Elliot, 2010; Botha, 2007; Cajete, 1999; Cronje et al., 2015; Hewson, 2015; Thomson, 2010; Wane, 2009). However, despite the efort made to indigenize school science, the old form of traditional instruction and assessment practices seem to prevail in many education systems. Consequently, science is still perceived as an imposed foreign culture to the majority of Indigenous students whose beliefs are completely diferent from the scientifc belief promoted at school (e.g., Cajete, 1999; Chinn, 2007; Garroutte, 1999; Nichol & Robinson, 2000). Although there is general consensus among the proponents of decolonization, there is no clearcut agreement on the procedure to be used to achieve this goal. A poignant issue in this regard is whether decolonization can actually be accomplished when the language of instruction is still the colonial language. It is a well-known fact that every language carries with it certain meanings and nuances that are not easily perceived by a non-native speaker. In situations where English, French, Portuguese, or Spanish is a second or third language, it is not difcult to see why most Indigenous students feel alienated from science. I shall elaborate on this in the section on the language of instruction. Unfortunately, decolonization is gradually slipping from its original elegance as a rallying symbol for accomplishing equity, justice, unity, and socioeconomic development to becoming a mere cliché that is bandied about in the literature but absolutely meaningless to most people advocating for it. Yet without a clear idea of what decolonization really means, it won’t be long before it is forgotten, like many other bright ideas about what sort of culturally responsive science education is all about. But before elaborating on this any further, it is necessary to talk briefy about a currently emerging issue, namely, the teaching of science in a multicultural classroom context.

Teaching Science in Multicultural Science Classrooms In response to the current multicultural classrooms as a result of globalization and human movement around the world, the need for teacher preparation institutions to develop compatible programs to address this issue has become an urgent necessity. Despite the fact that the Western world has increasingly rejected racial injustice in principle, a considerable number of stakeholders (including teachers) in these parts of the world are still reluctant to take necessary steps to eliminate this human tragedy from every aspect of their education system. For instance, the teachers being prepared in higher education are likely to be confronted with a congeries of complex social, political, and economic factors beyond their capacity to handle. As noted by Chisholm (1994) two decades ago, prospective teachers are faced with the task of teaching culturally diverse students, many of them coming from impoverished communities. Even today the situation is not likely to change considering the current pandemic, environmental disasters, and economic crisis. The current multicultural science classroom in many countries is characterized by all kinds of disparities in terms of cultures, languages, socioeconomic statuses, beliefs, expectations, age, gender, interests, values systems, worldviews, and so on. Certainly, all these diferences pose great challenges for science teachers, especially those who have been prepared in higher education to teach in monocultural classrooms. Indeed, the contextual realities of multicultural science classrooms today demand innovative instructional strategies that are capable of enabling all students to engage meaningfully with what is being taught. As has been pointed out in the literature on multiculturalism (e.g., Atwater et al., 2010; Cronje et al., 2015; Hewson & Ogunniyi, 2011; McKinley & Stewart, 2012), instructional strategies must of necessity be dynamic, participatory, communal, and inclusive. Ironically, many of our higher teacher education institutions today are still preparing teachers for the past despite the realities existing in a pluralistic society!

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Despite all the curriculum reforms that have taken place since the independence era, many African countries today are still very much in the throes of colonial curricula. Likewise, traditional instructional approaches are still the most prevalent method of teaching science. The focus is still largely on teacher-centered and examination-driven instructional practice. Our schools urgently need teachers who serve as facilitators rather than transmitters of knowledge. We need more of cultural bridge-builders and not the deprecators of minority cultures. We need teachers who appreciate the potentials of diverse cultures, not those who look down on their students with a disdainful gaze. Such teachers are more likely to create a learning environment where students are able to learn, thrive, and adapt more easily to a multicultural classroom context. Unfortunately, most preservice teachers worldwide lack the necessary knowledge, skills, attitudes, and habits of mind to work efectively in a multicultural classroom setting. Further, teachers confronted with a multicultural classroom must possess such vital ingredients such as valid understanding of nature of science Indigenous knowledge, valid pedagogic content knowledge, possessing knowledge of the school and classroom context and the society in which the school is located. Unless teachers possess these attributes and others they will be unable to prepare their students adequately for the current pluralistic society we live in. The list is simply endless, but it sufces to say that in view of the emergence of the current multicultural classrooms worldwide, it would be negligent on our part if we failed to prepare science teachers adequately for their chosen profession. In addition, science teacher educators must be well aware of the need to prepare teachers who are able to facilitate increased cultural self-awareness among their students, cultivate an awareness of and appreciation for diversity, increase cultural competencies, motivate all their students regardless of their socioeconomic backgrounds to want to succeed in science, and so on. More specifcally, teachers who will be teaching in a multicultural classroom must hold valid understanding of the nature of science and Indigenous knowledge systems.

Nature of Science and Indigenous Knowledge Systems Nature of Science A frequently mentioned objective in science education since the last half of the 20th century has been the need for science teachers and their learners to conceptualize the nature of science (e.g., Abd-El-Khalick, 2005; Abd-El-Khalick & Lederman, 2000; Lederman, 1992). Even today, virtually all science curricula around the world explicitly state the need for science teachers and their students to understand the nature of science (NOS). The underlying assumption to this belief without understanding this human enterprise implies that one is familiar with the substantive (product), the syntactical (procedural), and the ethical or regulative mechanisms in scientifc practice; otherwise, what is taught or learned would be a corruption of what is intended. Some of the rationales for understanding the nature of science have been well articulated by McComas (1998) and are not worth repeating here. No doubt, a lot of commendable eforts have been directed at understanding NOS, but a full treatment of this subject is beyond the scope of this chapter. As Ziman (2000) has rightly pointed out, “academic science” is a complex multifaceted enterprise pursued in a variety of ways. It is a sociocultural activity that has evolved among a group of practitioners with shared traditions that are articulated, transmitted, and reinforced by members of the group across political boundaries. Although academic science has no written code of conduct, all its members are constrained by their training to behave in a certain predictable and morally responsible manner. Nevertheless, while scientists are free to pursue their intellectual interests in an open manner, they are well aware that any claim they make would not go unchallenged by members of the scientifc community of practice. The NOS has been described in a variety of ways in the extant literature. In this regard, there are arguments and counterarguments about what aspects of it should be emphasized in the school science

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curriculum. For instance, in the 1950s–1960s the emphasis was on the process or methods of scientifc inquiry, while in the mid-1960s–1970s the focus was on the product of science, such as facts, concepts, laws, and theories. But despite the controversy surrounding NOS in the past seven decades, there is consensus in the literature that it deals with the ontology, epistemology, and the axiology of science (including the historical, psychological, and sociocultural aspects of scientifc practice) as a way of knowing and interpreting human experiences with nature (e.g., Abd-El-Khalick, 2005; Driver et al., 1996; Habermas, 1999; Lederman, 1992; Popper, 2001; Ziman, 2000). A common misconception about the NOS is that its epistemic authority rests in its exactness, reliable methodology, and unbiased objectivity. However, anyone a bit familiar with scientifc practice will be hesitant to accede to such a generalization. Newspapers and court cases are replete with examples in which frontline scientists marshal scientifc facts to support or rebut opposing viewpoints. Although science is generally counter-intuitive, anti-authoritarian, and driven largely by the tenets of empiricism, quantifcation, dualism, reductionism, and anthropocentricism, it also entails a multiplicity of approaches, such as the use of common sense, practical reasoning, intuition, creativity, and some elements of fortuitous serendipity. According to Medawar (1982, p. 108), “an imaginative or inspirational process enters into all scientifc reasoning at every level: it is not confned to ‘great’ discoveries, as the more simple-minded inductivists have supposed”. But despite the arguments surrounding the NOS, there is consensus amongst scholars that the construct generally refers to the unique professional way and habits of mind exemplifed by scientists as they probe into nature and interpret their observations (e.g., Popper, 2001; Driver et al., 1996; Ziman, 2000). The general belief among most postmodern scholars, however, is that as much as some knowledge of science and technology is vital for living a normal life, some knowledge of Indigenous knowledge held by their students is critical to fulflling the ethics of science or flling up the ethical, aesthetical, and knowledge gaps in Western science, especially in the attainment of a stable and sustainable environment (Snively & Corsiglia, 2001). Besides, educators and students face the challenge of cognitive border crossing between school science and the Indigenous worldviews prevalent in the communities where Indigenous/ethnic students live in on a daily basis (see Aikenhead & Jegede, 1999; Aikenhead & Ogawa, 2007; Ogunniyi, 2011).

Nature of Indigenous Knowledge Systems Like the NOS, the nature of Indigenous knowledge systems has been described in various ways in the extant literature. But despite this, there is general agreement that this human enterprise relates to the ways that the Indigenous peoples tend to interpret their experiences with nature. The term “Indigenous” is used here to refer to a conglomeration of knowledge systems encompassing the sciences, technologies, religions, languages, philosophies, and socioeconomic systems peculiar to certain Indigenous communities. Within this framework, Indigenous knowledge is not just about the artifacts per se but also the epistemologies, ontologies, and metaphysical systems underpinning such artifacts and the way they are used to create a sense of wholeness, relatedness, or complementarity amidst a collocation of human dilemmas. Another way to describe Indigenous knowledge systems is to consider them as a body of knowledge systems consisting of ideas and practices peculiar to the so-called natives of a particular geographical location or sociocultural environment. This implies that the knowledges embedded in Indigenous knowledge systems have not been borrowed from other localities or cultures, or if borrowed at all, they have become so assimilated into the new culture that it is difcult if not impossible to identify their sources of origin. Judging from human mobility and migratory characteristics, however, it might be difcult to talk about Indigenous knowledge in an absolute sense. Nevertheless, there is a wealth of knowledge in every people group that is not easily accessible to other groups for reason of diferences in language and other cultural diferences.

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To Hoppers (2002, p. 8): Indigenous knowledge systems refer to the combination of knowledge systems encompassing technology, social, economic, and philosophical learning, or educational, legal and governance systems. It is knowledge relating to the institutional, scientific and developmental, including those used in the liberation struggles. In other words, IKSs are redemptive, holistic, and transcendental views about human experiences with the cosmos. Unlike science, whose ethos is reductionism, IKSs celebrate pluralism, diversity, and holism of human experiences (e.g., Hoppers, 2002; Ogunniyi, 1988, 2004). From the foregoing, it is obvious that IKSs as a composite of knowledge systems is broader than science, hence the challenges normally encountered by teachers when they are required by certain science curricula to integrate Indigenous knowledge systems with school science (e.g., DOE, 2002; DBE, 2011). For the same reason, and in view of space limitation, however, only issues where the two systems of thought have the potential for conceptual integration will be considered in the chapter. Another epistemological obstacle to the issue of integration is that by and large Western science has not only usurped, marginalized, and preempted any meaningful coexistence with other ways of knowing, it has propagated the myth of being the only valid way of interpreting human experience with nature (Habermas, 1971; Hoppers, 2002). As stated earlier, policies solely based on modern scientifc practice at the expense of resident Indigenous sciences and practices have resulted in certain disastrous consequences of shattered dreams in many developing countries today. The use of chemical pesticides such as DDT and others (instead of sustainable plant-based pesticides) have caused the deaths of millions of people around the world. Similar disastrous efects have been reported in relation to medicine, agriculture, the destruction of biodiversity, ecological balance, and the abuse of intellectual property, to mention but a few. These adverse efects on the environment suggest the need to reevaluate the potential of IKS for mitigating such unwarranted consequences. According to Ogunniyi (2007a), IKSs as a construct are a conglomeration of knowledge systems. They are redemptive knowledge systems that describe human experience with the cosmos. Ogawa (1995) has identifed two types of Indigenous knowledge: (1) a body of stratifed and amalgamated knowledge and cosmology with several diferent kinds of precedent cultures or civilizations, such as the hunting and fruit gathering of the Jomon era (12,000 BC–300 BC) and the agricultural-nomadic lifestyle of the Yayoi era (300 BC–300 AD), and (2) the Indigenous science prevalent in contemporary Japanese ways of life. He has also explored how Indigenous science has been and is taught in educational settings, not just in school science (RIKA) programmes in Japan but also in a form Michael Apple would have called the “silent curriculum”. To Aikenhead and Ogawa (2007), the term “knowledge” within Indigenous and neo-Indigenous cultures is distinctly diferent from the way it is construed within Western science. In the former, knowledge and the knower are inextricably connected. For example, among the Nehiyawak people group in Canada, knowledge connotes a “coming to know” or a quest to becoming wiser in living harmoniously with one’s community and nature. Knowledge, a noun within Western science, is to the Indigenous peoples an action verb that can be translated as “ways of living” and sometimes “ways of being”. It can also be translated as a journey toward wisdom rather than reaching a destination. In short, knowledge is a way of living and behaving properly, the pursuit of wisdom, or simply, “wisdom in action”. Using the Japanese people’s experience with nature over the millennia, Aikenhead and Ogawa (2007) have gone further to substitute the term “Indigenous Ways of Living in Nature” (IWLN) with the “Japanese Way of Knowing Nature” (JWKN) to denote how one of the mainstream neo-Indigenous Asian cultures has evolved its own unique knowledge about nature (Shizen) and seigyo (roughly translated as subsistence) in the context of the Japanese culture

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(bunka). However, in contemporary Japanese society, the meanings of seigyo range from hobbies to real occupations. As stated earlier, a common experience in many countries with respect to curriculum reform is that the policymakers and curriculum planners are motivated mostly more by political exigencies than the readiness of the stakeholders, especially the teachers. For instance, policymakers are constantly on the watch to see how their students perform in international achievement assessments, e.g., Trends in International Mathematics and Science Study (TIMMS), Programme for International Student Assessment (PISA), and others (e.g., Department of Basic Education, 2011; Reddy et al., 2016), as indicated in the extant literature (e.g., Fuhai, 2017; Hewson & Ogunniyi, 2011; McKinley, 2005; Webb P. Xhosa, 2013). For the same reason, the demand on teachers to integrate science with Indigenous knowledge in many parts of the world has not been favorably received by teachers or the public at large. Another important factor not often considered in many reforms is that curricular reforms have taken place in an atmosphere of largely dysfunctional school settings, inadequate learning resources, large classes, shortage of qualifed science teachers, and so on.

The Integration of Science and Indigenous Knowledge Systems The issue of whether Indigenous knowledge should feature in school science is no longer considered a serious academic debate. The current environmental crisis, caused largely by scientifc and industrial activities, would make any such debate unwarranted. Likewise, the impact of globalization and concomitant human movement, and consequently the emergence of multicultural classrooms in many regions of the world, are further reasons why Indigenous knowledge that has sustained human life for centuries must be part of the science curriculum. Gilday, talking about the infuence of Western education on First Nations in Canada, to which she belongs, puts this so succinctly by asserting that: Our people have reconciled the relationship between the respect for wild animals and usage. There is a lot to learn from this, and that’s Caring-for-the-Earth ethics. For thousands of years, our people survived in this fragile environment. Their traditional knowledge is based on how they’ve interacted with the animals, their environment, and each other: what they have observed and tested through time and patience and relationship with the environment. (Gilday, 1995, p. 10) The previous statement suggests further that inclusivity, as opposed to exclusivity, should be the goal of any culturally sensitive science curriculum. Table 13.1 is not to compare the scientifc and Indigenous worldviews but to show that both are based on certain fundamental assumptions about the universe. It seems to me that what we need to do is to tap the potential of both worldviews as having certain areas of commonalities as well as differences. Also, it is to show students that the two reveal diferent ways of knowing and perceiving or interpreting experience with nature. In addition, we must be aware that much as the integration of the two knowledge corpuses is reasonable, the two are not easy to come by in a classroom context. But as many science educators working with Indigenous students (e.g., Aikenhead & Elliot, 2010; Kim, 2017) have warned, unless science is made more socially and culturally responsive to Indigenous students’ needs, many of them are likely to drop out of school. UN bodies, such the World Bank and United Nations Children’s Fund, working among Indigenous communities in about 200 countries have expressed similar concerns. To these UN bodies, education, including Indigenous education, is seen as the most potent tool for improving the quality of life and the reduction of poverty among the Indigenous communities around the world. In pursuance of this goal, the World Bank organized an international conference in Washington, DC in 1993 on Indigenous knowledge

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Meshach Mobolaji Ogunniyi Table 13.1 Fundamental Scientifc and Indigenous Assumptions About Nature Assumptions Underpinning the Scientifc Worldview

Assumptions Underpinning the IKS Worldview

It is not known whether the universe is fnite or infnite

The universe is infnite and is constantly undergoing dynamic changes

The universe most probably occurred by chance through an explosive force known as the “Big Bang” or other natural forces

The universe was created by a Supreme Being or supernatural forces

The universe functions like a mechanical system

The universe functions like a living system

Matter is real and exists within time and space

Matter is real and exists within time, space, and in the ethereal realm

Space is real and has defnite dimensions

Space is real and has both defnite and indefnite dimensions

Time is real and has a continuous, irreversible series of duration

Time is real, continuous, and cyclical in nature

The universe is orderly and predictable

The universe is orderly, predictable, and partly unpredictable

All events have natural causes

Events have both natural and supernatural causes

Humans are able to understand the natural universe

Humans have a limited understanding of the natural universe

and thereafter declared the frst decade in the 21st century as the Decade of Indigenous Knowledge (Davis & Ebbe, 1995). But making Indigenous knowledge part of formal education implies that there are instructional strategies to do so. It was in light of this that the Science and Indigenous Knowledge Systems Project (SIKSP), supported by the National Research Foundation in South Africa, was launched. In view of the efectiveness of argumentation instruction for discussing controversial subjects, e.g., the inclusion of Indigenous knowledge in the science curriculum, SIKSP developed a dialogical argumentation instructional model (DAIM) as a possible tool for integrating science and Indigenous knowledge in the classroom context.

The Potential of the Dialogical Argumentation Instructional Model (DAIM) for Indigenous/Ethnic Students’ Science Education In the last two decades, a number of studies have adopted diferent instructional strategies to integrate science and Indigenous knowledge in the classroom. For example, Cajete (1999) explored the spiritual, environmental, mythical, visionary, artistic, afective, and communal foundations of Indigenous education to present science to Native American students through an ethno-scientifc paradigm. By designing an inclusive and transformative science curriculum, he was able to inculcate certain aspects of Native American students’ cultural heritage in the students while at the same time grounding them in canonical school science. The positive ripple efects of this instructional strategy of course were not only benefcial to these students but also their White American counterparts as well. The latter became more aware about the life worlds of the former than was previously the case. Both groups were exposed to a form of ecological and multicultural thinking and aspirations critical to maintaining a sustainable environment. Based on his fndings from using this holistic approach, he advocated for the development of similar exemplary science curricula and appropriate instructional strategies to explore the values, interests, and potential of the spiritual, environmental, mythical, artistic, afective, and visionary domains of Indigenous education to enrich the present canonical

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science curricula. Similar approaches have been implemented in other countries (e.g., Aikenhead, 2017; Aikenhead & Elliot, 2010; Kim, 2017). In our own experience in South Africa in the SIKSP, we found it difcult at the initial stages to convince teachers and teacher educators that implementing science using the dialogical argumentation instructional model (DAIM) could facilitate their attempts at a science–Indigenous curriculum in their classrooms. After a number of seminars on the nature of science and Indigenous knowledge, however, many of the participants became interested in the project. The SIKSP team adopted DAIM because (1) it has been found to facilitate classroom discourse and promote the integration of knowledge systems, e.g., science and Indigenous knowledge; (2) it is amenable to arguments involving both logical and nonlogical but culturally valid arguments; (3) it provides ample opportunities for people to express their views freely without intimidation; and (4) it has been found to be efective for reducing possible occurrence of symbolic violence that tend to occur in the process of power sharing in classroom dialogues (e.g., Diwu & Ogunniyi, 2012; Gbebru & Ogunniyi, 2017; Iwuanyanwu, 2022; Ogunniyi, 2007a, 2007b). Figure 13.1 summarizes how DAIM works. As Bourdieu and Wacquant (1992) and Skoumios (2009) have contended, equity and freedom of expression are critical elements of a fair argument. Essentially, DAIM entails diferent levels of argument, starting with individual argument or self-conversation (i.e., intra-locutory arguments) that tend to occur during brainstorming exercises that occur when students attempt to perform individual tasks. After this he/she goes into the small group to contribute to the next task, again saturated with dialogues and arguments and decision-making concerning the tasks (i.e., inter-locutory arguments). Finally, the decisions reached at the individual and group level are again mobilized at the large-group session, usually articulated by the group representatives (i.e., trans-locutory arguments) where the teacher plays a facilitative role with the express purpose of reaching collaborative consensus. Besides this, at every stage the teacher plays the role of the devil’s advocate by asking thought-provoking questions to facilitate learning. As indicated earlier, DAIM shares some semblance of how a scientifc or Indigenous community of practice solves a given problem. Argumentation and dialogues are frequently used in the scientifc and Indigenous communities of practice. Although it is often not refected in the science education

1. Individual task

4. Whole-class media˜on (Facilitator coconstruc˜on)

5. Evolving cogni˜ve harmoniza˜on

3. Whole-class discussions (Group leader presenta˜ons)

Figure 13.1 Stages of the dialogical argumentation instructional model.

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2. Small group task

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literature, arguments and dialogues take various forms within the Indigenous communities. They may be expressed in various forms, e.g., proverbs, idioms, drama, songs, storytelling, and so on. These approaches are used to initiate the youth into the conversations of the adult community. DAIM assumes that thinking, at whatever stage, usually entails some form of argumentation (see Figure 13.1). It starts from the individual to the group, and fnally to the community efort in which arguments and dialogues play a critical role. DAIM, like other argumentation instruction, provides the students with ample opportunities to become deeply involved in the teaching–learning process. It encourages students to externalize their thoughts freely (including views that may not be compatible to the scientifc belief), clear their doubts, deepen their understanding, and even change their views, if necessary, especially after listening to others. The teacher’s role in a DAIM-based classroom is basically facilitative, and as such he/she should avoid dominating classroom discourse. In other words, he/she sees him/herself as a co-learner ready to learn new things beyond what can be found in a science textbook. But at the same time, he/she must strive to direct classroom discussion toward a valid understanding of the nature of science and Indigenous ways of interpreting experience with natural phenomena. The demand of the South African curriculum to integrate science concepts with Indigenous knowledge is premised perhaps in the general belief that all languages share to a certain extent some commonalities that must have arisen from the mere fact that only humans are known to have evolved this communication facility. According to the renowned linguist Noam Chomsky: We could not have acquired any language unless its fundamental properties were already in place, in advance of experience, as argued in the epistemic naturalism of early rationalist psychology. . . . By now, enough is known to indicate that the diferences among the languages may not be very impressive compared with the overwhelming commonality, at least from the standpoint we adopt towards organisms other than ourselves. (Chomsky, 2000, p. 48)

The Implications of the Language of Instruction for Indigenous Students’ Science Education When Indigenous students are forced to use the colonial language of instruction, which is their second or third language, to learn science or any subject for that matter, they tend to lose their confdence in participating in classroom discourses. If this problem is not attended to promptly by the teacher, these students may lose their interest in the subject altogether. It is obvious that one cannot talk about indigenizing the science curriculum in the absence of paying close attention to the language of instruction. Apart from the unique language of science in terms of the profusion of technical terms, the language in which science is taught or presented in textbooks or electronic media is largely foreign to the majority of learners of color. As Gill and Levidow (1989) have contended, “An uncritical teaching and learning of science as currently practiced inevitably engages the teacher and learner in maintaining structural racism” (p. 3). Indigenous/ethnic minority students in developed countries or the majority in most developing countries learn science in foreign languages. As a result, they tend to lack sufcient profciency to intellectually engage with the learning material, and the consequence is mass underachievement in science as commonly shown in international assessments, such as TIMMS and PISA. But it would be a mistake on our part if we assume that such a poor performance necessarily refects their true cognitive ability. More often than not, such achievement refects their profciency in the language of instruction and assessment than their conceptual or even linguistic ability. Often, what one reads in the research literature about the language of instruction carries with it a racial undertone and a condescending stance coated with a half-hearted empathy on the part of the

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researchers. The general complaint often heard about Indigenous languages being too undeveloped, descriptive, and lacking necessary vocabulary to accommodate science is indefensible. The poignant questions are: What languages (including the colonial languages) have ever evolved out of the blue without some efort to develop them? If that is the case, what prevents the government or concerned organizations from doing the same to some of the widely spoken Indigenous languages spoken by the majority of students? How culturally inclusive are the new science curricula and the pedagogical practices for implementing them? Should the goal of science education be to expect the Indigenous students to perform equally with their frst-language counterparts? What attempts have been made to truly indigenize the curriculum materials and instructional methods? Are the human and material resources available in all schools regardless of the socioeconomic backgrounds of the students? What specifc efort has been made to mitigate the efects of the existing disparities created by colonial/ apartheid governments? What these and similar questions suggest is that we cannot continue to blame the Indigenous/ethnic students for their poor performance in science assessments or poor attitudes toward science without attending to the injustices evident in the past education policies and practices. A thorny issue in most developing countries, and perhaps elsewhere, that warrants urgent attention is the continued use of foreign languages such as Afrikaans, English, French, Portuguese, and Spanish or a modifcation thereof without considering the present contextual realities of multicultural classrooms (e.g., Desai et al., 2010; Msimanga et al., 2017; Ogunniyi, 2004; Ogunniyi & Rollnick, 2015; Rogan & Grayson, 2003). It is in this regard that some sort of meta-language or translanguaging might facilitate the iIdigenous/ethnic students’ understanding of science concepts and principles by including the students’ Indigenous home languages alongside the language of instruction (BrockUtne et al., 2006; Cronje et al., 2015; Khuzwayo & Ogunniyi, 2016; Ogunniyi, 2007a). The same situation should be refected in the textbooks, other teaching materials, and even the languages used in the classroom. To indigenize science implies the inclusion of Indigenous languages in the materials to be taught or learned. However, this is not an easy task, as many Indigenous languages are still largely descriptive. Besides, the same word may be used for diferent concepts. Of course, the issue of inclusivity goes far beyond instructional materials; it also entails the deployment of appropriate culturally nuanced instructional strategies. In addition, class excursions to real-life situations of the students to learn from the adult community as practiced in Japan and other countries may prove enriching and instructive, and the involvement of Indigenous language experts is inevitable. The fact is that no language drops from the sky; people develop languages as an instrument for thought, communication, and productivity. To accomplish this goal, issues such as identifying suitable existing or coined Indigenous terms before resorting to loanwords. This of course implies that Indigenous vocabularies should be preferred over one that is borrowed ones. Where Indigenous words are nonexistent, e.g., atom, electron, molecule gene, chromosome, and so on, familiar coined or indigenized loanwords from the colonial language could be adopted, but as much as possible this should be explicit, clear, and precise in meaning. In terms of the frst principle, loanwords must be minimal compared to those from an Indigenous language, e.g., isiZulu, Setswana, Hausa, Amharic, Arabic, Tuareg, Swahili, and so on. Regarding the second principle, if a local language gives a misleading meaning to a given concept it would be preposterous to abandon a more valid or familiar colonial language. Also, for ease of reference, operational defnitions of the terms must be made and revised from time to time. With respect to the third principle, the symbols used in the text for each subject must be consistent to avoid confusion (Bamgbose, 1984). Also, where the same word is used for diferent terms, e.g., amandla for energy, power, and force in isiZulu, or simba in Shona, some consensus must be reached by creating some distinctions between the two, e.g., by adding some qualifers. In cases where certain concepts are completely missing in an Indigenous language, e.g., the limited number of colors, then descriptive terms are needed. For instance, red, orange, and yellow are all called “pupa” in Yoruba, but to distinguish between them descriptive words or phrases are usually added, such as “green as like a

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leaf ”, “yellow as gold, ripe as an orange or banana fruit” “orange like dye”, “red like blood” and so on. Likewise, an indigenized English term is spelled and intoned in the Indigenous way, e.g., “atom” becomes “atomu”, “molecule” becomes “molikulu”, “chromosome” as “kuromosomu”, and so on. Placing equivalent terms in the Indigenous students’ languages side by side with English words aford them with additional language facility to think and process information. It also helps them to clear their doubts and to gain a deeper insight into what is intended and even to change their minds on a subject matter. This is further reinforced when the instructional strategies used by the teacher encourage classroom discourses where they are free to converse, argue, and participate in dialogues using the diverse languages in their repertoires. Of course, the benefts derivable from the blending of English, French, Portuguese, or Spanish terms with equivalent Indigenous terms go beyond language per se. It also has import for conceptual development in that the more meaningful a concept is the more likely the students’ understanding of that concept is likely to be.

Using DAIM to Implement a Science–Indigenous Curriculum in a Multicultural Science Classroom In an attempt to create a learning environment that was conducive for classroom discourse, and to facilitate the process of border crossing, the SIKSP team normally exposed the participants to a series of DAIM-based lectures and seminars on the NOS and reading materials on the NOS and Indigenous knowledge systems. The lectures, seminars, and workshops involved the use of argumentative discourses alongside problem-solving activities in the performance of a set of cognitive tasks and the completion of worksheets on such tasks as foatation of objects in water, solutes and solution, genetics, metals and nonmetals, force, static electricity, water pollution, mechanical force, and so on. As stated earlier, DAIM draws inspiration from two theoretical constructs, namely, the Toulmin’s argument pattern (TAP), which in turn draws on the Aristotelian syllogistic form of reasoning, and the contiguity argumentation theory, which draws on Ubuntu, the central African belief that construes ideas as interrelated or interdependent (see Ogunniyi, 2007a). Table 13.2 is an example of an analysis of data derived from a cognitive task given to a cohort of 25 participants (11 science educators and 14 science teachers) to choose one out of three gari-processing methods it considered best for obtaining a high-quality gari and to support their choice based on valid arguments using TAP. The table is derived from the claims, counterclaims, and/or rebuttals made in three randomly selected groups designated as Groups 1, 2, and 3. Group 1 chose method A, which is based largely on the Indigenous method of gari processing, while Groups 2 and 3 chose

Table 13.2 Levels of TAP Used to Analyze the Discussions About Gari Processing in Three Groups Levels of TAP

Group 1

Group 2

Group 3

Level 1: Non-oppositional arguments or arguments with simple claims versus counterclaims

1

1

2

Level 2: Arguments with claims supported with grounds (data, warrants, and backings) but with no rebuttals

2

2

2

Level 3: Arguments with claims supported with grounds and only occasional weak rebuttals

9

4

6

Level 4: Arguments with claims supported with grounds and at least one strong rebuttal.

-

-

2

Level 5: Arguments with claims supported with grounds and more than one strong rebuttal

-

-

-

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Culturally Responsive Science Education Table 13.3 CAT Categories Derived From the Participants’ Accounts of Their Experiences on the Project CAT Cognitive Categories

Science Teachers

Science Educators

Science

IK

Science

IK

Dominant – a dominant worldview

9

3

5

-

Suppressed – a worldview dominated by a dominant worldview

-

16

-

4

Assimilated – a worldview that capitulates to a dominant worldview

3

3

2

Emergent – a worldview arising from a new experience

6

20

10

7

Equipollent – a worldview shaped by two coexisting worldviews

7

7

10

6

method B, which is based on a combination of both Indigenous and modern scientifc methods. Method C is based solely on the scientifc method. An examination of Table 13.2 shows the levels of TAP used among the three groups. Adopting a modifed TAP espoused by Erduran et al. (2004), the levels ranged from Level 1, that is, non-oppositional arguments, to Level 5, arguments with claims supported with grounds and at least one strong rebuttal. The same group of participants was asked to indicate what it gained from SIKSP in terms of understanding the NOS and Indigenous knowledge systems. Table 13.3 is a summary of the groups’ responses to the science–Indigenous systems questionnaire using the contiguity argumentation theory (CAT) as the unit of analysis. In terms of CAT, the scientifc arguments were the most dominant, while sometimes individuals used culturally embedded Indigenous arguments to support their scientifc arguments and vice versa, thus maintaining an equipollent, i.e., the use of science and Indigenous arguments. In line with CAT, their arguments seemed to be largely context dependent. Table 13.3 is based on the subjects’ responses to what we call the Refective Diaries Questionnaire. Analysis of their responses using CAT as the unit of analysis shows some disparities between the teachers’ and the science educators’ valuing of the diferent methods of gari processing. The table further exemplifes the nature of their perceptual shifts as a result of their encounter with DAIM. For instance, with few exceptions, the science educators used more scientifc arguments more frequently to support their arguments than the science teachers. Conversely, the science teachers supported their arguments with Indigenous sciences to support their views than the science educators. On the whole, both groups appear to subscribe to an equipollent worldview in valuing the scientifc/ Indigenous methods of science. In my view, there is nothing drastically wrong in their stance since no one permanently lives in a scientifc world (e.g., Aikenhead & Elliot, 2010; DOE, 2002; DBE, 2011; Gunstone  & White, 2000; Ogunniyi, 2011). In their criticism of the conceptual learning theory, Gunstone and White (2000) contend that what is important for students is replacing their Indigenous beliefs with the scientifc belief but to know what worldview is appropriate for a given learning context. As can be seen in Tables 13.2 and 13.3, the purpose of Toulmin’s argument pattern (TAP) and the contiguity argumentation theory are quite diferent. While the former is concerned with the complexity of classroom arguments, the latter deals with the nature of cognitive shifts (occurring in the classroom) or the thinking processes involved in classroom argumentative discourse and the role of the arousal of contextual changes (Hewson & Ogunniyi, 2011). Some of the following excerpts are derived from the subjects’ responses to the Refective Diaries Questionnaire. The names used are the pseudo-names of randomly selected participants in the project. For ease of reference, SIKSP stands for the Science and Indigenous Knowledge Systems Project, while IK and IKS stand for Indigenous knowledge and Indigenous knowledge systems, respectively.

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Doe, a 49-year-old male high school physical science teacher with 20 years’ experience in the chemical industry and 12 years teaching experience stated that: When I started, I felt that IKS and science where two different knowledges and that it was almost impossible to combine the two. . . . After attending the workshops and seminars, I came to understand that . . . many people are still using it [IKS] nowadays and hence is just knowledge which is authentic to a particular people’s experiences and by no means inferior to present day technologies. Berinda, A 34-year-old female chemistry science high school teacher with 13 years of teaching experience, said: My experiences in the SIKSP activities have reformed my way of thinking, doing things completely. When I started I did not appreciate indigenous knowledge nor did I realize its richness until I matured in these workshops. I have now reached a level where I am confident of integrating these two worldviews harmoniously. Lucinda, a 49-year-old female grade 3 teacher with 30 years of teaching experience, had this to say: Before the workshops I thought that Western Science is dominant over IKS. . . . In my view IKS was all about witchcraft. . . . After attending the workshops I realized that IKS is not something new to me, perhaps the terminology. . . . These workshops are so valuable to me because it made me realize once again how precious IKS is. Emily, a 45-year-old female science educator with 25 years of teaching experience, said, “Being involved with SIKSP has been an inspirational journey. The fact that the group is heterogeneous (students and lectures with diferent perspectives and knowledge about teaching IKS) has provided a very rich environment for sharing knowledge and feelings about IKS”. Damon, a 47-year-old male science/math educator with 22 years of university teaching experience, said: Before being part of the IKS group I was a bit skeptical about the role which IKS can play in our everyday life. . . . Having attended the workshops and seminars I have grown to understand that knowledge from both IKS and modern science are all the same. Further reflections have led me to conclude that two knowledge systems can actually co-exist. Similar views to the ones provided were expressed during the interviews as well, e.g., Dan, a 50-year-old physics teacher with over 20 years of teaching experience, stated that: “My exposure to . . . DAIM caused my previous ideas to begin to change. . . . I really felt ashamed that as a science teacher, I had never tolerated such a thing”. Saratu, a 47-year-old science educator with 18 years of higher education teaching experience, said: Initially, I did not appreciate the inclusion of IK into school science curriculum. . . . Over time, I developed new understanding and insight into the NOS and IK and what science-IK curriculum entails. This resulted in progressive shift in my mind-set . . . about the integration of science and IK. From the foregoing, it is obvious that despite the diferences in their views and initial dispositions toward the new science–IK curriculum, both groups agreed that DAIM, which stressed dialogue and

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arguments in a classroom discourse, (1) enhanced their awareness of the feasibility of implementing an inclusive science–Indigenous knowledge curriculum in a classroom situation; (2) facilitated their awareness about the educational and cultural values of argumentation instruction; (3) facilitated the process of confict resolution and consensual decision-making; and (4) showed them that despite the diferences between the scientifc and Indigenous ways of knowing and doing things the two thought systems do share some areas of commonalities (e.g., Diwu & Ogunniyi, 2012; Ghebru & Ogunniyi, 2017; Iwuanyanwu & Ogunniyi, 2020; Moyo & Kizito, 2014).

Conclusion Throughout this chapter my contention has been that the current performance and attitudes of Indigenous/ethnic students toward science may not be unrelated to the impact of remote historical factors, such as the slave trade and colonialism, as well as current factors, e.g., the environmental crisis largely caused by scientifc and industrial activities and the poor planning and implementation of many science curricula. According to Akena (2012), the general consensus among science educators is that the science–Indigenous curricula and the instructional strategies used have not succeeded in enhancing Indigenous/ethnic minority students’ performance in science. In light of this, the dialogical argumentation instructional model (DAIM) has been proposed in the chapter as an instructional model worthy of consideration by science educators working in the area. With regard to the issue of making science culturally responsive to Indigenous/ethnic students, e.g., by indigenizing school science, my view is that the continual use of a foreign colonial language as the language of instruction contradiction in terms and with little prospect of succeeding. The use of English or any colonial language as the language of instruction has often been pointed out in the extant literature as creating cognitive barrier dissonance among Indigenous students rather than enhancing their ability to study science (Brock-Utne et al., 2006; Desai et al., 2010). To mitigate this contradiction, I have proposed the need to mount up a meta-language project in science education that would facilitate Indigenous/ethnic students’ understanding of science (Ogunniyi, 2017). Further, my view is that unless the Indigenous languages are developed to the point that they can be deployed to explain scientifc concepts and generalizations not much can be achieved in the indigenization process. However, to achieve true indigenization of the science curriculum and instructional practices implies massive investment in Indigenous languages, which currently remain underdeveloped. Likewise, science curricula, textbooks, and science education programs in our higher institutions would require drastic reforms of their courses to make them more culturally responsive than has been the case so far. Also, there is an urgent need to mount innovate instructional strategies that combine Indigenous and modern instructional strategies to achieve the desired goal of true indigenization. In conclusion, it is imperative for educators who want to get into the heart of Indigenous/ethnic students to pay very close attention to the Indigenous knowledge systems shaping the beliefs, value systems, and worldviews of Indigenous students. According to Beets and le Grange (2005), such knowledge and cultural values provide the necessary milieu for the students to connect school science with the knowledge they bring into the classroom. Similarly, the level of commitment by science teachers must go beyond the transmission of a collocation of scientifc facts. They must become more sensitive to their Indigenous students’ contextual realities. Last but not the least, teachers must be well equipped with necessary knowledge and skills that will enable them to gain the necessary confdence to teach in the current multicultural science classrooms.

References Aikenhead, G. S. (2017). Enhancing school mathematics culturally: A path of reconciliation. Canadian Journal of Science, Mathematics and Technology Education, 17(2), 73–140. https://doi.org/10.1080/14926156.2017. 1308043

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Meshach Mobolaji Ogunniyi Aikenhead, G. S., & Jegede, O. J. (1999). Cross-cultural science education: A cognitive explanation of a cultural phenomenon. Journal of Research in Science Teaching, 36(3), 269–287. Abd-El-Khalick, F. (2005). Developing deeper understandings of nature of science: The impact of a philosophy of science course on preservice science teachers’ views of instructional planning. International Journal of Science Education, 27(1), 15–42. Abd-El-Khalick, F., & Lederman, N. G. (2000). Improving science teachers’ conceptions of nature of science: A critical review of literature. International Journal of Science Education, 22(7), 665–701. Aikenhead, G. S. (2006). Science education for everyday life: Evidence-based practice. Teachers College Press. Aikenhead, G. S., & Elliot, D. (2010). An emerging decolonizing science education in Canada. Canadian Journal of Science, Mathematics and Technology Education, 10(4), 321–338. Aikenhead, G. S., & Ogawa, M. (2007). Indigenous science revisited. Cultural Studies in Science Education, 2(3), 539–620. Akena, F. A. (2012). Critical analysis of the production of Western knowledge and its implications for Indigenous knowledge and decolonization. Journal of Black Studies, 43(6), 599–619. Atwater, M. M., Freeman, T. B., Butler, M. B., & Draper-Morris, J. (2010). A case study of science teacher candidates’ understandings and actions related to the culturally responsive teaching of “other” students. International Journal of Environmental and Science Education, 5(3), 287–318. Beets, P.,  & Le Grange, L. (2005). “Africanising” assessment practices: Does the notion of ubuntu hold any promise? South African Journal of Higher Education, 19, 1197–1207. Bishop, A. J. (1990). Western mathematics: The secret weapon of cultural imperialism. Race and Class, 32(2), 51–63. Botha, M. M. (2007). Africanising the curriculum: An exploratory study. South African Journal of Higher Education, 21(2), 202–216. Bourdieu, P., & Wacquant, J. D. (1992). An invitation to refective sociology. The University of Chicago Press. Brock-Utne, B., Desai, Z., & Qorro, M. (Eds.). (2006). Focus on fresh data on the language of instruction debate in Tanzania and South Africa. African Minds. Cajete, G. A. (1999). Igniting the sparkle: An indigenous science education model. Kivaki Press. Chinn, P. W. U. (2007). Decolonizing methodologies and indigenous knowledge: The role of culture, place and personal experience in professional development. Journal of Research in Science Teaching, 44(9), 1247–1268. Chisholm, I. M. (1994). Preparing teachers for multicultural classrooms. The Journal of Educational Issues of Language Minority Students, 14, 43–67. Chomsky, N. (2000). Language and thought. Moyer Bell. Cronje, A., De Beer, J., & Ankiewicz, P. (2015). The development and use of an instrument to investigate science teachers’ views on indigenous knowledge. African Journal for Research in Mathematics, Science and Technology Education, 19(3), 319–332. Davis, S. H., & Ebbe, K. (1995). Traditional knowledge and sustainable development. Proceedings of a conference held at the World Bank. Dei, G. J. S., Hall, B. L., & Rosenberg, D. G. (2000). Indigenous knowledges in global contexts: Multiple readings in our world. Toronto University of Toronto Press. Department of Basic Education. (2011). Curriculum and assessment policy statement grades 10–12. Physical sciences. Government Printing Works. Department of Education. (2002). Revised national curriculum statement for grades R-9 (Schools) – Natural sciences. Department of Education. Desai, Z., Qorro, M., & Brock-Utne, B. (2010). Educational challenges in multilingual societies. African Minds. Diwu, C., & Ogunniyi, M. B. (2012). Dialogical argumentation instruction as a catalytic agent for the integration of school science with indigenous knowledge systems. African Journal of Research in Mathematics, Science and Technology Education, 16(3), 333–347. Driver, R., Leach, J., Millar, R., & Scott, P. (1996). Young people’s images of science. Open University Press. Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Development in the use of Toulmin’s argumentation pattern in studying science discourse. Science Education, 88, 915–953. Fuhai, A. (2017). The basis for integrating local knowledge into the school curriculum for Tibetans in Southern Gansu. Chinese Education & Society, 50(1), 12–17. https://doi.org/10.1080/10611932.2016.1262182 Garroutte, E. M. (1999). American Indian science education: The second test. American Indian Culture and Research Journal, 23, 91–114. Ghebru, S.,  & Ogunniyi, M. (2017). Pre-service science teachers’ understanding of argumentation. African Journal of Research in Mathematics, Science and Technology Education, 21(1), 49–60. https://doi.org/10.1080/18 117295.2016.1254493 Gilday, C. (1995). Traditional knowledge, land, and the environment. In S. H. Davis & K. Ebbe (Eds.), Traditional knowledge and sustainable development (pp. 9–10). Proceedings of a conference held at the World Bank.

408

Culturally Responsive Science Education Gill, D., & Levidow, L. (1989). Anti-racist science teaching. Free Association Books. Govender, N. (2011). South African primary school teachers’ scientifc and indigenous conceptions of the earthmoon-sun system. African Journal of Research in Mathematics, Science and Technology Education, 15(2), 154–167. Gunstone, R., & White, R. (2000). Goals, methods and achievements of research in science education. In R. Millar, J. Leach, & J. Osborne (Eds.), Improving science education: The contribution of research. Open University Press. Habermas, J. (1971). Knowledge and human interest. Beacon Press. Habermas, J. (1999). The inclusion of the other. MIT Press. Hewson, M. G. (2015). Embracing indigenous knowledge in science and medical teaching. Springer. Hewson, M. G., & Ogunniyi, M. B. (2011). Argumentation-teaching as a method to introduce indigenous knowledge into science classrooms: Opportunities and challenges. Cultural Studies of Science Education, 6(3), 679–692. Iwuanyanwu, P. N. (2020). Nature of problem-solving skills for 21st Century STEM Learners: What teachers need to know. Journal of STEM Teacher Education, 55 (1), 27–40. https://doi.org/10.30707/JSTE55.1/MMDZ8325 Iwuanyanwu, P. N. (2022). What students gain by learning through argumentation. International Journal of Teaching and Learning in Higher Education, 34(1), 97–107. Iwuanyanwu, P. N., & Ogunniyi, M. B. (2018). Scientifc and indigenous worldviews of pre-service teachers in an interactive learning environment. In L. M. Semali & A. Singambi-(Eds.), Proceedings of the 4th Annual Conference of the African Association for the Study of Indigenous Knowledge Systems (pp. 287–304). Iwuanyanwu, P. N., & Ogunniyi, M. B. (2020). Efects of Dialogical Argumentation Instructional Model on pre-service teachers’ ability to solve conceptual mathematical problems in physics. African Journal of Research in Mathematics, Science and Technology Education, 24(1), 129–141. https://doi.org/10.1080/18117295.2020.1 748325 Khuzwayo, H., & Ogunniyi, M. B. (2016). Ending the “occupation of our minds”: The adoption of Ubuntu in teaching and learning of mathematics and science. Proceedings of the 2nd International Conference of the African Association for the Study of Indigenous Knowledge Systems (pp. 123–132). Universidade Pedagogica. Kim, M. (2017). Indigenous knowledge in Canadian science curricula: Cases from Western Canada. Cultural Studies of Science Education, 12, 605–613. https://doi.org/10.1007/s11422-016-9759-z Lederman, N. G. (1992). Students’ and teachers’ conceptions of the nature of science: A review of the research. Journal of Research in Science Teaching, 29(4), 331–359. McComas, W. F. (1998). The nature of science in science education: Rationales and strategies. Dordreecht: Kluver Academic Publishers. McKinley, E., & Gan, J. S. (2014). Culturally responsive science education for indigenous and ethnic minority students. In N. G. Lederman & S. K. Bell (Eds.), Second handbook of research in science education. Routledge. McKinley, E., & Stewart, G. M. (2012). Out of place: Indigenous knowledge (IK) ln the science curriculum. In B. Fraser, C. McRobbie, & K. Tobin (Eds.), Second international of science education handbook (pp. 541–554). Springer. Moyo, P., & Kizito, R. (2014). Prospects and challenges of using argumentation instructional method to indigenous school science teaching. African Journal of of Research in Mathematics, Science and Technology Education, 18(2), 113–124. McKinley, E. (2005). Locating the global: Culture, language and science education for indigenous students. International Journal of Science Education, 27(2), 27–41. Msimanga, A., Denley, P., & Gumede, N. (2017). The pedagogical role of language in science teaching and learning in South Africa: A review of research 1990–2015. African Journal of Research in Mathematics, Science and Technology Education, 21(3), 245–255. Nichol, R., & Robinson, J. (2000). Pedagogical challenges in making mathematics relevant for indigenous Australians. International Journal of Mathematics Education in Science & Technology, 31(4), 495–505. Ogawa, M. (1995). Science education in a multiscience perspectives. Science Education, 79(5), 583–593. Ogunniyi, M. B. (1988). Adapting western science to traditional African culture. International Journal of Science Education, 10(1), 1–9. Ogunniyi, M. B. (2004). The challenge of preparing and equipping science teachers in higher education with knowledge and skills to integrate science and indigenous knowledge systems for learners. South African Journal of Higher Education, 18(3), 289–304. Ogunniyi, M. B. (2007a). Teachers’ stances and practical arguments regarding a science-indigenous knowledge curriculum, paper 1. International Journal of Science Education, 29(8), 963–985. Ogunniyi, M. B. (2007b). Teachers’ stances and practical arguments regarding a science-indigenous knowledge curriculum, paper 2. International Journal of Science Education, 29(10), 1189–1207. Ogunniyi, M. B. (2011). The context of training teachers to implement a socially relevant science education in Africa. African Journal of Research in Mathematics, Science and Technology Education, 15(3), 98–121. Ogunniyi, M. B. (2017). African indigenous cosmologies and the African way of life. Plenary paper. Proceedings of the African Association for the Study of Indigenous Knowledge Systems (pp. 14–26). University of the Witwatersrand.

409

Meshach Mobolaji Ogunniyi Ogunniyi, M. B. (2020). Tapping the potential of ubuntu for a science that promotes social justice and moral responsibility. In H. A. Yacoubian & L. H. Hansson (Eds.), Nature of science for social justice (pp. 157–176). Springer. Ogunniyi, M. B., & Rollnick, M. (2015). Pre-service science teacher education in Africa: Prospects and challenges. Journal of Science Teacher Education, 26, 65–79. Popper, K. (2001). All life is problem solving. Routledge and Human Sciences Research Council. Reddy, V., Visser, M., Winnaar, L., Arends, F., Juan, A., Prinsloo, C. H., & Isdale, K. (2016). TIMSS 2015: Highlights of mathematics and science achievement of grade 9 South African learners. Human Sciences Research Council. http://hdl.handle.net/20.500.11910/10673 Rodney, W. (2005). How Europe underdeveloped Africa. Panaf Publishing, Inc. Rogan, J. M., & Grayson, D. J. (2003). Towards a theory of curriculum implementation with particular reference to science education in developing countries. International Journal of Science Education, 25(10), 1171–1204. Skoumios, M. (2009). The efect of socio-cognitive confict on students’ dialogical Argumentation about foating and sinking. International Journal of Environmental and Science Education, 4(4), 381–399. Snively, G., & Corsiglia, J. (2001). Discovering indigenous science: Implications for science education. Science Education, 85(1), 6–34. Sotero, M. C., Alves, Â. G. C., Arandas, J. K. G., & Modeiros, M. F. T. (2020). Local and scientifc knowledge in the school context: Characterization and content of published works. Journal of Ethnobiology and Ethnomedicine, 16, 23. https://doi.org/10.1186/s13002-020-00373-5 Thomson, N. (2010). Science education researchers as orthographers: Documenting keiyo (Kenya) knowledge, learning and narratives about snakes. International Journal of Science Education, 25(1), 89–115. Wane, N. N. (2009). Indigenous education and cultural resistance: A decolonizing project. Curriculum Inquiry, 39(1), 159–178. Webb, P. (2013). Xhosa indigenous knowledge: Stakeholder awareness, valeu, and choice. International Journal of Science and Mathematics Education, 11, 89–110. Yacoubian, H. A., & Hansson, L. (2020). Nature of science for social justice. Springer. Ziman, J. (2000). Real science: What it is, and what it means. Cambridge University Press.

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SECTION IV

Science Teaching Section Editors: Jan van Driel and Charlene Czerniak

14 DISCOURSE PRACTICES IN SCIENCE LEARNING Gregory J. Kelly, Bryan Brown, and María Pilar Jiménez-Aleixandre

Education events are constructed through discourse processes and social practices. The multimodal and interactive nature of science learning positions discourse studies as relevant for understanding ways that science is constructed, communicated, and taken up across a variety of education settings. This chapter provides a rationale for the study of discourse in science education research with consideration for the theoretical perspectives for researching discourse in science education. Discourse studies include consideration of social languages, speech genres, contextualization, para-linguistic communication, interactional contexts, the role of language in social practice, and the ideological ways that knowledge is framed through discourse. We review recent studies of discourse in science education across a number of domains of interest. After this review, we provide some methodological considerations for the study of discourse in science education before proposing a set of emerging areas for research. Studies of discourse in science education reviewed in this chapter were organized into four broad categories. These categories are not mutually exclusive and overlap across a number of dimensions. The frst group examines discourse practices across multiple learning contexts. These studies investigate diferent forms of science instruction and student engagement and include considerations of the recent afective turn in discourse studies; a focus on epistemic practices such as argumentation and critical thinking; and a recognition of the multiplicity of discourses and modalities employed in science contexts (e.g., Grimes et al., 2019; Hufnagel, 2015; Kelly & Cunningham, 2019; Monteira & Jiménez-Aleixandre, 2016). A second set of studies considers how access to science and school are constructed through interaction. These studies focus on ways that discourse practices, with associated ideological positions and values, serve to include or exclude potential learners. A third set of studies examine how discourse in science education contexts instantiates values and projects ideology. Research in this area is concerned with how certain voices and perspectives are chosen, privileged, and legitimized. The fourth category draws from the frst three categories to explore methodological considerations for the study of discourse in science learning contexts. Given the nature of discourse processes in educational events, any given study can be characterized in multiple ways. For example, a study of student groupwork in an out-of-school setting could include analysis of discourse processes that project an ideological stance, include privileged points of view, and involve a student investigation, all framed by a set of discourse practices established over time by the local participants. In this way, such a study could be characterized in multiple ways and cut across the categories we imposed on the literature for the purposes of this chapter. After reviewing the current knowledge of discourse

DOI: 10.4324/9780367855758-18

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practices in science education, the chapter concludes by proposing topics, issues, and audiences for future research directions.

Rationale for Discourse Studies Discourse is central to the ways communities collectively construct norms and expectations, defne common knowledge for the group, build afliation, frame disciplinary knowledge, and invite or limit participation. In short, educational events are constructed through discourse. Learning opportunities are supported or constrained by how participants make choices about how to communicate, interact, attend, and contribute to group processes. In science classrooms, the ways that teachers talk science, frame communicative norms, and engage students in the range of semiotics of the relevant discipline, construct the nature of the scientifc knowledge and practices available to be learned (Kelly, 2014). As communication and interpretation of this range of semiotic meanings are integral to teaching and learning, the study of discourse becomes most relevant to researchers interested in understanding how access to science is interactionally accomplished in educational settings. A critical stance toward the ways that science is constructed through discourses evinces the ways that choices of genre, register, and positioning of perspectives delimit participation and engagement among students. Across diferences in culture, language background, and communication styles, the potential for mutual misunderstanding increases. Furthermore, science has unique linguistic features that difer from the ways of being, speaking, and interaction common among students’ other discourse communities (Brown et al., 2005). These features of science discourse pose communication demands. Such demands pose diferential challenges. For example, to engage with issues such as scientifc and engineering practices for students who are deaf and hard of hearing (DHH), Enderle et  al. (2020) conducted a study of existing American Sign Language (ASL) scientifc vocabulary to consider ways of communicating about these practices and the associated interpretations of the nature of science, given currently available resources. To address the communicative challenges of science and engineering discourse, asset-based approaches to science learning consider how features of students’ everyday discourse practices can serve to promote learning (Harman et al., 2020; Warren et al., 2020). Interactional contexts are constructed through discourse in various settings. Research on discourse processes in science education has examined how knowledge and practice are communicated in different settings and how research processes themselves entail uses of discourse. Studies of discourse include examination of everyday talk and action in ongoing teaching and learning events, interviews with participants, and written texts in their various forms; as such, each research method lends itself to particular research questions. The study of social processes of science teaching and learning examines the work of participants as they construct everyday life in science classrooms. Social and discourse processes in naturalistic settings make available to analysts the construction of educational events in real time. As a result, the study of naturally occurring discourse in education provides ways of understanding the communication and uptake of conceptual knowledge, distribution of power, defnitions of roles, and ways that afliation and identity are constructed among participants. Discourse analysis can also be applied to the study of interviews. Often, educational researchers are interested in talking with participants in a given study about relevant research topics (SezenBarrie et al., 2020). However, such interviews are also discourse events, with norms and expectations, rules for interchanges, and contextualization through para-linguistic communication (Lemke et al., 2006; Mishler, 1986; Russ et al., 2012). Treating interviews as discourse events, where meaning is constructed among individuals, and relevant artifacts and contextualization are taken into account, can lead to diferences in what knowledge is made available by participants and interviewers alike (Schoultz et al., 2001). This suggests careful consideration of how meaning is constructed through the moment-to-moment turns of the interview conversation, rather than taking the interviewee’s

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comments at face value and representative of extant knowledge. Thus, the discourse practices of the interview itself can be the subject of study. Written texts can also be analyzed from a discourse point of view. Science textbooks, written products of students, and representations of scientifc knowledge in its many forms can be analyzed with a variety of textual analyses (Bazerman, 2006; Halliday & Martin, 1993). Textbooks, student writing, displayed student work, and images posted on the wall can serve as active texts to be carefully and systematically analyzed for meaning. Thus, discourse analytic research methods provide a set of tools to research the ways that teaching and learning, identity development, and patterns of power and control are constructed in educational settings.

Theoretical Considerations for Studying Discourse in Science Education Discourse is typically defned as language in use, or a stretch of language larger than a sentence or clause (Cameron, 2001; Jaworski & Coupland, 1999). This defnition can be limiting – from a sociolinguistic view of interaction, discourse is interpreted as situated in ongoing sociocultural practices with histories, intertextual references, social relationships, positions, and obligations of members of the relevant social group (Kelly, 2021). As discourse entails more than the ideational communication, these broader contexts of social groups, cultural practices, and interpersonal goals need to be taken into consideration when deciphering meaning in interactional contexts. Social norms, expectations, and practices are constructed through discourse processes over time, and in turn shape ways that discourse is evoked in each instance, thus instantiating the symbiotic relationship of discourse and sociocultural practices. Hence, discourse is embedded in social knowledge, practice, power, and identity (Fairclough, 1995; Gee, 2014; Rogers, 2004). In each instance of use, discourse is constructed among people in some context, with some history, projections of future actions, and ideological commitments. Such histories, assumptions, and commitments, although similarly constructed through cultural practices, play a role in the meaning derived from the social accomplishment of the discourse event. Therefore, discourse analysis generally focuses on more than the moment-tomoment use of language to consider broader patterns over time and the ways that such use is embedded in cultural practices and ideological commitments. Fairclough (1992) identifed three dimensions of discourse analysis: analysis of text, analysis of text production, and social analysis of the discursive events, conditions, and consequences for participants. Therefore, the study of discourse processes in science education should properly include a defnition of discourse as using language in social contexts. As Gee (2001) argued, discourse is connected to social practices, “ways of being in the world . . . forms of life which integrate words, acts, values, beliefs, attitudes, and social identities as well as gestures, glances, body positions, and clothes” (p. 526). For the purposes of this review, we adopt a broad defnition of discourse and consider a range of studies that encompass the many epistemological, ideological, and social dimensions of language use (Kelly, 2014). Scientifc discourse includes unique features, derived from the highly specialized nature of the epistemic communities constructing these discourse processes and practices. In professional and educational settings, scientifc discourse is characterized by multiple modes of semiotic communication, including spoken, written, representational, inscriptional, and symbolic. Studies of scientifc practice in situ have identifed the importance of discourse practices in constructing scientifc knowledge (Knorr-Cetina, 2009). From the sensemaking banter of investigations, to inscriptions of events, to the formalization of text in publications in specialized genres, scientifc communities dedicate considerable time to the production of spoken, written, and symbolic discourse (Bazerman, 1988; Kelly et al., 1993; Latour & Woolgar, 1986). This range of varied semiotic forms is often alien to science students’ ways of communicating in other aspects of their lives. This may pose challenges to learners of science, as the unique linguistic forms of science include passive voice and conditionals, technical vocabulary, interlocking taxonomies, abstraction and nominalization, and complex symbols and

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notational systems (Halliday & Martin, 1993). Studies of classroom discourse have documented that science is constructed through talk and action in ways that often alienate students, leaving the impression that science is difcult, reserved for cognitive elites, and regimented (Lemke, 1990). Although such features pose pedagogical challenges, educators are seeking ways to draw from students’ knowledge and use their communicative competencies as assets for learning. Understanding the role of these discursive exchanges ofers powerful insights into student learning, teacher communicative norms, and the social role of discourse in education. Discourse analysis refers to the study of language in use. To examine the range and types of communicative situations of discourse in science education, analysts have sought to understand how uses of discourse are situated both in social practice and over time. Social practices, norms for interacting, and expectations about communicative demands are tied to the ways that language is used. Discourse analysis considers how talk and action are shaped by the norms and expectations of the communicative events. This suggests the need for ethnographic and other research approaches that seek to understand broader cultural patterns of activity governing the uses of discourse (Gee  & Green, 1998; Green et  al., 2020; Gumperz, 2001). Such studies consider the micro-moments of interaction, the meso-level construction of practices through multiple interactions, and the macrolevel analysis of cultural practices, thus acknowledging the importance of ways specifc interactions are shaped by cultural norms and expectations. Spoken communication occurs through both verbal and nonverbal channels. To understand meaning in interactional contexts, discourse analysis needs to consider pitch, stress, intonation, pause structures, physical orientation, proxemic distance, and eye gaze, among other paralinguistic features of talk. This chapter is an update from previous handbook chapters (Kelly, 2007, 2014), and thus focuses primarily on recent publications in science education discourse. The review is limited to studies that specifcally examine how the form, function, and/or interactional aspects of language are used in an explicit manner. Generally, this means that studies that self-identify as explicitly related to language, literacy, and discourse are more likely to be included in the review. We sought to expand the domain of topics, rather than update the literature along the trajectory of the previous handbook chapters. Across both the range of research topics and span of educational settings, a number of communicative issues have been observed. Historically, much of the discussion in science classrooms is directed by teacher talk, which often closely follows science textbooks. Such talk in the interactional context of whole-class discussion falls into a pattern of teacher initiation, student response, and teacher evaluation (IRE) (Mehan, 1979; Lemke, 1990). This pattern of talk has implications for what is made available to learn, how the particular science discipline is positioned, and how students develop their identity with science. Educational reformers have argued for a more expansive range of interactional contexts that include opportunities for an active role of students in classroom conversations. Programs include situating learning in scientifc practices, project-based activities, reasoning and decision-making through socioscientifc issues, and inquiry investigations. Such varied contexts ofer expanded opportunities to learn science and diferent aspects of science, but also impose new communicative demands on students. To address the range of communicative situations, Mortimer and Scott (2003) proposed a model to examine fve important dimensions of classroom discourse: teaching purpose, science content, communicative approach, patterns of discourse, and teacher interventions. This model helps understand the nature of discourse events and provides a basis for designing teacher education with a focus on the centrality of discourse for science learning. Recent research on discourse processes have largely focused on students interacting in science learning.

Discourse Processes and Practices in Science Education Discourse studies are gaining in prominence and infuence in science education. The ready availability of video and audio equipment and facility of recording techniques have led to a large number

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of studies that examine interactions in educational settings. Many such studies record discourse processes, but only a subset examine the interactions from the point of view of interactionally accomplished discourse, meaning a close analysis of the social practices and examination of language in use that includes the proxemics, gestures, prosody, eye gaze, and other contextualization of communication. Rather than review all discourse studies, we focus on those studies that advance beyond the issues previously discussed in the Handbook of Research on Science Education (Kelly, 2007, 2014). We consider a group of topics that examine discourse practices across multiple learning contexts; studies of access and identity constructed through discourse processes; and discourse, values, and ideology in science education. From this body of research, we distill some methodological considerations for the study of discourse in science learning contexts before turning to some emerging research directions for studies of discourse practices in science education.

Discourse Practices Across Multiple Learning Contexts The study of discourse in the last decade has been opening new avenues. We explore: (1) the shift from domain-general research methods to study discourse toward domain-specifc methods in science (and engineering) education; (2) the afective, emotional, and aesthetic turn; (3) students’ engagement in epistemic practices; (4) the uses of evidence and argumentation; (5) critical thinking as discourse practice, for instance, in decision-making contexts; and (6) representations and multimodal discourses in science classrooms. Although the second, third, and fourth of these trends may address general teaching and learning (they are not necessarily centered on science content), all of them recognize that discourse practices are situated in educational and cultural contexts. In this section, we discuss these avenues with a focus on science education. It may be noted that in learning situations, as well as in research, two or more of these contexts may overlap, even though for systematic purposes we address them consecutively.

Domain-Specifc Methods in Science and Engineering Education There has been a gap in discourse studies about science and engineering education; and more specifcally, there is a limited research base about engineering education. The study of discourse has tended to have a stronger focus either on theoretical approaches – sometimes grounded in philosophy, science studies, or linguistics – or on empirical fndings, with less attention to methodological issues specifc to the feld. In their introduction to a domain-specifc edited volume, Kelly and Green (2019a) aim to make visible how science and engineering concepts, processes, and practices are socially constructed, through rigorous qualitative research. Their work is framed in a sociocultural approach, which posits that discourses and social practices are constructed over time by members of a sustaining social group. Thus, the volume contributors “make visible ways of conducting ethnographically informed, discourse studies of science and engineering education as socially constructed in everyday life in classrooms” (Kelly & Green, 2019a, p. 2), through the connectedness of knowledge production and research methodology. It should be noted that engineering education, a context often neglected, is encompassed in this work. This edited volume (Kelly & Green, 2019b) is among a recent trend to consider the domain-specifc characteristics of research approaches to the study of discourse in science and engineering education (Tang et al., 2021).

Affect, Emotion, and Aesthetics Discourse studies have experienced what we can call an afective turn. Scholars acknowledge the interactions between cognitive, social, and afective dimensions of learning, both generically and in science education (Baker et al., 2013). This means paying attention to the relevance of emotional

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sense-making of science issues, which Hufnagel (2015, 2019) has studied for climate change, and Polo et al. (2017) in the context of argumentation about water resources. Hufnagel (2015) conceives of emotions as evaluative mechanisms that indicate personal relevance and deep relationships to ideas or objects. She called aboutness, the specifcity of an emotion’s object, a category of usefulness for the study of emotions. In argumentation settings, Polo et al. (2017) consider that emotions are mobilized as argumentative resources within the participants’ discourses. In the context of scientifc argumentation, Asterhan (2013) explored two types of argumentative discourse, consensus-seeking and adversarial. She studied the risk that afective concerns may cause if students attend to the interpersonal dimension rather than to the epistemic dimension of the confict, in other words to the confict between ideas. If participants seek a quick consensus, argumentative discourse may be lacking the critical dimension, but if the climate is adversarial, the discourse may be void of collaborative knowledge construction. Isohätälä et al. (2018) studied how student teachers, in the context of a course on environmental science, struck a balance between engaging in argumentation and sustaining socio-emotional processes. Even though the groups sustained favorable socio-emotional processes, they mostly failed to engage in argumentation. The authors suggest that the challenging nature of argumentation may cause participants to attend to socio-emotional processes at the expense of cognitive ones. The acknowledgment of the afective dimension means also taking into account the aesthetic experience in science learning, as Wickman’s (2013, 2017) pioneer studies have revealed. Scientifc creativity and creative reasoning have also been suggested as dimensions to be attended to in classroom discourse (Ferguson, 2018; Ferguson & Prain, 2020), which may require adopting the Peircean logic of discovery in science classrooms and a reappraisal of abduction as a creative element of reasoning that drives discovery. The afective turn may pose new questions and challenges about the interactions between epistemic and emotional dimensions: In their study on how emotional tension frames an argumentative debate about diets, Jiménez-Aleixandre and Brocos (2021) suggest the need for fne-grained analyses of the relationships between emotional tension and participants’ perception of their own agency in socioscientifc argumentation.

Student Engagement in Epistemic Practices There has been a shift toward the study of students’ engagement in epistemic practices through discourse, both in theory and in policy, in connection with scientifc practices, as the Next Generation Science Standards (NGSS, 2013) attest. Epistemic practices include contexts related to argumentation, modeling, explanation, and inquiry. The study of epistemic cognition in science education has been reviewed by Elby et al. (2016) from developmental perspectives, through examination of students’ conceptions of the nature of science, to students’ engagement in science practices. Framing epistemic cognition and epistemic practices in social practices, Wickman and Östman foreran a third approach, which they termed practical epistemology analysis (e.g., Östman  & Wickman, 2014), that is concerned with ways disciplinary science practices are enacted in situated learning contexts, rather than with students’ propositional knowledge. In this view, participation in epistemic practices refects membership in a disciplinary community, revealing students’ underlying practical epistemologies. For instance, unraveling complex causality in gene expression provides epistemic resources to address determinist and racist discourses and makes specifc epistemic practices part of genetics (Jiménez-Aleixandre, 2014). Students’ engagement in these practices is relevant in a context of science reductionism, where public discourses attribute all causes to genes. Epistemic practices have been defned as “the socially organized and interactionally accomplished ways that members of a group propose, communicate, evaluate, and legitimize knowledge claims” (Kelly  & Licona, 2018, p.  140). Thus, they provide a broad frame for studying argumentation, modeling, explanation, and inquiry. Epistemic goals in science education, according to Kelly and

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Licona, are the understanding of the reasons and evidentiary bases for conceptual knowledge and models. Chinn and colleagues have developed the AIR model of components of epistemic cognition, comprising Aims, epistemic goals; Ideals, criteria and standards used to evaluate the goals achievement; and Reliable epistemic processes, procedures and strategies to achieve the aims (Chinn et al., 2014). Applying the AIR model to the design of science learning environments, Pluta et al. (2011) reported the elaboration of criteria to evaluate scientifc models by seventh graders after completing model-evaluation tasks. In order to develop meta-epistemic abilities, Chinn et al. (2020) proposed educational practices of explorations into knowing, involving a shift from discourse focused on disagreements about content or facts, as for instance, whether vaccines are harmful or safe, to meta-epistemic discourse focused on deep epistemic disagreements about appropriate ways of knowing, for instance, whether systematic studies are more reliable and why. The issue of shared epistemology, or the loss of it, and the disagreements about how to know, are serious challenges, both for civic education and for research in the current context of emerging post-truth narratives. Project-based science and engineering are appropriate contexts to promote the acculturation into scientifc discourses and to understand how learning to engage in epistemic practices occurs over time. Students’ participation in discourse processes across contexts are better supported through extended engagement in inquiry. Long-term projects provide such afordances. For example, a fvemonth project about snails favored the appropriation by kindergarten children of the discourse of science and of science representations, and their engagement in using evidence to revise their previous ideas (Monteira & Jiménez-Aleixandre, 2016). Kelly and Cunningham (2019) reported that engineering projects that built epistemic tools into the design process supported elementary students’ learning of disciplinary concepts and practices, and acculturation in the specialized discourse of the relevant epistemic community. On the other hand, in their review about project-based science and technology teaching and learning studies, Hasni et al. (2016) suggested that methodological improvement is needed in order to ascertain the benefts of project-based teaching.

Use of Evidence and Argumentation Within studies about students’ engagement in epistemic practices in science education, a strand focuses on the practice of argumentation, that is, an examination of students engaging in the evaluation of knowledge claims in the light of evidence. In the Organization for Economic Cooperation and Development (OECD, 2016) framework for the Programme for International Student Assessment (PISA), argumentation is framed as a competency, which as the characterization of scientifc practices, draws on three types of knowledge: content, procedural, and epistemic (Osborne et al., 2016). The evaluation of merits of claims, explanations supporting conclusions, and decisions regarding science in social issues is relevant for science education. Osborne (2014) proposed that science teaching should address epistemic goals, emphasizing how we know what we know, and why we believe what we do. This discussion focuses on recent shifts in argumentation studies since the 2014 handbook (Kelly, 2014); argumentation was conceived as a type of discourse, whereas currently it is rather viewed as a (scientifc and epistemic) practice, a practice that would require changes in the most prevalent classroom discourse, shifting toward students’ participation in discussions about how to formulate and assess evidence. Previous studies tended to focus on constructing and supporting arguments. However, Ford (2015) has suggested the centrality of critique in argumentation as an important dimension of understanding uses of evidence in scientifc practices. Rapanta and Felton (2019) identifed the emphasis on the analysis, critique, and evaluation of evidence as a feature of inquiry-based argumentation. Critique, according to Osborne et  al. (2016), is cognitively more demanding, and thus is placed higher in their learning progression about argumentation. Both argumentation and explanation require interpreting evidence to identify patterns, which involves epistemic judgments and poses challenges for students; in other words, students need to

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appropriate some of the tools for discourse analysis to acknowledge that data may be interpreted in diferent ways. These challenges have been addressed in recent works. McNeill and Berland (2017) found that students view data as factual, rather than understanding evidence as constructed and needing to be interpreted. These authors required students to manipulate information in order to fnd patterns. Bravo-Torija and Jiménez-Aleixandre (2018), in their learning progression, also reported that students experienced difculties recognizing patterns in data and explaining them with a relevant theory, combining empirical and theoretical discourses. Discourse studies in engineering education by Kelly and Cunningham (2019), focused on elementary school children’s ability to interpret data. They reported that the students could identify trends in tables of trial runs through the collective sharing and analysis of pooled data. Grounded in the AIR model, Duncan et al. (2018) proposed a framework called grasp of evidence, that aims to help citizens determine the credibility of scientifc claims in everyday communication, a type of discourse to which they are exposed daily, by tackling challenges related to evidence-based practices. To develop meta-epistemic understanding of methods to establish support for claims, they propose explicit discussions about the community norms for evidence quality and evidence strength. As Feinstein and Waddington (2020) argued, science education should respond to post-truth by focusing on how to help people work together to make appropriate use of science in social contexts, and for this purpose, aimed at citizenship, argumentation and a lay grasp of evidence may be useful. Studies of argumentation address a range of discursive contexts; on the one hand on arguments evaluating models and explanations, and on the other hand on arguments focusing on decisionmaking about issues of social relevance. Jiménez-Aleixandre and Brocos (2018) suggested the need for examining argumentative operations and specifc pedagogical discursive practices that are likely to difer in both contexts. They discuss instances with a focus on plausibility in the evaluation of models and explanations, and on acceptability in decision making. Thus, in decision making about socioscientifc issues, options are weighted in light of the evidence, but also in accordance with personal or social values, as is the case with the issue in their study about promoting a vegetarian diet. They suggested that in decision making, the focus is on the social context and the social interaction, whereas in the evaluation of explanations the focus tends to be on the individual learner. The role of values, emotions, and afective issues in argumentation is being acknowledged, as refected in studies such as Isohätälä et al.’s (2018), mentioned earlier. Henderson et al. (2018) discussed challenges and identifed the following potential directions for argumentation research: (1) recognizing challenges in establishing a classroom culture that values argumentation, which requires shifts in the classroom mindset; (2) the efect of diferent theoretical frameworks in how researchers communicate fndings; (3) the challenge of assessing various aspects of argumentation in a valid and reliable fashion; (4) pedagogical challenges in supporting student discourse and social collaboration; and (5) challenges concerning the professional development of teachers. Changes in classroom discourse that are needed for students to engage in the practice of argumentation, have been examined by González-Howard and McNeill (2019), focusing on the ways teachers framed goals for students. Two teachers’ instructional strategies were compared, emphasizing either individual or communal understanding. Even though both were successful, the second strategy was aligned with students’ building more frequently on each other’s ideas.

Critical Thinking as Discourse The focus on critical thinking as skills, rather of a domain-general character, has recently evolved toward a focus on critical thinking as discourse and practice. Thus, for Kuhn, “critical thinking is a dialogic practice people commit to and thereby become disposed to exercise, more than an individual ability or skill . . . engaged initially interactively and then with practice in interiorized form with the other only implicit” (Kuhn, 2019, pp. 148–149). She also pointed out that critical thinking,

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rather than being an individual ability or a fxed attribute, is a dynamic activity developed through engagement in the practice. A second change in the conceptualization of critical thinking, which draws from critical discourse analysis, is the inclusion of action. Criticality, encompasses critical thinking, critical refection, and critical action. The approach to critical thinking as discourse and practice may be a fruitful frame to integrate with science education (Jiménez-Aleixandre & Puig, 2021), particularly in times when addressing post-truth, discarding fake news, and distinguishing reliable from unreliable information in public discourses are social challenges that science education should meet. Feinstein and Waddington (2020) call for integrating science and civic discourse to make appropriate use of science in social contexts. Place-based science has also been proposed as an appropriate context to empower students to participate in democratic processes (Brocos & Jiménez-Aleixandre, 2020).

Representations and Multimodal Discourses A multiplicity of discourses exists in science classrooms, beyond spoken and written discourse. Discourse is mediated through various semiotic modes and artifacts, as emphasized in the pioneer work of Kress et al. (2001) about multimodality. They saw learning as a dynamic process of sign making, in which pupils reshaped meanings or “signs” to create new ones. Thus, scientifc texts and discourses are seen as semiotic hybrids (Lemke, 1998). Multimodality and multivocality are the focus of Solli et al.’s (2019) study based on dialogical theories of language – their analysis incorporated several perspectives and voices. The research analyzed how individual students are “in dialogue” with present as well as remote interlocutors and internet contexts in discussions about the socioscientifc issue of hydraulic fracturing, and the discursive means that students used to handle the many relevant perspectives. A recent special issue of Research in Science Education (2021, vol. 51[1]), focuses on the methodology of classroom discourse analysis in science education, bringing new insights into discourse studies (Kelly, 2021) and making visible multimodality, as in the studies by Wieselmann et al. (2021) and Knain et al. (2021). Representations are thus semiotic resources, cultural tools, and practices, and learning science includes learning to interpret and use representations. The varied representations of science are relevant for all ages, but in early childhood, when children are beginning to read and write, learning to understand and build representations is of the utmost importance. Monteira et  al. (2022) reported a longitudinal study carried out along three years in an early childhood education (ECE) classroom, examining the development of children’s (aged 3–6) science representations, as well as the afordances of the teacher’s scafolding of the production of these representations. The fndings indicated that children’s representations of science phenomena, such as evaporation, became more complex along dimensions such as accuracy and appropriation of visual codes from ECE Year 1 to ECE Year 3. They also showed increasing autonomy in the use of symbolic and iconic modes of representation in drawings, thus for instance, they used models to represent the three states of water (see Figure 14.1). The teacher, who was the same during these three ECE years, as is frequently the case in Spanish public schools, modulated her scafolding, fading its intensity, and provided afective support, acknowledging and legitimating children’s contributions. Olander et al. (2018) studied lower secondary school students’ meaning-making processes as they drew from multiple representations of the human body. Their analysis focused on the ways representations aforded the students’ ways of making sense of the content and on how continuity between the purposes of diferent inquiry activities could be sustained. The results indicated that continuity was established in multiple ways, for instance, through the use of metaphors, which gradually transformed from an everyday register through an interlanguage expression, toward a more scientifc register. Prain and Tytler (2012) presented a framework to study student-generated representations, integrating three perspectives, semiotic, epistemic  – related to the picture of knowledge-building practices  – and

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Figure 14.1 Child’s representation of the three states of matter for water.

epistemological, or how and what students can know through engaging in the challenge of representing causal accounts through these semiotic tools.

Constructing Access and Identity Through Discourse Processes Drawing from theoretical traditions of systemic functional linguistics (Halliday, 1993; Halliday  & Martin, 1993), sociolinguists (Green  & Bloome, 1997), and scholars of the culture of language (Agar, 1994; Erickson, 1982), science educators have called attention to ways that discourse represents and frames students’ science identity (Brown, 2004; Gilbert & Yerrick, 2001; Parkinson & Crouch, 2011; Visintainer, 2019). The increasing globalization of education systems throughout the world, and the incorporation of immigrant children to school systems, have posed new challenges and opportunities for educators. Issues of frst language, ethnic afliation, and home cultures intersect with experiences in education and emerging understandings of students’ views of themselves as learners, and science learners in particular. Often, teachers are not well prepared for the variation in student experience. For example, in U.S. schools, multilingual and multicultural students are often taught by monolingual teachers. Science teaching and learning creates classroom contexts that are rich in symbolic meaning and opportunities for students to take on and reject the identities associated with using the multifaceted aspects of science discourse practices (Brown, 2006; Maulucci, 2008; Reveles et al., 2004). Over the years, several paradigms of research have emerged to challenge and extend the feld’s thinking about the role of language and identity in science. We have organized the body of scholarship associated with discourse and identity into fve themes. Issues of identity and access to cultural knowledge cut across diferent dimensions of education; thus, there is overlap of domains of knowledge in these themes. First, studies of discourse examine the relationship among students’ home language, (school) science language, and the development of identities in and for science teaching and learning. Second, building on the frst theme, discourse studies consider ways that language and identity serve to foster or limit common ways of being and understanding. Third, studies in science education have identifed ways that conficting interactions prevent students from participating in science discourse. Recognizing of the importance of identity

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and discourse, another research direction focuses on emergent pedagogies that emphasize the relationships of identity development and cognitive understanding. Finally, a set of studies examine how educators might expand who has the opportunity to participate in science in meaningful ways by recrafting what counts as science discourse in formal and informal learning environments.

Making the Case for Identity and Discourse Science has authority in society due to demonstrable successes in producing verifable knowledge. Yet, this epistemic authority can also be questioned due to the restricted ways of knowing within the disciplinary communities. Epistemic practices of science are reproduced by those participating in the communities, and as these practices become concretized they can become taken-for-granted ways of being that may limit participation of alternative views. For example, one of the most thoroughly explored aspects of the role of science discourse examines how teacher discourse sends messages about the association of students’ identity and gender roles in science (Gilbert  & Yerrick, 2001; Reveles et al., 2004; Schnittka & Schnittka, 2016). The implications of this work suggest that the discourse of science frames how students understand gender identity. Gullberg et al. (2018) noted that the science discourse of pre- and in-service teachers consists of presumptions of children’s attributes as being “biologically predestined” (p. 709). They argued that the discourse of the classroom sends messages of what counts as gender and sends explicit messages about biological predestination. These messages, which are communicated through the features of classroom discourse, can be full of implicit bias, as educators associate students’ discourse practices with academic ability. Gender understandings and presumptions embedded in the science discourse of teachers is one example of how the framing of science intersects with students’ identity. This is not contained to gender – it also pertains to other groups often excluded from science, particularly students from less valued racial, ethnic, and socioeconomic classes. Godec (2018) examined how working-class girls from diverse ethnic backgrounds negotiated their identifcation with science, through a gender lens and an intersectional approach. The science-identifying girls’ discursive strategies ranged from rendering gender invisible to reframing “science people” as caring and nurturing to cultural discourses of the desirability of science. She suggested the need for legitimating more heterogeneous performances and experiences. Students’ sense of self and belonging can be tied to the ways that discourse practices manifest in various science education contexts. Varelas et al. (2012) examined how relationships between text, identity, and language shape opportunities for access to science. Similarly, Brown et al. (2005) identifed how science language served as a cue for cultural membership for students. Collectively, these studies share a common assumption that the symbolic meaning systems of science, including science words, symbols, argumentation strategies, and artifacts, all send messages of cultural belonging and distance for students. As teachers teach, they employ the complex mix of science discourse. This can include symbol systems, mathematical language, and charts and diagrams that are intended to promote clarity but can simultaneously serve to intimidate students who are new to these discourse practices. Visintainer (2019) suggested that identity is a representation of cultural membership and maintains implications for access to science. This work challenged scholars to move implicit assumptions about identity and language from the passive subtext of the classroom toward an explicit component of science teaching and learning. Collectively, studies of language and identity focus on the role of discourse in the broadest sense. Varelas et al. (2012) explored how students developed “textual relationships” with the language practices of science. Their idea of intertextuality demonstrated that the dynamics of the language practices of science communicate messages to students about who can be considered a scientist (Langman, 2014; Olitsky, 2007; Varelas et al., 2012; Varelas & Pappas, 2006). They argue that scientifc text, whether read or written, sends messages of belonging. This has potentially diferential and adverse infuences on students. For some, these messages are afrmations

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of belonging, while for others they are cues that the culture of science represents a culture in confict with their very being. Scholars have not only defned the problems associated with science language and identity but also ofered explorations of how to address these challenges. Langman (2014) suggested science teachers reframe the borders of what counts as language in science teaching for multilingual students. This perspective requires that educators reconsider the norms of scientifc language practices and how they might be modifed in educational contexts. In this study, the failure of teachers’ practices to link language and identity in science presented a challenge for students, English learners (ELs) in this case, learning science in their non–home language. Langman explained how presumptions made by science teachers about science discourse as reliant “on symbol systems and hands-on experiences” lead them to believe that these discourses would be “sufcient input of a non-linguistic nature to allow ELs to participate in English medium contexts” (p. 189). Langman’s insightful analysis implicated a failure of science educators to take seriously the depth and intensity of the role of language learning in science classrooms. To address ways of building access to science, Poza (2016) proposed the use of translanguaging practices, as he argued for the incorporation of multiple discourse practices that enabled students to think and communicate about science in both Spanish and English. Translanguaging allows students to draw from multiple linguistic resources and focus on meaning making. For example, Licona and Kelly (2020) identifed that teachers and EL students were able to engage in epistemic practices of science across languages by engaging freely in translanguaging. Language choice, cultural practices, and ideological assumptions about language frame the complexity of student identity development and afliation. Importantly, teachers’ choices of discourse practices for curriculum, instruction, and assessment position students as potential members or outsiders to science. Recognizing the potentially alienating role of discourse, Lemmi et al. (2019) explored the role of teachers’ ideologies and their approaches to assessing students’ science knowledge. In this study, teachers were provided with writing samples that varied in language use. The analysis explored how teachers’ existing ideologies impacted their assessment of student knowledge. In his text on urban education teaching, Emdin (2010) challenged teachers to explore the benefts of using the cultural and linguistic practices of hip-hop to mediate the challenges associated with science language and culture. Globally, scholars like Parkinson and Crouch (2011) continued to push this paradigm of expanding the language identity norms of science as they explored how science language can be exclusionary for African students. In a qualitative study of classroom learning, Parkinson and Crouch (2011) reported that students who were able to use their native Zulu language were aforded greater opportunities to learn science and develop supportive identities. The fndings of this study are similar to those of translanguaging studies. The ways that students are positioned to use language have educational and ideological implications for student success and identity development. Collectively, the emergent body of research on language and identity relationships in science challenges scholars to reconsider how existing instructional norms can serve as resources to limit or promote access to science learning. Across the body of research, there are some shared assumptions. First, all language refects cultural membership. Science is a culture with its own discursive norms and practices. This recognition evinces the ways that the rhetoric of science and choices made in educational contexts about its use infuence students’ understandings of themselves as science learners and potential members of the science community. Second, the depth of the relationships between science language and classroom norms is often framed as a nonessential aspect of contemporary teaching and learning. This has the unfortunate consequence of focusing science learning on conceptual knowledge (concepts, theories, laws), without a more careful consideration of the ways such knowledge is communicated through discourse practices. Third, the emerging and continual rediscovery of the subtle relationships between the positioning of science and students through discourse has led to explorations of how efective science learning environments can be designed in which all students thrive.

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Conficts and Access Through Discourse Studies of identity and discourse in science settings point to the need to consider ways that engaging in specifed ways of speaking, acting, and being can be alienating to students, particularly those underrepresented in science. In a qualitative study of student groups’ discourse practices, Moje et al. (2001) discovered that conficts in students’ understanding of the meaning of vernacular terms undermined their capacity to participate in classrooms in meaningful ways. Although the teacher was focused on ensuring students understood the new science terms in the lesson, the vernacular word being used to introduce the ideas caused greater confusion than the science labeling words. Olitsky (2007) ofered a potential solution for cultural conficts by pushing educators to restructure the boundaries of classroom discourse. Olitsky suggested that the shift from formal classroom structures to out-of-school structures provided more opportunities for identity development. Avraamidou (2020a, p. 338) pointed out the need for exploring the emotionality of identity to examine how power shapes discourses about emotions, “given that emotions are not just dialectically related, but are inextricably bound with recognition and various systems of oppression”. For her, science identity is not only personal, but political. While the relationship between language and identity in science is being redefned by scholars (Parkinson  & Crouch, 2011; Poza, 2016), much of their research is based on an assumption that science discourse can produce conficts for students (Brown, 2006; Lee, 2001, 2005). A bulk of the studies associated with confict explore how underrepresented students experience feelings of intimidation and isolation related to science discourse (Brown, 2004; Buxton et al., 2013; Buxton & Lee, 2014). In a discourse analysis study of high school students in an urban school, Brown (2004) found that students actively avoided using science discourse because of cultural norms. This pattern was found across other settings and studies. Recognizing the potential conficts that students bring to classrooms, Buxton and Lee (2014) explored the need to integrate language practices that afrm identity and science language practices. Brown et al. (2019) used psychological stress tests to document how students who were taught with complex science discourse experienced reduced working memory in the science classrooms. Others, like Hsu et al. (2018), explored ways to address potentially limiting gender dynamics and discovered that altering gender interactions in groups created improved opportunities for women to develop science identities. In this case, the researchers designed group interaction that allowed girls to talk more freely, reducing the negative impacts of male bias in group interactions. Similarly, Schnittka and Schnittka (2016) explored how designed-based activities should be viewed through a gender lens to maximize learning experiences and reduce the ways students experience conficts between their identities. Maulucci’s (2008) study of teachers’ practices suggested that the language of the teacher can shape access or denial of students’ identities in science classrooms. Where these perspectives fnd common ground is in their shared discovery of the nature of science conficts. Although the mechanism of the conficts ranged from symbol systems to science words to everyday words, each of the studies identifed how and when students’ participation was limited due to the discourse practices of the classroom. When left to master the highly specialized, and potentially alienating, language practices of science without structured pedagogy from the teacher, students not only did not participate but also developed negative associations with science learning. Said diferently, students struggled with what was not taught simply because teachers assumed the language of science would be learned passively. Thus, to address issues of equity, active recruitment and participation of diverse students, and building educational culture of belonging, research needs to examine the science discourses of schooling and consider alternatives that are more inviting to students.

Emergent Practices for Integration and Learning As the feld provided a landscape of language identity implications, it concurrently developed studies of practices that modeled ways to improve the relationship between science discourse and students’

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identities (Chen et al., 2019; van Eijck & Roth, 2011; Varelas & Pappas, 2006). Chen et al. (2019) found that underperforming boys benefted from the use of the modifed argument-driven inquiry (MADI) approach in their group discourse practices. Their work shifted toward a focus on how the provision of heuristics could help students master discourse practices and shift the norms of classroom communication. Oliveira et al. (2007) found that embedding dialogic discourse structures in university classrooms provided greater access for students to develop science identities. By shifting from having students sit passively to providing explicit structures for discourse, they found that students developed a greater appreciation for science. Rincke (2011) explored how helping students approach science as a new language or using an interlanguage approach improved learning for college students. Olander (2010) examined how students’ use of science language that used a combination of vernacular and scientifc practices enhanced their science learning. Brown and Ryoo (2008) conducted a quasi-experimental study of teaching science with simple language and documented how students demonstrated improved learning on a conceptual text of photosynthesis when taught with simpler language. Park et al. (2019) suggested that those who are hard of hearing and using multiple hybridized discourse (ASL) had difculty adapting to argumentation language practices. Chappell and Varelas (2020) proposed exploring alternative modes of discourse for promoting access to science. They ofer an example of Black students who used dance as arts-based practices to make connections to science identities. Clegg and Kolodner (2014) suggested that students who participate in kitchen science are provided an opportunity to fnd identities that are commensurate with science. The tie that binds these studies is a shared assumption that intentionally promoting students’ academic talk improved their learning and connection to science. The range of success documented across the context of these studies was striking. Studies from Brown and Ryoo (2008) focused on elementary school students, while Olander (2010) and Rincke (2011) researched high school and college students. Across the studies, students demonstrated improved learning outcomes. Additionally, the studies document how engagement with discourses practices that intentionally teach students the language of science seemed to promote a deeper connection to science as a discipline. The growing body of research investigating the relationship between science discourse and identity frames science teaching and learning as an environment rich in sociolinguistic interactions (Brown, 2006; Varelas  & Pappas, 2006; Yerrick  & Gilbert, 2011). These interactions range from discursive interactions that afrm students’ identity (Buxton et al., 2013; Emdin, 2010; Parkinson & Crouch, 2011) to providing students with opportunities to merge multiple linguistic resources into their teaching and learning experiences (Langman, 2014; Poza, 2016). The research on discourse and identity in science suggests that science discursive practices impact students’ cognitive understanding and cultural membership in and out of science, thus leading to changes in how they are viewed and view themselves as science learners (Brown et al., 2019; Olander, 2010; Rincke, 2011). There is a shared assumption that these interactions are potentially problematic if undefned and allowed to operate as a subtext of science classrooms (Chen et al., 2019; Lee, 2005; Lemmi et al., 2019). Scholars are making progress in recognizing potential ways to promote healthy identity through innovative approaches to science discourse (Chen et al., 2019; Oliveira et al., 2007; Poza, 2016). Adopting perspectives from sociolinguists has ofered a broader vision on the role of formal and informal interactions and its implications for student and teacher identity.

Discourse, Values, and Ideology Science is supported by a set of practices, epistemic frameworks, and ideologies (Bazerman, 1982; Roth & Lucas, 1997; Watson, 1995). Science communicates (often implicitly) these values through education, schooling, and other forms of communication. Research in science education and in the public communication of science has examined how the values of science have been communicated through the dynamic discourse practices embedded in the enterprise of science (Fang, 2005; Hakuta

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et al., 2013; Kelly, 2014). As discourse practices evolve (e.g., new genres of communication, social media, data visualizations), the need to understand the association between science discourse practices, their associated ideologies, and science teaching has increased in importance (Cheuk, 2016; Linder, 2013; Rosebery et al., 1992). As we have argued previously, scientifc knowledge is constructed through social interactions and further reifed and instantiated through theoretical and physical models, inscription devices, data representations, and genre-specifc written texts (Henze et  al., 2008; Gilbert et  al., 2000; Knain, 2001; Latour & Woolgar, 1986; Lenoir, 1998). This complex relationship between discourse tools and meaning making produces a complex matrix of communication with associated ideologies (Aronowitz, 1988; Stoddart, 2007). Knain (2001) made clear how such choices present themselves in education: Science education necessarily contains values, because nature does not automatically provide what is to be taught, and for what purposes. Science proper has to be recontextualized in order to be meaningful in school science discourse. It must be adapted to students’ age and prior knowledge, it must be divisible within the time units allocated and adapted to the physical resources of the school, and it must be ftted into a form that can be assessed. (p. 320) While Knain (2001) wisely highlighted how the translation from science to science education requires a re-presenting of the nature of science and its discourse, the subtext illuminates the challenge of translating discourse practices used by scientists to construct phenomena into discourse practices that can efectively scafold students into an epistemic community. Moreover, the science values and discursive tools that are used in meaning making in professional science endeavors must be reasonably translated to a K–12 context where students are experiencing approximations of authentic scientifc experiences. Thus, educators need to examine how science discourses, ostensibly used as teaching tools to help students make sense of phenomenon, also impose ideologies that are often opaque to teachers and students alike.

Sensemaking Values and Learning Science discourse refects a systematic approach to sensemaking that privileges certain values and goals, refective of the epistemic cultures of its production. This orientation serves the purposes of the respective disciplinary communities. The discourse practices used for sensemaking in science (e.g., charts, diagrams, words, symbols, and other epistemic tools) refect a set of values and ideologies (Rosebery et  al., 1992; Watson, 1995). Consequently, learning science requires integrating sensemaking practices that are communicated through classroom discourse (Grimes et al., 2019; McDonald & Kelly, 2012; Warren et al., 2001). Although science discourse helps to accomplish the epistemic (Kelly, 2011, 2014) and cognitive functions of scientifc communication (Fang, 2005; Halliday  & Martin, 1993), these discursive forms privilege certain ways of knowing (Barton & Tan, 2010; Córdova & Balcerzak, 2016). Thus, as science students learn to adopt the discourse practices of science, they simultaneously acquire access to the privileged subtexts associated with science discourse. As scholars consider how to make science accessible to all, they must also consider how providing access to the discursive tools of science equates to providing access to the values and ideologies embedded in scientifc epistemology (Culbertson  & Adger, 2014; Haraway, 2003; McDonald  & McIntyre, 2001; Oliveira et  al., 2007; Warren et  al., 2001). Thus, questions of access to science discourse are rooted in considerations of who has the right to talk, act, and engage in discourse practice and what is possible for diferent people to say and do (Cheuk, 2016; Gilbert & Yerrick, 2001; Halliday, 1993). Science teaching and learning includes assumptions about who has an opportunity

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to engage in science talk and what genres of science discourse are valued in science (Brown, 2004; Varelas, 2017).

Power and Ideology in Discourse Although language systems are used to communicate meaning, they are also associated with systems of thinking (Fourez, 1988; Friedrich, 1989). Diferent languages, registers, and genres are not interpreted equally in contexts of use. A language ideology lens allows us to view the ways in which language is viewed as a tool for meaning making and positioning (Lippi-Green, 2012; Seargeant, 2008). Woolard (1992) defned language ideology by suggesting that linguistic ideologies are shared bodies of common sense notions about the nature of language in the world. We mean to include cultural conceptions not only of language and language variation, but of the nature and purpose of communication, and of communicative behaviors as an enactment of collective order. (Woolard, 1992, p. 235) From this lens, ideas about what language matters, how language matters, and whose language has value are deeply connected to the ideologies held about the given language. Perceptions of language ideology can be conceived of three sorts. Several scholars adopt a social framework on language ideologies by suggesting that language practices are associated with communities, identities, and power relations (Friedrich, 1989; Lippi-Green, 2012; Seargeant, 2008). Bakhtin (1981) identifed the ways in which language practices are associated with ways of viewing people. Those using a particular language come to be interpreted as a particular type of person (Agar, 1991; Alim & Smitherman, 2012; Kroskrity, 2009). This social view suggests that language systems are connected with historical and social meaning, as people use language practices associated with a particular way of being. Another framework centers cognitive processes (Lemmi et al., 2019). Poza (2016) and Lippi-Green (2012) explored how the cognitive resources embedded in a language are valued or devalued by social norms (Lemmi et al., 2019; Stoddart et al., 2002; Van Dijk, 2009). As such, language ideologies frame which cognitive resources are valued as each language practice provides the speaker with access to cognitive tools that transcend language boundaries (Langman, 2014; Lemmi et al., 2019). Finally, a third framework on language ideology is rooted in political identity frameworks (Echevarria et al., 2016; Friedrich, 1989). Echevarria et al. (2016) examined how language norms are intentionally shaped and used to frame political identities (Friedrich, 1989; Woolard, 1992). The ranking and marginalizing of native language practices served to elevate some languages as more suitable for academic, political, and social engagement than others (Flores & Rosa, 2015; Rosa, 2010; Woolard, 1992). This framework, like others, is rooted on the premise that while understanding language is a foundational framework for how languages are used, adapting and understanding of the ways of being associated with those languages is rooted in larger ideological standpoints. A language ideology provides a framework to understand not only how languages are used, but how languages are viewed in societal terms. Foucault (1980) and Fairclough and Wodak (1997) challenged scholars across multiple disciplines to carefully examine the relationship between discourse, power, and ideology. Foucault explored how the relationship between discourse and power is shaped by who has access to discursive tools and how those tools accomplish political and social ends. Scholars of discourse ideology examined how science serves as a site for discursive power as people adopt the rhetorical structures and use the discourse tools of science (Duschl & Osborne, 2002; Hodson, 2003; Lemke, 1990). Although the power of science discourse and ideology have emerged over hundreds of years (Bazerman, 1982), these discursive practices allowed scientists to heighten perceptions of objectivity via discursive strategies such as

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through the use of passive voice or nominalization (Bazerman, 1982; Halliday & Martin, 1993) to reify knowledge claims through data representation (Latour & Woolgar, 1986) and to improve epistemic claims through the use of the rhetorical structures of science (Roth, 2014; Roth & Lucas, 1997). As scientists expanded their approach to communicating scientifc phenomena, they developed representational tools to enhance and refect their sensemaking practices (Bazerman, 1982). With these developing and ever-changing discourse practices and inscription devices comes the need for students to master these same discursive strategies. In explaining how physics students learn to master the discursive tools of physics, Linder (2013) explained: A person who has achieved fuency in the disciplinary discourse of physics is able to see the afordance attributes that are presented in the constitution of a collective disciplinary afordances. However, what is critical for the teaching and learning of physics is that a physics teacher is able to make this visible to learners. (p. 49) The idea that efective science teaching requires the teacher to help students develop an awareness of the rhetorical structures of science is a position shared by several science scholars of science education (Bazerman, 1988; Kelly, 2014; Lemke, 1990). The veiled relationship between science and science education presents some challenges as students are asked to learn science concepts while being introduced to an approximation of science discourse practices. These lessons are rich, complicated, and can present a number of challenges (Arons, 1977; Olander & Ingerman, 2011; Rincke, 2011). Although there is a wealth of scholarship on discourse, cultural practices, and ideology in the philosophy of science (e.g., Foucault, 1980; Harding, 2011; Rouse, 2002), this perspective has not been thoroughly integrated into science education, with a few exceptions. Lemke’s (1990) landmark work on talking science highlighted how the semantics patterns in science language refected meaningmaking patterns that were often overlooked by teachers. Warren and Rosebery (1992) called for the use of sensemaking as a framework to help science teachers understand how students’ discourse practices were rich with patterns that teachers needed to recognize. Rudolph and Horibe (2016) called for scholars to recognize the value of praxis and civic engagement to introduce students to the myriad of discourse and epistemic foundations of science. Across these studies there is a clear recognition that the failure to explicitly address and teach the relationships between science language and the implied values allows unexamined ideological assumptions to serve as a subtext of instruction. As science education shifts toward valuing argumentation, classrooms are reliant on teaching discourse practices that helps students engage in reasoning that is rooted in discourse. Thus, the language ideology relationships in discourse can emerge as a hidden curriculum in contemporary classrooms.

Invisible Ideological Curriculum The ideological positioning of science in education occurs through discourse practices of socially and culturally dominant groups. Ways of knowing, speaking, and being within science (and all) communities are constructed over time as members afliate and come to learn what it means to be a member of the group. The emergent discourse practices result from participation and potentially serve to exclude prospective members from the community. This occurs in educational contexts as well. Furthermore, many rules of the dominating discourses are typically not overt (Gee, 2014), in everyday life or in education, and occur with taken-for-granted assumptions about what it means to be a member. Breeches of the rules make visible the underlying assumptions, but also serve to identify membership and nonmembership. For newcomers, learning these

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aspects of discourse also means learning to value and judge yourself and others in terms of the dominating discourse. Learning to take part in discourse is thus not merely a question of learning what is true and certain. It also means learning to enact systematic ways of distinguishing who is to be included or excluded and what is relevant or irrelevant. Some privileging when learning and taking part in discourse is inevitable – it is precisely the exacting language and rigorous protocols for use that lead to scientifc success. However, research can make visible the implicit rules of discourse and how they are shaped by and for participants. Such research ofers the possibility of changing the discourse of apprenticeship and recognizing alternative ways to being, seeing, and acting. Where the borders of science discourse meet their limits, individuals learn to adopt and extend science discourse to engage in critical refections and analysis of how science serves as a tool for critical social participation (Hakuta et al., 2013; Kang et al., 2018; Miller et al., 2014; Moore et al., 2015).

Shift in Ideology Requires Shift in Discourse Although the relationship between science discourse and epistemic forms of scientifc knowledge coexists in classrooms, a major shift in the ideological positionings of science calls for a reexamination of the accompanying discourse (Aikenhead, 2001; Sadler, 2006; Stoddart, 2007). For example, the epistemic cultures of science value evidence. Evidence is often formulated through specifc argumentation strategies. The adoption of argumentation as a primary component of science instruction surfaced the need for a careful reconsideration of accompanying discourse practices (Ballenger, 1997; Berland & McNeill, 2012; McNeill & Pimentel, 2010; Sadler, 2006). This has occurred through the implementation of the Next Generation Science Standards (NGSS) (2013). As these standards are being adopted throughout the United States, research has pushed teachers and researchers to consider ways educators can introduce students to science argumentation through purposefully structured discourse practices (Bell & Linn, 2000; Berland & McNeill, 2012; Enderle et  al., 2013; Erduran et  al., 2004). Thus, the inextricable link between science argumentation as ideology and science discourse has emerged as a vital component of modern science instruction. Although the notion of scientifc argumentation presented here is broad, it refects a dynamic set of discursive tools. Whether these tools include evaluating data, constructing written arguments, or engaging in public conversations of science, a shift to a paradigm that values argumentation requires being attuned to the discursive practices that make scientifc epistemologies possible. Argumentation promotes an individual’s or group’s abilities to reason and justify claims. This highlights how inseparable discourse and ideology practices are in modern science teaching. As such, the growing adoption of argumentation in science classrooms requires a careful parsing of the discourse practices needed to achieve this type of reasoning.

Multilingual and Multi-Cognitive Ideologies A fnal consideration of ideology and science is how multilingual students access science ideology through multilingual practices (Cheuk, 2016; Erduran et  al., 2004; Poza, 2016; Probyn, 2015). Although discourse and argumentation are linked, the research on the particular impact on multilingual students requires scholars to further explore how multilingual students can connect their cognitive resources to multiple linguistic forms (Bell & Linn, 2000; Campbell & Filimon, 2018; González-Howard & McNeill, 2016). Multilingualism is a norm for the majority of people and can be an asset for learning and communication, yet many students learn science in a non-native language. Research on multilingual practices point to multilingual reasoning, which requires scholars to refect on the ways that science ideologies are expressed across language boundaries (Cheuk,

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2016; Lemmi et  al., 2019; Poza, 2016, Wu et  al., 2019). Classroom studies suggest that hybrid discourse practices produce better argumentation strategies for students. However, scholars have noted that learning the argumentative discourses of science, particularly for non-native speakers and recent immigrants, pose challenges for students. Careful attention needs to be placed on ways that argumentation, and other specialized discourses of science, are used with students of varied ability in the language of instruction. Cheuk (2016) called for educators to be cautious not to “disempower those who are learning the content and English language simultaneously” (p. 99). Cheuck suggested that educators find ways to use active discourse practices for classroom learning. To build on the heightened awareness of language English learners (ELs) possess, Cheuck noted that teachers need to include ideological discourse practices of argumentation as a central component of ELs’ education. O’Hallaron (2014) made a similar recommendation. She highlighted how teachers’ use of explicit scaffolds can support students’ use of their own discourse practices in argumentation. González Howard et al. (2017) offered a similar claim. They suggested that helping students understand why they use particular discourse is vital to their use of science discourse and adoption of argumentation practices. Argumentation in science education offers certain opportunities to learn about ways of using and aligning evidence. Argumentation emerges from epistemic cultures that privilege certain ways of using discourse. Properly scaffolded, argumentation can be one way to engage students in science discourses that draw on dynamic linguistic communities as assets for student learning. Bilingual classrooms provide a rich context for examining the potential of thinking beyond monolinguistic approaches to study discourse. Ünsal, Jakobson et al. (2018b) examined, through practical epistemology analysis, how emergent bilingual students used gestures to talk about the science content, when their language proficiency limited their possibilities to express themselves. Although the meaning of the gestures needed to be negotiated in some situations, they resulted in the continuation of the science activities. In another study, based on translanguaging and making part of the same project, Ünsal, Jakobson et al. (2018a) explored, in a multilingual elementary classroom, how bilingual students used both of their languages as resources during hands-on activities. And how, in other situations where they had to argue in one language, their Swedish language repertoire limited their possibilities of making meaning of the activities. In a bilingual English/Spanish middle school science classroom, Licona and Kelly (2020) studied teacher translanguaging and how this practice afforded opportunities for supporting scientific argumentation. The paper investigated how framing epistemic practices proved to be of paramount importance in supporting practices comprising scientific argumentation.

Discourse and Ideology in Science Education The body of research on discourse and ideology in science education offers a vision of nuanced relationships between the epistemic ideology of science and its associated discourse tools. Scholars have been vigilant in their calls to identify how privileged ideologies are embedded and inextricably linked to discursive tools (Berland & McNeill, 2012; Cheuk, 2016; González-Howard & McNeill, 2016). To this end, research exploring how to teach these practices to students highlights the need for detailed scaffolding (O’Hallaron, 2014; Wu et al., 2019). Classrooms throughout the world are increasingly multilingual; scholarship on argumentation and discourse has suggested that multilingual ideological positions add value to students’ experiences and provide them greater access to argumentation ideology (Cheuk, 2016; González-Howard & McNeill, 2016; Lemmi et al., 2019a; Poza, 2016). Though the fields still remain nascent, the focus on understanding student discourse resources and the need to scaffold them into the discourse practices of science as a means to engage in argumentation is a common refrain throughout the scholarship.

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Methodological Considerations for the Study of Discourse in Science Learning Contexts The substantive issues tied to the study of discourse and science education have both brought about new ways of investigating discourse processes and practices and have left open the need for imaginative solutions to emerging methodological challenges. The topics reviewed in previous sections of this chapter, from examination of discourse practices across multiple learning contexts to studies of identity construction to discourse, values, and ideology, each present the complexity of studying human interaction. In this section we consider four interrelated aspects of research methodology related to the study of discourse in science education contexts: the situated nature of discourse, the scope and nature of discourse features, data representations of discourse, and refexive questions about positioning the discourse of the researcher in discourse analysis.

Situated Nature of Discourse in Cultural Practices Discourse processes occur in specifc settings, within particular cultural contexts. From a research perspective, discourse is situated in time, place, culture, and the ongoing practices of a given community. Understanding the nuances of discourse and interaction requires analyses to understand such contexts and develop methodological approaches that take the broader implications of specifc uses of language into account. These cultural practices include, minimally, those of science, schooling (or other educational settings), and that of the participants. Cultural practices of science have developed over time to serve the purposes of the respective epistemic communities comprising the various disciplines. These practices may be epistemic in nature, such as the genre conventions of the scientifc research article, or interpersonal, such as the ways that a laboratory is organized. In educational settings, such as schools, the cultural practices may entail ways of being that include conventions for participating, speaking, and interacting. The science and schooling practices are mediated by relevant factors such as the teacher, technologies, and texts. An example illustrates this point. Ideological images of science, such as the distillation of the scientifc method as a means for testing hypotheses has become a trope in science classrooms. In this way, a distorted view of epistemic practices of science (a constellation of ways of adjudicating evidence) coalesces with teaching practices (ways to acculturate students into reasoning patterns) to produce not only a situationally defned view of science but also putative ways of generating knowledge in science. Studies of science discourse thus need to examine the history of ideas and the taken-for-granted ways of teaching to consider how science is framed in the schooling process and potentially taken up by students. Instances of science talk should be situated in ongoing cultural practices. Furthermore, students may have alternative ways of being and knowing that clash with the linearity of “the” scientifc method. Various cultural practices may intersect at instances of talk and action. Discourse analysts need to take these ideologies and ways of being into account. Sociolinguistic analyses often work across scales and examine discourse as contextualized in activity and time (Skukauskaitė & Girdzijauskienė, 2021). A recent study by Franco and Munford (2021) addressed the issue of understanding intercontextual relationships across discourse events and illustrated ways to situate discourse in cultural practices. They proposed an hourglass model – frst thinking broadly about the history of the group, then focusing on the specifcs in science classroom discourse events before expanding to think about how classroom events relate to other events and contexts. The hourglass model provides a useful metaphor for organizing methods of analysis for discourse studies. Franco and Munford’s detailed analysis examined the details of the local event and sought to consider the ideologies framing the construction of the event. They then drew implications for the science learning opportunities created through the events of the classroom. Patterson (2019) also provides an illustrative example of the situated nature of discourse. She considers issues of

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equity in science education by focusing on student voice, visibility, and agency/authority in interactions, rather than just on the outcomes of instruction in terms of equal participation or assessments of knowledge. To understand how students use voice in small group interactions, she created a set of event maps (Brown & Spang, 2008; Green et al., 2020) informed by the verbal exchanges and nonverbal cues. By looking across diferent units of analysis (Green & Kelly, 2019), Patterson was able to consider student voice based on the amount and quality of the participation. Due to the embedded nature of the discourse units of analysis, each instance of participation was understood within the ongoing activity of the classroom. This was demonstrated through the use of event maps that provided a sequential analysis of the ongoing activity, allowing the analysts to interpret the patterned ways of being in the two settings under study.

Scope and Nature of Discourse Features in Interaction Research in discourse studies requires detailed, meticulous analysis of human interaction. This includes the ways communication occurs through contextualization (such as proxemics, gestures, prosody, eye gaze, among other paralinguistic features of talk) and over time, as cultural practices evolve across scales (meso, ontogenetic, sociohistorical) (Kelly, 2008). Analysts need to balance the insights from close, fne-grained analysis with the nature of educational research questions that concern participation, learning, teaching, and assessment that occur across instances of uses of discourse. Discourses in science are often multimodal, involving spoken, written, graphic, and gestural modes (Monteira & Jiménez-Aleixandre, 2016; Wilmes & Siry, 2021). Thus, some of the best studies can “zoom in” to examine the nature of specifc interactions, and “zoom out” to set the interaction in a broader context and recognize the instance of the interaction in a larger pattern of activity. The studies by Franco and Munford (2021) and Patterson (2019) provide insights into how to interpret discourse within the ongoing activities in a cultural setting. Detailed analysis of discourse at the micro level can inform how meaning is constructed among participants. In this section we examine some of the methodological considerations needed to understand the multimodal communication in science education by providing examples of discourse contextualized in paralinguistic features of interaction. An emerging feld of science education attentive to the contextualization of talk and action is the study of emotions in science education. Studies of students’ emotional responses to science (Davis & Bellocchi, 2018; Hufnagel, 2015; King et al., 2015) and teachers’ emotional management (Bellocchi, 2019) rely on understanding not only what gets said, but how it is said and in what activity for what purpose, to understand what gets meant and heard. For example, Bellocchi (2019) presented a methodology, grounded in phenomenology of practice, microsociology, and ethnomethodology, to study emotional management and social bonds in a secondary science classroom. The primary data sources were video recordings of the lessons, diaries, and refective writing – each source is a form of discourse, and each provided valuable information for the purposes of the study. The uses of the video recordings are of particular importance for our purposes of understanding the nature of communication and emotion. The detailed frame-by-frame analysis of the talk and action included gestures, facial confgurations, and nonverbal actions such as intonations, speech rate, pitch, and loudness. These features worked together for the participants as they constructed the educational events (with associated emotions). Recordings were made available for the analysts to examine the interplay of emotion management with social bonds. From the detailed transcripts, tables were produced to visualize the forms of emotional management during the interactions. Importantly, fully understanding emotions in this case required such careful analysis. The detailed transcripts of interaction support the larger issues related to understanding emotion in science teaching and learning. Another important issue for science education where multimodal discourse transcripts evince analytic insight is that of bilingualism, multilingualism, and translanguaging. An example of this line of work that examined meaning-making discourse features in interaction was conducted by Ünsal,

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Jakobson, Wickman et al. (2018). This study focused on how emergent bilingual students used gesture as part of their language repertoire to communicate in science. Across two science education settings, the authors focused on the ways that language, gestures, and physical artifacts mediate meaning in science learning. The empirical study attended to how gesture contributed to meaning making in science learning for bilingual students of diferent national origin. The discourse was analyzed using practical epistemological analysis (Wickman & Östman, 2002), and much like Bellocchi’s study, included detailed transcripts of talk and action. Types and uses of gestures for communications were characterized and tabulated. The analysis showed how students used gestures to express meaning when their other modes of communication were limited. Examples from the classroom demonstrated how the gestural aspects of discourse assisted in the meaning-making for the students and teachers.

Data Representations of Discourse Practices Discourse analysis often begins at the level of the transcript – a social and discursive process in the making itself. Diferent kinds of transcripts re-present the associated events, bringing focus and recognition to only some of the key features (Green et al., 2020). Furthermore, as noted, discourse is situated in time, space, and cultural practices, and thus new and emerging forms of data display call attention to features of discourse practices. These research practices assist with analysis and communication of results to readers. Over the past couple of decades there has been a growing interest in helping students produce, assess, and critique knowledge claims. This work is often connected to various forms of argumentation analysis. For the purposes of discussing the methodological implications of data displays of discourse practices in science education research, we draw from a selection of studies of argumentation analysis. González-Howard and McNeill (2019) sought to understand how engaging students in scientifc practices could lead to improved epistemic understandings. In their study of two seventh-grade classrooms, the authors examined engagement in scientifc practices by considering the framing of the argumentation practices by the teachers and then the subsequent uses of argument by students. Social network analysis (SNA) of student dialogic interactions led the authors to create sociograms displaying the patterns of three types of dialogic interactions – questioning, critiquing, and building on others’ ideas. The sociograms provide a visual map of the interactions making visible the participants (nodes), their social relations (types of focal dialogic interactions), directionality of the utterances (arrows), and the quantity of the contributions of speakers (size of the respective nodes). The sociograms provided a visual means, grounded in the discourse analysis, to compare the nature of the dialogic interactions across the student groups. By doing so, González-Howard and McNeill advance research on argumentation and epistemic practices by making visible how students communicated in dialogical interactions. Two other studies created data displays to facilitate interpretation of the discourse practices associated with argumentation analysis. Lin and Hung (2016) constructed graphical representations, associated with the transcripts of students’ dialogic conversations. The two-dimensional graphical representations map assertations, warrants, and rebuttals, both for afrmation and negation, on the vertical axis and time across the horizontal axis. The students’ discursive actions, such as opinions, questions, and rebuttals, and cohesive ties to previous utterances were mapped onto this grid. The display was particularly useful for examining the reconciling strategies used by the teachers when conficts arose. The authors used this graphical representation to identify how teachers difused conficts by redirecting talk, modeling uses of qualifers, and extending students’ arguments to other science activities. A fnal example of efective use of data display is presented by Yun and Kim (2015) in a study of student argumentation in small group settings. Consistent with emerging interpretations of uses of argumentation in science education (McDonald & Kelly, 2012), this study considered how students’ participation was related to building group norms in the context of eighth-grade classrooms

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in Korea. The social and argumentation norms were dependent on each other. Importantly, classroom culture and norms were taken into account in the analysis. This included identifying a range of students’ utterances beyond just those typically associated with formalized arguments. To make visible the relationship of the interwoven nature of the interactions, Yun and Kim constructed a map of the ways the task and teacher infuenced the epistemic practices associated with student interactions leading to argumentation and social norms. These examples of data representations provide a means for developing analytic tools to address the previously mentioned dimensions of discourse analysis related to the situated nature of discourse and the scope and nature of discourse features in interaction.

Positioning the Discourse of the Researcher in Discourse Analysis The analysts in discourse studies take a point of view, provide a perspective on an interaction (ex post facto), and embody a history of ideas, cultural assumptions, and personal biases in understanding. Thus, although discourse events in science education can be viewed as promulgating an ideology, the same can be said of the analysts. Refexivity in discourse studies requires careful attention to the positionality of the analyst(s) and how the discourse of the analyses and presentation themselves embody ideological assumptions. This sort of refexivity can be viewed in the stance the researchers take in observing, interpreting, and communicating about the interactions constituting the discourse subject to the analysis. This stance can be made visible to the reader in the form of discussion about the analyst’s perspective, or more often, embedded in the overall orientation to the participants and issues subject to the analysis. In each instance of discourse analysis, the communication about the discourse events is itself a form of discourse with choices made about point of view, diction, reference, and assumed audience (Kelly, 2021). Thus, researchers instantiate a discourse of analysis and presentation that embody ideologies, identities, and values, as much as the discourses of the participants. An example of analysts’ point of view with respect to the participants’ discourse is found in a recent study by Monteira et al. (2022). In this case, the authors sought to understand young children’s (ages 3–6) engagement with science representations. The focus on children’s drawings took seriously the sensemaking among the early learners, as drawings are often the frst eforts at abstraction. The longitudinal study, over three years, included analysis of 482 drawings across two science domains, snails and clouds. The content and discourse analysis, taken from the point of view of children as knowledge producers, was able to identify the teachers’ strategies and scafolds, and evolving development of children’s representations. The study identifed how the children developed increased autonomy, accuracy, and attention to detail, and appropriated visual codes for communication, such as the use of colors to signify meaning. Another example comes from an ethnographic study based on interviews of Native American science professionals (Page-Reeves et al. (2019). Ethnographic interviews are discourse events. In this case, the researchers adopted a cultural analytic perspective to examine the “nature and meanings of the participant narratives” (p. 181). By taking this stance, the research team was able to learn from the participating scientists by listening to how their narratives were constructed and how they were presented. The interviewers dedicated a portion of the interview to explaining the history and purpose of the project. By situating the study in this way, the research team sought to foster mutual understanding. This positionality of the interviewers thus became recognized as relevant to the substance of the narratives of the participants.

Emerging Research Directions for Studies of Discourse Practices in Science Education In this fnal section of the chapter, we consider some emerging research directions for studies of discourse practices in science education. The feld has developed and grown from a focus primarily on teacher discourse to multiple foci across diferent social settings for science learning. We propose

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fve ways that discourse studies in science education can expand to contribute to the knowledge base informing science education. The increasing relevance of science and technology in modern society and attacks on the rationality of science suggest that future directions examine discourses of science and socioscientifc issues as they expand to new venues for learning and uses of science in society. The fve directions we propose are: (1) uses of discourse studies to examine the increased communicative demands of the goals of science education reform; (2) application of discourse studies to disciplinarity, interdisciplinarity, and transdisciplinarity in science, engineering, and technology education; (3) recognition of multilingualism and the importance of drawing on students’ cultural and identities assets for science learning; (4) reconsideration of scientifc literacy and citizenship in the new social media universe; and (5) understanding science discourse as (mis)educative in multiple (unsanctioned) settings. The history of ideas in science education documents a continual push away from the goal of learning current factual knowledge to an integration of knowledge with discovery, investigative processes, inquiry, and science set in social issues (DeBoer, 1991). Current reforms continue this trend with calls for ways to engage students in the epistemic practices of science and to understand the aims and values of science (Park et al., 2020). These reforms acknowledge the important ways that learning conceptual understanding and the bases for such understanding are connected through ways that disciplines construct and legitimize theoretical knowledge (Grob et al., 2017; NRC, 2013; OECD, 2016). While these ways of knowing may be framed as disciplinary practices or competencies, a common feature is the increased communicative demands on students. Engaging in the formulation, communication, assessment, and critique of knowledge claims requires that students draw from and employ a range of semantic and syntactic features unique to science. This engagement requires that students’ current communicative competence be used as a resource to develop new repertoires for sensemaking, speaking, and writing science. The study of the discourse processes of learning these sociolinguistic features of disciplinary practices and competencies may shed light on ways of creating learning environments supporting students. There are four ways that discourse studies are crucial for understanding the communicative demands of engagement. First, engagement in disciplinary practices relevant to knowledge claims entails a shift of the epistemic subject from that of the lone observer to a collective with common vocabulary (ontology) and ways of being, doing, and speaking. Understanding the ways that epistemic cultures construct, communicate, assess, and legitimate knowledge requires careful consideration of the discourse practices of the relevant community (Kelly, 2016). Second, disciplinary practices and competencies are framed, taken up, and transformed through discourse. The feld needs to examine ways that such practices are manifest in the everyday workings of science education across settings. Third, sense-making occurs through language use. Developing deep understanding of science requires that students understand and employ discourse practices and features of science in socially appropriate ways. Meaning making in science and engineering settings occurs through multimodal communication and requires careful sociolinguistic analysis (Martin et al., 2020; Wilmes & Siry, 2021). Fourth, student identities are constructed through discourse processes. As science reforms move to a greater use of inquiry and a focus on practices and competencies, there is a need for research to examine how students’ identity work is constructed through discourse processes and how the discourses of school and science intersect with other dimensions of students’ lives (e.g., gender, race, ethnicity, social class, religion) (Avraamidou, 2020b; Brown et al., 2017). A recent trend in science education has been a move to STEM – science, technology, engineering, and mathematics (Erduran, 2020). Although there are various interpretations of STEM, some including arts (STEAM), common understandings for what this means for teaching, learning, curriculum, and assessment are vague. Disciplinary distinctions have been identifed across sciences (e.g., Ault, 1998; Erduran, 2001; Rudolph & Stewart, 1998), and the importance of such distinctions for learning have been documented (Reynante et al., 2020). However, choices about how to divide up bodies of knowledge related to the natural and social world can be dubious. Furthermore,

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the emergence of interdisciplinary and transdisciplinary science and technology have made some distinctions appear anachronistic. Discourse studies have value for research in STEM education as the unique features of inquiry in any given area develop common ways of conducting work with common languages for communication. Discourse analysis can provide insight into ways that disciplinary, interdisciplinary, and transdisciplinary knowledge is articulated, used, and transformed through use in epistemic cultures. As views of science broaden to include engineering and technology, as well as citizen science, discourse studies need to probe how communities define what counts as science and how such local definitions of science may connect to alternative interpretations of natural phenomena. Such studies in educational contexts may examine ways that local interpretations of disciplinary knowledge(s) are constructed – for example, ways that positioning and power play out in engineering design challenges (Wieselmann et al., 2021). As the language of science, or STEM, or STEAM, expands, questions about the ideology and politics of science in society pose challenges for researchers. Currently, frameworks for understanding the epistemological assumptions of STEM for education are developing (Ortiz-Revilla et al., 2020). Discourse studies can contribute through textual and interactional analysis of how science, engineering, technology, and mathematics, and STEM are evoked, employed, interpreted, and legitimized in educational contexts. Discourse studies in science education are increasingly aligning with the worldwide norm of multilingualism. Students come to school with home languages and linguistic resources, which are beginning to be recognized as new opportunities for advancing pedagogy in science education contexts (Salloum & BouJaoude, 2020; Ünsal, Jakobson et al., 2018b). These linguistic resources have not typically been employed to serve learning goals, particularly when the language of instruction is different than that of the students’ home culture. Linguistic features of scientific discourse pose challenges for all learners and often alienate students whose experience and language use vary from the dominant discourses of instruction. Studies of discourse in science settings identify ways that teachers and students draw on linguistic assets of multilingualism to learn science. For example, a number of studies of translanguaging show how, through a focus on sensemaking and communication across different languages, students can access science and develop understandings of the knowledge and practices sought (Charamba, 2020; Licona, 2019; Salloum & BouJaoude, 2020). Mavuru and Ramnarain (2020) found that South African teachers were able to draw on learners’ home languages to facilitate conceptual understanding. The study considered both affordances and constraints of codes witching to support science learning and noted the value of teachers understanding and drawing from the cultural experiences of the students. Discourse studies can contribute to understanding of the value of translanguaging through analysis of the ways that meaning and engagement occur through verbal and nonverbal communication and across modalities of communication (Siry & Gorges, 2020). Furthermore, the practical work with materials in science, technology, and engineering provide avenues for students to express and understand meaning across modes of communication. This opens up new potential pedagogies that require careful analysis. The expansion of venues for information, whether accurate or not, poses new challenges for research in science education. Social media sources, including non-refereed platforms, such as Facebook, have become sources for knowledge claims (Höttecke & Allchin, 2020). Although this growth in information and the potential democratization of knowledge has the benefit of opening up for new voices for views about science in society, not all such information is accurate, and the potential for disinformation poses real risks (Treen et al., 2020; Xu et al., 2020). The world faces new and complex socioscientific and sociotechnical issues – pandemics, climate change, editing of genomes – with an increasing array of forums for information and opinion about science and technology in society. These challenges question not only science but also what counts as evidence and rational argument. For example, science bloggers make choices about how they choose, frame, and highlight features of science for their rhetorical purposes (Luzón, 2013). Discourse studies in science education need to examine both ways of preparing citizens through schooling as well as ways of developing

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critical thinking among adults across venues for (mis)information. Discourse is central to scientifc literacy. Because such literacy entails an expansion to multiple, complex, and ever-changing sources of information, the study of discourse in society around science and technology is increasingly important. Science education needs to address how students and citizens come to understand discourses of science, in the various genres and venues, and ways that science communication can be informed by evidence, credibility, expertise, and trust (Höttecke & Allchin, 2020). The rationality of science has a role to play in the post-truth era, and much of the work of reasoning occurs through the discourse processes entailed in dialogue, debate, and inquiry (Duschl, 2020). Related to the expansion of venues for (mis)information are the varied contexts in which science learning can occur. Discourse studies have generally occurred in educational settings, even as they have expanded into out-of-school and other less formal schooling contexts. The feld of discourse studies has had a school focus with more recent works emerging from studies in museums, science centers, and other organized educational locations and events. Yet, science learning occurs in the wild – in unsupervised, unintended, and naturally occurring events. Vedder-Weiss (2017) refers to this as serendipitous science engagement (SSE), occurring most readily in an undesigned environment and driven by the learner’s initiative. While the self-ethnography of Vedder-Weiss identifed multiple ways that three children and a mother made sense of the insects in their backyard and came to engage with science, this may not always be the case. Beyond the forums of social media, the actual learning of science or, increasingly, anti-science occurs in social confgurations outside the purview of educators. Access to information and such potential learning has increased and co-occurred with an increase in the potential to study science in the wild. New recording devices, cloud computing and storage, and ubiquitous wireless connections ofer the potential to engage in discourse studies as learning occurs outside of organized learning events (Land et  al., 2020). Such research has numerous challenges, including identifying how and where science manifests in every life and how reliable records can be collected to analyze ways that science is evoked in the wild. To address global citizenship discourse, analytic research needs to address the sources and uses of science at locations where it occurs.

Acknowledgments We would like to thank our critical friends, Christine Cunningham and Ashwin Mohan, for comments on an earlier version of this chapter, and the chapter reviewers, Danusa Munford and KokSing Tang, for their suggestions for improving the chapter. We gratefully acknowledge the mother of the young child for providing the sketch shown in Figure 14.1 and authorizing its use.

References Agar, M. (1991). The biculture in bilingual. Language in Society, 20(2), 167–182. Agar, M. (1994). The intercultural frame. International Journal of Intercultural Relations, 18(2), 221–237. Aikenhead, G. S. (2001). Students’ ease in crossing cultural borders into school science. Science Education, 85(2), 180–188. Alim, H. S., & Smitherman, G. (2012). Articulate while Black: Barack Obama, language, and race in the US. Oxford University Press. Aronowitz, S. (1988). Science as power: Discourse and ideology in modern society. University of Minnesota Press. Arons, A. B. (1977). The various language: An inquiry approach to the physical sciences. Oxford University Press. Asterhan, C. S. C. (2013). Epistemic and interpersonal dimension of peer argumentation: Conceptualization and quantitative assessment. In M. Baker, J. Andriessen, & S. Järvelä (Eds.), Afective learning together. Social and emotional dimensions of collaborative learning (pp. 251–271). Routledge. Ault, C. R. (1998). Criteria of excellence for geological inquiry: The necessity of ambiguity. Journal of Research in Science Teaching, 35, 189–212. Avraamidou, L. (2020a). Science identity as a landscape of becoming: Rethinking recognition and emotions through an intersectionality lens. Cultural Studies of Science Education, 15, 323–345. https://doi.org/10.1007/ s11422-019-09954-7

438

Discourse Practices in Science Learning Avraamidou, L. (2020b). “I am a young immigrant woman doing physics and on top of that I am Muslim”: Identities, intersections, and negotiations. Journal of Research in Science Teaching, 57, 311–341. Baker, M., Andriessen, J., & Järvelä, S. (Eds.). (2013). Introduction: Visions of learning together. In M. Baker, J. Andriessen, & S. Järvelä (Eds.), Afective learning together. Social and emotional dimensions of collaborative learning (pp. 1–30). Routledge. Bakhtin, M. M. (1981). The dialogic imagination: Four essays by M. M. Bakhtin (M. Holquist, Ed.; C. Emerson & M. Holquist, Trans.). University of Texas Press. Ballenger, C. (1997). Social identities, moral narratives, scientifc argumentation: Science talk in a bilingual classroom. Language and Education, 11(1), 1–14. Barton, A. C., & Tan, E. (2010). We be burnin’! Agency, identity, and science learning. Journal of the Learning Sciences, 19(2), 187–229. Bazerman, C. (1982). Discourse paths of diferent disciplines. Paper presented at the Annual Meeting of the Modern Language Association of America, Los Angeles. Bazerman, C. (1988). Shaping written knowledge: The genre and activity of the experimental article in science. University of Wisconsin Press. Bazerman, C. (2006). Analyzing the multidimensionality of texts. In J. L. Green, G. Camilli, & P. B. Elmore (Eds.), Handbook of complementary methods in education research (pp. 77–94). American Educational Research Association. Bell, P., & Linn, M. C. (2000). Scientifc arguments as learning artifacts: Designing for learning from the web with KIE. International Journal of Science Education, 22, 797–817. Bellocchi, A. (2019). Early career science teacher experiences of social bonds and emotion management. Journal of Research in Science Teaching, 56, 322–347. Berland, L. K., & McNeill, K. L. (2012). For whom is argument and explanation a necessary distinction? A response to Osborne and Patterson. Science Education, 96, 808–813. Bravo-Torija, B.,  & Jiménez-Aleixandre, M. P. (2018). Developing an initial learning progression for the use of evidence in decision-making contexts. International Journal of Science and Mathematics Education, 16, 619–638. Brocos, P.,  & Jiménez-Aleixandre, M. P. (2020). What to eat here and now: Contextualization of scientifc argumentation from a place-based perspective. In I. Sanchez-Tapia (Ed.), International perspectives on the contextualization of science education (pp. 15–46). Springer. Brown, B. A. (2004). Discursive identity: Assimilation into the culture of science and its implications for minority students. Journal of Research in Science Teaching, 41, 810–834. Brown, B. A. (2006). “It isn’t no slang that can be said about this stuf”: Language, identity, and appropriating science discourse. Journal of Research in Science Teaching, 43, 96–126. Brown, B. A., Donovan, B., & Wild, A. (2019). Language and cognitive interference: How using complex scientifc language limits cognitive performance. Science Education, 103, 750–769. Brown, B. A., Mangram, C., Sun, K., Cross, K., & Raab, E. (2017). Representing racial identity: Identity, race, the construction of the African American STEM students. Urban Education, 52(2), 170–206. Brown, B. A., Reveles, J. M.,  & Kelly, G. J. (2005). Scientifc literacy and discursive identity: A theoretical framework for understanding science learning. Science Education, 89, 779–802. Brown, B. A., & Ryoo, K. (2008). Teaching science as a language: A “content-frst” approach to science teaching. Journal of Research in Science Teaching, 45, 529–553. Brown, B. A., & Spang, E. (2008). Double talk: Synthesizing everyday and science language in the classroom. Science Education, 92, 708–732. Buxton, C. A., Allexsaht-Snider, M.,  & Rivera, C. (2013). Science, language, and families: Constructing a model of steps to college through language-rich science inquiry. In J. A. Bianchini, V. L. Akerson, A. C. Barton, O. Lee, & A. J. Rodriguez (Eds.), Moving the equity agenda forward (pp. 241–259). Springer Netherlands. Buxton, C. A., & Lee, O. (2014). English learners in science education. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. 2, pp. 204–222). Routledge. Cameron, D. (2001). Working with spoken discourse. Sage. Campbell, Y. C., & Filimon, C. (2018). Supporting the argumentative writing of students in linguistically diverse classrooms: An action research study. RMLE Online, 41(1), 1–10. Chappell, M. J., & Varelas, M. (2020). Ethnodance and identity: Black students representing science identities in the making. Science Education, 104, 193–221. Charamba, E. (2020). Translanguaging in a multilingual class: A study of the relation between students’ languages and epistemological access in science. International Journal of Science Education, 42, 1779–1798. Chen, H.-T., Wang, H.-H., Lu, Y.-Y., & Hong, Z.-R. (2019). Bridging the gender gap of children’s engagement in learning science and argumentation through a modifed argument-driven inquiry. International Journal of Science and Mathematics Education, 17(4), 635–655.

439

Kelly et al. Cheuk, T. (2016). Discourse practices in the new standards: The role of argumentation in common core-era next generation science standards classrooms for English language learners. The Electronic Journal for Research in Science & Mathematics Education, 20(3). Chinn, C. A., Barzilai, S., & Duncan, R. G. (2020). Disagreeing about how to know. The instructional value of explorations into knowing. Educational Psychologist, 55(3), 167–180. Chinn, C. A., Rinehart, R. W., & Buckland, L. A. (2014). Epistemic cognition and evaluating information: Applying the AIR model of epistemic cognition. In D. Rapp & J. Braasch (Eds.), Processing inaccurate information (pp. 425–454). MIT Press. Clegg, T., & Kolodner, J. (2014). Scientizing and cooking: Helping middle-school learners develop scientifc dispositions. Science Education, 98, 36–63. Córdova, R. A., & Balcerzak, P. (2016). Co-constructing cultural landscapes for disciplinary learning in and out of school: The next generation science standards and learning progressions in action. Cultural Studies of Science Education, 11, 1223–1242. Culbertson, J.,  & Adger, D. (2014). Language learners privilege structured meaning over surface frequency. Proceedings of the National Academy of Sciences, 111, 5842–5847. Davis, J. P., & Bellocchi, A. (2018). Objectivity, subjectivity, and emotion in school science inquiry. Journal of Research in Science Teaching, 55, 1419–1447. DeBoer, G. E. (1991). A history of ideas in science education: Implications for practice. Teacher College, Columbia University. Duncan, R. G., Chinn, C. A.,  & Barzilai, S. (2018). Grasp of evidence: Problematizing and expanding the next generation science standards’ conceptualization of evidence. Journal of Research in Science Teaching, 55, 907–937. Duschl, R. A. (2020). Practical reasoning and decision making in science: Struggles for truth. Educational Psychologist, 55(3), 187–192. Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39–72. Echevarria, J., Vogt, M., & Short, D. (2016). Making content comprehensible for English learners: The SIOP model (5th ed.). Pearson. Elby, A., Macrander, C., & Hammer, D. (2016). Epistemic cognition in science. In J. A. Greene, W. A. Sandoval, & I. Bråten (Eds.), Handbook of epistemic cognition. Routledge. Emdin, C. (2010). Urban science education for the hip-hop generation. Sense Publishers Rotterdam. Enderle, P., Cohen, S., & Scott, J. (2020). Communicating about science and engineering practices and the nature of science: An exploration of American Sign Language resources. Journal of Research in Science Teaching, 57, 968–995. Enderle, P., Grooms, J., & Sampson, V. (2013). The use of argumentation in science education to promote the development of science profciency: A comparative case study. Society for Research on Educational Efectiveness, Fall 2013, 1–10. https://eric.ed.gov/?id=ED564062 Erduran, S. (2001). Philosophy of chemistry: An emerging feld with implications for chemistry education. Science & Education, 10, 581–593. Erduran, S. (2020). Nature of “STEM”? Science & Education, 29, 781–784. Erduran, S., Simon, S., & Osborne, J. (2004). TAPping into argumentation: Developments in the application of Toulmin’s argument pattern for studying science discourse. Science Education, 88, 915–933. Erickson, F. (1982). Money tree, lasagna bush, salt and pepper: Social construction of topical cohesion in a conversation among Italian-Americans. Analyzing Discourse: Text and Talk, 43–70. Fairclough, N. (1992). Discourse and social change. Blackwell Publishing. Fairclough, N. (1995). Critical discourse analysis: The critical study of language. Longman. Fairclough, N., & Wodak, R. (1997). Critical discourse analysis. Discourse Studies: A Multidisciplinary Introduction, 2, 357–378. Fang, Z. (2005). Scientifc literacy: A systemic functional linguistics perspective. Science Education, 89, 335–347. Feinstein, N. W., & Waddington, D. I. (2020). Individual truth judgments or purposeful, collective sensemaking? Rethinking science education’s response to the post-truth era. Educational Psychologist, 55(3), 155–166. Ferguson, J. (2018). Students are not inferential-misfts: Naturalising logic in the science classroom. Educational Philosophy and Theory, 51(8), 852–865. Ferguson, J., & Prain, V. (2020). Revisiting Peirce’s account of scientifc creativity to inform classroom practice. Educational Philosophy and Theory, 52(5), 524–534. Flores, N., & Rosa, J. (2015). Undoing appropriateness: Raciolinguistic ideologies and language diversity in education. Harvard Educational Review, 85(2), 149–171. Ford, M. J. (2015). Educational implications of choosing “practice” to describe science in the next generation science standards. Science Education, 99, 1041–1048.

440

Discourse Practices in Science Learning Foucault, M. (1980). Power/knowledge: Selected interviews and other writings, 1972–1977. Vintage. Fourez, G. (1988). Ideologies and science teaching. Bulletin of Science Technology & Society, 8, 269–277. Franco, L. G., & Munford, D. (2021). The hourglass approach: Analysing science classroom discursive interactions through an intercontextual lens. Research in Science Education, 51, 13–33. Friedrich, P. (1989). Language, ideology, and political economy. American Anthropologist, 91(2), 295–312. Gee, J. P. (2001). Literacy, discourse, and linguistics: Introduction and what is literacy? In E. Cushman, E. R. Kintgen, B. M. Kroll, & M. Rose (Eds.), Literacy: A critical sourcebook (pp. 525–544). St. Martins. Gee, J. P. (2014). An introduction to discourse analysis: Theory and method. Routledge. Gee, J. P., & Green, J. L. (1998). Discourse analysis, learning, and social practice: A methodological study. Review of Research in Education, 23, 119–169. Gilbert, A., & Yerrick, R. (2001). Same school, separate worlds: A sociocultural study of identity, resistance, and negotiation in a rural, lower track science classroom. Journal of Research in Science Teaching, 38, 574–598. Gilbert, J. K., Boulter, C. J., & Elmer, R. (2000). Positioning models in science education and in design and technology education. In Developing models in science education (pp. 3–17). Springer. Godec, S. (2018). Sciencey girls: Discourse supporting working-class girls to identity with science. Education Sciences, 8(19), 1–19. González-Howard, M., & McNeill, K. L. (2016). Learning in a community of practice: Factors impacting Englishlearning students’ engagement in scientifc argumentation. Journal of Research in Science Teaching, 53, 527–553. González-Howard, M., & McNeill, K. L. (2019). Teachers’ framing of argumentation goals: Working together to develop individual versus communal understanding. Journal of Research in Science Teaching, 56, 821–844. González-Howard, M., McNeill, K. L., Marco-Bujosa, L. M., & Proctor, C. P. (2017). “Does it answer the question or is it French fries?”: An exploration of language supports for scientifc argumentation. International Journal of Science Education, 39, 528–547. Green, J. L., Baker, W. D., Chian, M., Vanderhoof, C., Hooper, L., Kelly, G. J., Skukauskaite, A., & Kalainof, M. (2020). Studying the over-time construction of knowledge in education settings: A microethnographicdiscourse analysis approach. Review of Research in Education, 44, 161–194. Green, J. L., & Bloome, D. (1997). Ethnography and ethnographers of and in education: A situated perspective. In J. Flood, S. B. Heath, & D. Lapp (Eds.), Research on teaching and literacy through the communicative and visual arts (pp. 181–202). Macmillan. Green, J. L., & Kelly, G. J. (2019). How we look at discourse: Defnitions of sociolinguistic units. In G. J. Kelly & J. L. Green (Eds.), Theory and methods for sociocultural research in science and engineering education (pp. 264–270). Routledge. Grimes, P., McDonald, S., & van Kampen, P. (2019). “We’re getting somewhere”: Development and implementation of a framework for the analysis of productive science discourse. Science Education, 103, 5–36. Grob, R., Holmeier, M.,  & Labudde, P. (2017). Formative assessment to support students’ competences in inquiry-based science education. Interdisciplinary Journal of Problem-Based Learning, 11(2), 6. Gullberg, A., Andersson, K., Danielsson, A., Scantlebury, K.,  & Hussénius, A. (2018). Pre-service teachers’ views of the child – Reproducing or challenging gender stereotypes in science in preschool. Research in Science Education, 48, 691–715. Gumperz, J. (2001). Interactional sociolingustics: A personal perspective. In D. Schifrin, D. Tannen, & H. E. Hamilton (Eds.), The handbook of discourse analysis (pp. 215–228). Blackwell Publishing. Hakuta, K., Santos, M., & Fang, Z. (2013). Challenges and opportunities for language learning in the context of the CCSS and the NGSS. Journal of Adolescent & Adult Literacy, 56(6), 451–454. Halliday, M. A. K. (1993). Towards a language-based theory of learning. Linguistics and Education, 5(2), 93–116. Halliday, M. A. K., & Martin, J. (1993). Writing science: Literacy and discursive power. Falmer Press. Haraway, D. (2003). Situated knowledges: The science question in feminism and the privilege of partial perspective. Turning Points in Qualitative Research: Tying Knots in a Handkerchief, 2003, 21–46. Harding, S. (Ed.). (2011). The postcolonial science and technology studies reader. Duke University Press. Harman, R., Buxton, C., Cardozo-Gaibisso, L., Jiang, L.,  & Bui, K. (2020). Culturally sustaining systemic functional linguistics praxis in science classrooms. Language and Education, 1–17. Hasni, A., Bousadra, F., Belletete, V., Benabdallah, A., Nicole, M.-C., & Dumais, N. (2016). Trends in research on project-based science and technology teaching and learning at K – 12 levels: A systematic review. Studies in Science Education, 52(2), 199–231. Henderson, J. B., McNeill, K. L., González-Howard, M., Close, K., & Evans, M. (2018). Key challenges and future directions for educational research on scientifc argumentation. Journal of Research in Science Teaching, 55, 5–18. Henze, I., van Driel, J. H., & Verloop, N. (2008). Development of experienced science teachers’ pedagogical content knowledge of models of the solar system and the universe. International Journal of Science Education, 30, 1321–1342.

441

Kelly et al. Hodson, D. (2003). Time for action: Science education for an alternative future. International Journal of Science Education, 25, 645–670. Höttecke, D., & Allchin, D. (2020). Reconceptualizing nature-of-science education in the age of social media. Science Education, 104, 641–666. Hsu, P.-S., Van Dyke, M., Smith, T. J., & Looi, C.-K. (2018). Argue like a scientist with technology: The efect of within-gender versus cross-gender team argumentation on science knowledge and argumentation skills among middle-level students. Educational Technology Research and Development, 66(3), 733–766. Hufnagel, E. (2015). Preservice elementary teachers’ emotional connections and disconnections to climate change in a science course. Journal of Research in Science Teaching, 52, 1296–1324. Hufnagel, E. (2019). Emotional discourse as constructed in an environmental science course. In G. J. Kelly & J. L. Green (Eds.), Theory and methods for sociocultural research in science and engineering education (pp. 155–180). Routledge. Isohätälä, J., Näykki, P., Järvelä, S., & Baker, M. J. (2018). Striking a balance: Socio-emotional processes during argumentation in collaborative learning interaction. Learning, Culture and Social Interaction, 16, 1–19. Jaworski, A., & Coupland, N. (Eds.). (1999). The discourse reader (Vol. 2). Routledge. Jiménez-Aleixandre, M. P. (2014). Determinism and underdetermination in Genetics: Implications for students’ engagement in argumentation and epistemic practices. Science & Education, 23(2), 465–484. Jiménez-Aleixandre, M. P., & Brocos, P. (2018). Shifts in epistemic statuses in argumentation and in conceptual change. In T. Amin & O. Levrini (Eds.), Converging perspectives on conceptual change: Mapping an emerging paradigm in the learning sciences (pp. 171–179). Routledge. Jiménez-Aleixandre, M. P., & Brocos, P. (2021). Emotional tension as a frame for argumentation and decisionmaking: Vegetarian vs. omnivorous diets. Frontiers in Psychology, 12, 662141 Jiménez-Aleixandre, M. P., & Puig, B. (2021). Educating critical citizens to face post-truth: The time is now. In B. Puig & M. P. Jiménez-Aleixandre (Eds.), Critical thinking in biology and environmental education: Facing challenges in a post-truth world. Springer. Kang, E. J., Donovan, C.,  & McCarthy, M. J. (2018). Exploring elementary teachers’ pedagogical content knowledge and confdence in implementing the NGSS science and engineering practices. Journal of Science Teacher Education, 29(1), 9–29. Kelly, G. J. (2007). Discourse in science classrooms. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (pp. 443–469). Lawrence Erlbaum Associates. Kelly, G. J. (2008). Learning science: Discursive practices. In Encyclopedia of language and education (pp. 1071– 1082). Springer. http://link.springer.com/10.1007/978-0-387-30424–3_82 Kelly, G. J. (2011). Scientifc literacy, discourse, and epistemic practices. In C. Linder, L. Östman, D. A. Roberts, P. Wickman, G. Erikson, & A. McKinnon (Eds.), Exploring the landscape of scientifc literacy (pp. 61–73). Routledge. Kelly, G. J. (2014). Discourse practices in science learning and teaching. In N. G. Lederman & S. K. Abell (Eds.), Handbook of research on science education (Vol. 2, pp. 321–336). Lawrence Erlbaum Associates. Kelly, G. J. (2016). Methodological considerations for the study of epistemic cognition in practice. In J. A. Greene, W. A. Sandoval, & I. Braten (Eds.), Handbook of epistemic cognition (pp. 393–408). Routledge. Kelly, G. J. (2021). Theory, methods, and expressive potential of discourse studies in science education. Research in Science Education, 51(1), 225–233. Kelly, G. J., Carlsen, W. S., & Cunningham, C. M. (1993). Science education in sociocultural context: Perspectives from the sociology of science. Science Education, 77, 207–220. Kelly, G. J., & Cunningham, C. (2019). Epistemic tools in engineering design for K – 12 education. Science Education, 103(4), 1080–1111. Kelly, G. J., & Green, J. (2019a). Framing issues of theory and methods for the study of science and engineering education. In G. J. Kelly & J. Green (Eds.), Theory and methods for sociocultural research in science and engineering education (pp. 1–28). Routledge. Kelly, G. J., & Green, J. (Eds.). (2019b). Theory and methods for sociocultural research in science and engineering education. Routledge. Kelly, G. J., & Licona, P. (2018). Epistemic practices and science education. In M. R. Matthews (Ed.), History, philosophy and science teaching (pp. 139–165). Springer. King, D., Ritchie, S., Sandhu, M., & Henderson, S. (2015). Emotionally intense science activities. International Journal of Science Education, 37, 1886–1914. Knain, E. (2001). Ideologies in school science textbooks. International Journal of Science Education, 23, 319–329. Knain, E., Fredlund, T., & Furberg, A (2021). Exploring student reasoning and representation construction in school science through the lenses of social semiotics and interaction analysis. Research in Science Education, 51, 93–111.

442

Discourse Practices in Science Learning Knorr-Cetina, K. (2009). Epistemic cultures: How the sciences make knowledge. Harvard University Press. Kress, G., Jewitt, C., Ogborn, J., & Tsatsarelis, C. (2001). Multimodal teaching and learnins: The rhetorics of the science classroom. Continuum. Kroskrity, P. V. (2009). Narrative reproductions: Ideologies of storytelling, authoritative words, and generic regimentation in the village of Tewa1. Journal of Linguistic Anthropology, 19(1), 40–56. Kuhn, D. (2019). Critical thinking as discourse. Human Development, 62, 146–164. Land, S. M., Zimmerman, H. T., Millet, C., & Choi, G. W. (2020). Supporting observing-on-the-move with proximity-based technology: Designing for children’s scientifc observations outdoors. Visitor Studies, 23(2), 182–204. Langman, J. (2014). Translanguaging, identity, and learning: Science teachers as engaged language planners. Language Policy, 13(2), 183–200. Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientifc facts. Princeton University Press. Lee, O. (2001). Culture and language in science education: What do we know and what do we need to know? Journal of Research in Science Teaching: The Ofcial Journal of the National Association for Research in Science Teaching, 38(5), 499–501. Lee, O. (2005). Science education with English language learners: Synthesis and research agenda. Review of Educational Research, 75(4), 491–530. Lemke, J. L. (1990). Talking science: Language, learning and values. Ablex. Lemke, J. L. (1998). Multiplying meaning: Visual and verbal semiotics in scientifc text. In J. Martins & R. Veel (Eds.), Reading science. Routledge. Lemke, J. L., Kelly, G., & Roth, W. M. (2006). Toward a phenomenology of interviews. Cultural Studies of Science Education, 1, 83–106. Lemmi, C., Brown, B. A., Wild, A., Zummo, L., & Sedlacek, Q. (2019). Language ideologies in science education. Science Education, 103, 854–874. Lenoir, T. (1998). Inscription practices and materialities of communication. Inscribing Science: Scientifc Texts and the Materiality of Communication, 1–19. Licona, P. R. (2019). Translanguaging about socioscientifc issues in middle school science. In G. J. Kelly & J. L. Green (Eds.), Theory and methods for sociocultural research in science and engineering education (pp. 73–100). Routledge. Licona, P. R., & Kelly, G. J. (2020). Translanguaging in a middle school science classroom: Constructing scientifc arguments in English and Spanish. Cultural Studies of Science Education, 15, 485–510. Lin, Y.-R., & Hung, J.-F. (2016). The analysis and reconciliation of students’ rebuttals in argumentation activities. International Journal of Science Education, 38, 130–155. http://doi.org/10.1080/09500693.2015.1134848 Linder, C. (2013). Disciplinary discourse, representation, and appresentation in the teaching and learning of science. European Journal of Science and Mathematics Education, 1(2), 43–49. Lippi-Green, R. (2012). English with an accent: Language, ideology and discrimination in the United States. Routledge. Luzón, M. J. (2013). Public communication of science in blogs: Recontextualizing scientifc discourse for a diversifed audience. Written Communication, 30(4), 428–457. Martin, J., Xu, L., & Seah, L. H. (2020). Discourse analysis and multimodal meaning making in a science classroom: Meta-methodological insights from three theoretical perspectives. Research in Science Education, 1–21. Maulucci, M. S. R. (2008). Intersections between immigration, language, identity, and emotions: A science teacher candidate’s journey. Cultural Studies of Science Education, 3(1), 17–42. https://doi.org/10.1007/ s11422-007-9074-9 Mavuru, L., & Ramnarain, U. (2020). Learners’ socio-cultural backgrounds and science teaching and learning: A case study of township schools in South Africa. Cultural Studies of Science Education, 15, 1067–1095. McDonald, C., & McIntyre, M. (2001). Reinstating the marginalized body in nursing science: Epistemological privilege and the lived life. Nursing Philosophy, 2(3), 234–239. McDonald, S., & Kelly, G. J. (2012). Beyond argumentation: Sense-making discourse in the science classroom. In M. S. Khine (Ed.), Perspectives on scientifc argumentation: Theory, practice, and research (pp. 265–281). Springer. McNeill, K. L., & Berland, L. (2017). What is (or should be) scientifc evidence use in k-12 classrooms? Journal of Research in Science Teaching, 54, 672–689. McNeill, K. L., & Pimentel, D. S. (2010). Scientifc discourse in three urban classrooms: The role of the teacher in engaging high school students in argumentation. Science Education, 94, 203–229. Mehan, H. (1979). Learning lessons: Social organization in the classroom. Harvard University Press. Miller, E., Laufer, H. B.,  & Messina, P. (2014). NGSS for English language learners. Science and Children, 51(5), 55. Mishler, E. G. (1986). Research interviewing: Context and narrative. Harvard University Press. Moje, E. B., Collazo, T., Carrillo, R., & Marx, R. W. (2001). “Maestro, what is ‘quality’?”: Language, literacy, and discourse in project-based science. Journal of Research in Science Teaching, 38, 469–498.

443

Kelly et al. Monteira, S. F., & Jiménez-Aleixandre, M. P. (2016). The practice of using evidence in kindergarten: The role of purposeful observation. Journal of Research in Science Teaching, 53, 1232–1258. Monteira, S. F., Jiménez-Aleixandre, M. P., & Siry, C. (2022). Scafolding children’s production of representations along the three years of ECE: A longitudinal study. Research in Science Education, 52, 127–158. http:// doi.org/10.1007/s11165-020-09931-z Moore, T. J., Tank, K. M., Glancy, A. W., & Kersten, J. A. (2015). NGSS and the landscape of engineering in K-12 state science standards. Journal of Research in Science Teaching, 52, 296–318. Mortimer, E. F., & Scott, P. H. (2003). Meaning making in secondary science classrooms. Open University Press. National Research Council. (2013). Monitoring progress toward successful K-12 STEM education: A nation advancing? National Academies Press. NGSS Lead States. (2013). Next generation science standards: For states, by states. The National Academies Press. O’Hallaron, C. L. (2014). Supporting ffth-grade ELLs’ argumentative writing development. Written Communication, 31(3), 304–331. Olander, C. (2010). Towards an interlanguage of biological evolution: Exploring students talk and writing as an arena for sense-making. https://gupea.ub.gu.se/handle/2077/21558 Olander, C., & Ingerman, Å. (2011). Towards an inter-language of talking science: Exploring students’ argumentation in relation to authentic language. Journal of Biological Education, 45(3), 158–164. Olander, C., Wickman, P.-O., Tytler, R.,  & Ingerman, Å. (2018). Representations as mediation between purposes as junior secondary science students learn about the human body. International Journal of Science Education, 40(2), 204–226. Olitsky, S. (2007). Facilitating identity formation, group membership, and learning in science classrooms: What can be learned from out-of-feld teaching in an urban school? Science Education, 91, 201–221. Oliveira, A. W., Sadler, T. D., & Suslak, D. F. (2007). The linguistic construction of expert identity in professor – Student discussions of science. Cultural Studies of Science Education, 2(1), 119–150. Organisation for Economic Co-operation and Development [OECD]. (2016). PISA 2015 results (volume I): Excellence and equity in education. OECD Publishing. Ortiz-Revilla, J., Adúriz-Bravo, A.,  & Greca, I. M. (2020). A framework for epistemological discussion on integrated STEM education. Science & Education, 29, 857–880. Osborne, J. F. (2014). Teaching scientifc practices: Meeting the challenge of change. Journal of Science Teacher Education, 25(2), 177–196. Osborne, J. F., Henderson, J. B., MacPherson, A., Szu, E., Wild, A., & Yao, S. Y. (2016). The development and validation of a learning progression for argumentation in science. Journal of Research in Science Teaching, 53, 821–846. Östman, L., & Wickman, P.-O. (2014). A pragmatic approach on epistemology, teaching and learning. Science Education, 98, 375–382. Page-Reeves, J., Marin, A., Mofett, M., DeerInWater, K., & Medin, D. (2019). Wayfnding as a concept for understanding success among Native Americans in STEM: “Learning how to map through life”. Cultural Studies of Science Education, 14, 177–197. Park, H., Kim, Y., & Jeong, S. (2019). The efect of a science festival for special education students on communicating science. Asia-Pacifc Science Education, 5(1), 1–21. Park, W., Wu, J. Y., & Erduran, S. (2020). The nature of STEM disciplines in the science education standards documents from the USA, Korea and Taiwan. Science & Education, 29, 899–927. Parkinson, J., & Crouch, A. (2011). Education, language, and identity amongst students at a South African university. Journal of Language, Identity, and Education, 10(2), 83–98. Patterson, A. D. (2019). Equity in groupwork: The social process of creating justice in a science classroom. Cultural Studies of Science Education, 14, 361–381. Pluta, W. J., Chinn, C. A., & Duncan, R. G. (2011). Learners’ epistemic criteria for good scientifc models. Journal of Research in Science Teaching, 48, 486–511. http://doi.org/10.1002/tea.20415 Polo, C., Plantin, C., Lund, K., & Niccolai, G. (2017). Group emotions in collective reasoning: A Model. Argumentation, 31(2), 301–329. Poza, L. E. (2016). The language of ciencia: Translanguaging and learning in a bilingual science classroom. International Journal of Bilingual Education and Bilingualism, 1–19. Prain, W., & Tytler, R. (2012). Learning through constructing representations in science: A framework of representational construction afordances. International Journal of Science Education, 34, 2751–2773. Probyn, M. (2015). Pedagogical translanguaging: Bridging discourses in South African science classrooms. Language and Education, 29(3), 218–234. Rapanta, C., & Felton, M. (2019). Mixed methods research in inquiry-based instruction: An integrative review. International Journal of Research & Method in Education, 42(3), 288–304.

444

Discourse Practices in Science Learning Reveles, J. M., Cordova, R., & Kelly, G. J. (2004). Science literacy and academic identity formulation. Journal of Research in Science Teaching, 41, 1111–1144. Reynante, B. M., Selbach-Allen, M. E., & Pimentel, D. R. (2020). Exploring the promises and perils of integrated STEM through disciplinary practices and epistemologies. Science & Education, 29, 785–803. Rincke, K. (2011). It’s rather like learning a language: Development of talk and conceptual understanding in mechanics lessons. International Journal of Science Education, 33, 229–258. Rogers, R. (2004). An introduction to Critical Discourse Analysis in education. In R. Rogers (Ed.), An introduction to Critical Discourse Analysis in education (pp. 1–18). Lawrence Erlbaum Associates. Rosa, J. D. (2010). Looking like a language, sounding like a race: Making Latina/o panethnicity and managing American anxieties. University of Chicago Press. Rosebery, A. S., Warren, B., & Conant, F. R. (1992). Appropriating scientific discourse: Findings from language minority classrooms. The Journal of the Learning Sciences, 2(1), 61–94. Roth, W.-M. (2014). Science language wanted alive: Through the dialectical/dialogical lens of vygotsky and the bakhtin circle. Journal of Research in Science Teaching, 51, 1049–1083. Roth, W.-M., & Lucas, K. B. (1997). From “truth” to “invented reality”: A discourse analysis of high school physics students’ talk about scientific knowledge. Journal of Research in Science Teaching, 34, 145–179. Rouse, J. (2002). How scientific practices matter: Reclaiming philosophical naturalism. University of Chicago Press. Rudolph, J. L., & Horibe, S. (2016). What do we mean by science education for civic engagement? Journal of Research in Science Teaching, 53, 805–820. Rudolph, J. L., & Stewart, J. (1998), Evolution and the nature of science: On the historical discord and its implications for education. Journal of Research in Science Teaching, 35, 1069–1089. Russ, R. S., Lee, V. R., & Sherin, B. L. (2012). Framing in cognitive clinical interviews about intuitive science knowledge: Dynamic student understandings of the discourse interaction. Science Education, 96, 573–599. Sadler, T. D. (2006). Promoting discourse and argumentation in science teacher education. Journal of Science Teacher Education, 17, 323–346. Salloum, S., & BouJaoude, S. (2020). Language in teaching and learning science in diverse Lebanese multilingual classrooms: Interactions and perspectives. International Journal of Science Education, 42, 2331–2363. Schnittka, J., & Schnittka, C. (2016). “Can I drop it this time?” Gender and collaborative group dynamics in an engineering design-based afterschool program. Journal of Pre-College Engineering Education Research (J-PEER), 6(2). https://doi.org/10.7771/2157-9288.1120 Schoultz, J., Säljo, R., & Wyndhamn, J. (2001). Heavenly talk: Discourse, artifacts, and children’s understanding of elementary astronomy. Human Development, 44, 103–118. Seargeant, P. (2008). Language, ideology and “English within a globalized context”. World Englishes, 27(2), 217–232. Sezen-Barrie, A., Stapleton, M. K.,  & Marbach-Ad, G. (2020). Science teachers’ sensemaking of the use of epistemic tools to scaffold students’ knowledge (re) construction in classrooms. Journal of Research in Science Teaching, 57, 1058–1092. Siry, C., & Gorges, A. (2020). Young students’ diverse resources for meaning making in science: Learning from multilingual contexts. International Journal of Science Education, 42, 2364–2386. Skukauskaitė, A., & Girdzijauskienė, R. (2021). Video analysis of contextual layers in teaching-learning interactions. Learning, Culture and Social Interaction, 29, 100499. Solli, A., Hillman, T., & Mäkitalo, Å. (2019). Navigating the complexity of socio-scientific controversies – How students make multiple voices present in discourse. Research in Science Education, 49, 1595–1623. Stoddart, M. C. (2007). Ideology, hegemony, discourse: A critical review of theories of knowledge and power. Social Thought & Research, 191–225. Stoddart, T., Pinal, A., Latzke, M., & Canaday, D. (2002). Integrating inquiry science and language development for English language learners. Journal of Research in Science Teaching, 39, 664–687. Tang, K. S., Tan, A. L., & Mortimer, E. F. (2021). The multi-timescale, multi-modal and multi-perspectival aspects of classroom discourse analysis in science education. Research in Science Education, 51, 1–11. Treen, K. M. D. I., Williams, H. T., & O’Neill, S. J. (2020). Online misinformation about climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(5), e665. Ünsal, Z., Jakobson, B., Molander, B.-O., & Wickman, P.-O. (2018a). Language use in a multi-lingual class: A study of the relation between bilingual students’ languages and their meaning-making in science. Research in Science Education, 48, 1027–1048. Ünsal, Z., Jakobson, B., Wickman, P. O., & Molander, B. O. (2018b). Gesticulating science: Emergent bilingual students’ use of gestures. Journal of Research in Science Teaching, 55, 121–144. Van Dijk, T. A. (2009). News, discourse, and ideology. In K. Wahl-Jorgensen & T. Hanitzsch (Eds.), The handbook of journalism studies (pp. 191–204). Routledge.

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Kelly et al. van Eijck, M., & Roth, W.-M. (2011). Cultural diversity in science education through novelization: Against the epicization of science and cultural centralization. Journal of Research in Science Teaching, 48, 824–847. Varelas, M. (2017). Dialectical relationships and how they shape (in)equitable science learning spaces and places. In L. Bryan & K. Tobin (Eds.), 13 questions: Reframing education’s conversation: Science. Peter Lang. Varelas, M., Kane, J. M., Tucker-Raymond, E., & Pappas, C. C. (2012). Science learning in urban elementary school classrooms: Liberatory education and issues of access, participation and achievement. In Second international handbook of science education (pp. 91–103). Springer. http://doi.org/10.1007/978-1-4020-9041-7_8 Varelas, M., & Pappas, C. C. (2006). Intertextuality in read-alouds of integrated science-literacy units in urban primary classrooms: Opportunities for the development of thought and language. Cognition and Instruction, 24(2), 211–259. Vedder-Weiss, D. (2017). Serendipitous science engagement: A family self-ethnography. Journal of Research in Science Teaching, 54, 350–378. Visintainer, T. (2019). “I think at frst glance people would not expect me to be interested in science”: Exploring the racialized science experiences of high school students of color. Journal of Research in Science Teaching, 57, 393–422. Warren, B., Ballenger, C., Ogonowski, M., Rosebery, A. S., & Hudicourt-Barnes, J. (2001). Rethinking diversity in learning science: The logic of everyday sense-making. Journal of Research in Science Teaching: The Ofcial Journal of the National Association for Research in Science Teaching, 38(5), 529–552. Warren, B., & Rosebery, A. S. (1992). Science Education As A Sense-Making Practice: Implications for assessment. In E. Statzner (Eds.), Proceedings of the Third National Research Symposium on Limited English Profcient Student Issues: Focus on Middle and High School Issues. US Department of Education. Warren, B., Vossoughi, S., Rosebery, A., Bang, M., & Taylor, E. (2020). Multiple ways of knowing: Re-imagining disciplinary learning. In N. S. Nasir, C. D. Lee, R. Pea, & M. McKinney de Royston (Eds.), Handbook of the cultural foundations of learning (pp. 277–294). Routledge. Watson, T. J. (1995). Rhetoric, discourse and argument in organizational sense making: A refexive tale. Organization Studies, 16(5), 805–821. Wickman, P.-O. (2013). Aesthetic experience in science education: Learning and meaning-making as situated talk and action. Routledge. Wickman, P.-O. (2017). Back to the drawing board-examining the philosophical foundations of educational research on aesthetics and emotions. In A. Bellocchi, K. Otrel-Cass, & C. Quigley (Eds.), Beyond cognition in science education: Considering the role of emotions, well-being, and aesthetics (pp. 9–37). Springer. Wickman, P. -O., & Östman, L. (2002). Learning as discourse change: A sociocultural mechanism. Science Education, 86, 601–623. Wieselmann, J. R., Keratithamkul, K., Dare, E. A., Ring-Whalen, E. A., & Roehrig, G. H. (2021). Discourse analysis in integrated STEM activities: Methods for examining power and positioning in small group interactions. Research in Science Education, 51(1), 113–133. Wilmes, S. E., & Siry, C. (2021). Multimodal interaction analysis: A powerful tool for examining plurilingual students’ engagement in science practices. Research in Science Education, 51(1), 1–21. Woolard, K. A. (1992). Language ideology: Issues and approaches. Pragmatics, 2(3), 235–249. Wu, S., Silveus, A., Vasquez, S., Bif, D., Silva, C., & Weinburgh (2019). Supporting ELLs’ use of hybrid language and argumentation during science instruction. Journal of Research in Science Instruction, 30, 24–43. Xu, Q., Song, Y., Yu, N., & Chen, S. (2020). Are you passing along something true or false? Dissemination of social media messages about genetically modifed organisms. Public Understanding of Science, 1–17. Yerrick, R. K., & Gilbert, A. (2011). Constraining the discourse community: How science discourse perpetuates marginalization of underrepresented students. Journal of Multicultural Discourses, 6(1), 67–91. Yun, S. M., & Kim, H. B. (2015). Changes in students’ participation and small group norms in scientifc argumentation. Research in Science Education, 45, 465–484.

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15 SYNERGIES BETWEEN LEARNING TECHNOLOGIES AND LEARNING SCIENCES Promoting Equitable Secondary School Teaching Marcia C. Linn, Dermot Donnelly-Hermosillo, and Libby Gerard

The research reported in this chapter was partially funded by National Science Foundation grants (Projects  & No: Using Natural Language Processing to Inform Science Instruction, 2101669; Supporting Teachers in Responsive Instruction for Developing Expertise in Science, 1813713; Graphing Research on Inquiry with Data in Science, 1418423; Project Learning with Automated, Networked Supports, 1451604; Continuous Learning and Automated Scoring in Science, 1119670). This research was partially funded by the William and Flora Hewlett Foundation (Anti-Racism Interactive Science Education #2020–2269; Personalizing Open Web-based Educational Resources, 2018–8067). Any opinions, fndings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily refect the views of the National Science Foundation or of the William and Flora Hewlett Foundation.

Introduction Advances in technology tools and learning sciences research are increasing equitable science outcomes by enabling students to take control of their own investigations and study issues aligned with their personal and cultural interests. Design teams are using advances in technology to design automated guidance that amplifes the ability of teachers to provide personalized guidance. Further, advances in learning analytics and natural-language processing (NLP) are beginning to scafold learners and support teachers to guide their students in real time. We report on a confuence of technological advances, including powerful visualizations, collaborative tools, and automated guidance, that, when combined in learning environments, support students to become self-directed learners. Emerging authorable and customizable environments (ACEs) have the potential to enable teachers to design and customize their instruction using evidence from their students’ work. ACEs can support dashboards that use learning analytics to synthesize student progress in ways that teachers can use both immediately and when planning future instruction. Further, as students beneft from opportunities to direct their own learning, they gain insights that reinforce their identity as science learners.

DOI: 10.4324/9780367855758-19

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Equitable Instruction That Welcomes Each Student This chapter documents how technological advances have helped broaden the goals for science education beyond preparing students for careers in science or promoting science appreciation (Dewey, 1904; Girod et al., 2003). Instead, these technologies support reconceptualizing science courses to welcome each student, independent of race, culture, sexual orientation, or economic status. The pipeline metaphor that focused primarily on whether students persisted in science has been eclipsed by a focus on empowering students to investigate science dilemmas they face throughout their lives (Cannady et al., 2014; Wu & Uttal, 2020). These dilemmas often reveal scientifc insights as well as social injustices that result from science advances (Jasanof, 2014; Lee, White et al., 2021). They motivate students to take action to reverse policies that cause harm (e.g., Morales-Doyle et al., 2019; Vakil, 2018). For example, students might identify social justice issues around COVID-19 exposure and treatment or analyze tradeofs between selecting an electric or a gasoline-powered car. Further, exploring science topics relevant to their lives may change students’ beliefs about the value of studying science while also building student identity as a science learner (Morales-Doyle, 2017; Nasir et al., 2014; Penuel et al., 2022; Pinkard et al., 2020; Vakil, 2018). Increased access to technological innovations that personalize learning for each student and amplify teachers’ ability to guide each student are a key factor in making science instruction more equitable. Since the last handbook, the COVID-19 pandemic has spurred schools to distribute computers and increase internet access, yet access disparities continue to exist, especially for low-income families (Pew, 2021). During the pandemic, 93% of US families reported that their children received some remote schooling, and 62% said it had gone somewhat or very well. Growing numbers believe that schools should provide computers to K–12 students. By 2021, to remedy disparities, 87% of US adults reported that schools should provide computers, especially to families who cannot aford them (Pew, 2021).

Combining Technology and Pedagogy Recent research has shown the value of combining pedagogical frameworks with learning technologies to promote integrated understanding for each student. Spurred by evidence that students come to learning situations with many ideas developed from their own experience, resulting in what has been called “knowledge in pieces” (diSessa, 2018; NRC, 2007), pedagogical frameworks have evolved to build on the ideas that students craft. Studies of the ICAP framework have shown that interactive engagement, demonstrated by co-generative collaborative behaviors, is superior to constructive engagement, characterized by generative behaviors such as self-explanation, as well as active or passive learning (Chi et al., 2018). The knowledge, learning, and instruction (KLI) framework that underlies many cognitive tutors highlights the value of designing responses for each student idea (Koedinger et  al., 2012). The storylines instructional framework emphasized the collaboration of materials developers, educational researchers, classroom educators, and educational leaders to align materials with disciplinary standards (Edelson et al., 2021; Penuel et al., 2022).

Knowledge Integration (KI) Pedagogy The knowledge integration (KI) pedagogical framework, featured in the last handbook, conceptualizes science instruction as building on the variety of ideas that students bring to science class, capitalizing on the strategies that the students used to formulate those ideas, and scafolding them to gather evidence to distinguish among the ideas (Krajcik & Mun, 2014; Linn, 1998; Linn & Eylon, 2011; Linn et al., 2016). The KI framework specifes processes for achieving equitable and self-directed

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learning by tracking the specifc ideas that students develop and guiding them to grapple with these and other ideas suggested by their peers, teachers, personal investigations, or classroom experiences. Unsurprisingly, students often reject ideas presented in lectures (like metals are good conductors), reverting to ideas that resonate with their own experience, such as arguing that metals at room temperature feel cold so they could cool things down (Linn & Eylon, 2011). By leveraging learning technologies to detect specifc student ideas and using customizable learning environments to scafold learning, researchers have investigated ways to engage each student in taking responsibility for integrating their ideas (e.g., Wiley et al., in press). A set of KI principles have emerged to guide designers of collaborative tools (Matuk & Linn, 2018), models (Vitale et al., 2016; Williams et al., 2012), computational thinking (Bradford et al., 2022; McBride et al., 2018), and professional development (Bichler et al., 2021; Wiley et al., in press). Specifcally, four learning processes focus designers on ways to combine activities to promote KI, scafold students to integrate their ideas, and help teachers give guidance that leads to KI:

Elicit Ideas By ensuring that students are grappling with all their insights rather than mainly considering the ideas they encounter in class, instruction guards against students reverting to their existing ideas after science class ends.

Discover New Ideas By engaging students in discovering new ideas using models, simulations, experiments, or other investigations, instruction encourages self-directed learning.

Distinguish Among Ideas When students distinguish between the ideas in their repertoire, they develop arguments to support one idea over another, leading to more coherent understanding.

Refect on Learning When students refect on their progress in understanding a complex scientifc phenomenon, they monitor their own learning strategies, and recognize their progress in becoming a science learner.

Chapter Overview In this chapter we review trends for secondary school science, reported in peer-reviewed articles, over the last ten years, building on the previous review (Krajcik & Mun, 2014). Drawing on the KI pedagogy, we discuss how advances in technology support students to become self-directed learners who refne their ideas, seek new evidence to resolve discrepancies, and build integrated, generative understanding. Each student comes to science classes with a plethora of ideas from everyday experience, interactions with peers and families, previous science classes, and natural curiosity. Efective instruction builds on these ideas. We describe synergies between advances in learning science research and learning technologies that lead to equitable instruction and promote self-directed learning from two perspectives. In the frst section of the chapter, we discuss how advances in learning technologies are strengthening curriculum materials. We discuss how advances are: (1) making scientifc phenomena visible by promoting discovery of new evidence and insights using models, simulations, and computation; (2) supporting

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learning from others with repositories and collaborative tools; and (3) guiding students to distinguish among their ideas and refect using natural-language processing (NLP) of written work and automated assessments of student work on concept maps, models, graphs, and other activities. In the second section of the chapter, we discuss how advances are empowering teachers to personalize instruction using web-based supports by (1) supporting teachers to customize instruction for each student and (2) designing dashboards that provide teachers with information they can use both in the moment and when preparing to teach the unit again. We integrate these two perspectives in the conclusions and implications section. These trends reveal how a combination of technology and pedagogy empowers each student to develop integrated understanding, thereby increasing equitable outcomes. We close with open questions and opportunities.

Strengthening Science Teaching We capture progress in strengthening science instruction over the past ten years by discussing the emergence of ACEs and by synthesizing advances in instructional designs that enhance science teaching. Informed by the KI pedagogy, we discuss advances that help students discover ideas, learn from others, and beneft from automated guidance. We illustrate how these advances make science instruction more equitable by supporting students to direct their own learning and enabling them to build identity as a science learner.

Developing Authoring and Customizing Environments (ACEs) Learning technologies for science have broadened from stand-alone tools to authoring and customizing environments (ACEs), powerful instructional delivery systems that empower designers (including research practice partnerships and classroom teachers) to create and customize instruction. Typically aligned with a pedagogical framework such as KI, ACEs include the functions of a learning management system (LMS) to register students, record student work, assign scores, and compile grades alongside pedagogically inspired scafolding. Blended and online science platforms such as edCrumble (Albó  & Hernandez-Leo, ́ 2018), Go-Lab Software, Inq-IT (de Jong et  al., 2021), nQuire-it (Aristeidou & Herodotou, 2020), and Web-based Inquiry Science Environment (WISE: DonnellyHermosillo et al., 2020; Linn & Eylon, 2011), have features of ACEs. ACEs make it easy to create inquiry activities that combine open educational resources (OERs) and align them with pedagogical frameworks (Dimitriadis et al., 2021; Moore et al., 2014). OERs developed by many groups, including ChemCollective, Concord Consortium, Physics Exploration Technologies (PHeT), Physlets, Olabs, and others (de Jong et  al., 2021; Donnelly et  al., 2014), include simulations, models, virtual labs, extended reality, and videos. For example, Concord Consortium provides a searchable list of resources organized by content area and suggested grade levels that take advantage of probeware/sensors, visualizations, and guidance (Bondaryk et al., 2021; Zhu et al., 2020). The learning sciences have informed designs of ACEs with insights on the evolving interactions between interdisciplinary communities, technology artifacts, the conceptualization of technology use in the classroom, and pedagogical frameworks (Pea  & Linn, 2020). For example, the WISE ACE is aligned with the KI pedagogy and managed by a design team composed of computer scientists, human–computer interaction experts, learning scientists, classroom teachers, and educational technologists. The team hosts a community, including other designers who add new functionality, teachers who access and customize the materials, and researchers who test and refne the innovations. Many ACEs are available on GitHub (https://github.com/) or a similar platform. Some states and districts have recognized the use of ACEs and OER as science curriculum options (DeBarger & Casserly, 2021).

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Discovering Relevant Scientifc Insights Discovering scientific ideas is central to the KI instructional framework and essential for selfdirected learning. When students discover relevant scientific insights by choosing the variables they want to investigate, gathering the evidence they think is important, and resolving conundrums when their results contradict their predictions, they develop self-directed learning capacities (Chiu & Linn, 2012; Lee, Gweon et al., 2021). Technology advances have increased support for discovery of ideas by (1) making scientific phenomena visible with models and embedding the scientific models in ACEs (McElhaney et  al., 2015) and (2) making relevant scientific phenomena accessible to students by enabling investigations of connections between environmental and social issues and engaging students in design of digital and physical artifacts both in and out of school (Kafai et al., 2014; Morales-Doyle & Gutstein, 2019; Pinkard et al., 2020). For example, students can design tests for their own novel ideas by using computational thinking to adapt scientific models and investigate personally relevant variables (Biswas et al., 2016; Emara et al., 2021). Iteratively refining these opportunities using learning science methods, such as design research (Bang  & Vossoughi, 2016; Design-Based Research Collective, 2003) and social design experiments (Gutiérrez & Jurow, 2016), helps each student become a self-directed learner.

Making Scientifc Phenomena Visible Many scientifc phenomena are challenging for learners to visualize due to aspects of size, scale, rate of change, etc. (McElhaney et al., 2015). Starting in the 1980s learning technologies supported students to discover new ideas by making phenomena visible with interactive activities involving models, simulations, and probeware (McElhaney et al., 2015). For example, probeware has helped by displaying changes in real time for topics such as kinematics (motion of a runner), thermodynamics (heat transfers in kitchen examples), and acid-base chemistry (food and drink products), as documented in a sustained research program (Mokros & Tinker, 1987; Shen et al., 2014; Zucker et al., 2008). With more computing power, visualization tools have been combined with graph representations to support variable manipulation for topics such as states of matter (ice melting), genetics (gene mutations), electricity (electric circuits for light bulbs), and mitosis (Donnelly-Hermosillo et al., 2020; Matuk et al., 2019a; Rutten et al., 2012). Researchers have increased the impact of models by embedding them in ACEs that scafold learning (e.g., Matuk et  al., 2019b; Quintana et  al., 2004). Recent technological advances show how scafolds can help students recognize salient features of a visualization and provide individualized guidance that increases equitable outcomes (Fischer et al., 2020; Gerard, Ryoo et al., 2015; Linn & Eylon, 2011). Creative design of data visualizations can support the construction of explanations by representing large amounts of data in unique ways, revealing hidden information in simulation processes, and in some cases, only displaying the information necessary for the purpose of an explanation (Jebeile, 2018). For example, representing buoyancy using a mass-by-volume grid helped students understand density (Vitale et al., 2019; see Figure 15.1).

Taking Advantage of Embedded Assessments to Increase Equity Embedded assessments in ACEs can broaden the image of what counts as success and promote identity as a science learner (de Royston et al., 2020). For example, indicators such as the number of experiments conducted, the breadth of ideas considered, and the comparison of alternatives capture aspects of self-directed learning. Broadening the indicators of progress also increases opportunities for scafolding student interactions (De Jong, 2019; Gerard & Linn, 2016; Gerard et al., 2019;

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Figure 15.1 Technological representation of buoyancy that captures results of experiments students conduct.

McBride et al., 2018; Ryoo & Linn, 2014; Vitale et al., 2019; Vitale et al., 2016). For example, studies clarify how best to design hints to increase the number of consequential experiments that students perform (Vitale et al., 2019) and how to promote agency by supporting students to choose and study topics of interest to them (Chen & Yang, 2019; Chen, Bradford et al., 2020). Studies illustrate ways to support students in self-directed investigations, such as learning by teaching (e.g., Zacharia et al., 2015; Kolodner et al., 2003). For example, one study explored the impact of metacognitive guidance on socioscientifc decision-making. Using an ACE called Reservoirs in Taiwan, the investigators studied the impact of embedded metacognitive guidance on 11th-grade Taiwanese students’ decision-making (Hsu  & Lin, 2017). All students learned about the functions of water reservoirs and their potential impacts on the environment using a simulation of reservoir placement. Students received either general or metacognitive prompts. The metacognitive prompts included eliciting criteria, distinguishing alternatives, using decision-making strategies, and evaluating evidence. The metacognitive prompts signifcantly increased the sophistication of decision-making about socioscientifc issues.

Comparing Pedagogies By embedding alternative designs in ACEs, researchers can compare pedagogies (e.g., Chi et  al., 2018). To investigate pedagogies for self-directed learning, one study compared KI to specifc scaffolds. KI scafolds encouraged students to distinguish ideas by analyzing features of their responses and seeking more information from the visualization. Specifc scafolds directly communicated ideas that were missing or misrepresented in student responses (Vitale et al., 2016). While specifc scafolds

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produced greater accuracy during instruction, KI scafolds produced stronger outcomes on a novel essay at post-test. Closer analysis revealed an association between the time spent revisiting the visualization and post-test scores on the summary essay for those students in the KI condition. These fndings suggest that the KI guidance introduced a desirable difculty that motivated students to use the visualization to sort out their alternative ideas (Bjork & Linn, 2006). This capability is valuable for self-directed learning.

Emerging Visualization Technologies Researchers are investigating emerging technologies, including embodied, virtual, and extended reality tools, to make science phenomena visible in three dimensions (Lindgren et al., 2016; Luo et al., 2021). Examples of topics supported by these technologies include nutrition (food chains), adaptations (biodiversity), the water cycle (water quality; Boda & Brown, 2020), and motion (Zohar et al., 2018). Rapid advances including low-cost virtual reality (VR) videos available on cell phones, are opening new opportunities for students’ science learning. For example, VR research is benefting from insights gained by studying modeling and simulation technologies. These insights include building on prior student knowledge, allowing students to test their own ideas, presenting and integrating information pictorially and verbally, focusing attention on relevant information, and controlling the rate of information display (McElhaney et  al., 2015). For example, VR research suggests that stereoscopic visuals and wider felds of visual displays are more valuable for student learning than improved visual quality or enhanced auditory content (Cummings & Bailenson, 2016). Preliminary meta-analyses suggest promise and reveal the need for additional design research. A preliminary meta-analysis identifed 115 efect sizes from 83 studies and found medium efect sizes for the presence of technological immersion (Cummings & Bailenson, 2016). A second review and meta-analysis explored the impact of VR on student learning across K–12 and university settings from 2000 to 2019 (Luo et al., 2021). Of the 149 articles identifed, only 22 provided necessary information for efect-size calculation (36 efect sizes across the 22 articles with 17 efect sizes from K–12 and 19 from university settings). This meta-analysis shows a medium efect of VR instruction on student learning outcomes (g = 0.723, SE = 0.181, CI = [0.367, 1.079], p < 0.001) with signifcant moderators for discipline favoring science and engineering over language and health sciences; level of immersion favoring less immersive VR; and level of scafolding favoring the teacher and the computer, rather than either individually. Caution is recommended in interpreting the results since there are only 22 studies; issues such as physical discomfort, safety, and technical glitches marred several studies; details such as duration and sample size were not consistently reported; and less immersive VR was more impactful.

Making Scientifc Phenomena Accessible Few science texts acknowledge the political nature of science and the power structures that shape the experiences of science learners (Agarwal & Sengupta-Irving, 2019; Niaz & Maza, 2011; Philip & Gupta, 2020). Research capturing how science applies to the lives of students often celebrates the benefts, such as cures for disease or energy-saving inventions. Recently, science educators have also called attention to the injustices resulting from scientifc advance and the perspectives that are silenced (Emejulu  & McGregor, 2019; Morales-Doyle, 2017; Philips  & Azevedo, 2017). For example, science texts often tout chemical companies as friendly to society even when they are the biggest polluters (Morales-Doyle et al., 2019). Units on endangered species and climate have the potential to showcase the infuence of powerful stakeholders yet rarely do (Plutzer et al., 2016; Vitale et al., 2016). Factors such as redlining are crucial to understanding the distribution of pollution from

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freeways or the locations of heat islands yet are rarely depicted in mainstream science curricula or addressed in science standards (Owens & Sadler, 2020).

Empowering Students to Test New Ideas Using Computational Thinking Spurred by national attention to the importance of computational thinking (Grover & Pea, 2018; NGSS Lead States, 2013; Wing, 2006) and the development of accessible programming languages such as Scratch and Snap! researchers have designed instruction that empowers students to design their own models and test novel ideas (e.g., Biswas et al., 2016; Emara et al., 2021; Weintrop & Wilensky, 2019). To promote self-directed learning, researchers have designed activities that enable students to investigate topics of personal interest. For example, students can investigate the increased risk of asthma for those living close to freeways or dangers from chemical waste due to industrial practices (e.g., Bradford et al., 2022; Gerard, Bradford et al., 2022; Morales-Doyle & Gutstein, 2019). Research programs have used Netlogo, a text-based OER programming language, to connect systems and graphs for topics such as predator/prey cycles, acid–base relationships, or percolation in soil. Results show that students gain understanding of the interactions between components in a system, ability to critique aspects of a computational model, and ability to recognize the range of a computational model’s application (Weintrop & Wilensky, 2019). One study using Snap!, an OER block-based programming language designed to be accessible for novice students, enabled high school students to display greater learning gains in a block-based environment than when they used a text-based alternative (Irgens et al., 2020). However, these differences were not maintained in a subsequent course when switching both treatments to using only text-based Java. Additional investigations of conceptual learning, attitudinal shifts, and programming practices are needed (Xu et al., 2019). Incorporating computational thinking enables students to investigate socioscientific issues that impact their communities, such as urban heat islands (Borunda, 2021; Gardner, 2021). Using Snap! programming integrated into the Web-based Inquiry Science Environment (WISE), students were empowered to investigate a crucial issue, especially with increasing temperature fluctuations (see Figure 15.2). Students select variables and study their impact by using Snap! to control an urban heat islands simulation. For example, they can add and adjust the percentage of reflective roofs in the

Figure 15.2

Computational thinking example: a Snap! activity where students program a model to test the impact of reflective roofs on the temperature for a community.

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community. Average temperature for each day is shown in real time on a connected graph as they compare their methods for mitigating the urban heat island (Bradford et al., 2022).

Designing Physical and Virtual Artifacts Giving students agency to create digital and physical artifacts can make science accessible by expanding the nature of scientifc discovery (Kafai et  al., 2019) and allowing for student choice (IsraelFishelson & Hershkovitz, 2022; NGSS Lead States, 2013). Students are highly motivated to design artifacts in maker spaces using robotics, e-textiles, multimedia, and crafting both in school and out of school (Kafai et al., 2014; Pinkard et al., 2020). These programs empower students to become self-directed learners while building computational literacy. Out-of-school programs have been especially important for enriching the science instruction ofered in under-resourced schools (Pinkard et al., 2020). For example, a design program for middle school girls from nondominant communities, Digital Youth Divas, introduces computational thinking in schools lacking courses in computer science. Results show both pre/post increases in science learning and increases in positive perceptions of computer science (Pinkard et al., 2020). The program takes advantage of an OER, the Virtual Environment Interactions (VENVI) introductory programming environment, which enables students to build on their interests, facilitates student sharing, and emphasizes deepening science practice. E-textiles engage high school students in using Lilypad Arduinos, LEDs, sensors, and switches to create artifacts (Litts et al., 2017). In an elective class, students implemented four distinct light patterns on a letter as part of a cloth sign showing the name of the school. Assessments of students’ knowledge of circuits showed gains consistent with other studies of e-textiles and also showed that many students gained ability to read the code to control a circuit. Students developed a more robust understanding of electrical circuits than a typical unit using batteries and bulbs, albeit as the result of extended instruction time. To design solar ovens and learn about thermodynamics, middle school student groups used engineering design practices, such as meeting specifcations, budgeting, and iterative refnement (McBride et al., 2018). This study showed that when students were guided to plan their designs prior to constructing their ovens they considered multiple options. If, instead, they were guided to evaluate their designs after construction, students primarily conducted confrmatory investigations rather than testing alternatives. These fndings align with design principles in the KI framework calling for eliciting student ideas prior to conducting investigations (Linn & Eylon, 2011). Students using this unit gained insight into thermodynamics and engineering design beyond that gained in typical courses, without adding instructional time. These studies illustrate the power of engineering design to motivate students and increase the accessibility of science. For example, when using e-textiles, students leveraged their science knowledge to make informed design decisions, thus deepening their understanding of the science. Further, for both e-textiles and solar oven design students worked in pairs to complete complex projects, thereby appreciating the social nature of scientifc work. Finally, both e-textiles and solar ovens reveal the importance of carefully planning instruction to ensure that students have the resources and guidance to complete their projects. A systematic review of empirical research on technology-rich maker activities reported that 15% of 60 studies from 2007 to 2020 provided psychometric evidence for the reliability and validity of assessments, and that few studies include analytic rubrics to assess student improvement (Lin et al., 2020). These 60 studies predominantly investigate tools such as Arduino, Makey Makey, and Scratch. They measure a range of outcomes across cognition, afect, and motivation. For the cognition category, assessments include STEM content knowledge (28/60 studies), computational thinking and programming knowledge (20/60 studies), and skills and competencies (15/60 studies).

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Incorporating engineering and artifact design into science can make science accessible to a wide range of students. These activities expand options for students by integrating data science and multidisciplinary learning into K–12 science classrooms (Lee & Campbell, 2020).

Simulated Versus Physical Laboratories For many schools, virtual laboratories can make science investigations accessible to a wider range of students. The success of ACEs that empower students to discover scientifc ideas has motivated investigators to analyze the relative afordances of virtual versus physical laboratories (De Jong et al., 2013; Klahr et al., 2006; Zacharia et al., 2015). For example, researchers have incorporated PhET interactive simulations into ACEs to support students in making predictions, understanding relationships between variables, and interpreting science phenomena. The PhET simulations span multiple science content areas and are primarily implemented in HTML5 so that they work across laptops, tablets, and cell phone devices. Although physical laboratories provide needed opportunities to manipulate apparatus, students often beneft from virtual laboratories with visualizations that capture unseen interactions among the materials (Hegarty et al., 2016; Puntambekar et al., 2021). Virtual laboratories fast track students to data interpretation and analysis by reducing the tedium of experimentation (Puntambekar et al., 2021). When implemented using guidance in an ACE, virtual laboratories support students to make connections between macroscopic, microscopic, and symbolic aspects of a phenomenon, potentially reducing the cognitive load associated with manipulating equipment. Further, aligning virtual laboratories with constructivist pedagogies can strengthen impacts of laboratory instruction. Implementing virtual laboratories with individualized scafolds in open-source ACEs can provide free, tested science curriculum materials that enable each student to get started with self-directed learning (e.g., Gerard, Bradford et al., 2022; Liu et al., 2022).

Summary of Efforts to Promote Discovery Research programs have identifed ways to capitalize on technology to enable students to test their own ideas by embedding powerful models in ACEs, incorporating programming languages to support students to build their own models, and empowering students to design physical or virtual artifacts, such as multimedia, e-textiles, and robots. Studies are taking advantage of OERs, including Concord Consortium, edCrumble, GoLab, PhET, and WISE, to support visualization of scientifc ideas (Zacharia et al., 2015). During the COVID-19 pandemic, with physical spaces unavailable, ACEs featuring visualizations supported students to engage in many scientifc practices, including developing and using models, using mathematics and computational thinking, and analyzing and interpreting data (Gerard, Wiley et al., 2022; Wu et al., 2021). Emerging technologies such as virtual and extended reality and embodied design are under investigation. One next step is to develop valid and reliable indicators of student progress to establish the impacts of these technologies for student learning. A key research question concerns how to design automated scafolds that empower students to become self-directed learners who guide their own knowledge integration. In the next section, we address the ways that collaborative tools are contributing to equitable and efective science education. After that we discuss progress in designing personalized, automated guidance that helps each student succeed.

Learning From Others Collaboration is a mainstay of scientifc advance and a mechanism for learning from others emphasized in the KI pedagogy. Computer-supported collaborative learning (CSCL) researchers have

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explored ways to use collaborative activities to help students learn science, develop an identity as a science learner, and support teams to jointly solve compelling scientifc dilemmas. Advances in collaborative technologies, including automated dialogue tools (e.g., Coomey & Stephenson, 2018) and NLP analysis of conversations, have increased our understanding of peer-to-peer interactions and facilitated the opportunities for students to learn from each other. Like other eforts to appropriate scientifc practices for education, the design of CSCL opportunities impacts outcomes (Zheng et al., 2020). Technologies designed for collaboration include shared knowledge repositories – digital spaces where students may contribute information for others to access and refne (Matuk et al., 2019a; Norris & Soloway, 2017; Scardamalia & Bereiter, 2014); peer critique activities, including consultancies on experiments and virtual “pinups” for engineering designs (Chi et al., 2018; Misiejuk & Wasson, 2021; Raviv et al., 2019); and collaborative project environments where students jointly investigate scientifc dilemmas as citizen scientists (National Academies of Sciences, Engineering, and Medicine, 2018), makers of artifacts (Kafai et al., 2014), engineering designers (McBride et al., 2018; Pinkard et al., 2020), and players of certain games (Bufum et  al., 2016). Logging online collaborative activities afords complex analysis of patterns of interaction, quality of contributions, and roles of specifc scafolds. Recent research has begun to characterize the mechanisms and designs that make collaborative learning efective and equitable. A major issue in making both scientifc and classroom collaboration equitable concerns power structures, where stereotyped beliefs about expertise can silence some participants (Philip & Gupta, 2020). Collaborative opportunities can feature science contexts that draw on students’ lived experiences and honor their cultural epistemologies (Calabrese-Barton & Tan, 2019; Linn & Hsi, 2000; Visintainer, 2020; Warren et  al., 2020). When students collaborate in citizen science projects or maker spaces by contributing their own valued ideas or by exploring solutions to problems that impact their lives, they may develop identity as a science learner. Pedagogical frameworks ofer conficting insights concerning the mechanisms governing CSCL. The KLI framework argues there is insufcient evidence to support the value of collaboration for learning (Koedinger et al., 2012). However, ICAP research suggests that students can deepen their understanding by watching how other students collaborate to address a disciplinary dilemma (Chi et al., 2018). Research on KI shows how collaborative scafolds can support students to discover new ideas, distinguish among their own ideas and those of others, and develop collaboration skills (Linn & Hsi, 2000; Matuk et al., 2019a). The limitations and strengths of collaborative learning are refected in a meta-analysis of studies of CSCL between 2000 and 2016 (Chen et al., 2018). Analysis of 425 empirical studies revealed positive efect sizes for knowledge gains (0.45), skill acquisition (0.53), student perceptions (0.51), group task performance (0.89), and social interactions (0.57). Yet studies were often fawed by lack of: (1) control for prior knowledge between treatment and control group, (2) tasks that require students to work together rather than complete work independently, (3) sample sizes beyond 30 students, (4) study durations beyond two hours, and (5) outcomes beyond counting of messages posted in forums. As these perspectives illustrate, along with promise, there are many unanswered questions about collaborative learning.

Knowledge Repositories Recently researchers have advanced understanding of designs for repositories where learners refne their own ideas while building a common understanding (Scardamalia & Bereiter, 2014) and distinguish among the ideas they encounter (Matuk et al., 2019a). Research on the design of repositories has increased understanding of how to guide learning from others, including ways to help students become more intentional about their interactions with others. Students can become better at appreciating the value of collaboration by using and contributing to a repository.

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Even young students are capable of selecting promising, high-quality ideas from among those of their peers when supported to make good choices and to use those choices to strengthen their own ideas (Chen, Bradford et al., 2020). Encouraging participants to contribute to a repository rather than lurk in the background is impacted by factors such as the quality of peer contributions, trust in known peers, social pressures to reciprocate, and the desire to present oneself in a positive light (e.g., Guan et al., 2018). Building awareness of how ideas of others’ impact their own ideas, a key component of metacognition, strengthens the value of interacting with a repository (Järvelä et al., 2021). Repository designers have investigated ways to reward students for adding valuable ideas and to direct users to promising ideas. Some knowledge repositories use rating systems, often determined by experts, to accord status to entries and individual contributions (Kelly et al., 2002). Others have explored crowdsourcing for fltering out high- from low-quality ideas (Riedl et al., 2013). Some studies imply that nonexperts rank new product and service ideas comparably to experts, ofering an afordable alternative to experts (Magnusson et al., 2016). Others assess student ideas based on their impact on users, fnding that the accuracy of an idea is often only part of its value for knowledge building or knowledge integration (Chi et al., 2018; Matuk & Linn, 2018). Evaluating the impact of repositories includes measuring shared understanding or knowledge convergence between users of collaborative learning environments (Scardamalia & Bereiter, 2014; Weinberger et al., 2007). Convergence is controversial, as illustrated by public opinion polls that difer from expert fndings and by examples of groupthink (Janis, 1992). When convergence occurs early on in a group’s progress it may predict group performance but does not necessarily foretell individual success (Kapur, 2016). Encountering convergent ideas may help individuals inspect their ideas more fully, motivating them to confrm, refne, and reinforce their existing views (Paulus et al., 2019). Alternatively, divergence is essential for exchange of views and for supporting students to negotiate among alternative ideas (Halatchliyski et al., 2011). Ultimately, both exploring alternative convergent ideas and distinguishing among divergent ideas has potential to engage students in deepening their exploration of their thinking. A series of studies have explored ways to scafold interactions with repositories to promote equitable opportunities and develop self-directed learners (Donnelly et al., 2016; Matuk & Linn, 2018; Matuk et al., 2019a, 2019b; Matuk et al., 2016; Tate et al., 2020). Using the Idea Manager within the WISE platform (wise.berkeley.edu; Creative Commons Attribution-ShareAlike 4.0 license), these studies are exploring ways to scafold collaborative knowledge integration while valuing each student’s ideas. The Idea Manager is a repository to support students to gradually develop more extended scientifc explanations (see Figure 15.3). Students analyze powerful visualizations and add ideas of 1–3 sentences to their Idea Basket throughout a two-week science unit. Secondary school students are challenged to add sophisticated explanations of their scientifc ideas and concepts often because they have limited experience with such writing activities. The Idea Manager guides students to express their ideas and compare them to the ideas of others. Experiments have explored various ways to structure these interactions. For example, studies show the beneft of using prompts alongside teacher guidance to help students distinguish among ideas were more valuable than those designed to promote adding ideas (Donnelly et al., 2016). Students develop insights or refne their explanations as they review the ideas their classmates share. Another series of studies have investigated the role of the actual and perceived impact of selecting and using divergent and convergent ideas from a repository (Matuk & Linn, 2015, 2018). Although the power of the study limited the ability to detect the actual impact of selecting convergent or divergent ideas, there was a signifcant diference in the perceived value of the ideas. Students reported a larger impact of divergent than convergent ideas on their thinking. This is compatible with students using divergent ideas to distinguish among alternative ideas.

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Figure 15.3 Idea Manager within WISE Mitosis. Note: The fgure illustrates the introduction to a biologist researching cancer treatments, and her friend who has contracted cancer. Students explore mitosis in a normal cell, and one treated by a cancer medicine. They test three cancer medicines. Students add evidence to an Idea Manager, sort their evidence to distinguish benefts among the medicines, and link ideas in an explanation recommending one of the plants as a cancer treatment.

Peer Critique Scientifc advance depends, in part, on peer critique. CSCL researchers have explored the value of peer critique for classroom learning, arguing that when students review peers’ ideas they have the opportunity to distinguish their own ideas from those of others, often reconsidering their ideas in the process (Raviv et al., 2019; Sato & Linn, 2014). This has proven efective when students observe a video of a peer critique dialogue (Chi et al., 2018). A meta-analysis of 58 studies showed a moderate efect (g = 0.29) for students who participate in peer critique, compared to those who do not; it also found that computer-mediated peer review is associated with greater learner benefts than paper-based

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approaches (Li et al., 2020). Another meta-analysis of 54 studies found a moderate efect of peer critique (g = 0.31, p = 0.004), although it was not signifcantly diferent from self-assessment (g = 0.23, p = 0.209) (Double et  al., 2020). A series of studies found that peer review can beneft both the reviewer and the person receiving the reviews (Chi et  al., 2018; Sato  & Linn, 2014). The metaanalysis and individual studies make clear that equitable reviews depend on reviewers having criteria for evaluating their peers (Cohen, 1994; Sato & Linn, 2014). When developing their own criteria, peer reviewers often primarily give positive feedback, focus on details such as grammar or spelling rather than reasoning, and when they are critical, tell the learner the right answer rather than explaining the steps necessary to solve the problem. In addition, ofering constructive guidance requires understanding of the discipline, critical reasoning skills, as well as insights into how people learn (Bransford et al., 1999; Harrison et al., 2018). Even experienced teachers report that fnding ways to give guidance that advances the learning of each student is challenging and time consuming (Gerard, Ryoo et al., 2015). Studies also show that students can detect inaccurate peer critiques and that power relationships in classrooms can inhibit the success of peer critique (Ogan et al., 2015; Bang & Vossoughi, 2016).

Collaborative Projects Many of the problems in science require the collaboration of multiple individuals from varied disciplines. Introducing this capability starting in kindergarten has benefts and is particularly well illustrated in Japanese elementary schools (Lewis  & Perry, 2017; Linn et  al., 2000). Recently citizen science projects, robotics activities, and some gaming environments have expanded the opportunities for collaboration and learning (Kajamaa et al., 2020). Research programs are grappling with ways to optimize collaboration, how best to use technology, and how to evaluate impacts. Citizen science projects, such as recording bird migration, documenting light pollution, tracking diesel pollution, or helping identify protein structures (FoldIt), engage volunteers in collaborative science investigations about climate, health, and other topics (Kullenberg  & Kasperowski, 2016; National Academies of Sciences, Engineering, and Medicine, 2018). The SciStarter website (https:// scistarter.org/), as an example, has 1,600 searchable citizen science projects, a community of 50,000+ citizen scientists, and a participant dashboard that documents their contributions while also supporting them to fnd people and projects of interest (Hofman et al., 2017). The data collected can be incorporated into science instruction and localized to the community of learners. Ensuring that students learn from these activities, rather than only contribute to the database, remains a challenge (Trouille et al., 2019). The West Oakland Environmental Indicators Project uses locally gathered data to respond to West Oakland residents who identifed diesel trafc as an issue of concern in their neighborhoods. In this community-based participatory research project, local residents collaborated with academic partners from the Pacifc Institute in order to evaluate the air quality in their homes, yards, parks, and schools. This collaborative project produced data for a collaborative science investigation that engages students in comparing the incidence of asthma and the distribution of pollutants in local communities. See the asthma case study reported in the section on equitable science instruction for details (González et al., 2011). The nQuire project investigates ways to make personally meaningful citizen science available to the public (Sharples et al., 2019). The website supports missions, including Spot-it, where people contribute observations of phenomena, such as images of severe weather. Win-it missions are questions or challenges for participants to answer, for example, to propose ways to attract bumblebees to gardens. Registered users can comment on a mission or on a contribution, creating conversations around contributed content. A study of citizen science projects suggests potential impacts on student learning depending on the project and the role of the students (National Academies of Sciences, Engineering, and Medicine,

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2018). However, a systematic review of ten empirical studies of online citizen science projects found that most featured fexible participation and relied on self-reports for assessment (Aristeidou  & Herodotou, 2020). Progress on linking citizen science to localized personally relevant topics is illustrated in the asthma project that can connect crowd-sourced data to incidence of disease (Bradford et al., 2021). Engaging students in science activities where they use science practices such as designing solutions builds identity as a scientist (Kafai et al., 2014). Building identity is often the goal of robotics and other maker activities, yet these are often accessible primarily to afuent families (Archer et al., 2012). In one summer science program, minoritized students reported enhanced identity as a scientist when they used a grid methodology to identify random plots, count the incidence of weeds, and record the data (Visintainer, 2020). Similarly, when families participated in science exhibits where they constructed artifacts to solve practical problems, such as building a system to amplify sound, they reported feeling like scientists or engineers (Wang et al., 2013). Design research is needed to clarify how best to support citizen science investigations in school and out-of-school settings. At the same time, existing research on maker activities indicates the potential of an engineering curriculum for welcoming students in science, building identity, and providing opportunities to collaborate in STEM (Lin et al., 2020; Schad & Jones, 2020).

Gaming Games designed for collaborative science learning feature role playing, simulations, competitions, and drill. In one study of gaming, collaborative activities initially advantaged those with expertise. However, carefully designed, extended collaborative activities bridged experience gaps and enabled all students to beneft from game-based learning (Bufum et al., 2016). While gaming activities are motivating in science, status and power relationships may advantage some students and not others (Clark et al., 2016). In addition, competitive games can increase motivation but also increase anxiety (Chen, Shih et al., 2020). A meta-analysis of 32 studies of gamifcation of instruction showed that students preferred gameinspired instruction (d = 0.50). Students reported that gamifcation was motivating, provided feedback on performance, fulflled their needs for recognition, and encouraged goal setting. They also noted that gamifcation caused anxiety and jealousy (Bai et al., 2020). For example, a comparison of Supercharged! versus an inquiry activity to support students’ learning about electromagnetic concepts found signifcant learning gains in favor of the game (Anderson & Barnett, 2013). Supercharged! is a 3D racing game where players must maneuver a spaceship through a set of charged obstacles across fve levels of increasing difculty. Players can adjust the type and magnitude of electric charge of the ship to maneuver it past obstacles. The study found that students in the game treatment provided more nuanced descriptions of electric felds and the infuence of distance on charges. This study calls for the intentional use of meta-cognitive activities within games rather than assuming that sufcient learning occurs organically through gameplay. Overall, research on gaming is still preliminary (De Freitas, 2018; Honey & Hilton, 2011).

Summary of Advances in Learning From Others In summary, there is potential for CSCL where each student is supported to contribute rather than where contributors are those who volunteer or are stereotyped to succeed. Design of collaborative spaces can empower the social presence of each student in online environments (Calabrese-Barton & Tan, 2019; Richardson et al., 2017). Adding collaborative features can enhance knowledge integration by expanding the breadth of ideas that students consider, support them to distinguish among alternatives, and encourage them to refect on the process. Teachers can augment the efectiveness of

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CSCL by monitoring student contributions and following up in the classroom. As a result of moving to entirely online instruction during the COVID-19 pandemic, 60% of parents of K–12 students expressed concerns about maintaining their childrens’ social connections and friendships (n = 2561; Pew, 2020). These trends reinforce the need for additional design research to establish efective ways to leverage collaboration with technology.

Guiding Students to Integrate Their Understanding Advances in technology make it possible for computers to amplify teacher guidance with automated guidance, an important component of KI pedagogy. Automated guidance, based on logs of student work, increase support for students to integrate their ideas (e.g., Wilson  & Czik, 2016; Zacharia et al., 2015). NLP tools can assess students’ written explanations and enable designers to create personalized guidance (Gerard & Linn, 2022; Lee, Gweon et al., 2021). NLP tools empower teachers to take advantage of written explanations (Gerard, Ryoo et al., 2015). Learning analytics that analyze student use of concept maps (Ryoo & Linn, 2016), collaborative learning (Matuk et al., 2017; Sato & Linn, 2014), models (Gobert et al., 2013), graphing (Vitale et al., 2019), and interactions between models and computational thinking (Weintrop & Wilensky, 2019) can be used to generate automated guidance. In studies using ACEs that combine written explanations, concept maps, visualizations, and collaborative activities, the RPP explored the impact of promoting self-directed learning by detecting foundering and ofering hints as well as alerting the teacher (Gerard & Linn, 2016). Further, researchers can use ACEs to explore interactions between learner trajectories and guidance strategies by randomly branching students to alternative conditions. Designers of guidance face a dilemma between efciently telling students the answer and giving hints to encourage self-directed learning (Koedinger & Aleven, 2007). Reviews of studies of guidance report conficting results for design of online adaptive guidance, depending on whether the goal is self-directed inquiry or rapid acquisition of detailed procedures (Koedinger et al., 2012; Gerard, Matuk et al., 2015). Supporting self-directed learning involves enabling students to monitor their own progress, look for opportunities to distinguish ideas, and refect on their learning. Advances in automated scoring of student work have spurred research on design of efective guidance using the automated scores. These scores can help support self-directed learning and achieve equity goals, although bias can limit impact (Philip & Azevedo, 2017). Automated scores can also be used to amplify teacher guidance. In the section on empowering teachers, we discuss questions such as which information about students’ progress is most valuable for teachers (e.g., accuracy of class responses, distribution of ideas, unique ideas) and when is it most useful (e.g., while students are learning, when planning instruction for the next day, when customizing for the next year).

Design of Effective Guidance Inspired by analysis of efective teacher guidance in lesson study (Lewis & Perry, 2017), inquiry learning (Furtak et al., 2012), ICAP (Chi et al., 2018), and KI (Gerard et al., 2010), automated guidance has explored the value of giving hints and asking questions rather than telling students the right answers. Studies show that instruction emphasizing gathering evidence to distinguish among alternatives is more efective than instruction focused on giving accurate responses (Booth et al., 2013; Chi et al., 2018; Vitale et al., 2016). Empirical studies examining desirable difculties (Richland et al., 2007; Ryoo & Linn, 2014) and productive failure (Kapur, 2016) support these fndings by illustrating that when students struggle to fnd solutions they often develop durable understanding. In addition, meta-analytic investigations show that students learn more when encouraged to generate their own responses rather than selecting the right answer among multiple choices (e.g., Bertsch et al., 2007).

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Several review articles document the potential of giving hints rather than supplying the right answer. One meta-analysis of 24 independent comparisons found that automated adaptive guidance was signifcantly more efective than guidance provided in typical instruction, particularly for students with low prior knowledge (Gerard, Matuk et al., 2015). This study also showed that hints led to greater learning gains than feedback providing the correct answer. Another meta-analysis found that automated KI guidance was more efective than no guidance, typical teacher guidance, and right answer guidance while being as efective as guidance designed by teachers who participated in a KI workshop and knew their students well. Comparison studies are distinguishing among alternative forms of guidance to encourage self-directed learning. For example, a design study of c-rater-ML automated guidance within a Concord Consortium water curriculum module for groundwater systems compares forms of automated guidance to support students’ revision of scientifc arguments (Lee, Gweon et  al., 2021). Students in this study were provided with argument-only guidance or argument and simulation guidance across three argumentation tasks. Overall, this study found that most students failed to “use simulations thoroughly enough to collect evidence” (p. 189). However, both treatments showed signifcantly improved scores for students who revised their arguments. Further, students receiving argument and simulation guidance were signifcantly more likely to rerun simulations (36% across the three tasks) than argument-only students (3%; p

 

Practices

Performance Expectations

Identifying science principles

Describe, measure, or classify observations

Using science principles

Explain observations of phenomena

Using scientifc inquiry

Design or critique aspects of scientifc investigations

Using technological design

Propose or critique solutions to problems given criteria and scientifc constraints

State or recognize correct science principles

Demonstrate relationships among closely related science principles Suggest examples of observations that illustrate a science principle

Demonstrate relationships among diferent representations of principles Predict Propose, analyze, observations of and/or evaluate phenomena alternative explanations or predictions Conduct Identify patterns in Use empirical scientifc data and/or relate evidence to investigations patterns in data to validate or criticize using appropriate theoretical models conclusions about tools and explanations and techniques predictions Identify Apply science scientifc trade- principles or data ofs in design to anticipate efects decisions and of technological choose among design decisions alternative solutions

Performance expectations – combining content and practices. The design of the NAEP science assessment is guided by the framework’s descriptions of both the science content and science practices to be assessed but with the key assumption that the practices are to be combined with a science content statement to generate specifc student performance expectations that serve as the target for assessment. Assessment items can then be developed based on the description of each specifc performance expectation. Based on the logic of specifc performance expectations as a guide for item development processes, items are then designed to vary the cognitive demands of tasks, which in turn infuences the 1079

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Figure 33.7 NAEP assessment item development model.

conclusions to be made about student performance. Such a process of item development can be represented schematically by Figure 33.7.

Composition of the Assessment The design and administration of the NAEP assessment is complex and makes use of a matrix sampling approach in which multiple item blocks are created, varying both content and item types as described next. The general rule is that any individual student is only tested for 50 minutes and is presented with selected item blocks that fall within that time limit. Students vary in the item blocks they respond to using a balanced incomplete block design. Details of the sampling procedures and block design can be found in technical documents from the National Center for Educational Statistics and are beyond the current presentation. Distribution of items. The distribution of items by content area is supposed to be as follows: approximately equal across physical science, life science, and earth and space sciences at grade 4; more emphasis on earth and space sciences at grade 8; and a shift to more emphasis on physical science and life science at grade 12. With respect to science practices, at all grades the greatest emphasis is supposed to be on identifying and using science principles and slightly less than a third of the time should be spent on items related to using scientifc inquiry. Item types. Item types for the NAEP science assessment fall into two broad categories: selectedresponse items (such as multiple choice) and constructed-response items (such as short answers). With respect to student response time, 50% of the assessment items at each grade level are supposed to be selected response and 50% should be constructed response. To further probe students’ abilities to combine their understanding with the investigative skills that refect practices, a subset of the students sampled receive an additional 30 minutes to complete hands-on performance or interactive computer tasks. At each grade, at least four of these tasks are included. Of these four tasks, there is supposed to be at least one hands-on performance task and one interactive computer task; the number of interactive computer tasks should not exceed the number of hands-on performance tasks. 1080

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In hands-on performance tasks, students manipulate selected physical objects and try to solve a scientifc problem involving the objects. NAEP hands-on performance tasks are designed to provide students with a concrete task (problem) along with equipment and materials. Students are given the opportunity to determine scientifcally justifable procedures for arriving at a solution. Students’ scores are to be based on both the solution and the procedures created for carrying out the investigation. There are four types of interactive computer tasks: (1) information search and analysis, (2) empirical investigation, (3) simulation, and (4) concept maps. Information search and analysis items pose a scientifc problem and ask students to query an information database and analyze relevant data to address the problem. Empirical investigation items place hands-on performance tasks on the computer and invite students to design and conduct a study to draw conclusions about a problem. Simulation items model systems (e.g., food webs) and ask students to manipulate variables and predict and explain resulting changes in the system. Concept map items probe aspects of the structure or organization of students’ scientifc knowledge by providing concept terms and having students create a logical graphic organizer.

Recent Science Administrations and Results In 2015, NAEP was administered as primarily a paper-and-pencil test using the item types described earlier. In 2019, a major shift occurred and NAEP science was administered for the frst time as an entirely digitally based assessment (DBA). The NAEP DBA consisted of stand-alone and discrete questions that were adapted from the prior paper-and-pencil test format, and scenario-based tasks comprising a connected sequence of questions. Scenario-based tasks were designed to engage students in scientifc inquiry through hands-on activities and computer simulations set in real-world contexts. The tasks provided students opportunities to demonstrate their knowledge and skills in each of the three science content areas and four science practices. The science assessment included two types of scenario-based tasks. Interactive computer tasks (ICTs). ICTs use real-world simulations to engage students in scientifc investigations that require the use of science inquiry skills and the application of scientifc knowledge to solve problems. Hybrid hands-on tasks (HHOTs). Students perform hands-on scientifc investigations using materials in kits provided by NCES. The “hybrid” in HHOTs denotes that these tasks combine hands-on investigations with digital activities. Students use supplied tablets to view kit instructions, record results and data, and answer assessment questions. Representative samples of students from across the nation participated in the 2019 assessment including: 30,400 4th graders from 1,090 schools; 31,400 8th graders from 1,070 schools; and 26,400 12th graders from 1,760 schools. The student samples were carefully constructed to ensure adequate representation of various demographic groups for purposes of subgroup reporting of results. As noted earlier, NAEP science results are reported in two ways: (1) on a 0–300 psychometrically determined scale and (2) in terms of the percentages of students whose performance is classifed as below basic, basic, profcient, and advanced achievement based on preestablished cut scores on the performance scale. The 2019 scale score results are shown in Figure 33.8 for each of the grade levels in comparison to prior administrations back to 2009. As can be seen in Figure 33.8, the average science score for the nation at grade 4 was lower by two points compared to 2015, whereas average scale scores at grades 8 and 12 did not signifcantly difer from 2015. At grades 4 and 8, average scale scores were higher when compared to 2009, while the average scale score at grade 12 was not signifcantly diferent across years. While the absolute levels of the scale scores and the trends in those scores are important indicators of student performance, of particular signifcance is the reporting of results in terms of achievement 1081

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Figure 33.8 NAEP 2019 scale scores in contrast to NAEP 2009 for grades 4, 8, and 12. * Signifcant diferent (p < .05) from 2019

levels. This form of interpretive reporting for NAEP assessments was begun in the 1990s and has been followed by several critical evaluations of both the process of setting these standards and their validity, which go well beyond the present discussion (National Assessment Governing Board, 2019). As shown in Figures 33.9, 33.10, and 33.11, the rates by which students were classifed into the achievement levels varied across the grades, with the highest rate of profcient classifcations occurring in grade 4, slightly lower levels of profciency at grade 8, and substantially lower student profciency classifcations at grade 12. Note that at all three grade levels, there is a very low level of classifcation of student performance at the Advanced level and this holds across years. Achievement level interpretation. To provide some context for the interpretation of performance at grade 8 as shown in Figure 33.10, Table 33.5 shows both the scale cut score for assignment of student performance to each achievement level, as well as a description of what each performance level implies in terms of what students are supposed to know and are able to do in science at that achievement level. As can be seen in the descriptions, the levels vary in both the knowledge and skills expected of students and their integration in terms of problem solving and application of science content knowledge in the context of the practices.

NAEP Technology and Engineering Literacy NAEP TEL Framework One important development in the NAEP assessment program is the inclusion of technology and engineering literacy (TEL) as an addition to its established assessment agenda. A TEL framework was developed for the frst TEL assessment in 2014 at grade 8 and was used again for the 2018 TEL at grade 8. It is currently scheduled to be used once more for the 2028 TEL administrations for grade 8. 1082

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Figure 33.9 Grade 4 achievement level performance. * Signifcantly diferent (p < .05) from 2019

Figure 33.10 Grade 8 achievement level performance. * Signifcantly diferent (p < .05) from 2019

Figure 33.11 Grade 12 achievement level performance. * Signifcantly diferent (p < .05) from 2019

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Understanding how technology is developed, how it afects our society and environments, and being able to design technological solutions to solve problems is critical for students to function in the rapidly changing world. In the science education community, there has long been a call for preparing students with technology and engineering literacy. The Science for All Americans report (American Association for the Advancement of Science, 1990) explicitly suggested that science education should incorporate technology and engineering as a form of scientifc inquiry. Bybee (2010) proposed an advance to STEM education by integrating technology and engineering with science and mathematics education. He argued that “there are very few other things that infuence our everyday existence more [than technology] and about which citizens know less” (Bybee, 2010, p. 30). For example, technology was among the most salient factors causing polarization in current labor markets, requiring critical skills and competencies (Goos et al., 2009). Bybee suggested extending the traditional information communication technology (ICT) education by integrating ICT with other subjects. He further pointed out that involving students in engineering activities could promote their abilities for both problem solving and innovation. He also acknowledged that engineering as typically presented in schools was inconsistent with its careers and contributions to society, and thus authentic scenarios needed to be developed for both learning and assessment (Bybee et al., 2009). Table 33.5 Eighth Grade NAEP Achievement Levels Levels (Cut Scores)

Achievement Description

NAEP Basic (141)

Students performing at the NAEP Basic level should be able to state or recognize correct science principles. They should be able to explain and predict observations of natural phenomena at multiple scales, from microscopic to global. They should be able to describe properties and common physical and chemical changes in materials; describe changes in potential and kinetic energy of moving objects; describe levels of organization of living systems – cells, multicellular organisms, and ecosystems; identify related organisms based on hereditary traits; describe a model of the solar system; and describe the processes of the water cycle. They should be able to design observational and experimental investigations employing appropriate tools for measuring variables. They should be able to propose and critique the scientifc validity of alternative individual and local community responses to design problems. Students performing at the NAEP Profcient level should be able to demonstrate relationships among closely related science principles. They should be able to identify evidence of chemical changes; explain and predict motions of objects using position time graphs; explain metabolism, growth, and reproduction in cells, organisms, and ecosystems; use observations of the sun, earth, and moon to explain visible motions in the sky; and predict surface and ground water movements in diferent regions of the world. They should be able to explain and predict observations of phenomena at multiple scales, from microscopic to macroscopic and local to global, and to suggest examples of observations that illustrate a science principle. They should be able to use evidence from investigations in arguments that accept, revise, or reject scientifc models. They should be able to use scientifc criteria to propose and critique alternative individual and local community responses to design problems. Students performing at the NAEP Advanced level should be able to develop alternative representations of science principles and explanations of observations. They should be able to use information from the periodic table to compare families of elements; explain changes of state in terms of energy fow; trace matter and energy through living systems at multiple scales; predict changes in populations through natural selection and reproduction; use lithospheric plate movement to explain geological phenomena; and identify relationships among regional weather and atmospheric and ocean circulation patterns. They should be able to design and critique investigations involving sampling processes, data quality review processes, and control of variables. They should be able to propose and critique alternative solutions that refect science-based trade-ofs for addressing local and regional problems.

NAEP Profcient (170)

NAEP Advanced (215)

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The 2012 NRC report (National Research Council, 2012a) Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century identified information literacy and ICT literacy as two of the most frequently mentioned critical competencies for students to succeed in the 21st century. That report discussed various foundations for education and STEM education in particular, including preparing future entrants to the labor market with the ability to adapt to technological changes of the society rather than simply acquiring static bits of knowledge (Nelson & Phelps, 1966). Similarly, another 2012 NRC report, the Framework for K–12 Science Education (NRC, 2012b), framed one of the overarching goals of science education as the development of students who “are careful consumers of scientific and technological information related to their everyday lives” (p.  1). The Framework explicitly includes “Engineering, Technology, and Applications of Science” as one of four disciplinary core ideas and describes “defining problems, design solutions, and using computational thinking” as critical components of science and engineering practices. These and other trends related to technology and engineering literacy spurred the National Assessment Governing Board (NAGB) to develop a TEL framework and include the TEL assessment as part of the NAEP program to obtain information about students’ understanding of technology and its effect on our society and environments, as well as students’ ability to design solutions to solve real-world problems. The TEL framework denotes TEL as the “capability to use, understand, and evaluate technology as well as to understand technological principles and strategies needed to develop solutions and achieve goals” (p. xi). Specifcally, the framework identifed three interconnected areas to be assessed (National Assessment Governing Board, 2018): Technology and society deals with the efects that technology has on society and the natural world and with the sorts of ethical questions that arise from those efects. Knowledge and capabilities in this area are crucial for understanding the issues surrounding the development and use of various technologies and for participating in decisions regarding their use. Design and systems covers the nature of technology, the engineering design process by which technologies are developed, and basic principles of dealing with everyday technologies, including maintenance and troubleshooting. An understanding of the design process is particularly valuable in assessing technologies, and it can also be applied in areas outside technology, since the design is a broadly applicable skill. Information and communication technology includes computers and software learning tools, networking systems and protocols, hand-held digital devices, and other technologies for accessing, creating, and communicating information and for facilitating creative expression. Although it is just one among several types of technologies, it has achieved special prominence in technology and engineering literacy because familiarity and facility with it are essential in virtually every profession in modern society. Students with TEL are expected to succeed in three types of practices, being able to think and reason in problem solving: • •

•

Understanding technological principles, which focuses on students’ knowledge and understanding of technology and their capacity to think and reason with that knowledge Developing solutions and achieving goals, which relates to students’ systematic application of technological knowledge, tools, and skills to address problems and achieve goals presented in societal, design, curriculum, and realistic contexts Communicating and collaborating, which relates to students’ capabilities to use contemporary technologies to communicate for a variety of purposes and in a variety of ways, individually or in teams (generated virtually)

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NAEP TEL Assessment Tasks The TEL assessment focused on developing scenario-based tasks to engage students in multimedia environments to gauge students’ understanding of technological and engineering principles and the ability to apply the principles to determine design solutions. Most of TEL’s assessment tasks are computer simulation problems associated with scenarios of technology and engineering, with a few exception questions that are not part of a concrete scenario. TEL’s assessment tasks are similar to those NGSS-aligned assessments, as they both underscore the necessity to assess practices. However, a comparison between the TEL framework and NGSS suggests that TEL difers from NGSS-aligned assessments by not requiring students to apply specifc knowledge of science to complete the tasks (Neidorf et al., 2016). This could be seen that some TEL assessment tasks measure student performance in more than one content area or practice. The practice-based assessment tasks required a relatively long time to complete, with long tasks for 30 minutes and short tasks for 10–20 minutes. Next we introduce one example item from the TEL 2014, recreation center task (see Figure 33.11). This task creates a scenario where a company ofered to build a new recreation center for the teenagers in the town. Students are required to engage in six activities to create digital products that will promote the benefts of building the recreation center, to convince some business owners who are opposed to the idea. This task aims to elicit multiple facets of students’ TEL competency using the six activities. By administering the task in TEL 2014, it was observed that students were able to identify relevant design elements for presenting ideas while less able to organize design elements and provide constructive feedback. Details are presented in Table 33.6. Assessing TEL faces many challenges, one of which is the validity of the inferences that can be drawn from assessment performance due to preexisting variation in students’ familiarity with computer-based tests. TEL assesses students in computerized environments, but not all students are equally familiar with computers given their diverse backgrounds. Familiarity with computers and computer-based testing thus may function as construct irrelevant variance in students’ scores. To examine this issue, Zhang et al. (2016) conducted a TEL pilot study and found that students’ everyday frequency of use of computers and technology devices was associated with their TEL scores. Students with medium frequency levels achieved the highest TEL scores. They also found that students’ self-efcacy regarding computer use and variations in home access to computers was positively associated with students’ TEL scores. These fndings indicate that researchers and policymakers should exercise caution when interpreting TEL scores.

Recent TEL Administrations and Results TEL has been administered at grade 8 in 2014 and 2018, respectively. The three practices are the primary assessment goals of TEL, with approximately the same percentages of assessment time for each practice. Specifically, in the 2014 TEL assessment 30% of the total time was devoted to the assessment of understanding technological principles, 40% for developing solutions and achieving goals, and 30% for communicating and collaborating. The 2014 TEL assessment was the first in the NAEP program to be entirely digitally based. The two TEL tests at grade 8 provided insights into students’ TEL competency and helped to identify relevant trends. On average, students scored two points higher in TEL 2018 than those who took part in TEL 2014 (see Figure 33.12). Overall, 46% of eighth graders performed at or above the NAEP Profcient level in the TEL 2018, in comparison to 43% in 2014. NAEP also reported results for fve selected percentiles (i.e., 10%, 25%, 50%, 75%, and 90%) of students’ performances in 2018, three of which were higher than 2014 (50th, 75th, and 90th). These fndings might be partially due to the fact that

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in 2018 more participating students (57%) had a chance to take at least one course related to technology or engineering compared to 2014 (52%). NAEP also reported a gender diference on TEL. Overall, female eighth-grade students consistently outperformed male eighth-grade students on both the 2014 and 2018 TEL tests. Specifcally, Table 33.6 Activities, Skills Measured, Student Performance, and 2014 Outcomes of Recreation Center Task Activities

Skills Measured

Student Performance

Outcomes

Choosing a podcast idea

Tempering communication with a knowledge of the audience and the communicative purpose.

In the frst part of the task, students record a podcast to convince opponents of the recreation center that building it is a good idea. They must select an idea for the podcast that best supports their message and is appropriate for the target audience. Students need to create an introduction to the podcast interview. They listen to six audio clips and must select the three clips that together form the best introduction. The clips must be placed in the proper order.

87% of students were able to identify an idea for the podcast that best supported the intended message about recreation centers.

Selecting Creating presentations, a podcast visualizations, and other introduction products that support their communicative purpose, adhering to appropriate visual conventions, and acknowledging sources of data and ideas. Attracting Tempering communicative podcast content with a knowledge listeners of the audience and the communicative purpose.

Students are informed that they need to attract listeners to the podcast by including a quotation from the interview on the recreation center website. To accomplish this, students must choose a quotation that would most likely encourage people to listen. Planning the Tempering communicative In this part of the task, students help video content with a knowledge create a video for the recreation center of the audience and the website to convince local business communicative purpose. leaders to support the recreation center. Students frst create a storyboard of the video. They need to select a picture and statements about recreation centers to include in the storyboard. Picking Tempering communicative In this section, students are shown background content with a knowledge several statements to be included as music of the audience and the text in the video. The students need communicative purpose. to select background music to be played along with the statements. The music should be appropriate for the message and the target audience of local businesspeople. Evaluating Recognizing various Students review a recreation center the video forms of collaboration video created by a peer who has asked and supporting forms of for feedback. The students need to collaborative technologies. provide constructive feedback in an Functioning efectively email to the peer. The feedback needs as part of a team, such as to include things that are good about by providing constructive the video as well as parts that could be feedback to team members. improved.

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24% of students correctly selected and organized audio clips in a way that supported the intended message.

69% of students chose an interview quote that would most likely encourage people to listen to the podcast.

88% of students selected the storyboard image that best supported the video’s purpose. 72% of students correctly chose statements that supported the video’s message about the benefts of recreation centers. 69% of students selected video background music that was most appropriate for the intended message and target audience.

41% of all students could provide constructive and balanced feedback about a video created by a peer.

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female students scored three points higher than male students in 2014, while the gap increased to fve points in 2018. The gender diferences were demonstrated in many of the TEL content and practices areas (see Figure 33.13).

Uses of the NAEP Assessments Because the information it generates is available to policymakers, educators, and the public, NAEP is often used as a tool for monitoring student achievement in subjects such as science at the national, state, and selected school district levels. For example, NAEP reports (known as The Nation’s Report Card) compare student performance in a given subject across states, within the subject over time, and among groups of students within the same grade. To the extent that individual state standards refect the knowledge and skills specifed in the framework for a given subject area such as science, state comparisons can legitimately be made. As shown earlier, NAEP data include achievement results, reported as both scale scores and achievement levels, at aggregate levels and for various subgroups. Based on questionnaires also administered with the assessment, NAEP reports include important background information about schools, teachers, and students at the subgroup level (e.g., course-taking patterns of various subgroups of students); and publicly released assessment questions, student responses, and scoring guides. The NAEP website (http://nces.ed.gov/nationsreportcard) contains user-friendly data analysis software to enable policymakers, researchers, and others to examine all aspects of NAEP data, perform signifcance tests, and create customized graphic displays of NAEP results. These data and software tools can be used to inform policymaking and for secondary analyses and other research purposes. NAEP assessment frameworks and specifcations documents, as well as examples of tasks and scoring procedures, are also used as resources for considerations of international, state, and local curricula and assessments. This is attributable in part to the broad-based process used in the development of the frameworks and task specifcations that attempts to capture current thinking and research about

Figure 33.12 Example TEL item – recreation center task.

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what students should know and be able to do in a given subject area like science. In addition, NAEP uses a rigorous and carefully designed process in developing the assessment instruments, which can serve as a model for other assessment developers.

A New Era for Large-Scale Assessment in Science Education As indicated earlier and in the previous edition of this handbook’s chapter on large-scale science assessment (Britton & Schneider, 2014), much has happened and continues to happen in the conceptualization, implementation, reporting, and interpretation of large-scale science assessments at both the international and national levels. We expect that such assessments will continue playing a critical role in monitoring the progress and outcomes of science education systems and in support of the development of a 21st-century STEM-literate workforce. Regularly occurring large-scale science and technology assessments not only provide informative policy-oriented indicators but also push participating countries to consider reform agendas in light of trends in the periodically released assessment outcomes. Evidence from international assessments has motivated various countries (e.g., China, Germany) to initiate implementation of national and/or local large-scale assessments of science and technology meant to serve specifc goals and needs of their own education systems, taking into consideration the cultural, economic, political, and historical issues in science and technology education and assessment unique to their nation. Eforts to develop and implement science assessments for large-scale deployment have deepened our understanding of what to assess, how to assess, and how to make use of the results to guide policy and practice at both national and international scales. It is clear from the evidence to date that large-scale science assessments will continue to evolve over the next decade. This will be driven by continuing developments in conceptualizing frameworks for science assessment, which will be combined with advances in practices of assessment design and development and further enhanced by technology-based delivery, data capture, and analytic tools. We can see these trends in the assessment programs discussed in this chapter over the past two decades. The importance of large-scale science assessments for international and national use will certainly not decrease and will, in all likelihood, increase as policymakers seek to understand the knowledge and skills of their populace relative to the demands of a globalized and technologically sophisticated society and workforce. Given rapid changes in society, in scientifc and technological knowledge, including epistemological and conceptual perspectives on the nature of the knowing and learning of science, it is very likely that even greater demands will be made on large-scale assessments of science to provide valid, reliable, and useful information about the state of science education and student achievement across the globe. We foresee a number of challenges and opportunities that will therefore need to be met by large-scale assessments of science and technology over the next decade, signifying a new era of large-scale assessments. Each of these is briefy touched on next. Continued movement toward multidimensional science frameworks to refect future challenges and opportunities. The world is facing unprecedented social, scientifc, and technological challenges, including environmental change, global pandemics, cyberattacks, food production, and pollution, among others. Future citizens must be educated in ways that develop the range of competencies needed to meet these challenges individually and collaboratively. Large-scale assessments of science and technology will need to provide evidence in terms of whether and to what degree education systems are preparing future citizens with the knowledge and reasoning skills sufcient to meet these challenges. Thus, change is underway across the globe in the conceptualization of science competency. A prime example is the vision of science competency described in the Framework for K–12 Science Education (National Research Council, 2012b) with its articulation of three dimensions of science knowledge and skill: disciplinary core ideas, science and engineering practices, and crosscutting concepts and the argument that competency is expressed in terms of performance expectations that refect the 1089

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1090 Figure 33.13 Student performance on TEL 2014 and 2018.

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Figure 33.14 Students’ performance on TEL 2014 and 2018 by gender.

intersection of all three dimensions simultaneously. Students are expected to use scientifc knowledge to fgure out solutions for authentic problems in their lives. This idea was further articulated in the Next Generation Science Standards (NGSS Lead States, 2013) for grades K–12. The implications for science assessments and the associated challenges in design and validation of assessments for multidimensional performance expectations have been articulated in Developing Assessments for Next Generation Science Standards (Pellegrino et al., 2014). Given that more than 40 states in the United States have adopted science standards aligned with the vision of the Framework (NRC, 2012b) and the NGSS (NGSS Lead States, 2013), it is highly likely that the NAEP science framework and the NAEP science and TEL assessments will undergo revisions to better align with this contemporary view of science and technology. This is especially the case since analyses of alignment between the current NAEP science and TEL frameworks and NGSS show signifcant diferences in both content and performance demands (Neidorf et al., 2016). OECD’s PISA framework is also undergoing 1091

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reconsideration. For example, the PISA 2025 steering science expert group (SEG) is seeking to reform PISA from a “science literacy framework” toward a “science framework” (OECD, 2023). Specifcally, the SEG is working to redefne the competency components to better refect the sustainability and socio-environmental needs of future citizens. Inspired by the diverse attitudes toward COVID-19, the SEG is considering extending the attitudes to science component to a broader and more inclusive concept, science identity (e.g., critical science disposition, valuing scientifc perspectives and approaches to equity, attitudes, and aspirations to science) (OECD, 2020). How these changes will be manifest in the 2025 PISA assessment when science is the focal assessment area is yet to be fully determined. Similarly, TIMSS 2023 is moving toward a comprehensive framework to better refect the nature of science learning. The committee is making a signifcant efort to better understand how science learning is associated with learning contexts, including school, family, policies, etc. TIMSS 2023 will continue working with participating countries to analyze the contextual factors to better understand the nature of science learning, and the fndings will be documented in the TIMSS Encyclopedia. Simultaneously, powerful and cutting-edge technologies, such as artifcial intelligence (AI), bring unprecedented opportunities as well as challenges for many facets of life (National Artifcial Intelligence Initiative Ofce, 2020). AI will dramatically change the future structure of work, the needs of the labor market, and the competencies needed for future citizens to accommodate the changing workforce. McKinsey Global Institution (Manyika et al., 2017) suggests that in the coming ten years, more than 60% of STEM occupations could be leveraged and changed by new innovative technologies such as AI. Consequently, future citizens will face challenges that demand multidimensional competencies to be productive in future society. Such competencies include being able to use scientifc knowledge to creatively solve unpredictable problems and fgure out solutions in complex situations. This poses a major challenge to current education systems across the globe. Large-scale assessments will need to advance to continue providing information regarding what students know and can do in areas of scientifc and technological thinking. Assessment outcomes from new and innovative multidimensional assessments could help participating countries modify their education systems to produce sufcient numbers of STEM-capable graduates with the multidimensional competencies needed for the global society of the future. Leveraging technology in and for assessment. As already indicated in this chapter, technology has had a profound efect on large-scale assessments of science and technology. PISA 2015 and 2018, TIMSS 2019, NAEP 2019, and TEL 2014 and 2018 have all been delivered via technology using various types of devices. Not only has technology changed assessment delivery but also the scoring. It has had a signifcant efect on assessment design, including the types of tasks and situations that can be presented to students that tap into various forms of scientifc thinking and reasoning aligned with the practices of science. Digital assessment environments allow for the creation of scenario-based tasks using simulations, videos, pictures, and other media resources that previous conventional assessments could not achieve. The involvement of these multimedia resources provides an opportunity for students to use scientifc knowledge and reasoning to solve real-world problems, a substantial goal of the Framework for K–12 Science Education (National Research Council, 2012b). While technology afords the enhancement of many aspects of large-scale assessment (Gane et al., 2018; Pellegrino & Quellmalz, 2010), concerns also arise about the equity and fairness of computerized assessments. Of concern is evidence regarding the comparability of results and validity of inferences derived from performance obtained across diferent modes of assessment, especially for varying groups of students (Berman et al., 2020). As the digital assessment world advances, a signifcant issue for future large-scale science and technology assessment is determining how student background characteristics, including familiarity with computers, language, culture, and educational experience, infuence performance on diferent types of tasks and innovative assessment designs that leverage the power of technology.

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Though multiple studies have collected empirical evidence for mode efects of large-scale assessments, more research is needed to ensure the equity and fairness of these tests (Way & Strain-Seymour, 2021). Digital data capture and enhanced opportunities for scoring and interpretation. One of the greatest benefts of digital assessment is the capacity to capture a wider range of data about student performance than simply correct fnal choices on selected-response tasks. Fortunately, most of the large-scale assessment data (e.g., PISA, TIMSS, NAEP) are publicly accessible or partially accessible to researchers for further analyses. Some tools developed for data analyses, such as IEA’s IDB Analyzer, are also available to researchers. Much has recently been written about student response process data and the opportunities it provides for capturing what students are doing when they solve problems and answer questions related to science and technology (see the chapters in Ercikan & Pellegrino, 2017). Such data include the time taken to perform various actions, the actual activities chosen, and their sequence and organization. The potential exists for examining the global and local strategies students use while solving assessment problems and the implications, including how such strategies relate to the accuracy or appropriateness of fnal responses. While capturing such data in a digital environment is “easy”, making sense of it is far more complicated. The same can be said for capturing data to constructed-response questions where students may be expressing in written form an argument or explanation about some scientifc problem or phenomenon or describing the design of a scientifc investigation (Zhai et al., 2021b). The data capture contexts described earlier are challenging in many ways, not the least of which is scoring and interpretation. It is here that AI and machine learning can play a signifcant role in future science assessments. Machine learning mimics human scoring processes by frst “learning” from scoring by human experts to develop algorithmic models and then applying those models to new student responses (Zhai et al., 2020b). This approach is especially helpful for constructed responses that require students to write their responses. Developments in machine learning may also allow researchers to analyze complex response process data of the type described earlier (Zhai, 2021). Such data are complex, and traditional statistical methods are often difcult or inappropriate to apply. Machine learning, however, might assist in analyzing the data to reveal patterns that provide important insights into students’ cognitive processes in problem solving (Zhai et al., 2020a). Such data may prove to be especially informative about student thinking and reasoning and thus add greatly to the knowledge we can gain about student competence from large-scale assessments above and beyond the performance accuracy data they now provide. Evidentiary inferences accounting for personal behaviors, context, and multiculturalism. Though large-scale assessment programs such as TIMMS and PISA make signifcant eforts to take into account various factors in performance, including behavioral, contextual, and cultural diferences, the interpretation of results from international as well as national large-scale assessments has much still to do in considering these impacts. Issues of comparability abound (see Berman et al., 2020). They are challenging and need to be attended to in the design of assessments used with students from diferent countries or jurisdictions to ensure fairness and comparability of the constructs being measured and the interpretations of scores. Evidentiary inference toward valid conclusions in assessment has to consider personal behaviors, which might challenge validity if appropriate care is not provided. An interesting example was provided in a study reported by Pohl et al. (2021). They revealed that diferences in student response processes of the type described earlier, when combined with scoring methods, signifcantly change the rankings of countries on an assessment such as PISA. They showed that current reporting practices confound diferences in test-taking behavior with diferences in competencies, and they can do so in a diferent way for diferent examinees, threatening the fairness of comparisons, such as country rankings. They went on to show that test-taking behavior is not a nuisance factor that may confound measurement but an aspect that provides important information on how examinees approach tasks, which can be relevant for real-life outcomes. Disentangling and reporting all of these factors as part of a portfolio of performance could be a method to result in fairer comparisons across

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groups and also allow for a better understanding and valid assessment of competencies, as well as for more tailored interventions, targeting possible causes of low performance. It is not uncommon that the interpretation of TIMSS, PISA, and NAEP results is made in isolation of other important contextual and cultural factors related to family, school, curricular, country, and other diferences that the assessment participants have experienced, as well as cultural diferences in responding that may be contributing to the results (He et  al., 2019). Inappropriate interpretation, whereby invalid causal conclusions are drawn about the meaning and implications of performance diferences between countries, is not uncommon when it comes to international assessment results, such as TIMMS or PISA scores (Bennett, 2018). As Carnoy et al. (2016) argued, language has become an inextricable part of the construct in large-scale assessments. Particularly during cross-country comparisons, cultural diversity along with linguistic diversity has been found to potentially contaminate the interpretations and uses of large-scale assessments (Solano-Flores & Milbourn, 2016). These factors need to be addressed in future large-scale assessments to better serve the purposes of these assessments, as well as the participating countries. Appropriate use of large-scale assessment results as policy indications. Though large-scale assessment results can provide valuable information for policymakers with regard to their educational systems, appropriate use of the assessment outcomes in terms of educational policy is of signifcant concern. Many researchers warn that policy inferences and decisions made from scores on large-scale international assessments should be made with great caution (Braun, 2018; Braun & Singer, 2019). Ercikan et al. (2015) raised two signifcant concerns about inference and uses of jurisdiction rankings to inform policy adoption and practices. They suggest that there is a variegated and complex picture of the relations between achievement rankings, and crucial factors to be considered before one can relate the rankings with specifc policies in a nation. Second, they suggest that using correlations of achievements in high-performing countries to inform other countries’ education reform policies should be done with caution. Diverse cultures, economics, demographics, curriculum coverage, and geography should not be ignored when interpreting relations among scores. For example, Huang et al. (2016) found that curriculum coverage played a signifcant role in diferential item functioning, which may impact the interpretation of the scores. The organization of a nation’s educational system could signifcantly impact curriculum development and coverage. In some highly centralized educational systems (e.g., China) where policies and relevant implementations are uniform, variations could be smaller than in some decentralized systems (e.g., the United States) where schools and teachers have considerable input in curriculum selection and implementation (Hooper et al., 2015).

Concluding Remarks Two decades ago, the National Research Council report Knowing What Students Know: The Science and Design of Educational Assessment (Pellegrino et al., 2001) stated, “assessment practices need to move beyond a focus on component skills and discrete bits of knowledge to encompass the more complex aspects of student achievement” (p. 3). The last two decades have continued to reveal that student achievement is indeed complex and multifaceted and that society increasingly needs students to demonstrate complex forms of competence appropriate to the global challenges facing society. During that time when large-scale science assessments have continued to evolve in an attempt to reveal facets of student achievement in science and technology that are relevant and critical to the educational research, practice, and policy communities. Only if the “complex aspects of student achievement” are included in our assessment frameworks, task designs, and interpretive methods can we make valid conclusions to inform those stakeholder communities. PISA, TIMMS, and NAEP have accomplished much in moving the feld of science assessment forward, and they have impacted international and national science education policies. They are, however, by no means identical and continue to evolve in multiple ways, including their assessment purpose, focus, item characteristics, delivery mechanisms, scoring and reporting procedures, and intended audiences and 1094

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interpretive uses. They are also not without their critics with respect to many of those characteristics (Zhao, 2020). Future versions no doubt will show changes and improvements taking into account both relevant criticisms as well as contemporary thinking about the learning and knowing of science with the goal of producing fndings that are reliable and conclusions that are valid and informative. Technologies, including AI and machine learning, are signifcantly changing the world we live in and will no doubt prove valuable in expanding the complex constructs we can target and the evidence-based inference processes we can apply in large-scale science assessments to make them more efcient, robust, and valid. In conclusion, we believe that large-scale science and technology assessments will continue to serve as some of the most important and infuential sources of evidence for informing and reforming educational practices and policies thus supporting the improvement of 21st-century educational systems and the scientifc and technological literacy of our youth.

Note 1 PISA 2018 assessed science as a minor domain, so we have focused on the prior PISA 2015, which included science as the major domain.

References American Association for the Advancement of Science. (1990). Science for all Americans. Oxford University Press. Anderson, L. W., Krathwohl, D. R., & Bloom, B. S. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman. Bennett, R. E. (2018). Educational assessment: What to watch in a rapidly changing world. Educational Measurement: Issues and Practice, 37(4), 7–15. Bloom, B., Englehart, M., Furst, E., Hill, W., & Krathwohl, D. (1956). Taxonomy of educational objectives: The classifcation of educational goals. Handbook I: Cognitive domain. Longmans, Green. Berman, A. I., Haertel, E. H., & Pellegrino, J. W. (2020). Comparability of large-scale educational assessments: Issues and recommendations. National Academy of Education. Braun, H. I. (2018). How long is the shadow? The relationships of family background to selected adult outcomes: Results from PIAAC. Large-Scale Assessments in Education, 6(1), 1–52. Braun, H. I., & Singer, J. D. (2019). Assessment for monitoring of education systems: International comparisons. The ANNALS of the American Academy of Political and Social Science, 683(1), 75–92. Britton, E. D., & Schneider, S. A. (2014). Large-scale assessments in science education. In Handbook of research on science education (pp. 1021–1054). Routledge. Bybee, R. W. (2010). Advancing STEM education: A 2020 vision. Technology and Engineering Teacher, 70(1), 30. Bybee, R. W., McCrae, B., & Laurie, R. (2009). PISA 2006: An assessment of scientifc literacy. Journal of Research in Science Teaching: The Ofcial Journal of the National Association for Research in Science Teaching, 46(8), 865–883. Carnoy, M., Khavenson, T., Loyalka, P., Schmidt, W. H., & Zakharov, A. (2016). Revisiting the relationship between international assessment outcomes and educational production: Evidence from a longitudinal PISATIMSS sample. American Educational Research Journal, 53(4), 1054–1085. Chi, S., Liu, X., Wang, Z., & Won Han, S. (2018). Moderation of the efects of scientifc inquiry activities on low SES students’ PISA 2015 science achievement by school teacher support and disciplinary climate in science classroom across gender. International Journal of Science Education, 40(11), 1284–1304. Denoël, E., Dorn, E., Goodman, A., Hiltunen, J., Krawitz, M., & Mourshed, M. (2017). Drivers of student performance: Insights from Europe. Mckinsey & Company. Douglas, K. A., Gane, B. D., Neumann, K., & Pellegrino, J. W. (2020). Contemporary methods of assessing integrated STEM competencies. In Handbook of research on STEM education (pp. 234–254). Routledge. Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (2007). Taking science to school: Learning and teaching science in grades K-8 (Vol. 500). National Academies Press. Ercikan, K., & Pellegrino, J. W. (2017). Validation of score meaning for the next generation of assessments: The use of response processes. Taylor & Francis. Ercikan, K., Roth, W.-M., & Asil, M. (2015). Cautions about inferences from international assessments: The case of PISA 2009. Teachers College Record, 117(1), 1–28. Fishbein, B., Martin, M. O., Mullis, I. V., & Foy, P. (2018). The TIMSS 2019 item equivalence study: Examining mode efects for computer-based assessment and implications for measuring trends. Large-Scale Assessments in Education, 6(1), 1–23.

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Xiaoming Zhai and James W. Pellegrino Gane, B. D., Zaidi, S. Z., & Pellegrino, J. W. (2018). Measuring what matters: Using technology to assess multidimensional learning. European Journal of Education, 53(2), 176–187. Goos, M., Manning, A., & Salomons, A. (2009). Job polarization in Europe. American Economic Review, 99(2), 58–63. Grek, S. (2009). Governing by numbers: The PISA “efect”in Europe. Journal of Education Policy, 24(1), 23–37. Gurria, A. (2016). PISA 2015 results in focus. PISA in Focus, 67(1). Harris, C. J., Krajcik, J. S., Pellegrino, J. W., & DeBarger, A. H. (2019). Designing knowledge-in-use assessments to promote deeper learning. Educational Measurement: Issues and Practice, 38(2), 53–67. He, J., Barrera-Pedemonte, F., & Buchholz, J. (2019). Cross-cultural comparability of noncognitive constructs in TIMSS and PISA. Assessment in Education: Principles, Policy & Practice, 26(4), 369–385. Hooper, M., Mullis, I. V., Martin, M. O., & Fishbein, B. (2015). TIMSS 2015 context questionnaire framework. Timss, 61–82. Huang, X., Wilson, M., & Wang, L. (2016). Exploring plausible causes of diferential item functioning in the PISA science assessment: Language, curriculum or culture. Educational Psychology, 36(2), 378–390. Kauertz, A., Neumann, K., & Haertig, H. (2012). Competence in science education. In Second international handbook of science education (pp. 711–721). Springer. Lau, K.-C., & Lam, T. Y.-P. (2017). Instructional practices and science performance of 10 top-performing regions in PISA 2015. International Journal of Science Education, 39(15), 2128–2149. Li, M., Ruiz-Primo, M. A., & Shavelson, R. J. (2006). Towards a science achievement framework: The case of TIMSS 1999. In Contexts of learning mathematics and science: Lessons learned from TIMSS (pp. 291–311). Routledge. Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute, 150. Martin, M. O., von Davier, M., & Mullis, I. V. (2020). Methods and procedures: TIMSS 2019 technical report. International Association for the Evaluation of Educational Achievement. McKinsey, C., & McKinsey, M. M. (2007). How the world’s best performing school systems come out on top. www. mckinsey.com/App_Media/Reports/SSO/Worlds_School_Systems_Final.pdf Mullis, I. V. S., & Martin, M. O. (2017). TIMSS 2019 assessment frameworks. ERIC. Mullis, I. V. S., Martin, M. O., Fishbein, B., Foy, P., & Moncaleano, S. (2021). Findings from the TIMSS 2019 problem solving and inquiry tasks. L. S. o. E. a. H. D. TIMSS & PIRLS International Study Center, Boston College and International Association for the Evaluation of Educational Achievement (IEA). Mullis, I. V. S., Martin, M. O., Foy, P., Kelly, D. L., & Fishbein, B. (2020). TIMSS 2019 international results in mathematics and science. https://timssandpirls.bc.edu/timss2019/international-results/ National Artifcial Intelligence Initiative Ofce. (2020). National artifcial intelligence initiative: Overseeing and implementing the United States national AI strategy. www.ai.gov/ National Assessment Governing Board. (2008). Science framework for the 2009 National Assessment of Educational Progress. ERIC Clearinghouse. National Assessment Governing Board. (2018). Technology and engineering literacy framework for the 2018 National Assessment of Educational Progress. ERIC Clearinghouse. National Assessment Governing Board. (2019). Science framework for the 2019 National Assessment of Educational Progress. ERIC Clearinghouse. National Center on Education and the Economy. (2021). NCEE’s blueprint for a high-performing education system. https://ncee.org/blueprint/ National Research Council. (2000). How people learn: Brain, mind, experience, and school: Expanded edition. National Academies Press. National Research Council. (2001). Knowing what students know: The science and design of educational assessment. National Academies Press. National Research Council. (2007). Taking science to school: Learning and teaching science in grades K-8. National Academies Press. National Research Council. (2012a). Education for life and work: Developing transferable knowledge and skills in the 21st century (J. W. Pellegrino & M. L. Hilton, Eds.). National Academies Press. National Research Council. (2012b). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press. Neidorf, T., Stephens, M., Lasseter, A., Gattis, K., Arora, A., Wang, Y., & Holmes, J. (2016). A comparison between the Next Generation Science Standards (NGSS) and the National Assessment of Educational Progress (NAEP) frameworks in science, technology and engineering literacy, and mathematics. US Department of Education. Nelson, R. R., & Phelps, E. S. (1966). Investment in humans, technological difusion, and economic growth. The American Economic Review, 56(1/2), 69–75. Neumann, K., Fischer, H. E., & Kauertz, A. (2010). From PISA to educational standards: The impact of large-scale assessments on science education in Germany. International Journal of Science and Mathematics Education, 8(3), 545–563. Ng, P. T. (2011). How the world’s most improved school systems keep getting better. Journal of Educational Change, 12, 463–468.

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Large-Scale Assessment in Science Education NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press. OECD. (2001). Measuring student knowledge and skills: A new framework for assessment. www.oecd.org/education/ school/programmeforinternationalstudentassessmentpisa/33693997.pdf OECD. (2003). The 2003 assessment framework: Mathematics, reading, science and problem solving knowledge and skills. Organisation for Economic Co-operation and Development. OECD. (2006). Assessing scientifc, reading and mathematical literacy: A framework for PISA 2006. www.oecd-ilibrary. org/docserver/9789264026407-en.pdf?expires=1629582441&id=id&accname=guest&checksum=67498B 2B817D8B3589DA9F060F88FBF4 OECD. (2017a). PISA 2015 assessment and analytical framework: Science, reading, mathematic, fnancial literacy and collaborative problem solving. O. Publishing. https://doi.org/10.1787/9789264281820-en OECD. (2017b). PISA 2015 technical report. www.oecd.org/pisa/data/2015-technical-report/PISA2015_ TechRep_Final.pdf OECD. (2020). PISA 2024 strategic vision and direction for science. www.oecd.org/pisa/publications/PISA2024-Science-Strategic-Vision-Proposal.pdf OECD. (2023). PISA 2025 Science Framework. Paris. Oliver, M., McConney, A., & Woods-McConney, A. (2019). The efcacy of inquiry-based instruction in science: A comparative analysis of six countries using PISA 2015. Research in Science Education, 1–22. Osborne, J. (2007). Science education for the twenty frst century. EURASIA Journal of Mathematics, Science and Technology Education, 3(3), 173–184. Osborne, J., Collins, S., Ratclife, M., Millar, R., & Duschl, R. (2003). What “ideas-about-science” should be taught in school science? A Delphi study of the expert community. Journal of Research in Science Teaching, 40(7), 692–720. Pellegrino, J. W. (2013). Profciency in science: Assessment challenges and opportunities. Science, 340(6130), 320–323. Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. ERIC. Pellegrino, J. W., & Quellmalz, E. S. (2010). Perspectives on the integration of technology and assessment. Journal of Research on Technology in Education, 43(2), 119–134. Pellegrino, J. W., Wilson, M. R., Koenig, J. A., & Beatty, A. S. (2014). Developing assessments for the next generation science standards. The National Academies Press. PISA, O. (2015). Released feld trial: Cognitive items. www.oecd.org/pisa/pisaproducts/PISA2015-Released-FTCognitive-Items.pdf Pohl, S., Ulitzsch, E., & von Davier, M. (2021). Reframing rankings in educational assessments. Science, 372(6540), 338–340. Ruiz-Primo, M. A., & Li, M. (2015). The relationship between item context characteristics and student performance: The case of the 2006 and 2009 PISA science items. Teachers College Record, 117(1), n1. Singer, J. D., & Braun, H. I. (2018). Testing international education assessments. Science, 360(6384), 38–40. Solano-Flores, G., & Milbourn, T. (2016). Assessment capacity, cultural validity and consequential validity in PISA. Relieve, 22(1), M12. Teltemann, J., & Klieme, E. (2016). The impact of international testing projects on policy and practice. In Handbook of human and social conditions in assessment (pp. 369–386). Taylor Francis. Travers, K. J., & Westbury, I. (1988). The IEA study of mathematics I: Analysis of mathematics curriculum (Vol. 1). Pergamon Press. Way, D., & Strain-Seymour, E. (2021). A framework for considering device and interface features that may afect student performance on the national assessment of educational progress. https://www.air.org/sites/default/fles/Frameworkfor-Considering-Device-and-Interface-Features-NAEP-NVS-Panel-March-2021.pdf Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology, 30(2), 1–11. Zhai, X., Haudek, K. C., Shi, L., Nehm, R., & Urban-Lurain, M. (2020a). From substitution to redefnition: A framework of machine learning-based science assessment. Journal of Research in Science Teaching, 57(9), 1430–1459. Zhai, X., Haudek, K. C., Wilson, C., & Stuhlsatz, M. (2021a). A framework of construct-irrelevant variance for contextualized constructed response assessment. Frontiers in Education, 6, 751283. https://doi.org/10.3389/ feduc.2021.751283 Zhai, X., Krajcik, J., & Pellegrino, J. (2021b). On the validity of machine learning-based next generation science assessments: A validity inferential network. Journal of Science Education and Technology, 30(2), 298–312. Zhai, X., & Li, M. (2021). Validating a partial-credit scoring approach for multiple-choice science items: An application of fundamental ideas in science. International Journal of Science Education, 43(10), 1640–1666. Zhai, X., Yin, Y., Pellegrino, J. W., Haudek, K. C., & Shi, L. (2020b). Applying machine learning in science assessment: A systematic review. Studies in Science Education, 56(1), 111–151. Zhang, T., Xie, Q., Park, B. J., Kim, Y. Y., Broer, M., & Bohrnstedt, G. (2016). Computer familiarity and its relationship to performance in three NAEP digital-based assessments. AIR-NAEP Working Paper# 01–2016. Zhao, Y. (2020). Two decades of havoc: A synthesis of criticism against PISA. Journal of Educational Change, 1–22.

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Science Teacher Education Section Editor: Saouma Boujaoude

34 SCIENCE TEACHER ATTITUDES AND BELIEFS Reforming Practice M. Gail Jones and Soonhye Park

Introduction There is increasing evidence that science teachers’ attitudes and beliefs are key to improving science education. Previous research has clearly shown that the success of innovative professional development programs, new science curricula and standards, and changes in teaching practices rest frmly on teachers’ attitudes and beliefs (c.f., Bryan, 2012; Jones & Carter, 2006; Jones & Legon, 2014; Wallace, 2014). In this chapter, we examine the research on science teachers’ attitudes and beliefs through the lens of science education reform. In the sections that follow, we review the research on science teacher attitudes and beliefs that has been published since the 2014 Handbook of Research on Science Education (Volume II). The review includes an examination of the defnitions of teacher attitudes and belief constructs, descriptions of the functions and stability of science teachers’ beliefs (in and out of the classroom), theoretical frameworks related to science teachers’ attitudes and beliefs (with a focus on expectancy-value and self-efcacy), studies related to science teachers’ epistemological beliefs and beliefs about reform-oriented science teaching and science, strategies to measure attitudes and beliefs, and a discussion of future research needed in science teacher education. Most of the previous research on teachers’ attitudes and beliefs focused on correlational studies that documented relationships that might exist between a set of beliefs or attitudes and instructional behaviors (e.g., Jones & Legon, 2014). In this review of research, we extend this correlational view of attitudes and beliefs to examining theoretical models that researchers apply in their studies as well as models that have explanatory and predictive capacities for understanding teachers’ practices in reform environments from the lens of teacher attitudes and beliefs.

Scope of the Review The review of research began with an identifcation of the constructs that are used by researchers to defne attitudes and beliefs related to science teachers and science teaching in science education as well as general teacher education studies that were relevant for defning teacher attitudes and beliefs more broadly. In this review, we selected only peer-reviewed, scholarly articles that were published in educational journals. The articles were found by searching the databases of ERIC, EBSCOhost, and APA PsycINFO for peer-reviewed science education research papers relevant to teacher beliefs and attitudes. The scope of the review focused on science education research that has taken place over the last six years. In the frst phase of the review, we located all studies that were identifed with the

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search terms of “science teacher beliefs” and “science teacher attitudes”. These two terms were also examined along with secondary terms that indicate particular aspects of teacher attitudes and beliefs such as “identity”, “controversial issues”, “worldview”, “self-efcacy”, “gender”, “religious beliefs”, “climate change”, “values”, “culture”, “science disciplines”, “self-concept”, “science practices”, “inquiry”, “assessment”, “evolution”, “technology”, “metacognition”, “professional development”, “epistemology”, and “epistemic beliefs”. The search included studies at all levels and contexts of science education (K–12, higher education, and informal education) as well as papers that described theoretical positions. As each paper was reviewed, we closely examined additional studies that were cited to extend the search to studies that may have been missed in the initial search using databases. Within each study, we examined the context, the methodology applied, the assessments utilized, the results, implications, and the recommendations for future research.

Attitude and Belief-Related Constructs One of the signifcant challenges that face researchers who examine science teacher attitudes and beliefs is the lack of clear and concise defnitions for these constructs. Without a consensus defnition, it is difcult for researchers to validate and replicate fndings or build new theoretical models. As shown in Table 34.1, teachers’ beliefs and attitudes are defned in relation to an array of constructs Table 34.1 Defnitions of Attitude and Belief-Related Constructs Related Constructs

Authors

Views, conceptions, mental representations Unconscious guides; individually held conceptions that are in constant relation to the context and teachers’ experiences; flters used in the evaluation of information entering the cognitive system Beliefs and emotions, though distinct conceptually, are nevertheless completely intertwined; beliefs are paradigms through which situations are interpreted

Caleon et al. (2018) Fives and Buehl (2012)

Beliefs are often defned relative to other mental constructs such as knowledge, dispositions, or attitudes; mental representation . . . active in cognition; a representation held in long-term memory Beliefs, self-efcacy, and attitude are interrelated Assumptions felt to be true Filters for experiences, frames for addressing problems, and guides for action Beliefs, like attitudes but in contrast to emotions, are relatively stable and are less intense . . . than both attitudes and emotions A functionally integrated cognitive system Dispositions about phenomena Values, worldviews; mental constructions Motivational constructs that infuence (or guide) the goals teachers set, their efort toward meeting those goals, their perseverance in the face of challenges, and how they feel while engaged in the task Multidimensional; overlap with opinions, drives, motivations; thoughts, beliefs, feelings toward teaching; afective states; anxiety, enjoyment, self-efcacy, perceptions Convictions Beliefs frame a person’s decisions, actions, and overall view of the world

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Gill and Hardin (2014) Goldin et al. (2016) Hutner and Markman (2016)

Kazempour and Sadler (2015) Kleickmann et al. (2016) Levin (2015) McLeod (1992) Rokeach (1968) Schraw and Olafson (2015) Skott (2015) Tschannen-Moran et al. (1998)

van Aalderen-Smeets and van der Molen (2013) Watt and Richardson (2015) Wong and Luft (2015)

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that include conceptions, representations, mental constructs, dispositions, afective states, cognitive factors, frames, and paradigms. As researchers have continued to document the infuence of beliefs and attitudes on educational reform, there is a growing consensus that beliefs are associated with motivation, interpretation of information, and prior experiences (e.g., Eccles & Wigfeld, 2020). Attitudes, unlike beliefs, have traditionally been defned as constructs associated with an evaluation of being favorable or unfavorable (Eagly & Chaiken, 1993). In early studies, researchers argued that beliefs were cognitive constructs and attitudes were afective constructs (Fishbein, 1967). As can be seen in the studies that are highlighted in this chapter, it is now recognized that beliefs and attitudes have cognitive as well as experiential dimensions (e.g., experiences situated in a context; Fives & Buehl, 2012).

Functions of Beliefs In this chapter, we frame the review around the roles that beliefs play in teacher practice and professional growth. These roles include flters, frames, and guides (Fives & Buehl, 2012), as shown in Figure 34.1. First, teachers’ interpretations of teaching experiences (prior and current) and new information are fltered through their beliefs both implicitly and explicitly. These beliefs can be about teaching roles, epistemology, instructional practices, or broader beliefs such as worldview. Beliefs can serve as interpretive frames for problems, teaching contexts, tasks, and new knowledge and skills such as beliefs about the likelihood of success for a new instructional practice or reform efort. Additionally, beliefs can serve as active guides that have predictive value in shaping how new knowledge or skills may be incorporated into preexisting schemas or how new knowledge is related to teachers’ expectancies for success. This later role of beliefs is refected in teachers’ self-efcacy and task value for new pedagogical approaches or strategies. Through those three major roles, science teachers’ beliefs greatly infuence their teaching practices that are in turn infuencing their beliefs.

Science Teacher Experiences ˜ Experiences as a student ˜ Experiences in teacher education ˜ Experiences with instruction ˜ Experiences in professional development ˜ Experiences with students

Beliefs as Filters

Beliefs as Frames

Beliefs as Guides

Interpreting: • New knowledge • New instructional practices • Curricular reforms • Epistemology

Defining: • Experiences • Knowledge • Skills • Problems • Tasks • Contexts

Shaping: • Instructional decisions and actions • Intentions to implement reformed practices

Science Teacher Practice (in and out of the Classroom) Planning, Teaching, & Reflection Figure 34.1 The roles of science teachers’ beliefs as flters, frames, and guides (modifed from Fives & Buehl, 2012).

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Stability of Science Teachers’ Beliefs Just as there is a myriad of defnitions of beliefs, researchers have recognized that the stability and malleability of beliefs vary across types of beliefs and contexts (e.g., Watt & Richardson, 2015). Recognizing that beliefs are highly related to instructional practices and the efectiveness of educational reform, some researchers are examining change in beliefs over time and strategies that promote these changes. Fives and Buehl (2012) have clarifed the meaning of teachers’ beliefs and report that contentrelated beliefs, such as beliefs about the nature of science, are more malleable than general beliefs (e.g., general teaching efcacy). Liljedahl (2010) noted that teachers often experience rapid and profound changes in beliefs. After examining 42 cases of these changes in beliefs in mathematics teachers, Liljedahl made the argument that changes in beliefs are the result of diferent processes that include “(1) conceptual change, (2) accommodating outliers, (3) reifcation, (4) leading belief change, and (5) push-pull rhythm of change” (p.  412). In later work, Liljedahl (2011) applied conceptual change theory to investigate the impact of cognitive confict as a driver for changes in mathematics teachers’ beliefs about the nature of mathematics, mathematics teaching, assessment, student knowledge, student learning, and student motivation. Liljedahl found that conceptual change as an intervention was a valid and efective way to support change in teachers’ initial beliefs. Hofman and Seidel (2015) also examined changes in beliefs and argued that teachers’ beliefs exist across a continuum (e.g., Fives & Buehl, 2012), with some beliefs subject to rapid change while other beliefs, such as those related to epistemology, are much more stable. For example, in an examination of secondary science teachers’ practices and beliefs, Boesdorfer et al. (2019) reported that beliefs about how students learn were particularly stable and shaped teachers’ instructional practices. Conversely, teaching experience and professional development experiences were not infuential in changing teachers’ beliefs. Wong and Luft (2015) as well as Martin et al. (2019) have also found that beliefs about how students learn infuence science instructional practices more than teachers’ beliefs about teaching. Across these studies, the research suggests that conceptual change approaches are emerging as an efective way to modify beliefs, but the studies also note that changing beliefs is related to the types of beliefs held by teachers. In the sections that follow, theoretical frameworks that explain factors infuencing science teachers’ changes in beliefs are reviewed.

Theoretical Frameworks for Teachers’ Attitudes and Beliefs Expectancy Value Theory: Beliefs and Motivation Across studies, researchers have found that developing science teachers’ professional competencies involves taking teachers’ attitudes, behaviors, and beliefs into account. Eccles and Wigfeld (2020) have argued that motivations and behavioral choices (e.g., teachers’ instructional decisions) are embedded in a complex model that includes the cultural milieu, personal characteristics (e.g., aptitude, sex, ethnic group membership), prior experiences, perceptions of social roles, goals, social and personal identities, self-concept and self-efcacy, afective memories, expectations of success, and subjective task value (e.g., utility and costs). Eccles and Wigfeld’s (2020) expectancy value theory model is adapted and applied here (see Figure 34.2) as a framework for examining the motivational components of science teachers’ beliefs and attitudes. This model allows researchers to integrate many of the attitude- and belief-related constructs that arise in studies of science teachers’ development and practices. Within this framework, teachers are situated in a cultural context, have personal characteristics that infuence their attitudes and beliefs, have had prior relevant experiences, and hold perceptions of other teachers’ beliefs 1104

Cultural Milieu 1. Gender and other social role systems 2. Stereotypes of teaching and the nature of abilies for teaching 3. Family demographics

Instruc˜onal Goals and Self-Schemata for Teaching 1. Teaching self-concept 2. Teaching self-schemata 3. Teacher identy 4. Short-term goals 5. Long-term goals

Expecta˜on of Success

Science Teacher Competencies 1. A˝tudes 2. Behaviors 3. Beliefs

Other Science Educators’ Beliefs and Behaviors

1105 Personal Characteris˜cs 1. Aptudes 2. Temperaments 3. Sex 4. Ethnic group

Previous Science Teaching-Related Experiences

A°ec˜ve Reac˜ons and Memories Interpreta˜on of Experience

Figure 34.2 The expectancy value theory model (modifed from Eccles & Wigfeld, 2020).

Subjec˜ve Task Value 1. Science teaching-related interest-enjoyment 2. Science teaching ulity value 3. Relave costs

Science Teacher Attitudes and Beliefs

Percep˜on of… 1. Other science teachers’ beliefs and behaviors 2. Gender and other social roles 3. Ed context characteriscs and demands 4. Possible acvies

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and attitudes, social roles, and contextual characteristics and demands. These contextual and background factors infuence teachers’ instructional goals and science teaching–related self-schemata (selfconcept, self-efcacy, identity, and goals). Within the model, instructional goals and self-schemata contribute to teachers’ afective reactions and memories, expectations of success, evaluations of the task value (attainment, interest, utility, and costs). Ultimately, these factors shape the attitudes, beliefs, and behaviors of science teachers. Previous studies have shown that eforts to reform instruction, curricula, and science teachers’ professional development are likely to fail if teachers do not believe the reform eforts are useful and ft in their instructional contexts (e.g., Czerniak & Lumpe, 1996; Feldman, 2002). Eccles and Wigfeld (2020) argue that unless individuals fnd that a task is congruent with their self-schema and identity such that they can see themselves as being successful with the task, the individual is not likely to adopt that behavior. The congruence of reform goals with teachers’ self-schema can be predictive of whether teachers will be successful in implementing reform-based science education initiatives. The expectancy value theoretical model outlined here can inform the “long-term ontogeny of the beliefs and memories underlying individuals’ motivated achievement-related choices’ (Eccles & Wigfeld, 2020, p. 1). Eccles and Wigfeld (2020) argue that the model is hierarchical and that an individual’s choices and behaviors are dependent on the other factors that mediate these outcomes. The mediating factors include science teachers’ self-concepts, work-related task perceptions, and teaching-related identities. The Eccles and Wigfeld model accounts for the individual complexities “developmental processes, situational processes, individual diferences, and individual context processes” (Eccles & Wigfeld, 2020, p. 3) that infuence teachers’ beliefs and, ultimately, behavior. The model is a useful tool that allows researchers to examine the intrinsically motivated factors such as interest and persistence, as well as extrinsically motivated factors such as perceptions of teachers’ roles, other teachers’ beliefs, and stereotypes of teaching and teachers. A less studied component of expectancy value theory is the cost factor. Costs, according to Eccles and Wigfeld (2020), include efort costs, emotional costs, and opportunity costs, or “the extent to which doing one task takes away from one’s ability or time to do other valued tasks” (3.2.1, para. 1). The cost factor is particularly salient for science teachers who face the ongoing challenges of updating their knowledge of advancements in science as well as integrating inquiry and laboratory experiences into their instruction. If science teachers perceive the costs of a new reform efort as higher than the perceived benefts of the reform, the reform is not likely to succeed. Some of the costs of educational reform can potentially be negated with professional development that addresses aspects of emotional costs (e.g., addressing anxiety or stress; Perez et al., 2014) as well as opportunity costs (e.g., providing teachers with time away from students to learn and practice new instructional skills). As shown in this section, science teachers’ beliefs are associated with an array of factors that ultimately infuence instructional attitudes, beliefs, and behaviors.

Science Teachers’ Self-Effcacy In recent years, science education researchers have increasingly applied expectancy value theory in studies of science teachers. Most often, teachers’ self-efcacy is measured as an outcome of a professional development experience. Self-efcacy is typically defned as a belief about the extent to which one can complete a task in the future (e.g., Bandura, 1977). Bandura (1977) argues that self-efcacy beliefs infuence choices an individual makes, the amount of efort individuals will expend on an activity, and the amount of time that individuals will expend if they face obstacles while performing an activity. In the studies described in this chapter, researchers have examined the relationships of science teaching self-efcacy to other variables that are associated with science teachers’ beliefs and attitudes (see Figure 34.2). 1106

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Science Teaching Self-Effcacy and Preservice Teacher Education One study on 112 preservice elementary teachers’ science teaching efcacy by Deehan et al. (2019) examined the impact of taking science courses over a four-year teacher education program. The study found that science teaching efcacy increased after taking science courses, and the level of efcacy remained stable even after the courses ended. The researchers noted that efcacy scores following the completion of a course were the highest when the course included embedded practical science teaching experiences. The impact of teacher preparation on preservice teachers was also studied by Velthuis et al. (2014), who compared the self-efcacy of 292 primary teachers in the Netherlands who had completed science classes to those who had completed science methods classes. The results showed that selfefcacy for teaching science was correlated with higher levels of self-rated science knowledge and science teaching experience. In addition, preservice teachers who had taken science classes had higher self-efcacy than preservice teachers who had taken science methods classes. When the teachers were followed into their second year of teacher training, there were no longer diferences in selfefcacy based on completed coursework. The impact of teacher education courses was also examined by Menon and Sadler (2016), who examined changes in preservice elementary teachers’ self-efcacy as a result of taking a science content course. The results of this study found that preservice teachers’ science self-efcacy beliefs and conceptual understandings of science improved. They also found there were moderate gains in personal science teaching efcacy beliefs as a result of taking the course as well as improved science teacher self-image. Other studies have also reported changes in elementary preservice teachers’ attitudes and selfefcacy toward science and science teaching as a result of taking a science methods course. Kazempour and Sadler (2015) found participating in a science methods class increased positive attitudes toward science and science teaching as well as increased science teaching self-efcacy. These researchers found diferences in the infuence of the class depending on the degree of self-efcacy students held at the beginning of the course. Using a diferent approach to examining preservice science education, Buldur (2017) studied preservice teachers’ beliefs about science teaching over a four-year teacher education program and reported that in the beginning teachers held conventional views of teaching (teacher-centered), but by the end of the teacher ed program students’ beliefs shifted to a student-centered orientation. This shift in views of teaching started as early as the frst science methods course taken in the teacher education program.

Science Teaching Self-Effcacy Beliefs and In-Service Teacher Education Recognizing the importance of science teaching self-efcacy as an indicator of teachers’ beliefs, researchers have increasingly measured changes in science teaching self-efcacy related to professional development initiatives. Sandholtz and Ringstaf (2014) examined K–2 teachers’ beliefs, science instruction, and self-efcacy after participating in a three-year professional development program that focused on developing teachers’ content knowledge and use of research-based instructional strategies. This study found teachers’ overall self-efcacy for teaching science, personal efcacy, and outcome expectancy efcacy increased signifcantly. The researchers also found that changes in self-efcacy were not correlated with changes in instructional time spent on science, but the study did fnd that as a result of participating in the professional development, teachers were more likely to engage in hands-on science activities with their students. Elementary science teachers’ self-efcacy was also examined in a study by Mintzes et al. (2013). These researchers examined how self-efcacy changed as a result of participating in a sustained science 1107

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professional learning community. The study found that there were signifcant gains in self-efcacy and the changes were attributed to having had a variety of mastery experiences along with emotional and social support (encouragement and opportunities to share ideas with others). Emotion and selfefcacy beliefs were also examined in a study by Brígido et al. (2013), who examined the relationship between self-efcacy beliefs and preservice teachers’ emotions about teaching. The researchers found that self-efcacy was correlated with positive emotions about teaching physics and chemistry. Similar to the positive impact of the social support reported by Mintzes et al. (2013), Kleickmann et al. (2016) reported that providing scafolding during professional development was instrumental in changing beliefs. In this study, the researchers examined the infuence of pedagogical and pedagogical content knowledge scafolding on elementary teachers’ professional development. Teachers who received scafolding experienced more positive changes in beliefs related to motivation increased instructional quality, and their students had higher achievement. To unpack some of the factors that infuence the development of science teaching self-efcacy, Wang et al. (2015) examined 233 Taiwanese elementary teachers’ sources of science teaching selfefcacy. This study utilized a mediational model approach and examined science teaching self-efcacy, teaching and learning conceptions, technological-pedagogical content knowledge (TPACK), as well as attitudes toward internet-based instruction. The study found that TPACK and attitudes mediated the relationship between science teaching efcacy and concepts of teaching and learning. Wang et al. suggested that the knowledge and attitudes toward internet-based instruction may have mediated the relationship between constructivist teaching conceptions and outcome expectancy. This study is one of the few studies that included both attitudes toward teaching science as well as science teaching self-efcacy. Enderle et al. (2014) conducted a study that examined the infuence of a science research experience on elementary, middle, and high school teachers’ beliefs about reform-based teaching (e.g., inquiry). The authors found that the teachers’ beliefs changed as a result of participating in the research program. Self-efcacy was measured in relation to teacher beliefs, gender, and school type in a study conducted by Lin and Chao (2014). This study found that female teachers had more positive teaching beliefs than male teachers about teacher–student relationships overall, but there were no diferences in self-efcacy by gender. There is an increased interest in examining changes in teacher beliefs over extended periods of time, as seen in a study by DeCoito and Myszkal (2018), who examined the infuence of a longitudinal STEM professional development program on middle grades science teachers’ personal teaching self-efcacy and beliefs. The authors reported that although teachers had confdence that they could teach STEM topics, there was a disconnect between their self-efcacy and the extent to which they implemented inquiry-based instruction.

Teachers’ Collective Effcacy A seldomly applied efcacy-related construct is that of collective efcacy. Bandura (2000) defned collective efcacy as the shared beliefs that individuals have about their collective capacity to produce results. Collective efcacy may be particularly relevant in the social structure of science professional learning communities (e.g., Voelkel & Chrispeels, 2017), science departments and schools (e.g., Donohoo et al., 2018), and through the teachers’ use of common science standards and curricula. Jugert et al. (2016) examined collective agency in the context of pro-environmental intentions (e.g., climate change) and found that collective efcacy was efective when it also worked to raise selfefcacy. Jugert et al. noted that pro-environmental actions have limited impact when they are kept as personal actions. The author argues, “It is only when personal actions are part of an efcacious collective movement and are perceived as collective behavior that people may conceive of their actions as afecting environmental crises” (pp. 36–37). 1108

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Science Teachers’ Epistemological Beliefs Epistemological beliefs refer to one’s beliefs about knowledge and knowing in terms of nature, limits, source, and justifcation, which is often called personal epistemology (Elby, 2009; Hofer & Bendixen, 2012; Schommer, 1990). In literature, epistemological beliefs and epistemic beliefs are often used interchangeably. Kitchener (1983) asserted that epistemic is the accurate modifer for the beliefs in that epistemic refers to knowledge and thus it denotes beliefs about knowledge rather than beliefs about epistemology that the modifer, epistemological, denotes. However, researchers whose work is reviewed in this section dominantly used the term epistemological beliefs. Research on science teachers’ epistemological beliefs addresses three focal areas: (1) how preservice and in-service science teachers’ epistemological beliefs are characterized, (2) how they are related to teaching practices and student learning, and (3) what factors infuence their development. Concerning the frst focal area, some studies center on disciplinary domain-general epistemological beliefs (i.e., beliefs about the structure of knowledge and how people come to know), whereas others center on epistemological beliefs in the discipline of science specifcally (i.e., what it means to know in science). For example, in an international comparison study of preservice and in-service chemistry teachers’ beliefs about teaching and learning chemistry in Jordan, Turkey, and Germany, Al-Amoush et al. (2014) conceptualized general epistemological beliefs (i.e., knowledge transmission view vs. constructivist view) as one of the three dimensions of beliefs about teaching and learning chemistry. This conception is aligned with the idea that people have domain-general epistemological belief systems and that these beliefs are specifed to disciplinary domains when they are measured at task-specifc levels (Buehl et al., 2002). In contrast, adopting the domain-specifc perspective on epistemological beliefs, Ding and Zhang (2016) explored a progression trend in Chinese preservice and in-service teachers’ epistemological beliefs about physics and learning physics across diferent stages of their teacher education programs and beyond, using a disciplinary domain-specifc measure, the Colorado Learning Attitudes about Science Survey (CLASS) (Adams et al., 2006). Regardless of discipline-general or discipline-specifc approaches, this line of research has suggested that epistemological beliefs are central to teachers’ learning to teach, especially preservice teachers during teacher education programs. Specifcally, preservice teachers’ epistemological beliefs are closely related to their constructivist conception of teaching and learning (Güneş & Bahçivan, 2018), their goal orientations (Kaya, 2017), their evaluation of the usefulness of educational research in teaching practices (Guilfoyle et al., 2020), and their value of general pedagogical knowledge they learn during teacher education programs (Merk et al., 2017). In this regard, the calls for teacher educators to focus on the epistemological development of preservice science teachers have grown.

Epistemological Beliefs and Teaching Practices in Science Education Reform Several research studies on the second focal area have shed light on empirical associations between teacher epistemological beliefs and teaching practices. For example, Sengul et al. (2020) investigated the relationship between three urban science teachers’ epistemological beliefs, pedagogical content knowledge (PCK) of argumentation, and classroom practices after they participated in a one-year professional development program focusing on argument-driven inquiry (ADI). Their fndings demonstrated close links between a teacher’s epistemological beliefs and PCK of argumentation, and subsequently, implementation of the ADI model. Specifcally, one teacher with an evaluativist view that considered knowledge as the product of a continuing process of examination, comparison, evaluation, and judgment of diferent explanations and perspectives (Kuhn, 1991) exhibited high-quality PCK of argumentation, which was refected in teaching practices aligned with key features of the ADI model. However, the other teachers, who were inclined toward a 1109

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multiplist view that considered knowledge as subjective, uncertain, and dependent on personal experience without a need for reason and expertise (Kuhn, 1991), modifed the ADI model failing to acknowledge the value of argumentation instruction involving the generation and critique of multiple perspectives. Similarly, Rinehart and colleagues (2020) conducted a study with 77 K–8 teachers in the United States to examine the relationship between the teachers’ epistemic cognition focusing on epistemic aims, reliable processes, and epistemic ideals and their dialogic practices in an argument-based inquiry science classroom. Epistemic aims are the goals one sets for achieving epistemic ends (e.g., creating high-quality arguments or developing evidence-based explanations). Reliable processes are a series of actions that one takes to obtain those ends, and epistemic ideals are the evaluative criteria used to determine if the targeted ends have been achieved (Chinn & Rinehart, 2016). Based on the analysis of recorded science lessons and interviews, Rinehart et al. (2020) found that teachers’ epistemic aims and ideals were connected to their dialogic feedback practices as well as the quality of their implementation of evidence-based argumentation in the classroom. The link between teacher epistemological beliefs and teaching practices is also evident for preservice science teachers. Tarmo’s (2016) study with six preservice science teachers in Tanzania found that the preservice teachers viewed science knowledge to be simple, unchanging, and handed down by authorities such as textbooks and scientists. Their epistemological beliefs were manifested in their practices in which they asked factual questions, sought predetermined textbook-based answers from students, and adopted transmissive teaching strategies. Although a growing number of studies highlight the importance of epistemological beliefs to the process of learning to teach and to the act of teaching, few studies published for the inclusion period of this review directly examine their impact on student outcomes. Only one study loosely related to student outcomes is Lin et al.’s (2014) study that investigated 1,048 Taiwanese high-school students’ and their 59 science teachers’ conceptions of learning science (COLS) and conceptions of science assessment (COSA). Their fndings revealed discrepancies between students’ and teachers’ COLS and COSA, which failed to support the assertion that teachers’ epistemological beliefs are critical to the development of their students’ epistemological beliefs as previous studies had suggested (Mansour, 2009). Fenstermacher and Richardson (2005) argued that the presumption of a simplistic causal relation between teaching practices and student learning is naïve because teaching and learning are related to each other in highly complex ways. Clearly, more research is needed to unpack the complexity of the connections among teacher epistemological beliefs, teaching practices, and student learning. With the close link between teachers’ epistemological beliefs and teaching approaches (e.g., Brownlee et al., 2011; Tsai, 2003; Yadav & Koehler, 2007), epistemological beliefs have become a crucial strand of research on teacher beliefs. However, epistemological beliefs have recently received growing attention in the feld of science education as reform movements (e.g., a framework for K–12 science education [NRC, 2012], and the Scottish Curriculum for Excellence [Wallace & Priestley, 2017]) have called for a shift away from a heavy emphasis on cognitive learning toward a balanced focus among the conceptual, epistemic, and social practices that science involves (Duschl, 2008; Kelly, 2008; OECD, 2019). This shift requires science teachers to implement teaching practices to support students’ dialogic knowledge-building, critiquing, and sense-making beyond knowledge acquisition (Ford, 2015; Furtak & Penuel, 2019; Nussbaum et al., 2008). However, creating such learning opportunities requires changes to teachers’ epistemological beliefs to be compatible with those reform-oriented teaching practices (JiménezAleixandre & Crujeiras, 2017; Wallace & Priestley, 2017). In this regard, to realize the vision of any science education reform that involves changing teaching practices, teacher educators should support the development of science teachers’ epistemological beliefs that undergird reform-based pedagogical approaches. 1110

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Factors Infuencing Science Teachers’ Epistemological Beliefs Multiple factors afect science teachers’epistemological beliefs, including prior experience as a learner, education background, teacher education program, and science research courses. Al-Amoush et al.’s (2014) international comparison study found that Jordanian chemistry preservice and in-service teachers held the most traditional, teacher-centered, and transmission-oriented beliefs, while German teachers held the most modern, student-centered, and constructivist beliefs. Turkish teachers’ beliefs lay in a middle ground in between. The authors attributed the diferences in epistemological beliefs to the diferences in teacher education programs and education systems as well as cultural contexts between the countries, suggesting less exposure to traditional teaching practices, as a student in secondary science classrooms would likely result in the student retaining more constructivist beliefs. The impact of prior experience in science classrooms on teachers’ epistemological beliefs and conceptions of teaching and learning are also supported by other researchers (e.g., Bahçivan & Aydin, 2020; Güneş & Bahçivan, 2018). With 484 Turkish preservice science teachers, Kaya (2017) investigated their scientifc epistemological beliefs, i.e., domain-specifc views of the nature of scientifc knowledge and how it is produced, shared, and validated, in relation to their goal orientations. In addition to the positive relation between them, Kaya (2017) found that the participants who successfully completed a scientifc research methods course had less traditional scientifc epistemological beliefs than the participants who had not taken such a course previously. In a similar vein, Guilfoyle and colleagues (2020) proposed that scientifc research experiences need to be included as activities for teachers’ epistemological development. A cluster of studies highlights the potential of teacher education programs to promote sophisticated epistemological beliefs in preservice teachers. Taylor and Booth’s (2015) phenomenological study examined eight South African secondary physical science teachers’ conceptions of science teaching who were schooled in an education system emphasizing knowledge transmission but were prepared in their initial teacher education programs focusing on learner-centered science teaching approaches. They found that the teachers’ conceptions of science teaching were compatible with learner-centered views, especially in terms of the nature of scientifc knowledge, teacher roles, and student roles. Based on this fnding, the authors suggested that a purposefully designed teacher education program could develop underlying epistemological beliefs for constructivist science teaching practices and such infuence could last some years after teachers graduated from their initial teacher education (the teachers’ years of teaching ranged from 1.9 to 6.4 years). Similarly, Kirmizigul and Bektas (2019) asserted that a course focusing on the nature of science during a teacher education program contributed to preservice teachers’ sophisticated epistemological beliefs. Considering that epistemological beliefs are subject to change and can be developed, researchers have suggested interventions and strategies for the development of teacher epistemological beliefs, such as engaging in argumentative practices (Güneş & Bahçivan, 2018), authentic science research (Kite et al., 2020), and discussion centering on the epistemology of science (Wendell et al., 2019). However, little research has been conducted to examine the impact of an intervention aiming at the development of teacher epistemological beliefs on teaching practices, and the mechanisms of the change in epistemological beliefs in the context of the intervention.

Teachers’ Beliefs About Reform-Oriented Science Teaching Practices Assuming that teachers’ classroom practices are infuenced by their beliefs, researchers have examined science teachers’ beliefs about reform-oriented teaching approaches as a means to obtain insights into how to support teacher implementation of those practices. This strand of research has particularly 1111

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centered on core practices of science, such as inquiry, argumentation, integration of computational thinking, modeling, scientifc explanation, and technology integration.

Teacher Beliefs About Reform-Oriented Science Teaching Approaches Recent research fndings regarding the relationship between teacher beliefs about an innovative science teaching approach and classroom practices are inconclusive. Some studies report a strong correlation between the two. In concert with previous research suggesting teacher beliefs about argumentation can impact teachers’ instructional choices related to argumentation (Sampson & Blanchard, 2012), Lin et al.’s (2017) study, conducted in a nursing high school in southwestern Taiwan, demonstrated a strong link between teacher beliefs about argumentation and instructional approaches with detailed descriptions of one novice and one experienced teacher. Specifcally, the novice teacher, who believed that the learning of argumentation should occur in a more student-centered manner than in a traditional lecture-based environment, spent a substantial amount of time engaging students with their peers’ ideas through discussion and collaboration. On the other hand, the experienced teacher believed that most students were capable of generating arguments, but few knew how to argue based on evidence. Aligned with her beliefs, to improve students’ ability to incorporate their understanding of scientifc knowledge into scientifc argumentation, she created opportunities for students to collect data from various resources and to construct their own knowledge framework. Similarly, using the concurrent mixed-methods research design, Zangori and Forbes (2014) examined how third-grade students (n = 59) and their teachers (n = 3) engaged in constructing scientifc explanations during enactment of the eight-week Full Option Science System (FOSS) unit on plant growth and development. Their fndings suggest a robust link between the teachers’ beliefs about scientifc explanations and their instructional support and scafolding for students’ scientifc explanation construction. As an illustration, two teachers who believed that there was a single correct explanation that they needed to help their students to learn throughout a lesson rarely provided opportunities for their students to construct scientifc explanations either through discourse or writing. In contrast, the other teacher, Grace, who believed that there was more than one explanation that could lead to an understanding of the science concept, more frequently supported students to engage in formulating scientifc explanations. Furthermore, Grace’s classroom had a statistically signifcant outcome for the increased presence of written explanations as compared to the other two classrooms, and her students’ explanations were more frequently grounded in evidence. Consistent with those two studies, Avraamidou’s (2017) single case study with one exemplary beginning elementary teacher, Sofa, in Cyprus, documented how Sofa’s beliefs about scientifc inquiry aligned with science education reforms were translated into practice in her ffth-grade classroom. Sofa believed that inquiry-based science is a means for supporting students’ engagement, science learning, and positive attitudes toward science. She perceived student engagement in scientifc investigations with authentic data and construction of scientifc claims from evidence as central aspects of inquiry-based science teaching. In accordance with her beliefs, she provided students with opportunities to work with data to respond to a posed question and to construct evidence-based explanations. Correia and Harrison’s (2020) case study of two secondary science teachers also showed that the teachers’ espoused beliefs about inquiry-based learning were congruent with not only their teaching approaches promoting student autonomy but also their formative assessment practices in the classroom. In contrast, some studies report inconsistencies between teacher beliefs and classroom practices related to reform-oriented science teaching practices. An example is Hundal et  al.’s (2014) study that examined teachers’ beliefs about argumentation in the context of their co-designing a curriculum focusing on argumentation for an after-school environmental health science club. They found that the teachers perceived the importance of argumentation in both science and students’ science 1112

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learning, but their implementation of argumentative practices was relegated due to their perceived constraints, such as the need to retain a high level of students’ interest, desire to cover classroomrelated content, and time pressure. The disparity between teacher beliefs and classroom practices is also reported in relation to inquiry-based science teaching. Ramnarain and Hlatswayo’s (2018) sequential explanatory mixedmethods study investigated the interaction between tenth-grade physical sciences teachers’ beliefs about inquiry-based learning and their practice of inquiry in their classrooms in under-resourced rural schools in South Africa. They found that participating teachers believed the benefts of inquiry, such as promoting students’ motivation to learn science and understanding of abstract concepts in physical sciences. Despite this positive belief toward inquiry-based learning, teachers were less inclined to enact inquiry-based learning in their lessons, with barriers including the lack of laboratory facilities and teaching materials, curriculum to cover, time constraints, and large classes. Blanchard and colleagues (2016) investigated the efects of the technology-enhanced professional development (TPD) designed to support reform-based teaching on 20 middle school science, mathematics, and technology teachers’ beliefs, practices, and student learning outcomes in rural, high-poverty school districts in the United States. Their quantitative analyses indicated a statistically signifcant shift in teacher beliefs about teaching toward more student-centered views and in their comfort using new technologies over the three years of the TPD. However, there was no statistically signifcant diference in teachers’ practices over the TPD, in terms of student-centered and inquirybased aspects as measured by the RTOP (Sawada et al., 2002). Particularly, the majority of the teachers adopted technology in ways that improved efciency and efectiveness without any substantial changes in their instruction rather than using technology in ways that transformed their roles and classroom practices. Similarly, Constantine et al. (2017) qualitatively described a disparity between teacher beliefs and practices in relation to technology integration and concluded: “teachers’ beliefs about technology integration in STEM were more ambitious than their actual practice” (p. 348). Mixed fndings regarding the association between teacher beliefs and reform-oriented teaching practices suggest that the translation of teacher beliefs into practice involves complex processes that are intervened by various internal and external factors, such as teacher knowledge of those practices (Windschitl, 2002) and perceived and experienced contextual hindrances (Fives & Buehl, 2012; Ramnarain & Hlatswayo, 2018). In addition, it implies that teacher beliefs exist like a system in which individual beliefs link to one another, and some beliefs are more central than others in guiding teacher instructional decision-making and actions (Fives & Buehl, 2012; Wallace, 2014). This is supported by Lebak’s (2015) single case study of one science teacher, Jerry, teaching in a high-poverty urban school that revealed that some beliefs emerged as stronger than others for infuencing practice, which resulted in the disconnect between the teacher’s espoused beliefs and enacted practice. In particular, Jerry’s defcit perspective of his urban students’ capabilities led to teacher-centered approaches despite his beliefs of the role of the teacher and students consistent with an inquiry-based approach to science instruction.

Factors Infuencing Science Teachers’ Beliefs About Reform-Oriented Teaching Approaches Research that sheds light on changes in teachers’ beliefs about a particular science teaching approach identifed teachers’ experience of enacting the teaching approach, educative curriculum materials, teacher preparation programs, and professional development (PD) programs as major triggers for the changes in the beliefs. The majority of studies reviewed for this section included an intervention purposefully designed to improve implementations of a reform-oriented teaching approach and examined the efectiveness of the intervention in terms of changes in teacher practices and teacher beliefs associated with the approach. For instance, in the context of a PD focusing on argumentation, 1113

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Knigh-Bardsley and McNeill (2016) examined the impact of the development of fve teachers’ beliefs and pedagogical content knowledge (PCK) through the PD on their instructional design and implementation of argumentation. The authors found that teachers who ofoaded their design of argumentation lessons onto resources and strategies presented during the PD, tried out argumentation lessons, and refected on argumentation instruction exhibited the largest change in their belief about argumentation, classroom practice, and PCK of student conceptions. In comparison, teachers who adapted the argumentation framework that the PD utilized to align with their current instruction and instructional goals, relied to a greater extent on their preexisting beliefs and PCK and did not show desirable changes in beliefs about argumentation as well as PCK. With in-depth qualitative analyses of the teachers’ instructional design and implementations, the authors concluded that the experience of trying out argumentation lessons and refecting on them changed their beliefs about the purposes and value of argumentation. In accordance with this, Loper and colleagues (2019) also found that enacting a curriculum focusing on argumentation was associated with positive changes in teachers’ beliefs about this practice. Specifcally, they conducted a randomized experimental study with 90 middle school science teachers in the United States to examine how teachers’ use of multimedia educative curriculum materials (MECMs) impact their beliefs related to teaching argumentation. Both control and experimental groups taught the same curriculum, using a web-based teacher’s guide, but experimental teachers received MECMs, including 24 videos and 17 interactive refective prompts. Results suggest that enacting the curriculum infuenced teachers’ beliefs about the importance of argumentation, confdence about teaching argumentation, and beliefs about how capable students with diverse backgrounds are in engaging in this science practice. The potential of an educative curriculum to improve teacher beliefs to be in support of reformoriented classroom practice is evident in McNeill et  al. (2016). The authors surveyed 42 middle school science teachers and conducted follow-up interviews with 25 who enacted educative earth and space science curricular units designed to support teachers as well as students’ learning of argumentation to examine their beliefs concerning their implementation of argumentation. All teachers indicated that argumentation was an important learning goal for their students regardless of students’ backgrounds and abilities. Interestingly, teachers responded that both their own science learning goals and broader academic goals had the greatest impact on their argumentation instruction, while context, policy, and assessment had the least impact. This is inconsistent with other studies reviewed earlier that highlight the great infuence of high-stake assessments and contextual hindrances on teachers’ reform-oriented practices (e.g., Hundal et al., 2014; Ramnarain & Hlatswayo, 2018). The authors interpreted that this group of teachers might have diferent views from other teachers in that they opted-in to enacting the reform-oriented curriculum with motivation and interest in the new pedagogical approach. In addition, the majority of the teachers explained that argumentation was not a focus for their state, which made them believe that high-stakes testing or policy had the least impact on their implementation of argumentation. A number of studies indicated that well-designed PDs can contribute to changes in teacher beliefs to be congruent with reform-oriented teaching practices. Both Lee et  al. (2017) and Blanchard et al. (2016) suggest that teacher beliefs can change to become more student-centered and inquiry-oriented over time through longitudinal technology professional development (TPD). However, in Lee and colleagues’ study, changes in teacher beliefs were not signifcant until after the second year of TPD. This implies that changes in teacher beliefs require prolonged time, and thus sustained professional support is necessary. Concerning critical components of PDs that infuence teacher beliefs and possibly subsequent classroom practices, research identifes refections on instruction (e.g., Knigh-Bardsley et al., 2016), collaboration with peers or researchers (e.g., Hundal et al., 2014; Lebak, 2015), and use of educative curriculum (e.g., Lee et al., 2017; McNeill et al., 2016) as being infuential. 1114

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On the other hand, a cluster of studies describe eforts to facilitate changes in preservice teachers’ beliefs about reform-oriented teaching practices. Herranen et al. (2019) conducted a case study with fve Finnish preservice science teachers enrolled in an inquiry-based chemistry education course with a focus on the context-dependent nature of inquiry. Over 14 weeks of the course, the authors examined preservice science teachers’ beliefs about inquiry and their implementation of inquiry in planning inquiry teaching sequences for their chosen contexts. At the end of the course, the authors identifed several prevalent features of inquiry that the preservice teachers were most frequently able to implement in their planned inquiry teaching sequences: Inquiry (1) includes a context, (2) is a way to act, (3) is a way to think, and (4) includes search and evaluation of information and argumentation. With these fndings, Herranen et al. (2019) suggested that a context-based inquiry teaching methods course emphasizing the context-dependent nature of inquiry are efective to promote preservice teachers’ implementations of inquiry that require situational knowledge as well as declarative knowledge from science. McGinnis et  al. (2020) designed and implemented a curricular module on computational thinking (CT) within an elementary science methods course and investigated preservice science teachers’ (n=39) beliefs about CT integration in elementary science education, its feasibility, and its value for their own teaching practice. After participating in the curricular module, preservice teachers highly valued CT integration with perceived benefts for student engagement, supported the pedagogical innovation of integrating CT in their science teaching, and generally believed that CT integration supported the implementation of what they understood as good science teaching practice. Taken together, changing teachers’ beliefs to be aligned with reform-oriented science teaching practices is shown to be difcult but possible with appropriate support and scafolding. Teacher beliefs provide a useful framework to understand why teachers enact reform-oriented science teaching approaches in varied ways in their classrooms even when they are ofered the same resources, including curricula, technology, and PD support (McNeill et al., 2016). In this regard, more research is necessary to unpack the complexity of the translation of teacher beliefs into classroom practices. This understanding will provide important insights into interventions and strategies to change teacher beliefs that stimulate changes in teaching practices.

Attitudes and Beliefs About Science As the research on beliefs has evolved, it is apparent that teachers can hold diferent beliefs (including self-efcacy) for science disciplines, science topics, the nature of science, as well as for the diferent components of science teaching. These varying and complex hierarchies of beliefs and belief systems make predicting and changing teachers’ beliefs challenging. A teacher can report feeling high efcacy for teaching biology and low efcacy for teaching another subject, such as physics. Research on topics such as climate change education has highlighted the complexity and diffculties that teacher educators face in changing attitudes and beliefs. Higde et al. (2017) surveyed 1,277 preservice science teachers to predict their uncertainty beliefs, values, and behaviors related to climate change. The researchers investigated uncertainty beliefs about the reality of climate change, value orientations related to climate change, awareness of climate change, and climate change–related behaviors using a survey. The researchers found that the preservice teachers held at least some level of knowledge of climate change, reported some pro-climate behaviors, and over half reported that they were interested in climate change and found it relevant to them personally. However, the reported beliefs about the role of humans in climate change were complex. Most preservice teachers reported that climate change is happening and is a problem, but on the other hand, almost 77% did not believe the consequences of climate change would be catastrophic. The researchers examined relationships among the value-related factors and found that ecocentrism (concern for living organisms) was a 1115

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signifcant predictor of positive climate change–related behaviors and anthropocentrism (concerns for humans) was negatively correlated with climate change–related behaviors. Although researchers often examine teachers’ attitudes and beliefs, few studies take the next step to examine both teachers’ beliefs and their students’ beliefs about a scientifc phenomenon. Stevenson et al. (2016) took on this challenge and examined middle school science teachers’ beliefs about climate change as well as the beliefs of the teachers’ students. These researchers reported that teachers’ beliefs that global warming is happening, and students’ knowledge of climate change predicted students’ belief that humans have an impact on global warming and that global warming does exist. The authors concluded that “as long as climate change information is presented in classrooms, students deduce anthropogenic causes” (p. 1). This study suggests a possible link between teachers’ beliefs and students’ beliefs about a socioscientifc issue, climate change in the study, that may be mediated by instructional practices guided by teachers’ beliefs. Further research is needed to test hypothetical relationships between teachers’ beliefs, teaching practices, and students’ beliefs as a learning outcome in the context of various (controversial) socioscientifc issues, including evolution, genetically modifed organisms, animal testing, etc. with larger sample sizes.

Assessing Beliefs and Attitudes Just as we noted in the last edition of the Handbook of Research in Science Education (Jones & Legon, 2014) there are signifcant problems in validly and reliably measuring beliefs and attitudes. The lack of valid assessment instruments limits researchers’ ability to determine the constructs being measured as well as compare results from one study to the next. Similar to the fndings from Jones and Legon’s (2014) review, studies selected for this review used a variety of quantitative and qualitative approaches to assess beliefs and attitudes, including surveys, questionnaires, interviews, written refections, and observations. One example of quantitative measures used in the reviewed studies is the Draw-AScience-Teacher-Test Checklist (DASTTC [Thomas et al., 2001]). Buldur (2017) used DASTTC with preservice science teachers across four years to measure changes in the student- or teacher-centeredness of teacher beliefs. Another example is Colorado Learning Attitudes about Science Survey (CLASS), utilized in Ding and Zhang’s (2016) study. CLASS was frst developed and validated by Adams and colleagues in 2006 to measure learners’ beliefs about a specifc science discipline (i.e., physics, chemistry, or biology) and about learning the science discipline. It consists of 42 Likert-scale items that measure 8 subscales: learners’ views on (1) personal interest, (2) real-world connection, (3) problem-solving general, (4) problem-solving confdence, (5) problem-solving sophistication, (6) sense-making and efort, (7) conceptual understanding, and (8) applied conceptual understanding. Although the original CLASS was validated in the United States and written in English, Ding and Zhang (2016) translated it for the Chinese version and validated it in China, van Aalderen-Smeets and Walma van der Molen (2015) utilized the Dimensions of Attitude toward Science (DAS) questionnaire to investigate the impact of an attitude-focused professional development program on primary teachers’ personal attitudes toward science and attitudes toward teaching science using a quasi-experimental pre-post test control group design. The DAS questionnaire was designed to assess preservice and in-service primary teachers’ attitudes toward teaching science and validated with over 500 pre- and in-service teachers in the Netherlands (van Aalderen-Smeets & Walma van der Molen, 2013). Since then, Turkish, Spanish, and English versions of the DAS have been validated (Korur et al., 2016, Wendt & Rockinson-Szapkiw, 2018). The DAS questionnaire is developed based on the theoretical framework for the construct of primary teachers’ attitudes toward teaching science that consists of three dimensions, i.e., cognition, afect, and perceived control (van Aalderen-Smeets & Walma van der Molen, 2013). The DAS questionnaire utilizes Likert-scale items to measure seven subcomponents of the three dimensions that represent diferent thoughts, beliefs, and/or feelings toward teaching science. 1116

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To assess teacher beliefs and attitudes with qualitative data such as interviews and observations, researchers usually quantify the qualitative data using scoring scales drawn from a set of categorizations or a pre-established coding scheme. For example, to examine the efects of microteaching with lesson study approach on preservice science teachers’ beliefs about teaching and learning, Yakar and Turgut (2017) conducted interviews with 58 preservice science teachers using the Teacher Beliefs Interview (TBI; Luft & Roehrig, 2007). The TBI consists of seven open-ended questions focusing on teacher epistemologies. Yakar and Turgut categorized teacher responses to each question with the fve categories of the TBI maps developed by Luft and Roehrig (2007). They quantifed a teacher’s response coded with each category by assigning a numerical value for statistical analyses: a traditional response −1 point; an instructive response −2 points; a transitional response −3 points; a responsive response −4 points; and a reform-based response −5 points. A notable trend in methods to assess teacher beliefs and attitudes is the increasing use of multiple methods. That is, a signifcant number of studies have employed quantitative instruments to directly measure a belief- or attitude-related construct under study in combination with qualitative data, such as interviews and observations, that require researchers to indirectly infer the construct based on what teachers say and what they do. This may be because of researchers’ growing awareness that beliefs and attitudes are too complex to be assessed with a single instrument.

Future Research Needed on Science Teachers’ Beliefs and Attitudes Studies over the past 5–10 years have documented the critical role that beliefs and attitudes play in shaping teachers’ instructional practices. Many of the studies examined beliefs and attitudes before and after a teacher education intervention. These studies have led to new questions about beliefs and attitudes that have yet to be fully understood. For example, the degree to which beliefs about specifc science topics and subtopics difer has not been fully explored. Al-Amoush et al. (2014) argued that we need more research on whether teachers hold diferent beliefs about teaching diferent subtopics within a domain. Fives and Buehl (2012) shared that, “(d)espite the widespread agreement that teachers’ beliefs exist in a system, few empirical investigations have examined beliefs as complex systems” (p. 477). As we noted earlier, few studies investigated the relationships and infuence of teachers’ beliefs on their students’ beliefs and achievement. More research is needed to examine this relationship. The conceptual framework of teacher beliefs as flters, frames, and guides introduced earlier in this chapter can ofer a useful lens to unfold the complex associations between teacher beliefs and other teacher-level and student-level variables. It is not clear if there are underlying developmental patterns in the development of teachers’ attitudes and beliefs. For example, as beginning teachers gain content knowledge, do they also gain self-efcacy related to teaching about that content? If we can identify and predict changes in beliefs, we may be better able to develop efective professional supports that promote desirable changes in teachers’ beliefs and subsequently in their practices. Fives and Buehl (2012) argue that identifying changes in beliefs across teachers may enable us to better understand and identify more malleable belief structures as well as those that are resistant to change. Another promising area of research is the study of the contexts that support and promote positive changes in attitudes and beliefs. Researchers have studied changes in individual teachers’ beliefs, but there are very few studies of beliefs and attitudes held by groups of teachers. With the increased use of professional learning communities, this focus on the collective development of beliefs may shed insight into how reform is enacted at the institutional level. We need more longitudinal research that documents changes in beliefs over longer periods of time because, as Levin (2015) noted, “changes in teachers’ beliefs may be temporary” p. 49) and may be continuously infuenced by experiences over time. In addition to longitudinal studies, we need 1117

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studies that examine attitudes and beliefs in the moment-to-moment teaching environment to document whether there are critical events that promote or change beliefs. Understanding critical events and instances can shed insight on how reform-based eforts are perceived and interpreted by teachers.

References Adams, W. K., Perkins, K. K., Podolefsky, N. S., Dubson, M., Finkelstein, N. D., & Wieman, C. E. (2006). New instrument for measuring student beliefs about physics and learning physics: The Colorado learning attitudes about science survey. Physical Review Special Topics-Physics Education Research, 2(1), 010101. Al-Amoush, S., Markic, S., Usak, M., Erdogan, M., & Eilks, I. (2014). Beliefs about chemistry teaching and learning: A comparison of teachers’ and student teachers’ beliefs from Jordan, Turkey, and Germany. International Journal of Science and Mathematics Education, 12(4), 767–792. Avraamidou, L. (2017). A well-started beginning elementary teacher’s beliefs and practices in relation to reform recommendations about inquiry-based science. Cultural Studies of Science Education, 12(2), 331–353. Bahçivan, E., & Aydin, Y. (2020). Pre-service science teachers’ teaching beliefs: Responding to “which”,“how” and “why” questions. Action in Teacher Education, 42(2), 120–136. Bandura, A. (1977). Self-efcacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191. Bandura, A. (2000). Self-efcacy. In A. E. Kazdin, (Ed.), Encyclopedia of psychology (pp. 212–213). Oxford University Press. Blanchard, M. R., LePrevost, C. E., Tolin, A. D., & Gutierrez, K. S. (2016). Investigating technology-enhanced teacher professional development in rural, high-poverty middle schools. Educational Researcher, 45(3), 207–220. Boesdorfer, S. B., Del Carlo, D. I., & Wayson, J. (2019). Secondary science teachers’ reported practices and beliefs on teaching and learning from a large national sample in the united states. Journal of Science Teacher Education, 30(8), 815–837. Brígido, M., Borrachero, A. B., Bermejo, M. L., & Mellado, V. (2013). Prospective primary teachers’ selfefcacy and emotions in science teaching. European Journal of Teacher Education, 36(2), 200–217. Brownlee, J. M., Schraw, G., & Berthelsen, D. C. (2011). Personal epistemology and teacher education: An emerging feld of research. In J. M. Brownlee, G. Schraw, & D. C. Berthelsen (Eds.), Personal epistemology and teacher education (pp. 3–24). Routledge. Bryan, L. A. (2012). Research on science teacher beliefs. In B. A. Frasier, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 477–495). Springer. Buehl, M. M., Alexander, P. A., & Murphy, P. K. (2002). Beliefs about schooled knowledge: Domain specifc or domain general? Contemporary Educational Psychology, 27, 415–449. https://doi.org/10.1006/ceps.2001.1103 Buldur, S. (2017). A longitudinal investigation of the preservice science teachers’ beliefs about science teaching during a science teacher training programme. International Journal of Science Education, 39(1), 1–19. Caleon, I. S., Tan, Y. S. M., & Cho, Y. H. (2018). Does teaching experience matter? The beliefs and practices of beginning and experienced physics teachers. Research in Science Education, 48(1), 117–149. Chinn, C. A., & Rinehart, R. W. (2016). Epistemic cognition and philosophy: Developing a new framework for epistemic cognition. In W. A. Sandoval, J. A. Greene, & I. Braten (Eds.), Handbook of epistemic cognition (pp. 460–478). Routledge. Constantine, A., Rózowa, P., Szostkowski, A., Ellis, J., & Roehrig, G. (2017). The “T” in STEM: How elementary science teachers’ beliefs of technology integration translate to practice during a co-developed STEM unit. Journal of Computers in Mathematics and Science Teaching, 36(4), 339–349. Correia, C. F., & Harrison, C. (2020). Teachers’ beliefs about inquiry-based learning and its impact on formative assessment practice. Research in Science & Technological Education, 38(3), 355–376. Czerniak, C., & Lumpe, A. (1996). Relationship between teacher beliefs and science education reform. Journal of Science Teacher Education, 7, 247–266. DeCoito, I., & Myszkal, P. (2018). Connecting science instruction and teachers’ self-efcacy and beliefs in STEM education. Journal of Science Teacher Education, 29(6), 485–503. Deehan, J., McKinnon, D. H., & Danaia, L. (2019). A long-term investigation of the science teaching efcacy beliefs of multiple cohorts of preservice elementary teachers. Journal of Science Teacher Education, 30(8), 923–945. Ding, L., & Zhang, P. (2016). Making of epistemologically sophisticated physics teachers: A cross-sequential study of epistemological progression from preservice to in-service teachers. Physical Review Physics Education Research, 12(2), 020137.

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Science Teacher Attitudes and Beliefs Donohoo, J., Hattie, J., & Eells, R. (2018). The power of collective efcacy. Educational Leadership, 75(6), 40–44. Duschl, R. (2008). Science education in three-part harmony: Balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32(1), 268–291. Eagly, A., & Chaiken, S. (1993). The psychology of attitudes. Wadsworth Group/Thomson Learning. Eccles, J. S., & Wigfeld, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, 101859. Elby, A. (2009). Defning personal epistemology: A response to Hofer & Pintrich (1997) and Sandoval (2005). Journal of the Learning Sciences, 18, 138–149. Enderle, P., Dentzau, M., Roseler, K., Southerland, S., Granger, E., Hughes, R., Golden, B., & Saka, Y. (2014). Examining the infuence of RETs on science teacher beliefs and practice. Science Education, 98(6), 1077–1108. Feldman, A. (2002). Multiple perspectives for the study of teaching: Knowledge, reason, understanding, and being. Journal of Research in Science Teaching, 39(10), 1032–1055. Fenstermacher, G. D., & Richardson, V. (2005). On making determinations of quality in teaching. Teachers College Record, 107(1), 186–213. Fishbein, M. (1967). A consideration of beliefs and their role in attitude measurement. In M. Fishbein (Ed.), Readings in attitude theory and measurement (pp. 257–266). John Wiley & Sons. Fives, H., & Buehl, M. M. (2012). Spring cleaning for the “messy” construct of teachers’ beliefs: What are they? Which have been examined? What can they tell us? In K. R. Harris, S. Graham, & T. Urdan (Eds.), APA educational psychology handbook, Vol 2: Individual diferences and cultural and contextual factors (pp. 471–499). American Psychological Association. Ford, M. J. (2015). Educational implications of choosing “practice” to describe science in the next generation science standards. Science Education, 99(6), 1041–1048. Furtak, E. M., & Penuel, W. R. (2019). Coming to terms: Addressing the persistence of “hands-on” and other reform terminology in the era of science as practice. Science Education, 103(1), 167–186. Gill, M. G., & Hardin, C. (2014). A “hot” mess. In International handbook of research on teachers’ beliefs (pp. 230– 245). Routledge. Guilfoyle, L., McCormack, O., & Erduran, S. (2020). The “tipping point” for educational research: The role of pre-service science teachers’ epistemic beliefs in evaluating the professional utility of educational research. Teaching and Teacher Education, 90, 103033. https://doi.org/10.1016/j.tate.2020.103033 Güneş, E., & Bahçivan, E. (2018). A mixed research-based model for pre-service science teachers’ digital literacy: Responses to “which beliefs” and “how and why they interact” questions. Computers & Education, 118, 96–106. Goldin, G., Hannula, M. S., Heyd-Metzuyanim, E., Janson, A., Kaasila, R., Lutovac, S., Di Martino, P., Morselli, F., Middleton, J., Pantziara, M., & Zhang, Q. (2016). Attitudes, beliefs, motivation and identity in mathematics education. Springer Open. Herranen, J., Kousa, P., Fooladi, E., & Aksela, M. (2019). Inquiry as a context-based practice: A case study of pre-service teachers’ beliefs and implementation of inquiry in context-based science teaching. International Journal of Science Education, 41(14), 1977–1998. Higde, E., Oztekin, C., & Sahin, E. (2017). Turkish pre-service science teachers’ awareness, beliefs, values, and behaviours pertinent to climate change. International Research in Geographical and Environmental Education, 26(3), 253–263. Hofer, B. K., & Bendixen, L. D. (2012). Personal epistemology: Theory, research, and future directions. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA handbooks in psychology®: APA educational psychology handbook, Vol. 1. Theories, constructs, and critical issues (pp.  227–256). American Psychological Association. Hofman, B. H., & Seidel, K. (2015). Measuring teachers’ beliefs. In International handbook of research on teachers’ beliefs (pp. 106–127). Routledge. Hundal, S., Levin, D. M., & Keselman, A. (2014). Lessons of researcher-teacher co-design of an environmental health afterschool club curriculum. International Journal of Science Education, 36(9), 1510–1530. Hutner, T. L., & Markman, A. B. (2016). Proposing an operational defnition of science teacher beliefs. Journal of Science Teacher Education, 27(6), 675–691. Jiménez-Aleixandre, M. P., & Crujeiras, B. (2017). Epistemic practices and scientifc practices in science education. In Science education (pp. 69–80). Sense Publishers. Jones, M. G., & Carter, G. (2006). Science teacher attitudes and beliefs. In S. Abel & N. Lederman (Eds.), Handbook of research on science teaching. Lawrence Erlbaum Associates, Publishers.

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M. Gail Jones and Soonhye Park Jones, M. G., & Legon, M. (2014). Teacher attitudes and beliefs: Reforming practice. In N. Lederman & S. Abell (Eds.), Handbook of research on science Ttaching (pp. 830–847). Routledge. Jugert, P., Greenaway, K. H., Barth, M., Büchner, R., Eisentraut, S., & Fritsche, I. (2016). Collective efcacy increases pro-environmental intentions through increasing self-efcacy. Journal of Environmental Psychology, 48, 12–23. Kaya, G. I. (2017). The relations between scientifc epistemological beliefs and goal orientations of pre-service teachers. Journal of Education and Training Studies, 5(10), 33–42. Kazempour, M., & Sadler, T. D. (2015). Pre-service teachers’ science beliefs, attitudes, and self-efcacy: A multicase study. Teaching Education, 26(3), 247–271. Kelly, G. (2008). Inquiry, activity and epistemic practice. In R. Duschl & R. Grandy (Eds.), Teaching scientifc inquiry: Recommendations for research and implementation (pp. 99–117, 288–291). Sense Publishers. Kirmizigul, A. S., & Bektas, O. (2019). Investigation of pre-service science teachers’ epistemological beliefs. Cypriot Journal of Educational Sciences, 14(1), 146. Kitchener, K. S. (1983). Cognition, metacognition, and epistemic cognition. Human Development, 26, 222–232. https://doi.org/10.1159/000272885 Kite, V., Park, S., McCance, K., & Seung, E. (2020). Secondary science teachers’ understandings of the epistemic nature of science practices. Journal of Science Teacher Education, 1–22. Kleickmann, T., Tröbst, S., Jonen, A., Vehmeyer, J., & Möller, K. (2016). The efects of expert scafolding in elementary science professional development on teachers’ beliefs and motivations, instructional practices, and student achievement. Journal of Educational Psychology, 108(1), 21. Knigh-Bardsley, A., & McNeill, K. L. (2016). Teachers’ pedagogical design capacity for scientifc argumentation. Science Education, 100(4), 645–672. Korur, F., Vargas, R. V., & Serrano, N. T. (2016). Attitude toward science teaching of Spanish and Turkish inservice elementary teachers: Multi-group confrmatory factor analysis. Eurasia Journal of Mathematics, Science and Technology Education, 12(2), 303–320. Kuhn, D. (1991). The skills of argument. Cambridge University Press. Lebak, K. (2015). Unpacking the complex relationship between beliefs, practice, and change related to inquirybased instruction of one science teacher. Journal of Science Teacher Education, 26(8), 695–713. Lee, H., Longhurst, M., & Campbell, T. (2017). Teacher learning in technology professional development and its impact on student achievement in science. International Journal of Science Education, 39(10), 1282–1303. Levin, B. B. (2015). The development of teachers’ beliefs. In International handbook of research on teachers’ beliefs (pp. 48–65). Routledge. Liljedahl, P. (2010). Noticing rapid and profound mathematics teacher change. Journal of Mathematics Teacher Education, 13(5), 411–423. Liljedahl, P. (2011). The theory of conceptual change as a theory for changing conceptions. Nordic Studies in Mathematics Education, 16(1–2), 101–124. Lin, S. C., & Chao, S. L. (2014). Relationships among senior high vocational school teacher teaching beliefs, self-efcacy, gender, and school type. Journal of Social Science Studies, 2(1), 1–12. Lin, Y.-R., Hung, C.-Y., & Hung, J.-F. (2017). Exploring teachers’ meta-strategic knowledge of science argumentation teaching with the repertory grid technique. International Journal of Science Education, 39(2), 105–134. Loper, S., McNeill, K. L., González-Howard, M., Marco-Bujosa, L. M., & O’Dwyer, L. M. (2019). The impact of multimedia educative curriculum materials (MECMs) on teachers’ beliefs about scientifc argumentation. Technology, Pedagogy and Education, 28(2), 173–190. Luft, J., & Roehrig, G. (2007). Capturing science teachers’ epistemological beliefs: The development of a teacher beliefs interview. Electronic Journal of Science Education, 11(2), 58–63. Mansour, N. (2009). Science teachers’ beliefs and practices: Issues, implications and research agenda. International Journal of Environmental & Science Education, 4, 25–48. Martin, A., Park, S., & Hand, B. (2019). What happens when a teacher’s science belief structure is in disequilibrium? Entangled nature of beliefs and practice. Research in Science Education, 49(3), 885–920. McGinnis, J. R., Hestness, E., Mills, K., Ketelhut, D., Cabrera, L., & Jeong, H. (2020). Preservice science teachers’ beliefs about computational thinking following a curricular module within an elementary science methods course. Contemporary Issues in Technology and Teacher Education, 20(1), 85–107. McLeod, D. B. (1992). Research on afect in mathematics education: A reconceptualization. Handbook of Research on Mathematics Teaching and Learning, 1, 575–596. McNeill, K. L., Katsh-Singer, R., González-Howard, M., & Loper, S. (2016). Factors impacting teachers’ argumentation instruction in their science classrooms. International Journal of Science Education, 38(12), 2026–2046.

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Science Teacher Attitudes and Beliefs Menon, D., & Sadler, T. D. (2016). Preservice elementary teachers’ science self-efcacy beliefs and science content knowledge. Journal of Science Teacher Education, 27(6), 649–673. Merk, S., Rosman, T., Rueß, J., Syring, M., & Schneider, J. (2017). Pre-service teachers’ perceived value of general pedagogical knowledge for practice: Relations with epistemic beliefs and source beliefs. PLoS One, 12(9), 1e25. Mintzes, J. J., Marcum, B., Messerschmidt-Yates, C., & Mark, A. (2013). Enhancing self-efcacy in elementary science teaching with professional learning communities. Journal of Science Teacher Education, 24(7), 1201–1218. National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academy Press. Nussbaum, E. M., Sinatra, G. M., & Poliquin, A. (2008). Role of epistemic beliefs and scientifc argumentation in science learning. International Journal of Science Education, 30(15), 1977–1999. OECD. (2019). PISA 2018 assessment and analytical framework. In OECD, PISA 2018 science framework (pp. 97–117). OECD Publishing. https://doi.org/10.1787/f30da688-en Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106, 315–329. Ramnarain, U., & Hlatswayo, M. (2018). Teacher beliefs and attitudes about inquiry-based learning in a rural school district in South Africa. South African Journal of Education, 38(1), 1–10. Rinehart, R. W., Kuhn, M., & Milford, T. M. (2020). The relationship between epistemic cognition and dialogic feedback in elementary and middle school science classrooms. Research in Science & Technological Education, ahead-of-print(ahead-of-print), 1–18. https://10.1080/02635143.2020.1799779 Rokeach, M. (1968). Beliefs, attitudes, and values: A theory of organization and change. Jossey-Bass. Sampson, V., & Blanchard, M. R. (2012). Science teachers and scientifc argumentation: Trends in views and practice. Journal of Research in Science Teaching, 49(9), 1122–1148. Sandholtz, J. H., & Ringstaf, C. (2014). Inspiring instructional change in elementary school science: The relationship between enhanced self-efcacy and teacher practices. Journal of Science Teacher Education, 25(6), 729–751. Sawada, D., Piburn, M. D., Judson, E., Turley, J., Falconer, K., Benford, R., & Bloom, I. (2002). Measuring reform practices in science and mathematics classrooms: The reformed teaching observation protocol. School Science and Mathematics, 102(6), 245–253. https://doi.org/10.1111/j.1949-8594.2002.tb17883.x Schommer, M. (1990). Efects of beliefs about the nature of knowledge on comprehension. Journal of Educational Psychology, 82, 498–504. Schraw, G., & Olafson, L. (2015). Assessing teachers’ beliefs. International handbook of research on teachers’ beliefs, 87–105. Sengul, O., Enderle, P. J., & Schwartz, R. S. (2020). Science teachers’ use of argumentation instructional model: Linking PCK of argumentation, epistemological beliefs, and practice. International Journal of Science Education, 42(7), 1068–1086. https://doi.org.prox.lib.ncsu.edu/10.1080/09500693.2020.1748250 Skott, J. (2015). The promises, problems, and prospects of research on teachers’ beliefs. In H. Fives & M. G. Gill (Eds.), International handbook of research on teacher beliefs (pp. 13–30). Routledge. Stevenson, K. T., Peterson, M. N., & Bradshaw, A. (2016). How climate change beliefs among US teachers do and do not translate to students. PLoS One, 11(9), e0161462. Tarmo, A. (2016). Pre-service science teachers’ epistemological beliefs and teaching reforms in Tanzania. Cogent Education, 3(1), 1178457. https://doi.org/10.1080/2331186X.2016.1178457 Taylor, D. L., & Booth, S. (2015). Secondary physical science teachers’ conceptions of science teaching in a context of change. International Journal of Science Education, 37(8), 1299–1320. https://doi.org/10.1080/095 00693.2015.1035356 Thomas, J. A., Pedersen, J. E., & Finson, K. D. (2001). Validating the draw-a-science-teacher-test checklist (DASTT-C): Exploring mental models and teacher beliefs. Journal of Science Teacher Education, 12(3), 295–310. Tsai, C. C. (2003). Taiwanese science students’ and teachers’ perceptions of the laboratory learning environments: Exploring epistemological gaps. International Journal of Science Education, 25(7), 847–860. Tschannen-Moran, M., Woolfolk-Hoy, A., & Hoy, W. (1998). Teacher efcacy: Its meaning and measure. Review of Educational Research, 68, 202–248. van Aalderen-Smeets, S. I., & Walma van der Molen, J. H.(2013). Measuring primary teachers’ attitudes toward teaching science: Development of the dimensions of attitude toward science (DAS) instrument. International Journal of Science Education, 35(4), 577–600. van Aalderen-Smeets, S. I., & Walma van der Molen, J. H. (2015). Improving primary teachers’ attitudes toward science by attitude-focused professional development. Journal of Research in Science Teaching, 52(5), 710–734.

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M. Gail Jones and Soonhye Park Velthuis, C., Fisser, P., & Pieters, J. (2014). Teacher training and pre-service primary teachers’ self-efcacy for science teaching. Journal of Science Teacher Education, 25(4), 445–464. Voelkel Jr., R. H., & Chrispeels, J. H. (2017). Understanding the link between professional learning communities and teacher collective efcacy. School Efectiveness and School Improvement, 28(4), 505–526. Wallace, C. S. (2014). Overview of the role of teacher beliefs in science education. In R. Evans, J. Luft, C. Czerniak, & C. Pea (Eds.), The role of science teachers’ beliefs in international classrooms (pp. 17–31). Sense Publishers. Wallace, C. S., & Priestley, M. R. (2017). Secondary science teachers as curriculum makers: Mapping and designing Scotland’s new curriculum for excellence. Journal of Research in Science Teaching, 54(3), 324–349. https://doi.org.prox.lib.ncsu.edu/10.1002/tea.21346 Wang, Y. L., Tsai, C. C., & Wei, S. H. (2015). The sources of science teaching self-efcacy among elementary school teachers: A mediational model approach. International Journal of Science Education, 37(14), 2264–2283. Watt, H. M. G., Richardson, P. W. (2015). A motivational analysis of teachers’ beliefs. In H. Fives, and M. Gill (Eds.), International handbook of research on teachers’ beliefs (1st ed., pp. 191–211). Routledge. Wendell, K. B., Swenson, J. E. S., & Dalvi, T. S. (2019). Epistemological framing and novice elementary teachers’ approaches to learning and teaching engineering design. Journal of Research in Science Teaching, 56(7), 956–982. https://doi.org/10.1002/tea.21541 Wendt, J. L., & Rockinson-Szapkiw, A. (2018). A psychometric evaluation of the English version of the dimensions of attitudes toward science instrument with a US population of elementary educators. Teaching and Teacher Education, 70, 24–33. Windschitl, M. (2002). Framing constructivism in practice as the negation of dilemmas: An analysis of the conceptual, pedagogical, cultural, and political challenges facing teachers. Review of Educational Research, 72, 131–175. https://doi.org/10.3102/00346543072002131 Wong, S. S., & Luft, J. A. (2015). Secondary science teachers’ beliefs and persistence: A longitudinal mixedmethods study. Journal of Science Teacher Education, 26(7), 619–645. Yadav, A., & Koehler, M. J. (2007). The role of epistemological beliefs in preservice teachers’ interpretation of video cases of early-grade literacy instruction. Journal of Technology and Teacher Education, 15(3), 335–361. Yakar, Z., & Turgut, D. (2017). Efectiveness of lesson study approach on preservice science teachers’ beliefs. International Education Studies, 10(6), 36–43. Zangori, L., & Forbes, C. T. (2014). Scientifc practices in elementary classrooms: Third-grade students’ scientifc explanations for seed structure and function. Science Education, 98(4), 614–639.

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35 RESEARCH ON SCIENCE TEACHER KNOWLEDGE AND ITS DEVELOPMENT Jan H. van Driel, Anne Hume, and Amanda Berry

Over the past 60 years, research on teachers and teaching has focused on diferent aspects to understand the role of teachers in educational processes, specifcally their impact on student learning. These aspects include teachers’ personality traits, their classroom behaviors (e.g., teachers’ ability to create a positive learning environment), and the knowledge and beliefs that inform teachers’ instructional decisions. This chapter focuses on science teacher knowledge and its development, both in the context of initial teacher education and of in-service teachers’ professional learning activities. It begins with an overview of teacher knowledge research, briefy discussing some of the diferent strands in this feld of research and some of the models that are used to frame this research. In the next part, fndings of recent empirical research on science teacher knowledge and its development are discussed. The chapter ends with a discussion of the progress that has been made in this feld since the publication of the second edition of this handbook and implications for practice and future research.

Situating This Review The Nature of Teacher Knowledge In this chapter, we adopt a broad view of teacher knowledge as the total knowledge a teacher has at their disposal to inform decision-making about teaching actions, and enable those actions, during a particular teaching and learning instance (Carter, 1990). This view recognizes that teachers’ knowledge includes elements that are more or less immediately linked with their actions and that not all the knowledge a teacher has plays a role in their actions. Teachers are selective in given teaching situations and can refrain, either consciously or not, from using certain insights during their teaching. Over time and through classroom experience, many of these teaching decisions underpinning actions can become intuitive and tacit in nature and exert strong infuences on a teacher’s pedagogical moves. Therefore, to understand teachers’ instructional actions, a comprehensive approach to investigating teacher knowledge and its development is required (Verloop et al., 2001). Teacher knowledge may have a variety of origins (Shulman, 1987), such as formal education, that is, initial disciplinary training and teacher education, or continued professional learning (cf. Calderhead, 1996), access to educational materials and structures (such as curricula and exam prescriptions), and practical experiences that occur in day-to-day teaching practice, including interactions 1123

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with peers. In this sense, teacher knowledge is not opposed to theoretical or academic knowledge, but rather it incorporates elements of that knowledge in ways that make it more suited to meeting pedagogical goals. Obviously, individual teachers difer enormously in the extent to which they have merged, integrated, or transformed knowledge from diferent sources into conceptual frameworks that guide their actions in practice. One of the criteria for teaching to qualify as a profession is the existence of a common or shared knowledge base of teaching, conceived as all profession-related insights that are potentially relevant to a teacher’s activities. Therefore, research on teacher knowledge aimed to produce such a knowledge base (Reynolds, 1989). Initially it was conceived in a rather static and prescriptive way (cf. “knowledge for teachers”, Fenstermacher, 1994), however, since the 1980s the emphasis shifted to investigating and appreciating the knowledge that teachers develop1 in the course of their education and professional practice (cf. “knowledge of teachers”, Fenstermacher, 1994) and the mechanisms underlying this development. To avoid research on teacher knowledge being limited to idiosyncratic descriptions (Tom & Valli, 1990), many scholars agreed that one should search for the shared components of teacher knowledge from teachers in their classroom practice, and attempt to fnd “certain overarching generalizable features which are common across teachers” (Brown & McIntyre, 1993, p. 19). Underlying this discussion are questions about the aim of research on teacher knowledge: Should this research result in sets of rules that specify a direct connection between knowledge and practice that can be used as prescriptions for teachers (e.g., in teacher education programs), or should the resulting knowledge be seen as a resource that can enlighten and enhance teachers’ pedagogical reasoning and decision-making (cf. “practical arguments”, Fenstermacher, 1986), and, subsequently, their options for action? Given that teacher knowledge, by defnition, is shaped by teachers’ personal and professional contexts, and those contexts are highly varied, the latter aim seems the more realistic and desirable option. It also makes sense to focus the search for shared teacher knowledge on groups of teachers that are in similar situations with respect to variables such as subject matter, level of education, and age of students. In the context of these discussions, Lee Shulman began a research program on teacher knowledge in the late 1980s that studied teachers from diferent disciplines, aiming to identify the knowledge that is essential for teaching. Importantly, this research program focused on identifying what teachers know about teaching specifc subject matter to certain student groups, thus considering the teacher as a “knower” (Fenstermacher, 1994). The approach adopted in the Shulman program has had a very substantial and worldwide infuence on research on science teacher knowledge over the last 35 years.

Models of Teacher Knowledge In two highly infuential publications, Shulman (1986, 1987) outlined what he termed, “the knowledge base for teaching”. His goal was to identify the knowledge that distinguishes teachers from disciplinary peers on the one hand and from teachers of other disciplines on the other hand. He argued that the key to distinguishing the knowledge base of teaching lies at the intersection of content and pedagogy, in the capacity of a teacher to transform the content knowledge he or she possesses into forms that are pedagogically powerful and yet adaptive to the variations in ability and background presented by the students. (Shulman, 1987, p. 15) This premise led Shulman to defne a new category of teacher knowledge he termed pedagogical content knowledge (PCK). Shulman identifed PCK as “[t]hat special amalgam of content and 1124

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pedagogy that is uniquely the province of teachers, their own special form of professional understanding” (Shulman, 1987, p. 8), which refected and promoted the idea of the teacher as “knower”. He conceptualized PCK as a knowledge form shaped by elements of teachers’ pedagogical knowledge (e.g., about classroom management, instructional principles, learning theories) and by their knowledge of content or subject matter (substantive and syntactic). When discussing the shaping role of content knowledge, which he described as “the knowledge, understanding, skill, and disposition that are to be learned by school children” (Shulman, 1987, pp. 8–9), Shulman emphasized the importance of teachers having a deep understanding of the structures of the subject matter they are teaching.2 At the heart of PCK lies what teachers know about how their students learn specifc subject matter (e.g., concepts, principles) and the difculties or misconceptions students may have regarding this subject matter, and how this knowledge relates to the varied representations (e.g., models, metaphors) and instructional activities (e.g., explications, experiments) teachers know to teach this specifc topic. These two components of PCK, that is, knowledge of students and knowledge of instructional strategies, are mutually related: the better teachers understand their students’ learning difculties with respect to a certain topic and the more representations and activities they have at their disposal, the more efectively they can teach this topic. As reported in our chapter in the second edition of this volume (Van Driel et  al., 2014), the construct of PCK has had a profound and far-reaching impact on research into teachers’ knowledge in science education. As with any new construct, various scholars elaborated on Shulman’s work, proposing diferent defnitions and conceptualizations of PCK in terms of the features included or integrated (for example, see Van Driel et al., 1998). These refnements often arose in response to perceived limitations of the original PCK construct and sought to highlight the importance and contribution of certain aspects of teacher knowledge to the development and nature of PCK, particularly the infuence of contextual factors (see Van Dijk & Kattman, 2007). One PCK model introduced by Magnusson et al. (1999) has proved to be particularly infuential in research on science teacher knowledge (Friedrichsen et al., 2011) since the early 2000s. This model (commonly referred to as the Magnusson model) presented a strong case for the existence of PCK as a separate and unique knowledge category, related to teaching of specifc topics. These authors conceptualized PCK as consisting of fve components, summarized as: (1) orientations to teaching science, (2) knowledge of science curricula, (3) knowledge of assessment of scientifc literacy, (4) knowledge of students’ understanding of science, and (5) knowledge of instructional strategies (Figure 35.1). While various scholars have proposed diferent adaptations of this model (e.g., Park & Oliver, 2008, who extended the model with teacher efcacy), the Magnusson model continues to be a conceptual framework for many empirical studies that seek to understand the nature and development of PCK. The lack of a shared defnition and the existence of multiple conceptualizations of PCK inspired a group of US scholars (i.e., Julie Gess-Newsome, Janet Carlson, and April Gardner) to organize a PCK Summit. The aims of this summit were to form “a professional learning community to explore the potential of a consensus model of PCK” and identify specifc next steps that would move science education research in this feld forward (Carlson et al., 2015, p. 15). The summit resulted in a consensus defnition of PCK and a comprehensive model of teacher professional knowledge and skill (TPK&S) that includes PCK. Both were published in a book edited by Berry et al. (2015). In this book, PCK is defned as “the knowledge of, reasoning behind, and enactment of the teaching of particular topics in a particular way with particular students for particular reasons for enhanced student outcomes” (Carlson et al., 2015, p. 24). This defnition expands upon earlier portrayals of PCK by acknowledging the dynamic aspects of PCK, notably the pedagogical reasoning and decision-making skills that link teacher thinking and actions in the teaching cycle, and the all-important place of students in the teaching cycle. In one of the book chapters, Gess-Newsome explained how the new model of teacher professional knowledge and skill, also termed the consensus model (CM), aimed to address the weaknesses in PCK research, some of which were raised by Lee Shulman in his 1125

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PCK includes

Orientations to Science Teaching

which shapes

which shapes

Knowledge of Science Curricula

which shapes which shapes

Knowledge of Students’ Understanding of Science

Knowledge of Assessment of Scientific Literacy

Knowledge of Instructional Strategies

Figure 35.1 Magnusson model of PCK. (simplifed version; from Friedrichsen et al., 2011, p. 361, copyright © 2011. Reprinted by Permission of Wiley)

opening address to the summit: The absence of emotional and afective considerations, the need for more emphasis on teacher actions, the broader social setting and the relation between teacher knowledge and student learning (Shulman, 2015, pp. 9–10). In response, the CM of TPK&S introduced a number of signifcant considerations. First, it distinguished topic-specifc professional knowledge, as a form of public and canonical knowledge, from personal PCK and PCK&S (PCK and skill). The latter indicated the personal knowledge and skills teachers draw on in the context of classroom practice. Second, teacher orientations and beliefs were positioned as a flter or amplifer between topic-specifc professional knowledge and personal PCK/PCK&S. Lastly, student outcomes were seen to be impacted by teachers’ personal PCK/PCK&S and mediated by student variables, such as prior knowledge and student beliefs (Gess-Newsome, 2015). However, a review of empirical investigations (n=99) into science teachers’ PCK published several years later (Chan & Hume, 2019) highlighted the diferent conceptualizations and operationalizations of PCK, even in studies that were published after the introduction of the CM. The continued use of many variants and components of PCK meant diverse approaches to its identifcation and measurement in empirical investigations remained the norm. Chan and Hume (2019) also found the rhetoric around the PCK construct in the reviewed work typically featured idiosyncratic terminology, suggesting researchers in the science education feld risked talking at cross-purposes. This ongoing absence of conceptual clarity about PCK made the building of a common understanding of research agendas and outcomes problematic (Chan & Hume, 2019). In the introduction to a special issue of the International Journal of Science Education, Neumann et al. (2019) proposed acting on these concerns “in an attempt to help raise the profle of PCK as a major contributor to teacher education policy and practice” (p. 848) by testing the viability of the CM as an explanation of PCK and its development. The special issue sought to return to and probe Shulman’s original construct of PCK as an amalgam of content knowledge (CK) and pedagogical knowledge (PK) (since most researchers could agree that these were the foundations of PCK) by examining 1126

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the collective empirical fndings of fve studies featuring investigations into PCK development and outcomes. The intent was to use insights from this post-hoc examination of fndings to evaluate the CM. (Note these studies will be reviewed later in this chapter.) Neumann et al. (2019) provide a thorough and useful literature review of PCK’s research history in their introduction to the special issue, as background to a detailed critique of the CM that reviews its afordances and limitations. In the concluding paper of the special issue, Kind and Chan (2019) take on the complex and difcult task of drawing conclusions from the many and varied fndings of the fve papers in relation to models of PCK. In their interpretation of the fndings, Kind and Chan confrm Shulman’s amalgam view of PCK as a useful construct on which to build understanding of teachers’ knowledge for classroom teaching and how it is developed. Their conclusions concerning the CM highlight aspects of PCK and its development that are missing or underspecifed. Among others, they argue that the model does not contribute to understanding of teachers’ developmental trajectories and how these are infuenced by contextual and collaborative factors. To address the limitations of the CM, a second PCK summit was organized to “reach consensus on a model of PCK that is strongly connected with empirical data of varying nature and can be used as a framework for the design of future PCK studies” (Cooper & Van Driel, 2019, p. 308). In a book that resulted from this summit, Carlson et al. (2019) illustrated how the lack of detail and specifcity in the CM could cause confusion about the placement of certain knowledge aspects within the model. Using knowledge of instructional strategies as an example of a well-established and recognized component of PCK, Carlson et al. pointed out that this component only appeared as part of the (public) topic-specifc professional knowledge of the CM. Thus, it could be inferred that knowledge of instructional strategies was not considered part of a teacher’s personal PCK when clearly it is. The participants of the second PCK summit jointly wrote a chapter in which they propose a Refned Consensus Model (RCM) of PCK for teaching science (Carlson et al., 2019). The RCM’s central focus is on teaching practice, which is enveloped by the “complex layers of knowledge and experiences that shape and inform teachers’ science practice throughout their professional journey and, in turn, mediate student outcomes” (Carlson et al., 2019, p. 82). The outermost layer retains long-held views that teacher’s broader professional knowledge bases (i.e., content knowledge, pedagogical knowledge, knowledge of students, curricular knowledge, and assessment knowledge) underlie and inform their professional knowledge for teaching (Figure 35.2). As inner layers, the RCM discerns three interrelated realms of PCK: collective, personal, and enacted. This distinction recognizes that shared forms of PCK exist along with highly individual forms that are connected via processes of two-way knowledge exchange resulting in knowledge transformation. Personal PCK (pPCK) refers to a teacher’s unique and specialized knowledge for teaching science, which they can access when planning and teaching. This knowledge (previously sometimes termed static or declarative PCK) develops over time and is infuenced and shaped through multiple experiences, amongst which classroom teaching is considered foremost. In the teaching cycle at the heart of the RCM, knowledge exchange (see double-headed arrows in Figure 35.2) occurs as teachers draw upon their pPCK and engage in the acts of planning, teaching, and refecting. Using responsive pedagogical reasoning to make “in-the-moment” instructional decisions, a teacher transforms elements of their existing pPCK into a fexible and tacit subset of PCK that exists only in that teaching moment (Alonzo et al., 2019). This new “in-action” dynamic form of PCK, called enacted PCK (ePCK), is highly specifc to a particular situation since it involves teaching one or several particular students a particular concept in a particular learning context. ePCK only becomes visible (or more accurately, inferable) when examining teachers’ actions, for example, their use of instructional strategies, how they engage with students’ ideas, and how they express ideas to ensure development of conceptual understanding. In return, knowledge gained from these teaching experiences over time are transformed into a teacher’s pPCK (Alonzo et al., 2019; see also Alonzo & Kim, 2016, whose pioneering research utilized a novel video-based methodology to distinguish between these 1127

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Figure 35.2 Refned consensus model of PCK for teaching science. (from Carlson et al., 2019, p. 83, copyright © 2019. Reprinted by Permission of Springer)

diferent forms of personal and enacted PCK and the relationship between them). While pPCK and ePCK are unique to individual teachers, when teachers share their knowledge with others, their personal PCK can be transformed into collective PCK (cPCK). This type of knowledge can be found in documented best practice information for teachers, such as curriculum guidelines and teaching resources, where it can be thought of as authenticated or canonical PCK. It is this collective knowledge that has typically been the focus of much previous PCK research (Carlson et al., 2019). cPCK can also be contextually bound shared knowledge, such as that generated by teachers within a science department. The RCM proposes pedagogical reasoning (Shulman, 1987) as the mechanism underpinning and driving teachers’ knowledge transformations. Specifcally, the development of PCK is indicated by the knowledge exchanges between layers in the RCM, which signal how one type of knowledge infuences another (cf., Gess-Newsome, 2015; Magnusson et  al., 1999). These exchanges predict that as a teacher is exposed to new ideas and information, such as might occur during a professional learning intervention or in a classroom interaction, their pedagogical reasoning and decision-making come into play. The degree of transformation resulting from these exchanges is mediated through amplifying and fltering factors. For example, teachers’ personal beliefs about teaching science will mediate the infuence of cPCK on their pPCK (Carlson et al., 2019; Gess-Newsome, 2015). The mediating infuence of the wider context, outside classroom teaching, is acknowledged in the RCM by the insertion of a learning context layer (between those of pPCK and cPCK; see Figure 35.2) to “symbolically situate science teaching and learning in time and space” (Carlson et al., 2019, p. 87). This component expects features of learning contexts (like curriculum guidelines, exam prescriptions, or a school’s special character) to have both fltering and amplifying efects as teachers decide what aspects of cPCK to transform into their pPCK. 1128

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The Focus of This Chapter This chapter builds on the one that we wrote for the second edition of this volume (Van Driel et al., 2014), which followed up on the chapter written by Sandra Abell in the frst edition (Abell, 2007). When it came out, the Abell chapter was the most comprehensive review of research on science teacher knowledge ever published, and it continues to be a valuable resource for anyone who is interested in this area.3 Our chapter in the second edition highlighted Abell’s recommendations for future research, which included: We need more studies that take place within a teaching context to examine how SMK [subject-matter knowledge] develops, how it plays out in teaching, and how it is related to other kinds of teacher knowledge. . . . More studies need to focus on the essence of PCKhow teachers transform SMK of specifc science topics into viable instruction . . . we know little about how teacher knowledge afects students. (Abell, 2007, p. 1134) In our chapter in the second edition, we concluded that although the research between 2005 and 2012 had made considerable progress in response to these calls, more research was still needed in the areas identifed by Abell (2007). Specifcally, we advocated for: • • •

Studies focusing on the relationships between teachers’ content knowledge, pedagogical knowledge, and PCK and how these develop in a classroom teaching context Qualitative and quantitative studies that relate science teacher knowledge to student learning Research on the expertise of science teacher educators and facilitators and their infuence on the development of science teacher knowledge

For our selection of studies to be included in the present chapter, we considered research on science teacher knowledge of in-service and preservice teachers at all levels of education. For this purpose, we searched the volumes between 2012 and 2020 of the major science education journals and some of the major teacher education journals.4 From this corpus, we initially selected articles that met at least one of the following criteria: they present or apply a novel or innovative (1) conceptualization or model of science teacher knowledge, or (2) research design or methodology, or (3) present fndings about the content or structure of science teacher knowledge and its development, that make a substantial contribution to what was included in our chapter in the second edition. Other criteria also infuenced our fnal selection. For example, given the close relationship between teacher knowledge, thinking, and beliefs, we included studies on science teacher thinking and beliefs only when these were investigated in combination with teacher knowledge. However, studies focusing on science teachers’ beliefs, attitudes, or dispositions were excluded, as these constitute the focus of Chapter 34. Conversely, while other chapters in this volume feature teacher learning and development in broader terms, in the context of preservice science teacher education programs (Chapter 36) or inservice science teachers’ professional development (Chapter 37) we did include studies in this chapter where the development of science teacher knowledge was the central focus of attention. We also excluded studies in which teachers were asked to assess or rate their own knowledge. An example concerns a study by Irmak and Yilmaz (2019), who developed a questionnaire to investigate science teachers’ perceived content knowledge (example item: “I am comfortable responding to questions about topics in genetics”), pedagogical knowledge (“I can adapt the pace of my teaching to meet students’ needs”) and PCK (“I can select teaching strategies that efectively support student learning in genetics”). In our view, instruments like these are investigating teachers’ beliefs or confdence and are not valid measures of teacher knowledge. 1129

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Outcomes of Empirical Studies on Science Teacher Knowledge and Its Development In this section, we review empirical studies published since 2012 that meet the criteria outlined earlier. First, we discuss studies on science teachers’ content knowledge and its development, followed by a review of research on science teachers’ PCK. Next, we address studies that explored relationships between diferent categories of science teacher knowledge, in particular, content knowledge, pedagogical knowledge, and PCK, before reviewing research that investigated relationships between science teacher knowledge and student variables (i.e., learning outcomes). Finally, we give an overview of research on science teacher educators’ expertise related to fostering the development of science teacher knowledge.

Research on Science Teacher Content Knowledge In her chapter in the frst edition of this volume, Abell (2007) revealed that science teacher content knowledge (CK) had been investigated since the 1980s with increasingly sophisticated methods and for diferent purposes. Whereas some studies focused on describing the CK of specifc groups of teachers (e.g., elementary or preservice), either in general or regarding specifc topics, other studies attempted to relate teachers’ CK to other teacher characteristics or to teaching practice. Summarizing the research fndings on science teachers’ CK up until 2005, Abell concluded that science teachers often demonstrate very similar misunderstandings and misconceptions of science as their students. Some studies found that CK improves through teaching; however, studies that compared teaching in and out of one’s certifcation area discovered that teachers with low content knowledge tend to rely heavily on textbooks, talk a lot, ask few questions, and avoid cognitively challenging activities. These fndings led Abell to conclude that a positive relationship exists between teachers’ CK and their teaching. In our chapter in the second edition, we concluded that the research on science teachers’ CK between 2005 and 2012 was still consistent with Abell’s observation that many teachers had limited knowledge of, or misconceptions about, the science topics under consideration. Also, the research between 2005 and 2012 seemed to confrm her conclusion that teachers’ CK increases with teaching experience, and that a higher level of CK is typically associated with more confdence and more interactive and adventurous ways of teaching. The remainder of this section concentrates on research on science teachers’ CK from 2012 onwards, focusing on studies that further contribute to this feld of study, either by challenging or adding depth to what has been found earlier. Based on emerging themes, the recent research is summarized next.

The Construct of Teachers’ Science CK Needs Further Elucidation Diamond et  al. (2014) raised issues around the nature of CK and its measurement. The authors argued that teachers’ science CK needed a number of measures to fully capture and elucidate its nature, given the recognized limitations of various single measures. Thus, to determine CK gains as a consequence of an intervention, the study (involving 223 elementary teachers) utilized a range of measures, including a science knowledge test (comprising selected items from standardized national and international student achievement tests at levels equal to and just above those expected of their students); a questionnaire (self-reporting CK gains); classroom observations (using a protocol to determine the extent to which the teacher has an accurate and comprehensive grasp of the science content of the lesson); and number of college science courses taken. Student achievement was measured using a “high-stakes science test”. The fndings revealed that the intervention had a signifcant 1130

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efect on the treatment group teachers’ science knowledge test scores and questionnaire responses compared to the control group, but not on the classroom observation ratings. This latter fnding was surprising and raised questions about the trustworthiness of this measure, particularly when fndings also showed that “[t]eachers’ scores on the science knowledge test were found to be the largest signifcant teacher-level predictor of student achievement outcomes regardless of participation in the intervention” (p. 635).

Disciplinary Education Makes a Difference but Doesn’t Guarantee Strong CK Several studies investigated the subject-matter knowledge (SMK) of science teachers in relation to their disciplinary background. Kind (2014) surveyed 256 preservice teachers (PSTs) from the UK, who graduated in diferent scientifc disciplines (i.e., chemistry, biology, or physics), to explore their understandings and misconceptions about fve selected chemistry concepts using 28 short diagnostic probes. PSTs with a chemistry degree outperformed biology and physics graduates in chemical bonding and combustion reactions, leading the author to conclude “that non-chemists’ CK is insufcient for teaching these chemistry concepts in high schools” (p. 1313). Similarly, in a small-scale qualitative study involving six novice science teachers in the United States, Nixon et al. (2016) reported that the three who held a degree in chemistry demonstrated stronger knowledge of the content of particular chemistry topics, that is, produced more chemistry-focused and more sophisticated and coherent explanations compared to those with a biology degree. Glaze and Goldston (2019) surveyed advance placement (AP) biology teachers (n=71) in the United States on their knowledge and acceptance of evolution and understandings of the nature of science. Participants had low to moderate mean scores in each of the three measures, which the authors considered to be worrying since AP biology courses are held in high regard for their rigor, depth, and breadth of content. The authors also found that some teachers accepted evolution, although their actual comprehension of it was low, whereas others demonstrated strong understanding of evolution yet actively rejected those parts of the theory that were not in alignment with their worldview.

Experience With Teaching Specifc Subject Matter Seems to Enhance Teachers’ CK To investigate the impact of teaching experience on teachers’ SMK, Nixon et al. (2017) conducted a longitudinal study involving 15 beginning secondary science teachers in the United States across their frst fve years of teaching. Participants constructed a concept map in their frst year of teaching and another in their ffth year of teaching. Comparing these concept maps showed that participants did not provide more sophisticated connections between concepts, nor did they demonstrate more complex knowledge structures in their second map. The authors concluded that the SMK of these teachers hadn’t changed signifcantly, despite having taught the subject matter for fve years. In another study, Nixon et al. (2019) focused on the SMK of elementary teachers relative to career stage. The authors found that teachers’ SMK was stronger for topics in the grade level they taught and increased with teaching experience, except for the most senior teachers (18+ years). The lower scores of the latter could be attributed to their attention having shifted to areas other than teaching science (e.g., leadership). The authors concluded that the association of stronger SMK with years of experience teaching specifc science topics could be attributed to teachers’ self-directed learning as part of their daily practice. The contrasting fndings of these two studies may be related to the diferent ways in which content knowledge was measured, that is, with concept maps (Nixon et al., 2017) or with the items that were selected in each study from the Misconceptions-Oriented Standards-Based Assessment Resources for Teachers (MOSART) test bank (Sadler et al., 2010; Nixon et al., 2019). 1131

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Specifc Interventions Can Contribute to Improving Teachers’ CK Whereas Nixon et al. (2019) reported that the amount of professional development (PD) teachers undertake is not associated with the quality or strength of their content knowledge, other studies have demonstrated that specifc interventions can positively impact science teachers’ CK. Donna and Hick (2017) conducted an experimental study with educative curriculum materials (ECM) designed to build preservice elementary teachers’ CK in an area of known difculties and misconceptions (i.e., convection as a thermal energy process). The authors found that the PSTs credited participating in the ECM lessons with building their content knowledge and their confdence. Also, participants who used the ECM to teach within a feld experience had signifcant gains in CK of the topic compared to those who did not use the materials in their teaching. Batiza et al. (2013) investigated the impact of an intervention about biology energy transfer on biology teachers’ CK and self-efcacy. The intervention consisted of a two-week workshop, followed up with regional meetings, a yearly classroom visit, and bimonthly online reporting. The authors applied a randomized control experimental design and found signifcant gains and large efect sizes in CK and self-efcacy, not only post-workshop but also one year later. These last two studies seem to demonstrate that providing teachers with carefully designed materials, which focus on understanding science content, can help to improve their understanding of that content and, in turn, their confdence or self-efcacy. As such, this is not a novel nor surprising conclusion. In summary, recent studies on science teachers’ content knowledge appear to confrm what was reported in the frst and second editions of this handbook. Research reveals shortcomings in CK, even among teachers with a degree in the respective discipline. These shortcomings can be addressed by specifc interventions focused on the teaching of the subject matter at hand. There is evidence that suggests teachers’ knowledge of particular content improves through teaching this content repeatedly; however, the evidence is not conclusive.

Research on Science Teacher Pedagogical Content Knowledge In her review, Abell (2007) observed that compared with research on teacher content knowledge in science, research on PCK was still very much in its infancy. Abell sign-posted several pressing needs in relation to future PCK studies: the need to understand the interaction of PCK with other knowledge categories, the need to make conceptual frameworks using PCK more explicit, and the need for more studies that focus on the essence of PCK, that is, how teachers transform their knowledge of specifc science topics into viable instruction. In our chapter in the second edition, we reported that studies investigating specifc components of science teachers’ PCK had continued to accumulate between 2005 and 2012, mostly focusing on science teaching orientations and knowledge of student understanding of science. Similar to the fndings Abell (2007) reported, these studies demonstrated that science teachers often have limited knowledge of how their students understand and learn science content; however, this knowledge tends to improve with teaching experience or as a result of specifc interventions in teacher education programs. Another conclusion was that these studies typically did not investigate PCK in the context of teaching practice, although some used artifacts from practice to elicit components of teachers’ PCK. We also reported that studies investigating the development of science teachers’ PCK were beginning to accumulate, with researchers identifying relationships resulting from the interaction and growth of diferent PCK components and further empirical evidence of the important role refection plays in the process of PCK development. Some studies between 2005 and 2012 showed how CK could act as a source or basis for the development of PCK, suggesting that a thorough understanding of subject matter is necessary, but not sufcient, as a requisite for the formation of PCK. However, 1132

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other studies reported that CK and PCK can develop simultaneously, implying that PCK development doesn’t have to “wait” for SMK to be developed (Kind, 2009), but the mechanisms by which CK and PK infuence the development of PCK remained mostly unclear. In this section, we review research on science teachers’ PCK since 2012. In recent years, several publications collating and reviewing research in this area have been published, notably, a special issue of the International Journal of Science Education (Neumann et al., 2019), and two books based on the aforementioned PCK summits. These publications raise important considerations and help inform our review as we report on developments in the feld from 2012 to 2020. We begin the review by discussing studies that are focused on the structure and nature of PCK. These studies aim to identify and capture the components that constitute PCK and determine how these components are related. Next, we review studies on the development of PCK through small-scale, classroom-based investigations. These studies center on how PCK develops in relation to its sources (i.e., content knowledge and pedagogical knowledge) and how this development is infuenced by specifc interventions in programs of initial teacher education or professional development.

Studies on the Structure of PCK and Relationships Between Its Components Adopting the Magnusson model (Figure 35.1), a group of studies explored relationships between components of PCK. This strand of research follows up on the work of Park and Chen (2012), reviewed in the second edition, where PCK maps for individual teachers on specifc topics were constructed that visualized connections between diferent PCK components and the relative strength of these connections. Soysal (2018) conducted a case study based on a 150-minute interview with one experienced Turkish elementary teacher that focused on her teaching of energy. The author applied a variation of the Park and Chen mapping approach and found that knowledge of student understanding was the most central component of PCK and was most often connected to knowledge of science curriculum. The author explained this fnding by referring to a longitudinal study (Arzi & White, 2007) that shows that the school science curriculum is “the single most powerful determinant of teacher knowledge, serving as both its organiser and source” (p. 221). The mapping approach was applied in a number of studies on PSTs in Turkey. Aydin et  al. (2015) investigated how interactions among PCK components of three PSTs developed throughout a 14-week practicum course using data collected from pre- and post-course content representations (CoRe; Loughran et al., 2004) and semi-structured interviews to prepare PCK maps. Comparing these maps pre- and post-course demonstrated that the interplay among PCK components was fragmented initially but integrated at the end of the course, while the development of the interplay was idiosyncratic to each teacher. Specifcally, the most signifcant development was seen in the connection of knowledge of science curriculum with other components, which the authors suggest could be a result of asking the PSTs to prepare a CoRe. Demirdöğen et al. (2016; see also Demirdöğen & Uzuntiryaki-Kondakci, 2016) investigated the impact of a two-semester elective course on the PCK of 30 chemistry PSTs related to the nature of science (NOS). The authors analyzed data from interviews and lesson plans using the PCK mapping approach to monitor the degree of integration and coherence among components of the PSTs’ developing PCK. This analysis revealed that PSTs’ development of PCK for NOS progressed from knowledge to application (in lesson plans) during the course, and they aligned their NOS-related science teaching orientations with their instructional strategy at the application level in their planning. All but three PSTs developed knowledge of instructional strategies for teaching NOS and to a greater extent than any other components. In contrast, the level of integration of PCK components differed amongst the PSTs, but those with more integrated PCK developed better lesson plans for teaching NOS. 1133

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In an attempt to relate PCK to the performance of PSTs, Reynolds and Park (2021) applied the PCK mapping approach to portfolios that are commonly used in the United States for performance assessment (i.e., edTPA). Via this method, the PCK levels of 36 PSTs were determined from their portfolios, and the authors reported a strong correlation between the participants’ edTPA scores and their PCK levels (r=0.949, p < 0.0001). Confrming fndings of previous research, the study found knowledge of student understanding and knowledge of instructional strategies to be the most developed and most frequently integrated components of PCK. However, compared to other studies, knowledge of assessments was more often integrated in PSTs’ PCK, which the authors suggest may have been due to the requirements of the edTPA portfolio. Compared to studies of in-service teachers, the PSTs did not frequently integrate science teaching orientations in their PCK. Hanuscin et  al. (2020) investigated the PCK about (properties of and changes in) matter in a cohort (n=37) of ffth-grade teachers, consisting of experienced, novice, and “reassigned” teachers. The last group consisted of teachers with more than fve years’ teaching experience but fewer than fve years at ffth grade. The authors used a standard test to measure teachers’ CK and explored their PCK using a lesson preparation task in combination with a CoRe, followed by an interview. A rubric was used to calculate scores for four PCK components of the Magnusson model and diagrams were developed to represent outcomes for groups of teachers with diferent levels of CK and teaching experience. Among other fndings, it appeared that reassigned teachers had weaker CK compared to novice teachers and those with more experience in ffth grade; however, reassigned teachers displayed higher levels of some PCK components than novice teachers. Their methodology allowed the authors to reveal interesting and sometimes unanticipated relationships between teachers’ CK, PCK components, and teaching experience. By analyzing and visualizing the relationships between PCK components as per the Magnusson model (see Figure 35.1), these studies go beyond older studies that investigated these components in isolation (cf. Friedrichsen et al., 2011). PCK maps are based on counting the co-occurrence of PCK components in units of analysis in the data (e.g., “PCK episodes” in a portfolio), which implies that inferences about the nature or the quality of the relationships between components cannot be drawn from these maps. To address these limitations of PCK mapping, Park et  al. (2018) proposed two additional types of PCK measures – a survey and a rubric. Their survey asked respondents to identify student misconceptions about the topic of photosynthesis that were revealed in a given scenario and then suggest instructional strategies, plus a justifcation, to address the identifed misconceptions. The method of principal component analysis revealed a strong association between knowledge of student understanding and knowledge of instructional strategies and representations. The PCK rubric was applied to pre- and post-teaching interviews and video clips of classroom teaching, and it was discovered that scoring necessitated a considerable amount of time, even for well-trained and experienced scorers. The authors concluded that “[a]lthough a set of substantial evidence has been collected to support the validity of the PCK measures, further validation eforts are necessary to make them more useful and operational for future research on PCK” (p. 566). In summary, recent studies on relationships between components of PCK tend to confrm the central association between knowledge of student understanding and knowledge of instructional strategies related to a particular topic. Other linkages identifed with the PCK mapping approach appear to result from contextual infuences, such as the requirements of a school curriculum or those of national assessment, or methodological diferences between studies.

Studies on the Development of PCK in the Context of Classroom Practice This section reviews recent empirical studies on the development of PCK. Most researchers in these studies regard PCK as the knowledge teachers draw on and enact in their classroom teaching. These studies seek to determine in a qualitative manner what PCK comprises, how it is developed 1134

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in relation to CK and PK as its sources, and how to support that development. First, we will review research on PCK development of PSTs in the context of initial teacher education and then turn to studies of in-service teachers of science in the context of curriculum development or professional learning programs. Finally, we cover a study featuring tertiary science teachers that resulted in signifcant PCK gains.

PCK Development of Preservice Science Teachers A set of South African studies focused on the development of preservice teachers’ PCK related to specifc science topics (topic-specifc PCK or TSPCK) by comparing PCK before and after an intervention that included classroom teaching. In a longitudinal qualitative case study involving seven chemistry PSTs, Miheso and Mavhunga (2020) introduced the participants to TSPCK through lesson planning for organic chemistry or stoichiometry. Teacher tests specifcally designed to assess TSPCK for these two topics when planning lessons were administered before and after the introduction of TSPCK in the last year of their teacher education program and for a third time two years later into their practice as beginning teachers. Findings revealed growth and retention of TSPCK over the two years and supported the claim that PSTs’ early introduction to TSPCK expedites the development of PCK for teaching science in the beginning years of their professional teaching career. In a similar study involving 14 novice, uncertifed science teachers, Pitjeng-Mosabala and Rollnick (2018) tracked the TSPCK development of four participants as case studies during a ten-month-long intervention. This program comprised an initial two weeks of induction training, featuring an introduction to TSPCK and construction of CoRes by the participants on the topic “particle nature of matter”; followed by classroom teaching experience in schools; and concluded by the construction of fnal CoRes on the same topic after teaching. In addition to PCK tests, data sources included the CoRes constructed before and after teaching, video-recorded lessons, feld notes, and interviews. Findings showed that all 14 participants had developed their TSPCK after the initial two-week induction. However, for the four case-study teachers, the only participants who had the opportunity to directly teach the particle nature of matter topic to classes, there was evidence of further TSPCK development emerging from their teaching. These four teachers showed greater improvement in TSPCK than others in the study, which the authors attributed to the importance of the feedback loop between classroom practice and TSPCK in the consensus model (considering TSPCK equivalent to TSPK in this model; Gess-Newsome, 2015). In the third TSPCK study of this set Coetzee et al. (2020) used a similar methodology to the previous two but introduced the RCM (Figure 35.2) into their overall conceptual framework to interpret how three PSTs enacted TSPCK about electromagnetism when teaching the topic for the frst time. The study aimed to fnd out the extent to which the PSTs applied the knowledge taught during a preceding physical science method course and focused on their enacted PCK (ePCK) as they taught electromagnetism in classrooms. Data were captured for each part of the teaching cycle by CoRes during the training period (plan), video-taped lesson observations during teaching in schools (teach), and video-stimulated recall interviews after teaching the topic (refect). A rubric was used to assess the quality of enacted PCK teaching as restricted, adequate, or rich. The interviews aforded the opportunity to elicit PSTs’ pedagogical reasoning for certain classroom actions. Findings indicated that the PSTs enacted certain components of the PCK taught during the method course and were able to reason pedagogically about their teaching, though not at the same level for all main ideas of the topic electromagnetism. PSTs’ confdence about SMK appeared to be linked to the quality of their ePCK. Two other studies involving PSTs closely examined their development of PCK for teaching the NOS. Hanuscin (2013) focused on the transition of one PST over a two-year period from her elementary science methods course, through her early teaching experiences in an informal science 1135

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setting, and fnally student teaching. Through construction of critical incident vignettes, narrative inquiry was employed to illustrate the changes in her PCK about NOS over time, and the experiences that facilitated these changes. The study shed light on potential sources of PCK within the PST’s teacher preparation, the interplay between the various knowledge components comprising PCK, and barriers and supports that impacted the development of her PCK. Findings supported the assertion that teacher knowledge builds from more generalized pedagogical knowledge and isolated bits of PCK to more integrated PCK. This frst study highlighted the importance of mentoring to bridge the gap between a methods course, feld experiences, and student teaching. The second study (Mesci et al., 2020) explored the factors infuencing PSTs’ PCK for targeted aspects of NOS and nature of scientifc inquiry (NOSI) in a 13-month teacher education program in the United States. Data collection included lesson plans, interviews, and observations, and analysis targeted evidence for CK, PK, self-efcacy, and the PCK components of the Magnusson model. Focusing on a pair of PSTs, the study described how PSTs can develop and enact PCK for NOS/NOSI over time in their teaching of science topics. Specifcally, the two PSTs successfully used their knowledge for teaching NOS and NOSI aspects, including CK and PCK components (i.e., knowledge of student difculties and misconceptions, of representations and instructional strategies, and of assessment) to engage students and support their construction of a conceptual framework for NOS and NOSI. The authors conclude: “This integrated perspective, with additional infuences such as self-efcacy and goals, exemplifes the complexity of PCK as a construct. In other words, it is messy, making research on PCK likewise messy” (p. 285). In summary, these studies highlight the complex interplay of elements within teacher education programs (e.g., method courses, mentors, feld experiences) that occurs throughout the emergence of PSTs’ PCK from their core content and pedagogical knowledge forms.

PCK Development of In-Service Science Teachers The implementation of new curriculum and curricular materials and professional development interventions are frequently the context of studies on PCK development. Often, these studies were with experienced teachers to gauge how and in what ways they accommodated new educational resources and pedagogical approaches. For example, in their classroom-based study, Bayram-Jacobs et al. (2019) investigated the PCK development of 30 science teachers from four European countries as they taught a specially designed socioscientifc issues (SSI) curriculum module as part of a European Commission project. In their analysis of data collected through a lesson preparation form (PCK-before), a lesson refection form (PCK-after), and a lesson observation table (PCK-in-action), the researchers asserted that both the richness of the respective PCK components and their alignment with the goals of the lesson indicate the strength of PCK and therefore its transference to classroom practice. The fndings revealed that by using the curriculum module most of the teachers developed their knowledge about students’ understanding of science and instructional strategies. Recognition of student difculties prompted teachers to consider specifc teaching strategies in line with the learning objectives to improve student understanding. The authors concluded that when professional development programs and curriculum materials for teaching SSI target the development of strong interconnections between the PCK components understanding of students’ difculties in SSI learning and knowledge of appropriate instructional strategies, and equally focus on science content and SSI skills, they contribute to and foster PCK for SSI teaching. In another setting, this time a large-scale national implementation of curriculum materials to accompany a new science curriculum in China, Chen and Wei (2015) investigated how fve experienced middle school chemistry teachers used and enacted these materials in their classroom teaching. They observed the teachers’ existing PCK had a strong infuence on how they interpreted and implemented the materials. From interviews with the teachers before, during, and after their teaching of 1136

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selected chemistry topics, and observations of their teaching, the researchers discovered teachers made decisions to omit or refne aspects of the intended curriculum in their teaching. These decisions and actions drew on components of the teachers’ PCK, especially their knowledge of students’ understanding of science and knowledge of assessment in science and were infuenced strongly by national examinations. The authors recommended that future development of curriculum materials would beneft from more involvement of experienced teachers in their design. Barendsen and Henze (2019) also revealed dissonance between the intent of curriculum reforms and their enactment in the classroom through an in-depth case study of a chemistry teacher as he planned and taught a contextbased lesson module on (Poly) Lactic Acid to his grade 9 science class in the Netherlands. The teacher’s PCK was captured before and during teaching with a CoRe form (Loughran et al., 2004) and an observation table to monitor classroom interactions. The study revealed that the teacher’s previously espoused beliefs in the context-based instructional paradigm of the national curriculum were not refected in his teaching practice, that is, his PCK pre-teaching lacked topic specifcity, and there was little alignment with the context-based approach to teaching science in the PCK that was inferred from his teaching. The authors discuss the teacher’s pedagogical reasoning and decisionmaking whilst in action to explain the relationship between his knowledge and his teaching moves. Chan and Yung (2015) sought to capture how the enacted PCK of four experienced Hong Kong science teachers developed during their classroom teaching, as they frst attempted to teach a new topic (i.e., polymerase chain reaction). The authors explored the veterans’ planning, teaching, and refecting on the new curricular content using data sources that included classroom observations, feld notes, classroom artifacts, semi-structured interviews, and video-stimulated recall interviews. The fndings were presented as classroom vignettes featuring a detailed description of the classroom interaction, the instructional strategies and representations used, and the teachers’ thought processes, notably their pedagogical reasoning and refection. The teachers were observed inventing new instructional strategies and representations on the spot during the lesson, referred to as onsite PCK development. To explain this type of PCK development, the authors advanced a model that depicts a three-step inventive process comprising a stimulus for PCK development, an integration process, and a response. Factors facilitating onsite PCK development in the case studies included teachers’ strong knowledge of the topic, strong PK, and knowledge of students to do with their prior learning experience and their potential learning difculties related to the topic. In another paper emerging from this same study, Chan and Yung (2018) identifed that an experienced teacher who had the habit of mind to carry out content analysis in initial lesson planning for a new topic and to plan formative assessment opportunities was more able to capitalize on his existing SMK to develop new PCK than teachers in the study without this disposition. The combination of pedagogical strategies (i.e., content analysis and formative assessment) that guided the teacher’s refective thinking and pedagogical reasoning as he undertook the task of planning new teaching topics was termed a “generalized mental framework” by the authors. The authors identifed the CoRe template as another example of a mental framework that directs teachers to analyze and consider the content knowledge of the topic in relation to several important aspects of teaching (e.g., student thinking, their learning difculties, and ways of assessing the ideas). They recommend that supporting teachers to acquire the disposition of structuring their refections purposefully in the planning phase of a new topic should be a focus for teacher professional learning, given continuous curriculum changes. A study by Rollnick (2017) also investigated experienced teachers teaching a new science topic in the senior secondary curriculum (semiconductors), this time in the context of action research as seven South African high school teachers participated and collaborated with colleagues and university researchers. The study reports on the role the teachers’ developing CK played in relation to developing their TSPCK for the new topic. In the collective case study approach, data was gathered as the teachers engaged in collaborative CoRe design, concept mapping, lesson planning and teaching, and ongoing refection. Findings showed how teachers’ learning of content took place alongside 1137

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PCK development, that is, the extent and type of CK the teachers developed informed their choice of teaching approaches. Teachers increased their understanding of how to teach the topic in almost all cases, as evidenced by their increased ability to design teaching strategies and their use of representations and suitable assessment tasks for their lessons. The following group of studies in this section did not involve interventions or curriculum implementation per se; rather, they investigated how the PCK of in-service teachers might be enhanced by classroom enactment accompanied by refection, either personal or provided as feedback by observers. For example, the study by Schultze and Nilsson (2018) revealed how an experienced Swedish chemistry teacher gained and refned her PCK through cooperation with two grade 12 students (age 18) as co-teachers. In a grade 10 class, the teacher and senior students co-planned, co-taught, and co-evaluated lessons in chemical bonding, while the researchers observed and looked for evidence of transformations in components of the teacher’s PCK (as per the Magnusson model) as a measure of PCK development. The study found that the teacher’s decision-making and actions in class were mediated by the observations of her two co-teachers, who helped the teacher to identify the content the students fnd difcult to learn and how to develop instructional strategies to address these learning difculties. As a result, the teacher’s PCK was refned. In a similar collaborative inquiry (Nilsson & Vikström, 2015), six experienced secondary science teachers in Sweden sought to improve their classroom teaching of content they identifed as problematic when it comes to student learning. Using learning cycles informed by variation theory, the teachers worked collaboratively and iteratively in teams of three with a researcher to: explore their own teaching activities; identify what was critical for their students’ learning; and implement those insights into their teaching practice. Data from interviews and video-recorded lessons were analyzed to reveal instances where diferent PCK components interacted in a dynamic way to develop new knowledge. Using vignettes to give a more holistic view of the PCK development that was occurring, four of the six teachers were shown to actively change their teaching of the specifc content in a way that provided new possibilities for students to grasp the object of learning. These vignettes included how the object of learning was defned and focused, how the examples that were presented to the students were chosen, how the lessons were structured, and how the nature of student learning changed. Wongsopawiro et al. (2017) also explored processes of PCK development as 12 in-service teachers addressed problems they were experiencing in their own science teaching as part of a yearlong action research program in the United States Using the Interconnected Model of Teacher Professional Growth (IMTPG; Clarke & Hollingsworth, 2002) as an analytical tool, the authors examined the teachers’ action research reports, electronic refective journals, and responses from semi-structured interviews (conducted at the end of the project) to understand how the design features of the action research program impacted changes in the teachers’ PCK. This analysis of teacher-reported data enabled the researchers to identify specifc pathways of change in the teachers’ PCK as they researched solutions to their practice dilemmas. Pictograms (pictorial representations) were produced to show these change pathways for each component of a teacher’s PCK (using four components from the Magnusson model), which appeared an efective way of tracing the pedagogical reasoning and action that occur in individual learning pathways. Wongsopawiro et al. concluded that the IMTPG proved useful in monitoring long-term PCK development by making it possible to describe the changes and reveal the underlying processes of enactment and refection for research purposes, showing diferences between teachers’ PCK development processes, acknowledging PCK as personal and context bound, and illustrating that professional development is a complex network of processes sometimes occurring simultaneously. The fnal study in this section represents an interesting adaptation of an existing approach to study PCK development to suit teaching in the tertiary science teaching context. In an Australian investigation into the characteristics of the PCK of chemistry lecturers, Schultz et al. (2018) piloted, refned, and applied the CoRe tool for collating the collective TSPK (cf., cPCK) of over 80 tertiary 1138

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chemistry teachers with a range of levels of teaching experience. Analysis of the CoRe responses provided insights into the participants’ cPCK, and in an innovative move Shultz et al. built a website to share and maintain the large collection of teaching strategies that was generated through the study. In addition, the researchers observed that participation in the CoRe design workshops led to a signifcant gain in pPCK for some individuals, and they recommend their modifed CoRe as a useful tool for the PCK development of tertiary teachers. In summary, many of the studies in this section refect Shulman’s (1986, 1987) original conception of PCK as a specialized form of teacher knowledge for classroom teaching that is transformed from PK and CK through teachers’ processes of pedagogical reasoning and action. By using qualitative methodologies, often featuring case-study approaches and data-gathering methods, such as classroom observations (video-recorded in some instances), interviews, and journaling, these studies have built rich databases that allow researchers to explore individual teachers’ decision-making processes and teaching actions in authentic classroom situations. The studies included in this section reported insights from the analysis and interpretation of these data to better understand the nature and development of PCK (including its components and their interplay), typically in the context of programs for teacher professional learning, as it plays out in classroom teaching. Some of the studies used new conceptual or theoretical frameworks and novel tools or existing ones in innovative ways to conceptualize and represent the development of PCK. Some studies addressed the role of CK and PK in the development of PCK, and the relationship between these knowledge categories is the focus of the next section.

Research on Relations Between Categories of Science Teacher Knowledge Based on our review of studies between 2005 and 2012 in the previous edition, we concluded that research from that period supported the claim that PCK and CK are separate categories of science teacher knowledge that can be empirically distinguished. This conclusion was in line with fndings from research on mathematics teacher knowledge (Baumert et al., 2010). Since 2013 there has been an increasing occurrence of studies that investigated relationships between categories of science teachers’ knowledge, most frequently CK, PCK, and PK. These tend to be large-scale, quantitative studies that use surveys and paper-and-pencil tests to explore associations between these knowledge categories. These studies are reviewed in this section. First, however, we review a qualitative study by Andrews et al. (2019) that described how undergraduate biology instructors (n=13) from fve universities in the United States use components of PCK and PK to design and implement opportunities for students to generate their own understandings in large classes (>150 students). Teacher knowledge was elicited in the context of the instructors’ own teaching using a pre-instruction interview, flming of a “target class” session, and a post-instruction stimulated recall interview. The authors found that instructors who engaged students in generative instruction integrated topic-specifc knowledge about student learning (i.e., PCK) and more generalizable knowledge about how people learn (i.e., PK) to plan lessons to target student difculties. Specifcally, they demonstrated how these instructors drew on their PK to design lessons featuring a focus on students generating reasoning with instructional strategies that made this reasoning explicit, which in turn created opportunities to develop new PCK. A number of studies from Germany explored relationships between CK, PK, and PCK with paper-and-pencil tests. Following up on a previous study discussed in the second edition of this handbook (Kirschner et al., 2011), Kirschner et al. (2016) described a model of professional knowledge consisting of CK, PK, and PCK where PCK was conceptualized in terms of three knowledge areas: declarative (what), procedural (how), and conditional (why). The PCK test consisted of openended items that asked teachers to respond to vignettes that contain a realistic description of a teaching sequence or teaching material. Diferent approaches to evaluating the PCK test are reported, 1139

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including the analysis of construct validity by testing teachers across diferent school subjects, teachers from diferent school types, preservice teachers, and physicists. Findings demonstrated that the PCK test distinguished physics teachers from the other groups. Multidimensional Rasch analysis showed physics teachers’ CK, PCK, and PK are distinct categories of teacher professional knowledge that are moderately (PCK-CK: 0.5) to weakly (PK-CK and PK-PCK both around 0.2) correlated. These fndings were consistent with the authors’ earlier study (Kirschner et al., 2011). Following up on their previous work, as discussed in the second edition of this handbook (i.e., Schmelzing et al., 2013; Jüttner & Neuhaus, 2012), Jüttner et al. (2013) described the stepwise development of an instrument to test the CK and PCK for four biological topics. As in the Kirschner et  al. (2016) study, the authors distinguished between declarative (what), procedural (how), and conditional (why) PCK. Short-answer, open-ended, and ranking items were developed, for instance, asking how teachers would handle a particular model in a lesson. A random sample of 158 in-service biology teachers completed the two tests. Based on their analyses, the authors claim that the instruments measured teachers’ CK and PCK in an objective, valid, and reliable way. A statistically low but signifcant correlation (r=0.22, p=0.006) was found when CK measures of all respondents were compared to PCK measures. This result is lower than the correlation reported in other studies; however, it confrms that CK and PCK are interacting but distinct categories of teacher knowledge. Applying a similar design, Großschedl et al. (2019) developed a PCK instrument with open and true/false items and evaluated it in three studies with biology PSTs. On the basis of their analysis of the responses, the authors concluded that the fnal 34-item version of the instrument, focusing on knowledge of the students’ understanding (13 items) and instructional strategies for teaching (21 items), is “unidimensional, provides objective test scores and enables reliable and valid registration of pre-service biology teachers’ PCK” (p. 402). In one of the three studies they explored relationships between the PCK, CK, and PK of biology PSTs, fnding correlations between PCK and CK (0.82), PCK and PK (0.78), and CK and PK (0.51) that were substantially higher compared to those identifed in the studies of Jüttner et al. (2013) and Kirschner et al. (2016). In another study, Großschedl et al. (2015) applied a similar approach to measuring CK, PCK, and PK that showed they are three unique and separable but correlated constructs. Correlations varied between 0.68 (CK-PCK), 0.35 (PK-PCK), and 0.11 (PK-CK). Moreover, and confrming fndings from previous research, results indicated that CK and teaching experience are relevant for PCK development. In a study by Paulick et al. (2016), the relations between PSTs’ academic self-concept and their professional knowledge were central. Self-concept was measured by asking preservice teachers to apply the same fve items (e.g., “I can give an overview of the topic of . . .”) to three categories of professional knowledge, namely, CK, PCK, and PK, using a four-point scale. For PCK, CK, and PK, instruments with open-ended, short-answer, and multiple-choice items were used that were based on existing instruments. From the correlations found between self-concept scales and knowledge categories, the authors concluded that academic self-concept is empirically separable into CK, PCK, and PK and that self-concept measures provide an alternative means to assess PSTs’ professional knowledge in comparison with traditional performance measures. Sorge et  al. (2019) also examined quantitative relationships among CK, PCK, and PK. The study involved physics PSTs (n=200) enrolled in diferent years of teacher education at 12 major teacher education universities in Germany, dividing the sample into groups of beginning (n=91) and advanced (n=109) PSTs. Using structural equation modeling, the authors confrmed the conclusions from studies mentioned earlier that CK, PCK, and PK represent distinct types of knowledge. Correlations between these three categories were high and very similar to those found by Großschedl et al. (2019). Interestingly, this study indicated that for beginning PSTs, PCK is more closely related with general PK, while for the advanced PSTs, PCK is more closely associated with CK, “suggesting that [PCK] develops from a general knowledge about teaching and learning into knowledge about 1140

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the teaching and learning of specifc content” (p. 862). Finally, informal learning opportunities, such as observing expert teachers, were identifed as contributing to the development of PCK. In a project conducted in the United States, Jin et al. (2015) explored the CK and PCK of teachers after they participated in a professional development program focused on a teaching unit about plant growth. The authors developed a CK test and a PCK test for this topic based on a learning progression framework from a previous study. CK was measured with a student assessment test; in other words, teachers’ content knowledge refected what students were expected to learn (cf. Shulman, 1987). The PCK test focused on two PCK components: knowledge of student thinking and knowledge of instructional strategies. Open-ended items were developed, which asked teachers to react to contexts that are commonly encountered when teaching photosynthesis and cellular respiration. A rubric was constructed to score teachers’ responses. One hundred ninety-four middle school teachers participated in the study. Overall, teachers’ performance on CK was slightly higher than PCK, but not signifcantly diferent. As for CK, 61.5% of responses were at the highest level, suggesting the majority of teachers were able to use scientifc reasoning to explain phenomena about plant growth and functioning. Regarding PCK, most teachers were able to recognize incorrect content in students’ responses, but they struggled with identifying students’ intuitive ideas. In a follow-up study with a subgroup of 25 teachers who taught the plant growth unit and assessed their students, a statistically signifcant relationship was found between teacher knowledge (i.e., a combined score on the CK and PCK tests) and student learning gains. Finally, three studies investigated CK in relation to PCK, where PCK was limited to teachers’ knowledge of student learning difculties or (alternative) conceptions. Lucero et  al. (2017) conducted a small-scale study with four biology teachers in the United States from the same high school focusing on the teaching of evolution through natural selection. Main data sources were teacher thinking aloud responses to questions about specifc subject matter (i.e., from the concept inventory natural selection [CINS]) and predictions of what their students’ most common alternative conceptions to these questions would be. Teachers’ predictions, when compared with actual students’ responses to the CINS, were considered evidence (or not) of the PCK component under investigation. Participating teachers exhibited rather high ability to predict student conceptions; however, they were unsure how to modify their instructional practices so that student ideas could be used for productive learning. The authors concluded that their own knowledge of evolution through natural selection and knowledge of students’ conceptions of this topic were not strongly connected, and on this basis argued the need for teachers to reach a minimum threshold of SMK to be able to recognize student alternative conceptions. In a similar study from Turkey, Kaya et al. (2021) also explored the relationship between CK and PCK, the latter operationalized as the knowledge of student learning difculties (KSLD) component of PCK. The authors used open-ended surveys and vignette-based individual interviews to measure CK and KSLD, respectively, for the topics acid rain and the processes of photosynthesis and cellular respiration. A sample of science PSTs (n=73) participated. The results indicated that CK and KSLD were signifcantly correlated; however, the average scores on both knowledge categories were low. The authors suggested a model that posits KSLD as a plausible pathway connecting CK and PCK. A similar focus is apparent in Lin (2017), who compared the professional knowledge of experienced and preservice Taiwanese science teachers in terms of their CK and a component of PCK, that is, the ability to predict student conceptions about physics concepts (i.e., electric circuits). The author used an existing knowledge test with multiple-choice and two-tier items and found that experienced teachers (n=76) demonstrated higher levels of CK and confdence than the PSTs (n=85). However, both groups’ predictions of students’ conceptions about electric circuits were not signifcantly diferent for two-thirds of the test items. In summary, quantitative studies on the relationships between categories of science teachers’ knowledge confrms the conclusion from previous research that CK, PCK, and PK are distinct categories of knowledge. Most studies report connections between these categories, usually in the form 1141

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of quantitative measures (e.g., correlation coefcients). Some authors speculate about the nature of the relationships, for instance, how one category may develop in relation to another. Some studies (e.g., Sorge et al., 2019) looked at the correlational structure of knowledge categories to obtain information on how knowledge structures difer between beginning and advanced PSTs. Others (Andrews et al., 2019) revealed qualitative relationships between PCK and PK. We think that these studies are helpful in modeling science teacher knowledge and understanding how the diferent categories relate to each other. In addition, they provide insights into the knowledge structures of diferent groups of science teachers, for example, novice or experienced teachers.

Research on Science Teacher Knowledge in Relation to Student Variables In the frst edition of this handbook, Abell (2007) concluded her chapter by stating, “The ultimate goal of science teacher knowledge research must be not only to understand teacher knowledge, but also to improve practice, thereby improving student learning” (p.  1134). Five years later, in their handbook chapter, Fischer et al. (2012) concluded, “Most of the research on CK and PCK . . . still remains on a descriptive level” (p. 443). In the second edition of this handbook, we observed that research that relates science teacher knowledge to student variables was still sparse. Most of the studies between 2005 and 2012 in this area adopted a statistical approach, that is, measures of teacher knowledge were somehow quantifed and correlated to student variables, such as achievement or interest. One of these studies showed that gains in teachers’ CK (as a result of participating in a certain intervention) were associated with gains in student learning of science content (Gess-Newsome et al., 2011, 2019). However, studies that aimed to relate PCK to student variables had mixed results. We rationalized this inability to reach clarity by pointing at methodological issues, such as misalignments between measures of teachers’ PCK and student variables. Our conclusion advocated for more studies, both qualitative and quantitative, that relate science teacher knowledge to student learning. We now turn to the studies that have been reported in recent years on relationships between teacher knowledge and student variables in the feld of science education. Despite the aforementioned calls for research in this area, relatively few studies have been published since 2012. Most of these studies were conducted either in the United States or in Germany. Several aspects of teacher knowledge were included in these studies, including CK, PCK (or its components), and curricular knowledge. Student variables were typically focused on cognitive achievement but also included student motivation for science. Roth et al. (2019) compared the impact of two diferent professional learning programs on teachers’ CK, PCK, classroom practice, and student learning. Two cohorts of elementary teachers participated in the STeLLA analysis-of-practice program (n=71) or a content deepening program (n=66). Student and teacher CK was measured using a series of assessments matched to the topics implemented by the teachers during the study. A rubric was used to score teacher analyses of video clips as a measure of their PCK. Video recordings of the participating teachers were coded and scored as a measure of classroom practice. The STeLLA program focused on analysis of lesson videos and student work and spent less time on activities designed specifcally for deepening teacher CK compared to the other program. Nevertheless, teachers in STeLLA developed stronger CK than their counterparts in the other program, and so did their students. Whereas the STeLLA program had sizable efects on all three teacher outcomes, only teacher practice had a positive relationship with student achievement. Sadler et al. (2013) aimed to determine which category of science teachers’ content-related professional knowledge most heavily impacts students’ science performance. Teachers’ SMK and their knowledge of student misconceptions about selected science topics (matter, forces and motion, energy) were assessed with a multiple-choice instrument based on the National Science Education Standards from the United States. The instrument was administered twice during a school year to physical science teachers (n=181) and to their middle school students (n= 9,556). Teachers’ SMK 1142

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emerged as an important predictor of student learning. Some items included a popular student misconception as an answer option and teachers who could identify this misconception had larger classroom gains than teachers who knew only the correct answer. These fndings suggest that a teacher’s SMK in combination with their ability to identify students’ most common wrong answer on multiple-choice items related to a specifc science concept (a form of PCK) is associated with student learning of that concept. A smaller follow-up study by the same group (Chen et al., 2020) replicated these fndings. This study focused on other science content (i.e., life science) and involved 79 biology teachers from the United States and their 2,749 high school students. The authors found an association between students’ correct answers to an item on the post-test and their teachers’ correct answers to the same item. Teachers’ ability to predict students’ most common wrong answer for an item was associated with even better student performance. The authors conclude that “it is not enough for teachers to only understand the science concepts that they teach, but they also must have a working knowledge of the ideas that students have when entering their classrooms” (p. 11). In another study from the United States, Gunckel et al. (2018) explored the impact of specifc resources for teachers (i.e., curriculum materials and a weeklong professional learning workshop in support of teaching about water in environmental systems) on teachers’ CK and PCK and their student achievement. PCK was broken down into knowledge of learning goals, knowledge of student thinking, and knowledge of instruction. CK and PCK were measured at two moments in time (one year apart) with three and nine items, respectively, and a rubric was developed to score items at three levels. Fifty teachers participated, and 29 reported that they had used the curriculum materials. Twenty of the 29 teachers who used the curriculum materials also administered pre- and post-assessments to their students. Teacher gains were strongest for those who had used the materials; however, even for these teachers they were modest for both CK and PCK. Of the PCK components, teachers with the highest scores on knowledge of instruction produced better learning gains compared to teachers who scored lowly. The authors pointed at the mismatch between traditional school science approaches and their innovative curriculum materials to account for the modest improvements of teachers’ CK and PCK. A similar design was applied in two related studies by Yang et al. (2018, 2020), who investigated the impact of a professional development (PD) program on science teachers’ PCK, their practice and the learning gains of their students. Over 200 primary and secondary teachers from the United States joined a PD program on interdisciplinary science inquiry, which included a placement to engage in interdisciplinary science research, workshops, and participation in a professional learning community. A large number of variables (school, teacher, and student) were included in the study. PCK was measured with existing tests, focused on one of the science disciplines rather than on interdisciplinary science, and teaching practice was explored through surveys among teachers and students. Student understanding of crosscutting concepts was assessed with a test consisting of 20 multiple-choice items. The authors found signifcant associations between PD participation on interdisciplinary science, teacher characteristics, school characteristics, and teacher PCK. However, PCK test scores were found to be unrelated to students’ understanding of crosscutting concepts, which could be due to the fact that a disciplinary PCK test was used. A set of studies from Germany investigated the relationships between science teachers’ professional knowledge, classroom practice, and student performance. Keller et al. (2017) also included afective variables in the research design, when exploring relationships between teachers’ PCK and motivation to teach physics and student achievement and interest in learning physics. Seventy-seven physics teachers from Switzerland and Germany and their 1,614 students participated in the study. Teachers’ PCK (about electricity) and motivation to teach physics were measured with two tests, prior to each teacher delivering a lesson of 90 minutes on electrical energy, and video recordings of the lessons were coded for cognitively activating instruction. Student achievement (related to electricity) and interest in learning physics were tested prior to and after the lesson. Findings showed PCK 1143

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was positively associated with students’ achievement and this relationship was facilitated through cognitively activating instruction. Also, teachers’ motivation predicted students’ interest, which was mediated by enthusiastic teaching as perceived by students. However, PCK was not associated with students’ interest, nor was teacher motivation with student achievement. The authors concluded that teacher knowledge (i.e., PCK) and motivation are both important but independent qualities that have distinct efects on students’ cognitive and afective outcomes. Other studies omitted afective variables and examined relationships between secondary teachers’ CK, PCK, teacher practice, and student learning outcomes. A study by Förtsch et al. (2016) included 39 German biology teachers and about 800 students. Teachers’ PCK and CK were assessed by a shortened version of the professional knowledge test developed by Jüttner et al. (2013). Analysis of video recordings of teachers’ lessons about neurobiology revealed a rather low level of cognitive activation across the sample. Multilevel path analysis results showed a positive signifcant efect of cognitive activation on students’ learning. Consistent with Keller et al. (2017), the authors found an indirect efect of teachers’ PCK on students’ learning outcomes that was mediated through cognitive activation. Teachers’ CK did not seem to afect the level of cognitive activation nor student achievement. Applying a similar design, Mahler et  al. (2017) explored the relationship between teachers’ content-related professional knowledge and students’ performance, where content-related professional knowledge was conceptualized as CK, PCK, and curricular knowledge. Forty-eight German biology teachers and their students (13–14-year-olds; n=1,036) participated in this study. The teachers were asked to complete a paper-and-pencil test measuring their CK, PCK, and curricular knowledge prior to planning a unit of four lessons about a certain ecosystem (i.e., the Wadden Sea). Subscales for CK and PCK both referred specifcally to the topic of the unit, whereas the subscale for curricular knowledge addressed the entire school subject of biology. Students’ performance was measured with a paper-and-pencil test and a concept map before and after the unit was taught to them. The authors found a signifcant positive relationship between biology teachers’ PCK and their students’ performance; however, neither CK nor curricular knowledge was associated with students’ performance. The authors attributed the unexpected lack of a relationship between CK and student achievement to diferent ways of operationalizing the subject matter in the teacher and student assessments. They concluded that PCK seems to be the driving force for improvement of students’ science performance and that CK and curricular knowledge impact student learning indirectly through their associations with PCK. In the last study in this set, Liepertz and Borowski (2019) explored relationships between science teachers’ professional knowledge, classroom practice, and students’ performance, related to the teaching of a specifcally designed lesson about mechanics. In this study, teacher knowledge comprised CK, PCK, and PK. Thirty-fve physics teachers and their grade 8 or 9 classes (n=907 students) participated. The frst lesson of a unit on mechanics, focusing on the topic of force, was taught by each of the teachers and videotaped. CK and PCK were measured with a test developed by Kirschner et al. (2016), and PK was measured using text vignettes. Student achievement was assessed with a test that was taken by the students before and after the teaching of the unit. The results revealed an association between teachers’ CK and PCK, but not between PK and PCK. Multilevel analyses revealed a small negative regression coefcient for PCK in relation to students’ post-test scores. The authors concluded that measuring professional knowledge with paper-and-pencil tests might not be sufcient: “To measure PCK or professional knowledge, which has a real impact on teachers’ in-class actions, their pedagogical reasoning has to be taken into account” (p. 906). In summary, research focusing on relations between science teachers’ knowledge (especially CK and PCK) and student outcomes has increased in recent years, typically employing paper-and-pencil tests to measure teacher knowledge and student outcomes and exploring statistical relationships between these variables. Outcomes vary between studies; however, across the board it seems that both CK and PCK are positively related to student achievement. Some studies found that only PCK was associated with student learning gains, whereas others reported no relationship or even a negative 1144

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one between PCK and student outcomes. These diferences may arise because the instruments in these studies are related to knowledge categories that are located in the outer circles of the refned consensus model (RCM; Figure 35.2), that is, CK and cPCK, which are not immediately or directly linked to teaching practice. We will elaborate on this argument in the discussion section. In the absence of studies that connect CK and cPCK, through pPCK and cPCK, with classroom practice, our understanding of teacher knowledge on student learning remains very limited.

Research on Science Teacher Educators In our chapter in the previous edition of this handbook, we noted the limited research attention that has been paid to the role and expertise of science teacher educators. Since the publication of that chapter, this feld has remained relatively small, although it is growing. A recently published special issue of the International Journal of Science and Mathematics Education on “Science and Mathematics teacher educators and their professional growth” (Park Rogers et al., 2021) marks recognition of the body of work that is accumulating in this feld. However, the editors of the special issue note that to date, much more research has been published about mathematics teacher educators than science teacher educators. We recognize that the term “teacher educator” can refer to those who work in university settings teaching pre- and in-service teachers, those who work in professional development settings but who are not university academics, school-based mentors of preservice teachers, and those who lead the learning of colleagues in schools or across a school district (e.g., professional learning leaders). In this chapter, we use the term “science teacher educator” (STE) to include all these diferent groups; however, we recognize that the nature of the expertise and its sources may difer across these groups. Within the small number of studies focusing on STEs, there is general agreement that STEs need specialist knowledge that is diferent from that needed by science teachers: “there seems to be a consensus that there is a specialized knowledge base that is needed for teaching science teachers that is distinct from the knowledge needed by a K-12 classroom science teacher” (Bradbury et al., 2018, p. 1389). Yet, what that knowledge base actually comprises, and how it is developed, is still far from settled. It could be argued that science teacher educators need at least well-developed PCK for teaching specifc school subject matter in science, as well as knowledge of how to teach teachers about that SMK so that they are able to teach it efectively to their students. Abell et al. (2009) captured this latter idea as “a parallel form of PCK” for teacher educators that includes “knowledge about curriculum, instruction, and assessment for teaching science methods courses and supervising feld experiences, as well as . . . knowledge about preservice teachers and orientations to teaching science teachers” (p. 79). There appear to be two main strands of research emerging in the feld of STE expertise: (1) research focusing on STEs’ conceptualization and development of PCK and (2) research on other components of STEs’ expertise.

Studies on the PCK of Science Teacher Educators and Its Development Abell et al. (2009) proposed that a distinct PCK exists for teaching science teachers. Their conception, based on the Magnusson model, includes orientations to science teaching as a lens through which other aspects of PCK are fltered, and includes: “knowledge about curriculum, instruction, and assessment for teaching science methods courses and supervising feld experiences, as well as his/ her knowledge about preservice teachers and orientations to teaching science teachers” (p. 79). In terms of their knowledge of teaching about science teaching, Abell et al. (2009) argued that The science teacher educator should understand the points of resistance that prospective teachers might experience when learning about science teaching. Furthermore, the science 1145

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teacher educator should know strategies for helping future teachers confront their naïve conceptions of science teaching and learning and fnd suitable alternative views. (p. 79) Further, they proposed that just as the PCK of classroom teachers can change over time, so too does the PCK of STEs, resulting in a professional learning continuum for teacher educators that changes with experience. The amount of research on PCK of STEs is limited: “Though PCK has been posited as a framework for the knowledge needed by science teacher educators, there are few studies that directly investigate the development of PCK for teaching science teachers” (Bradbury et al., 2018, p. 1390). These authors also observed that most of the published studies in this feld are conducted in the form of self-studies, and by novice STEs. Empirical studies on STEs’ PCK and its development are limited to university-based STEs working with both primary and secondary PSTs. Several STEs conducted self-studies, which led them to further conceptualize the construct of PCK for STEs. Faikhamta, a beginning STE, investigated his own PCK alongside how he supported secondary science PSTs to develop their PCK (Faikhamta & Clarke 2013). In their conceptualization of PCK of STEs, Faikhamta and Clarke (2013) echoed the ideas of Abell et al. (2009) that STEs need two kinds of PCK: “strong PCK for teaching science, but also PCK for teaching science teachers” (p. 960) and emphasized the important role of beliefs that “are refected in the ways they [STEs] teach student teachers in a classroom and [that] are closely intertwined with PCK components” (p. 960). They further distinguished two kinds of SMK required by STEs as “science content and knowledge for teaching science” (p. 960). The authors defned STEs’ PCK as a teacher educator’s understanding of how to help science teachers develop PCK for teaching science which includes (a) orientations towards teaching about teaching science, (b) knowledge of science teachers’ conceptions and learning, (c) knowledge of a science methods course curriculum, (d) knowledge of instructional strategies for teaching the course, and (e) knowledge of assessment for science teachers’ learning. (p. 964) Using a self-study approach to the research design, the researchers collected and analyzed a range of qualitative data in the form of journal entries, student assignments, and course syllabi to understand more about how he sought to develop PSTs’ PCK. An important insight from his study was that although Faikhamta’s own PCK for teaching science was strong, his PCK of teaching science teachers was limited, especially in terms of knowledge of instructional strategies and knowledge of assessment of science teachers’ learning. Hume (2016) studied the growth and sources of her own PCK as a beginning STE in her transition from classroom chemistry teacher to science teacher educator. Using a self-study approach, she applied the Magnusson model as an analytical framework. Her study outcomes emphasized the importance of engaging in forms of scholarship to support the transformation of her knowledge of classroom teaching to knowledge required for educating prospective science teachers. Engagement in scholarship occurred through science education research, a university professional development course that included an action research component, and collaboration and co-planning of her teaching with a trusted colleague. Refecting on a collection of action research projects of her teaching of both primary and secondary PSTs, Hume’s fndings diferentiate between the PCK STEs need to support primary or secondary PSTs. Through the study she proposed a modifed version of the Magnusson model for the PCK of STEs, including orientations toward science education teaching, knowledge of science education curriculum, knowledge of PSTs’ understanding of science teaching, knowledge of instructional strategies, and knowledge of assessment. 1146

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Bradbury et  al. (2018) investigated the PCK development of two STEs in the United States, using a self-study methodology. The STEs partnered with two experienced elementary teachers to plan, implement, and refect on a unit taught in second-grade classrooms that integrated science and language arts as a means to enhance the STEs’ PCK for teaching elementary science teaching. Data included transcripts of planning meetings, oral refections about their experiences, and videos of the unit being enacted. Using Park and Oliver’s (2008) model of PCK, which incorporates selfefcacy as their analytic tool, the STEs sought to fnd out which aspects of their PCK they developed through their collaborative planning and teaching. Findings showed that working in the secondgrade classrooms strengthened their sense of self-efcacy through experiencing successful teaching episodes and reinforced their reform-based orientations to science teaching. Additionally, their PCK was enhanced in the areas of knowledge of science curriculum, knowledge of instructional strategies, and knowledge of students’ understanding in science. Demirdöğen et al. (2015) also conducted a self-study of their PCK development as they redesigned and taught a practicum course for teaching secondary science teachers in Turkey. As in the previous studies, the authors used Abell et al.’s (2009) PCK model to analyze the development of diferent PCK components, for each of them, over one year. Data was collected in the form of journal entries, CoRes, refection papers, observations of PSTs’ teaching and meetings with PSTs, that were subsequently developed by each author into vignettes for each PCK component. The authors reported development in all components of their PCK for teaching science teachers as a result of their experiences. Their PCK development was further supported by engaging with research literature and working with colleagues. Cite et al. (2017) investigated their PCK development for teaching specifc science content (i.e., light and shadows) to in-service elementary teachers in a professional development program in the United States. This study is the only one that investigates STEs’ PCK for a specifc science topic. The authors developed CoRes and pedagogical and professional experience repertoires (PaP-eRs; Loughran et al., 2006) based on data collected during the program to document growth in their PCK. They identifed critical incidents from the data that led to new insights about teaching science to teachers. Through this process of analysis, they came to recognize gaps in their own PCK for teaching this content area, for example, having a sufcient “repertoire of representations on which to draw” (p. 286). Writing short vignettes of their experiences in the form of PaP-eRs was found to be helpful by the authors in articulating theory-practice links, and alignment between their STE beliefs and practices.

Studies Focusing on Other Components of Science Teacher Educators’ Expertise Several studies focused on STEs’ knowledge of pedagogy as a component of their expertise. Cooper (2013) investigated the role of critical experiences in the development of STEs’ pedagogical knowledge (PK), as well as the ways that STEs’ notions of PK develop and change over their careers, and the dispositions necessary to developing an understanding of PK for science teacher education. Drawing on interview data from a small sample of highly experienced Australian science teacher educators, Cooper applied Morine-Dershimer and Kent’s (1999) model of PK to analyze shifts and changes in the STEs’ thinking and teaching practice, as revealed through their critical experiences. Study outcomes highlighted how STEs’ personal backgrounds, beliefs, and dispositions played out quite diferently in their career trajectories and in shaping the development of their PK for science teacher education. Davis et al. (2017) investigated how a group of three university-based STEs in the United States understood and utilized a particular pedagogical approach of “rehearsals” to facilitate PSTs’ learning to support students’ sensemaking discussions in science. Rehearsals are similar to microteaching 1147

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sessions where PSTs present a lesson to peers who assume the role of school students. However, an important feature of “rehearsals” is the ability to “pause” a lesson at any time to focus on what is happening in a particular moment and ofer (and potentially use) alternative courses of action. While the use of rehearsals is increasingly common in US teacher education courses, the knowledge required of STEs to efectively facilitate this practice is not well understood. Through collaborative analysis of their pedagogies of rehearsal, and in particular their use of pauses, the STEs in this study “expanded their knowledge base” (p. 289) of rehearsal pedagogy about sensemaking discussions in science and became more explicit and purposeful in their approach by identifying conceptual tools to support PSTs, developing a protocol for supporting planning for rehearsals, and introducing a new practice of analyzing video of the actual enacted lesson in a later science methods class. In another study that focused on STEs’ pedagogical expertise, Underwood and Moore Mensah (2018) investigated the perceptions and practices of a group of 11 university-based science teacher educators in the United States, related to culturally relevant pedagogy (CRP). Given that “teaching to and for diversity” (p. 58) is considered important for increasing scientifc literacy for all, CRP is one approach that has been found to usefully serve this purpose. Study fndings revealed that while the STEs were able to articulate the need for ideological change in science education to empower all students, they were unable to describe how CRP or other substantial pedagogical changes could be used to address the needs of historically underserved or marginalized students in science classrooms. Implications of this study relate to the need for STEs to build their understandings of personal bias and how it plays out in their own teaching and to explicitly model social justice approaches to teaching with their science PSTs. Finally, Hanuscin et al. (2021) studied STE expertise more broadly in a group of nine universitybased STEs in the United States, to understand how a particular professional learning approach of shadowing supported the development of the STEs’ “specialized knowledge-in-practice”. Each of the STEs shadowed a colleague through observation of their method classes, followed by collaborative refection and debriefng on their learnings. Findings from the study showed that shadowing was helpful in supporting several diferent aspects of STE expertise development, including deepening disciplinary and subject-matter knowledge and expanding STEs’ knowledge base both within and across their respective science disciplinary felds, enhancing practical knowledge in ways of thinking about and practicing science teacher education, and building relational knowledge through diferent kinds of social interactions and identity work as teacher educators. The shadowing experiences aforded unique opportunities for the faculty members, who held a joint appointment between a teacher education program and a science department, to learn about other science disciplinary perspectives and to refect on the nature of their professional knowledge and practice, and its development. In summary, research on STEs’ expertise is still fairly sparse, but it is growing. The majority of this work appears to focus on STEs in university initial teacher education programs, is small scale, qualitative, and tends to be conducted by STEs themselves, primarily through self-study. Various PCK models have been adapted for use by STEs to investigate their own knowledge and its development. Most authors reference Abell et al. (2009). Those that draw on the Magnusson model tend to look for parallel forms of its components, for example, knowledge of student understanding (Magnusson model) becoming knowledge of science teachers’ conceptions and learning (Faikhamta & Clarke, 2013).

Discussion Since the second edition of this handbook appeared in 2014, research on science teacher knowledge has continued to attract attention from scholars around the globe. Many of these studies take place in the context of preparing new science teachers (i.e., in an initial teacher education program). Also, much research on science teacher knowledge is done in the context of science education reform, 1148

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which is ongoing across the globe, and is typically accompanied by programs to support teachers. Usually, these studies are informed by an interest to monitor what teachers know, or need to know, to teach science efectively and to explore how science teachers can be supported to enhance or expand their knowledge and improve their practice. In this fnal section, we will frst evaluate the progress that has been made in research on science teacher knowledge since the publication of the second edition of this handbook, particularly in relation to the areas that were identifed in previous reviews as needing further research, that is, (1) studies on the relationships between teachers’ CK, PK, and PCK, and how these develop in a classroom teaching context; (2) studies that relate science teacher knowledge to student learning; and (3) research on the expertise of STEs related to supporting the development of science teacher knowledge. We conclude this chapter with recommendations for science teacher education and professional development and suggestions for future research.

Progress in Research on Science Teacher Knowledge and Its Development Research on Science Teachers’ CK, PK, and PCK and Their Development In our 2014 handbook chapter, we identifed that research on science teachers’ CK was predominantly inspired by concerns about teachers having a sound understanding of science content, especially beginning and elementary teachers. Recent studies in this domain continue to reveal shortcomings in teachers’ CK, even among teachers with a degree in the respective discipline. Some studies concluded that teachers’ knowledge of particular science content improves through teaching this content repeatedly; however, the evidence is not conclusive. Other studies demonstrated how teachers’ knowledge of specifc science content can be improved by interventions, provided these are focused on the teaching of this content. However, some researchers report CK remains an under-researched construct (e.g., Diamond et al., 2014), and we argue this knowledge gap is refected in the ongoing ambiguity surrounding what constitutes a teacher’s CK and the issues this uncertainty presents for investigations into science teacher knowledge that call for measurements of CK. In some instances, CK refers to wider and deeper disciplinary knowledge, while in others, to selected discipline content contained in curriculum (e.g., mandated guidelines/standards, text, school plans), or to the specifc science concepts and skills that a teacher draws on and enacts in classroom teaching. These diferent parameters for CK have the potential to raise methodological issues of validity and reliability around measures of CK, which can in turn make comparisons between studies problematic. For example, in some studies teachers’ science content knowledge was measured with tests that were initially developed to test students’ understanding of the same content (e.g., Sadler et al., 2013). This measure suggests that the researchers see teachers’ CK as limited to what is taught to students, or what is included in the curriculum, whereas others would maintain that teachers need a much more comprehensive and deeper understanding of the content they are teaching, including the syntactic and substantive structures of the disciplinary subject matter of science (cf. Shulman, 1987). It also infers that the boundaries between CK and knowledge of science curricula, one of the PCK components in the often-used Magnusson model, may be somewhat blurred. To further confuse matters, while most authors refer to content knowledge, or CK, some prefer the term subject-matter knowledge (SMK). This practice raises the question of whether these terms can be used interchangeably, and again, what content or subject matter is actually being referred to? In some PCK studies, the content is defned broadly, such as, socioscientifc issues (e.g., BayramJacobs et al., 2019) or nature of science (e.g., Hanuscin, 2013). In others, the content is narrowed or limited to particular topics within a certain discipline, such as stoichiometry (Miheso & Mavhunga, 2020) or semiconductors (Rollnick, 2017) in chemistry. These authors often refer to topic-specifc PCK, or TSPCK, to highlight their focus on specifc content. In studies on STEs, the meaning of 1149

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subject matter or content may be considered diferently, for instance, Faikhamta and Clarke (2013) argued that STEs need two types of SMK, that is, knowledge of science content and “knowledge for teaching science”. This lack of clarity or blurriness around what constitutes CK has the potential for confusion among researchers and calls for more careful articulation of exactly what CK refers to in a given study. In our chapter in the second edition of this handbook, we observed a shift from descriptive studies of certain aspects or components of the PCK of a small number of science teachers at a particular moment in time, to studies that explored the impact of specifc interventions (either in preservice teacher education or professional development programs) on the development of science teachers’ PCK. Since 2013, research on science teachers’ PCK and its development has shown an increased focus on the structure and nature of PCK, and how it is developed in a classroom teaching context. Studies that have chosen to explore relationships between components of PCK using the mapping approach introduced by Park and Chen (2012) have shed further light on the connections between these components and how these may be shaped by contextual factors. These studies, typically adopting the Magnusson model or a variation of it, tend to confrm the central association between knowledge of student understanding and knowledge of instructional strategies related to a particular topic. As these two components were central in Shulman’s original conceptualization of PCK, this fnding raises the question of whether other knowledge components, such as those included in the Magnusson model, should be seen as part of PCK or rather as foundations, or infuences? It could be argued the latter stance is refected in the refned consensus model (RCM), where assessment knowledge and curricular knowledge are represented as separate knowledge categories that underlie and inform teachers’ collective PCK (cPCK) and, mediated through the learning context, feed into teachers’ personal (pPCK) and enacted PCK (ePCK). Recent research on the development of science teachers’ PCK also refects Shulman’s original view of PCK as a specialized form of teacher knowledge for teaching particular content that is built through iterative processes of pedagogical reasoning and refection on classroom practice (Shulman, 1987). Studies using observations or video recordings of classroom teaching, interviews, and journaling have built rich databases that provide insights into individual teachers’ decision-making processes and teaching actions in authentic classroom situations. In the context of programs for teacher professional learning, such studies help us to better understand the nature and the development of PCK as it plays out in classroom teaching. Similarly, recent studies on the PCK development of preservice science teachers have paid more attention to the role of classroom feld experiences and mentors and have contributed to a better understanding of the complex interplay between school-based and university-based elements of teacher education programs (e.g., method courses) that occurs during the early stages of PCK development of prospective science teachers. This increased attention to PCK development in authentic science classroom contexts aligns with the design of the RCM, which places the cycle of planning, teaching, and refection at its core (Figure 35.2). Although this model was published very recently (Carlson et al., 2019), it is rapidly gaining traction as evidenced by the number of citations (over 150 in Google Scholar to date). We note that the RCM is beginning to be used in published research on science teachers’ PCK, for instance, by Coetzee et al. (2020), who explored how preservice teachers were adopting elements of a physical science method course into their classroom practice. The study fndings highlight the importance of actual classroom teaching combined with critical refection on these teaching experiences and the infuence of cPCK introduced in the method course on preservice teachers’ pPCK and/or ePCK. Preservice teachers’ feelings of confdence about their SMK also appeared to be connected with the quality of their ePCK. Viewed through the perspective of the RCM, Coetzee et al.’s (2020) study helps to shed light on ways in which knowledge exchanges occur between the layers of the RCM and how these exchanges are mediated through various amplifying and fltering factors (in this case, confdence to teach particular subject matter) and teachers’ pedagogical reasoning. 1150

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Research reviewed in our 2014 handbook chapter demonstrated that CK and PCK are separate categories of science teacher knowledge that each exists in their own right. Studies on the relationships between CK and PCK revealed diferent mechanisms of how CK and PCK may impact each other. Specifcally, teachers who have a thorough understanding of the science content before they start to teach this content, appear to develop PCK on the basis of their CK. Moreover, strong CK tends to be associated with strong confdence to teach. However, some studies showed how, without strong CK, teachers’ general PK can play a supportive role in the simultaneous development of PCK and CK. Recent research again confrms that CK, PCK, and PK are distinct but related categories of knowledge. Most of these studies have focused on quantitative measures to represent the relationships between these knowledge categories; however, they have not provided empirical evidence about the processes or mechanisms that explain how these knowledge categories interact and develop in relation to each other.

Research on Relationships Between Science Teacher Knowledge and Student Learning In our recommendations for future research (Van Driel et al., 2014), we advocated studies that aimed to relate science teacher knowledge to student learning to incorporate a combination of instruments that capture not only what is in a teacher’s mind (CK and “declarative PCK”; cf. Schmelzing et al., 2013), but also how teachers enact their understanding in classroom situations, [and] to align measures of teacher knowledge, in particular PCK, with measures of student learning (p. 866) We also argued that these measures should be consistent with the learning objectives that teachers aim to achieve among their students. Although we have observed an increase in research focusing on the relationships between science teachers’ knowledge (especially CK and PCK) and student outcomes in recent years, it appears to be limited to studies that employ paper-and-pencil tests to measure teacher knowledge and student outcomes. Such studies focused on determining statistical relationships and, across the board, reported positive impacts of both CK and PCK on student achievement. However, some scholars found that only PCK was associated with student learning gains, and others reported no or a slightly negative relationship between PCK and student outcomes. Some studies (e.g., Keller et al., 2017; Förtsch et al., 2016) highlighted the mediating role of classroom teaching and showed how teachers’ PCK was associated with the use of cognitively activating teaching strategies, which in turn had a positive efect on student learning. From the perspective of the RCM, we could say that studies that employ paper-and-pencil tests to investigate PCK are focused on collective PCK. According to the RCM, the relationship between cPCK and student learning is mediated by teachers’ processes of pedagogical reasoning frst through the learning context, which acts as a flter and/or amplifer to inform teachers’ personal PCK, which in turn plays a part in their ePCK through cycles of planning, teaching, and refecting. Therefore, attending to pedagogical reasoning is essential to gain insight into teachers’ complex decision-making processes that draw on their rich knowledge of and sensitivity to context. Student learning, according to this model, is infuenced by teachers’ pPCK and ePCK rather than their cPCK, which in our view are important assumptions that warrant further investigation if the RCM is to continue as a viable model for PCK research and development. Specifcally, we need to better understand the conditions that support the development of pPCK and ePCK from cPCK that contributes to student learning. This understanding would beneft the design of teacher education programs. 1151

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In our 2014 chapter, we distinguished between studies that regarded PCK as “knowledge of teachers” and studies based on PCK as “knowledge for teachers” (Fenstermacher, 1994). Viewed through the perspective of the RCM, the former types of studies tend to align with the notion of pPCK and ePCK, whereas the latter studies align with the notion of cPCK. In this respect, we consider the distinctions between diferent types of PCK in the RCM to be helpful for researchers in locating the specifc emphasis of their investigations and recognizing the complex and multifaceted nature of PCK. Further, distinguishing between types of PCK may help to diferentiate those aspects that are shared or common across a larger or smaller community of teachers (cPCK) and thus measurable, compared with those that are situational and personal (pPCK, ePCK). Custom-designed tests and exemplars of topic-specifc cPCK are useful for measuring cPCK. In the area of pPCK and ePCK, researchers have an emerging repertoire of potentially useful strategies for capturing the essence of these unique knowledge forms, including PaP-eRs, PCK mapping, and rubrics. Assessing pPCK and ePCK validly requires approaches that acknowledge the core characteristics of high-quality, idiosyncratic PCK. This includes knowing when to apply a certain strategy in recognition of students’ actual learning needs and understanding why a certain teaching approach may be useful in some situations (i.e., a particular class of students at a certain moment) but not in others and ways of enacting this knowledge. The challenge for researchers assessing the PCK of an individual teacher is how to do justice to the situational and personal aspects that are inherently part of a teacher’s PCK, as they engage in classroom-based pedagogical reasoning and action while at the same time being able to clearly distinguish more or less sophisticated levels of PCK.

Research on the Expertise of Science Teacher Educators In our 2014 handbook chapter, we noted that few studies focused on science teacher educators (STEs). Since then, research on STEs’ expertise has remained fairly sparse, although it is growing. Most of this work appears to focus on university-based STEs in initial teacher education programs, is small-scale, qualitative, and tends to be conducted by STEs themselves, primarily through forms of self-study. Various PCK models have been adapted for use by STEs to investigate their own knowledge and its development. Most authors base their studies on the model of PCK for teaching science teachers developed by Abell et al. (2009) or the Magnusson model. (As we noted earlier, Abell et al.’s model is adapted from Magnusson et al., 1999). Those studies that draw on the Magnusson model tend to look for parallel forms of its components in teacher education, for example, knowledge of student understanding (Magnusson model) becoming knowledge of science teachers’ conceptions and learning (Faikhamta & Clarke, 2013). This trend suggests that research on STEs might still be considered in its “pre-science” phase, as Abell (2007) characterized research in PCK at that time, that can be ascribed to ongoing diferences in PCK conceptualizations amongst researchers and subsequent approaches to studying it. In their epilogue to a recent special issue on “Science and Mathematics teacher educators and their professional growth”, Park Rogers et al. (2021) identify three areas requiring further research: • • •

What and how teacher educators’ knowledge continues to grow throughout a career The role of subject-matter or disciplinary knowledge in establishing a specialized knowledge of teaching teachers The development of a shared language to describe the processes of professional growth for science and mathematics teacher educators working across diferent contexts that can help to defne their expertise and development

These suggestions will be elaborated next. 1152

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Implications for Science Teacher Education and Professional Development In our chapter in the second edition, we concluded that programs for science teacher education and professional development, rather than targeting specifc aspects of science teacher knowledge, whether content knowledge or (components) of PCK, should aim at the development of this knowledge in relation to the practice of teaching, or learning to teach, science. We discussed specifc features that could be incorporated in the design of such programs, such as including opportunities to plan and design ways to teach certain science content and trying these out in classroom situations. Ultimately, such programs should contribute to establishing science teaching practices that promote students’ understanding of, and interest in, science. The studies in this review revealed that efective teacher education embraces a range of considerations in its design and employs multiple strategies to accommodate for the diversity that exists within cohorts of teachers (e.g., prospective or early career teachers versus experienced teachers, out-offeld teachers, and career changers) and across school contexts in which teachers operate. For teacher educators, the task of addressing this breadth of needs and considerations in teacher education can be almost insurmountable at frst glance, especially in contexts where change is the norm (e.g., curriculum reform, introduction of teacher standards and entry qualifcations, changing teacher roles). Fortunately, the studies in this review have provided very useful pointers for efecting rich and meaningful professional learning for diverse learners (teachers) in diverse learning situations. For example, providing a classroom-based professional development program in combination with curriculum support materials, where both components actively promote strong linkages between understanding of students’ difculties in learning science and knowledge of appropriate instructional strategies (Bayram-Jacobs et al., 2019). Or the use of CoRes in a preservice teacher education program to collaboratively develop TSPCK for PSTs from diferent disciplinary backgrounds before they plan and teach lessons on that topic during feld experience (e.g., Coetzee et al., 2020). Perhaps most importantly, these studies reinforce understanding of the iterative processes (e.g., noticing, refecting, reasoning, planning, decision-making, and classroom teaching) by which teachers build professional knowledge for classroom teaching, specifcally PCK, and the conditions that prompt and nurture this growth. The studies provide exemplars of how to structure and support opportunities for teachers to participate in these processes, often collaboratively with peers and/or mentors. Experiencing knowledge building as they work through pedagogical problems in their own practice, teach new content curriculum, or introduce new teaching resources proved to be essential. Initiating and embedding habits of mind in teachers that underpin self-directed professional learning, through the use of general mental representations (e.g., content analysis and CoRes) as recommended by Chan and Yung (2018), makes a very valuable contribution to our thinking around sustainable PCK development in the context of ongoing educational change and diversity.

Recommendations for Future Research In our 2014 chapter, we concluded that researchers between 2007 and 2012 had made progress in response to the repeated call to do more empirical research on the relations between professional knowledge of science teachers (which includes CK, PK, and PCK), classroom practice, and student learning outcomes. Despite this, we recommended more research in these areas, in particular, studies with a classroom teaching component that investigate how teachers enact and develop their knowledge (CK, PK, and PCK) in interaction with students, and how these practices impact on student learning and appreciation of science. Such studies would contribute to our understanding of how the processes of teaching and learning of science interact and lead to outcomes for teachers and students. 1153

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Refecting on the progress in this area in published research since 2013, we recommend that rather than more studies that focus on CK or PCK as such, we would like to see future studies on the development of these knowledge categories in relation to each other and to other knowledge categories, such as PK. Specifcally, we would welcome studies that contribute to a better understanding of the mechanisms and processes underlying the development of these knowledge categories. We also recommend qualitative studies to further our understanding of the relationships between teacher knowledge and student variables. Applying the RCM means that we need to focus on teachers’ pPCK and ePCK if we want to understand the impact of science teacher knowledge on student learning. Chan et al. (2021) argued that ePCK is conceptually similar to the construct of teacher noticing, and therefore recommended future studies of ePCK be explicitly connected with teacher noticing “to better understand the situational and dynamic teaching expertise crucial to promoting students’ understanding of specifc science subject matter” (p. 36). Wilson et al. (2019) proposed a framework for future research related to the RCM, which included suggestions for research on the structure and development of PCK. Among other recommendations, they advocated for longitudinal studies that explore the pathways or learning progressions from novice to expert science teacher. In a refection on the outcomes of the second PCK summit, and the RCM in particular, Tepner and Sumfeth (2019) called for future research on science teacher knowledge to pay more explicit attention to the roles of teachers’ perceptions, attitudes, and beliefs as flters and amplifers that signifcantly infuence teachers’ classroom practice and, subsequently, student learning of science. Finally, we noticed that very few studies on science teacher knowledge to date have been conducted in early childhood settings (Nilsson & Elm, 2017; Leuchter et al., 2020). Also, studies on the PCK of tertiary science educators are just beginning to emerge (Fraser, 2016). More research in these sectors would be welcome. In summary, we recommend that future studies on science teacher knowledge adopt more holistic and comprehensive designs. The assessment or measurement of science teachers’ PCK remains an area that is in need of further research. While paper-and-pencil tests can be useful to investigate how much teachers know about the collective PCK of a particular topic or discipline, they usually don’t take into account how personal and contextual factors shape the PCK that expert teachers enact to serve the learning needs of a particular group of students in a particular situation. This lack of sensitivity could result in such teachers not choosing what is considered by the researchers as the correct or best answer to an item in a PCK test. In some tests, rather than scoring answers as either correct or wrong, teachers’ justifcations are assessed (Keller et al., 2017). As diferent types of knowledge require diferent assessments, a combination of (qualitative and quantitative) instruments is needed to give a comprehensive picture of teachers’ knowledge. Recently, and partly related to the two PCK summits, a number of instruments have been proposed to address these issues. Specifcally, several authors have focused on the use of rubrics to assess science teachers’ PCK. Chan et al. (2019) proposed a “grand rubric template” for this purpose, while Carpendale and Hume (2019) developed a detailed rubric to analyze changes in the ePCK of science teachers who participated in an intervention focused on collaborative CoRe design. Finally, Kind (2019) developed a combined CK/PCK rubric based on responses of a cohort of PSTs to vignettes, which were analyzed in terms of relevant, relevant but incomplete, irrelevant, or (for CK only) incorrect. These approaches to assessing PCK have in common that they distinguish between types of PCK (such as cPCK and pPCK; Chan et al., 2019) and that they consider the quality of PCK to vary on a spectrum from lower to higher levels. Kind’s (2019) rubric also gives insights into teachers’ underlying thinking. We believe that the increased use of data-gathering tools like video recording, journaling, and refective interviews, in combination with analytical tools like PCK mapping, rubrics, and learning pathways and representations such as CoRes and PaP-eRs and vignettes, is key for researchers to understanding and representing the processes of knowledge transformation 1154

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that are at the heart of science teacher knowledge development and we recommend their use in future research. Areas for further research on the expertise of science teacher educators (STEs) include: •

• • •

Studies to determine how the PCK of STEs is diferent from the PCK of science teachers. For instance, how do conceptualizations of STEs’ PCK (i.e., variations of Magnusson and Abell) compare to those of science teachers, such as the RCM? Studies of pedagogical reasoning of STEs. Studies relating general and discipline specifc aspects of STEs’ PCK (cf. Faikhamta & Clarke, 2013). Since we found no studies about school-based STEs or facilitators of professional development, there is a need for research that looks at the nature and development of knowledge of these nonuniversity-based STEs.

Given increasing recognition of teacher educators as a specifc professional group with distinct expertise, responsibilities, and commitments (Kelchtermans et al., 2018) and their important role in the preparation of future teachers of science, we encourage research that can increase our understanding of this important, but rather neglected, group.

Acknowledgments The authors would like to acknowledge Knut Neumann and Kennedy Kam Ho Chan for their review of a draft version of this chapter.

Notes 1 Throughout this chapter we use the phrase “knowledge development” instead of “learning”. Science teacher learning and the process of learning to teach science are the focus of Chapters 37 and 40, respectively. Our use of the phrase “knowledge development” typically refers to studies that compare data at diferent points in time and make inferences from the diferences. We considered “knowledge growth” or “knowledge change” as alternatives but chose “knowledge development”, as this phrase is most commonly used in the studies we reviewed. 2 In research on science teacher knowledge, the terms “content knowledge” and “subject-matter knowledge” are used interchangeably. In this chapter, and following Shulman, we will refer to content knowledge (CK), except when reviewing publications in which the term subject-matter knowledge (SMK) is used. 3 Abell’s chapter in the frst edition of this handbook (2007) has been cited nearly 1,200 times on Google Scholar (700 times since 2014), whereas our chapter in the second edition of the handbook (2014) has accumulated just over 150 citations to date. 4 The following science education journals were included in our search: Journal of Research in Science Teaching, Science Education, Science & Education, International Journal of Science Education, Research in Science Education, Studies in Science Education, and Journal of Science Teacher Education. In addition, the following education journals were included: American Educational Research Journal, Teaching and Teacher Education, Journal of Teacher Education, and Teachers and Teaching: Theory and Practice. Due to the scarcity of articles focusing on science teacher educator knowledge, we broadened our search to include any peer-reviewed published research that met at least one of our criteria, related to science teacher educators.

References Abell, S. K. (2007). Research on science teacher knowledge. In S. Abell & N. Lederman (Eds.), Handbook of research on science education (pp. 1105–1149). Lawrence Erlbaum Associates. Abell, S. K., Park Rogers, M. A., Hanuscin, D. L., Lee, M. H., & Gagnon, M. J. (2009). Preparing the next generation of science teacher educators: A model for developing PCK for teaching science teachers. Journal of Science Teacher Education, 20, 77–93.

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Jan H. van Driel, Anne Hume, and Amanda Berry Alonzo, A. C., Berry, A., & Nilsson, P. (2019). Unpacking the complexity of science teachers’ pedagogical content knowledge. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 271–288). Australia Springer. Alonzo, A. C., & Kim, J. (2016). Declarative and dynamic pedagogical content knowledge as elicited through two video-based interview methods. Journal of Research in Science Teaching, 53(8), 1259–1286. Andrews, T. C., Auerbach, A. J. J., & Grant, E. F. (2019). Exploring the relationship between teacher knowledge and active-learning implementation in large college biology courses. CBE – Life Sciences Education, 18(4), ar48. Arzi, H., & White, R. (2007). Change in teachers’ knowledge of subject matter: A 17-year longitudinal study. Science Education 92 (2), 221–251. Aydin, S., Demirdöğen, B., Akin, F. N., Uzuntiryaki-Kondakci, E., & Tarkin, A. (2015). The nature and development of interaction among components of pedagogical content knowledge in practicum. Teaching and Teacher Education, 46, 37–50. https://doi.org/10.1016/j.tate.2014.10.008 Barendsen, E., & Henze, I. (2019). Relating teacher PCK and teacher practice using classroom observation. Research in Science Education, 49, 1141–1175. https://doi.org/10.1007/s11165–017–9637-z Batiza, A. F., Gruhl, M., Zhang, B., Harrington, T., Roberts, M., LaFlamme, D., Haasch, M. A., Knopp, J., Vogt, G., Goodsell, D., Hagedorn, E., Marcey, D., Hoelzer, M., & Nelson, D. (2013). The efects of the SUN project on teacher knowledge and self-efcacy regarding biological energy transfer are signifcant and long-lasting: Results of a randomized controlled trial. CBE-Life Science Education, 12, 287–305. https://doi. org/10.1187/cbe.12–09–0155 Baumert, J., Kunter, M., Blum, W., Brunner, M., Voss, T., Jordan, A., Klusmann, U., Krauss, S., Neubrand, M., & Tsai, Y.-M. (2010). Teachers’ mathematical knowledge, cognitive activation in the classroom, and student progress. American Educational Research Journal, 47(1), 133–180. Bayram-Jacobs, D., Henze, I., Evagorou, M., Shwartz, Y., Aschim, E. L., Alcaraz-Dominguez, S., Barajas, M., & Dagan, E. (2019). Science teachers’ pedagogical content knowledge development during enactment of socioscientifc curriculum materials. Journal of Research in Science Teaching, 56, 1207–1233. https://doi. org/10.1002/tea.21550 Berry, A., Friedrichsen, P., & Loughran, J. (Eds.). (2015). Re-examining pedagogical content knowledge in science education. Routledge. Bradbury, L. U., Wilson, R. E., & Brookshire, L. E. (2018). Developing elementary science PCK for teacher education: Lessons learned from a second grade partnership. Research in Science Education, 48, 1387–1408. https://doi.org/10.1007/s11165–016–9607-x Brown, S., & McIntyre, D. (1993). Making sense of teaching. Open University Press. Calderhead, J. (1996). Teachers: Beliefs and knowledge. In D. Berliner & R. Calfee (Eds.), Handbook of educational psychology (pp. 709–725). Macmillan. Carlson, J., Daehler, K. R., Alonzo, A. C., Barendsen, E., Berry, A., Borowski, A., Carpendale, J., Chan, K. K. H., Cooper, R., Friedrichsen, P., Gess-Newsome, J., Henze-Rietveld, I., Hume, A., Kirschner, S., Liepertz, S., Loughran, J., Mavhunga, E., Neumann, K., Nilsson, P., . . . Wilson, C. D. (2019). Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp.  77–94). Australia Springer. Carlson, J., Stokes, L., Helms, J., Gess-Newsome, J., & Gardner, A. (2015). The PCK Summit: A process and structure for challenging current ideas, provoking future work, and considering new directions. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 14–27). Routledge. Carpendale, J., & Hume, A. (2019). Investigating practising science teachers’ pPCK and ePCK development as a result of collaborative CoRe design. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 223–250). Australia Springer. Carter, K. (1990). Teachers’ knowledge and learning to teach. In W. R. Houston (Ed.), Handbook of research on teacher education (pp. 291–310). Macmillan. Chan, K. K. H., & Hume, A. (2019). Towards a consensus model: Literature review of how science teachers’ pedagogical content knowledge is investigated in empirical studies. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 3–76). Australia Springer. Chan, K. K. H., Rollnick, M., & Gess-Newsome, J. (2019). A grand rubric for measuring science teachers’ pedagogical content knowledge. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 251–269). Australia Springer. Chan, K. K. H., Xu, L., Cooper, R., Berry, A., & Van Driel, J. H. (2021). Teacher noticing in science education: Do you see what I see? Studies in Science Education, 57(1), 1–44. https://doi.org/10.1080/03057267.2 020.1755803

1156

Research on Science Teacher Knowledge and Its Development Chan, K. K. H., & Yung, B. H. W. (2015). On-site pedagogical content knowledge development. International Journal of Science Education, 37, 1246–1278. https://doi.org/10.1080/09500693.2015.1033777 Chan, K. K. H., & Yung, B. H. W. (2018). Developing pedagogical content knowledge for teaching a new topic: More than teaching experience and subject matter knowledge. Research in Science Education, 48, 233–265. https://doi.org/10.1007/s11165-016-9567-1 Chen, B., & Wei, B. (2015). Examining chemistry teachers’ use of curriculum materials: In view of teachers’ pedagogical content knowledge. Chemistry Education Research and Practice, 16, 260–272. https://doi. org/10.1039/c4rp00237g Chen, C., Sonnert, G., Sadler, P. M., & Sunbury, S. (2020). The impact of high school life science teachers’ subject matter knowledge and knowledge of student misconceptions on students’ learning. CBE-Life Sciences Education, 19(ar9), 1–16. https://doi.org/10.1187/cbe.19–08–0164 Cite, S., Lee, E., Menon, D., & Hanuscin, D. L. (2017). Learning from Rookie mistakes: Critical incidents in developing pedagogical content knowledge for teaching science to teachers. Studying Teacher Education, 13, 275–293. https://doi.org/10.1080/17425964.2017.1366306 Clarke, D., & Hollingsworth, H. (2002). Elaborating a model of teacher professional growth. Teaching and Teacher Education, 18, 947–967. Coetzee, C., Rollnick, M., & Gaigher, E. (2020). Teaching electromagnetism for the frst time: A case study of pre-service science teachers’ enacted pedagogical content knowledge. Research in Science Education. https:// doi.org/10.1007/s11165-020-09948-4 Cooper, R. (2013). Using critical experiences to build understanding of science teacher educators’ pedagogical knowledge. Teacher Education and Practice, 26(4), 637–650. Cooper, R., & Van Driel, J. H. (2019). Developing research on PCK as a community. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 301–313). Australia Springer. Davis, E. A., Kloser, M., Wells, A., Windschitl, M., Carlson, J., & Marino, J. C. (2017). Teaching the practice of leading sense-making discussions in science: Science teacher educators using rehearsals. Journal of Science Teacher Education, 28, 275–293. https://doi.org/10.1080/1046560X.2017.1302729 Demirdöğen, B., Aydin, S., & Tarkin, A. (2015). Looking at the mirror: A self-study of science teacher educators’ PCK for teaching teachers. Eurasia, Journal of Mathematics, Science and Technology Education, 11, 189–205. https://doi.org/10.12973/eurasia.2015.1315a Demirdöğen, B., Hanuscin, D. L., Uzuntiryaki-Kondakci, E., & Koseoglu, F. (2016). Development and nature of preservice chemistry teachers’ pedagogical content knowledge for nature of science. Research in Science Education, 46, 575–612. https://doi.org/10.1007/s11165–015–9472-z Demirdöğen, B., & Uzuntiryaki-Kondakci, E. (2016). Closing the gap between beliefs and practice: Change of pre-service chemistry teachers’ orientations during a PCK-based NOS course. Chemistry Education Research and Practice, 17, 818–841. https://doi.org/10.1039/c6rp00062b Diamond, B. S., Maerten-Rivera, J., Rohrer, R. E., & Lee, O. (2014). Efectiveness of a curricular and professional development intervention at improving elementary teachers’ science content knowledge and student achievement outcomes: Year 1 results. Journal of Research in Science Teaching, 51(5), 635–658. Donna, J. D., & Hick, S. R. (2017). Developing elementary preservice teacher subject matter knowledge through the use of educative science curriculum materials. Journal of Science Teacher Education, 28, 92–110. https://doi.org/10.1080/1046560x.2017.1279510 Faikhamta, C., & Clarke, A. (2013). A self-study of a Thai teacher educator developing a better understanding of PCK for teaching about teaching science. Research in Science Education, 43, 955–979. https://doi. org/10.1007/s11165-012-9300-7 Fenstermacher, G. D. (1986). Philosophy of research on teaching: Three aspects. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 37–49). Macmillan. Fenstermacher, G. D. (1994). The knower and the known: The nature of knowledge in research on teaching. In L. Darling-Hammond (Ed.), Review of research in education (pp. 3–56). American Educational Research Association. Fischer, H. E., Borowksi, A., & Tepner, O. (2012). Professional knowledge of science teachers. In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education (pp. 435–448). Springer. https://doi.org/10.1007/978-1-4020-9041-7_30 Förtsch, C., Werner, S., von Kotzebue, L., & Neuhaus, B. J. (2016). Efects of biology teachers’ professional knowledge and cognitive activation on students’ achievement. International Journal of Science Education, 38, 2642–2666. https://doi.org/10.1080/09500693.2016.1257170 Fraser, S. P. (2016). Pedagogical content knowledge (PCK): Exploring its usefulness for science lecturers in higher education. Research in Science Education, 46(1), 141–161.

1157

Jan H. van Driel, Anne Hume, and Amanda Berry Friedrichsen, P., Van Driel, J., & Abell, S. (2011). Taking a closer look at science teaching orientations. Science Education, 95, 358–376. Gess-Newsome, J. (2015). A model of teacher professional knowledge and skill including PCK: Results of the thinking from the PCK Summit. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 28–42). Routledge. Gess-Newsome, J., Cardenas, S., Austin, B. A., Carlson, J., Gardner, A. L., Stuhlsatz, M. A. M., Taylor, J. A., & Wilson, C. D. (2011). Impact of educative materials and transformative professional development on teachers PCK, practice, and student achievement. Paper presented at the Annual Meeting of the National Association for Research in Science Teaching, April 6. Gess-Newsome, J., Taylor, J., Carlson, J., Gardner, A. L., Wilson, C. D., & Stuhlsatz, M. A. M. (2019). Teacher pedagogical content knowledge, practice, and student achievement. International Journal of Science Education, 41, 944–963. https://doi.org/10.1080/09500693.2016.1265158 Glaze, A., & Goldston, J. (2019). Acceptance, understanding & experience: Exploring obstacles to evolution education among advanced placement teachers. American Biology Teacher, 81, 71–76. https://doi.org/10.1525/ abt.2019.81.2.71 Großschedl, J., Harms, U., Kleickmann T., & Glowinski, I. (2015). Preservice biology teachers’ professional knowledge: Structure and learning opportunities. Journal of Science Teacher Education, 26, 291–318. https:// doi.org/10.1007/s10972-015-9423-6 Großschedl, J., Welter, V., & Harms, U. (2019). A new instrument for measuring pre-service biology teachers’ pedagogical content knowledge: The PCK-IBI. Journal of Research in Science Teaching, 56, 402–439. https:// doi.org/10.1002/tea.21482 Gunckel, K. L., Covitt, B. A., & Salinas, I. (2018). Learning progressions as tools for supporting teacher content knowledge and pedagogical content knowledge about water in environmental systems. Journal of Research in Science Teaching, 55, 1339–1362. https://doi.org/10.1002/tea.21454 Hanuscin, D. L. (2013). Critical incidents in the development of pedagogical content knowledge for teaching the nature of science: A prospective elementary teacher’s journey. Journal of Science Teacher Education, 24, 933–956. https://doi.org/10.1007/s10972-013-9341-4 Hanuscin, D. L., de Araujo, Z., Cisterna, D., Lipsitz, K., & van Garderen, D. (2020). The re-novicing of elementary teachers in science? Grade level reassignment and teacher PCK. Journal of Science Teacher Education, 31, 780–801. https://doi.org/10.1080/1046560X.2020. 1778845 Hanuscin, D. L., Donova, D., Acevedo-Gutiérrez, A., Borda, E., DeBari, S., Melton, J., Le, T., Morrison, W., & Ronca, R. (2021). Supporting the professional development of science teacher educators through shadowing. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-021-10154-5 Hume, A. (2016). Finding the means to initiate and sustain a teacher educator’s pedagogical content knowledge (PCK) development in science education. In G. A. Buck & V. L. Akerson (Eds.), Enhancing professional knowledge of pre-service science teacher education by self-study research turning a critical eye on our practice (pp. 317–339). Springer Publishers. Irmak, M., & Yilmaz-Tuzun, O. (2019). Investigating pre-service science teachers’ perceived technological pedagogical content knowledge (TPACK) regarding genetics. Research in Science & Technological Education, 37, 127–146. https://doi.org/10.1080/02635143.2018. 1466778 Jin, H., Shin, H., Johnson, M. E., Kim, J., & Anderson, C. W. (2015). Developing learning progression-based teacher knowledge measures. Journal of Research in Science Teaching, 52, 1269–1295. https://doi.org/10.1002/ tea.21243 Jüttner, M., Boone, W., Park, S., & Neuhaus, B. J. (2013). Development and use of a test instrument to measure biology teachers’ content knowledge (CK) and pedagogical content knowledge (PCK). Educational Assessment, Evaluation and Accountability, 25, 45–67. https://doi.org/10.1007/s11092–013–9157-y Jüttner, M., & Neuhaus, B. (2012). Development of items for a pedagogical content knowledge-test based on empirical analysis of pupils’ errors. International Journal of Science Education, 34, 1125–1143. Kaya, Z., Kaya, O. N., Aydemir, S., & Ebenezer, J. (2021). Knowledge of student learning difculties as a plausible conceptual change pathway between content knowledge and pedagogical content knowledge. Research in Science Education. https://doi.org/10.1007/s11165-020-09971-5 Kelchtermans, G., Smith, K., & Vanderlinde, R. (2018). Towards an “international forum for teacher educator development”: An agenda for research and action. European Journal of Teacher Education, 41, 120–134. https:// doi.org/10.1080/02619768.2017.1372743 Keller, M. M., Neumann, K., & Fischer, H. E. (2017). The impact of physics teachers’ pedagogical content knowledge and motivation on students’ achievement and interest. Journal of Research in Science Teaching, 54, 586–614. https://doi.org/10.1002/tea.21378

1158

Research on Science Teacher Knowledge and Its Development Kind, V. (2009). A confict in your head: An exploration of trainee science teachers’ subject matter knowledge development and its impact on teacher self-confdence. International Journal of Science Education, 31, 1529–1562. Kind, V. (2014). A degree is not enough: A quantitative study of aspects of pre-service science teachers’ chemistry content knowledge. International Journal of Science Education, 36, 1313–1345. https://doi.org/10.1080/ 09500693.2013.860497 Kind, V. (2019). Development of evidence-based, student-learning-oriented rubrics for pre-service science teachers’ pedagogical content knowledge. International Journal of Science Education, 41, 911–943. https://doi. org/10.1080/09500693.2017.1311049 Kind, V., & Chan, K. K. H. (2019). Resolving the amalgam: Connecting pedagogical content knowledge, content knowledge and pedagogical knowledge. International Journal of Science Education, 41, 964–978. https:// doi.org/10.1080/09500693.2019.1584931 Kirschner, S., Borowksi, A., & Fischer, H. E. (2011). Measuring physics teachers’ pedagogical content knowledge. Paper presented at the Biannual Conference of the European Science Education Research Association, September 8. Kirschner, S., Borowksi, A., & Fischer, H. E. (2016). Developing and evaluating a paper-and-pencil test to assess components of physics teachers’ pedagogical content knowledge. International Journal of Science Education, 38, 1343–1372. https://doi.org/10.1080/09500693. 2016.1190479 Leuchter, M., Saalbach, H., Studhalter, U., & Tettenborn, A. (2020). Teaching for conceptual change in preschool science: Relations among teachers’ professional beliefs, knowledge, and instructional practice. International Journal of Science Education, 42(12), 1941–1967. Liepertz, S., & Borowski, A. (2019). Testing the consensus model: Relationships among physics teachers’ professional knowledge, interconnectedness of content structure and student achievement. International Journal of Science Education, 41, 890–910. https://doi.org/10.1080/09500693.2018.1478165 Lin, J. W. (2017). A comparison of experienced and preservice elementary school teachers’ content knowledge and pedagogical content knowledge about electric circuits. Eurasia, Journal of Mathematics, Science and Technology Education, 13, 835–856. https://doi.org/10.12973/eurasia.2017.00646a Loughran, J. J., Mulhall, P., &. Berry, A. (2004). In search of pedagogical content knowledge in science: Developing ways of articulating and documenting professional practice. Journal of Research in Science Teaching, 41, 370–391. Loughran, J. J., Mulhall, P., & Berry, A. (2006). Understanding and developing science teachers’ pedagogical content knowledge. Sense Publishers. Lucero, M. M., Petrosino, A. J., & Delgado, C. (2017). Exploring the relationship between secondary science teachers’ subject matter knowledge and knowledge of student conceptions while teaching evolution by natural selection. Journal of Research in Science Teaching, 54, 219–246. https://doi.org/10.1002/tea.21344 Magnusson, S., Krajcik, J., & Borko, H. (1999). Nature, sources and development of pedagogical content knowledge. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 95–132). Kluwer Academic. Mahler, D., Großschedl, J., & Harms, U. (2017). Using doubly latent multilevel analysis to elucidate relationships between science teachers’ professional knowledge and students’ performance. International Journal of Science Education, 39, 213–237. https://doi.org/10.1080/09500693.2016.1276641 Mesci, G., Schwartz, R. S., & Pleasants, B. A. S. (2020). Enabling factors of preservice science teachers’ pedagogical content knowledge for nature of science and nature of scientifc inquiry. Science & Education, 29, 263–297. https://doi.org/10.1007/s11191–019–00090-w Miheso, J. M., & Mavhunga, E. (2020). The retention of topic specifc PCK: A longitudinal study with beginning chemistry teachers. Chemistry Education Research and Practice, 21, 789–805. https://doi.org/10.1039/ d0rp00008f Morine-Dershimer, G., & Kent, T. (1999). The complex nature and sources of teachers’ pedagogical knowledge. In J. Gess-Newsome & N. G. Lederman (Eds.), Examining pedagogical content knowledge (pp. 21–50). Kluwer Academic. Neumann, K., Kind, V., & Harms, U. (2019). Probing the amalgam: The relationship between science teachers’ content, pedagogical and pedagogical content knowledge. International Journal of Science Education, 41, 847–861. https://doi.org/10.1080/09500693.2018.1497217 Nilsson, P., & Elm, A. (2017). Capturing and developing early childhood teachers’ science pedagogical content knowledge through CoRes. Journal of Science Teacher Education, 28(5), 406–424. Nilsson, P., & Vikström, A. (2015). Making PCK explicit-capturing science teachers’ pedagogical content knowledge (PCK) in the science classroom. International Journal of Science Education, 37, 2836–2857. https:// doi.org/10.1080/09500693.2015.1106614 Nixon, R. S., Campbell, B. K., & Luft, J. A. (2016). Efects of subject-area degree and classroom experience on new chemistry teachers’ subject matter knowledge. International Journal of Science Education, 38, 1636–1654. https://doi.org/10.1080/09500693.2016.1204482

1159

Jan H. van Driel, Anne Hume, and Amanda Berry Nixon, R. S., Hill, K. M., & Luft, J. A. (2017). Secondary science teachers’ subject matter knowledge development across the frst 5 years. Journal of Science Teacher Education, 28, 574–589. https://doi.org/10.1080/104 6560x.2017.1388086 Nixon, R. S., Smith, L. K., & Sudweeks, R. R. (2019). Elementary teachers’ science subject matter knowledge across the teacher career cycle. Journal of Research in Science Teaching, 56, 707–731. https://doi.org/10.1002/ tea.21524 Park, S., & Chen, Y. C. (2012). Mapping out the integration of the components of pedagogical content knowledge (PCK): Examples from high school biology classroom. Journal of Research in Science Teaching, 49, 922–941. Park, S., & Oliver, J. S. (2008). Revisiting the conceptualisation of pedagogical content knowledge (PCK): PCK as a conceptual tool to understand teachers as professionals. Research in Science Education, 38, 261–284. Park, S., Suh, J., & Seo, K. (2018). Development and validation of measures of secondary science teachers’ PCK for teaching photosynthesis. Research in Science Education, 48, 549–573. https://doi.org/10.1007/ s11165–016–9578-y Park Rogers, M., Berry, A., Krainer, K., & Even, R. (2021). Finding common ground: A synthesis of science and mathematics teacher educators experiences with professional growth. International Journal of Science and Mathematics Education, 19, 167–180. Paulick, I., Großschedl, J., Harms, U., & Moller, J. (2016). Preservice teachers’ professional knowledge and its relation to academic self-concept. Journal of Teacher Education, 67, 173–182. https://doi. org/10.1177/0022487116639263 Pitjeng-Mosabala, P., & Rollnick, M. (2018). Exploring the development of novice unqualifed graduate teachers’ topic-specifc PCK in teaching the particulate nature of matter in South Africa’s classrooms. International Journal of Science Education, 40, 742–770. https://doi.org/10.1080/09500693.2018.1446569 Reynolds, M. C. (Ed.). (1989). The knowledge base for the beginning teacher. Pergamon Press. Reynolds, W. M., & Park, S. (2021). Examining the relationship between the educative teacher performance assessment and preservice teachers’ pedagogical content knowledge. Journal of Research in Science Teaching, 58(5), 721–758. https://doi.org/10.1002/tea.21676 Rollnick, M. (2017). Developing and evaluating a paper-and-pencil test to assess components of physics teachers’ pedagogical content knowledge. International Journal of Science Education, 38, 1343–1372. https://doi.org/10 .1080/09500693.2016.1190479 Roth, K. J., Wilson, C. D., Taylor, J. A., Stuhlsatz, M. A., & Hvidsten, C. (2019). Comparing the efects of analysis-of-practice and content-based professional development on teacher and student outcomes in science. American Educational Research Journal, 56(4), 1217–1253. Sadler, P. M., Coyle, H., Miller, J. L., Cook-Smith, N., Dussault, M., & Gould, R. R. (2010). The astronomy and space science concept inventory: Development and validation of assessment instruments aligned with the K-12 national science standards. Astronomy Education Review, 8(010111), 1–26. https://doi.org/10.3847/ AER2009024 Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013). The infuence of teachers’ knowledge on student learning in middle school physical science classrooms. American Educational Research Journal, 50, 1020–1049. https://doi.org/10.3102/0002831213477680 Schmelzing, S., Van Driel, J. H., Jüttner, M., Brandenbusch, S., Sandmann, A., & Neuhaus, B. J. (2013). Development, evaluation, and validation of a paper-and-pencil test for measuring two components of biology teachers’ pedagogical content knowledge concerning the “Cardiovascular System”. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-012-9384-6 Schultz, M., Lawrie, G. A., Bailey, C. H., & Dargaville, B. L. (2018). Characterisation of teacher professional knowledge and skill through content representations from tertiary chemistry educators. Chemistry Education Research and Practice, 19(2), 508–519. Schultze, F., & Nilsson, P. (2018). Coteaching with senior students: A way to refne teachers’ PCK for teaching chemical bonding in upper secondary school. International Journal of Science Education, 40, 688–706. https:// doi.org/10.1080/09500693.2018.1436792 Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational researcher, 15, 4–14. Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57, 1–22. Shulman, L. S. (2015). PCK: Its genesis and exodus. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Reexamining pedagogical content knowledge in science education (pp. 3–13). Routledge. Sorge, S., Kröger, J., Petersen, S., & Neumann, K. (2019). Structure and development of pre-service physics teachers’ professional knowledge. International Journal of Science Education, 41, 862–889. https://doi.org/10.1 080/09500693.2017.1346326 Soysal, Y. (2018). An exploration of the interactions among the components of an experienced elementary science teacher’s pedagogical content knowledge. Educational Studies, 44, 1–25. https://doi.org/10.1080/0305 5698.2017.1331839

1160

Research on Science Teacher Knowledge and Its Development Tepner, O., & Sumfeth, E. (2019). Postscript: Considerations from an external perspective. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 315–329). Australia Springer. Tom, A. R., & Valli, L. (1990). Professional knowledge for teachers. In W. R. Houston (Ed.), Handbook of research on teacher education (pp. 372–392). MacMillan. Underwood, J. B., & Moore Mensah, F. (2018). An investigation of science teacher educators’ perceptions of culturally relevant pedagogy. Journal of Science Teacher Education, 29, 46–64. https://doi.org/10.1080/10465 60X.2017.1423457 Van Dijk, E. M., & Kattman, U. (2007). A research model for the study of science teachers’ PCK and improving teacher education. Teaching and Teacher Education, 23, 885–897. Van Driel, J. H., Berry, A., & Meirink, J. A. (2014). Research on science teacher knowledge. In N. Lederman & S. K. Abell (Eds.), Handbook of research on science education (2nd ed., pp. 848–870). Taylor & Francis. Van Driel, J. H., Verloop, N., & De Vos, W. (1998). Developing science teachers’ pedagogical content knowledge. Journal of Research in Science Teaching, 35, 673–695. Verloop, N., van Driel, J., & Meijer, P. (2001). Teacher knowledge and the knowledge base of teaching. International Journal of Educational Research, 35(5), 441–461. Wilson, C. D., Borowski, A., & Van Driel, J. H. (2019). Perspectives on the future of PCK research in science education and beyond. In A. Hume, R. Cooper, & A. Borowski (Eds.), Repositioning pedagogical content knowledge in teachers’ knowledge for teaching science (pp. 289–300). Australia Springer. Wongsopawiro, D., Zwart, R. C., & Van Driel, J. H. (2017). Identifying pathways of teachers’ PCK development. Teachers and Teaching: Theory & Practice, 23, 2, 191–210. https://doi.org/10.1080/13540602.2016.12 04286 Yang, Y., Liu, X. F., & Gardella, J. A. (2018). Efects of professional development on teacher pedagogical content knowledge, inquiry teaching practices, and Ssudent understanding of interdisciplinary science. Journal of Science Teacher Education, 29, 263–282. https://doi.org/10.1080/1046560x.2018.1439262 Yang, Y., Liu, X. F., & Gardella, J. A. (2020). Efects of a professional development program on science teacher knowledge and practice, and student understanding of interdisciplinary science concepts. Journal of Research in Science Teaching, 57, 1028–1057. https://doi.org/10.1002/tea.21620

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36 LEARNING TO TEACH SCIENCE Tom Russell and Andrea K. Martin

We begin this exploration and analysis of learning to teach science with a quotation more than 90 years old as a reminder that eforts to improve the quality of science teaching go back to the early years of compulsory schooling: Remember the inevitable danger of lectures. A preacher is supposed to be a teacher, but sermons are proverbially narcotic. . . . Above all things let your method be such as to compel your pupils to think and to reason. Let your method be logical. Let the facts and the hypotheses which link them be set and seen in a clear picture. . . . Beware of the pseudo method of discovery. “Pour H2SO4 on granulated zinc, and you will discover that hydrogen is given of”! Beware of verifcation methods. “Show that ferrous ammonium sulphate contains oneseventh of its own weight of iron.” This is simply asking for the evidence to be cooked. When a boy [sic] works an experiment, keep him [sic] just enough in the dark as to the probable outcome of the experiment, just enough in the attitude of a discoverer, to leave him [sic] unprejudiced in his [sic] observations. Do not adopt the heuristic extremist’s principle that a pupil must not be permitted to take anything second hand. Life is too short. (Westaway, 1929, p. 39) Not surprisingly, it has been well noted over the years that science teachers often feel caught between the teaching of facts and theories on the one hand and the ultimate impact of their teaching methods on the other. Similarly, science teacher educators may well feel the tension between the teaching of educational theories and ways to improve teaching and the overall impact of their teaching methods on those learning to teach science. Chapters in research handbooks often provide comprehensive surveys of published research. While we are attentive to such research, our major goal in this chapter is to build on earlier research to stimulate fresh perspectives for thinking about the assumptions and beliefs embedded in preservice programs for those learning to teach science. Challenging those assumptions can stimulate changes in beliefs that inspire changes in actions. We summarize the overall argument in the following points: 1.

Calls for change to how science is taught in schools and universities are longstanding. For example, Dewey’s (1938) contrast between traditional and progressive education shows just how

DOI: 10.4324/9780367855758-42

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2.

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little the fundamentals of school culture (Sarason, 1996) have changed. Willingham’s (2009) study posing the question “Why don’t students like school?” reminds us that calls for change have persisted for decades. A dominant theme in the science education research literature has been teaching for conceptual development and change. Ironically, only a small fraction of that research considers how individuals learn to teach science in preservice programs (Fitzgerald et al., 2021). As Sarason (1996) argues by seeing the school having a unique culture, teaching practices are quite stable, more so than those who call for change often realize (see Handelsman et al., 2004). Logic and reason alone do not change teaching practices that were initially learned indirectly and unintentionally from one’s own teachers (Lortie, 1975). Learning from experience (Munby & Russell, 1994; Munby et  al., 2001) is an undervalued and neglected aspect of science teaching and learning that is similarly undervalued in programs where individuals learn to teach science. This undervaluation is rooted in the ultimate value that the university assigns to rigorous argument and positivist epistemology (Schön, 1995). It is also rooted in Franklin’s (1994) insight that science separates knowledge from experience. Research on conceptual change ofers many suggestions for teaching that supports inquiry learning, deeper learning, and metacognition (Carpendale & Cooper, 2021; Linn, 2006). Conceptual change research indicates that achieving more complete conceptual understanding does not necessarily achieve the signifcant epistemological change that must accompany that understanding (Elby, 2001). Learning to teach science requires attention to conceptual change approaches, not only for teaching fundamental concepts of science but also for teaching fundamental concepts of teaching and learning (Linn, 2006). The range of metaphors apparent in each individual’s thinking about teaching and learning science require explicit attention in a teacher education program as they contribute to probing belief systems (Loughran, 2006; Buaraphan, 2011). There is a profoundly important distinction between what is taught (curriculum content) and how it is taught (pedagogy), and this applies to how one learns to teach science just as much as to how one learns science. Metacognitive teaching strategies are essential elements of teaching science and also of analyzing the impact of what and how we teach and learn (Baird & Mitchell, 1986; Willingham, 2016).

This chapter highlights the need for explicit attention to epistemological issues associated with teaching science and learning to teach science. If the dominant epistemology of the university continues to be essentially positivistic and closely linked to how we know in the various disciplines of science, then the long-sought breakthroughs in how science is taught and learned will not be achieved. For most future science teachers, learning to teach science begins on entrance to a teacher education program. Many teacher candidates fail to recognize that many years in science classes have acted as models for how science is and should be taught. Lortie (1975) made this point clearly for all teachers, and it applies equally to teacher educators. We proceed from an assumption that most science teacher education programs fall short as models of productive teaching and learning due to a lack of attention to creating conditions for quality in teaching and learning. Northfeld’s (1998, p. 698) insights identifed the issue: Consider the proposition that teacher educators could overestimate what they can teach new teachers, while also underestimating their ability to provide appropriate conditions for them to learn about teaching. Such a proposition serves to shift the teacher education task (at both preservice and inservice levels) from one of delivering what has to be known by teachers to one of providing better conditions for learning about teaching. 1163

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As this quotation clearly suggests, this chapter is constructed less with a focus on what knowledge can be taught to teacher candidates in propositional form and more on how science teacher educators might create better learning conditions by modeling productive teaching strategies that include fostering metacognition. Science curriculum content and evidence-based theories defnitely have their place; we argue here that productive teaching must be learned by experiencing it as well as by reading about it and discussing it. Doubts about the impact of formal knowledge “distributed” in teacher education programs are longstanding: There is a wealth of evidence from the research literature in education and the broader learning sciences that the absorption model is antiquated and insufcient. (Linn & Eylon, 2011, p. 6) With regard to the infuence of the formal knowledge distributed in education courses on teacher development, there is much evidence that the pedagogical methods and content knowledge introduced to students in campus courses has little infuence on the subsequent actions of students even during initial training. (Zeichner et al., 1987, p. 26) At various points we present Narrative Boxes to report former students’ comments that provide relevant perspectives from individuals who were learning to teach science or have recently begun to teach science. Narrative Box 36.1 continues the theme of how teacher education courses are taught.

Narrative Box 36.1 Teacher Education Courses Lacked Practical Experience In my B.Ed. year, I had a naïve view of what teaching entailed. Unsure of what else to do, I fell back onto teaching the way that I was taught when I was a student. This was the case on my practicum, but it persisted into my frst and even second year of teaching. B.Ed. classes varied in their usefulness in practice: Some were at too high a level to be useful, while the subject-specifc courses were helpful, but I didn’t have the practical experience to get as much out of them as I would have liked. The practicums were just enough to give me a taste of what the job would be like. Learning to teach is a much slower process than there is time for in a one- or two-year program. It’s just the reality that much of our learning about how to teach comes from on-the-job experience and trial and error. How we teach is so individual and connected to our personalities that I don’t see how it could be otherwise. (S. Dinicol, personal communication, December 1, 2020)

More than three decades ago, McIntyre (1988) described three insights into how people learn to teach, and we fnd them particularly relevant to the challenges associated with learning to teach science: What I conclude then from our limited knowledge about how people learn to teach is, frst, that they have their own extensive repertoires and their own agendas; and that we as teacher educators, if we are realistic, need to accept that we can only help them in their eforts, not defne the enterprise in which they are engaged. Second, I conclude that even if we did believe that we had “the answers”, reliable knowledge about how best to teach, student-teachers would not accept it but would want to test it for themselves in various ways; we can probably exert more infuence by encouraging this process of testing than by pretending it is not necessary. And third, we can have some confdence that if we do not put student-teachers into situations 1164

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which overwhelm or seriously threaten them, we have good reason to believe that they will explore the problems of teaching with a big degree of objectivity about their own performances and rationality in their investigations. (McIntyre, 1988, pp. 104–105, emphasis added) McIntyre’s three insights suggest the following questions for teacher educators seeking to better support their students in learning to teach science, or as Northfeld (1998) described, create conditions for quality teaching and learning in teacher education programs: • • • • • •

How can we help those learning to teach science recognize and identify “their own extensive repertoires and their own agendas”? Are we as teacher educators trying to “defne the enterprise in which they are engaged”? How often do we as teacher educators teach in ways that suggest that we have “the answers”? How can we support those learning to teach science in developing ways to test reliable knowledge for themselves? Does our program structure suggest that testing reliable knowledge is not necessary or beyond their skills? Are we ready to trust those learning to teach science that they can be objective about their own teaching and rational in their study of their own teaching? How can we best prepare and support them in that process?

It may be helpful for readers to keep these questions in mind as they continue through the chapter. As additional background for readers, it is important to introduce the concept of craft knowledge, which we contrast with the propositional or book knowledge that is commonly and perhaps predominantly taught in teacher education classes. Here we turn to the extensive review of “craft knowledge and the education of teachers” by Grimmett and Mackinnon (1992), who provide this defnition of craft knowledge: As a form of professional expertise, craft knowledge is neither technical skill, the application of theory or general principles to practice, nor critical analysis; rather, it represents the construction of situated, learner-focused, procedural and content-related pedagogical knowledge through “deliberate action”. (p. 393) In the following passage, they identify the signifcance of craft knowledge for teacher education programs: That craft knowledge exists as a powerful determinant of teachers’ practice is neither new nor controversial. What is new is the possibility that such knowledge could become an integral part of teacher education; what is controversial is the debate over whether this would be a productive direction to take. We argue here that, in addition to codifed knowledge bases framed around university-based research, teacher education could beneft from the contribution that craft knowledge can make to the formation of skillful, refective, and empowered teachers. (p. 388, emphasis in original) Although the concept of craft knowledge does not appear frequently in the teacher education literature, our own learning from experience indicates that it is essential to include the concept of craft knowledge in our understanding of how individuals learn to teach science. Book knowledge and 1165

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research are critical, but alone they do not address what every teacher learns from personal teaching experience.

Learning From Experience and the Authority of Experience In our culture, we speak easily of “learning from experience” in everyday life, yet we also hear many stories in which people seem not to have learned from experience. Just as propositional knowledge claims are easily forgotten and links are not always made from one context to another, so it is with learning from experience, which seems to be a marginal feature of many classrooms in the formal learning contexts of schools and universities. Science teachers are often credited with an advantage of being able to work with everyday materials, yet laboratory experiences are rarely described by students as major contributing activities in their learning of concepts. Because learning from experience is not a signifcant feature of many classrooms, when those learning to teach science begin a professional preparation program, the role of learning from experience may never have been considered. Quite universally, student teachers report that the practicum is the most signifcant element of their preparation for teaching, yet this does not mean that new science teachers understand how they learn from experience or that they are profcient in learning from experience. Munby and Russell (1994) addressed this issue when they introduced the term “authority of experience”. Listening to one’s own experience is not the same as listening to the experience of others, and the [physics method] students seem to indicate that they still place much more authority with those who have experience and with those who speak with confdence about how teaching should be done. They seem reluctant to listen to or to trust their own experiences as an authoritative source of knowledge about teaching. We wonder how and to what extent they will begin to hear the voice of their own experiences as they begin their teaching careers. The basic tension in teacher education derives for us from preservice students wanting to move from being under authority to being in authority, without appreciating the potential that the authority of experience can give to their learning to teach. The challenge for teacher education is to help new teachers recognize and identify the place and function of the authority of experience. If this is not done, the authority of experience can fall victim to the danger that accompanies all versions of authority: mere possession is not enough because authority can be abused. (Munby & Russell, 1994, pp. 93–94)

Narrative Box 36.2 Learning to Trust Learning from Experience The time between my frst and second feld placements gave me ample opportunity to refect on my experiences and determine exactly what I had learned, if anything at all. I believe it was during this time that I began to develop a more sophisticated understanding of what it is to teach. . . . The result was ultimately a radical shift in my thinking about teaching and learning by putting the needs of learners front and centre. . . . My professional development did not really commence until I understood that no one was going to tell me how to teach, and for that matter, it was not even possible to do so . . . . One avenue for improving practice is for teachers to monitor their own experiences and to use them as a source of knowledge about teaching. Munby and Russell (1994) point out that this “authority of experience” is something beginning teachers must learn to trust in order to develop as professionals. (Harrison, 2014, pp. 3–5, emphasis added)

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Dewey maintained that familiar educational patterns persist as tradition, not on the basis of their rationale (Dewey, 1938, pp. 28–29). Bringing the authority of experience into programs for learning to teach science involves all the challenges of learning from experience that are very familiar to experienced teachers. Beginning teachers need not only to know who Dewey was but also what he had to teach them, which is as relevant today as it was when he wrote: There is no discipline in the world so severe as the discipline of experience subjected to the tests of intelligent development and direction. . . . The road of the new education is not an easier one to follow than the old road but a more strenuous and difcult one. . . . The greatest danger that attends its future is, I believe, the idea that it is an easy way to follow. (Dewey, 1938, p. 90) We fnd it interesting that learning from experience and the associated epistemological issues tend not to be raised in the conceptual change literature. Here we call attention to the issue of learning from experience because it represents an important, even essential, perspective for helping individuals learn to teach science. A strong case for recognizing the authority of experience in the science classroom appears in the fndings and recommendations reported in a book intended for those who teach frst-year undergraduate courses in physics. Knight (2004) summarizes 25 years of physics education research on students’ concepts and problem-solving strategies with three conclusions that have direct implications not only for teaching science but also for learning to teach science: • • •

Students enter our classroom not as ‘blank slates,’ tabula rasa, but flled with many prior concepts. Students’ prior concepts are remarkably resistant to change. Students’ knowledge is not organized in any coherent framework. (Knight, 2004, p. 25)

These statements remind us that, in contrast to what is learned from textbooks, that which is learned from experience can be very powerful without being coherently organized. Knight closes his analysis with the report that “the results of physics education research can be sliced and rearranged in many patterns, but I see Five Lessons for teachers”. 1. 2. 3. 4. 5.

Keep students actively engaged and provide rapid feedback (p. 42). Focus on phenomena rather than abstractions (p. 42). Deal explicitly with students’ alternative conceptions (p. 43). Teach and use explicit problem-solving skills and strategies (p. 44). Write homework and exam problems that go beyond symbol manipulation to engage students in the qualitative and conceptual analysis of physical phenomena (p. 44).

The frst four lessons can be translated directly from teaching science to learning to teach science. The ffth lesson could easily be reshaped to “engage students in the qualitative and conceptual analysis of educational phenomena”. In traditional preservice teacher education programs, one might view these as research fndings to include in a “knowledge base” to be transmitted to preservice science teachers. Our analysis of the research literature confrms that it is entirely counter-productive to simply transmit such lessons to teachers as content. Rather, preservice science teacher education programs must explore the implications of these lessons through all the learning experiences created in teacher education classrooms. 1167

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Refection by Those Learning to Teach Bryan and Abell (1999) provided a case study of a student teacher named “Barbara”. Early in their argument, the authors declare their perspectives on the role of experience in learning to teach: The heart of knowing how to teach cannot be learned from coursework alone. The construction of professional knowledge requires experience.  .  .  . Experience infuences the frames that teachers employ in identifying problems of practice, in approaching those problems and implementing solutions, and in making sense of the outcomes of their actions. (pp. 121–122) The case of Barbara begins with an account of what Barbara believed about science teaching and learning and moves on to describe her vision for teaching elementary science as well as the tensions within her thinking about her professional responsibilities. Of particular interest is Barbara’s initial premise that a teacher should continue to teach a scientifc concept until all children show that they understand it. Once the process of refection became apparent, “Barbara began to shift her perspective and reframe the tension between her vision and practice. Her professional experience provided feedback that forced her to confront the idea that in teaching science, teachers need to consider more than students getting it” (p. 131). This case study of Barbara is one that could help new science teachers anticipate the challenges and prospects of student teaching, although the real help would probably be realized during rather than before the student teacher assignment. The implications for further study of learning from experience are clear: Barbara’s case implicitly underscores the fallacy of certain assumptions underlying traditional teacher education programs: (a) that propositional knowledge from course readings and lectures can be translated directly into practice, and (b) that prospective teachers develop professional knowledge before experience rather than in conjunction with experience. . . . Teacher educators are challenged to coach prospective teachers to purposefully and systematically inquire into their own practices, encouraging them to make such inquiry a habit. (p. 136) Just as a conceptual change approach to teaching science begins with students’ experiences, so Bryan and Abell conclude that “the genesis of the process of developing professional knowledge should be seen as inherent in experience” (p. 136). “A preeminent goal of science teacher education should be to help prospective teachers challenge and refne their ideas about teaching and learning science and learn how to learn from experience” (p. 137).

Narrative Box 36.3 Learning to Teach Requires Experience “How can we replicate the success that self-directed learning has in other subjects in subjects like math and physics?” Upon my return to classes, this question led me to complete an assignment on self-directed learning, specifcally in physics. In my fnal weeks of classes in this program, I’ve surprisingly more enjoyed completing assignments than going to class. With all the experiences I’ve had through this year, I feel that it has gotten harder to learn about teaching through listening to a professor. Listening in class can bring up interesting topics to think about, but I am not sure I am learning anything until I experience it, either through the completion of an assignment or through physically trying things. (A. Chan, personal communication, April 24, 2018)

Zembal-Saul et al. (2002) build on the conclusion by Bryan and Abell (1999, p. 121) that “experience plays a signifcant role in developing professional knowledge”. To this they add their own 1168

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conclusion that “what we do know . . . is that experience alone is not enough. It needs to be coupled with thoughtful refection on action” (p. 460). Their overall conclusions make important points that remind science teacher educators yet again of the importance of the cooperating teacher in supporting the student teacher’s professional learning. There is evidence that cooperating teachers who facilitate students’ meaningful learning in general and support student teachers in their eforts to continue to emphasize science content representation can positively infuence the territory student teachers attempt to master. Conversely, cooperating teachers who fail to support student teachers in continuing the process of planning, teaching, and refection on substantive issues of content representation are likely to reroute the entire process of learning to teach. (p. 460) They also remind us that our collective understanding of how experience contributes to learning to teach still requires attention and development: There is an urgent need to understand better the role of experience in learning to teach, in particular the aspects of teaching experiences that support or hinder new teachers’ continuing development in the often-fragile domain of science content knowledge and its representations. (p. 461) We turn next to the extensive and ever-growing literature of research on conceptual change.

The Complex Challenge of Conceptual Change Conceptual change is central both to learning science and to teaching science. Learning science requires conceptual change if the concepts acquired from everyday experience are to be modifed and developed. Learning to teach science requires conceptual change to develop the concepts acquired by years of watching teachers. Duschl and Hamilton (1998) describe science learning as a process of conceptual change that is concerned with issues about the development of scientifc knowledge. This entails understanding how learners decide among competing or alternative views, models, or theories of the natural world. Because students build conceptions from their everyday experiences and carry these with them into the classroom, they often confict with science conceptions presented in school. Learning science is especially difcult in felds in which students’ preinstructional conceptions are deeply rooted in daily life experiences. Conceptions that are based on empirical evidence through sense experiences (like the process of seeing, thermal phenomena, and conceptions of forces and motions) fall into this category as do everyday ways of speaking about natural and technical phenomena. (Duit & Treagust, 1998, p. 15) As Duschl and Hamilton (1998) point out, conceptual change involves the restructuring of both declarative and procedural knowledge. They contend that too often science education research is focused on the nature and organization of relevant declarative knowledge and the necessary changes that take place, or need to take place, in that knowledge or its organization. More attention and study need to be directed toward the changes in, or attempts to change, the strategic use of restructured knowledge. Prospective teachers need to reframe their understanding of science learning to recognize the inherent challenges attached when prevailing concepts are subjected to scrutiny and 1169

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validation. Unless and until new science teachers understand why conceptual change is so complex, they are unlikely to be able to efect changes and grasp why those changes are so challenging.

Teaching Conceptual Change The extensive work of Novak (1987, 1989, 1993) provides a useful framework both for understanding why conceptual change is so critical if students are to learn how to learn in science and for understanding why instruction often fails. Novak builds on Ausubel’s (1968) hypothesis that the single most important factor infuencing learning is prior knowledge and Kelly’s (1995) personal construct theory that emphasizes the view that knowledge is constructed and is highly personal, idiosyncratic, and socially negotiated. Novak and Gowin (1984) have advanced a set of three knowledge claims about students’ preconceptions that are carried into their science classes, with subsequent efects on their learning (Wandersee et al., 1994). •

•

•

The frst claim suggests that learners are not “empty vessels” but bring with them a fnite but diverse set of ideas about natural objects and events. These notions are often inconsistent with scientists’ and science teachers’ explanations. The second claim proposes that students’ alternative conceptions cut across age, ability, gender, and cultural boundaries, and these ideas are tenacious and resistant to extinction by conventional teaching strategies. The third claim is broad-based and engages the sociocultural context, viewing alternative conceptions as the product of a diverse set of personal experiences that include direct observation of natural objects and events, peer culture, everyday language, the mass media, as well as teachers’ explanations and instructional materials.

Furthermore, the authors also report that teachers often subscribe to the same alternative conceptions as their students (Mintzes et al., 1997). Additionally, Novak and his group have made three claims regarding successful science learners: 1. 2. 3.

The process of constructing meanings relies on the development of elaborate, strongly hierarchical, well-diferentiated, and highly integrated frameworks of related concepts. Conceptual change requires that knowledge is restructured by making and breaking interconnections between concepts and replacing or substituting one concept with another. Successful science learners regularly use strategies that enable them to plan, monitor, control, and regulate their own learning. (Mintzes et al., 1997)

Here again, the complex challenges of conceptual change are quite evident. Duit and Treagust (1998) describe Posner et al.’s (1982) theory of conceptual change as the most infuential theory on conceptual change in science education, with wide-ranging applications in other felds as well. Posner et al. (1982) propose that conceptual change will not occur unless learners experience some level of dissatisfaction with their current beliefs or understandings. As long as an existing conception has a successful explanatory track record, learners will hold fast to their initial conception and be satisfed with it. Further complicating matters is the impact of prior knowledge. Insufcient prior knowledge means that a new concept may be incompletely understood. Thus, prior knowledge is a necessary but not sufcient condition for conceptual change. If a new idea is to be accepted, three conditions must be met: intelligibility (understandable), plausibility (reasonable), and fruitfulness (useful). Intelligibility matters because learners need to understand an idea’s meaning and its potential or actual utility, as well as why scientists are concerned with its 1170

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coherence and internal consistency. Plausibility requires that learners make sense of an idea and reconcile it with their own beliefs. “Making sense” in scientifc terms poses complex challenges to one’s commonsense views (Hodson, 1998). Fruitfulness requires that learners gain something of value as a result of their eforts; this may include assistance in problem solving, predicting, or arriving at new insights, as well as direction toward new areas for further study and inquiry. Kagan (1992) summarizes the recommendations by Posner et al. (1982) for steps teachers can take to promote students’ conceptual change. Teachers must help students to make their implicit beliefs explicit, confront students with the inadequacies and inconsistencies of their beliefs, and provide extended opportunities for integrating and diferentiating old and new knowledge. Discussions of conceptual change should also include consideration of Piagetian ideas, specifcally assimilation, accommodation, disequilibrium, and equilibration (Duit & Treagust, 1998). When new events do not ft with existing schemes, then a state of disequilibrium or “mental discomfort” exists, and this can stimulate eforts to make sense of observations and puzzling events (McDevitt & Ormrod, 2002). Through accommodation – replacing, reorganizing, or more efectively integrating their perspectives – learners can resolve discordances and achieve a new state of equilibrium. Equilibration necessitates active involvement of learners and is premised on learners’ developing increasingly complex understandings. These understandings do not occur in isolation but require interaction with one’s environment. In light of its impact on conceptual change and the development of constructivist ideas as well as its contrast to Piagetian theory, Vygotsky’s work must also be acknowledged. Central to Vygotskyian theory is the infuence of sociocultural factors on cognitive development. Where Piaget saw the social environment as another source of information or experience that generated confict and adaptation for the child, Vygotsky saw the sociocultural environment as not just the trigger but the source of the child’s higher cognitive processes (Duschl & Hamilton, 1998). Thus, knowledge acquisition, use, and change are contingent upon children’s social activities and interactions (e.g., conversations, disagreements, etc.), as well as on the merging of thought and language during their early years, and the acculturation provided by parents, other adults, and formal schooling (McDevitt & Ormrod, 2002). The dialogical relationship of teacher and student that should be fostered while negotiating the zone of proximal development is embedded in Vygotskian theory. The familiar metaphor of scaffolding to capture the teacher–child interaction tends to obscure the fact that the more competent, as well as the less competent, individual can proft from the interaction (Tudge & Scrimsher, 2003). Duschl and Hamilton (1998) credit Vygotsky’s work for stimulating research on the social context of cognition and learning. This includes work in the areas of reciprocal teaching, collaborative learning, guided participation, and authentic approaches to teaching, learning, and assessment. In addition, constructs such as situated cognition, cognitive apprenticeships, and the social construction of meaning can be linked to Vygotskyian theory. Each involves the contextual nature of learning and the interrelation of individual, interpersonal, and cultural-historical factors in development (Tudge & Scrimsher, 2003). Overall, we must recognize the confuence of factors infuencing conceptual change and the importance of considering the range of experiences students bring into the classroom, the dynamics and discourse of the classroom context, and the cultural-historical imprint on schooling itself.

Teaching for Conceptual Change A few cautionary notes are in order: misconceptions are persistent and highly resistant to change (Duit & Treagust, 1998; Guzetti et al., 1993; Mintzes et al., 1997). During the late 1970s and into the 1980s, the predominant assumption was that students’ misconceptions had to be extinguished before they could be replaced by the correct scientifc view; however, there appears to be no study that confrms that a particular student’s conception could be totally extinguished and then replaced (Duit & Treagust, 1998). Rather, most studies reveal that the preexisting idea stays “alive” in particular contexts (Duit & Treagust, 1998), what diSessa (1993) describes as refnement, rather than replacement, 1171

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of concepts. Similarly, Chinn and Brewer (1998) address the question of the fate of the old knowledge and the new information after knowledge change takes place. They suggest that there are signifcant instructional implications. If a teacher believes that the old knowledge is simply replaced by the new, then there is no cause for concern about the old interfering with subsequent learning. However, if the old knowledge coexists with and infuences understanding of new knowledge, then instruction must be designed to gradually reduce the potency of the old knowledge. The essence of teaching for conceptual change is restructuring of knowledge (Mintzes et  al., 1997). Of course, this is far easier said than done when the range and variability of students’ responses to cognitive restructuring are reviewed. Hodson (1998) provides a helpful overview of students’ resistant responses. If an idea(s) has been useful in the past, then there will be little, if any, need to replace it. Therefore, rather than replacement, students may hold on to their views by denying the efcacy or accuracy of the new data, or they may rework the data so that it meshes with their existing beliefs. A variation of this is to distort the new idea until it is compatible with the old. Some students look for evidence to confrm their ideas rather than disconfrm. In this case, their original notion prevails, rather like selective perception where one sees only what one chooses to see. Hodson (1998) also points to variations in personality traits that may make some students more receptive to new ideas. Others, however, may adopt a more cavalier attitude and disengage from eforts to resolve discordances, essentially the “I don’t care and can’t be bothered” stance. And some students may be reluctant to pursue alternatives because what they know (or think that they know) is consistent with their own cognitive schema. Therefore, if they hold on to what they know, they do not have the anxiety and stress that can accompany what is unknown, uncertain, or unfamiliar. We are quick to point out that these “resistant responses” can also be employed by future science teachers who are being taught how to teach science; the challenges to teacher educators continue. The work of the Children’s Learning in Science (CLIS) group at the University of Leeds (e.g., Driver, 1989; Scott et al., 1992; Scott & Driver, 1998) is seminal when considering constructivist approaches to conceptual change. They suggest that there are certain commonalities that extend across scientifc disciplines and that support reconceptualization. These can be characterized as instructional activities/sequences that involve a teaching approach designed to address a particular learning demand (Scott et  al., 1992; Scott & Driver, 1998). These are sequenced as follows: (1) orientation or “messing about”, which uses students’ prior knowledge and existing conceptions as a starting point that can then be extended when working toward developing a scientifc point of view; (2) an externalizing or elicitation phase where conceptions that are global and ill-defned are diferentiated (e.g., heat and temperature, weight and mass); (3) modifcation or restructuring where experiential bridges are built to a new conception; and (4) the construction of new conceptions through practice or application. At this point, students’ preconceptions may be incommensurate with scientifc conceptions. If so, Scott and Driver’s (1998) recommendation is for the teacher to acknowledge and discuss the students’ ideas and then indicate that scientists hold an alternate view and present that model. The students can subsequently revisit the scientifc model in relation to their own prior ideas. Creating conditions for cognitive confict where teachers challenge students to look for limitations in their views or deliberately provide examples of discrepant or surprising events, often through hands-on demonstrations or activities, can spur reconceptualization (Hodson, 1998). We question the extent to which preservice teacher education anchors science courses within a conceptual change framework, explores conceptual change theory, probes the concepts that teacher candidates hold about science and learning science, provokes cognitive confict, and exposes candidates to instructional approaches and strategies to support conceptual change. Richardson (1996) has suggested that preservice teachers’ beliefs may be so strong that they cannot be changed within teacher education programs. Unless prospective teachers are directly challenged to confront their own alternative conceptions and to work through the process of conceptual change, it is highly unlikely that they will be able to support their own students in doing so. 1172

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Teaching for Conceptual Change in Preservice Science Teacher Education In this section of the argument, we turn specifcally to conceptual change in the context of learning to teach science. An article by Elby (2001) signals the potential signifcance of epistemological issues when teaching for conceptual change in physics, and we extend Elby’s insights to the signifcance of epistemological issues associated with concepts of teaching and learning. The following excerpts suggest a way forward, and the crucial feature is the view that attention to epistemological development must be explicit. Many of the best research-based reformed physics curricula, ones that help students obtain a measurably deeper conceptual understanding, generally fail to spur signifcant epistemological development. Apparently, students can participate in activities that help them learn more efectively without refecting upon and changing their beliefs about how to learn efectively. These students may revert to their old learning strategies in subsequent courses. (Elby, 2001, p. S54, emphasis in original) In concluding his paper, Elby summarizes his reasoning as follows: First, the fact that so many excellent physics courses fail to foster signifcant epistemological change . . . suggests that isolated pieces of epistemologically focused curriculum aren’t enough. Instead, the epistemological focus must sufuse every aspect of the course. Second, the classroom atmosphere created by the instructor, and the way he/she interacts with individual students, undoubtedly plays a large role in fostering refection about learning. (Elby, 2001, p. S63) Students’ epistemological beliefs – their views about the nature of knowledge and learning – afect their mindset, metacognitive practices, and study habits in a physics course. Even the best reform curricula, however, have not been very successful at helping students develop more sophisticated epistemological beliefs. (Elby, 2001, p. S64) This conclusion must be connected immediately and explicitly to the context of learning to teach science, for it indicates that deliberate attention must be given to the epistemological beliefs of both students in schools and prospective science teachers. Redish (2021) makes this point explicitly in the context of teaching physics, and it seems natural to extend it to the context of teaching how to teach science: It helps to think about students’ expectations about the nature of the knowledge they are learning. These are internal stimuli that guide and constrain how students respond to learning in our classes. These expectations may include ideas like, “I know this class is about memorizing equations. I just have to fnd which equation has the right symbol in it” or “I know I have bad intuition about physics so I have to trust my math even if the result looks crazy.” I refer to this kind of expectation as epistemology – knowledge about knowledge. Students’ epistemological expectations can have profound efects on what they hear and how they think about what they’re learning. (p. 316) Thus, we submit that this perspective of epistemological expectations is relevant not only to the learning of science concepts but particularly to the learning of educational concepts in terms of beliefs that future science teachers bring to a preservice program. 1173

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To provide credibility to this extension, we turn to an argument by McGoey and Ross (1999), both secondary science teachers, in which they provide a vivid account of student resistance to conceptual change and the complex teaching skills needed to negotiate it. We suspect that almost every teacher who has used a [conceptual change] model in the classroom has borne the brunt of student anger, frustration, and criticism. Students do not like having their ideas elicited in a nonjudgmental manner, only to have those ideas revealed as inadequate (whether it be mere seconds or days later). Some students eventually just stop giving their ideas. “Don’t express your thoughts, wait until someone else does, wait for the right answer to be transmitted and memorize it.” Dealing with this without disafecting students emotionally and intellectually requires delicate, precise, and theoretically sound skills of the teacher. (p. 118) The challenges continue when students respond in ways that indicate that they do hold signifcant epistemological beliefs: The really messy stuf appears when the teacher gets a range of diferent (though adequate) models from the students. Now the fat is really in the fre. If the teacher refuses to give a single answer, positivist-minded students demand the right answer. Give a single answer and you may promote positivism. Give them a few rules (beware logical empiricism!) and the students interpret it as carte blanche for relativism or conventionalism. Another response of students is to challenge the teacher’s practice outright. These attacks assert that since everybody knows that science is simply a universal body of facts and methods, just give us the recipe and tell us the answer so we can study for the test. (McGoey & Ross, 1999, pp. 118–119) These two teachers then extend their discussion to teacher education and to the stress that future teachers experience when cognitive confict arises from relying extensively on content knowledge. Again, we see that epistemological assumptions about teaching and learning are implicit. Teacher interns are often deeply troubled to have their content knowledge questioned. They are already nervous enough about whether they can get in front of 30 adolescents for 80 minutes. . . . Content knowledge is often their major life-saving device. When student teachers engage in action research activities that undermine overreliance upon content knowledge, they experience considerable distress. The experience is extremely unsettling. (McGoey & Ross, 1999, p. 119) To confront these challenging circumstances, science teacher educators must move well beyond transmission-style teaching practices. When we shared these quotations with a former student, the phrase “considerable distress” attracted the following comment: It brings back so many thoughts and feelings about teaching and learning [from my frst two years of teaching]. The section about the “considerable distress” experienced by student teachers when they confront the research which “undermines overreliance upon content knowledge” was particularly evocative.  .  . . There was maybe some additional context missing from that quote – I remember that it wasn’t just the cognitive dissonance that was distressing, but also all of the external pressures that push teachers towards less efective teaching techniques. (L. Brown, personal communication, June 19, 2021) 1174

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Here we are reminded that both science teachers and science teacher educators continue to face multiple pressures that impede or limit any eforts to encourage metacognition and other transformative teaching procedures in science and science teacher education classrooms. Windschitl (2002) has provided a powerful and practical list of constructivist teaching strategies that ofer excellent ways to begin to improve the quality of learning in their classrooms. When used by science teacher educators in teacher education programs, the efects of these strategies can be experienced personally and directly by those learning to teach science. When the efects are then identifed explicitly and explored metacognitively, future science teachers could gain access to both theoretical and practical bases for using them in classrooms of their own. • • • • • •

• •

Teachers elicit students’ ideas and experiences in relation to key topics, then fashion learning situations that help students elaborate on or restructure their current knowledge. Students are given frequent opportunities to engage in complex, meaningful, problem-based activities. Teachers provide students with a variety of information resources as well as the tools (technological and conceptual) necessary to mediate learning. Students work collaboratively and are given support to engage in task-oriented dialogue with one another. Teachers make their own thinking processes explicit to learners and encourage students to do the same through dialogue, writing, drawings, or other representations. Students are routinely asked to apply knowledge in diverse and authentic contexts, to explain ideas, interpret texts, predict phenomena, and construct arguments based on evidence, rather than to focus exclusively on the acquisition of predetermined “right answers”. Teachers encourage students’ refective and autonomous thinking in conjunction with the conditions listed previously. Teachers employ a variety of assessment strategies to understand how students’ ideas are evolving and to give feedback on the processes as well as the products of their thinking. (p. 137)

Here we suggest that readers compare Windschitl’s list to the list (in Narrative Box 36.4) of lessons learned from experience by one beginning science teacher.

Narrative Box 36.4 Lessons Learned in the First Two Years of Teaching 1. You will never be able to solve all of your students’ problems; however, you can give them 75 minutes every day where they feel comfortable and safe. 2. Students learn to problem solve by understanding the problem and making connections to previous knowledge/experiences/skills. 3. It is possible to talk to every student in the class every day. 4. The content of your class is not the most important thing in the student’s life, nor should you pretend that it is. 5. Students are afraid to take risks and make mistakes. They should be given this opportunity regularly in a safe environment, so they get comfortable taking risks, making mistakes, and learning from them. 6. If you fnd an activity boring, the students will as well. If you are excited to try something, the students will be as well. Easy tends to be boring. 7. School community is as important as classroom community. 8. Be your students’ number one fan. They may not get the praise or acknowledgment from anyone else. (D. Kernaghan, personal communication, May 26, 2020)

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This extended discussion of conceptual change in the context of learning to teach science has provided an overview of major arguments with respect to conceptual change in science teaching as well as an indication of practical challenges and ways to address them. We believe that Elby’s (2001) attention to epistemological beliefs is essential for progress beyond frequent but largely inefective calls for changes to how science is taught in schools and in teacher education programs. We turn next to consideration of research that proposes a knowledge integration framework to guide the learning of science and learning to teach science.

Conceptual Change in a Knowledge Integration Framework Linn and colleagues (Linn, 2006; Linn & Eylon, 2006; Linn, 2008; Linn et al., 2008; Linn & Eylon, 2011) have synthesized a great deal of research on conceptual change by building a knowledge integration framework that yields important recommendations both for the teaching of science and for learning to teach science. Linn (2008) introduces knowledge integration in the following way: An emerging group of researchers advocates a “knowledge in pieces” or “knowledge integration” view. This group suggests that students build individual ideas in various ways and that these ideas may exist alongside each other in a repertoire. . . . This group generally respects the intellectual work that produced the ideas and argues that the goal of instruction is to introduce more powerful ideas, as well as to encourage students to inspect, distinguish, and evaluate the ideas in a way that leads to some form of reconciliation or coherence. (p. 695) The knowledge integration framework calls for capitalizing on students’ ability to make sense of scientifc phenomena by empowering them to distinguish among ideas, consider new ideas, and promote the most promising ones. (p. 702) Linn and Eylon (2006) describe ten “design patterns”, each of which is based on “four interrelated processes of knowledge integration. . . . Instruction typically interleaves the four processes, moving among them rather than following a linear sequence” (p. 523). The four processes are “elicit or generate ideas from repertoire of ideas, add new ideas to help distinguish or link ideas, evaluate ideas and identify criteria, [and] sort out ideas by promoting, demoting, merging, and reorganizing” (p.  526). They then proceed to list ten design patterns “that best represent current research on instruction” (p. 525). The ten patterns for instruction are “orient, diagnose, and guide; predict, observe, explain; illustrate ideas; experiment; explore a simulation; create an artifact; construct an argument; critique; collaborate; refect” (pp. 525–534). Research points to four main processes that work together to promote knowledge integration: eliciting ideas, adding ideas, developing criteria, and sorting out ideas. Instruction often neglects the processes of developing criteria and sorting out ideas. . . . The activity sequences making up the design patterns represent essential elements in successful instruction. Design patterns transcend disciplinary knowledge, but raise questions about which design patterns are successful for which disciplinary topics. (p. 536) When the four processes and ten design patterns are set out in a table (p. 526) that makes explicit how each of the four knowledge integration processes contributes to each of the ten design patterns, teachers are provided with a robust guide to the knowledge integration approach. 1176

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Linn and Eylon (2011) also list four principles that promote knowledge integration: “make science accessible, make thinking visible, help students learn from others, [and] promote autonomy” (p. 109) The knowledge integration perspective connects conceptual change and efective instruction. . . . Variability in student ideas is [seen as] fundamentally a valuable feature and . . . instruction designed to capitalize on the variability and the creativity of student ideas has potential for facilitating conceptual change. The knowledge integration framework does not advocate unguided discovery or radical constructivism, but rather argues that understanding that students generate new ideas to make sense of science leads to important and essential design decisions. (Linn, 2008, p. 715) In our interpretation of the work of Linn and her colleagues, principles and patterns for learning to teach science can also serve as principles for learning to teach people how to teach. The knowledge integration framework can guide science teacher education just as powerfully as it can guide the teaching of science. Given Northfeld’s (1998, p. 698) focus on “appropriate conditions for [new teachers] to learn about teaching”, we see the four processes and ten patterns of the knowledge integration framework as a sound basis for providing appropriate conditions for learning about teaching and learning.

Recurring Themes in Research on Learning to Teach Science Anderson and Mitchener’s (1996) extensive review of research on science teacher education provides a strong foundation for the issues of learning to teach science that are explored and developed in this chapter. They describe a “traditional model” of preservice science teacher education that has three elements – educational foundations, methods courses, and feld experiences and student teaching. Anderson and Mitchener conclude their review with these powerful statements: Looking back, this three-pronged traditional model of preservice teacher education has survived relatively intact since its birth in the normal school. . . . The challenge facing science teacher educators today is this: how will you address in a coherent, comprehensive manner such emerging issues as new views of content knowledge, constructivist approaches to teaching and learning, and a refective disposition to educating teachers. In addition, thoughtful science teacher educators need to attend to the theoretical orientation of their programs and how important professional issues are addressed within these orientations. (Anderson & Mitchener, 1996, p. 19, emphasis added) These reviewers went on to identify six dominant themes in research on the preservice curriculum in the 20th century: an “established preservice model”, “inadequate subject matter preparation”, “haphazard education preparation”, the “importance of inquiry”, “reliance on the laboratory”, and “valued educational technologies” (pp. 21–22). We fnd little to indicate that these dominant themes have changed since publication. Anderson and Mitchener describe criticisms directed at the traditional model and then ofer important conclusions: One would expect a review of preservice science teacher education programs to portray a rich landscape, complete with diverse views, cohesive images, and defned detail. Research on these programs, however, is neither accessible nor diverse. Indeed, there is a dearth of literature describing preservice science teacher education programs. . . . Actual portrayals of comprehensive programs – including conceptual and structural components – are rare. 1177

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Diferences that do exist among programs are most often found at the course level. Innovative eforts in reforming science teacher preparation usually are directed at changing one or two isolated components within a program, as opposed to the program as a whole. (p. 23) Our review of literature available since Anderson and Mitchener’s review leads us to the conclusion that the six dominant themes they identifed continue to appear in research related to learning to teach science, despite many subsequent arguments for change and reform in science education and in preservice teacher education. We turn now to a little-known but powerful example of research that has changed how science is taught in a range of Australian classrooms. Here we do fnd a “rich landscape”, “cohesive images”, and “defned detail” of the work of teachers striving to improve the quality of student learning.

The Project for Enhancing Effective Learning The Project for Enhancing Efective Learning (PEEL) is a unique example of a teacher-directed, teacher-sustained collaborative action research. PEEL is a comprehensive school-based program for improving the quality of teaching in schools. With supportive links to nearby universities, PEEL began in 1985 in one school in the western suburbs of Melbourne, Australia. The key issues were deceptively simple: The major aim of PEEL is to improve the quality of school learning and teaching. Training for this improvement is centred on having students become more willing and able to accept responsibility and control for their own learning. Training has three aspects: increasing students’ knowledge of what learning is and how it works; enhancing students’ awareness of learning progress and outcome; improving students’ control of learning through more purposeful decision making. (Baird & Mitchell, 1986, p. iii) Overtly, PEEL aims to improve the quality of both learning and teaching. Thus, it is a comprehensive program of in-service professional development for teachers as well as a project for enhancing efective student learning. Rather than criticize students for their poor learning tendencies, teachers can reward students for good learning behaviors, and thus help students develop from being passive to being metacognitive in their stance toward their own learning. One example involves the contrast between the poor learning tendency, “Staying stuck”, in which a student sees no alternative but to ask for and wait for help from the teacher, and the good learning behavior, “Refers to earlier work before asking for help”. How many students use “waiting for the teacher’s help” as an excuse to do nothing but wait? The shift in perspective for both teacher and student is positive when students are taught that there are constructive alternatives to “staying stuck”. In this interpretation of PEEL, a central element involves reframing the activities of teachers and the activities of students within the classroom context. Most teachers are aware of numerous reform eforts that seek to change how teachers and students interact. The power of PEEL resides in its recognition of the fact that much learning in schools is passive. PEEL challenged teachers to develop teaching procedures that promoted metacognition. The result of several decades of such development is an extensive and thoughtfully organized array of specifc and practical procedures for the various small steps that are inevitably involved in working to a larger goal. The following list of Principles of Teaching for Quality Learning developed within PEEL illustrates many of the larger goals to which science teachers (and all teachers) should aspire. We see these principles as summaries of the craft knowledge 1178

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developed by teachers who participated in the project. Interestingly, these principles address many of the concerns about learning explored by Willingham (2009 from a cognitive science perspective). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Share intellectual control with students. Look for occasions when students can work out part (or all) of the content or instructions. Provide opportunities for choice and independent decision-making. Provide a diverse range of ways of experiencing success. Promote talk that is exploratory, tentative, and hypothetical. Encourage students to learn from other students’ questions and comments. Build a classroom environment that supports risk-taking. Use a wide variety of intellectually challenging teaching procedures. Use teaching procedures that are designed to promote specifc aspects of quality learning. Develop students’ awareness of the big picture: how the various activities ft together and link to the big ideas. 11. Regularly raise students’ awareness of the nature of diferent aspects of quality learning. 12. Promote assessment as part of the learning process. (Mitchell et al., 2004) As attractive as these principles might appear to the professional eye of the experienced teacher, they are broad principles that emerged within PEEL after years of work developing and sharing collections of specifc teaching procedures. To present such a list to a beginning science teacher with no teaching experience would accomplish little in terms of changing beliefs and actions. Using such a list to help beginning teachers interpret their early teaching experiences could contribute to promoting conceptual change. When teacher educators model PEEL principles in their classrooms, then future science teachers can experience their efects directly. We maintain that, as powerful as modeling new practices can be, it also requires the metacognitive step of engaging in explicit discussion to complete the contribution to conceptual change. Narrative Box 36.5 illustrates the impact of modeling in a science education classroom, as revealed in blogging with a frst-year science teacher. Notice the metacognitive stance in the dialogue.

Narrative Box 36.5 The Impact of Explicit Modeling and Encouraging a Metacognitive Stance [After completing his teacher education program, Liam Brown taught at a school in Mexico for two years. He wrote in a blog almost daily and Tom responded regularly. His analysis of that experience reveals the value of a teacher educator listening actively to a frst-year teacher.] Our blog posts reveal some distinctive patterns. I used the blog to articulate my thoughts, clarify my thinking and track my progress. Tom’s open ear and experienced-based advice provided much-needed emotional support; in looking over our posts, it is clear that he was efective in helping me keep my own professional goals in mind as I battled through the day-to-day challenges of teaching. Although classroom management was a constant struggle for me, and although [students] consistently performed below my expectations, I learned important lessons about forging positive and respectful relationships. . . . My struggle to teach students who presented so many obstacles to their own learning reinforced my pedagogical ideas and challenged me to fnd many new strategies to encourage active learning in a physics classroom. The most important realization I arrived at during my time in Mexico involves a deep understanding about what I wanted people to take from my class. [We] often discussed the idea of focusing on what students will remember 5 years later. In [Tom’s] words, it is not what you teach but how you teach that is most likely to be remembered. In his education class, that meant modeling the kind of teaching and learning he hoped we would try to achieve in our classrooms. In my physics class, that meant modelling the kind of respectful relationships, intellectual curiosity and active learning that I hoped my students would continue to pursue throughout their lives. (Brown & Russell, 2012, p. 27, emphasis added)

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We recommend White and Mitchell’s (1994) article as a practical account of the links between PEEL, metacognition, and improving the quality of learning. We turn next to the topics of knowledge acquisition and construction in learning to teach. White and Gunstone (2008) have contributed to the conceptual change literature from the perspective of the teaching of science. Their analysis of the development of research on alternative conceptions and conceptual change indicates that some research “implies that it is inevitable that, for many phenomena, people will acquire a primitive model, which is not totally discarded on later learning of the scientifc explanation” (p. 627). We maintain that much the same is true of beginning teachers, who enter an initial teacher education program with a primitive model of how teaching and learning occur and relate to each other. “One approach to alternative conceptions and conceptual change would be to get learners to be refective, open to new beliefs, and able to recognize contradictions between beliefs and resolve them” (p. 627). Schön (1983) gave considerable impetus to the “teacher as refective practitioner” perspective with his distinction between problem solving and problem setting (pp. 39–42). His point is that we often focus solely on problem solving without taking time to analyze a situation in terms of problem setting – how we are conceptualizing the problem we wish to solve. Reframing problems to develop and enact new approaches became an attractive image for teachers thinking about their work. The argument has intrinsic appeal in the context of teacher education and learning to teach, and it readily extends to the conceptual change approaches so often advocated in the science education community. We suggest that it is the context in which an individual confronts a problem that will determine whether a primitive model or a more complex and robust model will be used to guide problem setting and problem solving. Linn (2008, pp. 694–695) has also drawn a contrast between “extinguishing” and “distinguishing” students’ beliefs, pointing out that some researchers suggest that students can replace one set of beliefs with another, thereby “extinguishing” the primitive beliefs, while other researchers (herself included) prefer to assume that the introduction of new ideas can enable students to analyze conficting ideas, “distinguishing” them from one another with a view to achieving coherence. The knowledge integration framework clearly focuses on the richer and more metacognitive task of teaching students how to identify, evaluate, and distinguish beliefs. While Linn and colleagues do not address explicitly the place of laboratory or practical work in science teaching, Abrahams and Millar (2008, p. 1967) explored the issue of the efectiveness of practical work and drew conclusions that are consistent with a knowledge integration perspective: Given the clear importance in any practical task of helping the students to do what the teacher intends with objects and materials in the limited time available, “recipes” are likely to continue to have a significant role in science practical work. If, however, the scale of the cognitive challenge for students in linking their actions and observations to a framework of ideas were recognized, teachers might then divide practical lesson time more equitably between “doing” and “learning”. These do not, of course, have to be rigidly separated, but teachers need, on the basis of our data in this study, to devote a greater proportion of the lesson time to helping students use ideas associated with the phenomena they have produced, rather than seeing the successful production of the phenomenon as an end in itself. Here we draw several conclusions about learning to teach science that are important to our overall argument. 1. 2.

The learning processes of students, future teachers, experienced teachers, and teacher educators have much in common. Conceptual change, or reframing, is central for the student of science and the future teacher of science, just as it is for experienced science teachers and science teacher educators. 1180

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3.

4.

Reframing, or conceptual change, involves processes that cannot be made deliberate or sequenced in a timetable. Reframing appears to occur for individuals who have the confdence to actively seek new ways of seeing their learning, be it learning of science or learning how to teach science. Making explicit the metacognitive dimensions of reframing and conceptual change contribute signifcantly to the quality of learning.

This concludes our account of important arguments about acquiring and constructing knowledge in the context of learning to teach. Programs for learning to teach science continue to operate on patterns guided more by tradition than by arguments such as these.

Developing New Habits and Frames of Mind for Learning to Teach Science Early in the 20th century, when schools as we know them today were frst established and all children were expected to attend school, society appears to have been satisfed with approaches that are today characterized by terms such as transmission, absorption, and instructionism. As Hoetker and Ahlbrand (1969) concluded in their article about “the persistence of the recitation”, teacher–student patterns of interaction have been highly stable and often expect students to be passive. Those who would improve the quality of learning in school classrooms often use terms such as metacognition, active learning, engagement, deep learning, transformative learning, and most recently, knowledge integration. Linn and Eylon (2011) suggest that a traditional teaching approach that settles for absorption is inadequate and fails to meet the needs of students or their teachers: Expecting students to absorb information implies that their pre-existing ideas are of limited value. Furthermore, most students are cognitive economists, seeking to use their cognitive resources economically. When instruction emphasizes absorption, it sends the message that students do not need to evaluate evidence critically or attempt to reconcile apparent contradictions. Indeed, the absorption approach may convince students to avoid making sense of science at all. (p. 7) They set out their argument in favor of knowledge integration in these terms: When absorption fails, it is common to argue that (a) students are not sufciently motivated or do not work hard enough, (b) students need to develop a larger vocabulary, master some set of facts or details, or develop more powerful reasoning skills before they can understand the material, or (c) students are inhibited by misconceptions or naïve ideas that interfere with their ability to absorb the new knowledge. The absorption approach guides the design of most textbooks, lectures, and even laboratory experiences. (p. 4) As Linn and Eylon (2011, p. 10) have indicated, the knowledge integration framework builds on a large body of research on the topic of learning by inquiry. In “inquiry-rich environments, students often participate in the full inquiry cycle including posing their own questions, gathering and analyzing data, and evaluating evidence against hypotheses and theories” (Jeong & Songer, 2008, p. 180). The stance toward teaching taken by Linn and colleagues seems highly compatible with perspectives in the recent work of Hattie (2008, 2012), in which he makes a strong, research-driven appeal to teachers to focus frst and foremost on their impact on the learning of their students. Where Linn and colleagues describe ten design patterns, Hattie argues for “multiple ways of knowing”, “multiple 1181

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ways of interacting”, “multiple opportunities for practicing”, and “knowing that we are learning” (pp.  101–102). Hattie summarizes his analysis of teachers’ work by presenting eight mind frames (pp. 160–168). “The claim is that teachers and school leaders who develop these ways of thinking are more likely to have major impacts on student learning” (p. 160). “Frames of mind”, “mind frames”, and “ways of thinking” are three phrases calling attention to the importance of how teachers think about their work from a big-picture or metacognitive perspective. “Frames” is a potentially useful term because it fts well with Schön’s (1983) term, “reframing”, whereby an unexpected or puzzling student response might trigger a new way of thinking that in turn suggests a possible new response to a student or situation; Schön called this “refection-inaction”, where the crucial phrase is “in action”. Reframing seems much more likely to occur in the context of classroom action than in a lecture or reading about classroom action. When a new action meets up with teachers’ and students’ existing, well-established patterns of classroom behavior, the potential for confict and tension is obvious. If we remind ourselves that these typical patterns of behavior are habits – habits that were learned originally by observing teachers during many years as a student – then what we know about the difculties of changing habits (Duhigg, 2012) tells us that both frames and habits must be considered in any attempt to develop the professional skills of a teacher. Narrative Box 36.6 illustrates the surprise, reframing, and new actions that occur in a professional’s refection-in-action.

Narrative Box 36.6 Refection-in-Action in a Science Practicum Placement It’s Friday now in my Grade 10 science class, and things have not signifcantly improved since Wednesday. I’ve been trying to remember all the little things, but it’s proving a lot harder than expected to break out of my way of thinking. Also, the scramble between classes is turning out to be another unconsidered hurdle. I’m teaching ionic bonding; today’s lesson is about ionic compound nomenclature. The driest of dry, and difcult because it requires an explicit understanding of ionic charge. I’m still not feeling very confdent in front of the class. . . . I’m teaching and the concepts remain confusing for the students; many cry out in protest. These details are not the point, only context, to probably the most critical incident of my entire practicum. One of my students, always vocal and gregarious, but frequently pushing the line when it came to respecting authority, beckoned me over after the lesson. He proceeded to declare that he, thanks to his observant nature, had noticed me habitually glancing over at Mr. X [my mentor teacher] while I taught. He then asked me why I kept looking over and if I had even noticed what I’d been doing. I gave a polite response but was surprised that I had not been aware of all the faces watching me while I’d been doing it, nor had I realized why I’d been doing it until that instant. Clearly, things weren’t going as well as I wanted them to. I’d been looking over at Mr. X for some sort of look of reassurance that what I was doing was all right, that I wasn’t making a completely mockery of myself up there. But that insecurity, that tacit need for approval, was noticeable and, I’d argue, detrimental to the students. As much as I wanted to care for their needs, my frst instinct was to fnd a way for my own needs to be met. This moment had a profound impact on me. I realized that if I wanted to gain control over the class, to teach for the students instead of myself, I had to shrug of my insecurities, I had to believe in myself as a teacher, and I had to tackle the class with more focus and purpose than what came naturally from me. I managed to do just that. I put myself aside, gave up on worrying about approval, and took steps to actually learn. That class turned around, and I’ve never felt more confdent than when I was in front of that class by the end of week 4. The students, who I knew were just pushing to see how far they could cross a line, all learned to respect me and work with me. I developed a strong rapport with them even though it didn’t come easily. I was able to implement new strategies and teach in a way that gave them opportunity for continual practice, which led to tangible improvements in their learning. (B. Courtin, personal communication, March 17, 2017, emphasis added)

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Bullock’s (2011) detailed analysis of the professional learning of fve science teacher candidates as they moved between a science methods class and various practicum experiences gives further insight into the complex interaction of prior views of teaching and learning, new frames of mind, and the development of productive habits for science teaching. A series of case studies of science teacher educators analyzing their own teacher education practices (Bullock & Russell, 2012) sheds further light on the complex interaction between frames and habits. As both Hattie and Linn suggest, one of the major diferences between absorption and knowledge integration as frames for thinking about learning is the demand that knowledge integration places on teachers; making student learning visible means that teachers must do far more listening to students than is required by an absorption mindset. Cook-Sather (2002) put the issues very clearly: The work of authorizing student perspectives is essential because of the various ways that it can improve current educational practice, re-inform existing conversations about educational reform, and point to the discussions and reform eforts yet to be undertaken. Authorizing student perspectives can directly improve educational practice because when teachers listen to and learn from students, they can begin to see the world from those students’ perspectives. (p. 3) Listening to students’ voices may be one of the most challenging new habits required of those learning to teach science; they have neither the mindset nor the experiences of being listened to by their former teachers. The authorizing of student perspectives for which I am arguing here is not simply about including students as a gesture. It is about including students to change the terms and the outcomes of the conversations about educational policy and practice. Such a reform cannot take place within the dominant and persistent ways of thinking or the old structures for participation. (p. 12, emphasis added) From the arguments presented, we conclude that creating contexts for the development of new habits and mind frames, in part by giving voice to students (Featherstone & Grade 10 students, 1997), is essential if we are to create better conditions for learning to teach science.

Creating Better Conditions for Learning to Teach Science In this section we explore the question of how teacher educators challenge their students’ conceptions of science and the extent to which prospective teachers have been schooled in fnal-form science. White (2001) has suggested that the two decades from 1980 to 2000 produced a revolution in research on science teaching: “The change in the amount of research is sufcient alone to warrant the term revolution, but even more signifcant is its nature” (p. 457, emphasis in original). Against a background of revolution, the foreground ofers clarion calls for reform and the improvement of science education (e.g., American Association for the Advancement of Science, 1989, 1993, 2001; Council of Ministers of Education, Canada, 1997; Curriculum Corporation, 1994a, 1994b; National Research Council, 1996, 2006). Prominent among the recommendations are changes in science classrooms whereby instruction is situated in a context that supports students’ explorations of questions that develop deeper understandings of science content and processes and encourages learners to share developing ideas and information (Crawford et al., 1999; Krajcik & Blumenfeld, 2006). More broadly, reform eforts urge closer attention to students’ conceptions of the nature of science and scientifc inquiry (Lederman, 1998). Lederman makes the case that, unless teachers have a functional understanding of these concepts, there is little hope of achieving the vision of science teaching and 1183

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learning that is detailed in the reform literature. It is but a small step to argue that these types of understandings must be embedded in teacher education programs if prospective teachers are to move beyond the rhetoric of reform and become scientifcally capable themselves and enable their students to do likewise. Hodson (1998), building on the discussion document of the Scottish Consultative Council on the Curriculum (1996), describes scientifc capability as far more than the acquisition of scientifc knowledge, understanding, and skills. “It also involves the development of personal qualities and attitudes, the formulation of one’s own views on a wide range of issues that have a scientifc and/ or technological dimension and the establishment of an underlying value position” (p. 3).

Learning Science as a Discipline Duit and Treagust (1998) relate learning science to the conceptions held by students and teachers of science content, conceptions of the nature of science, the aims of science instruction, the purpose of teaching events, and the nature of the learning process. The complexity of the construct “learning science”, with its multiple components, points to many of the issues that confound science teacher education. These include the tenacity of students’ conceptions about science and scientifc inquiry as well as the tenacity of their experiences learning science – the procedural aspects, in addition to the propositional, the pedagogy they were exposed to in their science classes, and the (subconscious) interpretation they attached to it. Lederman (1998) contends that science education reforms have presented an ambiguous picture when it comes to scientifc inquiry. Inquiry can be perceived as (1) a set of skills that students learn and combine when they undertake scientifc investigation, (2) a cognitive outcome that students should achieve that involves not just “doing” but “knowing why they are doing”, or (3) a pedagogy scafolded on the belief that students need to do science as opposed to learning about science and learning how to do science. Too often it is only the pedagogical aspect of inquiry that science teachers glean from reform documents, “with the two former senses lost in the shufe” (Lederman, p. 17). Here, then, is an avenue that science teacher educators need to pursue. Unless prospective teachers can discriminate between and among these perspectives, they will be hard-pressed to efect inquiry in their own classrooms that does little more than reproduce the scientifc method, at worst, or that absents knowing from doing, at best. Seeing science as a discipline that is continually questioning itself is predicated on justifcation of knowledge claims. When students develop and cling to a fnal-form view of science, it becomes clear that the tentative nature of scientifc-knowledge claims is poorly understood and authoritarian views of science as absolute truth and fnal form prevail (Duschl, 1990).

The Signifcance of the Discourse of Science Without careful attention to the particular language of science that is used to understand the world, scientifc understanding cannot be had. Without the particular and technical language that science uses to constructs its worldview, “science is unthinkable” (Martin, 1990, p. 115). Wildy and Wallace (1995) see good science teachers as valuing the structures and conventions of the discipline and as teaching for understanding by helping students to accept and use scientifc language and protocols. This entails identifying how scientifc language is used to explain, classify, and decompose and making explicit how its protocols and conventions are used to both defne and structure the discipline. Costa (1993) likens school science to a rite of passage where students are inducted into membership of a scientifc community by virtue of familiarity with and understanding of its specialized language and agreed-upon procedures. Two points arise: (1) for some students, this rite of passage may more closely approximate trial by fre: We hardly do anything except copy notes that the teacher has written (not our own words) and do experiments that the teacher does for us.  .  .  . All we do is sit there and watch 1184

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demonstrations and listen to the teacher talk. Everyone just sits there and looks like they’re listening. I hate science. (Baird et al., 1990, p. 13) and (2) whether school science can claim to approach induction into a scientifc community has been the subject of some debate: When science is removed from contexts that match and support its goals of inquiry and experiment, its character can change. School science is distinct from experimental science because it is practiced in an institution whose goals are not the goals of science, and so school science becomes an inauthentic representation of experimental science. (Munby et al., 2000, p. 208) Put most simply, science cannot be learned – or taught – in the absence of its discourse. Similarly, we contend, the teaching of science cannot be learned – or taught – in the absence of its discourse. Programs of science teacher education must always struggle to avoid the criticism that its version of learning to teach science has become an inauthentic representation of the teaching of science in schools. The knowledge integration framework ofers powerful support for those who would create better conditions for learning to teach science. Linn (2008) identifed fve central issues of conceptual change that need to be considered collectively: Memory and forgetting as infuences on the trajectory of understanding, factors afecting rate of student development, the signifcance of the learning context, distinguishing types of explanations of scientifc phenomena, and explaining student responses to instructional activities that promote understanding and self-monitoring (pp. 695–702). The knowledge integration perspective connects conceptual change and efective instruction. It suggests that research on conceptual change will be most informative when combined with instructional investigations.  .  .  . Variability in student ideas is fundamentally a valuable feature and .  .  . instruction designed to capitalize on the variability and the creativity of student ideas has potential for facilitating conceptual change. The knowledge integration framework does not advocate unguided discovery or radical constructivism, but rather argues that understanding that students generate new ideas to make sense of science leads to important and essential design decisions. (Linn, 2008, p. 715) In closing this section, we return to Windschitl (2002), who provided strategies that can be used to explore metacognitively each constructivist strategy that is modeled by teacher educators in their classrooms: • • • • •

Why this is an important practice In what context it should be used How it can be adapted to their students and their classroom context How it should be combined with other, perhaps more traditional strategies (direct instruction, for example) How it relates to the assessment of learning (pp. 160–161)

Windschitl comments that use of these strategies “means that teachers should be able to both relate the practice back to its origins in learning theory (to some degree) and project the likely consequences (pitfalls and benefts) of using the technique with their students” (p. 161). Narrative Box 36.7 illustrates how one individual learning to teach science was in the early stages of developing constructivist teaching strategies. 1185

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Narrative Box 36.7 Professional Learning Described Metacognitively by a Preservice Science Teacher The following is a metacognitive analysis of my personal and teaching habits as well as my mind frames regarding how students learn. It is intended to provide deeper insight into my development over the past 2 months, as my analyses of my own teaching and learning have led me [to] discover some of the profound lessons that all teachers should know. Firstly, I learned that teachers need to have their cake and eat it too. Secondly, I realized that rewards can actually hurt students. During my frst practicum I really embraced active learning (Knight, 2004). I did lots of POEs (predict-observe-explain) and I incorporated several PEEL (Project for Enhancing Efective Learning) procedures into my lesson plans. Sadly, when I got back to classes at Queen’s I couldn’t say how much my students had actually learned. That insight was very disorienting. What grounded me again was a connection to Hattie’s (2012) description of how a “passionate, inspired teacher” (p. 24) plans lessons: by focusing on the learning that needs to happen before thinking about how to conduct the lesson. Accordingly, for my next practicum I consulted the science curriculum document (Ontario Ministry of Education, 2008) to fnd the expectations that I would be responsible for teaching. Then I focused on having “the mind frame to foster intellectual demand, challenge, and learning” (Hattie, 2012, p. 35). And . . . it worked! Students learned relativity well. I became a focused, determined, exhausted teacher. With all my focus on the learning, I had lost sight of the various methods of teaching. Still I had made tremendous strides toward connecting with the students. . . . To address my methodlessness, I revisited the PEEL procedures and discovered a whole new world of pedagogical insights. No longer was this just a database of diferent teaching methods; it was a tool box with various procedures to fx learning problems. . . . The idea of “working with” students aligned seamlessly with Ian Mitchell’s talk about sharing intellectual control with students. This idea also extends beyond teaching content, even though it has content-learning implications . . . implications that I have felt, myself, when I was given trust and decisionmaking power over my own learning. The most important efect was on HOW I learn. Under such conditions, not only was my learning more enjoyable, but also the intrinsic value was amplifed by the fact that I wanted the learning that I had decided to pursue to be valid. I want my students to have that kind of enjoyment – the pleasure of fnding things out. . . . I’m sure that if I can present any content as an interesting problem, then students can have choices by being given autonomy over how to solve the problem. That way they can learn more than what’s on the page; they can learn why it’s worth being on the page in the frst place. . . . I need to give them freedom to decide where they see potential value. Nevertheless, I am a leader in the classroom and my attitude toward what they are learning will afect their interest as well as the value they place in the subject. So, if I can focus on the learning as well as on how to teach, aford students the respect and choices necessary to encourage vulnerability and risk-taking, and also set an example for the kind of person I want students to be, then I can discover more ways of helping students learn. (M. Brown, personal communication, February 12, 2013, emphasis added)

The Challenging Role for Teacher Educators Thus far, we have illustrated the process of learning to teach science from many diferent perspectives. Here we speak directly to some of the challenges that follow for the role of the science teacher educator. We begin by reiterating the importance of metacognition and listening to those learning to teach. Pajares (1992) makes a powerful case for attending to the beliefs of all science teachers and teacher educators, anchoring them as central to professional learning and understanding one’s professional practice. I come full circle now to restate my premise. Attention to the beliefs of teachers and teacher candidates can inform educational practice in ways that prevailing research agendas have not 1186

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and cannot. The study of beliefs is critical to education precisely because, as Kagan (1992) concluded, “the more one reads studies of teacher belief, the more strongly one suspects that this piebald of personal knowledge lies at the very heart of teaching” (p. 85). If the hesitancy of many researchers to study beliefs and of educators to make them a focus of teaching and teacher preparation has been due to, as one colleague put it to me, the concern that beliefs are “messy” things, I suggest that the construct is less messy, far cleaner, and conceptually clearer than it may appear. When they are clearly conceptualized, when their key assumptions are examined, when precise meanings are consistently understood and adhered to, and when specifc belief constructs are properly assessed and investigated, beliefs can be . . . the single most important construct in educational research. (Pajares, 1992, p. 329) Accepting Pajares’s position and fnding new and appropriate teaching practices will challenge many teacher educators, but the challenge must be embraced. We are confdent in stating that learning how to teach science will not improve until teacher education practices change. Loughran (2008) has summarized quite clearly the importance of reframing teacher education to meet the realities faced by those learning to teach science. It is time for teacher educators to explicitly frame teacher education as being problematic in such a way as to illustrate that what they know, how they know it, why it matters and what it looks [like] in practice is central to that which might be described as quality in teaching about teaching. Teacher educators are daily confronted by issues associated with . . . the tacit nature of teaching . . .; the theory-practice gap . . .; the complexity of practice . . .; [and] the tensions between developing students’ authority of experience and the allure of their authority of position. . . . It is time for the work of teacher educators to boldly shape that which is the problem of teacher education and to respond in positive, well informed and meaningful ways. Seriously pushing ahead with an agenda for the development of a pedagogy of teacher education, I would suggest, is one such way. (p. 1181, emphasis in original) There is more than enough evidence that the time is now for reframing our science teacher education practices. We hope that this chapter makes a useful contribution to the efort to improve the experiences of those learning to teach science.

The Importance of Metacognition in Learning to Teach Science: A Case Study In this penultimate section of the chapter, we ofer insights gained from our shared personal experiences in the 16-month preservice teacher education program at Queen’s University. In Tom’s last year before retirement (2018–2019), he planned a self-study of his teaching of a physics methods course (72 hours in two 2-hour classes per week over 8 months, with practicum periods interspersed) and obtained ethical clearance from the university (Russell et al., 2020). In the role of critical friend, Andrea attended about half of those classes. Tom planned three signifcant changes from previous teaching: (1) invite all students to be participants in a self-study of how he was teaching them; (2) replace the terms “theory” and “practice” with book knowledge and craft knowledge; and (3) use the last 15 minutes of every class to discuss what had been learned and how it was learned. On the frst day of the course, Tom explained how and why he would be studying his own teaching and invited students to be participants; all 13 agreed and signed letters of consent. The students’ responses confrmed Tom’s longstanding conviction (craft knowledge) that a teacher who wants to introduce practices unfamiliar to students should do so in the frst week of the school 1187

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year. Tom was certainly the only teacher educator who announced that he planned to study his own teaching during his course. Andrea’s presence at many of the classes was unusual but readily accepted. Andrea became well known to the students for her thoughtful comments and questions, and she was asked at times to comment on issues related to her expertise in special education.

Change 1 – Studying His Own Teaching On Day 1, Tom explained that he had obtained ethical clearance for a self-study of his teaching practices throughout the course; all students signed the consent form to participate. Two weeks before the end of the course, Tom asked students to respond in writing to four questions about the course. He was particularly interested in their responses to the frst-day introduction of his self-study, so he placed it second in the list of four to avoid suggesting it was the question of greatest interest. Other questions asked about their big-picture ideas from the course, about what they expect to do diferently as a result of the course, and about whether the professor had pushed their thinking too much or too little. During the course, Tom made little reference to the self-study; Andrea’s frequent presence and the daily use of a video camera could have reminded them of the study. Tom was impressed that their responses indicated such positive reactions to the idea; of course, he was pleased that they linked it to the importance of their studying their own teaching. The reference to the signifcance of modeling teaching practices in class seemed particularly important, as teachers are so easily criticized for not practicing what they preach.

Change 2 – Using New Terminology In the frst class, Tom suggested replacing the familiar terms “theory” and “practice” with the terms “book knowledge” and “craft knowledge”. He used book knowledge in place of McIntyre and Hagger’s (1996) propositional knowledge to keep the terms as clear and straightforward as possible. Tom has long accepted teacher candidates’ views that practicum experience is more powerful than the content of many education courses. Framing class discussions in terms of two diferent categories of knowledge seemed likely to improve understanding of the tension between courses and practicum experiences. When teacher candidates returned from their frst practicum (7 weeks), discussion focused on their development of craft knowledge. Later, in Class 17 (midpoint of the course), they

Table 36.1 Teacher Candidates’ Comments About Their Professor’s Self-Study In our second class on the frst day we met in September, I explained that I was planning to study my own practice in my last year as a teacher educator. Everyone signed a consent form to participate. Do you remember what your reaction was to that idea? Has it afected how you have looked at this course over the last 8 months? • I remember being blown away by this idea. . . . The idea of still trying to study yourself and learn and experiment with new concepts even in your fnal year earned a lot of respect. • My reaction was to be extremely impressed that he is studying his teaching the year before he retired. He is one of the only [professors] in this program who practices everything he preaches and as a result I have deeply respected both him and this course from day one. • A mix of confusion and inspiration! Inspiring that you were willing and enthusiastic to continue studying your practice. It set the tone for the year as well, demonstrating the importance of recording our own attitudes and practice. • It has helped me look at this course as only the beginning of my development as a teacher. You continuing to study your own practice has helped me to see this course as a starting point to continuously develop my own teaching.

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Learning to Teach Science Table 36.2 Teacher Candidates’ Comments About Book Knowledge and Craft Knowledge • They both represent teachers’ essential knowledge and understanding. Both terms gave me some ideas on what I should aim to learn and how I can learn them. Understanding craft knowledge helped me to transform everyday experience during the practicum into intuitive and refective learning and thus bring positive changes and stronger results on my performance. • The terms book knowledge and craft knowledge have been critical to my understanding of the teaching process. This is because they delineate two concepts which are quite distinct, yet integral to the art of teaching. . . . The concepts that we learned in class (book knowledge) could not impart craft knowledge, and this became clear immediately upon teaching my frst lesson during my practicum. • Using the terms book knowledge and craft knowledge has been helpful to me because they distinguish between two modes of knowledge that we use to help navigate the world of teaching. Craft knowledge allows us to construct a model of how to teach from experience based on difering circumstances, whereas book knowledge is too vague to be of any sort of practical importance. At the point where book and craft knowledge contact is where it has been most important for me as an educator.

spent an hour in class typing responses to a list of previously prepared questions, one of which asked about the value of the terms book knowledge and craft knowledge. Table 36.2 provides examples of their responses. The initial impact seemed positive, as the term “craft knowledge” seemed intuitively related to practice and required little explanation. All who chose to write about the use of these two terms at the midpoint of the course spoke positively about them. Written work submitted through the course often used both terms spontaneously.

Change 3 – Ending Each Class With Discussion This practice was successful from the outset but became much richer as one class led to another. At the halfway point of the course, the students asked if they could set the agenda for these discussions, and as a group they quickly developed a list of topics. Serendipitously, none of the students had a class immediately following Tom’s class, and many (sometimes all) began to stay longer to continue the discussion. Some discussions ran for 60 minutes or more; the very last discussion ran for 150 minutes. Many commented that this had become the only opportunity in their 16-month program where they could discuss issues that were important to them. The theme was always metacognitive – sharing experiences that linked to what they were learning and how they were learning it. Just after the midpoint of the course, Tom was absent from two classes to consult at a university in Chile. As he prepared for this absence, he implicitly assumed that the students would meet in his absence and participate in activities planned by the group. All 36 classes were video recorded, providing Tom with the opportunity to see that all but one student attended the frst of his two absences; the discussion included the fact that many felt that they had to attend just to see how many others would attend. For most, it was their very frst experience attending a class with no teacher present. Table 36.3 illustrates comments about the practice of ending each class with a signifcant period of (metacognitive) discussion. While the data that emerged in the course of studying his practice could be read as trying to please their teacher, the overall experience of the year, with students committing themselves so strongly to the end-of-class discussions, suggested that the comments were genuine. Andrea’s participation in many of these discussions in the role of critical friend provided a second way to confrm the authenticity of the comments. After four weeks of classes and seven weeks in a practicum placement, the group seemed to have accepted that the discussion was for them, not for their teacher. Once they had shared and analyzed 1189

Tom Russell and Andrea K. Martin Table 36.3 Comments About End-of Class Discussions • The end-of-class discussions (and accompanying exit cards) have been excellent for consolidating my teaching experiences and take-aways from class. They have allowed me to capture essential book knowledge during a discussion or lesson and refect on craft knowledge from my practicum. • I have found the end-of-class discussions to be one of the most valuable contributions to my program so far. The relaxed, informal tone of the discussions allows us to feel comfortable bringing up anything we wish to discuss. • I appreciate the informality of it all. In so many other courses there are pressures to withhold one’s true feelings about a certain topic or how they’re feeling. I think these ending discussions provide an exceptional environment where we, as teacher-candidates, can speak freely about what we fnd useful and fnd a support system that validates our feelings/anxieties about teaching. • The discussions allow for time to touch on the main points over again. Personally, I fnd I remember more from these discussions than I do for any other class because we have time to understand, think, and discuss what we have learned. • The discussions allow me and other teacher candidates a sense of ownership in the class and learning. With that, I feel more engaged in learning. • Having these discussions has been one of the only helpful things that I’ve taken from the program thus far. • I feel like the end-of-class discussions are where I think the most critically and deeply about my educational beliefs.

their practicum experiences, the discussions tended to turn to issues that they were unable to discuss in other classes. The time tended to focus on big-picture perspectives on their learning to teach and on learning to teach science. Participants ranged from those with strong opinions to those who rarely spoke, but all found the discussions to be productive. For many years, Tom had ended every class by asking students to use the last two minutes to write comments on a small piece of paper (a “ticket out of class”) about their most important learning and what they wanted to understand better. The formal end-of-class discussions were an extension of that earlier practice. Introducing the practice in the frst class was obviously essential. Ultimately, these three changes proved to be the most productive changes in practice of Tom’s career in science teacher education, a form of craft knowledge that paid rich dividends. We have concluded that deliberate moves to foster metacognitive activities are essential features of courses focused on learning to teach science.

Conclusion Many people are pessimistic about the prospect of actually moving beyond the rhetoric of reform. In this chapter, our goal has been to show that moving forward requires an epistemological and conceptual revolution, reframing not only how we think about teaching science but also how we think about learning to teach science. Progress requires and demands that perspectives that move us forward in teaching science be extended to the context of learning to teach science. The two contexts tend to proceed independently because of the university’s implicit epistemology that suggests that research fndings can and should be passed on to practitioners, verbally or in text. This implicit epistemology fails to acknowledge how practitioners learn, while also failing to acknowledge that those who teach in universities are practitioners as well as researchers. We concur with Schön’s (1995) long-neglected call for a new epistemology that still needs to be explored and developed both in universities and in schools. A way forward involves accepting both epistemologies, the traditional epistemology of book knowledge and the epistemology of practice associated with craft knowledge (Russell & Martin, 2017). Although it continues to be easy to pin the hopes for improved teaching of science on those who are just entering the teaching profession, research on conceptual change suggests that this approach 1190

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is fundamentally fawed. Experienced teachers and teacher educators who ask of new teachers what they have not attempted themselves are ignoring the reality that we learn to teach more by what is modeled than by what is told. Our review of literature about learning to teach science suggests that, in general, science teacher educators continue to be reluctant to practice in their own teaching the procedures and strategies that research suggests. This reluctance is consistent with the university’s longstanding and persistent emphasis on research and publication, with little reward for careful attention to one’s teaching. Similarly, we have observed that many tenured faculty in teacher education programs seem to avoid and are not rewarded for practicum supervision, where they could observe and participate in the development of future teachers’ craft knowledge. Teachers in schools are learning that action research is a way to explore in practice the challenges of teaching for conceptual change. Similarly, teacher educators must explore the same challenges of teaching for conceptual change and developing metacognition in their own practice as they work with those learning to teach science. The growing literature of self-study of teacher education practices provides both guidance and illustration (Loughran et al., 2004; Kitchen et al., 2020). Buaraphan (2011) ofers a statement that summarizes many of the issues we have explored concerning beliefs, metaphors, conceptual change, and the need to model exemplary teaching and learning. This statement also points the way forward both for teacher educators and for those learning to teach. Metaphorical schemas held by pre-service science teachers are sometimes mismatched with the goals of teacher preparation programmes and the learning reform movement. Teaching and learning innovations that confict with student teachers’ beliefs are often met with resistance and doubt. . . . One major responsibility of science teacher educators is to help student teachers’ shift their metaphorical schemata to align with the goals of teacher preparation programmes and learning reform. In doing so, science teacher educators should provide ample opportunities for student teachers to critically refect on, and become aware of, their teaching and learning beliefs, and to evaluate them in the light of the goals of learning reform. Science teacher educators must model exemplary teaching and learning for student teachers, and they must encourage students to believe that those exemplary approaches are practical, plausible, and achievable, at diferent stages in their lifelong teaching career. (Buaraphan, 2011, p. 1591, emphasis added) Buaraphan emphasizes the importance of student teachers changing their metaphors to frame in new ways both problems and their roles as science teachers. We must remember that those learning to teach science begin teacher education programs not as blank slates but with well-formed referents and metaphors developed over years of watching their own teachers. While much of our attention has focused on the individual science teacher educator and the individual learning to teach science, Labaree (2004) reminds us of the challenge on the institutional level, calling on schools, colleges, and faculties of education to act at the organizational level. An ed school must be profcient at developing both theoretical and practical understandings of education and must work vigorously to establish viable links between the two. The ideal is to encourage the development of teachers and other educators who are truly “refective practitioners,” able to draw on theory to inform their instructional practice. The fip side of this aim is to encourage university professors to become practice-oriented theoreticians, able to draw on issues from practice in their theory building and to produce theories with potential use value. That is the ideal. But no one would argue that education schools . . . come close to meeting this ideal. The natural tendency . . . is to fall on one side of the border or the other with 1191

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only weak crossover ability, rather than to hold the middle ground and retain the ability to work well in both domains. (Labaree, 2004, p. 204) One way forward is to replace the references to theory and practice, or to theoretical and practical understandings of education, with the terms such as book knowledge (or propositional knowledge) and craft knowledge. The conclusions reached by Hagger and McIntyre (2006) are incredibly helpful in this regard: Teaching expertise must be understood as it is found, embedded in the practice of individual teachers. The term we use to characterize this way of understanding expertise is professional craft knowledge. (p. 33, emphasis in original) For us . . ., professional craft knowledge is not rules of practice, but is instead all the complex, largely tacit knowledge that informs the contextualized professional judgements made by individual teachers in their daily practice. (p. 34) Hagger and McIntyre also ofer suggestions for what initial teacher education [ITE] should involve, including the following: • • •

•

Good practice will not be developed by student teachers through learning any easily accessible standard practices. Student teaching can only be the frst phase of a long process of developing classroom teaching expertise. Since classroom teaching expertise involves a very diferent kind of knowledge from that which student teachers are accustomed to learning in their academic studies, they are likely to need considerable help in learning how to learn it. It is not obvious that we have inherited carefully and appropriately conceived ways of helping student teachers to learn classroom teaching expertise. (p. 37)

Further, these authors provided a strong sense of the challenge confronting teacher education that genuinely seeks to be as helpful as possible to those learning to teach: The theory-into-practice conception of ITE that dominated the twentieth century is fundamentally fawed and needs to be replaced. The notion that student teachers should learn good theoretical ideas in universities, and then put them into practice in schools, is fawed in many ways but most obviously in that it is based on quite false conceptions of the nature of teaching expertise and of how such expertise is developed. . . . It is a conception of ITE that has scandalously, if less obviously, neglected the expertise of experienced teachers. (Hagger & McIntyre, 2006, p. 158) In closing, we have argued that improvements in learning to teach science call for attention to a wide range of perspectives. These include an examination of belief systems and conceptual change, modeling inquiry and more productive teaching strategies, and drawing students into metacognitive analyses of their professional learning. The entire argument for better conditions for learning to teach science always needs to complete the circle of reasoning about theory and practice, potentially 1192

Learning to Teach Science Table 36.4 Suggested Focal Points for Improving Learning to Teach Science Ideas for people learning to teach science

Ideas for those who teach people learning to teach science

• Learn how to learn from experience

• Share how you learned from experience and model the content you teach • Visit at least a few students during practicum placements to understand how they learn

• Review the extent to which your beliefs about teaching were forged by years watching your own teachers • Attend carefully to what you can learn from unexpected student responses during practicum placements (refection-in-action) • Gradually introduce metacognitive strategies into your teaching

• Share what you are learning from unexpected student responses in the course you are teaching • Seize opportunities to encourage and develop metacognition

reframed as book knowledge and craft knowledge. In the process, we must fnd ways to recognize and develop the authority of experience and the epistemology of practice within our teaching and learning practices.

References Abrahams, I., & Millar, R. (2008). Does practical work really work? A study of the efectiveness of practical work as a teaching and learning method in school science. International Journal of Science Education, 30(14), 1945–1969. American Association for the Advancement of Science. (1989). Science for all Americans. Oxford University Press. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. Oxford University Press. American Association for the Advancement of Science. (2001). Designs for science literacy. Oxford University Press. Anderson, R. D., & Mitchener, C. P. (1996). Research on science teacher education. In D. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 3–44). Macmillan. Ausubel, D. (1968). Educational psychology: A cognitive view. Holt, Rinehart & Winston. Baird, J. R., Gunstone, R. F., Penna, C., Fensham, P. J., & White, R. T. (1990). Researching balance between cognition and afect in science teaching and learning. Research in Science Education, 20, 11–20. Baird, J. R., & Mitchell, I. M. (Eds.). (1986). Improving the quality of teaching and learning: An Australian case study: The PEEL project. Monash University. Brown, C. L., & Russell, T. (2012). A collaborative self-study of a physics teacher’s frst two years of teaching. In S. M. Bullock & T. Russell (Eds.), Self-study, science teaching, and science teacher education (pp. 9–29). Springer. Bryan, L. A., & Abell, S. K. (1999). The development of professional knowledge in learning to teach elementary science. Journal of Research in Science Teaching, 36, 121–139. Buaraphan, K. (2011). Metaphorical roots of beliefs about teaching and learning science and their modifcations in the standard-based science teacher preparation programme. International Journal of Science Education, 33(11), 1571–1595. Bullock, S. M. (2011). Inside teacher education: Challenging prior views of teaching and learning. Sense Publishers. Bullock, S. M., & Russell, T. (Eds.). (2012). Self-studies of science teacher education practices. Springer. Carpendale, J., & Cooper, R. (2021). Conceptual understanding procedure to elicit metacognition with pre service physics teachers. Physics Education, 56(2), 025008. https://doi.org/10.1088/1361–6552/abc8fd Chinn, C. A., & Brewer, W. F. (1998). Theories of knowledge acquisition. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education, part one (pp. 97–113). Kluwer Academic Publishers. Cook-Sather, A. (2002). Authorizing students’ perspectives: Toward trust, dialogue, and change in education. Educational Researcher, 31(4), 3–14. Costa, V. B. (1993). School science as a rite of passage: A new frame for familiar problems. Journal of Research in Science Teaching, 30, 649–668. Council of Ministers of Education, Canada. (1997). Pan-Canadian protocol for collaboration on school curriculum: Common framework of science learning outcomes K-12. Author. https://science.cmec.ca/framework/index.htm

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Tom Russell and Andrea K. Martin Crawford, B. A., Krajcik, J. S., & Marx, R. W. (1999). Elements of a community of learners in a middle school science classroom. Science Education, 83, 701–723. Curriculum Corporation. (1994a). Science: A curriculum profle for Australian schools. Curriculum Corporation. Curriculum Corporation. (1994b). A statement on science for Australian schools. Curriculum Corporation. Dewey, J. (1938). Experience and education. Macmillan. DiSessa, A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225. Driver, R. (1989). Changing conceptions. In P. Adey, J. Bliss, J. Head, & M. Shayer (Eds.), Adolescent development and school science (pp. 79–99). Falmer Press. Duhigg, C. (2012). The power of habit: Why we do what we do in life and business. Doubleday Canada. Duit, R., & Treagust, D. F. (1998). Learning in science: From behaviourism towards social constructivism and beyond. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education, part one (pp. 3–26). Kluwer Academic Publishers. Duschl, R. A. (1990). Restructuring science education: The importance of theories and their development. Teachers College Press. Duschl, R. A., & Hamilton, R. J. (1998). Conceptual change in science and in the learning of science. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education, part two (pp. 1047–1065). Kluwer Academic Publishers. Elby, A. (2001). Helping physics students learn how to learn. American Journal of Physics, Physics Education Research Supplement, 69(7), S54–S64. Featherstone, D., & Grade 10 science students. (1997). Students as critical friends: Helping students fnd voices. In D. Featherstone, H. Munby, & T. Russell (Eds.), Finding a voice while learning to teach (pp.  120–136). Falmer Press. Fitzgerald, A., Pressick-Kilborn, K., & Mills, R. (2021). Primary teacher educators’ practices in and perspectives on inquiry-based science education: Insights into the Australian landscape. Education 3–13, International Journal of Primary, Elementary and Early Years Education, 49 (3), 344–356. https://doi.org/10.1080/0300427 9.2020.1854962 Franklin, U. (1994, November). Making connections: Science and the future of citizenship. Paper presented at the Meeting of the Science Teachers Association of Ontario. Grimmett, P. P., & MacKinnon, A. M. (1992). Craft knowledge and the education of teachers. In G. Grant (Ed.), Review of research in education (Vol. 18, pp. 385–456). American Educational Research Association. Guzetti, B. J., Snyder, T. E., Glass, G. V., & Gamas, W. S. (1993). Meta-analysis of instructional interventions from reading education and science education to promote conceptual change in science. Reading Research Quarterly, 28(2), 116–161. Hagger, H., & McIntyre, D. (2006). Learning teaching from teachers: Realising the potential of school-based teacher education. Open University Press. Handelsman, J., Ebert-May, D., Beichner, R., Bruns, P., Chang, A., DeHaan, R., Gentile, J., Laufer, S., Stewart, J., Tilghman, S. M., & Wood, W. B. (2004). Scientifc teaching. Science, 304, 521–522. Harrison, D. E. (2014). The evolution of a pedagogy: A preservice teacher’s learning experience. Unpublished Manuscript, Queen’s University. Hattie, J. (2008). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge. Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge. Hodson, D. (1998). Teaching and learning science: Towards a personalized approach. Open University Press. Hoetker, J., & Ahlbrand, W. P. (1969). The persistence of the recitation. American Educational Research Journal, 6(2), 145–167. Jeong, H., & Songer, N. B. (2008). Understanding scientifc evidence and the data collection process: Explorations of why, who, when, what, and how. In C. L. Petroselli (Ed.), Science education issues and developments (pp. 169–200). Nova Science Publishers. Kagan, D. M. (1992). Implications of research on teacher belief. Educational Psychologist, 27(1), 65–90. Kelly, G. (1995). The psychology of personal constructs. Norton. Kitchen, J., Berry, A., Bullock, S. M., Crowe, A. R., Taylor, M., Guðjónsdóttir, H., & Thomas, L. (2020). Second international handbook of self-study of teaching and teacher education practices. Springer. Knight, R. D. (2004). Five easy lessons: Strategies for successful physics teaching. Addison-Wesley. Krajcik, J. S., & Blumenfeld, P. C. (2006). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 317–333). Cambridge University Press. Labaree, D. F. (2004). The trouble with ed schools. Yale University Press. Lederman, N. G. (1998). The state of science education: Subject matter without context. Electronic Journal of Science Education, 3(2), 1–12. Linn, M. C. (2006). The knowledge integration perspective on learning and instruction. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 243–264). Cambridge University Press.

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Learning to Teach Science Linn, M. C. (2008). Teaching for conceptual change: Distinguish or extinguish ideas. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 694–722). Routledge. Linn, M. C., & Eylon, B.-S. (2006). Science education: Integrating views of learning and instruction. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 511–544). Lawrence Erlbaum Associates. Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. Routledge. Linn, M. C., Kali, Y., Davis, E. A., & Horwitz, P. (2008). Policies to promote coherence. In Y. Kali, M. C. Linn, & J. E. Roseman (Eds.), Designing coherent science education: Implications for curriculum, instruction, and policy (pp. 201–210). Teachers College Press. Lortie, D. C. (1975). Schoolteacher; A sociological study. University of Chicago Press. Loughran, J. (2006). Developing a pedagogy of teacher education. Routledge. Loughran, J. (2008). Toward a better understanding of teaching and learning about teaching. In M. CochranSmith, S. Feiman-Nemser, & D. J. McIntyre (Eds.), Handbook of research on teacher education (3rd ed., pp. 1177– 1182). Routledge. Loughran, J. J., Hamilton, M. L., LaBoskey, V. K., & Russell, T. (Eds.). (2004). International handbook of self-study of teaching and teacher education practices. Kluwer Academic Publishers. Martin, J. R. (1990). Literacy in science: Learning to handle text as technology. In R. Christie (Ed.), Literacy for a changing world (pp. 79–117). Australian Council for Educational Research. McDevitt, T. M., & Ormrod, J. E. (2002). Child development and education. Merrill Prentice Hall. McGoey, J., & Ross, J. (1999). Research, practice, and teacher internship. Journal of Research in Science Teaching, 36(2), 117–120. McIntyre, D. (1988). Designing a teacher education curriculum from research and theory on teacher knowledge. In J. Claderhead (Ed), Teachers’ professional learning (pp. 97–114). Falmer. McIntyre, D., & Hagger, H. (Eds.). (1996). Mentors in schools: Developing the profession of teaching. Routledge. Mintzes, J. J., Wandersee, J. H., & Novak, J. D. (1997). Meaningful learning in science: The human constructivist perspective. In G. D. Phye (Ed.), Handbook of academic learning: Construction of knowledge (pp. 404–447). Academic Press. Mitchell, I., Mitchell, J., McKinnon, R., & Scheele, S. (2004). PEEL in practice: 1100 ideas for quality teaching. PEEL Publishing. Munby, H., Cunningham, M., & Lock, C. (2000). School science culture: A case study of barriers to developing professional knowledge. Science Education, 84(2), 193–211. Munby, H., & Russell, T. (1994). The authority of experience in learning to teach: Messages from a physics method class. Journal of Teacher Education, 45(2), 86–95. Munby, H., Russell, T., & Martin, A. K. (2001). Teachers’ knowledge and how it develops. In V. Richardson (Ed.), Handbook of research on teaching (4th ed., pp. 877–904). American Educational Research Association. National Research Council. (1996). National science education standards. National Academy Press. National Research Council. (2006). Learning to think spatially. National Academy Press. Northfeld, J. (1998). Teacher education and the practice of science teacher education. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science teacher education (pp. 695–706). Kluwer. Novak, J. (1987). Human constructivism: Toward a unity of psychological and epistemological meaning making. In J. D. Novak (Ed.), Proceedings of the Second International Seminar on Misconceptions and Educational Strategies in Science and Mathematics (Vol. 1, pp. 349–360). Cornell University Department of Education. Novak, J. (1989). The use of metacognitive tools to facilitate meaningful learning. In P. Adey (Ed.), Adolescent development and school science (pp. 227–239). Falmer Press. Novak, J. (1993). Human constructivism: A unifcation of psychological and epistemological phenomena in meaning making. International Journal of Personal Construct Psychology, 6, 167–193. Novak, J., & Gowin, D. B. (1984). Learning how to learn. Cambridge University Press. Ontario Ministry of Education. (2008). The Ontario curriculum grades 11 and 12: Science. Queen’s Printer. Pajares, M. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of Educational Research, 62, 307–332. Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientifc conception: Toward a theory of conceptual change. Science Education, 66, 211–227. Redish, E. F. (2021). Using math in physics: Overview. The Physics Teacher, 318–322. https://doi. org/10.1119/5.0021129 Richardson, V. (1996). The role of attitudes and beliefs in learning to teach. In J. P. Sikula, T. J. Buttery, & E. Guyton (Eds.), Handbook of research on teacher education (2nd ed., pp. 102–119). Association of Teacher Educators. Russell, T., & Martin, A. K. (2017). Teacher education needs an epistemology of practice. In J. Mena et al. (Eds.), Search and research: Teacher education for contemporary contexts (pp. 111–118). Ediciones Universidad Salamanca.

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Tom Russell and Andrea K. Martin Russell, T., Martin, A. K., & Loughran, J. (2020). Introducing new practices in a teacher education classroom: Lessons learned, insights gained. In C. Edge, A. Cameron-Standerford, & B. Bergh (Eds.), Textiles and tapestries. EdTech Books. https://edtechbooks.org/textiles_tapestries_self_study/chapter_115 Sarason, S. B. (1996). Revisiting “The culture of the school and the problem of change.” Teachers College Press. Schön, D. A. (1983). The refective practitioner: How professionals think in action. Basic Books. Schön, D. A. (1995). The new scholarship requires a new epistemology. Change, 27, 27–34. Scott, P. H., Asoko, H., & Driver, R. (1992). Teaching for conceptual change: A review of strategies. In R. Duit, F. Goldberg, & H. Neidderer (Eds.), Research in physics learning: Theoretical issues and empirical studies (pp. 310–329). Schmidt & Klannig. Scott, P. H., & Driver, R. H. (1998). Learning about science teaching: Perspectives from an action research project. In B. J. Fraser & K. G. Tobin (Eds.), International handbook of science education, part one (pp. 67–80). Kluwer Academic Publishers. Scottish Consultative Council on the Curriculum. (1996). Science education in Scottish schools: Looking to the future. Author. Tudge, J., & Scrimsher, S. (2003). Lev S. Vygotsky on education: A cultural-historical, interpersonal, and individual approach to development. In B. J. Zimmerman & D. H. Schunk (Eds.), Educational psychology, a century of contributions (pp. 207–228). Lawrence Erlbaum Associates. Wandersee, J., Mintzes, J., & Novak, J. (1994). Research on alternative conceptions in science. In D. Gabel (Ed.), Handbook of research on science teaching and learning (pp. 177–210). Macmillan. Westaway, F. W. (1929). Science teaching: What it was – what it is – what it might be. Blackie & Son Limited. White, R. T. (2001). The revolution in research on science teaching. In V. Richardson (Ed.), Handbook of research on teaching (4th ed., pp. 457–471). American Educational Research Association. White, R. T., & Gunstone, R. F. (2008). The conceptual change approach and the teaching of science. In S. Bosniadou (Ed.), International handbook of research on conceptual change (pp. 619–628). Routledge. White, R. T., & Mitchell, I. J. (1994). Metacognition and the quality of learning. Studies in Science Education, 23, 21–37. Wildy, H., & Wallace, J. (1995). Understanding teaching or teaching for understanding: Alternative frameworks for science classrooms. Journal of Research in Science Teaching, 32, 143–156. Willingham, D. T. (2009). Why don’t students like school? Jossey-Bass. Willingham, D. T. (2016, March). Knowledge and practice: The real keys to critical thinking. Issue Briefs, 1, 1–8. https://knowledgematterscampaign.org/dig-deeper/#issuebriefs Windschitl, M. (2002). Framing constructivism in practice as the negotiation of dilemmas: An analysis of the conceptual, pedagogical, cultural, and political challenges facing teachers. Review of Educational Research, 72(2), 131–175. Zeichner, K., & Liston, D. (1987). Teaching student teachers to refect. Harvard Educational Review, 57(1), 23–49. Zembal-Saul, C., Krajcik, J., & Blumenfeld, P. (2002). Elementary student teachers’ science content representations. Journal of Research in Science Teaching, 39, 443–463.

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37 RESEARCH ON TEACHER PROFESSIONAL DEVELOPMENT PROGRAMS IN SCIENCE Gillian Roehrig

In educational circles, it is widely accepted that teacher professional development (TPD) can foster improvements in teaching practices, and consequently student learning (e.g., Kennedy, 2016; Loucks-Horsley et  al., 2003). TPD refers to formal processes and activities designed to improve teachers’ knowledge and change their classroom practices, with the ultimate goal of improving student outcomes (Guskey, 2003). However, TPD experiences vary widely, from short one-day workshops to sustained, multiyear TPD programs; unfortunately, most teachers still experience TPD as less than an eight-hour duration (Darling-Hammond et al., 2017), which has been shown to be inefective in promoting measurable educational change (Fullan, 2007). A signifcant body of literature from the past 25 years is driven by a consistent theory of change for efective TPD (Figure 37.1). This theory of change assumes that participation in TPD leads to changes in teacher quality, which in turn positively impacts student outcomes (e.g., Blank et al., 2008; Cohen & Hill, 2000; Fishman et al., 2003; Desimone, 2009; Garet et al., 2001; Kennedy, 1998; Loucks-Horsley & Matsumoto, 1999; Supovitz, 2001; Yoon et al., 2007). This simplifed model is prevalent within the education literature, including science education (e.g., Kleickmann et al., 2016; Yang, Porter et al., 2020). However, this model fails to acknowledge the complexity of teacher change (Clarke & Hollingsworth, 2002). The role of teacher beliefs within this theory of change is debated, most commonly used is the model by Guskey (1986), which argues that it is not until a positive efect on students occurs that teachers will modify their beliefs. However, the improved model still avoids the complexities and dynamic nature of teacher change and the complex role of beliefs in teacher learning. For example, Lotter et al. (2020) describe the role of beliefs as fltering teachers’ learning within the TPD, and their theory of change includes the infuence of teachers’ incoming beliefs and knowledge, as well as other contextual factors, on teacher learning within TPD, and thus teacher outcomes. The Interconnected Model of Teacher Professional Growth (IMTPG) (Clarke & Hollingsworth, 2002) provides a model of teacher change that refects the dynamic relationships between four domains: (1) personal domain of the teacher (knowledge, beliefs, and attitudes), (2) practice domain (instructional practices), (3) consequence domain (impact on salient TPD outcomes, including student learning), and (4) external domain (sources of information, stimuli, or support) (see Figure 37.2). The external domain acknowledges the role of TPD content, structures, and interactions, as well as external factors, such as national and state standards and policies, high-stakes testing, and district pacing guides and curriculum that defne the context in which the TPD operates. Clarke and Hollingsworth (2002) describe that change occurs through the mediating processes of refection and 1197

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External Standards and Policies, High-Stakes Tes
ng Changes in Teachers’ Atudes, Beliefs, and Knowledge

Teacher Professional Development

Changes in Teachers’ Classroom Prac
ces

Changes in Student Outcomes

Figure 37.1 Teacher professional development theory of change.

The Change Environment External Domain External Sources of Informaon Personal Domain

Domain of Pracce

Knowledge, Beliefs, and Atudes

Professional Experimentaon

Salient Outcomes Enactment Refecon

Domain of Consequence

Figure 37.2 Interconnected model of teacher professional growth (IMTPG) (Clarke & Hollingsworth, 2002).

enactment, represented by the arrows in Figure 37.2. Enactment describes the active role of the teacher both with the TPD as a learner and within their classroom as a teacher working to implement TPD strategies in practice. Clarke and Hollingsworth (2002) invoke Dewey describing refection as “active, persistent and careful consideration” (p. 954) of new knowledge and strategies. Thus, the goal of TPD is to structure learning experiences that create opportunities for enactment and refection, placing focus on the process of teacher learning and not just the measurable outcomes. This same body of literature that underpins models of teacher change was used in support of Desimone’s (2009) consensus framework that high-quality TPD shown to have an impact on teacher outcomes consists of the following characteristics: (1) content focus, (2) coherence, (3) active learning, (4) collective participation, and (5) duration. More recently, Darling-Hammond and colleagues (2017) reviewed studies in the literature across all felds, not just science education, that used quasi-experimental or randomized control research designs to consider the impact of TPD on student outcomes. They expanded Desimone’s (2009) framework to include the following seven characteristics of TPD: (1) is content focused; (2) incorporates active learning; (3) supports collaboration, (4) uses models, 1198

Teacher Professional Development Programs in Science Table 37.1 Characteristics of Efective Teacher Professional Development Characteristic

Description

Content-focused

Content-focus refers to what teachers learn during TPD. This consists of: (1) subjectmatter knowledge and (2) knowledge of how students learn that content. Coherence refers to the extent that TPD content is aligned with: (1) teachers’ knowledge and beliefs and (2) school, district, and state reforms and policies. Active learning addresses how teachers learn during TPD. Teachers should be directly engaged in the practices they are learning. Collaboration, refection, and inquiry are central to teacher learning. Curricular and instructional models and modeling of instruction help teachers to have a vision of practice. High-quality TPD creates spaces for teachers to share ideas and collaborate in their learning. Collective participation in school-based teams is a specifc form of collaboration that refers to the extent to which multiple teachers from the same school participate in the same TPD. Duration considers both the total hours of TPD and the length of time over which the TPD occurs. Efective TPD provides teachers with adequate time to learn, practice, and refect upon new learning, for example, working with a mentor or classroom coach.

Coherence Active learning

Grounded in efective models of instruction Collaboration and collective participation

Duration

and modeling of, efective practice; (5) provides coaching and expert support; (6) ofers opportunities for feedback and refection; and (7) is of sustained duration. There is considerable overlap between characteristics across and within these frameworks. For example, collective participation (Desimone, 2009) is a specifc form of the broader characteristic of collaboration (Darling-Hammond et al., 2017) and coaching (Darling-Hammond et  al., 2017) is a specifc form of follow-up to TPD extending the duration of learning (Desimone, 2009). Table 37.1 summarizes the consensus characteristics of efective TPD. Descriptions of these characteristics and the supporting research draw primarily upon the general education literature. However, throughout the rest of the chapter, these characteristics of quality TPD are considered within the specifc context of science education.

Review of Research Process This chapter builds upon prior comprehensive reviews of the science teacher professional development research literature (Luft & Hewson, 2014; van Driel et al., 2012). Both reviews included studies published through 2012 providing comprehensive overviews of TPD within science education up to the release of the Framework for K–12 Science Education (National Research Council [NRC], 2012) and the Next Generation Science Standards (NGSS Lead States, 2013). The Framework authors clearly state that: “The framework and subsequent standards will not lead to improvements in K-12 science education unless the other components of the system – curriculum, instruction, professional development, and assessment -change so that they are aligned with the Framework’s vision” (NRC, 2012, p. 20). They further articulate that TPD should “not only be rich in scientifc and engineering practices, crosscutting concepts, and disciplinary core ideas but also be closely linked to teachers’ classroom practices and needs” (NRC, 2012, p. 259). Given the call for increased TPD to assist schools and teachers in the implementation of the NGSS, this chapter focuses on literature published from 2013 to 2021. It is important to note that the NGSS is a US-based reform initiative; however, the related, rapid expansion of integrated STEM (science, technology, engineering, mathematics) is a global phenomenon (Freeman et  al., 2014). 1199

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STEM policy documents across the globe (e.g., Australian Curriculum, Assessment, and Reporting Authority, 2016; European Commission, 2015; Hong, 2017; NRC, 2012; NGSS Lead States, 2013) have also argued for interdisciplinary or integrated instruction rather than disciplinary-specifc approaches to the teaching of science, technology, engineering, and mathematics. Following the approach of van Driel and colleagues (2012), an extended literature search was conducted through databases including PsycINFO, Academic search premier, and Google Scholar for journal articles published from 2013 to the present. Search terms included those used in the 2012 review, professional development and science teacher(s)/teaching, and expanded to include engineering and STEM. In addition, four major science education journals (Journal of Research in Science Teaching, Science Education, International Journal of Science Education, and Journal of Science Teacher Education) were reviewed in an efort to capture any science education studies not located in the database searches. This initial search resulted in 290 articles. Again, guided by the parameters of the van Driel et al. (2012) review, the following criteria were used: 1. 2. 3. 4.

Studies included a formal TPD intervention for in-service K–12 science teachers (i.e., no studies of preservice teacher education or TPD for university faculty). Studies reported on TPD outcomes related to teacher learning and/or student learning. Studies included a rigorous methodology, and quantitative, qualitative, and mixed-methods studies were included. Studies described the TPD intervention sufciently to determine the presence of quality characteristics of TPD (see Table 37.1).

A review of abstracts using these criteria resulted in 157 articles. The full text of these articles was then coded for: (1) country, (2) TPD format (in-person, hybrid, or online), (3) grade band focus for the TPD program (PreK, elementary, middle, and high school), (4) TPD outcomes (teacher- and/ or student-focused), (5) TPD focus, and (6) presence and description of characteristics of quality TPD. Next, following van Driel et al. (2012), TPD outcomes were categorized using the domains from Clarke and Hollingsworth’s (2002) Interconnected Model of Teacher Professional Growth (IMTPG). Finally, the consensus characteristics of efective TPD (see Table 37.1) were summarized for each study.

Findings From Systematic Literature Review Notably, there has been a signifcant increase on the number of TPD studies specifc to science education since the release of the NGSS in 2013. van Driel et  al. (2012) reported on 44 studies (2007–2012), and Luft and Hewson (2014) reported on 50 studies (2002–2012) compared to 157 studies reported in this chapter (2013–2021); even accounting for the diference in the number of years included within each review, there is a noted uptick in studies on TPD. The fndings are structured to frst present broad features of TPD (country, grade-level focus (PreK, elementary, middle, and/or high school), and mode of TPD delivery (in-person, hybrid, and online). Second, fndings are presented by TPD outcomes (personal, practice, consequence, and external). Third, fndings are discussed by consensus characteristics of efective TPD.

Broad Features of TPD Studies As reported by van Driel et al. (2012) and Luft and Hewson (2014), the majority of published studies were conducted within the United States (80%), with the remaining studies conducted in Argentina, Australia, Austria, Canada (2), Chile, China, Germany (4), Greece, Jordan, Mauritius, Netherlands (2), New Zealand, Norway (3), Oman, Peru, Qatar, Singapore, Sri Lanka, Sweden, Taiwan, Turkey 1200

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(2), and the United Kingdom (3). This review also revealed diferences in teaching assignment grade levels supported through TPD: 5% of studies included PreK teachers, 38% included elementary teachers, 42% middle school teachers, 36% high school teachers (several TPD programs supported both elementary and middle school teachers or both middle and high school teachers, with seven TPD programs including teachers across all K–12). Finally, it was noted that while the majority of TPD programs are still conducted in-person, 12 studies described hybrid TPD formats, and seven studies described fully online TPD. None of these studies were in response to the COVID-19 pandemic; however, due to the pandemic it is expected that the feld will see an increase in online TPD.

TPD Outcomes In order to compare with previous systematic literature reviews, Table 37.2 reports the percentage of studies within each IMTPG domain, or group of domains, for the current review as compared to results from van Driel et al. (2012). A far higher percentage of studies focused solely on the personal domain (19%) or the practice domain (18%), compared to 7% and 9%, respectively, in van Driel et al. (2012). It is likely that this is related to the desire to understand knowledge, beliefs, and practices related to the new NGSS standards. As argued by Pleasants et al. (2020), an objective for many TPD programs “is to improve teachers’ understanding of the nature of the engineering discipline” as “teachers are unlikely to accurately teach what they do not understand, and given the rapid introduction of the NGSS, the feld of science education is facing an urgent need to provide inservice and preservice teacher education” (p. 363). In response, several TPD studies have explored the impact of TPD on teachers’ conceptions of STEM (e.g., Ring et al., 2017), teachers’ understanding of engineering and engineering practices (e.g., Pleasants et al., 2020), and the ways in which teachers engage students in the engineering design process (e.g., Billiar et al., 2014; Dare et al., 2018; Maeng et al., 2018; Nesmith & Cooper, 2019). Conversely, only 25% of studies included outcomes within the consequence domain, either separately or in combination with personal and practice domain outcomes, compared to 34% of previous studies exploring the consequence domain in conjunction with the personal and practice domains. However, it should be noted that only 13.5% of the studies reviewed by van Driel et  al. (2012) used direct measures of student outcomes with the other studies using indirect measures such as teacher report of impact on students. Unlike van Driel et  al. (2012), this review revealed several quasi-experimental and randomized control designs that studied the impact of TPD on student achievement. For example, Johnson and Fargo (2014) reported that participation in TPD focused on reform-based science practices, and culturally relevant pedagogy had a signifcant impact on student

Table 37.2 Categorization of TPD Outcomes IMTPG Domain(s)

Percentage of Studies (n = 157)

Percentage of Studies (van Driel et al., 2012) (n = 44)

Personal Practice Personal and practice Personal, practice, and consequence Consequence Practice and consequence External

19 19 25 9 10 6 12

7 9 50 34

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achievement in the treatment elementary school after two years compared to the control school. Similarly, Nichol et al. (2018) reported on the impact of a yearlong TPD on ffth-grade state science scores. Even though teachers were out of the classroom for 20% of the academic year, student test scores were not diferent to the control teachers. Most importantly, students in the treatment teachers’ classroom signifcantly outperformed the control group, suggesting student outcomes might not be realized until after the TPD as teachers solidify new teaching practices. Marshall and Alston (2014) used a quasi-experimental design to demonstrate the impact of an inquiry-focused TPD for middle school teachers in diverse schools on improving student learning for all students and a narrowing of the achievement gap for minority students relative to White students. While there is an increase in the number of large experimental design studies on the impact of TPD on student learning in science, the review revealed only one study that explored the impact of integrated STEM TPD on student learning in science (Robinson et al., 2014) and one study that explored student learning in engineering (Crotty et al., 2017). Studies such as these are important in terms of policy and showing generalizable impact on student learning, as well as teacher outcomes in the personal and practice domains. However, it is apparent that TPD research in the science education feld has focused almost exclusively on impact within the personal, practice, and consequence domains. Only 12% of the studies in this review incorporated the external domain and a subset of these incorporated an understanding of the processes of teacher learning related to the arrows with the IMTPG (Figure 37.2). For example, Sandholtz and Ringstaf (2016) investigated how contextual factors infuenced the sustainability of outcomes from a three-year TPD for early elementary teachers in rural schools that provided science assistance for K–2 teachers in small, rural school districts. Factors impacting sustainability of the TPD goals were principal and collegial support, resources, and personal commitment. Other studies explored facilitation and collaborative discussion within TPD. For example, Watkins et al. (2020) investigated instructor facilitation as critical driver of teacher learning and refection. They characterized the responsive nature of instructors’ facilitation within an online TPD in supporting teachers’ individual and collective inquiry. Tang and Shao (2014) used discourse analysis to explore a process referred to as lesson-polishing, similar to lesson study, and how engaging in this process infuences learning and the development of a community of learners. They described how the iterative and collaborative development of lessons facilitated learning, although teachers had to navigate the tendency to refne every lesson detail, overcome existing beliefs about teaching, and authoritarian voices to move their work forward. Finkelstein et al. (2019) used critical discourse analysis to explore the afective and relational work happening as they engaged in content deepening during the TPD. The analysis surfaced tensions between participants, as well as between participants and facilitators, related to three primary themes: “(a) questioning the value of lingering on one science topic; (b) expressing anxiety over being positioned as ‘smart’ or ‘stupid’; and (c) seeking clarity about course logistics” (p. 354). Alonzo and Kim (2018) explored the use of discussion of classroom video clips to support physics teachers’ content-specifc judgments about evidence of student thinking. The quality of the discussions was ultimately related to teachers’ judgments about student thinking. Teachers were able to engage in high-quality discussion with limited facilitation by challenging each other and moving each other’s thinking forward. Other studies explored the interactions between mentors and teachers. For example, Tolbert (2015) investigated how cultural facilitators engaged science teachers in refective conversations around culturally sustaining science instruction. She identifed four key themes (racism, relevance, relationships, and instructional complexity) from these mentoring conversations that could guide future work related to culturally responsible mentoring. McFadden and Roehrig (2020) explored the interactions between a coach and teacher participants designed to help develop integrated STEM curriculum. The primary focus was on discourse moves made by the coach that aforded or constrained teacher learning, and the study suggests coaching strategies for future integrated STEM 1202

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TPD. McNally (2016) studied the interactions of mentors and beginning secondary science teachers within the online induction program, e-Mentoring for Student Success (eMSS). Specifcally, they studied mentor–mentee interactions as they used video to explore aspects of teaching of interest to the mentee. Critical to the success of these mentoring sessions was a focus on impact on the mentee’s practices on student learning using evidence from the classroom video.

TPD Consensus Characteristics TPD Duration Three broad TPD structures were identifed across the 157 studies: summer meeting sessions, workshops, or “institutes” (21%); summer TPD with academic year follow-up (42%); and full academic year programs (37%). Duration varied from half-day or single-day workshops to intensive, long-term programs spanning multiple years (90–120 hours per year). Of the 157 studies, 11 TPD programs were less than two days in duration and another 20 TPD programs were one week or less in duration. Despite clear evidence that one-time, short-term science TPD has, at best, limited impact (Kowalski et al., 2020; National Academies of Sciences, Engineering, and Medicine [NASEM], 2015; Scher & O’Reilly, 2009), studies on short duration forms of TPD continue to be peer-reviewed and published. As noted by van Driel et al. (2012), details on the duration of TPD were unclear in many cases, making it difcult to determine the exact number of hours. This was particularly problematic for follow-up from a summer institute, which was often vaguely described using phrases such as “monthly follow-up meetings” or “follow-up classroom coaching throughout the year”. In a meta-analysis of science and mathematics TPD, Scher and O’Reilly (2009) found evidence supporting TPD lasting at least one year over one-time, short-term interventions. In a more expansive meta-analysis, Kowalski et al. (2020) examined 162 science PD intervention studies (including both in-service and preservice teacher outcomes). This meta-analysis coded the duration of the TPD using two measures: intensity (hours per week) and duration (weeks). Kowalski et al. (2020) found that intensity of science PD interventions ranged from 0.2 to 60 hours per week (M=6 hours per week) and duration ranged from 0.2 to 78 weeks (M=11.9 weeks). Additionally, their metaregression results showed that both higher intensity and longer duration predict larger average efect size estimates, suggesting that “an ideal duration of an intervention might range from 12 to 15 weeks, with a duration of 18 weeks or longer also signifcantly more efective than interventions of less than 3 weeks” (p. 452). In other words, TPD needs to extend beyond the summer, into the academic year to maximize efectiveness (Kowalski et al., 2020; Scher & O’Reilly, 2009). Some studies suggest that teachers who participate in a TPD for a second year have greater implementation of TPD practices with students in their classrooms (e.g., Lewis et al., 2015). In another recent meta-analysis of science and mathematics TPD, Lynch et  al. (2019) used a diferent approach to evaluating the impact of TPD duration, specifcally a continuous measure of contact hours with a separate measure for time span. They found that limited-duration TPD still had generally positive impacts on student outcomes (measured by standardized tests), particularly with programs that combined curricular materials with short TPD. For example, within the reviewed 157 studies, Spatz et al. (2019) described a half-day workshop that was successful in supporting teachers’ implementation of a new middle school Newtonian mechanics curriculum. Wiener et al. (2018) reported on a short-term TPD (two three-hour sessions) that introduced teachers to a new curriculum on the subatomic structure of matter, as well as a new assessment process using one-on-one student interviews. Their results show promise for this short-term TPD with teachers’ implementation of the student interviews in impacting teachers’ PCK with respect to the topic of subatomic structure. Lynch et al. (2019) posited that rather than extensive hours and duration of TPD, “the core condition for program efectiveness was valuable content; more hours of a given 1203

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intervention will not help if the intervention content is not useful” (p. 285). Very few studies have conducted in experiments to determine the impact of TPD duration on desired outcomes. In a rare example, Schuchardt et al. (2017) investigated the impact of reducing TPD contact hours on two student outcomes related to mathematical sense-making in biology classrooms (quantitative predictions and qualitative predictions). Their fndings showed that the educative curriculum materials alone without any TPD did not change student gains in qualitative predictions as compared to educative curriculum materials with TPD. However, student gains in quantitative predictions decreased signifcantly upon reducing the hours of TPD. In other words, the amount of TPD needed to support student-learning gains depends on the nature of the desired outcome. More studies of this nature are necessary to understand how the duration of TPD is connected to specifc teacher and student outcomes.

TPD Program Coherence Coherence is defned as “the extent to which teacher learning is consistent with teachers’ knowledge and beliefs” and “the consistency of school, district and state reforms and policies, with what is taught in professional development” (Desimone, 2009, p.  184). Current arguments for TPD within the Framework for –-12 Science Education (NRC, 2012) and NGSS (NGSS Lead States, 2013) articulate the signifcant shifts in teaching practices needed to achieve the goals of these current science education reforms (Reiser, 2013; Wilson, 2013). While many studies addressed teacher beliefs (e.g., Enderle et al., 2014; Kleickmann et al., 2016; Lotter et al., 2020) and teacher science content knowledge (e.g., Diamond et al., 2014; Greene et al., 2013; Sandholtz et al., 2016) in terms of teacher outcomes, no studies explicitly attended to prior knowledge and beliefs in terms of determining TPD structure and content. Kowalski et al. (2020) also noted in their meta-analysis of science TPD that not enough information was included to code for beliefs or district- or school-level policies. Thus, coding of papers reviewed for this chapter focused on coherence with state or national standards (e.g., NGSS). The 157 studies represented a range of TPD content foci (see Table 37.3). As reported by van Driel et al. (2012), TPD for science teachers is largely focused on current science reform eforts. Coherence to these reforms was evident; for US teachers PD content was aligned with either the National Science Education Standards (NRC, 1996) or NGSS (NGSS Lead States, 2013) and for non-US teachers PD content was aligned with relevant national curriculum guidelines. Studies included a signifcant focus on inquiry-based instruction (33%), as the sample includes studies that were completed pre-NGSS, as well as many studies focused on specifc aspects of NGSS, particularly

Table 37.3 Rank-Ordered Categorization of TPD Focus IMTPG Domain(s)

Percentage of Studies

Inquiry-based instruction Engineering and integrated STEM Discipline-specifc content (e.g., earth and space science topics, force and motion, nanotechnology) Equity, cultural relevance, and EL/MLL Specifc aspects of NGSS (e.g., argumentation, modeling, systems thinking) Technology and computational thinking NOS Formative assessment

33 23 14 10 9 4 4 3

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engineering content and practices (32%). Aligned with calls to close the academic achievement gap, there was also a growing focus on attending to equity and diversity within science classrooms (10%) (Bancroft & Nyirenda, 2020). Parallel to reforms with NGSS that call for the integration of engineering into science classrooms (NGSS Lead States, 2013; NRC, 2012), policymakers call for interdisciplinary approaches to science teaching (e.g., Moore et al., 2020; National Academy of Engineering [NAE] and NRC, 2014; National Science and Technology Council, 2018). These calls for interdisciplinary approaches to science teaching, or integrated STEM, concurrently called for TPD programs to support science teachers in the implementation of integrated STEM (e.g., NAE and NRC, 2014). Research related to STEM education, including TPD research, has been plagued by the lack of consensus on a conceptual framework and specifc characteristics of integrated STEM (Bybee, 2010; Sgro et al., 2020). This was evident within the 28 studies within this review that focused on interdisciplinary and integrated STEM approaches to science teaching. Without a widely agreed-upon framework for integrated STEM, it was unsurprising to see a wide range of approaches to TPD focused on STEM. For example, some studies treated STEM as coding, including activities such as robotic applications and LEGO-coding kits (e.g., Altan & Ercan, 2016; Nadelson et al., 2013). Others aligned their TPD with a STEM problem-based learning framework, defned as a project-based instructional approach using content from at least two of the four STEM disciplines (e.g., Han et al., 2015). Most common (17 studies) was an approach to STEM focused on an engineering design challenge (e.g., Guzey et al., 2014; Nesmith & Cooper, 2019; Ring-Whalen et al., 2018) drawing on frameworks such as the Framework for Quality K–12 Engineering Education (Moore et al., 2014). In other words, there is a lack of coherence within STEM TPD that make it difcult to draw conclusions about the efects of STEM TPD on science teachers’ curriculum and instruction.

TPD Content Focus While there is consensus across the literature that a core feature of quality TPD is a focus on content (e.g., Borko et al., 2010; Desimone, 2009), what constitutes as a focus on content varies across the research literature. In a recent meta-analysis of science TPD, Kowalski and colleagues (2020) defned a focus on content as the presence of content-deepening experiences (direct instruction on science content) and the use of curriculum materials to support K–12 classroom instruction. They coded specifc discussion of pedagogical strategies within a TPD separately, arguing that explicit instruction on pedagogical strategies is not a documented key feature of TPD. Following Desimone (2009), who defnes content focus as including both a focus on subject-matter content and how students learn that content, van Driel and colleagues (2012) argued for a focus on research on both teaching and learning grounded in pedagogical content knowledge, drawing upon evidence-based teaching strategies within the TPD (e.g., Yoon et al., 2007). Darling-Hammond and colleagues (2017) include content focus and the use of models of efective practice as separate characteristics of quality TPD, with content focus defned as TPD that focuses on “teaching strategies associated with specifc curriculum content [which] supports teacher learning within teachers’ classroom contexts” (p. v). Whereas the TPD characteristic of using models of efective practice calls for teachers to be provided with concrete examples of best practices, including lesson plans, student work examples, and teaching cases (video or written) Through the provision of such concrete examples, teachers are provided with instruction on pedagogical and instructional strategies. Thus, for the analysis of studies in this review, multiple diferent aspects of content focus were attended to: (1) use of curriculum materials (Darling-Hammond et al., 2017; Kowalski et al., 2020), (2) content deepening (Darling-Hammond et al., 2017; Desimone 2009; Kowalski et al., 2020), and (3) the inclusion of instruction on pedagogical strategies as part of the TPD content focus (Desimone 2009, NASEM, 2015; van Driel et al., 2012; Wilson, 2013). 1205

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Role of Curricular Resources and Materials Approximately 31% of the studies included curriculum materials as central to the structure of the TPD. These studies suggest that a critical component of efective TPD is situating teacher learning within educative curriculum (e.g., Kleickmann et al., 2016; Roth et al., 2019 Taylor et al., 2015). Simply providing teachers with a classroom curriculum is less impactful on student learning than when teachers have opportunities to engage in TPD with the curricular activities as learners with expert scafolding (e.g., Kleickmann et al., 2016). In addition, analysis of the TPD using curriculum materials in the current study revealed that these TPD were complex and used curriculum materials in ways that often expanded beyond simply science content deepening activities (e.g., teachers experiencing sample lessons as learners). For example, Furman et al. (2019) reported on a two-month TPD focused on improving PreK teachers’ questioning strategies during inquiry lessons. Their TPD program used two science curricular units, which were developed by the researchers based on early childhood inquiry frameworks. The curriculum was taught and videotaped by a classroom coach, and the videos were then used in the TPD to have teachers refect on the teacher–student conversations elicited by the questions. Each week, these teachers worked directly with the classroom coach to implement the curriculum and refect on their use of questioning. Gropen et al. (2017) described a similar TPD approach focused on developing PreK teachers’ PCK and reform-based teaching practices. They used weekly TPD sessions that modeled content and pedagogy with classroom-based assignments to implement curricular activities in their own classrooms with opportunities to video and refect on the curriculum implementation. Lotter et al. (2016) implemented content-deepening activities that allowed teachers to experience the project-based curricular lessons as students. These middle school curricular units engaged students in scientifc inquiry, developing explanations for the focal science concepts that were then applied to an engineering design challenge. These content-deepening sessions were complemented by explicit instruction on specifc pedagogical practices, for example, using McNeill and Krajcik’s (2008) Claim, Evidence Reasoning Protocol. Teachers also had opportunities to immediately practice these new strategies within the TPD after observing the TPD instructors teach the frst two lessons of the curriculum at a summer STEM program. Teams of teachers then planned, implemented, and refected on subsequent lessons. As can be seen in these examples of TPD supported by curricular materials, their use is complex since curricular materials are not only used as model lessons for teachers to experience as learners for content-deepening purposes. These high-quality TPD also integrated explicit instruction and modeling of specifc pedagogical strategies into the content-deepening activities. They also included opportunities for teachers to practice and refne these pedagogical strategies through the implementation of curricular activities with students, either using summer programs for TPD centered around a summer institute (e.g., Lotter et al., 2016) and/or in teachers’ classrooms for TPD embedded into the academic year (e.g., Furman et al., 2019; Gropen et al., 2017; Roth et al., 2019 Yoon et al., 2015).

Teacher Design Teams The majority of TPD programs not using prepared classroom curriculum materials engaged teachers in some level of lesson planning. There was a wide range of detail and time spent on lesson planning, but few studies provided rich detail about the nature of lesson-planning expectations. Several studies (17%) included extensive and formal curriculum-writing structures using teacher design teams (TDTs). Research documents the professional learning benefts from engaging in practice-oriented conversations (Boschman et  al., 2014; Huizinga et  al., 2014; Kelly & Staver, 2005; Peercy et  al., 2015). As teachers engage in curriculum design supported by a more-knowledgeable facilitator or 1206

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coach (Binkhorst et al., 2015; McFadden & Roehrig, 2020), they are deeply engaged in practiceoriented discourse. For example, Maeng et al. (2018) implemented a four-week summer TPD with 14 hours of follow-up during the academic year focused on STEM problem-based learning (PBL). Within the summer TPD, participants co-planned a PBL unit, which was implemented (including daily refections) within a summer STEM camp. Teachers also planned a PBL unit with teachers from their school, supported by a coach, which was implemented in their own classrooms during the academic year. An identical structure was used to support the development and implementation of integrated STEM curriculum for elementary and middle school science teachers (e.g., McFadden & Roehrig, 2020; Ring-Whalen et al., 2018). In these approaches, curriculum development followed TPD activities that introduced and modeled relevant frameworks designed to guide the work of the teacher development teams and support coherence with education reforms. A diferent approach placed the emphasis on curriculum development and the incorporation of science experts into the design team; much of the design work was conducted virtually over an extended period, with just-in-time content deepening and skills development provided for teachers (e.g., Hammond et al., 2019; Sgouros & Stavrou, 2019).

Content Deepening Two primary modes of content deepening were present in the TPD studies: (1) engaging in model lessons as learners and (2) engaging in authentic research. While engagement in curriculum development with TDTs could be argued to present opportunities for content deepening, it was focused on the application of specifc frameworks and the promotion of specifc classroom practices (e.g., McFadden & Roehrig, 2020; Ring-Whalen et al., 2018; Sgouros & Stavrou, 2019). While teachers attended to content knowledge in the development of curriculum units, content deepening was a secondary concern to the pedagogical knowledge and classroom practices, whereas participation in model lessons and authentic research opportunities have the explicit goal of content deepening.

Model Lessons Most common across the TPD studies was the use of TPD activities that engaged teachers as learners in modeled lessons (63%), often supported by TPD-provided curricular materials intended for future classroom implementation. Doppelt et al. (2009) clearly articulate the goal of content deepening through engaging teachers in lessons as learner, stating that the TPD facilitator should “challenge teachers’ naïve conceptions by engaging teachers in scientifc investigations, giving (counter-) examples, prompting analogies, and stimulating discussions” (p. 26). Many of the studies explicitly engaged teachers as learners in model lessons with the goal of addressing teachers’ content knowledge. For example, Dubinsky et al. (2019) described the impact on teachers’ content knowledge and classroom practices of a TPD that focused on learning neuroscience. These modeled lessons served a dual purpose: neuroscience content deepening as well as development of pedagogical understanding and strategies for implementing inquiry-based instruction. Rarely was lesson modeling focused only on teachers’ science content knowledge. For example, Cofre et al. (2017) and Mulveya and Bell (2017) focused on improving teachers’ knowledge of evolutionary biology and/or the nature of science (NOS), reporting only on gains in teachers’ content knowledge. TPD focused primarily on teachers’ content knowledge was most often in the form of engaging teachers in authentic research experiences, as described in the following section. Similarly, some studies used lesson modeling to focus primarily on teachers’ pedagogical practices. Most often these studies were focused on teachers’ development of specifc pedagogical skills, such as formative assessment (e.g., Furtak et al., 2016; Wylie & Lyon, 2015), argumentation (McNeill & Knight, 2013; Osborne et al., 2013), and technology integration (Blanchard et al., 2016; Campbell 1207

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et al., 2015; Ekanayake & Wishart, 2015). For example, Furtak et al. (2016) worked with a group of high school biology teachers over a three-year period to support teachers in developing formative assessment tasks. While the focus was on teachers rehearsing, enacting, and refecting on the implementation of collaboratively designed formative assessment tasks, this learning cycle started with an exploration of not only student ideas but also the teachers’ own understanding of the relevant scientifc concepts. Campbell et al. (2015) focused their TPD on enhancing teacher and student learning through technology integration strategies, such as informative and computational technologies (ICTs). The TPD used two educative curriculum modules to support teachers’ learning through exemplar technology integration.

Authentic Scientifc Research Thirteen studies included partnerships with STEM faculty to provide opportunities for teachers to engage in authentic scientifc research (e.g., Blanchard et al., 2008; Dixon & Wilke, 2007; Enderle et  al., 2014; Lederman & Lederman, 2019; Yang, Liu et  al., 2020). These studies were a formal Research Experience for Teachers (RET) program or based more loosely on the RET model. A typical RET consists of a six- to eight-week summer placement in a university laboratory or feld setting with the goal of providing teachers an authentic research experience. Teachers are expected to develop lesson or unit plans based on their research experience, which are typically presented in a showcase at the end of the RET program. As described by Grove et al. (2009), teachers “were left to extract the elements and relate them to their own students, school and curriculum so the RET experience was as valuable to each participant as possible” (p. 250). Enderle et al. (2014) described an alternative RET model that engaged teachers “in scientifc research and an in-depth, refective study of the learning that occurred as well as how to translate that learning into classroom teaching practice” (p. 1085). They compared this model with an equal focus on content and pedagogy to the traditional model focused almost exclusively on content, while both models were efective in improving teachers’ beliefs and self-efcacy related to reform-based teaching, the model with explicit focus on pedagogy had a great impact on teachers’ beliefs and was the only program to show a positive infuence on teachers’ classroom practices.

Summary Each of the TPDs that engaged teachers as learners in content deepening varied in the degree of focus on learning content knowledge vs. pedagogical strategies, with very few TPDs focusing exclusively on one or the other. In a recent meta-analysis, Kowalski et al. (2020) found that both curriculum materials and content-deepening TPD experiences “were positively correlated with efect size estimates, with the use of curriculum materials showing a stronger relationship with efect size estimates than science content deepening experiences”, although, neither was statistically signifcant. They suggest that the large number of studies including content deepening (95%) may make it difcult to detect variation. As noted previously, this meta-analysis coded instruction on pedagogical strategies separately to content deepening and reported a positive, but nonsignifcant, impact of pedagogical instruction on teacher outcomes. Lynch et al. (2019) also reported larger efects when TPD programs focused on how to use curriculum materials and both teachers’ content knowledge and how students learn that content. Like Enderle et al. (2014) and Lynch et al. (2019), Scher and O’Reilly (2009) reported that pairing pedagogical instruction with content deepening produced larger efect sizes than TPD that focused only on pedagogy or only on content. Clearly, additional attention is needed to understand the ways in which TPD engages teachers in content deepening and identifying fruitful approaches to integrating instruction on pedagogical strategies into content-deepening activities. Rarely do TPD studies provide enough detail about the TPD activities to develop more 1208

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fne-grained codes that would describe diferent approaches to content deepening, or indeed other critical characteristics of quality TPD. In other words, it is critical to consider the ways in which teachers are engaged in content deepening and pedagogical instruction. There are potential overlaps with the critical TPD characteristics of active learning and collaboration (Darling-Hammond et al., 2017; Desimone, 2009) that at least partially address how teachers engage in learning during TPD.

Active Learning Darling-Hammond et al. (2017) describe active learning as “an ‘umbrella’ element that often incorporates the elements of collaboration, coaching, feedback, and refection and the use of models and modeling” (p. 7). During TPD, active learning engages teachers in opportunities to engage in “the same style of learning they are designing for their students” (Darling-Hammond et al., 2017, p. v), actively construct their own understandings, and lesson planning to integrated content and teaching strategies modeled within the TPD (e.g., Borko et al., 2010, Darling-Hammond et al., 2017; Desimone, 2009; Wilson, 2013). It is difcult to separate the characteristics of active learning and content focus, approaches such as engaging teachers as learners in model lesson, authentic research, and curriculum writing are optimally designed to actively engage teachers in content deepening and/ or learning about pedagogical strategies. Rarely, however, are details provided about the nature of active learning, making it difcult to do more than simply code for the presence of active learning.

Video Analysis A notable TPD approach for engaging teachers’ in developing an understanding of specifc pedagogical strategies, in addition to engaging teachers directly in model lessons, was video analysis. Almost 12% of the studies in this systematic literature review reported on the inclusion of lesson analysis through video within their TPD (e.g., Alonzo & Kim, 2018; Kleickmann et al., 2016; Roth et al., 2019; Smithenry et al., 2013; Taylor et al., 2015; Tekkumru-Kisa et al., 2018; Watkins et al., 2018). NASEM (2015) argued for the inclusion of the lesson analysis through video as an additional characteristic of quality TPD. Kowalski et al. (2020) found too few experimental or quasi-experimental studies using video analysis to include this variable in their meta-analysis. However, this review provides strong evidence that the use of video-based lesson analysis within TPD produces positive outcomes for both teachers and students. For example, Roth et al. (2019) compared two TPD programs, both of which incorporated all features from the TPD consensus model (Desimone, 2009), with the treatment TPD (Science Teachers Learning through Lesson Analysis [STeLLA]) including videobased analysis-of-practice. The PD program resulted in stronger student learning outcomes than the content-deepening program. STeLLA signifcantly impacted teachers’ knowledge and practice in comparison to the content-deepening model. Within the STeLLA TPD, teachers were engaged in active learning through the analysis of teaching and learning using video cases of the associated curriculum enacted in teachers’ classrooms. Video clips were purposefully selected to align with a specifc teaching strategy from the conceptual framework that guided the TPD (Roth et al., 2011). The conceptual framework also serves as an analytical tool for video analysis. TPD follow-up. As described earlier, there is clear evidence that an extended duration of TPD for at least 15 weeks is optimal for improving teacher outcomes (e.g., Kowalski et al., 2020). In other words, TPD needs to incorporate follow-up support beyond summer TPD into the academic year (Desimone, 2009; Yoon et al., 2007). These follow-up activities provide further opportunities for teachers to implement and refect on their implementation of new pedagogical approaches through activities such as video analysis of practice (e.g., Brown & Crippen, 2016a; Roth et al., 2019), coaching (e.g., Brenneman et al., 2019; Furman et al., 2019; Gropen et al., 2017; Maeng et al., 2018; McFadden & Roehrig, 2020), and teacher action research (Bianchini et al., 2015; Goodnough, 2018; McFadden et al., 2014). 1209

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In some TPD programs, the use of video analysis within TPD sessions was extended into the academic year through small groups or PLCs with teachers sharing and analyzing video collected in their own classrooms (e.g., Ellis et al., 2015; Manz & Suárez, 2018; Roth et al., 2011). Other TPDs incorporated video into refective implementation cycles as teachers worked to integrate new strategies into their instructional practice. For example, McNally (2016) described refective cycles for beginning science teachers to work with an assigned mentor by identifying a problem of practice as a focus discussion followed by sharing and refecting on video to develop solutions to the identifed problem of practice. Brown and Crippen (2016a) developed a TPD program, Science Teachers are Responsive to Students (STARTS), to foster science teachers’ growth as culturally responsive educators. STARTS included professional growth tasks that facilitated teacher learning around self-identifed topics and areas for growth centered on culturally relevant teaching. Brown and Crippen (2016a) provide specifc examples of teacher growth as teachers engaged in video analysis of their instruction guided by the Growing Awareness Inventory (GAIn) protocol (Brown & Crippen, 2016b). It is important to note that many studies complete classroom observations and collect classroom video as empirical data to address research questions related to changes in teachers’ classroom practices, but not all studies used these classroom observations for the purpose of measuring or documenting teacher learning.

Coaching Approximately 13% of the TPD studies reported used coaching as a TPD strategy, both within the formal TPD and as part of the academic year follow-up. Coaching is not specifed in most TPD frameworks, only Darling-Hammond et  al. (2017) specify coaching as a characteristic of quality TPD. They identifed coaching in 85% of the studies in their review, although it is critical to note that they include expert support or facilitation of follow-up activities as coaching. For example, in STeLLA (Roth et al., 2011, 2019) an expert PD provider facilitated learning and video analysis in academic year PLCs, which is not described as “coaching” per se by the developers of STeLLA. For the TPD studies in this review that referred to coaching, there was a range of approaches to coaching, often with little detail provided about the nature of the coaching. For example, Furman et al. (2019) explored a two-month, job-embedded TPD that included weekly, one-on-one coaching. However, coaching activities were only briefy described as including a coach modeling and debriefng lessons in the teachers’ classroom in preparation for the teacher implementing the same lessons in another classroom. Gropen et al. (2017) described a job-embedded TPD with individual and small group coaching sessions occurring between TPD workshops. This study provided details about how the coaches were prepared, as well as specifc details about the goals and structures for each coaching session. Most often, coaches were connected to the TPD team rather than school personnel (e.g., Maeng et  al., 2018; McFadden & Roehrig, 2020); however, some studies used school and district coaches. For example, Brenneman et al. (2019) adopted multiple refective coaching cycles, as well as periodic small group PLCs facilitated by a district coach. This TPD initially incorporated both a district coach and research team member before shifting to district coach-led coaching sessions. Critical to approaches that use district coaches is that the coach is familiar with the TPD framework and strategies to be able to support ongoing teacher learning. Other large TPDs with a focus on engaging teachers in curriculum writing (e.g., Maeng et al., 2018; McFadden & Roehrig, 2020) employed coaches connected to the TPD team both with the summer TPD to help co-plan curriculum units and to support teachers during implementation of the curriculum during the school year, thus providing coherence with the TPD goals and strategies. Of all the TPD studies reviewed, only one described and researched the process of coaching. McFadden and Roehrig (2020) explored the nature of coaching moves and discourse as one novice coach worked with a team of elementary teachers on implementing and refning their co-designed STEM lessons. This study revealed the complexity of coaching and the need to better understand 1210

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the role of coaching in promoting specifc TPD outcomes, as well as the appropriate preparation of coaches. More research is needed to better understand the role of coaching in supporting TPD, as the research is silent on specifc coaching strategies and approaches that might be more benefcial for teaching learning. While the use of classroom coaches within science TPD is new and understudied, there is a wealth of coaching research within the feld of literacy education and mathematics education that can guide this work (e.g., Ketelaar et al., 2012; Robertson et al., 2019).

Teacher Action Research A less common form of active learning within the academic year was engaging teachers in research within their own classrooms. Only nine studies formally engaged teachers in teacher action research as a form of TPD, although it should be noted that structures such as refective coaching cycles (e.g., Brenneman et al., 2019) or video-based lesson analysis in PLCs (e.g., Roth et al., 2019) could also be considered as small action research cycles within which teachers interrogated a specifc problem of practice. An important diference in the TPD studies coded as action research is that the individual teacher determined the focus of the research cycle. For example, Goodnough (2018) engaged teachers in collaborative action research to support changes in practice related to STEM education. Each teacher completed one cycle of planning, implementing, and refecting on a research question developed collaboratively within a teacher team. Teachers met throughout the cycle to plan, share, and debrief, culminating in sharing their fndings at a conference. McFadden et al. (2014) used a similar action research cycle to promote the implementation of reform-based practices within an online induction program; however, with more of a focus on individual versus collaborative planning, implementation, and refection. Bianchini et al. (2015) engaged teachers in critical examination of equity issues by supporting teachers to develop and address their own research questions related to equity within their own classrooms.

Summary This review of the ways in which science TPD incorporated active learning as a critical feature of TPD revealed a wide range of approaches to active learning. Indeed, many TPD programs engaged teachers in multiple forms of active learning, both within TPD workshops and during follow-up activities. It was also difcult to disaggregate active learning from other characteristics of quality TPD; for example, almost all content deepening occurred through active learning approaches. Similarly, most active learning occurred in collaborative settings such as groups of teachers engaged as learners in a model lesson, teacher design teams collaboratively producing curriculum units, and PLCs. In their meta-analysis of science TPD, Kowalski et al. (2020) did not code for active learning, as the range of defnitions and implementations was too varied to meaningfully group studies. Lynch et al. (2019) coded for TPD activities such as lesson planning, analysis of student work, and engaging in model lessons as learners. They report that any one activity does not have a signifcant efect on student outcomes, but a combination of fve or more TPD activities did signifcantly predict positive student outcomes. They also reported that program with follow-up implementation meetings yielded signifcantly larger average efect sizes, as well as the inclusion of summer workshops as part of the TPD program.

Collaboration and Collective Participation Desimone’s (2009) framework specifes opportunities for collective participation, in other words, teachers within a district, school, or grade level participate as a team in TPD. However, this characteristic is more often now extended to include opportunities for teachers to collaborate across school boundaries (e.g., Darling-Hammond et al. 2017). For example, online TPD opportunities provide opportunities for rural teachers, who may be the only science teacher in their school, to collaborate 1211

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with peers from across their state and/or country (e.g., Al-Balushi & Al-Abdali, 2015; Fishman et al., 2013; McFadden et al., 2014; McNally, 2016; Yoon et al., 2020). All TPD studies reviewed included some aspect of collaborative learning; however, what varied across TPD programs was the amount of time spent on individual learning vs. collaborative learning. For example, RET programs were individually placed within a research setting and developed their own independent work; however, opportunities existed throughout the TPD where teachers came together to share and learn together collectively. Alternatively, teachers in a large-scale integrated STEM TPD worked throughout the summer in teacher teams, planning and piloting their curriculum collaboratively. While each teacher individually implemented the curriculum in their classroom, there were ongoing opportunities for collaborative refection and modifcation of the co-constructed curriculum (e.g., Ring-Whalen et al., 2018). Collective participation was less common across the reviewed TPD studies. Johnson et al. (2016) reported on a fve-year district partnership in which all science teachers at its two middle schools participated in the TPD. Nichol et al. (2018) reported on a TPD for ffth-grade teachers that purposefully provided TPD workshops and related activities during the school day to promote collective participation. School principals were invested in the program by providing substitutes and a daily schedule conducive to PLC collaboration. Similarly, Diamond et al. (2014) described a school-wide initiative where all ffth-grade science teachers in the treatment schools were invited to participate, with all TPD activities scheduled during the academic year. Out of the 260 eligible teachers from the 64 schools, only 13 teachers chose not to participate. In many cases, while specifc partner districts were identifed, not enough information was provided to understand if teachers participated in school-based teams. Indeed, more often teachers were grouped by grade level or subject-specifc teams, rather than by school, to provide a common motivation for participation (e.g., Ring-Whalen et al., 2018). In the case of TPD ofered during the summer, collective participation is more difcult to achieve. Interestingly, in their meta-analysis, Kowalski et  al. (2020) reported that interventions that included either collaboration or collective participation had outcomes that were either no diferent from or even lower than outcomes from interventions that did not include collaboration or collective participation. They hypothesize that collective participation changes the nature of the participants from willing volunteers to a mix that includes unwilling or coerced participation. Contrary fndings were reported by Lynch et al. (2019), who reported that same-school collaboration had a signifcant efect on student outcomes. A greater understanding of the school context is needed to understand why TPD using collective participation has mixed results. Only one study in this review explicitly explored the efect of school factors, such as principal support, on the impact of TPD (Nichol et al., 2018).

Future Considerations for Teacher Professional Development Research It is also important to note considerations for science TPD from this review that are not evident from the categorization of TPD studies by IMPTG domains (Figure 37.2) or consensus characteristics of quality TPD (Table 37.1). Notably absent within this review were studies of district- or school-led TPD, suggesting that the fndings from these studies are relevant only within signifcant grant funding and/or support from university or other academic personnel. Also notable is the small number of studies exploring hybrid and online TPD, despite prior calls for more research on TPD within online environments (Luft & Hewson, 2014).

District and School TPD While ample evidence exists that high-quality PD can produce positive outcomes for both teachers and students, the issue remains that these programs are time-intensive and expensive. Federal, state, and district policy mandates for TPD are increasingly impacting K–12 teachers, particularly those 1212

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in high-needs settings (Kragler et al., 2008). Indeed, Lynch et al. (2019) suggest that school districts spend 1–6% of their budgets on TPD, yet most of the science TPD programs reported in the literature are grant funded with district science coordinators having little ability to implement instructional change in science classrooms given limited authority and budget (Whitworth et al., 2017). It appears in most cases that an external grant covers some or all of the TPD costs and that the TPD ends at the conclusion of the grant funding. Districts are unable to support these intensive forms of grant-funded science TPD. However, few studies explicitly explored the impact of diferent cost savings measures, such as reducing TPD duration or facilitating TPD with a teacher-leader instead of an outside expert. Kleickmann et al. (2016) explored the diferences between a curriculum implementation program with accompanying TPD and teacher self-study of the new curriculum. The inclusion of TPD was signifcantly superior to curriculum self-study in terms of teacher beliefs and motivation, classroom practices, and student achievement. In a similar study, Schuchardt et al. (2017) investigated the impact of reducing contact hours during a TPD tied to the use of educative curriculum materials; they found that limited TPD was successful in impacting some forms of student learning but not others. Osborne et al. (2013) explored a “train the trainer” model of TPD to understand if minimal forms of TPD could still afect teacher practices and student learning. They provided 30 hours of TPD to teacher-leaders on argumentation; these teacher-leaders then provided 10–14 hours of TPD to the other science teachers in their school. This minimal TPD intervention had limited success, suggesting that substantive changes in teachers’ knowledge and practices require more sustained and intensive TPD supported by an expert. From a practical perspective, it is important that TPD studies consider programmatic cost, and that research develops a stronger empirical basis for understanding the consequences of removing diferent TPD components on teacher and student outcomes. There is also a need to explore science TPD developed and implemented at the district or school level. As noted by Whitworth and Chiu (2015), School and district science coordinators “play a signifcant role in the planning and implementation of professional development, as well as providing ongoing leadership to support teacher change” (p. 121). Yet, as noted by van Driel et al. (2012), there is a dearth of studies that investigate both the preparation and instructional practices of TPD providers. More recently, in their national study of district science coordinators, Whitworth et al. (2017) reported on the limited PD opportunities for science coordinators, suggesting that an important avenue for future research is exploring the PD needs of district and school science leaders and developing their expertise in supporting science TPD.

Online Professional Development The TPD literature provides limited information about how to scale up successful TPD programs. With increasing access to technologies that can support distance learning, there is growing interest in online TPD to supplement or even replace in-person TPD programs. Online TPD ofers an opportunity to scale access to teachers by providing fexibility in terms of time (Merritt, 2016) and geographical isolation (Peltola et al., 2017). Research has shown that online PD can be a productive, cost-efective mechanism supporting teacher learning (Dede et al., 2009; NASEM, 2015). For example, Fishman et al. (2013) compared a one-week face-to-face curriculum adoption TPD with a series of online, self-paced short courses led by expert facilitators. No signifcant diference related to teacher outcomes (i.e., beliefs and practice) and student outcomes were found between the two diferent conditions. The studies in this systematic literature review primarily reported on in-person TPD; however, it is notable that 19 studies used full or partially online TPD programming (e.g., Al-Balushi & AlAbdali, 2015; Fishman et  al., 2013; McFadden et  al., 2014; McNally, 2016; Yoon et  al., 2020). Hybrid models included strategic in-person workshops to provide opportunities such as engaging as learners in model lessons and feld trips to research facilities. Ongoing online forums provided 1213

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further access to content experts, allowing teachers to select relevant webinars for the topic of their teaching module, as well as providing feedback on each other’s lesson plans and sharing implementation results (e.g., Seraphin et al., 2013; Sgouros & Stavrou, 2019). Seraphin et al. (2013) specifcally invoke cost related to travel within Hawaii as the reason for employing online mechanisms to extend their TPD beyond a two-day workshop. However, they cautioned that shifting to online may have resulted in some attrition of teachers. These online TPD programs were almost exclusively asynchronous. For example, Yoon et  al. (2020) transferred a successful in-person TPD involving a computer-supported complex systems biology curriculum (Yoon et al., 2016) into a fully asynchronous environment. Results from this small-scale pilot suggest that the online environment with use of carefully constructed prompts that promote teacher learning show promise for scaling up TPD to reach a larger audience. Similarly, pilot results from the transformation of the successful in-person STeLLA TPD (Roth et al., 2019) into an online environment are adding to the minimal knowledge base related to online TPD (Kowalski et al., 2021; Roehrig et al., 2021). Interestingly, STeLLA Online employs a combination of asynchronous and synchronous teacher learning opportunities compared to the prevalent approach of asynchronous TPD. Luft and Hewson (2014), in their TPD chapter within the previous Handbook of Research on Science Education, noted that the use of distance learning approaches to support teacher was preliminary. This remains the case, with a growing, but still small, number of studies related to online approaches to TPD. Luft and Hewson (2014) argued that “there is a need to explore how these [online] environments can support teacher and student learning in new ways . . . and to consider how to maximize teacher learning through various e-learning opportunities”. The need for further research related to online science TPD remains, as it is likely that the consensus characteristics of TPD take diferent forms in an online learning environment. As reported in previous TPD reviews (e.g., Capps et al., 2012; Luft & Hewson, 2014; van Driel et al., 2012), there is still a preponderance of small-scale qualitative studies. Luft and Hewson (2014) called for fewer studies reporting on general outcomes of TPD and already known fndings related to TPD and more synthesis studies, noting that such studies are challenging if TPD studies do not provide enough methodological details. This review incorporated the fndings from two recent metaanalysis studies on TPD (Kowalski et al., 2020; Lynch et al., 2019). While nearly singular, these two meta-analyses provide some important information about the impact of TPD on teacher outcomes (Kowalski et al., 2020) and student outcomes (Lynch et al., 2019). These studies provide guidance for other science TPD researchers on the specifc information needed for future meta-analysis and synthesis studies, including an efect-size calculator that “can help provide some clarity about whether a given efect from a particular PD intervention is ‘large’ or ‘small’ in context” (Kowalski et al., 2020, p. 454). These studies also suggest that the choice of research instruments is an important consideration in the design of TPD research. For example, Lynch et  al. (2019) coded for the nature of diferent student assessments, such as state standardized tests and researcher-designed tests. As expected, researcher-designed tests were more sensitive to targeted instructional goals from the TPD. Thus, rigorous test and research instrument design is important, and studies need to address the reliability and validity of researcher-designed instruments. This is particularly true for the growing number of TPD studies related to integrated STEM, where there is a need for more robust measures of student and teacher learning, including teacher practices, that are aligned with such integrated, interdisciplinary approaches to science teaching. This parallel need for the development of new research tools aligned with integrated STEM instruction complicates the development of robust TPD research related to integrated STEM. For example, in the absence of integrated STEM observation protocols for quantitative research designs, much of the STEM TPD research focuses on qualitative cases studies (e.g., Dare et al., 2018; Ring-Whalen et al., 2018). As new instruments, such as the STEM Observation Protocol (Dare et al., 2021) are developed, the STEM TPD research can be expanded to larger-scale 1214

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quantitative designs and address the call for more research that explores how TPD can better support science teachers to implement the interdisciplinary approaches to teaching and learning called for in current reforms (Luft & Hewson, 2014). Finally, Luft and Hewson (2014) called for studies that explore “how teachers learn about a concept as they engage in a [TPD], and how specifc strategies best support teacher learning and student learning”. A small number of studies in this review addressed this call (e.g., Alonzo & Kim, 2018; Finkelstein et al., 2019; McFadden & Roehrig, 2020; Tang & Shao, 2014; Tolbert, 2015). These studies reveal the potential of TPD research that explores the mechanisms of teacher learning within diferent TPD activities. Research methods such as discourse analysis provide useful tools to understand how teacher learning occurs. There is a need for more research that explores how teachers interact to move their knowledge forward and develop lessons aligned with current reform eforts.

References Al-Balushi, S. M., & Al-Abdali, N. S. (2015). Using a Moodle-based professional development program to train science teachers to teach for creativity and its efectiveness on their teaching practices. Journal of Science Education and Technology, 24(4), 461–475. Alonzo, A. C., & Kim, J. (2018). Afordances of video-based professional development for supporting physics teachers’ judgments about evidence of student thinking. Teaching and Teacher Education, 76, 283–297. Altan, E. B., & Ercan, S. (2016). STEM education program for science teachers: Perceptions and competencies. Journal of Turkish Science Education, 13, 103–117. Australian Curriculum, Assessment, and Reporting Authority. (2016). www.australiancurriculum.edu.au/ media/3220/stem-connections-report.pdf Bancroft, S. F., & Nyirenda, E. M. (2020). Equity-focused K-12 science teacher professional development: A review of the literature 2001–2017. Journal of Science Teacher Education, 31(2), 151–207. Bianchini, J. A., Dwyer, H. A., Brenner, M. E., & Wearly, A. J. (2015). Facilitating science and mathematics teachers’ talk about equity: What are the strengths and limitations of four strategies for professional learning? Science Education, 99(3), 577–610. Billiar, K., Hubelbank, J., Thomas, O., & Camesano, T. (2014). Teaching STEM by design. Advances in Engineering Education, 4(1), 1–21. Binkhorst, F., Handelzalts, A., Poortman, C. L., & Van Joolingen, W. R. (2015). Understanding teacher design teams: A mixed methods approach to developing a descriptive framework. Teaching and Teacher Education, 51, 213–224. Blanchard, M. R., LePrevost, C. E., Tolin, A. D., & Gutierrez, K. S. (2016). Investigating technology-enhanced teacher professional development in rural, high-poverty middle schools. Educational Researcher, 45(3), 207–220. Blanchard, M. R., Southerland, S. A., & Granger, D. E. (2008). No silver bullet for inquiry: Making sense of teacher change following an inquiry-based research experience for teachers. Science Education, 93(2), 322–360. Blank, R. K., de las Alas, N., & Smith, C. (2008). Does teacher professional development have efects on teaching and learning? Analysis of evaluation fndings from programs for mathematics and science teachers in 14 states. Council of Chief State School Ofcers. Borko, H., Jacobs, J., & Koellner, K. (2010). Contemporary approaches to teacher professional development. In P. Peterson, E. Baker, & B. McGaw (Eds.), International encyclopedia of education (Vol. 7, pp. 548–556). Elsevier. Boschman, F., McKenny, S., & Voogt, J. (2014). Understanding decision making in teachers’ curriculum design approaches. Educational Technology Research and Development, 62, 393–416. Brenneman, K., Lange, A., & Nayfeld, I. (2019). Integrating STEM into preschool education; designing a professional development model in diverse settings. Early Childhood Education Journal, 47, 15–28. Brown, J. C., & Crippen, K. J. (2016a). Designing for culturally responsive science education through professional development. International Journal of Science Education, 38(3), 470–492. Brown, J. C., & Crippen, K. J. (2016b). The growing awareness inventory: Building capacity for culturally responsive science and mathematics with a structured observation protocol. School Science and Mathematics, 113(6), 127–138. Bybee, R. W. (2010). Advancing STEM education: A 2020 vision. Technology and Engineering Teacher, 70(1), 30–35. Campbell, T., Longhurst, M. L., Wang, S.-K., Hsu, H.-Y., & Coster, D. C. (2015). Technologies and reformedbased science instruction: The examination of a professional development model focused on supporting science teaching and learning with technologies. Journal of Science Education and Technology, 24(5), 562–579.

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Gillian Roehrig Capps, D. K., Crawford, B. A., & Constas, M. A. (2012). A review of empirical literature on inquiry professional development: Alignment with best practices and a critique of the fndings. Journal of Science Teacher Education, 23, 291–318. Clarke, D. J., & Hollingsworth, H. (2002). Elaborating a model of teacher professional growth. Teaching and Teacher Education, 18(8), 947–967. Cofre, H., Cuevas, E., & Becerra, B. (2017). The relationship between biology teachers’ understanding of the nature of science and the understanding and acceptance of the theory of evolution. International Journal of Science Education, 39(16), 2243–2260. Cohen, D. K., & Hill, H. (2000). Instructional policy and classroom performance: The mathematics reform in California. The Teachers College Record, 102, 294–343. Crotty, E. A., Guzey, S., Roehrig, G. H., Glancy, A. W., Ring-Whalen, E. A., & Moore, T. J. (2017). Approaches to integrating engineering in STEM units and student achievement gains. Journal of Pre-College Engineering Education Research (J-PEER), 7(2), 1–14. Dare, E. A., Ellis, J. A., & Roehrig, G. H. (2018). Understanding science teachers’ implementations of integrated STEM curricular units through a phenomenological multiple case study. International Journal of STEM Education, 5(4). https://doi.org/10.1186/s40594-018-0101-z Dare, E. A., Hiwatig, B., Keratithamkul, K., Ellis, J. A., Roehrig, G. H., Ring-Whalen, E. A., Rouleau, M. D., Faruqi, F., Rice, C., Titu, P., Li, F., Wieselmann, J. R., & Crotty, E. A. (2021). Improving STEM education: The design and development of a K-12 STEM observation protocol (STEM-OP) (RTP). ASEE 2021 Annual Conference & Exposition. Virtual Conference. Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Efective teacher professional development. Learning Policy Institute. Dede, C., Ketelhut, D. J., Whitehouse, P., Breit, L., & McCloskey, E. M. (2009). A research agenda for online teacher professional development. Journal of Teacher Education, 60, 8–19. Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38, 181–199. Diamond, B. S., Maerten-Rivera, J., Rohrer, R. E., & Lee, O. (2014). Efectiveness of a curricular and professional development intervention at improving elementary teachers’ science content knowledge and student achievement outcomes: Year 1 results. Journal of Research in Science Teaching, 51(5), 635–658. Dixon, P., & Wilke, R. (2007). The infuence of a teacher research experience on elementary teachers’ thinking and instruction. Journal of Elementary Science Education, 19(1), 25–43. Doppelt, Y., Schunn, C. D., Silk, E. M., Mehalik, M. M., Reynolds, B., & Ward, E. (2009). Evaluating the impact of facilitated learning community approach to professional development on teacher practice and student achievement. Research in Science and Technological Education, 27(3), 339–354. Dubinsky, J. M., Guzey, S. S., Schwartz, M. S., Roehrig, G. H., MacNabb, C., Schmied, A., Hinesley, V., Hoelscher, M., Michlin, M., Schmitt, L., Ellingson, C., Chang, Z., & Cooper, J. C. (2019). Contributions of neuroscience knowledge to teachers and their practice. The Neuroscientist, 5(5), 394–407. Ekanayake, S. Y., & Wishart, J. (2015). Integrating mobile phones into teaching and learning: A case study of teacher training through professional development workshops. British Journal of Educational Technology, 46(1), 173–189. Ellis, J., McFadden, J., Anwar, T., & Roehrig, G. H. (2015). Investigating the social interactions of beginning teachers using a video annotation tool. Contemporary Issues in Technology & Teacher Education, 15(3). www. citejournal.org/vol15/iss3/general/article1.cfm Enderle, P., Dentzau, M., Roseler, K., Southerland, S., Granger, E., Hughes, R., Golden, B., & Saka, Y. (2014). Examining the infuence of RETs on science teacher beliefs and practice. Science Education, 98(6), 1077–1108. European Commission. (2015). Science education for responsible citizenship. European Union. Finkelstein, C., Jaber, L. Z., & Dini, V. (2019). “Do I feel threatened? No . . . I’m learning!”: Afective and relational dynamics in science professional development. Science Education, 103(2), 338–361. Fishman, B. J., Konstantopoulos, S., Kubitskey, B. W., Vath, R., Park, G., Johnson, H., & Edelson, D. C. (2013). Comparing the impact of online and face-to-face professional development in the context of curriculum implementation. Journal of Teacher Education, 64(5), 426–438. Fishman, B. J., Marx, R. W., Best, S., & Tal, R. T. (2003). Linking teacher and student learning to improve professional development in systemic reform. Teaching and Teacher Education, 19(6), 643–658. Freeman, B., Marginson, S., & Tytler, R. (Eds.). (2014). The age of STEM: Educational policy and practice across the world in science, technology, engineering and mathematics. Routledge. Fullan, M. (2007). The new meaning of educational change (4th ed., 35). Teachers College, Columbia University. Furman, M., Luzuriaga, M., Taylor, I., Jarvis, D., Prost, E. D., & Podestá, M. E. (2019). The use of questions in early years science: a case study in Argentine preschools. International Journal of Early Years Education, 27(3), 271–286.

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Teacher Professional Development Programs in Science Furtak, E. M., Kiemer, K., Circi, R. K., Swanson, R., de Leon, V., Morrison, D., & Heredia, S. C. (2016). Teachers’ formative assessment abilities and their relationship to student learning: Findings from a four-year intervention study. Instructional Science, 44(3), 267–291. Garet, M. S., Porter, A. C., Desimone, L., Birman, B. F., & Yoon, K. S. (2001). What makes professional development efective? Results from a national sample of teachers. American Educational Research Journal, 38(4), 915–945. Goodnough, K. (2018). Addressing contradictions in teachers’ practice through professional learning: An activity theory perspective. International Journal of Science Education, 40(17), 2181–2204. Greene, B. A., Lubin, I. A., Slater, J. L., & Walden, S. E. (2013). Mapping changes in science teachers’ content knowledge: Concept maps and authentic professional development. Journal of Science Education and Technology, 22(3), 287–299. Gropen, J., Kook, J. F., Hoisington, C., & Clark-Ciarelli, N. (2017). Foundations of science literacy: Efcacy of a preschool professional development program in science on classroom instruction, teachers’ pedagogical content knowledge, and children’s observations and predictions. Early Education and Development, 28(5), 607–631. Grove, C. M., Dixon, P. J., & Pop, M. M. (2009). Research experiences for teachers: Infuences related to expectancy and value of changes to practice in the American classroom. Professional Development in Education, 35(2), 247–260. Guskey, T. R. (1986). Staf development and the process of teacher change. Educational Researcher, 15(5), 5–12. Guskey, T. R. (2003). What makes professional development efective? Phi Delta Kappan, 80, 748–750. Guzey, S., Tank, K. G., Wang, H., Roehrig, G. H., & Moore, T. J. (2014). A high-quality professional development for teachers of grades 3–6 for implementing engineering into classrooms. School Science and Mathematics, 114(3), 139–149. Hammond, T., Bodzin, A., Popejoy, K., Anastasio, D., Holland, B., & Sahagian, D. (2019). Shoulder to shoulder: Teacher professional development and curriculum design and development for geospatial technology integration with science and social studies teachers. Contemporary Issues in Technology & Teacher Education, 19(2), 279–301. Han, S., Bugrahan, Y., Capraro, M. M., & Capraro, R. M. (2015). In-service teachers’ implementation and understanding of STEM project-based learning. Eurasia Journal of Mathematics, Science & Technology Education, 11(1), 2015, 63–76. Hong, O. (2017). STEAM education in Korea: Current policies and future directions. Policy Trajectories and Initiatives in STEM Education, 8(2), 92–102. Huizinga, T., Handelzalts, A., Nieveen, N., & Voogt, J. M. (2014). Teacher involvement in curriculum design: Need for support to enhance teachers’ design expertise. Journal of Curriculum Studies, 46(1), 33–57. Johnson, C. C., Bolshakova, V. L. J., & Waldron, T. (2016). When good intentions and reality meet: Large-scale reform of science teaching in urban schools with predominantly Latino ELL students. Urban Education, 51(5), 476–513. Johnson, C. C., & Fargo, J. D. (2014). A study of the impact of transformative professional development on Hispanic student performance on state mandated assessments of science in elementary school. Journal of Science Teacher Education, 25(7), 845–859. Kelly, M. P., & Staver, J. R. (2005). A case study of one school system’s adoption and implementation of an elementary science program. Journal of Research in Science Teaching, 42(1), 25–52. Kennedy, M. (1998). Form and substance of inservice teacher education Research Monograph No. 13. National Institute for Science Education, University of Wisconsin. Kennedy, M. M. (2016). How does professional development improve teaching? Review of Educational Research, 86(4), 945–980. Ketelaar, E., Beijaard, D., Boshuizen, H. P. A., & Den Brok, P. J. (2012). Teachers’ positioning towards an educational innovation in the light of ownership, sense-making and agency. Teaching and Teacher Education, 28(2), 273–282. Kleickmann, T., Trobst, S., Jonen, A., Vehmeyer, J., & Moller, K. (2016). The efects of expert scafolding in elementary science professional development on teachers’ beliefs and motivations, instructional practices, and student achievement. Journal of Educational Psychology, 108(1), 21–42. Kowalski, S. M., Hvidsten, C., Belcastro, A., Constantine, A., Askinas, K., Devaul, R., Roehrig, G. H., & Faruqi, F. (2021). Design decisions in translating face-to-face video-based elementary science professional development to an online environment. Paper presented at the American Educational Research Association Conference (virtual). Kowalski, S. M., Taylor, J. A., Askinas, K. M., Wang, Q., Zhang, Q., Maddix, W. P., & Tipton, E. (2020). Examining factors contributing to variation in efect size estimates of teacher outcomes from studies of science teacher professional development. Journal of Research on Educational Efectiveness, 13(3), 430–458. Kragler, S., Martin, L., & Kroeger, D. (2008). Money down the drain: Mandated professional development. Journal of School Leadership, 18, 528–550.

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Gillian Roehrig Lederman, N. G., & Lederman, J. S. (2019). Teaching and learning of nature of scientifc knowledge and scientifc inquiry: Building capacity through systematic research-based professional development. Journal of Science Teacher Education, 30(7), 737–762. Lewis, E. B., Baker, D. R., & Helding, B. A. (2015). Science teaching reform through professional development: Teachers’ use of a scientifc classroom discourse community model. Science Education, 99(5), 896–931. Lotter, C., Carnes, N., Marshall, J. C., Hoppmann, R., Kiernan, D. A., Barth, S. G., & Smith, C. (2020). Teachers’ content knowledge, beliefs, and practice after a project-based professional development program with ultrasound scanning. Journal of Science Teacher Education, 31(3), 311–334. Lotter, C., Smiley, W., Thompson, S., & Dickenson, T. (2016). The impact of a professional development model on middle school science teachers’ efcacy and implementation of inquiry. International Journal of Science Education, 38(18), 2712–2741. Loucks-Horsley, S., Love, N., Stiles, K. E., Mundry, S. E., & Hewson, P. (2003). Designing professional development for teachers of science and mathematics (2nd ed.). Corwin Press. Loucks-Horsley, S., & Matsumoto, C. (1999). Research on professional development for teachers of mathematics and science: The state of the scene. School Science and Mathematics, 99(5), 258–271. Luft, J. A., & Hewson, P. W. (2014). Research on teacher professional development programs in science. In S. K. Abell & N. G. Lederman (Eds.), Handbook of research on science education (Vol. 2). Routledge. Lynch, S., Hill, H. C., Gonzalez, K. E., & Pollard, C. (2019). Strengthening the research base that informs STEM instructional improvement eforts: A meta-analysis. Educational Evaluation and Policy Analysis, 41(3), 260–293. Maeng, J. L., Whitworth, B. A., Gonczi, A. L., Navy, S. L., & Wheeler, L. B. (2018). Elementary science teachers’ integration of engineering design into science instruction: Results from a randomized controlled trial. International Journal of Science Education, 39(11), 1529–1548. Manz, E., & Suárez, E. (2018). Supporting teachers to negotiate uncertainty for science, students, and teaching. Science Education, 102, 771–795. Marshall, J. C., & Alston, D. M. (2014). Efective, sustained inquiry-based instruction promotes higher science profciency among all groups: A 5-year analysis. Journal of Science Teacher Education, 25(7), 807–821. McFadden, J. F., Ellis, J., Anwar, T., & Roehrig, G. (2014). Beginning science teachers’ use of a digital video annotation tool to promote refective practices. Journal of Science Education and Technology, 23(3), 458–470. McFadden, J. F., & Roehrig, G. H. (2020). Missed expectations: Teacher and coach tensions at the boundary of STEM integration in an elementary classroom. Disciplinary and Interdisciplinary Science Education Research, 2(4). https://doi.org/10.1186/s43031-020-00021-8 McNally, J. C. (2016). Learning from one’s own teaching: New science teachers analyzing their practice through classroom observation cycles. Journal of Research in Science Teaching, 53(3), 473–501. McNeill, K. L., & Knight, A. M. (2013). Teachers’ pedagogical content knowledge of scientifc argumentation: The impact of professional development on K-12 teachers. Science Education, 97(6), 936–972. McNeil, K., & Krajcik, J. S. (2008). Inquiry and scientifc explanations: Helping students use evidence and reasoning. In J. A. Luft, R. L. Bell, & J. Gess-Newsome (Eds.), Science as inquiry in the secondary setting. NSTA Press. Merritt, E. G. (2016). Time for teacher learning, planning critical for school reform: Students aren’t the only ones who need more time to learn; teachers also need more and better time for learning and for planning. Phi Delta Kappan, 98(4), 31–36. Moore, T. J., Glancy, A. W., Tank, K. M., Kersten, J. A., Smith, K. A., & Stohlmann, M. S. (2014). A framework for quality K-12 engineering education: Research and development. Journal of Pre-College Engineering Education Research (J-PEER), 4(1), 1–13. Moore, T. J., Johnston, A. C., & Glancy, A. W. (2020). STEM integration: A synthesis of conceptual frameworks and defnitions. In C. C. Johnson, M. J. Mohr-Schroeder, T. J. Moore, & L. D. English (Eds.), Handbook of research in STEM education (pp. 3–16). Routledge. Mulveya, B. K., & Bell, R. L. (2017). Making learning last: Teachers’ long-term retention of improved nature of science conceptions and instructional rationales. International Journal of Science Education, 39(1), 62–85. Nadelson, L. S., Callahan, J., Pyke, P., Hay, A., Dance, M., & Pfester, J. (2013). Teacher STEM perception and preparation: Inquiry-based STEM professional development for elementary teachers. The Journal of Educational Research, 106(2), 157–168. National Academies of Sciences, Engineering, and Medicine. (2015). Science teachers’ learning: Enhancing opportunities, creating supportive contexts. The National Academies Press. National Academy of Engineering and National Research Council. (2014). STEM integration in K-12 education: Status, prospects, and an agenda for research. National Academies Press. National Research Council. (1996). National science education standards. The National Academies Press. National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. National Academies Press.

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Teacher Professional Development Programs in Science National Science and Technology Council Committee on STEM Education. (2018). Charting a course for success: America’s strategy for STEM education. National Science and Technology Council. www.whitehouse.gov/wpcontent/uploads/2018/12/STEM-Education-Strategic-Plan-2018.pdf Nesmith, S. M., & Cooper, S. (2019). Engineering process as a focus: STEM professional development with elementary STEM-focused professional development schools. School Science and Mathematics, 119, 487–498. NGSS Lead States. (2013). Next generation science standards: For states, by states. National Academies Press. Nichol, C., Chow, A., & Furtwengler, S. (2018). Year-long teacher professional development on ffth grade student science outcomes. International Journal of Science Education, 40(17), 2099–2117. Osborne, J., Shirley, S., Christodoulou, A., Howell-Richardson, C., & Richardson, K. (2013). Learning to argue: A study of four schools and their attempt to develop the use of argumentation as a common instructional practice and its impact on students. Journal of Research in Science Teaching, 50(3), 315–347. Peercy, M. M., Martin-Beltrán, M., Silverman, R. D., & Daniel, S. (2015). Curricular design and implementation as a site of teacher expertise and learning. Teachers and Teaching, 21(7), 867–893. Peltola, P., Haynes, E., Clymer, L., McMillan, A., & Williams, H. (2017). Opportunities for teacher professional development in Oklahoma rural and nonrural schools (REL 2017–273). Department of Education, Institute of Education Sciences, National Center for education evaluation and regional assistance, regional educational Laboratory southwest. http://ies.ed.gov/ncee/edlabs Pleasants, J., Olson, J. K., & De La Cruz, I. (2020). Accuracy of elementary teachers’ representations of the projects and processes of engineering: Results of a professional development program. Journal of Science Teacher Education, 31(4), 362–383. Reiser, B. J. (2013). What professional development strategies are needed for successful implementation of the next generation science standards? Paper prepared for K12 Center at ETS Invitational Symposium on Science Assessment. www.k12center.org/rsc/pdf/reiser.pdf Ring, E. A., Dare, E. A., Crotty, E. A., & Roehrig, G. H. (2017). The evolution of teacher conceptions of STEM education throughout an intensive professional development experience. Journal of Science Teacher Education, 28(5), 444–467. Ring-Whalen, E. A., Dare, E. A., Roehrig, G. H., Titu, P., & Crotty, E. (2018). How teachers’ conceptions of integrated STEM education are refected in curriculum writing. International Journal of Education in Mathematics, Science and Technology, 6(4), 343–362. Robertson, D. A., Ford-Connors, E., Frahm, T., Bock, K., Paratore, J. R. (2019). Unpacking productive coaching interactions: Identifying coaching approaches that supports instructional uptake. Professional Development in Education, 46(5), 1–19. Robinson, A., Dailey, D., Hughes, G., & Cotabish, A. (2014). The efects of a science-focused STEM intervention on gifted elementary students’ science knowledge and skills. Journal of Advanced Academics, 25(3), 189–213. Roehrig, G. H., Constantine, A., Faruqi, F., Hvidsten, C., Belcastro, A., Askinas, K., DeVaul, R., & Kowalski, S. (2021). Online Professional Development for Elementary Science Teachers. Paper presented at the Association for Science Teacher Education Conference (virtual). Roth, K. J., Garnier, H. E., Chen, C., Lemmens, M., Schwille, K., & Wickler, N. I. Z. (2011). Video-based lesson analysis: Efective science PD for teacher and student learning. Journal of Research in Science Teaching, 48, 117–148. Roth, K. J., Wilson, C. D., Taylor, J. A., Stuhlsatz, M. A., & Hvidsten, C. (2019). Comparing the efects of analysis-of-practice and content-based professional development on teacher and student outcomes in science. American Educational Research Journal, 56(4), 1217–1253. Sandholtz, J. H., & Ringstaf, C. (2016). The infuence of contextual factors on the sustainability of professional development outcomes. Journal of Science Teacher Education, 27(2), 205–226. Sandholtz, J. H., Ringstaf, C., & Matlen, B. (2016). Temporary fx or lasting solution? Investigating the longitudinal impact of teacher professional development on K-2 science instruction. The Elementary School Journal, 117(2), 192–215. Scher, L., & O’Reilly, F. (2009). Professional development for K-12 math and science teachers: What do we really know? Journal of Research on Educational Efectiveness, 2(3), 209–249. Schuchardt, A. M., Tekkumru-Kisa, M., Schunn, C. D., Stein, M. K., & Reynolds, B. (2017). How much professional development is needed with educative curriculum materials? It depends upon the intended student learning outcomes. Science Education, 101, 1015–1033. Seraphin, K. D., Philippof, J., Parisky, A., Degnan, K., & Warren, D. P. (2013). Teaching energy science as inquiry: Refections on professional development as a tool to build inquiry teaching skills for middle and high school teachers. Journal of Science Education and Technology, 22(3), 235–251. Sgouros, G., & Stavrou, D. (2019). Teachers’ professional development in nanoscience and nanotechnology in the context of a community of learners. International Journal of Science Education, 41(15), 2070–2093.

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Gillian Roehrig Sgro, C. M., Bobowski, T., & Oliveira, A. W. (2020). Current praxis and conceptualization of STEM education: A call for greater clarity in integrated curriculum development. In V. Akerson & G. Buck (Eds.), Contemporary trends and issues in science education: Critical questions in STEM rducation (pp. 185–210). Springer. Smithenry, D. W., Prouty, J., & Capobianco, B. M. (2013). Collaboratively exploring the use of a video casebased book as a professional development tool. Journal of Science Education and Technology, 22(5), 735–750. Spatz, V., Wilhelm, T., Hopf, M., Waltner, C., & Wiesner, H. (2019). Teachers using a novel curriculum on an introduction to Newtonian mechanics: The efects of a short-term professional development program. Journal of Science Teacher Education, 30(2), 159–178. Supovitz, J. A. (2001). Translating teaching practice into improved student performance. In S. Fuhrman (Ed.), From the capitol to the classroom: Standards-based reform in the states (pp. 81–98). University of Chicago Press. Tang, X., & Shao, F. (2014). On the evolution of a lesson: Group preparation for teaching contest as teacher professional development activity for Chinese elementary science teachers. Journal of Science Education and Technology, 23(2), 252–266. Taylor, J. A., Getty, S. R., Kowalski, S. M., Wilson, C. D., Carlson, J., & Van Scooter, P. (2015). An efcacy trial of research-based curriculum materials with curriculum-based professional development. American Educational Research Journal, 52(5), 984–1017. Tekkumru-Kisa, M., Stein, M. K., & Coker, R. (2018). Teachers’ learning to facilitate high-level student thinking: Impact of a video-based professional development. Journal of Research in Science Teaching, 55, 479–502. Tolbert, P. S., & Hall, R. H. (2015). Organizations: Structures, processes and outcomes. Routledge. van Driel, J. H., Meirink, J. A., van Veen, K., & Zwart, R. C. (2012). Current trends and missing links in studies on teacher professional development in science education: A review of design features and quality of research. Studies in Science Education, 48(2), 129–160. Watkins, J., Jaber, L. Z., & Dini, V. (2020). Facilitating scientifc engagement online: Responsive teaching in a science professional development program. Journal of Science Teacher Education, 31(5), 515–536. Watkins, J., McCormick, M., Wendell, K. B., Spencer, K., Milto, E., Portsmore, M., & Hammer, D. (2018). Data-based conjectures for supporting responsive teaching in engineering design with elementary teachers. Science Education, 102(3), 548–570. Whitworth, B. A., & Chiu, J. L. (2015). Professional development and teacher change: The missing leadership link. Journal of Science Teacher Education, 26(2), 121–137. Whitworth, B. A., Maeng, J. L., Wheeler, L. B., & Chiu, J. L. (2017). Investigating the role of a district science coordinator. Journal of Research in Science Teaching, 54, 914–936. Wiener, G. J., Schmeling, S. M., & Hopf, M. (2018). The technique of probing acceptance as a tool for teachers’ professional development: A PCK study. Journal of Research in Science Teaching, 55, 849–875. Wilson, S. M. (2013). Professional development for science teachers. Science, 340(6130), 310–313. Wylie, C. E., & Lyon, C. J. (2015). The fdelity of formative assessment implementation: Issues of breadth and quality. Assessment in Education: Principles, Policy & Practice, 22(1), 140–160. Yang, R., Porter, A. C., Massey, C. M., Merlino, J. F., & Desimone, L. M. (2020). Curriculum-based teacher professional development in middle school science: A comparison of training focused on cognitive science principles versus content knowledge. Journal of Research in Science Teaching, 57, 536–566. Yang, Y., Liu, X., & Gardella, J. A. (2020). Efects of a professional development program on science teacher knowledge and practice, and student understanding of interdisciplinary science concepts. Journal of Research in Science Teaching, 57(7), 1028–1057. Yoon, K. S., Duncan, T., Lee, S. W. Y., Scarloss, B., & Shapley, K. (2007). Reviewing the evidence on how teacher professional development afects student achievement. Issues and Answers Report, REL2007-No.033. U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southwest. Yoon, S. A., Klopfer, E., Anderson, E., Koehler-Yom, J., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub, H., Oztok, M., Evans, C., & Goh, S.-E. (2016). Designing computer-supported complex systems curricula for the next generation science standards in high school science classrooms. Systems, 4(38), 1–18. Yoon, S. A., Koehler-Yorn, J., Anderson, E., Lin, J., & Klopfer, E. (2015). Using an adaptive expertise lens to understand the quality of teachers’ classroom implementation of computer-supported complex systems curricula in high school science. Research in Science & Technological Education, 33(2), 237–251. Yoon, S. A., Miller, K., Richman, T., Wendel, D., Schoenfeld, I., Anderson, E., & Shim, J. (2020). Encouraging collaboration and building community in online asynchronous professional development: Designing for social capital. International Journal of Computer-Supported Collaborative Learning, 15(3), 351–371.

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INDEX

Note: Information in figures and tables is indicated by page numbers in italics and bold. AAC see augmentative and alternative communication (AAC) aboutness 418 ACEs see authorable and customizable environments (ACEs) achievement: student attitudes, toward science and 169–170 advanced placement (AP) 994 aesthetics 179–182 affect object/circumstance 160 Africa, standards in 529 agency, epistemic 111 America 2000 825–826, 828–829 analytics, learning 478–479 analytic vs. synthetic categories 159 AP see advanced placement (AP) Apt-AIR framework 106–108 AR see augmented reality (AR) argumentation 105–106, 419–420, 665–670, 667, 669, 756–761, 909–913 argument lines 808 artifacts, physical and virtual 455–456 artificial intelligence (AI): chemistry education research and 675–676; dashboard to support students’ inquiry 1011–1038, 1017–1018, 1019–1023, 1027, 1029, 1033–1037 ASD see autism spectrum disorder (ASD) Asia, standards in 529 aspirations: to learn 244–245; student 178–179; to teach 244–245 assessments: alerting dashboards for formative 1016–1017; aligning, with interdisciplinary approaches and integrated STEM 572; “assessment for all” 345–346; curricula and 1045–1046, 1052; design of, to undergird instruction 1015–1017; embedded 451–452; ethnicity and 247–248;

evolving landscape of nature of science 858–863; formative, of practices to support instruction 1015–1017; international 1047, 1048–1051; lack of, for assessing students’ competencies 1014– 1015; large-scale, in science education 1045–1095, 1048–1051, 1052, 1054–1067, 1068, 1071, 1072, 1074, 1079, 1080, 1082–1083, 1084, 1087, 1088, 1090–1091; of learning environments 194–200; learning progression and 134, 137–138; multilingual learners and 310–312; performance outcomes and 1046; race and 247–248; research, in nature of science 858–866, 859–860; of student attitudes, toward science 169–170; of teacher beliefs 1115–1118 Atlas for Science Literacy 131 attitudes see student attitudes, toward science; teacher beliefs augmentative and alternative communication (AAC) 338 augmented reality (AR) 673–674 authentic learning environments 568 authorable and customizable environments (ACEs) 337, 450–452, 456, 469, 472–473 autism spectrum disorder (ASD) 336–337 autonomy 101 Bakhtinian dialogic theory 64 behavioral theories of learning 90 belonging: gender and 267–268; identity and 275–276 Benchmarks for Science Literacy 826–828, 827 bias, gender 266–267 biology education: cell biology in 603–606, 604; developments in 586–587; ecology in 596–600, 597; evolution in 589–593, 590; future directions in 608–609; genetics in 593, 593–596; human biology in 600, 600–603; inheritance in 593,

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Index 593–596; metabolism in 606, 606–608; and nature of biology as science 587–588; overview of 586; teaching and learning in 588–608, 589, 590, 593, 597, 600, 604, 606; trends in 588, 589 blindness 341–342 CAI see computer-assisted instruction (CAI) capital, science 178–179 case studies 67–71, 68 CCCs see crosscutting concepts (CCCs) CEC see Classroom Emotional Climate (CEC) CED see class ethnicity distribution (CED) cell biology 603–606, 604 CER see chemistry education research (CER) CES see Classroom Environment Scale (CES) character development 917–920 chemistry education research (CER): argumentation and 665–670, 667, 669; artificial intelligence and 675–676; augmented reality and 673–674; COVID-19 and 657; defining 659–660; epistemic core in 662, 663; future directions in 682–683; nature of chemistry and 662–665, 663–664; recent trends in 660, 660–662; references about instruments related to 1003; STEM education research and 677–679, 678; technological pedagogical content knowledge and 669, 669–670; technology and 669; 3D printing and 676–677; virtual chemistry laboratory and 670–672, 672; virtual reality in 674–675; virtual tools and 672–673, 673 Chemistry Laboratory Environment Inventory (CLEI) 202 Chicago River Project 16 children see early childhood; students citizenship 917–920 CK see content knowledge (CK) class ethnicity distribution (CED) 245 classroom(s): complexity, quantitative research and 53–54, 54; emotional climate of 205–206, 206; instruction, quantitative research and 46–48; multicultural 395–396, 404, 404–407, 405; pedagogical content knowledge and 1134–1136; in rural settings 366–367; science, identity and 174–175; in urban settings 364–365; see also learning environment(s) Classroom Emotional Climate (CEC) 205, 206 Classroom Environment Scale (CES) 195–196, 205, 217 CLEI see Chemistry Laboratory Environment Inventory (CLEI) CLES see Constructivist Learning Environment Survey (CLES) clickers 807–808 climate change education 728–732 CMFs see contextual mitigating factors (CMFs) cognition 30–31; learning and 124–125 cognitive activation 629–630 cognitive theories of learning 90–92, 97

COLES see Constructivist-Oriented Learning Environment Survey (COLES) collaborative learning 98–99, 456–457, 460–461 collective wisdom 81–82 College and University Environment Inventory (CUCEI) 196, 217 colonlialism 391–393 colorblindness, race and 222 Common Core Standards 835–842, 840 community: funds of knowledge and 375–376; participationist learning and 92–93 community ethnography 64, 65–66 community-school partnership 16 comparative cases 76–77 competencies of learning 7 competitiveness, economic 560–562 computational thinking 454, 454–455 computer-assisted instruction (CAI) 33=8 computer-supported collaborative learning (CSCL) 456–457, 461 concept maps 807 concepts: learning and 124–125 conceptual change: curriculum coherence and 129; factors influencing 129–130; Framework for K-12 Science Education and 132–133; in knowledge integration framework 1176–1177; learning and 101–102, 127–129; learning over time and 130; meaningful context and 129; mechanisms of 128–129; prior knowledge and 129; teacher learning and 1169–1177; teaching 1170–1171; teaching for 1171–1176 conceptual schema 90–91 consequential learning 64 constructivism 19, 91–93, 197, 231, 238, 241, 697 Constructivist Learning Environment Survey (CLES) 197–198, 208, 217 Constructivist-Oriented Learning Environment Survey (COLES) 199–200, 208, 217 construct validity 36–37 content deepening 1207 content knowledge (CK) 1130–1133, 1149–1151 content validity 36 context, meaningful, conceptual change and 129 contextual mitigating factors (CMFs) 229–230 convergence, knowledge 458 correlative studies 38 COVID-19; chemistry education and 657; learning environments and 201–202; nature of science and 883–888; nature of scientific inquiry and 764; online instruction and 462, 484–485; rural communities and 359–360 criterion validity 36 critical discourse: within group 80; and hermeneutical conversations across groups 80–81; qualitative research and 79–81; regarding public reason 80 critical race theory (CRT): counternarratives 225–226; ethnicity in 224–248; intersectionality

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Index and 227–229; microaggressions and 226; race in 224–248 critical theory research: designs in 18; features of 17–19; paradigm 14–19; philosophical background in 14–15; quality standards in 18; report styles in 18–19; researcher role in 18; studies, examples of 15–17; theoretical framework in 14–15; topics in 17–18, 20 crosscutting concepts (CCCs) 132–133, 134, 139, 142–143, 145 cross-sectional panel studies 48–49 CRT see critical race theory (CRT) CSCL see computer-supported collaborative learning (CSCL) CUCEI see College and University Environment Inventory (CUCEI) cultural achievements of science 793–794 cultural anthropology 64 cultural-historical activity theory 68 cultural hybridity 64 culturally-relevant instruction 237–239 cultural negotiation 64 cultural scientific literacy 787 culture: components of 360; ethnicity and 229–237; funds of knowledge and 373–374; identity and 229–237; race and 229–237; student attitudes, toward science and 165, 170–171 curriculum: assessments and 1045–1046, 1052; authorable and customizable environments in 472–473; coherence of, conceptual change and 129; content standards and 817–846, 840; customization 472–473; early childhood 507– 510; engineering 971; ethnicity and 237–242; ideological 429–430; Indigenous students and 393–395, 404, 404–407, 405; integrated STEM and 569–571, 574; learning progressions and 134, 136–137, 144; minorities and 393–395; nature of science in 866–868; pedagogical framework in 474–476; race and 237–242; socioscientific issues and 901, 903–907; teacher empowerment and 471–476, 475; in teacher professional development 1206–1208 DAIM see Dialogical Argumentation Instructional Model (DAIM) dashboards 479–482, 480 DBR see design-based research (DBR) DCIs see disciplinary core ideas (DCIs) deaf and hard-of-hearing (DHH) students 342–343 decision-making, value of science for 796–799 decolonization 394–395 deep learning: knowledge-in-use and 125–126 democratic equality 68, 69–70 descriptive studies 38 design-based research (DBR) 71–76, 72 designs, research: in critical theory research 18, 20; experimental 38; in interpretivist/constructivist research 13, 20; in mixed-method research 20, 22;

nonexperimental 38; in post-positivist research 8, 20; in quantitative research 38; randomized 46 developmental disability (DD) 336–339 developmental evaluation 942–943 DHH see deaf and hard-of-hearing (DHH) students Dialogical Argumentation Instructional Model (DAIM) 400–402, 401, 404, 404–407, 405, 407 DIF see differential item functioning (DIF) differential item functioning (DIF) 42–43 disciplinary content knowledge 566–567 disciplinary core ideas (DCIs) 132–133, 134, 139, 142–143, 145 disciplinary literacy 333–334 discourse analysis 416 discourse practices: access and 422–426; across multiple learning contexts 417; aesthetics and 417–422; affect and 417–422; argumentation and 419–420; conflicts and 425; critical thinking and 420–421; cultural practices and situated nature of 432–435; data representations of 434–435; defined 415; discourse features in interaction and 433–434; discourse processes and 416–417, 422–426; domain-specific methods and 417; emerging research directions with 435–438; emotion and 417–422; epistemic practices and 418–422; evidence and 419–420; identity and 422–426; ideology and 426–431; methodological considerations with 432–435; multilingual ideologies and 430–431; multimodal discourses and 421–422; power and 428–429; rationale for discourse studies 414–416; representations and 421–422; theoretical considerations with 415–416; values and 426–431 discovery 456 distributed cognitive learning theory 72 drawing 508 early childhood education (ECE): accessibility of science education in 510–511; curricula 507–510; drawing 508; emotions in 505–507; engagement in scientific inquiry by 501–504; environmental education in 514–515; everyday science and 508; family participation and 515–516; holistic understandings and 512–514; infants 517; integration of other subject areas with science in 512–514; language-science relation in 511–512; literacy in 509; materiality and 516–517; mathematics in 509; motivation in 505–507; outdoor education in 514–515; play in 504–505; reasons for science education in 499–500; themes 501–517; toddlers 517 earth and space science research, learning progressions and 134, 135–136 earth science education: affective outcomes with 705–706; behavioral outcomes and 705–706; cognitive outcomes and 703–705; cross-national efforts and 706–708; environmental insight and 699–700; geoethics and 700–702; recent research

1223

Index advances in 696–708, 698; responsibility and 700–702; secondary 693–695; systems thinking hierarchy and 697–698, 698; university level 695–696 EBD see emotional behavioral disabilities (EBD) ECB see evaluation capacity building (ECB) ECE see early childhood education (ECE) ecology 596–600, 597 economic competitiveness 560–562 “Educating Americans for the 21st Century” 819–821 Education for Sustainable Development (ESD) 7–8, 514 effect size 39 elementary level: delivery of science content in 541–546; engineering in 542–546; field-based science and 534; methods coursework and 533–534; nature of scientific knowledge and 534–538, 552; physics education in 621–623; professional development at 533–534; references about instruments related to 1002; scientific inquiry instruction in 538–541; scientific literacy and 528–552; standards and 528–531; STEM in 546–551; teacher coursework at 532–533; teacher self-efficacy and 531–534 embedded assessments 451–452 emotional behavioral disabilities (EBD) 339–340 emotional climate, of classroom 205–206, 206 emotions: student attitudes, toward science and 171–173 empowerment, teacher 471–476, 475, 482–483 engagement 92; attitude and 168; cognitive 107; epistemic practices and 418–422; student attitudes, toward science engineering 542–546, 574, 964–965 engineering education, precollege: community and 980; contemporary studies of 968; crossdisciplinary integration with 985; curricula 971; disciplinary content in 972–974; early tensions between science and technology education 961–964; epistemic practices for 965–972, 979–980, 985; equity and 979–980, 986; future directions in 985–986; history of 960–964; identity and 974–978; interest development in 974–978; professional development in 982–984; purposes of 967–968, 978–981; reconsideration of purposes of 978–981, 986; references about instruments related to 1004–1005; social capital and 980–981; social justice and 979; teachers and teacher education in 981–985 environmental education: climate change education and 728–732; contemporary standards and 734– 736; criticisms of 738–739; in early childhood education 514–515; emotive dimensions of 726–728; future directions in 739–740; health and 725–739; history of 717–718; history of research in 719–721; history of term 718–719; as part of science education 733–734; place-based education and 721–725; science teachers and 736–738;

sustainability and 732–733, 736–737; trends and topics in 721–725; well-being and 725–739 epistemic agency 111, 753–754 epistemic growth: argumentation and 105–106; epistemic performance and 106–108; learning and 105–112 epistemic practices 418–422 epistemic thinking components 107 epistemic value of science 793–794 epistemological beliefs, in science education reform 1109–1111 equitable instruction 448 equity: learning and 96–97; student attitudes and 175 equity-oriented pedagogy 68, 69 error, measurement 41–42 ESD see education for sustainable development (ESD) ESSA see Every Student Succeeds Act (ESSA) e-textiles 455 ethnicity: assessments and 247–248; construction of 224; contextual mitigating factors and 229–230; in critical race theory 224–248; culturally-relevant instruction and 237–239; curriculum and 237–242; language and 242–244; linguistically-relevant instruction 237–239; pedagogy and 237–242; race vs. 221; sociocultural identity and 231–237; sociocultural perspectives and 229–237; special needs and 328–329; STEM-focused programs and 239–241; teacher perceptions and 246–247 ethnic minorities 16–17 ethnography: auto 65; community 64, 65–66; critical 65; critical theory research and 17; defined 63; interpretivist/constructivist research and 12; qualitative research and 63–67, 64; of and in science education 63–67, 64 evaluation see program evaluation: project evaluation evaluation capacity building (ECB) 939–940 everyday science 508 Every Student Succeeds Act (ESSA) 293, 300, 345 evidence: discourse practices and 419–420; obtaining 30–31; qualitative research and 76–77; in quantitative research 29–33; theory and 31–33; trustworthiness of 29–30 evolution 12, 589–593, 590 EVT see expectancy value theory (EVT) expectancy value theory (EVT) 164, 171, 1104– 1106, 1105 experience, teacher learning and 1166–1169 experimental designs 38 external validity 36 face validity 35 family: student attitudes, toward science and 171 femininity 270–273 FoK see funds of knowledge (FoK) four corners 808 Framework for K-12 Science Education 132–133 funds of knowledge (FoK) 373–376

1224

Index gaming 461 gender 263–285; belonging and 267–268; bias 266– 267; cognitive abilities and 265–266; emerging perspectives on 277–283; femininity and 270–273; gaps 264–269; identity and 175–176, 274–275; in identity-based approaches 270–277; imposter syndrome and 267; in intersectional approaches 277–279; LGBTQ+ students and 279–281; microaggressions and 268–269; motivation and 265–266; participation 264–265; performance 264–265; posthumanism and 281–282; queer theory and 279; self-determination and 267–268; self-efficacy and 267–268; stereotypes 266–267; student attitudes, toward science and 170–171, 175–176; in testing 265 gendered harassment 268–269 gender performativity 68, 70–71 generality: learning and 95 genetics 11–12, 593, 593–596 gifted learners 330–331, 343–345 globalization 393 Goals 2000 830–831 hard-of-hearing students 342–343 hierarchical linear modeling (HLM) 44–45, 48, 53–54 hierarchical structure, of data 44–45 HLM see hierarchical linear modeling (HLM) hope 81–82 IASA see Improving America’s Schools Act (IASA) ICEQ see Individualized Classroom Environment Questionnaire (ICEQ) ID see intellectual disability (ID) IDEA see Individuals with Disabilities Education Act (IDEA) identity: belonging and 275–276; construct, in science education 173–176; culture and 229–237; defining 176–178; discourse and 423–424; discourse processes and 422–426; ethnicity and 229–237; gender and 175–176, 274–275; learning and 159–160; measuring 176–178; participationist theories of learning and 93; race and 229–237; recognition and 68, 70–71; science 371–373; science classrooms and 174–175; sociocultural 231–237; sociocultural theories of 64, 64, 65; student attitudes and 173–178 ideological curriculum 429–430 IESO see International Earth Science Education Olympiad (IESO) IKSs see Indigenous knowledge systems (IKSs) imposter syndrome 267 Improving America’s Schools Act (IASA) 831–834 IMTPG see Interconnected Model of Teacher Professional Growth (IMTPG) Indigenous knowledge systems (IKSs) 397–399 Indigenous students: attitudes of 176; colonialism and 391–393; culturally-responsive education for

389–407, 400, 401, 404–405; curriculum and 393–395, 404, 404–407, 405; decolonization and 394–395; Dialogical Argumentation Instructional Model and 400–402, 401; funds of knowledge and 376; Industrial Revolution and 391–393; language of instruction and 402–404; nature of science and 396–397; place-based education and 378; slave trade and 391–393 Individualized Classroom Environment Questionnaire (ICEQ) 196, 217 Individuals with Disabilities Education Act (IDEA) 327–329, 328, 329, 331, 334–336 Industrial Revolution 391–393 inequality: in critical theory research 14 inert knowledge 125 infants 517; see also early childhood inheritance 593, 593–596 Inq-Blotter 1017, 1017–1038, 1018, 1019–1023, 1027, 1029, 1033–1037 Inq-ITS 1017, 1017–1038, 1018, 1019–1023, 1027, 1029, 1033–1037 inquiry: artificial intelligence dashboard for supporting student 1011–1038, 1017–1018, 1019–1023, 1027, 1029, 1033–1037; doing of 538–540; learning and 103–104; and need for reform 1013–1014; scientific inquiry instruction 538–541; students’ difficulties with 1012 inquiry-based science 332–333 insideness, masculinity and 274 integrated STEM see interdisciplinary approaches and integrated STEM intellectual disability (ID) 336–339 Interconnected Model of Teacher Professional Growth (IMTPG) 1197–1198 interdisciplinary approaches and integrated STEM; affective outcomes with 562–563; in Africa 561–562; aligning assessments with 572; in Asia 561; authentic learning environments and 568; challenges to 565–573; content knowledge and 566–567; curricula in 569–571, 574; definitions with 560; economic competitiveness and 560; in Europe 561; historical context with 560–562; in North America 560–561; in Oceania 562; pedagogies in 567–568; professional development and 569, 574; research supporting 562–565; school structures and 571; skill-based outcomes in 562–563; in South America 561; specialized schools and 572–573; special populations and 565, 574; standardized testing and 564–565; student academic outcomes and 563–564; teacher education programs and 568; teacher views on integration in 566 internal validity 36 International Earth Science Education Olympiad (IESO) 706–707 interpretivism 9–10 interpretivist/constructivist research: features of 12–14; paradigm 9–14; quality standards in

1225

Index 13; report styles in 13; research designs in 13; researcher-participant relationship in 13; studies, examples of 10–14; topics in 12–13, 20 intersectionality 68; in community-based settings 228–229; critical race theory and 227–229; gender and 277–279; identity and 70–71; in school-based settings 228–229; in science teacher education 227–228 intertexuality 423–424 KI see knowledge integration (KI) knowledge: dimensions of scientific 139; inert 125; prior, conceptual change and 129; see also pedagogical content knowledge (PCK); teacher knowledge knowledge, learning, and instruction (KLI) framework 448, 457 knowledge convergence 458 knowledge integration (KI) 448–450, 452–453, 463–468, 477–478, 1176–1177 knowledge-in-use: deep learning and 125–126; development of 122; learning progressions and 122–124, 123 knowledge repositories 457–458 knowledge systems, Indigenous 397–399 laboratories, simulated vs. physical 456 language: in early childhood 511–512; ethnicity and 242–244; Indigenous students and 402–404; natural-language processing 447; race and 242–244; translanguaging 306 LDs see learning disabilities (LDs) learning: argumentation and 105–106; autonomy and 101; behavioral theories of 90; big ideas 139–140; in biological content areas 588–608, 589, 590, 593, 597, 600, 604, 606; cognition and 124–125; cognitive theories of 90–92, 97; collaborative 98–99, 456–457, 460–461; competencies 7; concepts and 124–125; conceptual background of 139–140; conceptual change and 101–102, 127– 129; consequential 64; deep, knowledge-in-use and 125–126; deep, of three-dimensional learning progressions 144–145; by direct instruction, inquiry vs. 103–104; distributed cognitive learning theory 72; engagement and 92; epistemic growth and 105–112; epistemic performance and 106–108; equity and 96–97; generality and 95; and ideas across time 126; identity and 159–160; inquiry and 103–104; metacognition and 99–100, 108; misinformation and 108–112; motivation and 100–101; over time 130, 139–140; participationist theories of 92–95, 97–102; post-truth and 108–112; professional, learning progressions and 144; queering 280–281; scaffolding and 97–98; self-regulated 99–100; situativity and 95–96; social environment and 93; socioscientific inquiry-based 914–915; teacher knowledge and 1151–1152; to teach science 1162–1193, 1188–1190, 1193;

theories of 89–112; three-dimensional 503; transfer and 94–95; variables in 28; zone of proximal development and 93–94; see also teacher learning learning analytics 478–479 learning disabilities (LDs) 334–336; see also special needs learning environment(s): assessment of 194–200; COVID-19 and 201–202; cross-national studies on 204; developments in data analysis on 206–207; educational innovation evaluation and 200–202; emotional climate of 205–206, 206; evolution of research on 193–194; improving 208–209; mixedmethods approaches to 194–195; multiple, links between 203–204; physical 207–208; in secondary analysis 203; self-determination theory and 194; student outcomes and 202–203; typologies of 205; see also classroom(s) Learning Environment Inventory (LEI) 195–196, 217 learning management system (LMS) 450 learning progression (LP): assessment and measurement and 134, 137–138; Atlas for Science Literacy and 131; components of 142–144; comprehensible levels and 143; curriculum and 134, 136–137, 144; description of progress in, across levels 143–144; earth and space science research and 134, 135–136; Framework for K-12 Science Education and 132–133; future research directions on 144–147; history of 130–133; knowledge-in-use and 122–124, 123; life sciences research and 134, 135; lower anchors of 143; multi-dimensions and 134, 136; multiple paths with 141–142; No Child Left Behind and 130; physical science research and 133–135; professional learning and 144; research-based development and 140–141; research studies on 133–138, 134; scientific practices and 134, 136; Systems for State Science Assessment and 131; Taking Science to School and 131–132; three-dimensional, deep learning of 144–145; three-dimensional, example 150–151; three-dimensional, features of 140–142, 141; three-dimensional, testing and validating 145–146; upper anchors of 143; views on 138 LEI see Learning Environment Inventory (LEI) LGBTQ+ students 279–281 life sciences research, learning progressions and 134, 135 linear measures, in quantitative research 40–45, 42–43 linguistically-relevant instruction 237–239 linguistically responsive teaching (LRT) 314–315 listening triads 807 literacy: disciplinary 333–334; in early childhood 509; elementary science teaching and 528–552; see also scientific literacy LMS see learning management system (LMS) longitudinal studies 48–49 LP see learning progression (LP) LRT see linguistically responsive teaching (LRT)

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Index marginalization 16 Marxism 14 masculinity 273–274 materiality 516–517 measurement error 41–42 metabolism 606, 606–608 metacognition 99–100, 108, 1187–1190 microaggressions 226, 268–269 minorities 16–17; culturally-responsive education for 389–407, 400, 401, 404–405; curriculum and 393–395; funds of knowledge and 375 misinformation 108–112 mixed-method research: designs in 20, 22; ethnicity and 248–249; features of 22; on learning environments 194–195; quality standards in 20, 22; race and 248–249; report styles in 20, 22; researcher-participant relationship in 20, 22; sociocultural identity and, in race and ethnicity 232–233; studies, examples of 21–22; topics in 20, 22 mixed-methods research: paradigm 19–22, 20; quality standards in 20; topics in 20 mobility impairment 340–341 motivation: attitude and 162, 162, 167; in early childhood 505–507; gender and 265–266; to learn 244–245; learning and 100–101; student 6–7; to teach 244–245; see also student attitudes, toward science multi-cognitive ideologies 430–431 multicultural science classrooms 395–396, 404, 404–407, 405 multilingual learners: advancement of field with 318; centering science and engineering practices to guide language use in 303–304; contemporary perspectives on 303, 308, 310, 313; content learning and 306; contextualization with 315; defined 292; instructional approaches with 296; integration of science and language with 313–315; language ideologies and 311–312; linguistically responsive teaching and 314–315; multimodal communication and 304–305; Next Generation Science Standards and 310–311; No Child Left Behind and 292–293; pedagogical tools with 308–309; policy legislation with 293–295; science assessment and 310–312; teacher decision-making with 309; teacher preparation and 312–315; terms for 292–293; themes in research on 299–300; theoretical perspectives on 295; traditional perspectives on 302–303, 307–308, 310, 312–313; translanguaging and 306 multimodal communication 304–305 NAEP see National Assessment of Educational Progress (NAEP) National Assessment of Educational Progress (NAEP) 329, 541, 833–835, 846n7, 935, 1045–1047, 1077–1089, 1078, 1080, 1082–1083, 1084, 1087, 1088

National Commission on Excellence in Education (NCEE) 818–819 National Council on Education Standards and Testing (NCEST) 828 National Education Standards and Improvement Council (NESIC) 829–831 National Science Education Standards (NSES) 826–828, 827 “Nation at Risk, A” 818–819 natural-language processing (NLP) 447 natural selection 12 nature of chemistry 662–665, 663–664 nature of science (NOS): assessment, evolving landscape of 858–863; assessment research 858– 866, 859–860; chemistry education research and 587–588; construct 855–858, 856–857; COVID19 pandemic and 883–888; creative aspect of 856; culturally responsive science education and 396–397; in curricula 866–868; descriptive studies of 871–874; elementary level and 534–538, 552; empirical aspect of 856; inclusion criteria for literature on 851–852; inferential aspect of 856; in instructional materials 869–870; intervention studies in 874–883; learner conceptions of 871–872, 874–877; research landscape 852–855; scientific law aspect of 856; scientific method myth as aspect of 856; scientific theory aspect of 856; social and cultural embeddedness of science aspect of 857; social dimensions of science aspect of 857; socioscientific issues and 908; standards and 866–868; teacher conceptions of 872–874, 877–881; tentative aspect of 856; in textbooks 868–869; theory-driven aspect of 856 nature of scientific inquiry (NOSI) 764–769, 766–767 NCEE see National Commission on Excellence in Education (NCEE) NCEST see National Council on Education Standards and Testing (NCEST) NCLB see No Child Left Behind (NCLB) NESIC see National Education Standards and Improvement Council (NESIC) Next Generation Science Standards (NGSS) 128– 129, 132–133, 294, 310–311, 326–327, 694–695, 839–840, 840, 841–842, 940–941, 945–947, 963, 979, 1013–1014 NGSS see Next Generation Science Standards (NGSS) NLP see natural-language processing (NLP) No Child Left Behind (NCLB) 292, 833–834; learning progression and 130 NOS see nature of science (NOS) NOSI see nature of scientific inquiry (NOSI) NSES see National Science Education Standards (NSES) objectivity: in quantitative research 33 OERs see open education resources (OERs) open education resources (OERs) 485

1227

Index optics 640–641 orthopedic impairment 340–341 outdoor education 514–515 outsideness, masculinity and 273–274 paradigms, research: critical theory 14–19; diversity of 4; importance of 3–5; interpretivist/ constructivist 9–14; mixed-methods 19–22, 20; post-positivist 5–9; in science education research 3–23, 20; shifts 3 participationist theories of learning 92–95, 97–102 PBE see place-based education (PBE) PCK see pedagogical content knowledge (PCK) pedagogical content knowledge (PCK) 587, 759– 760, 770–771, 1109, 1124–1128, 1126, 1128, 1132–1147, 1149–1152, 1203–1204 pedagogical knowledge (PK) 36, 1126, 1133, 1135–1137, 1139, 1144, 1147, 1149–1151 PEEL see Project for Enhancing Effective Learning (PEEL) peer critique 457, 459–461 peer-mediated instruction (PMI) 338 person measure 43, 43–44 phenomenological variant of ecological systems theory (PVEST) 543 physical disability 340–341 physical science research, learning progressions and 133–135 physics education: classroom management and 630; cognitive activation and 629–630; content areas in 638–646; core concepts and practices in 632–638, 636; different sciences in 621; electricity in 641–642; emotional support and 630; energy in 633; epistemology in 624; everyday meanings and 631; experimentation in 638; force in 634–635; goals of 621–629, 626; in lower secondary 623–625; matter in 633–634; mechanics in 639–640; modeling instruction in 639; optics in 640–641; physical-mathematical modeling in 635–637, 636; in primary level 621–623; problem solving in 637–638; process-oriented teaching and 630–631; quality dimensions of 629–631; quantum mechanics in 643–645; references about instruments related to 1003–1004; science propaedeutics and 627; scientific literacy and 619; special relativity theory in 645–646; thermodynamics in 642–643; in upper secondary 625–629, 626 PISA see Program for International Student Assessment (PISA) PK see pedagogical knowledge (PK) place-based education (PBE) 376–379, 721–725 play 504–505 PMI see peer-mediated instruction (PMI) positioning: participationist theories of learning and 93 positivism 5 postcolonial theory 64

posthumanism 281–282 post-positivist research: designs in 8; features of 8–9; paradigms 5–9; quality standards in 9; report styles in 9; researcher-participant role in 8–9; studies, examples of 6–8; topics in 8 post-truth 108–112 poverty 16–17, 370–371 power dynamics: critical theory research 14 pragmatist approaches 179–180 problem solving: in physics education 637–638; value of science for 795–796 program evaluation: future directions with 1008– 1009; references related to 997–1005; support for 1007–1008; trends in 1005–1007; see also project evaluation Program for International Student Assessment (PISA) 165, 170–171, 176, 203, 333, 370–371, 399, 402, 419, 620, 761–762, 787, 843, 935, 994, 1045–1047, 1048–1051, 1070–1077, 1072, 1074, 1090–1091, 1094 Project 2061 131–132, 821–822, 826 project evaluation: capacity building 939–940; case for 930–939; developmental 942–943; dissemination of 951–952; equity in 950; Next Generation Science Standards and 945–947; program-level support for 949; project research and 949–950; researcher-practitioner partnerships and 941–942; resource investment for 950–951; shifting focus of 943–948; systemic reform and 951; teacher effectiveness and 945–947; transformative evaluation in 940–941; workforce science literacy and 944–945; see also program evaluation Project for Enhancing Effective Learning (PEEL) 1178–1181 propaedeutics 627 protectionism 797 purpose, and student attitudes toward science 159 PVEST see phenomenological variant of ecological systems theory (PVEST) QTI see Questionnaire on Teacher Interaction (QTI) qualitative research: applications of 62–76, 64, 68, 72; case studies and 67–71, 68; collective wisdom and 81–82; comparative cases in 76–77; critical dialogues in 79–81; as culture and practice 60–83, 64, 68, 72; data sets and 76; defined 60–61; design-based research and 71–76, 72; epistemic culture and 62; epistemological considerations with 77–79; ethnography and 63–67, 64; evidence and 76–77; framing 60–62; hope and 81–82; illustrative examples of 62–76, 64, 68, 72; methodology in 77–79; need for 61; quantitative vs. 28; as questions about questions 60–62; science of 61–62; solidarity and 81–82; standards for 78–79; in strategies of science teaching evaluation 45–46 quantitative research: classroom instruction investigation in 46–48; complexity of classrooms

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Index and 53–54, 54; cross-sectional panel studies in 48–49; design in 38; differential item functioning in 42–43; effect size in 39; evidence in 29–33; hierarchical structure of data and 44–45; linear measures in 40–45, 42–43; longitudinal studies in 48–49; methodological considerations with 33–38; objectivity in 33; qualitative vs. 28; Rasch analysis in 40–45, 42–43; reliability in 34, 34–35; sample size in 39; in science education research 45–54, 54; significance in 37–38; sociocultural identity and, in race and ethnicity 232–233; statistical procedures in 39–40; theoretical considerations with 33–38; validity in 34, 35–37; video analysis in 50–53 queer theory 279 Questionnaire on Teacher Interaction (QTI) 196–197, 202 race: assessments and 247–248; construction of 224; contextual mitigating factors and 229–230; in critical race theory 224–248; culturally-relevant instruction and 237–239; curriculum and 237–242; ethnicity vs. 221; identity and 229–237; language and 242–244; linguistically-relevant instruction 237–239; neutrality 222; pedagogy and 237–242; reality of 221; sociocultural identity and 231–237; sociocultural perspectives and 229–237; special needs and 328–329; STEM-focused programs and 239–241; teacher perceptions and 246–247 randomized experimental designs 46 Rasch analysis 40–45, 42–43 reading, in science 803–804 reform-oriented teaching practices 1111–1115 relationality theory 72, 73–74 reliability: in quantitative research 34; in video analysis 52 report styles: in critical theory research 18–19, 20; in interpretivist/constructivist research 13, 20; in mixed-method research 20, 22; in post-positivist research 9, 20 research-based development, learning progressions and 140–141 researcher-participant relationship: in critical theory research 20; in interpretivist/constructivist research 13, 20; in mixed-method research 20, 22; in postpositivist research 8–9, 20 researcher role: in critical theory research 18 rightful presence 64 rural settings: classrooms in 366–367; conceptual tools with 368–379; contemporary science education research in 366–367; COVID-19 pandemic and 359–360; culture and 360; defined 367; disruption of urban-rural binary 379–381; educational inequities between urban and 360; focus on 359; funds of knowledge and 373–376; opportunities and 366; place-based education and 376–379; science identities and 371–373; socioeconomic status and 369–371

sample size 39 scaffolding 97–98 schema, conceptual 90–91 school-community partnership 16 science identities 371–373 Science Laboratory Environment Inventory (SLEI) 197–198, 217 science propaedeutics 627 scientific and engineering practices (SEPs) 132–133, 134, 139, 142–143, 145 scientific discourse, in students 11 scientific enterprise 789–793, 790, 791 scientific inquiry literacy: argumentation and 756–761; authenticity and 763–764; critique and evaluation of scientific arguments in 758; discourse and 759; epistemic agency and 753–754; evidence and 757–758; factors impacting outcomes with 762–763; nature of scientific inquiry and 764–769, 766–767; recommendations on 773–775; research tools and 771, 772–773; scientific investigations and 754–756; scientific literacy and 749–750; status of scientific inquiry and 751–752; student outcomes and 761–764; student resources and 758–759; teacher pedagogical content knowledge and 759–760; teachers’ pedagogical content knowledge and 770–771; trends in research 752–753; uncertainty and 756–757 scientific literacy 16–17, 74–75, 619; alternative visions of 808–810; as constructive process 800–801; cultural 787; in derived sense 793–799; diverse meanings of 786–789; at elementary level 528–552; functional 787; in “fundamental sense” 799–808; project evaluation and 944–945; reading and 803–804; talk and 806–808; “true” 787; workforce and 944–945; writing and 804–806 scientific playworlds 505 SDT see self-determination theory (SDT) SEE-SEP model 905–906 self-concept: student attitudes, toward science and 168 self-determination 267–268 self-determination theory (SDT) 100–101, 194 self-efficacy 267–268, 1106–1108; student attitudes, toward science and 168; teacher, elementary level 531–534 self-regulated learning 99–100 self-regulated strategy development (SRSD) 339 self-regulation, student 6–7 SEM see structural equation models (SEM) semiotics, aesthetics and 182 sensemaking, socioscientific issues and 907–913 SEPs see scientific and engineering practices (SEPs) serendipitous science engagement (SSE) 438 SES see socioeconomic status (SES) sexual harassment 268–269 shared knowledge repositories 457 significance: in quantitative research 37–38 situativity: learning and 95–96

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Index slavery 391–393 SLEI see Science Laboratory Environment Inventory (SLEI) SMK see subject-matter knowledge (SMK) SNA see social network analysis (SNA) social environment 93 social justice 72, 74–75 social mobility 68, 69–70 social network analysis (SNA) 68–69, 434 socioeconomic status (SES) 369–371; argumentation and 909–913; as character development 901; modeling and 909–913; outside of conventional classrooms 913–917 socioscientific inquiry-based learning (SSIBL) 914–915 socioscientific issues (SSI): character development and 917–920; citizenship and 917–920; conceptualization of 899–900; curriculum and 901, 903–907; epistemic practices and 907–913; framing of 900–902; nature of science and 908; perspective taking and 917–920; in philosophical perspective 902–903; place-based education and 722–725, 915–916; in psychological perspective 902–903; sensemaking practices and 907–913; in sociological perspective 902–903; teacher pedagogical beliefs and 903–907 socioscientific perspective taking (SSPT) 918–919 socioscientific reasoning (SSR) 918 socio-technical systems 72 solidarity 81–82 special needs: “assessment for all” and 345–346; augmentative and alternative communication with 338; deaf students 342–343; developmental disability 336–339; disciplinary literacy and 333– 334; emotional behavioral disabilities 339–340; future research on 348; gifted and talented learners 330–331, 343–345; hard-of-hearing students 342–343; inquiry-based science and 332–333; intellectual disability 336–339; learning disabilities 334–336; mobility impairment 340–341; orthopedic impairment 340–341; peer-mediated instruction with 338; practices with 331–332; science teacher education and 346–348; selfregulated strategy development with 339; special educational needs 327–330, 328, 329; standards with 331–332; twice exceptional learners and 332; Universal Design for Learning and 331, 335, 348; visual impairments 341–342 SRSD see self-regulated strategy development (SRSD) SSE see serendipitous science engagement (SSE) SSI see socioscientific issues (SSI) SSIBL see socioscientific inquiry-based learning (SSIBL) SSPT see socioscientific perspective taking (SSPT) SSR see socioscientific reasoning (SSR) SSSA see Systems for State Science Assessment (SSSA)

standards: in Africa 529; in Asia 529; Common Core 835–837; curriculum reform and 817–846, 840; effects on students 843; in Europe 529; federal legislation on 828–836; future of 842–843; historical background on 817–824; Improving America’s Schools Act 831–834; movement, beginning of 818; national 7; National Science Education Standards 826–828, 827; nature of science and 866–868; Next Generation Science Standards 128–129, 132–133, 294, 310–311, 326– 327, 694–695, 839–840, 840, 841–842; No Child Left Behind 833–834; in North America 531; in Oceania 530; push for, by states 824–828, 827; in qualitative research 78–79; in South America 531 standards, quality: in critical theory research 18, 20; in interpretivist/constructivist research 13, 20; in mixed-method research 20, 22; in post-positivist research 9, 20 Standards for Technological Literacy (STL) 961–962 statistical procedures, in quantitative research 39–40 STEM education: in critical theory research 17 stereotypes, gender 266–267 STL see Standards for Technological Literacy (STL) structural equation models (SEM) 44–45, 48–49 student aspirations 178–179 student attitudes, toward science: achievement and 169–170; affect in, research on 163–165; affect object and circumstance in 160; and analyticsynthetic category distinction 159; coordinating 161, 161–163, 162; correlate studies 164; culture and 165, 170–171; defining 161, 161–163, 162; emotions and 171–173; engagement and 168; equity and 175; expectancy value theory and 164, 171; family and 171; gender and 170–171, 175–176; identity and 173–178; Indigenous students and 176; interest and 165–167; international assessments of 169–170; measuring 161, 161–163, 162, 163–168; motivation and 162, 162, 167; purpose and 159; science classrooms and 174–175; self-concept and 168; self-efficacy and 168; sociocultural influences on 170–171; teaching practices and 169–170 student motivation 6–7 students: female 12; marginalization of 16; scientific discourse in 11; understanding of nature of scientific inquiry 766, 766–769 student self-regulation 6–7 subjectivity 10 subject-matter knowledge (SMK) 365, 541, 569, 605, 607, 762, 1077, 1131, 1142–1143 synthetic categories, analytic vs. 159 Systems for State Science Assessment (SSSA) 131 systems thinking 7–8 systems thinking hierarchy (STH) 697–698, 698 Taking Science to School (TSTS) 131–132, 837 talented learners 330–331, 343–345 talk, in science 806–808

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Index TAP see Teacher Action Planner (TAP) taste, for science 180–182 Teacher Action Planner (TAP) 480–481 teacher attitudes and beliefs 1101–1118, 1102, 1103, 1105 teacher beliefs: about science 1115–1118; assessment of 1115–1118; constructs related to 1102; epistemological 1109–1111; expectancy value theory and 1104–1106, 1105; functions of 1103, 1103–1104; future research on 1117–1118; on reform-oriented science teaching practices 1111–1115; theoretical frameworks for 1104– 1111, 1105 teacher conceptions, of nature of science 872–874, 877–881 teacher effectiveness, project evaluation and 945–947 teacher empowerment 471–476, 475, 482–483 teacher knowledge: content 1130–1133; disciplinary education and 1131; expertise and 1147–1148, 1152; future research in 1153–1155; models of 1124–1128, 1126, 1128; nature of 1123–1124; professional development and 1153; progress in research on 1149–1152; relations between categories of 1139–1142; specific subject matter teaching experience and 1131–1132; student learning and 1151–1152; student variables and 1142–1145; see also pedagogical content knowledge (PCK) teacher learning: conceptual change and 1169–1177; conditions for 1183–1190; discourse in science and 1184–1186; educator role challenges with 1186–1187; experience in 1166–1169; habits and frames of mind for 1181–1183; knowledge integration and 1176–1177; metacognition and 1187–1190; Narrative Boxes and 1164; overview of 1162–1163; Project for Enhancing Effective Learning and 1178–1181; recurring themes in 1177–1181; reflection and 1168–1169; of science as discipline 1184 teacher pedagogical beliefs 903–907 teacher perceptions, ethnicity and 246–247 teacher professional development (TPD): active learning in 1209; characteristics of effective 1199; coaching and 1210–1211; collaboration in 1211–1212; collective participation in 1211– 1212; content deepening and 1207; content focus 1205; curricular resources and materials in 1206–1208; district and school 1212–1213; duration of 1203–1204; at elementary level 533– 534; engineering education and 982–984; future considerations for 1212–1215; Interconnected Model of Teacher Professional Growth and 1197–1198; interdisciplinary approaches and integrated STEM and 569; model lessons in 1207–1208; online 1213–1215; outcomes 1201, 1201–1203; program coherence 1204, 1204– 1205; studies in, broad features of 1200–1201;

teacher design teams in 1206–1208; video analysis and 1209–1210 teachers’ collective efficacy 1108 teacher self-efficacy 531–534, 1106–1108 teachers’ epistemological beliefs 1109–1111 teachers’ pedagogical content knowledge and 759–760, 770–771 technological pedagogical content knowledge (TPACK) 669, 669–670 technology: chemistry education research and 669; pedagogy and 448–449 theory: evidence and 31–33 Third International Mathematics and Science Study (TIMSS) 8, 164, 528–529, 620, 833, 843, 946, 994, 1045–1047, 1048–1051, 1052–1070, 1054– 1067, 1068, 1071, 1076–1077, 1092, 1094 three-dimensional learning 503 3D printing 676–677 time: ideas across 126; learning over, conceptual change and 130; learning over, of big ideas 139–140 TIMSS see Third International Mathematics and Science Study (TIMSS) toddlers 517; see also early childhood topics, research: in critical theory research 17–18, 20; in interpretivist/constructivist research 12–13; in mixed-method research 20, 22; in post-positivist research 8, 20 TPACK see technological pedagogical content knowledge (TPACK) TPD see teacher professional development (TPD) transfer: learning and 94–95 transformative evaluation 940–941 translanguaging 306 trends: in biology education 588, 589; in chemistry education research 660, 660–662; in environmental education 721–725; in evaluation 995–996; in science education 993–995; in science education program evaluation 1005–1007; in scientific inquiry literacy 752–753 trustworthiness, of evidence 29–30 TSTS see Taking Science to School (TSTS) turn and talk 807 twice exceptional learners 332 UDL see Universal Design for Learning (UDL) Universal Design for Learning (UDL) 331, 335, 340, 348 urban settings: classrooms in 364–365; conceptual tools with 368–379; contemporary science education research in 362–365; culture and 360; defined 365; disruption of rural-urban binary 379–381; educational inequities between rural and 360; funds of knowledge and 373–376; place-based education and 376–379; science identities and 371–373; socioeconomic status and 369–371; teacher preparation and development for 363–364

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Index validity: construct 36–37; content 36; criterion 36; external 36; face 35; internal 36; in quantitative research 34, 35–37; with Rasch analysis 41 variables, in learning 28 VASI see Views About Scientific Inquiry (VASI) VCD see video case diagnosis (VCD) VI see visual impairment (VI) video analysis 50–53 video case diagnosis (VCD) 545 Views About Scientific Inquiry (VASI) 765–766 virtual chemistry laboratory 670–672, 672 virtual reality (VR) 453, 674–675 visibility, of scientific phenomena 451 visual impairment (VI) 341–342 visualization technologies 453 VNT see voluntary national test (VNT)

voluntary national test (VNT) 833 VR see virtual reality (VR) What Is Happening in This Class? (WIHIC) 198–199, 203, 205, 217 WIHIC see What Is Happening in This Class? (WIHIC) wisdom, collective 81–82 women 12 writing, in science 804–806 YCVS see Young Children’s View of Science (YCVS) Young Children’s View of Science (YCVS) 345 zone of proximal development 93–94

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