Research Methods for Education in the Digital Age 9781474245630, 9781474245623, 9781474245685, 9781474245647

Citation preview

Research Methods for Education in the Digital Age

Bloomsbury Research Methods for Education series Edited by Melanie Nind, University of Southampton, UK The Bloomsbury Research Methods for Education series provides overviews of the range of sometimes interconnected and diverse methodological possibilities for researching aspects of education such as education contexts, sectors, problems or phenomena. Each volume discusses prevailing, less obvious and more innovative methods and approaches for the particular area of educational research. More targeted than general methods textbooks, these authoritative yet accessible books are invaluable resources for students and researchers planning their research design and wanting to explore methodological possibilities to make well-informed decisions regarding their choice of methods.

Also available in the series: Research Methods for Pedagogy, Melanie Nind, Alicia Curtin and Kathy Hall

Place-Based Methods for Researching Schools, Pat Thomson and Christine Hall

Forthcoming: Research Methods for Understanding Practitioner Learning, Vivienne Baumfield, Elaine Hall, Rachel Lofthouse and Kate Wall

BLOOMSBURY RESEARCH METHODS FOR EDUCATION

Research Methods for Education in the Digital Age Maggi Savin-Baden and Gemma Tombs Bloomsbury Academic An imprint of Bloomsbury Publishing Plc

LON DON • OX F O R D • N E W YO R K • N E W D E L H I • SY DN EY

Bloomsbury Academic An imprint of Bloomsbury Publishing Plc

50 Bedford Square London WC1B 3DP UK

1385 Broadway New York NY 10018 USA

www.bloomsbury.com BLOOMSBURY and the Diana logo are trademarks of Bloomsbury Publishing Plc First published 2017 © Maggi Savin-Baden and Gemma Tombs, 2017 Maggi Savin-Baden and Gemma Tombs have asserted their right under the Copyright, Designs and Patents Act, 1988, to be identified as Authors of this work. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage or retrieval system, without prior permission in writing from the publishers. No responsibility for loss caused to any individual or organization acting on or refraining from action as a result of the material in this publication can be accepted by Bloomsbury or the author. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. ISBN: HB: 978-1-4742-4563-0 PB: 978-1-4742-4562-3 ePDF: 978-1-4742-4564-7 ePub: 978-1-4742-4566-1 Library of Congress Cataloging-in-Publication Data Names: Savin-Baden, Maggi, 1960- author. | Tombs, Gemma, editor. Title: Research methods for education in the digital age / Maggi Savin-Baden, Gemma Tombs. Description: London; New York: Bloomsbury Academic, 2017. | Series: Bloomsbury research methods for education | Includes bibliographical references. Identifiers: LCCN 2016018819 (print) | LCCN 2016031712 (ebook) | ISBN 9781474245630 (hardback) | ISBN 9781474245623 (paperback) | ISBN 9781474245647 (ePDF) | ISBN 9781474245661 (ePub) | ISBN 9781474245647 (epdf) | ISBN 9781474245661 (epub) Subjects: LCSH: Education–Research–Methodology. | Education–Effect of technological innovations on–Research–Methodology. | BISAC: EDUCATION / Study Skills. | EDUCATION / Research. | EDUCATION / Computers & Technology. Classification: LCC LB1028 .S2293 2017 (print) | LCC LB1028 (ebook) | DDC 370.7–dc23 LC record available at https://lccn.loc.gov/2016018819 Series: Bloomsbury Research Methods for Education Typeset by Deanta Global Publishing Services, Chennai, India

It may be that the gulfs will wash us down: It may be we shall touch the Happy Isles, And see the great Achilles, whom we knew. Tho’ much is taken, much abides; and tho’ We are not now that strength which in old days Moved earth and heaven, that which we are, we are; One equal temper of heroic hearts, Made weak by time and fate, but strong in will To strive, to seek, to find, and not to yield. Ulysses Tennyson (1833)

vi

For Cathy, Mark and Alyssa

viii

Contents

List of Figures  xi List of Tables  xii Acknowledgements  xiii Series Editor’s P ­ reface  xiv A Glossary of Research Methods and Approaches  xvi

Introduction 1 1 I ssues in researching education in the digital age 9 2 New methodologies? 33 3 Ethnographies for the digital age 55 4 A  dapting research approaches for educational research in a digital age 79 5 Quantitative data in digital contexts 99 6 Digital ethics 121 7 Digital data creation and collection 143 8 Data management 167

x

CONTENTS

 9 R  epresentation and portrayal in qualitative research 195 10 Digital impact 215 References  241 Index  269

List of Figures

Figure 1.1  Developing a conceptual framework  19 Figure 9.1  Concepts of portrayal  208

List of Tables

Table 0.1 Terms used in learning in the digital age 2 Table 1.1 New typology of data 11 Table 1.2 Philosophies that inform our research practice 17 Table 1.3 Question development for research in the digital age 20 Table 2.1 Methodologies related to digital visual methods 37 Table 2.2 An overview of arts-related approaches 41 Table 2.3 Types of digital stories 51 Table 3.1 Positioning ethnographies 60 Table 3.2 Forms of ethnography for the digital age 61 Table 5.1 Types of quantitative educational data 101 Table 5.2 Modes of online survey delivery 106 Table 5.3 Quantitative educational data for virtual worlds research 115 Table 7.1 Paradigm influences on researcher roles and data collection 146 Table 7.2 Observational approaches in digital spaces 153 Table 7.3 Interview approaches in digital spaces 161 Table 8.1 Types of digital data 170 Table 8.2 Digital tools for analysis 176 Table 8.3 Theories for interpreting educational research data in a digital age 191 Table 9.1 Researcher stance in representation 203 Table 10.1 Key terms in open education and open research 228

Acknowledgements

Many people have supported the development of this book and helped us to explore ideas and ways of reviewing research. We would like to thank Melanie Nind for asking us to take on this project and providing critical comments. Maggi would like to thank the University of Worcester for their support in this project and particularly Ann Jordan, Head of the Institute of Education, and David Green, Vice Chancellor, for their enthusiasm for her work. Gemma would like to thank the Disruptive Media Learning Lab and particularly Oliver Wood for his artistic and technical expertise in creating the figures contained in this book. Our final thanks go to John Savin-Baden and Julie Tombs for the food, care and support they provided as well as for their detailed checking and critical comments during the completion of the manuscript.

Series Editor’s ­Preface

The idea of the Bloomsbury Research Methods for Education series is to provide books that are useful to researchers wanting to think about research methods in the context of their research area, research problem or research aims. While researchers may use any methods textbook for ideas and inspiration, the onus falls on them to apply something from social science research methods to education in particular, or from education to a particular dimension of education (pedagogy, schools, the digital dimension and practitioner learning, to name some examples). This application of ideas is not beyond us and has led to some great research and also to methodological development. In this series though, the books are more targeted, making them a good place to start for the student, researcher or person wanting to craft a research proposal. Each book brings together in one place the range of sometimes interconnected and often diverse methodological possibilities for researching one educational context, research problem or phenomenon. A quick look at the opening glossary will give you an idea of the methods you will find included within each book. You can expect a discussion of those methods that is critical, authoritative and situated. In each text, the authors use powerful examples of the methods in use in the arena with which you are concerned. There are other features that make this series distinctive. In each of the books the authors draw on their own research and on the research of others making alternative methodological choices. In this way, they address the affordances of the methods in terms of real studies; they illustrate the potential with real data. The authors also discuss the rationale behind the choice of methods and the way researchers put them together in research designs. As readers, you

SERIES EDITOR’S ­P REFACE

xv

will get behind the scenes of published research and into the kind of methodological decision making that you are grappling with. In each of the books you will find yourself moving between methods, theory and data; you will find theoretical concepts to think with and with which you might be able to enhance your methods. You will find that the authors develop arguments about methods rather than just describing them. In Research Methods for Education in a Digital Age, Maggi Savin-Baden and Gemma Tombs address the particular methods challenges and developments pertinent to researching education in a digital age – this is the kind of research they do, and the kind of book they would have found useful when starting out. You may be researching blended learning, online learning, teachers’ professional development through a virtual learning environment or any such aspect that necessitates a focus on the digital domain. Alternatively, the digital domain might just be influencing your methodological focus. Whatever brings you to this book, you will find a cutting-edge analysis of possibilities for the methods you could adopt. Maggi and Gemma combine an ability to demystify terms and concepts with a facility to open up the latest digital developments for educational researchers. They share both their enthusiasm and their willingness to appraise critically all that is going on in the domain of digital research. This necessitates their moving beyond the boundaries of education research to where the latest developments can be seen, raising our awareness of possibilities for application in education. This book cannot be the only book you need to read to formulate, justify and implement your research methods. Other books will cover a broader range of methods or more operational detail. The aim for this series, though, is to provide books that take you to the heart of the methods thinking you will want and need to do. They are books by authors who are equally passionate about their substantive topic and about research methods and they are books that will be invaluable for inspiring deep and informed methods thinking. Melanie Nind Series editor

A Glossary of Research Methods and Approaches

This glossary comprises only those methods and approaches covered in this book. Activity theory – This is a conceptual framework or descriptive metatheory that covers an entire activity system. The underlying idea of the framework is purposeful activity and the framework is used to examine the interactions between actors (subjects) and the world. Actor Network Theory – This is a means of exploring the relational ties within a network, although it is more of a method than a theory. It focuses on exploring networks and on the impact between networks and actors, and the controversies inherent in these. Age of data – The idea that data are valuable for understanding people’s movements, attitudes, the way they communicate, what they look like, who they are with and where they live. Altmetrics – Metrics that offer an alternative to traditional journal or book citation metrics, usually measuring attention via social media. Analysis – The process of breaking apart a unit into its component parts. Arts-related research – Research that uses arts, in the broadest sense, to explore, understand and represent human action and experience. It has emerged as a concept and practice from the interaction between art and social science; early studies began

A GLOSSARY OF RESEARCH METHODS AND APPROACHES

xvii

with artist-researchers using and following their creative process as their research method. Assemblage – The idea that data are collected and constructed from different sources and points in time in order to assemble relatively whole (rather than partial) depictions of participants and their lives, contexts and stories. Assemblage is the creation of as holistic a description as possible of the research and the people involved, including the impact on the researcher as he or she saw, interpreted and created the portrayal of the findings of the study. Attribution – The linking of research participants’ names to their data in publications or datasets, with agreement from the respective participants. This allows their work, opinions, physical image or other to be recognized as theirs. When data are attributed to a particular participant, that participant should be given the opportunity to review and critique the analysis and interpretation. Autoethnography – An approach that combines life history, ethnography and self-narrative in either an ethnographic study of oneself in the social and cultural context or an autobiographical account that includes ethnographic data. See also ethnography. Avatar – A tool used to navigate 3D virtual worlds, which can appear to be human, animal, fantasy character, or other representation. When the avatar is designed to appear human, it can be the bodily manifestation of one’s self in the virtual space. Big data – Definitions of big data are wide and varied; for instance, there are definitions that concentrate on scale or diversity, and others that focus on the economics of big data. In an educational setting, the term ‘big data’ includes ‘learning analytics’, ‘academic analytics’ and ‘educational data mining’, often with no clear distinction between the terms. Cartography – Cartography is defined as the study and practice of making maps where decisions about how to portray geographical data are made. Researchers are cartographers on tour who collect, co-construct, represent and then portray data – sometimes in ways that are troublesome and messy, and at other times in ways that are tidy, manageable and managed.

xviii A GLOSSARY OF RESEARCH METHODS AND APPROACHES

Case study – In-depth, intensive analysis of the single (or multiple) case within its naturalistic context, valuing its particularity, complexity and relationships with the context. This approach uses multiple methods and perspectives to look at the case holistically. Chatbots – Characters not controlled by a user within a virtual world or website. The chatbots are also known as ‘Non-Player Characters (NPCs)’ or pedagogical agents that can be commanded to do certain actions by the facilitator, such as moving around. Coding – A system of symbols used to represent themes and concepts. Computer-aided analysis – The use of a particular digital device or software to support analysis, such as NVivo or SPSS. Conceptual or theoretical framework – An existing concept or proven theory that serves to guide study design as well as interpretations. Digital connectivity – The opportunity to be always connected to the internet and to people in some way through digital media. Digital literacy – The ability to assemble knowledge, evaluate information, search, and navigate as well as to locate, organize, understand and evaluate information using digital technology. Digital immortality – The continuation of an active or passive digital presence after death. Digital métissage – The idea of blurring genres, texts, histories and stories in digital formats that recognize the value and spaces between and across cultures, generations and representational forms. Digital fluency – The ability to shift easily between and across digital media, often unconsciously, with a sense of understanding of the value and possibilities of their use and function. Digital capabilities – The range of skills and understandings needed to operate digital media, collaborate and share with others as well as being digitally literate and fluent.

A GLOSSARY OF RESEARCH METHODS AND APPROACHES

xix

Digital media – Media such as text, audio, video and graphics that are electronically transmitted over the internet or computer networks. Digital spaces – Those spaces in which communication and interaction are assisted, created or enhanced by digital media. Digital tethering – The constant interaction and engagement with digital technology, the sense of being ‘always on’, ‘always engaged’; characterized by the wearing of a mobile device, texting at dinner, and driving illegally while ‘Facebooking’. Empirical studies – Social science studies in which research findings are derived from evidence, rather than simply theory alone. The study, then, whether qualitative or qualitative, involves collecting data in the field. Ethnography – A research approach aimed at understanding an insider perspective on a particular community, practice or setting by focusing on the meaning of social action from the point of view of the participants. Methods of progressively focused observation and interview are used by the researcher who is immersed in the situation, generating complex, detailed data to enable deep descriptions and theorization of the cultural context. Folding – The idea that research should not be portrayed as straightforward and one-dimensional: a fold helps us to see portrayal as a means of being and becoming part of the data and the endings. Folding allows for a multiplicity of portrayal whilst helping readers see some kind of sense in the findings, as well as possible continuities and labyrinths with other research. Impact – Impact is particularly difficult to define, and is defined very differently across international borders. In the UK higher education context, it is defined as ‘an effect on, change or benefit to the economy, society, culture, public policy or service, health, the environment or quality of life, beyond academia’. Informed consent – Informed consent is the legal embodiment of the idea that a researcher should provide information to participants about the potential risks and benefits of participating

xx

A GLOSSARY OF RESEARCH METHODS AND APPROACHES

in a study and should make clear their rights as participants so that they can make informed decisions about whether to take part. Internet of things – This was originally used to argue that computers and the internet are dependent upon human beings for information so that as people possess limited time and accuracy, computers need to be enabled to gather data and observe the world without the need for human intervention. This has become broader and is also referred to as the web of things whereby everyday objects, whether fridges, running watches or sensors, can be accessed through the internet. Interpretivism – The perspective that knowledge, contexts, meanings and ideas are a matter of interpretation; thus researchers analyze the meaning people confer upon their own and others’ actions. Learning analytics – In education, educational research focuses on the process of learning and all that is inherent in that: measurement, collection, analysis and reporting of data about learners and their contexts. Member checking – A process for ensuring plausibility, in which participants (in the case of synthesis, subjects or authors) are asked whether data interpretations or findings are accurate. Mustering – The process of researchers gathering themselves, girding their thoughts and ideas and beginning the process of data portrayal. It involves making decisions about voice, colour, text, what is to be included and how to account for what is to be portrayed. Open education – A collective term used to describe multiple practices and initiatives designed to broaden access to learning, such as open educational resources, open educational practices, open content and open source software. See Table 10.1. Open research – A collective term used to describe multiple practices and initiatives designed to broaden access to research, such as open access literature and open data. See Table 10.1.

A GLOSSARY OF RESEARCH METHODS AND APPROACHES

xxi

Participatory research – A research process that involves those being researched or implicated in the research in the decision making and conduct of the research. Plausibility – A technique for ensuring rigour in qualitative research synthesis that involves locating the truths and the realities in the study, adopting a critical approach and acknowledging the complexities of managing ‘truths’ in research. Positivism – A philosophical system that recognizes only positive facts and observable phenomena; thus the only reliable knowledge of any field of phenomena reduces to knowledge of particular instances of patterns. Therefore, reality is single and tangible, research is value free and generalizations are possible. Post-positivism – A philosophical approach that argues that realities are multiple, and that research is value bound and is affected by time and context. Portrayal – The means by which the researcher has chosen to position people and their perspectives. Portrayal tends to be imbued with a sense of not only positioning but also a contextual painting of a person in a particular way. Reflexivity – An approach seeking to continually challenge our biases and examining our stances, perspectives and views as researchers. This is not meant to be a notion of ‘situating oneself’ as formulaic, as pronouncing a particular positioned identity connected with class, gender or race, but, rather, situating oneself in order to interpret data demands so as to engage with critical questions. Reliability – Ensuring that the experiments can repeatedly measure these variables accurately. Representation – Tends to refer to the way in which a researcher provides warranted accounts of data collected. Thus the main way the term ‘representation’ is used is in the sense of a proxy; the researcher is (re) presenting the views of the participants.

xxii

A GLOSSARY OF RESEARCH METHODS AND APPROACHES

Researcher stance – The philosophical position and personal position the researcher takes towards his or her research methodology and design. Supercomplexity – Influenced by postmodern theory, supercomplexity represents the idea that the future is fundamentally unknown and unknowable, and that knowledge of this shapes society and education. Thick description – Thick description involves explanation of the context as well as the importance of interpretation; thus it is not just reporting detail, but, instead, demands interpretation that goes beyond meaning and motivations. Tethered integrity – The idea that many of those who are ‘always on’, who are digitally tethered, do, in fact, have a degree of integrity with regard to their use of social networking sites. Transparency – Ensuring research processes are documented and presented as rigorously as possible to make the research process clear. Troublesome knowledge – Perkins (1999) described conceptually difficult knowledge as ‘troublesome knowledge’. This is knowledge that appears, for example, counter-intuitive, alien (emanating from another culture or discourse) or incoherent (discrete aspects are unproblematic but there is no organizing principle). Trustworthiness – The process of checking with participants that data collected are valid and that data interpretations are agreed upon a shared truth. It is evidence of research accountability and involves both integrity and rigour. Unit of analysis – This simply means the specific element about which the researcher will be able to say something at the end of the study; the unit then drives the analysis. Validity – Criteria for judging the soundness of qualitative research; thus strategies are developed to ensure that there is some kind of qualifying check to make sure the research is sound and credible.

A GLOSSARY OF RESEARCH METHODS AND APPROACHES xxiii

Verisimilitude – Demonstrating the appearance of truth; the quality of seeming to be true, which is arguably a more realistic quest than uncovering ‘truth’. Viral methodologies – Instead of research methodologies being specifically ‘located’ in areas such as post-structuralism and constructivism, the underlying theories are seen as mutable and liquid. Virtual ethnography – Methodology that seeks to understand, from a sociological perspective, what people ‘do’ on the Internet (Hine 2000). Virtual reality – A simulated computer environment in either a real or an imaginary world. Most virtual reality emphasizes immersion, so that the user suspends belief and accepts it as a real environment, and uses head-mounted displays to enhance this.

Introduction This book began, as do many in education, with an interesting conversation. The series editor, Melanie Nind, approached me (Maggi) by email, saying that she had heard that busy people were very effective writers and asking if I might consider writing a book for her series. I was both sceptical and intrigued – and not sure it was something I would be able to take on. After meeting Melanie for the first time over a glass of wine at a conference in Porto, Portugal, I was persuaded. Yet, I felt I was not as up to date as I might have been. Meanwhile, I had been storing some lounge chairs for Gemma, an excellent researcher who had been helpfully critical of my work for years. I felt we wrote well together. As she loaded the chairs into the car boot, I said, ‘I’ve got this book I have been asked to write, do you fancy doing it?’ She said yes immediately. As a former doctoral student of Maggi’s, later a colleague, and now a friend and collaborator, I (Gemma) was keen to write my first book alongside an experienced author and mentor. This text is, in many ways, a representation of the kind of book I wished I had read when I first began researching education in the digital age. What we hope we provide here is a critical stance towards the issues born out of undertaking and often struggling to make sense of doing research in digital spaces. We also include thoughts, suggestions and ideas that have emerged from our conversations about what it means to do research in the digital age, and how as researchers we manage liquidity and flexibility with rigour and honesty. The result is Research Methods for Education in the Digital Age, a text designed to provide critical discussion and guidance for educational researchers wanting to undertake sound and rigorous research in the digital domain. We begin by examining some of the key terms used in education for the digital age, in Table 0.1, as it is helpful at the outset to see the range

2

Research Methods for Education in the Digital Age

and complexities of digital technologies that are available in higher education, as well as the varying ways in which they are described.

Table 0.1  Terms used in learning in the digital age Term used

Abbreviation

Description

Authors

Blended Learning

Blended

Learning that takes place Oliver and Trigwell as a combination of face- (2005) to-face and online learning.

Online Learning

OL

Studying for a recognized Moore, Dicksonqualification by learning Deane and Gaylen (2011) online and not needing to attend the educational institution.

E-Learning

EL

Learning through electronic Moore, Dicksonmeans, and often seen as Deane and Gaylen being broader than online (2011) learning.

Digital Learning

DL

Learning using digital Warschauer (2007) technology (often used instead of online learning or e-learning) and includes Web 2 and Web 3 technologies.

Digital technologies used in education Virtual VLE Learning Environment

A web-based platform Aleshidi and for the digital aspects Zeki (2011)  of courses of study, usually within educational institutions; examples include Moodle and Blackboard.

Highly HIVE Interactive Virtual Environment

Students use games, sim- Aldrich (2009) ulations and virtual worlds in a distributed classroom environment (i.e. one in which students are not face-to-face with each other or with the teacher).

3

INTRODUCTION

Table 0.1 (Continued ) Term used

Abbreviation

Description

Authors

Virtual VE Environment

Digital spaces that De Freitas and combine the use of virtual Neumann (2009) worlds with other forms Schroeder (2008) of learning.

Educational EVE Virtual Environment

A virtual learning environ- Mikropoulos and Natsis (2011) ment that is based on a certain pedagogical model and learning outcomes.

Multi-User MUVE Virtual Environment

A multi-user virtual environment; it is used to denote a difference between MMORGs that are games-related, and environments such as Second Life, which are not usually seen as games. A MUVE is a more general reference to virtual worlds.

Massively MMORG Multiplayer Online Role-Playing Game

The focus in these games Peterson (2012) is on role play as opposed to MUVEs. The games tend to have some form of progression and social interaction within the game as well as in-game culture.

Virtual World VW

3D virtual reality spaces adapted to be used for learning in education (such as Second Life) with user-generated objects. The worlds are aimed at individual users focused primarily on social interaction. They have customizable avatars (tools by which users navigate the world), either fantasy or humanoid, with varying degrees of anthromorphism.

Immersive IVW Virtual World Three3D IVW Dimensional Immersive Virtual Worlds

Perez-Garcia (2009) Ketelhut et al. (2010) Heid and Kretschmer (2009)

Bayne (2008) Boellstorff (2010) Girvan and Savage (2010) Savin-Baden (2010) Dittmer (2010) Dalgarno et al. (2011)

4

Research Methods for Education in the Digital Age

This table highlights why researching education in the digital age can be challenging; as a comparatively new domain of research, there is little consistency around terms and definitions, and thus educational researchers should take care to familiarize themselves with the terms in their particular area and identify possible inconsistencies in their use or in assumptions underpinning their use. For example, the term ‘blended learning’ implies a smooth transition between online and face-to-face environments, in a manner akin to Deleuze and Guattari’s smooth spaces ([1980] 1987). Yet, what is called blended learning is more often an assemblage of online environments and face-to-face lectures or seminars with the inclusion of some digital technologies. The extent to which students and educators experience the context as blended depends upon far more than the design of the course and often reflects the experiences and practices of the individuals involved. If one student ignores the virtual learning environment entirely, and another does not attend lectures but passes the class based upon his or her use of the extensive online resources provided, are either of their experiences truly blended? If a researcher states that he or she is researching a blended-learning class, should not the first questions be: ‘What is blended, and on what basis is this class considered to be blended?’ Similarly, Mawer (2012) has cautioned against the use of ‘immersive virtual worlds’ to describe technologies such as World of Warcraft or Minecraft. Using this definition, he has argued, implies that immersion is a universal experience of users in the environment, which is not the case. Our goal in this text, therefore, is to examine the assumptions that underpin theories, methodologies and methods in education, and encourage a critical stance in educational research. In doing so, we offer suggestions for practice that are appropriate across multiple contexts. Research Methods for Education in the Digital Age explores important challenges such as the following: 1 The fractures between research-focused fields and

technology-focused fields, and the consequential lack of research approaches that are immersive, adaptive, reusable and pedagogically sound. 2 The uncertainty around which types of research work best in which contexts, and why this is the case.

INTRODUCTION

5

3 The resultant lack of mapping of different technology

research activities across different disciplines. 4 The poverty of contextually based, technology-enabled

research projects. 5 The ways in which effective learning situations are created,

and adapted, for learning in digital spaces. This text, therefore, reviews current practices in qualitative research, identifying the successful adoption and adaption of theories, methodologies, methods and analytical practices for undertaking educational research. In particular, it outlines the major challenges faced by today’s digital researchers, such as the large volume of (often partial) data ‘in and on’ the internet, issues of veracity, and digital dissemination. The book begins by examining the dilemmas of researching education in the digital age, and presents new and emerging methodologies. It explores approaches to digital ethics, digital data collection, analysis and dissemination and suggests helpful ways of dealing with the complexities and ethical challenges of undertaking research in and for digital spaces. Chapter 1, Issues in Researching Education in the Digital Age, begins by reflecting on what research in education is, and what it is for, in the digital age. It suggests that some reconceptualization of research needs to be done, and provides an initial critique of current practices. It begins by exploring the notion of the age of data, the internet of things, digital tethering and digital immortality. Whilst we argue that methodology, methods and philosophical and personal stances are still important in the digital age, we also suggest other issues that need to be considered. The final section raises questions and makes suggestions about research approaches and technologies adapted for the twenty-first century. Chapter 2 titled New Methodologies? delineates different types of methodologies and examines the possibilities and impacts of using such approaches. We then go on to explore digital-artsbased inquiry and digital visual methodologies. Building on this, we move on in Chapter 3, Ethnographies for the Digital Age, to describe newer forms of ethnographies and then explore some of the dilemmas and possibilities of these new formulations. Here, we argue that, in the main, those using ethnography tend to take a strongly interpretivist stance to data management, whilst we

6

Research Methods for Education in the Digital Age

suggest that ethnography should be located at the boundaries of constructivist and constructionist theories of research. In the final section of the chapter, we suggest alternate approaches for educational researchers’ design and undertaking of ethnographies for the digital age, providing examples of sound practice. Chapter 4, Adapting Research Approaches for Education in a Digital Age, moves away from specific exploration of methodologies and methods, and examines how theories for research might be adapted and adopted. Examples include design-based research, which combines both research and practice, and future technology workshop, which is an approach in which people with knowledge of the use of a particular area of technology envision future activities related to technology design. Actor network theory and activity theory are often adapted for research in the digital age, and tend to be used in order to try to understand individual construction of knowledge. Researchers using actor network theory and activity theory see it is as their role to understand the ways in which individuals construct meaning, since knowledge, truth and reality are created rather than constructed. However, many of these approaches remain unexplored philosophically and this chapter will examine and unpack their relative value as research approaches for education in the digital age. Chapter 5, Quantitative Data in the Digital Age, turns to the development and choice of research methods that are appropriate for the digital age, examining issues such as big data and learning analytics, as well as social media analytics and the changing nature of online observations. In particular, this chapter questions what is meant by ‘quantitative’ and ‘qualitative’ methods and considers the ways in which they overlap, and the value of such binary distinctions in the digital age. It begins by presenting different types of quantitative data available to researchers. In the second section of the chapter, some of the key methods employed for gathering quantitative data are presented, along with examples of innovative educational research practice using digital methods. As educational practices and research practices are adapted for the digital age, new ethical questions are brought to light. Chapter 6, Digital Ethics, commences with a definition of ethics in digital settings, and examines the often neglected ethical issues that emerge when creating, collecting and analysing educational data in the digital age and raises new challenges for researchers. Whilst we

INTRODUCTION

7

do not seek to suggest which choices are ‘correct’ or ‘incorrect’, we do offer suggestions for researchers to consider when determining what ‘ethical practice’ is in their individual contexts. We argue that these questions are too important to respectful, excellent and innovative educational research to be neglected as they currently are in many research publications, and suggest a need for researchers to acknowledge and embrace these challenges. The chapter concludes by identifying the multiple frameworks within which researchers must work when working in the digital age. In many areas of research across the disciplines, data collection is invariably seen as straightforward, but Chapter 7, Digital Data Creation and Collection, interrupts this stance and, instead, examines how data are ‘created’ and ‘collected’ in the digital age, acknowledging social constructivist stances in which the researcher plays an integral role in data ‘creation’ and the binary divisions between ‘researcher’ and ‘researched’ are blurred. We address how research observations might be undertaken in digital spaces, in which presence is constituted and experienced in ever-changing ways, and focus upon the integration between theories for digital research (as addressed in Chapter 4) and digital creation and collection methods, providing guidance for ensuring that theory is soundly embedded into research practice. Building on this, Chapter 8, Data Management, examines the ways in which digital data are managed, analysed and interpreted, exploring the pros and cons associated with the use of data software. We propose ways of integrating increasingly disparate forms of digital data, such as student YouTube videos with online forum analyses, with learning analytics. We then offer suggestions for analysis approaches, concluding by identifying innovative and creative theories for interpreting data in the digital age. Whilst we have treated this chapter as distinct from Chapter 7 for ease of understanding and reference, in this chapter we also address the interrelated natures of data creation/collection and data analysis. The final two chapters of the book examine portrayal, representation and impact. Chapter 9, Portrayal and Representation of Data, examines representation in terms of different representation claims, and we make arguments about how these might be implemented in practice. We then suggest that there need to be new perspectives about portrayal and concept, and provide ideas that offer a different view, namely the notions of mustering, folding,

8

Research Methods for Education in the Digital Age

cartography and assemblage. The final section of the chapter considers the interfaces between portal and representation as a space of friction that needs to be explored further in educational research. The final chapter of this book, Chapter 10, Digital Impact, examines the ways in which research is and might be disseminated for the digital age. We address critical issues such as the open source movement and the sharing of academic research via blogs, wikis and social media. This chapter also considers the possible consequences for the nature of educational research in the digital age, such as changes in the nature of peer reviewing, and the ever-changing definitions of ‘impact’ for educational research. This chapter begins by exploring what is meant by impact, the political underpinnings of the term and the most common approaches to measuring impact. It then addresses critical issues such as the dissemination of research through the open education movement and the sharing of academic research via blogs, wikis and social media. This chapter recognizes that dissemination of research does not automatically result in impact, and examines the nuanced relationship between portrayal, representation, dissemination and impact. It concludes by turning to the issue of teaching research methods in the digital age, arguing that current approaches need to be radically overhauled in order to support new researchers in education. Within this text, then, we focus on new and emerging methodologies and educational practices in the digital age. Our intent is to examine the relationships between these practices and the theories that underpin them, whilst recognizing that research in the digital age needs to be fluid and transparent. We begin, therefore, by exploring four key issues for researching in the digital age: the age of data, the internet of things, digital tethering and digital immortality.

chapter one

Issues in researching education in the digital age Introduction This chapter begins by reflecting on what research in education is, and what it is for, in the digital age. It suggests that some reconceptualization of research needs to be done, and provides an initial critique of current practices. We begin by exploring the notions of the age of data, the internet of things, digital tethering and digital immortality. Whilst we argue that methodology, methods and philosophical and personal stances are still important in the digital age, we also make suggestions about other issues that need to be considered. The final section raises questions and makes suggestions about research approaches and technologies adapted for the twenty-first century.

Current issues Researching education in the digital age has arguably become both more complex and more exciting in terms of the issues that need to be considered and the range of possibilities available. Today young people, small children and older adults are all using the internet and digital technologies in diverse ways and for different needs. In terms of research in education, the issues about what counts as learning, who

10

Research Methods for Education in the Digital Age

decides this and how it is changing and moving are also areas that bear consideration in the digital age. Whilst once upon a time, universities, schools, classrooms, further education colleges and nurseries were seen as research sites, in the twenty-first century, it is more difficult to locate and define what a research site might be. Furthermore, learning spaces are on the move, data gathering and portrayal differ, and means of representation are varied and wide reaching. Ideas such as the age of data, the internet of things, digital tethering and digital immortality are affecting educational research methods and data portrayal, often in ways that are yet to be clearly delineated.

The age of data? The notion of the age of data was the result of a study by Elliot, Purdam and Mackey (2013), who explored new methods that might be needed to analyse new types of research data. In practice, they interviewed stakeholders (although it was not clear who these were exactly) and undertook an exploratory online survey. The authors argued that we are in the age of data, meaning that data are valuable for understanding people’s movements, attitudes, the way they communicate, what they look like, who they are with and where they live: Such data include information on: attitudes, images of people and places, people’s movement and communications. This data revolution includes: life-long health and prescription records, brain scans, genetic, bio marker profiles and family histories, satellite images, digital passports, databases from product warranty forms, consumption transactions, online browsing records, email and web communications (including self generated blogs and Twitter postings), geo-coded information on movement and mobile phone use, and synthetic data. … Hence, we use the term the age of data to capture the historical phase that large parts of society has just entered to evoke the reality of the new relationship between humans and what is known about them – the data. (Elliot, Purdam and Mackey 2013: 8–9) This vast array of data ranges from tweets to digitized archive material, some collected for research purposes, whilst other data

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

11

are waiting to be captured or are already being harvested, often covertly, by companies following our daily lives and shopping habits. What is particularly helpful about this study is the delineation of new typologies of data, which are re-presented and annotated in Table 1.1.

Table 1.1  New typology of data Data type

Definition

Challenges for researchers

Orthodox This kind of data are intentional data those collected via interviews, surveys and focus groups with explicit consent of the participant.

The lines between online and offline data collection can become a challenge since even if data are collected face-to-face, other data are communicated and discussed often via email and, therefore, the notion of what is orthodox becomes complicated.

Participative Data are collected intentional data through participatory methods such as participatory action research and appreciative inquiry. Other methods such as crowdsourcing are also included.

Participatory methods that are both face to face and online generally enable the researchers to have a sense of who the participants are, but issues of consent can be difficult in participatory settings.

Consequential data

Data are collected as a result of participating in some other activity, such as health records and commercial transaction data.

These datasets tend to be incomplete; there may not only be missing data but also duplication.

Self-published data

These data mainly include blogs and personal websites, which can be used with or without permission.

These sites tend to be broad and contain large amounts of data. It is difficult to manage such large sets, and it is also challenging to decide on sampling approaches and the kinds of consent that might be required. (Continued)

12 Research Methods for Education in the Digital Age

Table 1.1 (Continued) Data type

Definition

Challenges for researchers

Social media data

These data are generally used without permission as they are in the public domain and comprise data from spaces such as MySpace, Twitter and Facebook.

It is often difficult to decide on the provenance of these data, their relative truthfulness and the extent to which short data and feeds can be seen as having sound validity.

Data traces

These are data left behind, data left through search histories and social networking sites.

These data can be used by other people without permission and often are. Researchers need to consider what might be honest and trustworthy uses of these data.

Found data

Data available in the public domain; observations of people in public spaces such as stations, supermarkets and flash mobs.

These data are often found through covert research methods. Thus, there are ethical difficulties inherent in using them.

Source: Adapted from Elliot, Purdam and Mackey 2013: 15.

The internet of things This phrase, coined by Ashton (2009), was originally used to argue that computers and the internet are dependent upon human beings for information. Ashton argued that as people possess limited time and accuracy, computers need to be enabled to gather data and observe the world without the need for human intervention. In mainland Europe, in 2009, discussions centred around a policy framework focused on governance, protection, global awareness and the ‘silence of chips’ (the idea that individuals should be able to disconnect from their networked environment at any time). Since 2009, there have been wide and varied discussions about what his term means. In the United States, it was initially used to refer to things like smart grids, smart objects and cloud computing, and it currently is seen as both a technology and a market development

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

13

based on the interconnection of everyday objects. In 2015, the concept of the internet of things became broader and, now, it is also referred to as the web of things (McKelvey, Curran and Subaginy 2015), whereby everyday objects, whether fridges, running watches or sensors, can be accessed through the internet, a recent example in 2016 being Amazon Echo. Saied et al. (2014) have argued that there will soon be more things than people on the internet, meaning that there will be more objects and devices connected via smart technology to the internet than people. However, the range of connections, networks and devices is resulting in increasing risk of security attacks, so invasion of privacy is a concern, particularly in terms of identity theft. Government surveillance and the monitoring of our shopping habits have been taking place for many years, yet the sophistication of these threats has changed. For example, software such as Zeus is a Trojan horse used for malicious and criminal tasks, such as stealing banking and credit card information, and malware that tricks users into unwittingly installing it.

Digital tethering Digital tethering is defined as both a way of being and a set of practices that are associated with it. Being digitally tethered would generally be associated with carrying, wearing or holding a device that enables one to be constantly and continually in touch with digital media of whatever kind (Savin-Baden 2015). Practices associated with digital tethering include those of being ‘always on’ and ‘always engaged’: texting at dinner, or driving illegally while ‘Facebooking on the phone’. With the increasing use of technology across home, work and school, most of us are digitally tethered. Across the media, there has been considerable criticism about schoolchildren’s use of mobile devices, along with anecdotes across educational contexts about students being continually distracted by technology. Thus, there are questions to be asked about the value and impact of digital tethering and it is vital that all teaching staff, in whatever context, consider these issues. There are still many university staff and schoolteachers who are concerned about technological determinism: the idea that those who have grown up in the digital age are necessarily different and that their persistent ‘connectivity’ damages them. Issues need to be

14 Research Methods for Education in the Digital Age

explored, such as who is doing the tethering and harnessing and how much coercion is or is not involved. It is not clear whether digital tethering really is a problem, or whether it promotes and allows for a different form of interrogation: students and young people are highly critical of games, apps and hardware. Perhaps what we are dealing with is different ways/forms of ‘reading’ and ‘interrogating’ that we have not yet come to understand, as well as different ways of being and managing identities left behind.

Digital immortality The concept of digital immortality has emerged over the past decade and is defined here as the continuation of an active or passive digital presence after death. As people establish online identities and repositories, the likelihood that their digital presences will persist beyond their death increases, especially as the use of virtual personal assistants grows. Going beyond simple memorial pages (Frost 2014), there have already been cases where people receive ‘beyond the grave’ updates from dead friends (McAlear 2011), as well as companies dedicated to creating digitally immortal personas (Lives On n.d.). However, advances in data mining and artificial intelligence are now making a more active presence after death possible. Thus, as people establish online identities and repositories, particularly on social media sites, the likelihood that these digital presences will persist beyond their death increases. This has resulted in digital presences that will, and have already, persisted beyond death as well as companies dedicated to creating digitally immortal online personas. More serious work includes the military looking at how to use virtual soldiers to support families in their country of origin, and NASA investigating the use of virtual families to support distant astronauts. However, the implications of what these applications could be when their user dies and they become a lasting, and active, memorial do not appear to have been rigorously thought through. Facebook has now put in place measures to control the digital legacy of pages on their site (Skelton 2012; Buck 2013), and recent work by authors such as Adali and Golbeck (2014) illustrates that it is possible to generate accurate predictions of personality from online traces. Advances in data mining and artificial intelligence are now making a more active presence after death possible; thus it

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

15

would seem to be possible to create artificially intelligent systems that could generate new commentary on media events in the style of a particular deceased person, for whom an online profile had been created before the person’s death. There are still questions that remain about the ideological underpinnings of digital immortality, since the search for such immortality is grounded in a human desire for control over life as well as death. Such a situation plays on the fears and hopes of humans whose lives have been about amassing personal resources they would wish to retain beyond death. Thus, the concepts of the age of data, the internet of things, digital tethering and digital immortality pose intriguing challenges for educational researchers. Those who work and are engaged in the field of education are connected (in some cases, perhaps tethered) to digital technologies that collect, utilize and store data about them, perhaps beyond the end of their lifetimes. Methodologies and methods for the digital age, therefore, need to be flexible enough to be applicable across multiple and shifting environments, yet also grounded in a theoretical and ethical design that acknowledges the likely longevity of the data and the ways in which it may be (re) presented and adapted in the future.

Methodology and methods Often people get confused between methodology and methods; here is an attempt to explain the difference: ●●

●●

Methodology is the particular kind of research undertaken. It refers to the rationale and the philosophical assumptions that underlie the study, whether qualitative, mixed methods or quantitative. A qualitative example would be ethnography or phenomenology. Some researchers call these research strategies, research designs, research approaches, or research traditions. Methods are the tools used to collect data, such as interviewing or focus groups, that need to fit with the methodology. It is no good doing a quick fifteen-minute interview when using narrative inquiry, as you will have little narrative and no story!

16 Research Methods for Education in the Digital Age

Research philosophies Undertaking research requires that researchers position themselves in terms of their personal and philosophical stance, since this informs the methodology they adopt. In some cases, a methodology is chosen from a purely pragmatic position because it fits the research questions, but more often a methodology is affected by personal and philosophical stances. A personal stance relates to people’s belief systems and the views they have about the world. For example, Maggi explains her stance:

Maggi’s philosophical stance

A

s a white, middle-class woman who has been educated at prestigious universities, I see myself as being privileged. However, as someone who failed at school and had strongminded critical parents, I wish to understand other people’s stories, learn about people and their lives and recognize that I see failure not as something bad, but as something through which it is possible to grow and learn. I realise that my personal stance has strong links with critical pedagogy and the arguments of Freire (1970) who criticized schools’ ‘banking education’ approach, which did not work for me. I also believe that the decontextualization of curriculum is unhelpful and that learning through real-life problem management is more useful than banking. This stance has led me to hold constructionism as one of the guiding philosophies of my research and to see, hear and represent people, their lives and their stories as the cornerstone of rigorous research.

What is interesting about our personal stance is that it not only influences our research but also the way we see others, often in ways we may not expect. Salmon has offered an example of this: I asked all the participants to attend to their perception of me, as a particular person standing in front of them. What kind of person, on this preliminary acquaintance, did I seem to be? I invited them to note down, for their eyes only, what kind of

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

17

person, provisionally, they would define me as being. Then I asked them to think about these perceptions as referring to their own personal stance towards me. (Salmon 1989: 231) A further challenge is the way in which we position ourselves philosophically, which requires a consideration of how we view the nature of knowledge. Our worldviews are underpinned by philosophies that inform our research practice, as seen in Table 1.2.

Table 1.2  Philosophies that inform our research practice Paradigms or philosophies

Description

Examples

Positivism

Positive knowledge exists and is based upon natural phenomena, their properties and relations, and may be discovered through the scientific method (quantitative).

Malinowski, B. (1922) – An ethnography of the Trobriand people, with significant influence on ethnographic research.

Post-positivism

Positive knowledge exists but is imperfectly understandable, and it may be uncovered through falsification (primarily quantitative).

Becker et al. (1961) – A classic in medical and sociological research that details the journey of becoming a physician.

Critical social theory

Positive knowledge exists and may be discovered through historical approaches.

Horkheimer, M. (1982) – A leading work in critical theory.

Pragmatism

Reality exists for individuals, but knowledge is contextually contingent; knowledge may be discovered by examining the usefulness of theory in practice.

Mead, M. (1928) – An anthropological study of youth on Tu’a on the Samoan Islands, which is one of the most widely read anthropological texts. (Continued)

18 Research Methods for Education in the Digital Age

Table 1.2 (Continued ) Paradigms or philosophies

Description

Examples

Phenomenology

Reality and knowledge reside in the mind, as the individual perceives and experiences it, and knowledge may be discovered by exploring human experiences.

Husserl, E. (1907/1964) – A leading work in phenomenology.

Post-structuralism and Postmodernism

Knowledge may be found deeply embedded in structures; a later view is that human agency is problematic since there is no unified truth but, rather, many truths and systems, and such systems impose linguistic codes and structures. Examining codes and structures can help researchers uncover knowledge.

Fine et al. (2000) – This work focuses particularly upon the importance of reflexivity in research, arguing that ethical research requires acknowledgement of the different contexts, cultures and communities within which social change occurs.

Constructionism

Reality and knowledge are socially constructed; knowledge may be gained by examining the ways in which individuals co-create knowledge.

Berger, P. L. and T. Luckmann (1966) – This work introduced the term ‘social constructionism’ into the social sciences.

Constructivism

Reality and knowledge reside in the minds of individuals. Knowledge may be uncovered by unpacking individual experiences.

von Glasersfeld, E. (1989) – A leading work in constructivism, and the discussion of radical constructivism.

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

19

Research questions and conceptual frameworks When undertaking research in the digital spaces, it is vital to have a conceptual framework, which is based on your personal stance, philosophical position and your methodology, an exemplar of which is presented in Figure 1.1. There are many newer methodologies that have been developed for researching education in digital spaces, as well as older ones that can be adapted (see e.g. Savin-Baden 2010; Savin-Baden et al. 2010; Savin-Baden and Tombs 2010). When undertaking research, it is important to ask a number of questions that will enable the development of a strong conceptual framework and a robust design for the research. This is because some staff and students who undertake qualitative research methods often assume that ‘interviewing’ is qualitative research, or that ‘surveys’ are quantitative research, and fail to understand the importance of a researcher stance and a conceptual framework for the study. At a very basic level, questions that need to be asked are as follows: 1 What do you want to know? 2 How will you find this out? 3 What is manageable, in terms of timeframe, place and

participants? 4 How will you ensure your study is robust?

Figure 1.1  Developing a conceptual framework

20 Research Methods for Education in the Digital Age

Table 1.3  Question development for research in the digital age Question purpose

Why am I developing the question in the first place? What knowledge will be gained by conducting this study using digital tools or in online settings? Who are the beneficiaries? What will the research achieve?

Question source

From where did the idea for the question germinate? What experiences led me to this question in a digital context?

Question about dissemination

What are the secondary questions I need to consider? Who will be the audiences for this study within and beyond the digital? What will be the impact of the findings?

Table 1.3 has been designed to support the development of research questions.

Data challenges Although we deal in detail with how data are ‘created’ and ‘collected’ in the digital age in Chapter 7, it is worth considering some approaches that researchers deliberate over when doing research in the digital age. In the following section, we address three key methods for research in the digital age; online observations, online interviews and online focus groups.

Online observations There is currently a huge body of guidance and discussion, as well as data on the use of observational methods for data collection, but relatively little debate about their use in online settings and virtual

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

21

worlds. Observation can be defined in terms of the different ways in which the researcher participates in the study: ●●

●●

●●

●●

Covert participation: This is a marginal role with the researcher on the edges of the action. In real life, this may be undertaken using two-way mirrors. In digital educational research, it is more likely to occur through lurking or covert observation, for example, in online forums, or when watching Twitter discussions. Passive participation: Here the researcher has only minimal involvement, for example, by small and discrete intervention in discussion forums or being present, but saying and doing little, in virtual worlds. Thus, the researcher operates as a bystander and observes in a detached way, rather than engaging with what is taking place. Active participation: Here the researcher operates as the central character in the research and, therefore, participates as well as observes. This would be an approach that a researcher would tend to use in virtual worlds, possibly also in discussion forums. Active participants in the research and research setting might be teacher-researchers, for example. Complete participation: Here the researcher is a full and active participant in the research; he or she is considered to be an insider researcher and, therefore, is seen as a member of the group or culture being studied. For example, an insider researcher might be a student studying his or her own cohort. This requires high-level reflexivity on the part of the researcher; the danger is in getting too close to the data and being unable to appreciate the impact of his or her own influence on the study.

Mawer (2014: 10) has undertaken a review of the literature and suggests that four issues need to be taken into account when undertaking observation in virtual worlds: 1 Defining and delimiting field sites by bounding and scoping

them. 2 Discerning attention: by understanding what the participant is focusing on

22 Research Methods for Education in the Digital Age

3 Charting actions: recognizing that the actions of an avatar

may seem to mimic physical real-world behaviour, but is more or less than implied 4 Attributing intention: recognizing that observing intention in virtual worlds is complex, since action may be the result of either intentional acts, unintentional failures in control or technical failures Mawer argued that researchers must be cautious in how they interpret their observational data due to difficulties in determining where participants’ attention is being directed, what actions participants are taking at a given moment and whether actions identified by the researcher are the result of intentional behaviours or unintended control or technical errors. These interpretative dilemmas place limitations on the capacity of observation to provide in situ data on participants’ behaviours without considerable assumptions on the part of the researcher. (Mawer 2014: 11) Mawer’s points are particularly interesting for educational researchers. It is often assumed, for example, that eye contact between learners and tutors means that the learner is paying attention and comprehends what is being discussed. Similarly, body language is often used to inform understanding of social dynamics in group work. These cues are not always applicable in online observations, and therefore educational researchers working in the digital should consider how ‘engagement’ might be represented in different settings.

Online interviews Online interviews span a broad spectrum, from asynchronous interviews undertaken via email with written response, Skype interviews, interviews using SMS and interviews using avatars in virtual worlds. When undertaking online interviews, it is important to consider issues such as participants’ attitudes towards using technology, what they choose to use, how and when

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

23

they use it and what their preferences are. For example, many young people would use a mobile phone rather than a desktop when they are searching, playing games, watching YouTube or doing homework. This would mean that any online survey would need to be portable to mobiles and therefore be straightforward to administer. However, other people, teachers for example, may use iPads or Windows desktops, so the survey will also need to be compatible across platforms. Other issues to consider would include the following: ●●

●●

●●

●●

Safety: How is sensitive information managed? For example, what might be done if illegal practices are revealed? Anonymity: Will this be assured, and if so how? For example, using crypotchat may ensure this, but if a transcript of that chat is retained, can anonymity still be assured? Expectations: How can participants’ expectation be managed? Most people expect an email response within 24 hours and, therefore, it is important to let participants know when you may respond to any queries and questions they may have. How are different data types to be managed, interpreted and presented, for example, synchronous versus asynchronous?

Online and in-world focus groups There are many different ways of using focus groups, which include nominal groups and the Delphi technique. In practice, whether faceto-face or online, a focus group is a gathering of a limited number of individuals who, through conversation with each other, provide information about a specific topic, issue or subject. Some researchers see the use of online focus groups as an opportunity for saving time and travel costs and for enabling the meetings to be more flexible than face-to-face ones. However, there are also disadvantages in terms of loss of cues and bodily markers, which may, for example, indicate power dynamics amongst a group of learners. Further, as Turkle (2005) has suggested, computers are not merely objects that

24 Research Methods for Education in the Digital Age

make our lives more efficient, but are subjects that are intimately and ultimately linked to our social and emotional lives. The result, then, is that computers change not only what we do, but also how we think about ourselves in the online or immersive worlds. Nevertheless, it is important to ensure that online focus groups have a clear purpose and also that participants, however diverse and widely spread across the globe, feel connected. Further, it is also helpful to ensure that the focus group is methodologically situated so that it is clear about what kinds of data are being collected. For example, a grounded theory focus group might seek to work with participants online to build theory together, whereas a narrative focus group would centre on the elicitation of stories about particular issues. This is an example from Maggi about her reflections on undertaking an in-world focus group in the virtual world Second Life:

Maggi’s reflection

T

he process of sharing and debating ‘stories’ is vital to the understanding of space and spatial encounters. This is because narrative requires recounting events to construct with the reader a particular way of ‘knowing about’, which as Martin (2008) suggested, moves towards meaning making. Bruner (1990) also believed that narrative is a process of meaning making, particularly when encountering unusual events or issues. Although some researchers would argue that narratives are structured with a beginning, middle and an end, held together by some kind of plot and resolution (Sarbin 1986), narratives in this study were not expected to have a plot or structured story line, but were seen as interruptions of reflections in/on a storied life and thus are also affected by issues of representation. Narrative approaches, such as that adopted in this study, generally focus on developing understanding through an exploration of story, interpretation and discourse. (Savin-Baden 2013:62)

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

25

Long and short data? One of the issues that would seem to require further investigation is how diverse digital data are analysed and interpreted, especially when using online interviews such as email and SMS. This is because using these methods of collecting data can become somewhat divorced from the participants and their context. This then raises issues about how to deal with different and diverse types of data in the same study. It also seems that little attention is paid to the ways in which different types of data are managed – there seems to be an assumption that examining subtext, valuing thick description and exploring opposition talk (for example) should and can be applied across all types of data in the same kinds of ways – but should this be the case? There are also questions to be asked about how what we term ‘short data’ are managed – such as tweets and SMS – and whether we need to be viewing and analysing these in new or particular ways. Qualitative researchers, in the main, tend to believe and rely on ‘thick description’, which was developed by Geertz (1973) but there has been criticism of Geertz’s notion, as Flewitt has noted: Geertz’ work has been strongly critiqued for defining ‘text’ intuitively and variously (e.g. Schneider 1987), and the concept of thick description begs the question of what data has been selected for description, inviting criticisms that ethnography risks making rather than reflecting culture (e.g. Clifford and Marcus 1986). (Flewitt 2011: 294)

Questions that bear consideration Amidst these new and emerging phenomena, there are questions to be asked about ‘the digital’, about the range of choices available and how research should/might be conducted in the digital age.

Does digital research really exist? There have been many discussions about whether research that is being undertaken in digital spaces is really any different from other

26 Research Methods for Education in the Digital Age

forms of research. Hine (2015) has suggested that internet research might be seen as both inward and outward looking: outwards into diverse frames of meaning making, and inwards into what we term ‘new reflexivities’ (which we explore further in Chapter 8). This is where the information, practices, presentations and portrayals of research we see in and on the internet prompt us to reconsider our stances as researchers and how we are being shaped by new information and experiences. Hine (2015) suggested a helpful delineation, which she terms the embedded internet, the embodied internet and the everyday internet, which we summarise below. In the embedded internet, the internet is embedded in our daily lives, in our interactions with our towns, schools, households or devices. The result is that the internet can be both specific to and have an impact on a particular context as well as being subject to multiple frames of meaning making. This means that, as researchers, the projects and studies we undertake can be both arbitrary, since we follow our own interests, and at the same time highly consequential in terms of the impact they could have. The embodied internet concept suggests that we have an embodied relationship with the internet (such as being digitally tethered). In practice, going online is not a discrete experience; it is part of us and complements (and challenges, we suggest) other embodied ways of being. The idea of the everyday internet holds that the internet is part of our lives to the extent that much of what occurs, and practices that develop, are seen as, or become, normalized. Thus, it is important for researchers to examine what is taken for granted and seen as normal. We need to explore which spaces, sites and social media are more visible than others are and why this might be the case, in addition to exploring where the silences are in internet discourses and practices. Perhaps the question is not ‘whether digital research exists’ but how we might see research in the context of the internet. ‘The digital’ in itself is problematic; for example, Horst and Miller (2012) noted that most researchers possess a different conceptualization of ‘the digital’. For some, this might involve exploring smart technology use and development, whilst for others this may mean using digital installations or virtual displays. Researchers may see the digital as innovative methodologies, or as opportunities to explore the impact of new media on learning.

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

27

New practices, new philosophical frameworks and shifting stances? Although the debate about digital natives and digital immigrants is now passé, there is no doubt that the internet and mobile technologies, of whatever sort, have ushered rapid change since the 1990s. The ability to be connected, to search with ease and to think differently about ways of doing research has brought us all wider views and greater possibilities. We suggest that, as a consequence, ways of collecting, managing and portraying data have shifted, resulting in new practices. Perhaps we are still using many of the same philosophical frameworks, although these, too, are on the move. There are authors such as Selwyn (2013, 2014) who have suggested that there is a tendency to see educational technology as being neutral, which is resulting in educational provision and practices with neo-liberal values. This, he believes, has changed the nature of education away from a public good and, instead, moved it towards the individualistic tendencies of twenty-first century capitalism. Perhaps stances have changed and we are becoming more individualistic, or perhaps we are too tethered. Certainly, there are many instances of organizations funding technology-enhanced learning projects where the focus is on the technology rather than the learning, and Bayne (2014) has offered a scathing attack on such practices. At the same time, the idea of the embedded internet does point to changes in our lives and across the globe, whether it is the development of artificial intelligence in unsettling ways or the ability to use solar energy to power mobile devices in sub-Saharan Africa.

Are there too many choices? It is argued here that there are now too many choices about what to research, how to research and what to use, is possibly the case. Yet, such a wide range of options should help us to make better and more informed choices. Whilst we tend to carve up philosophy, methodology and methods into frameworks that help us to make sense of our worlds, we do not, in fact, use one medium, framework or technology at a time, but flow between them. Further, Furlong and Davies (2012) argued that young people’s (and we would include students in this) engagement with new technologies is

28 Research Methods for Education in the Digital Age

fundamentally bound up with their own identity, for example how they choose to use technology to engage with others, represent themselves and manage their worlds. We suggest that there is much to be learned from young people about research and the process of ‘participatory pioneering’ (Savin-Baden 2015), which is the process by which people learn and teach each other collaboratively, through digital media, to invent, create and remix in ways that are both pioneering and disruptive in their use of media. Perhaps we need to move away from frameworks to digital métissage. This captures the idea of blurring genres, texts, histories and stories in digital formats that recognize the value and spaces between and across cultures, generations and representational forms (Savin-Baden and Wimpenny 2014). Research and meaning making in the digital age means that trajectories are not straightforward, and managing this digital métissage offers interesting, if challenging, possibilities.

Are we lured by the readily available? One of the difficulties with information being so easily accessible is that it is easy to take advantage of what is available and perceived to be ready for harvesting, for example data located in online forums and virtual learning environments used in universities. However, despite the need to develop new ways of creating and accessing data, we believe it is important to take responsibility for crafting research projects in ways that fit with the research questions. It could be argued that poor and unethical research has always been undertaken and that it has always been possible to collect easy data in bad ways. The internet does seem to offer a great range of possibilities to do things badly; therefore, it is important to ensure that researchers are reflexive and ethical, and use critical friends to ensure that what is being undertaken is both honest and rigorous. However, because research in the digital age is constantly on the move, it is also important to take a responsive stance, so that methodologies and methods can deal with new circumstances that might arise as the research progresses.

Does ‘the digital’ encourage us to be more participatory researchers? It is not yet clear whether undertaking research in and on the internet and using digital devices does prompt a shift to more

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

29

participatory forms of research. It would seem that, given the wide range of options and data available, it would be wise to adopt participatory approaches. One such approach that might be implemented is participatory action synthesis. This approach was developed from a project that sought to examine the sociopolitical impact of virtual world learning on higher education. Three PhD students examined different areas, namely student experience, identity and pedagogy. At the end of the study, the research team developed this new methodological approach, in which researchers are able to synthesize, interpret and construct greater meaning from qualitative data sets (e.g. from data accumulated across several studies), in a participatory process, to maximize knowledge production, dissemination, relevance and scientific knowledge (Steils et al. 2014; Wimpenny and Savin-Baden 2012). What makes participatory action synthesis different from other approaches to qualitative synthesis includes the following: 1 A focus on synthesis of primary data from several discrete

studies, which involves ●●

●●

●●

Focusing on a research question that transcends the discrete studies A strong well-argued reason for undertaking the synthesis across the studies Access to all the primary data from the discrete studies

2 The research team working across the data sets 3 Researchers who collect the initial primary data sharing

and critiquing it with all members of the team, who take a broader stance and wider view. This is just one example of a new approach, but it is participatory research that combines collaborative data sharing and analysis, bringing rigour to studies undertaking research in the digital arena. It is also an approach that offers researchers the opportunity to be reflexive and examine the research process both individually and as a team.

Conclusion This chapter has tackled a range of issues relating to undertaking research in the digital age. It is clear that much of this is not

30 Research Methods for Education in the Digital Age

straightforward and decisions will need to be made by researchers about whether they choose to rely on traditional methodologies, adapt them or create new ones. Although we have begun to explore some of the questions about new and emerging research concepts and practices, the rest of this book provides detailed guidance on this. Whilst we recognize that theories and methodologies are constantly being modified, there is often discomfort with creating new approaches that are mutable and liquid, and this is what we begin to explore in Chapter 2.

Further reading Elliot, M., K. Purdam and E. Mackey (2013), Data Horizons. London: ESRC. Elliot, Purdam and Mackey report on the Data Horizons project, in which they proposed the typology of data discussed in this chapter. What is perhaps more interesting, though, is their discussion of methodological developments. They highlight the need for researchers to work directly with developers of analysis software (as in the Software Sustainability Initiative, for example), for new collaborations to be developed between researchers and commercial companies who collect public data, and for researchers to take greater advantage of Freedom of Information requests. This does, however, raise questions about researcher capacity and ever-extending responsibilities; Elliot, Purdam and Mackey appear to suggest that such collaborations can support researchers in working ‘smarter, not harder’. McKelvey, N., K. Curran and N. Subaginy (2015), ‘The Internet of Things’, in M. Khosrow-Pour (ed.), Encyclopedia of Information Science and Technology, 3rd edn., 5777–83. Hershey, PA: Information Science Reference. McKelvey, Curran and Subaginy report a vision of a possible future associated with the Internet of Things, in which an individual’s day-to-day life is supported by technology’s automated actions. They then examine the technical challenges associated with such a vision, providing an accessible summary for researchers unfamiliar with these technological details. This paper, however, does seem to conflate possible futures with certain futures, as there is little acknowledgement of the agency of individuals in choosing, customizing and hacking technologies in the Internet of Things.

ISSUES IN RESEARCHING EDUCATION IN THE DIGITAL AGE

31

Savin-Baden, M. and C. Major (2013), Qualitative Research: The Essential Guide to Theory and Practice. London: Routledge. This qualitative research text provides an accessible entrance into research for newer researchers, and a timely reminder for current researchers who are adapting their practices for a current age. It pays particular attention to the theories underpinning qualitative research, and, as a text written by both English and American authors in addition to several international contributors, it is applicable internationally. Savin-Baden, M. (2015), Rethinking Learning in an Age of Digital Fluency: Is Being Digitally Tethered a New Learning Nexus? London: Routledge. This text by Savin-Baden represents a more considered extension of the often-critiqued digital natives theory. She posits that many students, particularly younger students, are digitally tethered or ‘always connected’ to the internet via mobile devices, and that such tethering should not be perceived automatically as harmful to the students’ learning experience. Instead, she argues, researchers need to move beyond technological essentialism and explore new forms of pedagogy that can take advantage of digital tethering.

32

chapter two

New methodologies? Introduction There are a number of new methodologies that can be used for educational research, which embrace the complexities of research in the digital age. For example, new methodologies are ones that, instead of being specifically ‘located’ in areas such as poststructuralism and constructivism, argue for the use of theory in ways that are mutable and liquid. This chapter delineates different types of methodologies and examines the possibilities and impacts of using such approaches. It will also explore digital-arts-based inquiry and digital visual methodologies and the particular research methods used within these.

A pragmatic stance? To date, there is a range of types, methodologies and methods of research being used by qualitative researchers working in educational contexts. There are still many academics who see qualitative research as a pragmatic approach involving collecting data through interviews and focus groups. We suggest that this is particularly so in digital educational research settings, where many researchers are overwhelmed by the novelty and vastness of research fields. Whilst this is understandable, to some extent, in a new field of research or a subject or field that largely uses quantitative

34 Research Methods for Education in the Digital Age

methods, it does tend to mean that research is undertaken in ways that often lack rigour and plausibility, because of the lack of a strong methodological position and guiding conceptual framework, as mentioned in Chapter 1 and exemplified in Figure 1.1. Without a methodology, there is likely to be a lack of congruence between the methods adopted and the way data are managed and interpreted. However, it is also important to recognise that many people do use a pragmatic approach to research, and that pragmatic qualitative research has a long history in this field of inquiry. This form of research emerged from the work of early pragmatists in the 1930s, such as Durkheim (1955): Pragmatic qualitative research is just what its name implies: an approach that draws upon the most sensible and practical methods available in order to answer a given research question (italics in original). (Savin-Baden and Major 2013: 171) Thus, it is evident that there is no one particular type of pragmatic research design for guiding a study; rather, researchers take eclectic approaches depending on what is most useful for undertaking the study. This can result in difficulties, because as yet there is no commonly accepted and understood approach or clear way of delineating how to carry out pragmatic research. Those undertaking it often find themselves needing to defend their position and stance by including lengthy descriptions of methods. However, a pragmatic stance shares such challenges with some of the new methodologies in qualitative research, which are less well known or seen as not being mainstream as yet. For example, there are many claims to methodological innovations in qualitative research, yet some are merely adaptions of current approaches. As Nind et al. argue (2013), there are tensions between developing innovative approaches over short periods of time and the need to critique them so that they are, and are seen as being, robust, which we discuss in this chapter.

Liquid methodologies? The notion of liquid methodologies is based on the idea that while it is useful to have underpinning philosophies from which to draw, it

NEW METHODOLOGIES?

35

is also vital when undertaking research in digital spaces to recognise the need for liquidity. We use the notion of liquid methodologies, drawing on Bauman’s (2000) notion of ‘the liquid’, and suggest that engaging with a world of liquid uncertainties might bring to light new understandings in terms of new notions of methodology and methods, as well as different understandings of space and spatial practices, and a recognition that research spaces are increasingly hybridized, extended and mixed. For example, the notion of viral methodologies is that, instead of methodologies being strongly ‘located’ philosophically, there is a sense of looser coupling and a greater liquidity between them, so that underlying theories are seen as mutable and liquid. Although such methodologies are emergent and there is currently little written about them, they are based on the idea of viral learning (for example, Downes 2005). These methodologies are similar to emergent design, whereby the design emerges in response to the participants and contexts (Lincoln and Guba 1985). However, we are not suggesting that methodological frameworks are ignored; rather, we are suggesting that having a sense of the liquid and the viral can enable methodologies to be matched and drawn together, such as using narrative inquiry and deliberative inquiry together. However, it is also important to be aware that methodologies might clash, such as the use of phenomenology and autoethnography. Husserl ([1907] 1964) believed it was necessary to bracket off the world and suspend judgement in order to see phenomena clearly using phenomenology, whereas autoethnography sees identity and personal stance as central to the research process, portrayal and presentation. There are a number of newer methodologies that have emerged from arts and humanities that have a sense of the liquid, yet are based on constructivism, which are now discussed in turn.

Digital and visual methods Visual research methods today are still largely dominated by the use of photographs in some way and methodologically tend to be located in some form of narrative inquiry. The use of other forms of image is growing, some of which are similar to photographs but are tagged and used in different ways, such as Snapchat, Instagram, traffic cameras and digital maps. There is a difficulty in qualitative

36 Research Methods for Education in the Digital Age

research currently about whether such methods are deemed to be digital visual or just visual methods. The challenge is that much of what now exists and is created is digital or digitised in some way, whether it be a drama that is filmed and uploaded, a photograph put up on Facebook or a story told in a virtual world. In many ways, everything these days could be said to be digital or have the ability to become digitised; we discuss this in more detail in Chapter 8. One of the difficulties with the rise of the use of digital visual methods is that they are often argued for on the basis of our both living and being in a visual culture and the digital age. Although such arguments are not particularly new, there does seem to be a proliferation of these methods. Kress and van Leeuwen first began the discussion in 2001, by suggesting that there has been a recent shift away from monomodality (text-based versus image) towards multimodality (text and image), with the screen rather than the book becoming the most common form of representation. Yet, this theory merely reinforces an unhelpful distinction between print and non-print texts, which is problematic because much of what is now in use is print on screen, with an increasing shift to image on screen. There is increasingly little distinction between what is digital and what is not. It is important, therefore, not to conflate multimodality as being something more than the use of several methods, or assume that visual methods inherently and necessarily promote an affective response. Such visual methods need to be methodologically located, as shown in Table 2.1. Although there has been an increase in the use of visual methods, it does seem to be an area that has received relatively little critique. Rose (2014) has suggested that the relationship between contemporary visual culture and visual research methods has not been investigated and argues: When they are used,‘visual research methods’ create neither a ‘social’ articulated through culturally mediated images, nor a ‘research participant’ competency in using such images. Instead, [I state] that the intersection of visual culture and ‘visual research methods’ should be located in their shared way of using images, since in both, images tend to be deployed much more as communicational tools than as representational texts (italics in original). (Rose 2014: 24)

Exploring the benefits of drama and arts activities for improving and maintaining the health and well-being of isolated and marginalized adults aged 50+.

The use of arts to inform the ways in which the research is undertaken: as a means of representing both findings and a response to the situation being studied.

The development of theory from the study of visual artefacts.

Using visual media to facilitate the portrayal of the process, learning and outcomes of evaluation.

Using visual artefacts to create and tell Students collected photographic documentation stories. and then reflected on the spaces they inhabited and how they used those spaces as well as the objects within them.

Artsinformed inquiry

Grounded theory

Evaluation

Narrative inquiry

Campbell and McDonagh (2009)

The presentation used different art forms such as Simons and music, poems, paintings by the evaluator, which McCormack depicted stages of the evaluation process. (2007)

Collecting and coding photographs of schools in Margolis (2005) the Farm Security Administration  Office of War Information Archives into emergent categories using a three-phase process.

Wimpenny and Savin-Baden (2014)

Coffey et al. (2006) used a number of Coffey et al. ethnographic methods, including photographs and (2006) video, to explore how scientific knowledge was produced and (re)presented by visitors (particularly schoolchildren) in a science discovery centre.

The use of the web, photography, motion pictures, virtual reality and hypermedia as means of capturing and expressing perceptions and social realities of people.

Visual ethnography

Authors

Example

Methodology Overview

Table 2.1  Methodologies related to digital visual methods

NEW METHODOLOGIES? 37

38 Research Methods for Education in the Digital Age

It is, however, important to be clear about what is meant by visual research methods. We suggest that such methods are those that use visual materials for the generation of research questions, for the collection of data and as a means of representing and portraying data. As with other approaches to research, we argue that such methods need to be methodologically located. Some researchers have argued for visual ethnography, such as Pink (2007: 1), who saw visual artefacts ‘as cultural texts, as representations of ethnographic knowledge and as sites of cultural production, social interaction and individual experience that themselves constitute ethnographic fieldwork locales’. However, since such methods include materials ranging from self-portraits, video diaries, drawings and sequential art to big visual data (Manovich 2013) and software-supported spatiality (Sheller 2009), we suggest that visual methods should be seen as being broader than visual ethnography. We propose that visual methods need to be defined as those that bring together theory and tools, as well as recognizing that meaning making is haphazard because of the demand for continual interaction between researchers, contexts, participants, audiences and representation. Furthermore, there is also a need to locate how such media are used and repurposed as texts, images and a means of social portrayal (through Facebook and Instagram, for example) in the context of everyday lives. Such visual methods raise interesting opportunities for educational researchers working with digital and visual methods. Data sources might include the images used within a classroom presentation, the wallpapers used by students on their mobile devices, the emojis or GIFs included in forum interactions, or the icons and colours included in a digital mind-map. All of these methods can provide unique insights into student experiences and attitudes, yet they might not be initially identified as sources of data and methods for data collection. In practice, then, we suggest that visual methods can be seen as images imbued with meaning, tools for thinking with, media for collecting data, and schemas and processes for portrayal.

Images imbued with meaning Researchers collect, work with, co-construct and use diverse types of images, whether photographs, video, motion capture and virtual

NEW METHODOLOGIES?

39

worlds, in order to create visual artefacts that can be used as data, as a means of promoting a response or as a mechanism to create meaning.

Tools for thinking with Visual artefacts can be used in a variety of ways, but, in particular, they may be used to enable participants to display and voice their views in order that they can help shape the design, process and product of the research. Further, these media can be employed to encourage those in the research to reflect upon their practices, and sessions could be recorded in order to discuss what took place and to examine reactions to the media. In education, for example, students might alter photographs or videos of a group working session, in order to convey their individual and collective experiences of working together.

Media for collecting data Visual artefacts can be used to ask participants to portray a response to a situation or be used to help provoke a response in participants. Collecting data in this way can enable participants to understand and unpack issues such as power, racism and stereotyping. The Question Bridge project, for example, defines itself as a collective trans-media project that is creating a ‘mega-logue’ among black men in the United States. In this project, individuals pose questions to a camera, using either a mobile application or the main QuestionBridge website, and share the question on the website. Other individuals are then able to view the question and upload their own responses, developing an ongoing and open conversation about issues that are important to black men in the United States. Based upon this project, an education curriculum has been developed that ‘focuses on themes of broad identity, conflict resolution, and inclusion’ (QuestionBridge.com n.d.).

Schemas and processes for portrayal There are a wide variety of ways of portraying data, which we discuss further in Chapter 9, and of using visual media to help participants to represent and communicate their views to others.

40 Research Methods for Education in the Digital Age

Digital-arts-related research Digital-arts-related research is based on arts-related research, which uses arts in the broadest sense, to explore, understand and represent human action and experience. Arts-related research developed the interaction between art and social science with artist-researchers using the creative process as a research approach. Although the focus of this approach began with the artistic process, arts-related research has changed and developed into a number of different forms, which we define and explain in Table 2.2, based on the works of Savin-Baden and Wimpenny (2014) who defined six different types of arts-related research. The typologies in Table 2.2 capture arts-related research that uses media to provoke questions, as well as to create artefacts, for understanding and examining the experiences of the participants and researchers involved in the arts-based research. This research uses media to create, examine and interpret issues in ways that illustrate both the artistic process and the impact of arts and issues on peoples’ lives. Thus digital-arts-related research is the use of digital media as a means of capturing and collecting data, and analysing, interpreting and portraying data.

Digital installation art This includes interactive videos, virtual world interactions, plays and shows as well as installations. For example, Upton (2012) integrated virtual worlds into real spaces in order to gain audience response and interaction. The Extract/Insert Project, for example, was an installation in Second Life and The Herbert Art Gallery, Coventry, United Kingdom, which took place over a week. In practice, a large screen was used to project the virtual world Second Life into the gallery and visitors could engage with avatars from around the world. Upton’s installation attracted over 5,000 visitors and as he explained: We succeeded in enabling a physical audience to engage and interact with a global virtual audience within an immersive mixed reality space. Participants were able to socialize and be extracted and inserted into each other’s alternative realities ... we created an installation ‘that inspired participants to consider the nature of space, identity, reality and communication’. (Upton 2012, n.p.)

Arts-informed inquiry

Where art is used to represent the findings of a study. Where art is used to represent a response to the findings of an issue or situation studied.

Artistic process is used Use of arts for personal by artists, researchers exploration of concern or and participants in order issue. to understand the art itself or understand a phenomenon through the artistic process.

Arts-based inquiry

Use of art to enhance understanding, reach multiple audiences and make findings accessible.

Centralizing the artistic process, criticality and inquiry and central learning processes.

Using arts to teach inquiry and enable learning to occur through exploration and inquiry.

Arts-inquiring pedagogy

Key features

Definition

Type

Table 2.2  An overview of arts-related approaches

Issues of representation

Artistic process

Use of inquiry for learning

Focus

Related theorists

Postmodernism Post-structuralism Constructivism

Postmodernism Post-structuralism Constructivism

(Continued)

Saldaña (2011)

McNiff (2008)

Critical awareness/ Dewey (1938) developmental Mezirow (1991) learning theories Rogers (1983)

Paradigms

NEW METHODOLOGIES? 41

Art is used in order to evoke a response from an audience (in the broadest sense) to a situation or issue; the response may or may not be captured.

The use of art to engage communities/ marginalized groups.

The use of the artistic process to undertake, represent and disseminate evaluation.

Artsinforming inquiry

Arts-engaging inquiry

Arts-related evaluation

Mezirow (1991) Freire (1970) hooks (1994) Simons and McCormack (2007) Cancienne and Snowber (2003)

Constructivism/ Constructionism Constructivism/ Constructionism

Change and transformation Evaluation of and through the artistic process

Creative processes central to understanding an issue through evaluation.

Herman (2005)

Engaging community and provoking change.

Constructivism/ Constructionism

Issues of response

Related theorists

Making meaning through complex performances/ products that have power and are evocative.

Paradigms

Focus

Key features

Source: Savin-Baden, M. and K. Wimpenny (2014), A Practical Guide to Arts-related Research. Rotterdam: Sense.

Definition

Type

Table 2.2 (Continued)

42 Research Methods for Education in the Digital Age

NEW METHODOLOGIES?

43

The installation was evaluated and Upton found that younger children and older adults engaged with the installation more than older children and teenagers, who often found it discomforting. This seemed to indicate that this kind of installation prompted a sense of dis-ease, as well as challenging notions of arts and virtual interaction. The questions of ‘what is real?’ and what counts as immersion in such spaces remains a contested area of research that bears further exploration. For example, Dede (1995) suggested that within learning environments, immersion can be created through the capacity to execute actions, through semantics and semiotics, and through physical and sensory provision, creating a feeling that the user is surrounded by the 3D virtual world. A study by Benford et al. (1995) explored user embodiment on collaborative virtual environments. The purpose of the study was to provide users with what the researchers considered to be appropriate body images with which to represent themselves. The underlying argument here was that given that reallife bodies provide continuous information, feedback and means of communication, embodiment in virtual environments should be identical, so that participants can represent themselves as accurately as possible. Digital installation art is a method of portraying art in the digital realm; however, a/r/tography combines both the research and the means of portrayal in one methodology.

A/r/tography A/r/tography is defined by the artist/researcher/teacher, as the frame of reference through which art practice is explored, examined and presented: 1 The Artist en-acts and embodies creative and critical

inquiry; 2 The Researcher acts in relation to the culture of the research community; 3 The Teacher re-acts in ways that involve others in artistic inquiry and educational outcomes. (Sullivan 2006: 25) A/r/tography is a process of inquiring in the world through the process of art making, in which the components of writing and art

44 Research Methods for Education in the Digital Age

are woven in ways that indicate that they are interconnected and related to one another. Works of this sort are seen as interactions between text and art so that inquiry, identity, metaphor and interrogation coalesce. The arguments for this approach stem from a belief that if forms of arts-based research are to be taken seriously as emerging fields within educational research, then perhaps they need to be understood as methodologies in their own right, not as extensions of qualitative research. This entails moving beyond the use of existing criteria that exists for qualitative research and toward an understanding of interdisciplinarity not as a patchwork of different disciplines and methodologies but as a loss, a shift, or a rupture where in absence, new courses of action unfold. (Springgay, Irwin and Kind 2005: 898) The danger with this approach is that the research can become dislocated from a methodological stance. As mentioned at the outset of this chapter, it is important that the research approach fits with the research paradigm. This does not mean that methods and methodologies cannot be adapted, but, rather, that researchers should consider how they position themselves in relation to their philosophies, whether consciously or unconsciously, as they consider how to undertake a given study. Thus, it is important to be clear about the choice of philosophy underpinning the research, when using a/r/ tography.

Digital narrative inquiry Digital narrative inquiry is based on narrative inquiry and uses digital media and artefacts as a means of collecting and portraying stories: Digital narrative inquiry refers to a representational form that features narratives of experience told and re-told, and storied and re-storied, through the narrative inquiry research method. Products of rigorous investigation, these digital narratives capture and communicate life as lived in context and understood in individuals’

NEW METHODOLOGIES?

45

own terms. In these digital narrative accounts, relationships between researchers-research participants are critically important because they fuel the personal/social understandings that become featured in the lifelike representations. (Craig 2013: 8) Narrative inquiry, as a research approach, is built on the notions of temporality, sociality and place (Connelly and Clandinin 2005), but in this approach, researchers also acknowledge that all stories are partial and that other perspectives still exist beyond those represented in the accounts given. The idea of narrative inquiry is that stories are collected as a means of understanding experience as lived and told, through both research and literature, however partial these may be. Those who use this research method argue that stories are the closest we can come to sharing experience and seeking to understanding experience. Polkinghorne (1995) has suggested that the term ‘narrative’, as used by qualitative researchers, has a whole variety of meanings. He has argued that narrative within narrative inquiry is ‘a discourse form in which events and happenings are configured into a temporal unity by means of a plot’ and has located Bruner’s (1996) classification of narrative inquiry into two distinct groups, namely narrative analysis and paradigmatictype analysis (Polkinghorne 1995: 5). Polkinghorne built on Bruner’s (1996) classification in order to draw a clear distinction between analysis of narratives, and narrative analysis. The first refers to studies in which the data consist of narratives that are then analysed to produce categories. The latter refers to ‘studies whose data consist of actions, events, and happenings but whose analysis produces stories’ (Polkinghorne 1995: 6). He argued that both of these classifications are central processes when undertaking narrative inquiry, but we suggest they are not easily separated when using digital media. Further, what counts as ‘story’ varies across methodological fields. The biographical-interpretative method was first developed by German sociologists to produce accounts of the lives of Holocaust survivors and Nazi soldiers. This method is part of the narrative tradition and the main theoretical principle in this method is the idea that there is a gestalt, informing each person’s life. Thus the biographers need to elicit this ‘meaning frame’ rather than following their own concerns. Consequently, when using

46 Research Methods for Education in the Digital Age

narrative inquiry, it is important that the researcher is able to both ask questions that elicit stories and position himself or herself so that stories can be analysed effectively. For example, some researchers within the ethnography tradition would argue that stories largely emerge from interview questions asked by the researcher. However, there are those within the interpretive tradition who disagree with this stance, and would always ask participants to tell and define their story in a way that would convey the meaning that they, as participants, would wish to be heard. Data sources in narrative inquiry include the following: ●●

Field notes of shared experiences

●●

Journal records of participants

●●

Unstructured (online) interviews

●●

Storytelling

●●

Shared reflections about an event or experience

●●

Recorded reflections (film, oral)

●●

Autobiographical and biographical writing

Narrative approaches generally focus on developing understanding through an exploration of story, interpretation and discourse (Leggo 2008). They are also valuable for gaining data in areas where it may be difficult to obtain them using other methods, such as undertaking a study into the history of prejudice, where storytelling is central to understanding. Stogner (2014) suggested that today’s media is changing cultural narratives and that we now have an explosive rise of storytelling technologies; the medium is instant messaging: Today’s narratives ebb and flow in a sea of continual action and reaction. Frequently, there is no beginning, middle, and end. No plot. No heroic archetypal characters. No narrator. The publicat-large is contributor, critic, and curator. (Stogner 2014: 14) Work by Stogner in Washington, United States of America, mirrored the changing landscape of narrative research and, in

NEW METHODOLOGIES?

47

Example Harrison, B. (2002), ‘Photographic Visions and Narrative Inquiry’, Narrative Inquiry, 12 (1): 87–111. This paper examines the ways in which photographic images can be used in narrative inquiry. After introducing the renewed interest in visual methodology, the first section examines the ways in which researchers have utilized the camera or photographic images in research studies that are broadly similar to forms of narrative inquiry such as auto/biography, photographic journals, video diaries and photo-voice. It then draws on the published literature in relation to the author’s own empirical research into everyday photography. Here the extent to which the practices that are part of everyday photography can be seen as forms of storytelling and provide access to both narratives and counter-narratives, are explored. Ideas about memory and identity construction are considered. A critical area of argument centres on the relationship of images to other texts, and asks whether it is possible for photographs to narrate independently of written or oral word. It concludes with some remarks about how photographs can be used in research and as a resource for narrative inquiry. This necessitates an understanding of what it is people do with photographs in everyday life.

particular, the impact of digital media. She suggested that today’s media are changing cultural norms and are resulting in a rise in storytelling technologies, which are often characterised by usercentric storytelling that is fragmented, adaptable and repurposed. Her research illustrated three new narrative forms: participatory narratives, collective narratives and mobile narratives. Participatory narratives involve the process of creating, critiquing and curating so that everyday heroes create their own quests and journeys. In practice, people share fragments of their stories, photos, virtual exhibitions and videos. An extension of participatory narratives is, Stogner suggests, purposeful participation, whereby technologies are used to create shared opportunities that add value to the shared artefacts and the users themselves. She cites the interactive installation, ‘From Memory to Action’, at the

48 Research Methods for Education in the Digital Age

U.S. Holocaust Memorial Museum in Washington D.C., which enables users to watch video testimonies about genocide, select stories of personal interest and share them (Ushmm.org n.d.). It introduces visitors to the concept and law of genocide, and to three contemporary cases of genocide – Rwanda, Srebrenica in BosniaHerzegovina and the Darfur region of Sudan. Collective narratives are where people engage with disparate others across time and space. The authorial voice is formed by the contributions of many, who, in turn, create aggregated mega stories. An example is the Question Bridge project (QuestionBridge.com n.d.), which defines itself as a collective trans-media project that is creating a ‘mega-logue’ among black men in the United States. It is a platform for black men of all ages and backgrounds to ask and respond to questions about life in America and was created to facilitate understanding as well as sharing diverse views and identities. Mobile narratives are told through whatever sources people choose to use on the move, whether through apps or superimposing video, poems and animation in any environment. An example of this is ‘Gradually Melt the Sky’, which creates a performance event ‘at once cosmic and mundane, an action painting and a protest’ to ‘overlay, intervene and challenge the physical world’ (Skwarek and Pappenheimer 2011). In terms of the future of digital narrative inquiry, Craig (2013) argued that the research process in this form of research collapses the gaps between what is lived and told and what is re-written and re-told, in ways that do not occur in written forms of narrative inquiry. Craig claimed: Emotions and attitudes were easier to apprehend in the digital representations of this narrative inquiry research. These included such things as tears misting in people’s eyes and quivering voices. Of particular importance was the fact that the research process from which the narrative representations arose made them qualitatively different from digital stories approached through technological means (more interactive because living/re-living was interlaced with telling/re-telling) and quantitatively different due to the productions being more than twice as long as the average digital story. (Craig 2013: 37)

NEW METHODOLOGIES?

49

However, we suggest that a number of questions still remain about digital narrative research: 1 Should technology itself be seen as a representational form,

or as part of the representational form, or as just a means of enabling the presentation of data? 2 To what extent might this approach replace or change current forms of narrative inquiry? 3 To what extent can the complexities of narrative research really be captured in a short (20-minute) digital presentation?

Digital story telling Digital storytelling was developed by Atchley and Lambert (Lambert 2006), and is a form of storytelling that is created through a short digital media presentation. Digital storytelling involves asking participants to present their point of view using images. It requires them to consider the use of their voice, their choice of music and the way their story is paced and presented. A digital story is usually short, often presented in the first person and is a video narrative created through a combination of voice, music, and still and moving images. One way of creating a digital story is by using the software that is freely downloadable, for example Photostory, as well as other tools such as iMovie, Premiere, Photoshop Elements, iPhoto, Audacity and SoundStudio. These short stories, in many ways, often look quite straightforward. However, after working out their design, creating and developing them is an iterative process that occurs through drafting, revising and gaining feedback from others before developing a storyboard. Lambert’s (2006) original seven principles included the following: 1 Point of view: The main point of the story and the

perspective of the author in relation to the story. 2 A dramatic question: A key question that keeps the viewer’s attention and will be answered by the end of the story. 3 Emotional content: Serious issues that come alive in a personal and powerful way and connect the audience emotionally to the story.

50 Research Methods for Education in the Digital Age

4 The gift of your voice: A way to personalize the story to

help the audience understand the context and get a stronger sense of the person behind the story. 5 The power of the soundtrack: Music or other sounds that support and embellish the story. 6 Economy: Using just enough content to tell the story without overloading the viewer. 7 Pacing: The rhythm of the story and how slowly or quickly it progresses. For some authors, the creation of the digital story is seen as more important than the story itself (for example, Ohler 2009), and for other authors, retelling the story is what matters (for example, Stenhouse et al. 2013). One of the current difficulties with digital storytelling is that all types tend to be described in the same way, which then makes it complicated to decide how to undertake digital storytelling. We suggest the typology summary in Table 2.3 as a useful analysis of the different types.

Digital métissage Digital métissage is based on the idea of literary métissage as outlined by Hasebe-Ludt, Chambers and Leggo (2009). Literary métissage is the process of creating stories that are braided together and rooted in history and memory, as well as being stories of­ be-coming. Literary métissage provokes engagement with dominant discourse(s) in order to challenge and change them. Digital métissage captures the idea of blurring genres, texts, histories and stories in digital formats that recognise the value and spaces between and across cultures, generations and representational forms. The notion of métissage (French meaning hybridization or fusion) brings with it the sense of braiding, so that the process of digital métissage requires co-production and co-creation with participants in ways that braid data and stories. Through collecting stories, researchers and participants undertake digital braiding, so the data and representation are both individual and collective. Such métissage enables researchers to work in innovative participatory ways that

The process of storytelling and using media to Burgess (2006) – The use of digital storytelling to explore the support the development of an autobiographical participatory cultural studies approach to research. account, as both a media form and a field of cultural practice.

Interpretation undertaken across digital stories that have already been analysed, in order to examine themes across all the stories at a meta level.

Digital storytelling

Digital meta stories

(Continued )

Chen (2014) – Found that the core content requirement Micro movie A short film lasting from a few seconds to no more than 5 minutes, which combines personal of the new media is ‘story’ and that these forms of digital writing, photographic images or video footage, production challenge the status quo. narrative, sound effects and music.

Craig (2013) – Traversed four narrative inquiries, and the digital exemplars produced for each, to show how digital narrative inquiries capture particular issues and other queries at the intersection where narrative inquiry and digital story meet.

A story created through the use of digital media, Stenhouse et al. (2013) – Creating digital stories with people facilitated by an individual or an expert in order with dementia: Digital stories were made with seven people to portray a particular concern or issue. with early-stage dementia as part of a learning package for student nurses.

Digital story

Example

Description

Type

Table 2.3  Types of digital stories

NEW METHODOLOGIES? 51

Students work together to create a story one section at a time; the catch is that students can only read the last paragraph of the story.

Fold that story

Fold That Story is a folding story game that has been designed for classroom use.

FoldingStory™ is a game in which players write one line of a story, fold the paper, and pass it to the next person online.

Folding story A group storytelling game

Story (2015) – Professional photographer and author Derrick Story publishes daily tech news, photo tips, techniques, reviews and a weekly photography podcast. An example of this is interactive fiction in which readers (children) choose their own path through the story, such as Goodbrey (2015) who created a unique hypercomic adventure syndrome.

A programme (lecture, story) made available in digital format for automatic download over the internet.

Podcast

Example

Hypertext environments that facilitate the Hypertext interactive story in which the ‘reader’ chooses stories and story games optional paths to explore.

Description

Type

Table 2.3 (Continued )

52 Research Methods for Education in the Digital Age

NEW METHODOLOGIES?

53

enable the creation and illustration of visual and emotional aspects of the stories, artefacts and research. The focus on ‘the digital’ also recognises the importance of connectivity as a complex and contested concept. In Chambers et al. (2008), notions of curriculum, language, culture, place and identity are explored: Place and space, memory and history, ancestry and mixed race, language and literacy, familiar and strange are braided with strands of tradition, ambiguity, becoming, (re)creation, and renewal. (Chambers et al. 2008: 152) The authors use poetry, pictures, storytelling and narrative, and mixed and braided genres. The inked examples in this text also illustrate how this can be transferred into digital forms online.

Conclusion Many of the approaches presented in this chapter are not entirely new; some are older approaches that are being used in different and diverse ways in digital spaces. However, what this chapter illustrates is that most methods can be adapted for digital qualitative research, and some of the new options and experiments, particularly within digital stories and digital métissage, offer opportunities to explore, challenge and disrupt the status quo and ask questions about what is meant by ‘the digital’, what is meant by ‘research’, and what is meant by ‘education’. This discussion is taken up in Chapter 3, which explores the use and value of ethnography for educational research in the digital age.

Further reading Nind, M., R. Wiles, A. Bengry-Howell and G. Crow (2013), ‘Methodological Innovation and Research Ethics: Forces in Tension or Forces in Harmony?’, Qualitative Research Journal, 13 (6): 650–67. In this study, Nind et al. explore three types of methodological innovation: netnography, child-led research and creative visual research

54 Research Methods for Education in the Digital Age

methods. Each of these methodologies is explored through interviews with the respective creators and researchers attempting to apply these methods in context. This paper thus provides several excellent examples of how researchers manage and adapt methodologies in practice, and how the originators of particular methodologies adapt their practice according to the surrounding contexts. Pink, S. (ed.) (2012), Advances in Visual Methodology. London: Sage. This collection brings together innovative essays from key scholars in the field of visual methodology. It is particularly focused upon the application of visual methodologies in contemporary and digital settings, making it applicable for readers of this text. Pink’s contributors are well chosen; they bring together theory and practice from several disciplines, establishing visual methodology as a leading interdisciplinary methodology. Savin-Baden, M. and K. Wimpenny (2014), A Practical Guide to Artsrelated Research. Rotterdam: Sense. Savin-Baden and Wimpenny approach arts-related research as teachers, researchers, methodologists and artists. This guide represents the creativity, innovation and opportunities inherent in the arts-related research field, but its most important contribution is the discussion of rigorous methodology in practice. It is also particularly useful in terms of considering one’s own position as a researcher and the ways in which teaching, researching and other identities can intersect and, occasionally, conflict. This book provides a wide range of examples written and created by international arts-related researchers.

chapter THREE

Ethnographies for the digital age Introduction The places and spaces in which researchers have conducted ethnographies have moved from the exotic to the local, familiar and on to the digital. We begin this chapter by providing a brief history of ethnography and then describing newer forms of ethnographies. We then introduce and discuss some of the newer forms of ethnography that have been developed and adapted for the digital age and explore some of the dilemmas and possibilities they present. In the context of methodology, we suggest that ethnography should be located at the boundaries of constructivism and constructionism, but that, in the main, those using it tend to take a strongly interpretivist stance to data management. In the final section of the chapter, we suggest how educational researchers might design and do ethnography in ways that are realistic, and we provide some examples of sound practice.

A brief history of ethnography Ethnography appears to have had a broad, chequered history and, in recent years, there have been many discussions at conferences about how possible and realistic it is, and even about what

56 Research Methods for Education in the Digital Age

counts as ethnography in the twenty-first century. This is because ethnography tends to rely on long-term (years of) engagement in the field, which these days is rarely possible financially. Early ethnography emerged from anthropological studies that sought to understand other cultures, countries and ways of life, and dates at least as far back as the 1700s. These forms of ethnography focused on collection of data by an anthropologist, and the provision of an in-depth description of the setting from which these data were collected. Criticism of this kind of ethnography, which focused on difference, stemmed from the origins of ethnography as an approach of studying the ‘other’, in many cases indigenous populations (Vidich and Lyman 2000). In the mid-to-late-twentieth century, new forms of ethnography emerged that sought to disrupt notions of the researcher studying the ‘other’ and the resultant challenges around power, authority, authenticity and agency in research. For example, autoethnography was developed as a research approach that sought to examine personal experiences to understand cultural experiences, containing both autobiographical and ethnographic elements. Autoethnographic accounts established the importance of researchers from disempowered, marginalized, or under-researched communities voicing their own experiences and those of their research participants. Although there are many different views about what counts as ethnography, broadly speaking, it is still an approach that we believe requires intensive fieldwork to gain a comprehensive view of a social group and its setting.

The term ‘ethnography’ comes from the Greek, ethnos, and is translated as ‘folk’. Ethnography is the study of people, cultures and values.

Whilst ethnography was developed though the seminal work of researchers in the 1920s, such as Malinowski’s ([1922] 1984) study of the social life of islanders in the Trobriand Islands, and Mead’s ([1928] 2001) Samoan study concerning adolescence, childbearing and the influence of culture on personality, much has changed since then. Early forms of ethnography sought to

ETHNOGRAPHIES FOR THE DIGITAL AGE

57

understand and interpret cultural groups, and Malinowski argued that ethnography has a goal, of which an Ethnographer should never lose sight. This goal is, briefly, to grasp the native’s point of view, his relation to life, to realize his vision of his world ... . In each culture, we find different institutions in which man pursues his life-interest, different customs by which he satisfies his aspirations, different codes of law and morality which reward his virtues or punish his defections. To study the institutions, customs, and codes or to study the behaviour and mentality without the subjective desire of feeling by what these people live, of realising the substance of their happiness – is, in my opinion, to miss the greatest reward which we can hope to obtain from the study of man. Malinowski ([1922] 1984: 25) In education, ethnography has had a long history of being used in schools, exemplified by work such as that by Hammersley, who in the late 1960s, saw schools as bastions of imperialism. His books such as Reading Ethnographic Research (1991) and What’s Wrong with Ethnography? (1991) had a marked effect on education. Work at the University of Anglia in the United Kingdom was also at the forefront of developing ethnography and authors like Helen Simons and Maggie MacLure have been highly imfluential in this area. In order to undertake such longitudinal studies, ethnographers (used to) spend time, often years, within the culture, trying to understand its ways, customs and hierarchies. There have been many debates about whether in these early studies, it was possible to grasp the ‘indigenous point of view’. The result has been complex discussions about stance, identity and agency, which have brought to the fore not only new forms of ethnography but also new ways of considering it as a methodology. For example, critical ethnography approaches, as Thomas (1993: vii) has argued, ‘offer a more direct style of thinking about the relationships among knowledge, society, and political action’. However, for many, it is still seen as largely an observational approach in which the researcher is immersed in a given culture. Thus, the expectation is that it is an approach that can create and represent an understanding of those being studied. Authors such as Pink (2011) have challenged this stance, reflecting on Atkinson’s argument that ‘we need to retain a structural, formal

58 Research Methods for Education in the Digital Age

sense of the multiple orderings of talk, action, things, places and so on’ (Atkinson 2005: para. 19) and Atkinson’s subsequent calls for an ethnography that attends to Clifford Geertz’s (1973) notion of ‘thick description’. Instead, Pink (2011: 270) suggested a need for an ethnography that should involve ‘learning in and as part of the world, and seeking routes through which to share or imaginatively empathize with the actions of people in it.’ Amidst the discussion about what might count as ethnography, there are also a range of terms related to using ethnography in/ on the internet. For example, in the early 2000s, the term ‘cyber ethnography’ was developed, which seems, certainly in the United Kingdom at least, to have been superseded by virtual ethnography or ethnography for the internet (Hine 2000, 2015) as well as digital ethnography (Madson 2014). Cyber ethnography is defined largely as an online ethnography that is action orientated and is based on epistemologies of doing. In practice, it tends to include interactive interviews, participant observation, and exploration about identities that are created and reimagined, image manipulation, creation of avatars, digital video and audio. Robinson and Schulz (2009) argued that cyberethnographic inquiry developed through three phases, which are pioneering, legitimizing and multimodal. Pioneering cyberethnographic inquiry is defined as early exploration of online interaction and online performance, much of which was in the area of online gaming and role-playing games. This is compared with offline experience. Legitimizing cyberethnographic inquiry involves seeking to combine and explore online and offline interaction and methodologies, seeing the interaction between them as a flow between realities, positing also that virtual text is both medium and data: Cyberethnographers claimed that where in the offline world the ethnographer must translate the field site into field notes or text, online members took on the ethnographer’s task by translating the offline range of interactive cuing mechanisms into textual format. (Robinson and Schulz 2009: 691) Multimodal cyberethnographic inquiry is seen as the need for ethnographers to examine visual, aural and non-text-based outputs in virtual spaces, which may be user driven or seen as spaces of political engagement. Further, ethnographers in this phase question traditional modes of dissemination and representation.

ETHNOGRAPHIES FOR THE DIGITAL AGE

59

Whilst this approach by Robinson and Schulz (2009) is interesting, it does rather oversimplify the growth of different ethnographies and understate the importance of their philosophical underpinnings. Madson’s (2014) argument for the term ‘digital ethnography’ appears to be more cogent: Digital ethnography is a many-stranded mesh, encompassing virtual ethnography (Dominguez et al. 2007; Hine 2000), internet ethnography (Sade-Beck 2004), connective ethnography (Hine 2007), netnography (Kozinets 2010), sensory ethnography (Pink 2009), multimodal ethnography (Dicks, Soyinka and Coffey 2006), hypermedia ethnography (Coffey et al. 2006), as well as the more recent expanded ethnography (BeneitoMontagut 2011), trace ethnography (Geiger and Ribes 2011), and extreme ethnography (Rotman et al. 2012). These strands differ in genealogy, applications, and epistemic foci, yet they all implement digital technologies at some point. Hence, digital ethnography might be most succinctly and inclusively defined as ‘ethnography mediated by digital technologies’ (Murthy 2011: 159). (Madson 2014: 68–9) Whatever the arguments about ethnography, it seems to us that there are many types, some of which are delineated below. For us, the term ‘ethnography for the digital age’ seems to be the most suitable overarching term, since it is research that takes place in digital spaces using digital devices to create and collate data whose roots are based firmly in the ethnographic tradition.

Ethnographies for the digital age? It is clear that amidst the diverse approaches to ethnography in the digital age, there exist fragmented views about what counts as ethnography. The result is that particular forms of data collected on the internet are seen as more ethnographic than others, such as interviewing and participant observation, since they are reminiscent of more traditional ethnography adapted for online use. There are others who suggest that there is a need for new, different and flexible approaches, which, while still nodding to ethnographic

60 Research Methods for Education in the Digital Age

Table 3.1  Positioning ethnographies Critical social theory

Post-positivism Constructionism Constructivism

Positive knowledge exists and may be discovered through historical approaches, aimed at change and emancipation.

Reality exists but is imperfectly understandable and it may be uncovered through falsification (primarily quantitative).

Reality and knowledge are socially constructed; knowledge may be gained by examining the ways in which individuals co-create knowledge.

Reality and knowledge reside in the minds of individuals. Knowledge may be uncovered by unpacking individual experiences.

Purpose To transform social realities through enlightenment

To understand realities through logical processes

To understand socially constructed realities

To understand constructed realities

Example Critical ethnography

Netnography

Connective ethnography Visual ethnography

Immersive ethnography Sensory ethnography

Theory View of reality

principles, tend to be more collaborative. What appears to be lacking in the debates about ethnographies for the digital age is the relationship between the different types, and the ways in which these relate to their underpinning philosophy. We argue that it is important to understand the underlying premise of an ethnography, in order to realize the impact each type will have on data gathering and representation of findings. In Table 3.1, we locate these ethnographies in relation to their philosophical position, and then present the key features in Table 3.2. We argue that, across these different types, there are some shared characteristics that locate these different forms within the ethnographic tradition, including the following: ●●

Prolonged engagement: The researcher tends to spend long periods of time in the setting, getting to know the participants over months or years.

Leander and Combining online and McKim (2003) offline ethnographic approaches.

Savin-Baden (2010)

Pink (2007)

Madison (2005)

Connective ethnography

Immersive ethnography

Visual ethnography

Critical ethnography

Challenging the existing status quo of political, social and other structures, by researching the struggles of those weak and powerless.

Exploring the storied relationships among people, places and things.

Reflection upon researcher purpose, intentions and frames of analysis as researchers. Ensuring that research contributes to equity, freedom and justice.

The use of photos, maps and computer graphics in its representations.

Undertaking research in virtual worlds Living and working immersively in virtual worlds for in a deeply immersive way. over a year, collecting data through residing in virtual worlds and observing. Seeing everything in virtual worlds as data.

Examines the cognitive processes occurring online that are influenced by offline activities.

Shift away from participant observation as listening and watching towards multisensory participation, for example walking with, eating with and sensing with.

Pink (2009)

Sensory ethnography

Re-thinking of ethnographic methods in which attention is paid to sensory perception, experience and categories.

Providing guidelines for the adaptation The use of 6 overlapping steps to examine community of participant-observation procedures and culture. to the contingencies of online community and culture.

Kozinets (2010, 2015)

Boundaries, especially between the virtual and the real, are not taken for granted. It is a process of intermittent engagement, rather than long-term immersion.

Key features

Netnography

Focus

Exploring of online actions and online spaces in order to understand what people do on the internet.

Key authors

Ethnography for Hine (2015) the internet

Ethnography

Table 3.2  Forms of ethnography for the digital age

ETHNOGRAPHIES FOR THE DIGITAL AGE 61

62 Research Methods for Education in the Digital Age

●●

●●

●●

Deep immersion: Researchers spend time not only in the setting but also in getting to know the participants and their lives, and thus they may join a Gorean community in a virtual world. A focus on seeing everyday life: The idea is not to focus on what is unusual, but, instead, on what is ‘normal’ within a context. For example, what are the values and norms of community life in virtual worlds? In-depth data collection: Researchers tend to adopt participant observation because by participating in the community, it is possible to come to understand it better. Thus by becoming part of a Second Life community, it may be possible to understand its values, norms and power structures.

Ethnography for the internet The work of Hine (2000, 2015) in this field has been seminal in terms of seeking to understand, from a sociological perspective, what people do on the internet. Her stance has not been to focus on immersions as in more traditional ethnographic studies (for example, Geertz 1973), but, instead, to suggest that it was important not to assume that by merely examining online actions and spaces, it would be possible to understand what was, or might be, significant or meaningful. Thus virtual ethnography is seen as ethnography of, in and through the virtual – we learn about the Internet by immersing ourselves in it and conducting our ethnography using it, as well as talking with people about it, watching them use it and seeing it manifest in other social settings. (Hine 2000: 65) Her recent work, as discussed in detail in Chapter 1, argued for the need for what is known as the E3 internet framework. The first aspect of this framework focuses upon an internet that is embedded, as families, organizations and cultures make the internet their own and experience it differently across groups. The second aspect is

ETHNOGRAPHIES FOR THE DIGITAL AGE

63

that the internet is embodied: that is, the idea that a particular person’s embodied context and circumstance shapes his or her experience of the internet. The third aspect is that the internet is every day since in many ways it has become taken for granted, invisible and a way of making sense of what we do. In practice, she argued the need to legitimize these kinds of approaches to research into the internet as standard approaches to virtual ethnographies, and suggested that there is additionally a need to understand how to undertake multimodal studies effectively. She suggested that, to date, small-scale studies (she cites Boellstorff 2008) have explored particular cultures online but that there still remains an online/ offline boundary, and that researching in this sphere is challenging because of the constant need to create new research strategies for diverse and differing situations. Her strapline for her research work is ‘finding out what people think they’re up to when they’re using the Internet’ (Ford 2013: para. 15).

Netnography This is defined by Kozinets (2010) as a form of online ethnography focused on the gathering and managing of social media data as a type of online or Internet, ethnography; Netnography provides guidelines for the adaptation of participant-observation procedures to the contingencies of online community and culture that manifest through computer-mediated communications. (Kozinets 2010: 193) It was an approach developed first in 1995 as a response to changes in online social interaction, because of an increasing recognition of the value of using a cultural frame of reference to understand these interactions. The focus in this approach is having a common set of standards to ensure stability, consistency and legitimacy. The idea was to create a distinct set of procedures for this kind of research, and the author suggested four critical differences between online and face-to-face cultural and social interactions: 1 The nature of the social and cultural interaction is altered

by the nature and rules of the technological medium.

64 Research Methods for Education in the Digital Age

2 The interaction can be optimally anonymous or

pseudonymous and real identities can be suspect. 3 Social interaction is widely accessible, thus ensuring that data collection possibilities are abundant. 4 The automatic archiving of data that we see in online social worlds transforms data collection and analysis. (Summarized from Kozinets 2012: 39) Although Kozinets underpinned the method of netnography with two concepts of community and culture, he argued for six overlapping steps, which seem to differ little from standard research designs: 1 Research planning 2 Entrée 3 Data collection 4 Interpretation 5 Ensuring ethical standards 6 Research representation

Netnography emerged from marketing and appears in some respects to draw upon post-positivism; for example, there is a sense that Kozinets is taking a Popperian stance. Popper (1959) argued that empirical observations could never prove a universal rule to be true, but, instead, provide evidence to support it. In doing so, he questioned the way science verified its claims and argued that the essence of science was the extent to which it could be refuted. The early work by Kozinets in 2002 focused on a step-by-step model for collecting data, and narrowed ethnography into something manageable rather than something liquid. Thus, traditionally, netnography focused on the observation of textual discourse, and used content analysis in order to code data easily. However, more recent work in this area would seem to indicate that it has become less positivist and pragmatic in its stance and implementation. For example, whilst field note observations were considered to be important to this methodology, a review of 116 netnographies undertaken by Bengry-Howell et al. (2011) revealed that 75% of the papers made no mention of field notes but, instead, focused upon online discussion and interaction. In this respect, it

ETHNOGRAPHIES FOR THE DIGITAL AGE

65

would seem that many applications of netnography are, instead, pragmatic research approaches that use content analysis to study online interaction. Pragmatic qualitative research is an approach that draws upon the most sensible and practical methods available in order to answer a given research question (Savin-Baden and Major 2013). These kinds of studies provide a clear summary of events in everyday research terms, aiming for a description of an experience or event as interpreted by the researcher (Neergaard et al. 2009). Whilst at one level this is unproblematic, the lack of focus upon the experience of individuals in context means that netnography does not seem to fit well with the underlying philosophy of ethnography.

Sensory ethnography This was described by Pink (2007) as a re-thinking of ethnographic methods in which attention is paid to sensory perception, experience and categories. Thus, she proposed that it is not merely ethnographic research about the senses, and we suggest it is a shift away from other similar approaches such as visual anthropology, which still largely focuses on visual representation. Even today ethnographic films only use the same basic senses of vision and hearing. Sensory ethnography goes beyond other forms of ethnography in that it is informed by an understanding of the interconnected senses and uses ‘innovative methods’ that move beyond listening and watching, such as multiple media. Further, it uses a variety of media for representation and portrayal, such as arts-related methods. Pink’s argument for this approach centred on the idea that there is a need to move away from ethnography as a site of watching and listening, and recognizes the importance of how we as researchers position ourselves in the environment – emplacement; how we realize sensory perception and sensory categories – the interconnected senses; and how we recognize that there are forms of knowing that are not necessarily expressible in words – knowing in practice.

Differences in methods Pink argued for a shift away from participant observation as listening and watching and, instead, for multisensory participation,

66 Research Methods for Education in the Digital Age

for example walking with, eating with and sensing with. In practice, the sensory ethnography approach rethinks the interview as a multisensory encounter and as part of a specific environment, and whilst interview conversations are still of interest, these need to be located within understandings of places and practices. Thus, Pink suggested that the interview, too, should be a multisensory ‘event’ and be used to probe sensory categories. In terms of representation, it seems that since Pink’s initial text in 2007, new practices for communicating the findings of sensory ethnography are emerging, which include new forms of ethnographic filmmaking and collaborations. However, she argued (Pink 2009) that there is a need for those using sensory ethnography to both report on their findings and reflect on the methods adopted, in order to contribute to the development of this field. Whilst there is an array of publications in this area, the work of Lund (2005) illustrates some of the key principles of this work:

Example Lund, K. (2005), ‘Seeing in Motion and the Touching Eye: Walking over Scotland’s Mountains’, SENSES, 18 (1): 27–42. In this paper I examine the senses of vision and touch in mountaineering. My aim is to demonstrate how approaches to vision in the Western context have been limited to the observing eye. During fieldwork with mountaineers in Scotland I learnt that how one senses the environment has to be considered in relation to the actual movement of the body and, thus, needs to be examined in relation to how the body measures itself to the ground. The body meets the ground and the touch affects the view because the walker’s attention shifts between focusing on the ground and looking into the distance. As a result the gaze into the distance cannot be taken out of the context of how the body treads the ground, which concludes that when approaching vision one needs to examine the eye, that not only sees, but also touches.

ETHNOGRAPHIES FOR THE DIGITAL AGE

67

Connective ethnography Connective ethnography is based on the idea of moving away from ethnography, which is necessarily seen as place-based. It was originally suggested by Leander and McKim (2003), as a means of combining online and offline ethnographic methods. This approach has developed since 2003, and the focus tends to be not only on online and offline spaces but also on how boundaries are constructed around and across these spaces. Ethnographers may have a specific research question or idea to pursue, but they generally do not clearly define how they will use online and offline methods in relation to one another. In connective ethnography, however, the idea is to study connections between these spaces, thereby seeing the research field as a network rather than a geographical space with clearly located

Example Fields, D. and Y. B. Kafai (2009), ‘A Connective Ethnography of Peer Knowledge Sharing and Diffusion in a Tween Virtual World’, Computer-Supported Collaborative Learning, 1 (4): 47–68. In this paper, we describe and analyse how an insider gaming practice spread across a group of tween players ages 9–12 years in an after-school gaming club that simultaneously participated in a virtual world called Whyville.net. In order to understand how this practice proliferated, we followed the club members as they interacted with each other and members of the virtual world at large. Employing connective ethnography to trace the movements in learning and teaching this practice, we coordinated data records from videos, tracking data, field notes, and interviews. We found that club members took advantage of the different spaces, people, and times available to them across Whyville, the club, and even home and classroom spaces. By using an insider gaming practice, namely teleporting, rather than the more traditional individual person as our analytical lens, we were able to examine knowledge sharing and diffusion across the gaming spaces, including events in local small groups as well as encounters in the virtual world.

68 Research Methods for Education in the Digital Age

boundaries. Most connective ethnography combines more traditional forms of ethnography with online approaches in order to connect across methods, fields and spaces. Some studies have used this approach to trace people and their travelling practices (for example, Leander and Lovvorn 2006), while others like Fields and Kafai (2009) have explored practice initiation in tween gaming groups. There are also studies that explore online and offline practices together and also combine research methods. For example, Huizing and van der Wal (2014) used connective ethnography to explore the rise and fall of the Warez MP3 scene. Warez is a virtual, global network of people copying, sharing and distributing copyrighted digital artifacts, and the researchers sought to examine the rise and the fall of the MP3 scene through statistical analysis, online semistructured interviews, online participant observation and literature study. Thus, it is evident there are a number of ways of implementing connective ethnography, and perhaps Hine’s stance is one that clearly reflects this approach: she argued that this approach is a ‘methodological response to e-science that builds on ethnographic traditions for understanding scientific practice’ (Hine 2007: 618). Thus it seeks to combine science and meaning making within the same methodology. Connective ethnography focuses on exploration and construction of boundaries, spaces, connections and mobility, rather than static field sites, to examine the ways in which cultures and communication are embedded in people’s lives.

Immersive ethnography This is a methodology Savin-Baden (2010) defined and developed particularly for virtual worlds, as it is one that combines ethnography with studies on immersion. In practice, it is located at the boundaries of constructivism and constructionism, but takes a strongly interpretivist stance to data management. Immersive ethnography moves beyond virtual ethnography, suggesting that to gain in-depth understanding of virtual worlds, researchers need to: ●● ●●

Live and work immersively in virtual worlds for over a year. Collect data through residing in virtual worlds and observing and participating in activities.

ETHNOGRAPHIES FOR THE DIGITAL AGE

●●

Problematize cultural norms.

●●

See everything in virtual worlds as data.

69

Such immersion will result in the researchers feeling ‘in’, or are ‘part of’ a virtual environment as they become absorbed or deeply involved. As mentioned earlier, immersion is a complex concept, and thus for immersive research to take place, it requires in-depth engagement with physical senses and mental processes within the environment, in order to understand the types of interaction, emotion, embodiment and technology involved. This approach, therefore, moves beyond just collecting interviews and watching what people do inworld. Rather, the focus is on in-depth immersion, whereby the researcher experiences a collision of being researcher, researched and participant.

Visual ethnography Visual ethnography has strong links with, and in many ways appears to have emerged from, visual anthropology, which studies ethnographic photography, film and, since the mid1990s, new media. Visual anthropology is based on the premise that culture is evident through visible symbols that are located in ceremonies, rituals and stories of lives, as well as objects and gestures. Whilst originally there were positivist overtones to visual anthropology, in the sense that it is a belief in the existence of an observable objective reality, there have been shifts towards a recognition that cultures are socially constructed. Nevertheless, the relationship between visual ethnography and visual anthropology appears to remain unclear, exemplified in the stance taken by Ruby (1996): Anthropology is a word-driven discipline. It has tended to ignore the visual-pictorial world perhaps because of distrust of the ability of images to convey abstract ideas. When engaged in ethnography, the researcher must convert the complex experience of fieldwork to words in a notebook and then transform those words into other words shifted through analytic methods and theories. This logocentric approach to understanding denies much of the multisensory experience of trying to know another

70 Research Methods for Education in the Digital Age

culture. The promise of visual anthropology is that it might provide an alternative way of perceiving culture-perception constructed through the lens. (Ruby 1996: 1351) We take the stance that visual ethnography is, indeed, a means of seeing cultural perceptions through a lens, since it uses photos, maps and computer graphics in its representations (Pink 2001). It is, therefore, a method that combines (often static) visual media with an ethnographic stance. Schembri and Boyle (2013) have noted that visual ethnographies are not focused on interactions but, rather, on the interpretation of phenomena through defined lenses. However, for others this is not the case. Pink (2001) suggested that visual ethnography is not merely the process of combining words to produce a desired result, but, instead, requires the coupling of visual images with narrative in order to document, represent and portray the participants. Pink argued: There are no fixed criteria that determine which photographs are ethnographic. Any photograph may have ethnographic interest, significance or meanings at a particular time or for a specific reason. The meanings of photographs are arbitrary and subjective; they depend on who is looking. The same photographic image may have a variety of (perhaps conflicting) meanings invested in it at different stages of ethnographic research and representation, as it is viewed by different eyes and audiences in diverse temporal historical, spatial, and cultural contexts. (Pink 2001: 51) Varied visual representations, whether film, photography, hypermedia or virtual worlds, are used to capture peoples’ lives and explain social worlds. Recent work in this area is enhancing the argument that visual ethnography should be seen as more than purely visual since it is vital that visual aspects of culture are seen as central to any research process. Further, it seems that visual ethnography is an approach that is increasingly being used to cross methodological and disciplinary boundaries, as exemplified in Schurr’s (2012) study.

ETHNOGRAPHIES FOR THE DIGITAL AGE

71

Example Schurr, C. (2012), ‘Visual Ethnography for Performative Geographies: How Women Politicians Perform Identities on Ecuadorian Political Stages’, Geographica Helvetica, 67 (4): 195–202. While there has been intense discussion of the theories of performativity in human geography, little has been said about the methodological implications of the performative turn. This paper suggests visual ethnography as a suitable methodology for performative geographies, since it focuses explicitly on the embodied and non-textual performances that bring both subjectivities and spatialities into being. In order to be able to connect the observed performances with performativity, a visual ethnography of performativity needs to be developed that combines visual research methods with insights about visual culture. By drawing on a visual ethnographic case study of politicians’ identity performances in Ecuador, I show in the empirical section of this paper how the filmed identity performances can be linked and contrasted to hegemonic discourses around masculinity, femininity, whiteness, and indigenousness represented in Ecuador’s visual culture. This visual ethnography reveals the ambivalence of their identity performances in which the politicians are constantly torn between responding to and simultaneously resisting hegemonic discourses around the masculinity and whiteness of the political.

Critical ethnography This form of ethnography is political, as its focus is on challenging the existing status quo of political, social and other structures, by researching the experiences of those who are discriminated against in society, with the overall aim, then, of seeking to change society. Critical ethnography begins with an ethical responsibility to address processes of unfairness or injustice within a particular lived domain. (Madison 2005: 5)

72 Research Methods for Education in the Digital Age

Critical ethnography is based on critical theory, and emerged from the work of German philosophers and economists such as Horkheimer (1982) and Adorno (Horkheimer and Adorno 1972) in the early 1900s, which investigated power relationships. Critical theory sought to critique, change and improve society. It ‘strive[s] for a state of affairs in which there will be no exploitation or oppression, in which an all-embracing subject, namely self-aware mankind, exists’ (Horkheimer [1972] 2002: 241). The focus of research in this area is to aim for transformation, and thus researchers question and examine power structures. Madison has suggested that the questions a critical ethnographer needs to ask are: 1 How do we reflect upon and evaluate our own purpose,

intentions and frames of analysis as researchers? 2 How do we predict consequences or evaluate our own

potential to do harm? 3 How do we create and maintain a dialogue of collaboration in our research projects between ourselves and others? 4 How is the specificity of the local story relevant to the broader meanings and operations of the human condition? 5 How – in what location or through what intervention – will our work make the greatest contribution to equity, freedom and justice? (Madison 2005: 6) In research studies, people should be seen as participants, and the ideologies of critical theory should inform and affect research throughout the research process, as well as the way findings are presented. Thus, those who adopt this approach acknowledge that all research is value laden, while asking strongly political questions that are designed to challenge and interrupt the status quo, such as ‘Why is it so?’ Areas that tend to be explored through critical ethnography are general social issues such as poverty, racism, sexism and inequality. For example, Rodriguez (2014) explored how Latino immigrant youth make sense of their educational experience, identity and belonging in an urban, public high school in the United States and used the study to examine their social interactions. The context of the study was that youth live in a segregated neighbourhood largely abandoned by policymakers.

ETHNOGRAPHIES FOR THE DIGITAL AGE

73

Her findings indicated that the youth interpreted the notion of community as a set of social relations across diverse spaces, such as a protest, or an act of civil disobedience. She explained: The data lead us to consider how schools often fail to reward social identities and alternative pedagogic spaces, provided through community-school partnerships, such as a protest or a service trip, but there exists cultural and symbolic value when asserting a particular social identity. The research offers insight into the disconnection between how institutional forces and policies situate youth and then abandon or intervene through false assumptions. (Rodriguez 2014: xii) As Rodriguez’s study illustrated, researchers using critical ethnography tend to use contradictions, imponderables and tension as features of the research process. What is central to this approach is the idea of exploring everyday life and cultural reality through a critical framework. The researcher must be highly reflexive, whilst recognizing both the value-laden-ness of research and that only a partial view can ever be attained.

Ethnographies in practice There are many forms of ethnography and we have presented just a few of them here. As a research methodology, it is challenging because it requires both in-depth prolonged engagement and the need to become immersed in an online community or virtual world in order to research it effectively. Nevertheless, in terms of researching digital spaces and research in digital spaces, ethnography does offer huge opportunities to gain insights into cultures, practices and norms that may be less evident when using other research approaches (such as grounded theory or evaluation). Yet, at the same time, ethnography has become a form of métissage, whereby different forms have become braided together in ways that are often confused and confusing; visual ethnography is a particular example of this. We suggest that dilemmas raised by Clifford and Marcus (1986) remain, and are particularly important in the context of researching

74 Research Methods for Education in the Digital Age

education for the digital age. They raised two concerns: the crises of legitimacy and representation. The crisis of legitimacy questioned whether, and to what extent, ethnographers should measure the legitimacy of an action either by using the norms of their own worldview, or by using the norms, beliefs and practices of the culture being studied. The crisis of representation focused on how ethnographers interpreted and then presented the individuals and groups they were researching. It is vital that those using ethnography for research in digital spaces are clear about the purposes and boundaries of their study, as well as the ethical complexities of being a participant observer over a long period of time. Being involved in such qualitative longitudinal studies may be challenging and demanding, but it is increasingly evident that such studies are vital for understanding the impact of cultures and practices on the internet. For example, although undertaken over ten years ago, the study by Constable (2003) illustrates seminal ethnographic work. She examined the ‘mail order bride’ phenomenon: people seeking foreign marriage partners. Constable undertook this by following private chats and news groups and by studying online introduction services. She also examined personal web pages and photographs, and communicated with men and women from all over the United States and from different regions of China and the Philippines. This analysis of a global community of men and women involved in correspondence, courtship and marriage illustrates how the internet enabled the emergence of new types of communities, and the researcher noted: Although these imagined communities of men and women are in a sense outside and beyond the state, and thus serve as a ‘space of contestation’, men and women who ultimately marry across state boundaries nonetheless depend on the state, reify the state, and reinforce its boundaries while also crossing them. (Constable 2003: 32) This study illustrates the importance of such longitudinal in-depth studies. The focus of ethnography has generally been on immersion in a given ‘face-to-face’ setting, through exploring patterns of cultural and social influence. A recent example in education is that of Roth and Erstad (2016), who examined how young people viewed themselves as learners within educational trajectories, and the ways

ETHNOGRAPHIES FOR THE DIGITAL AGE

75

in which they positioned themselves as they made the transition from one school to another. The ethnographic data were collected over a two-year period as part of a large-scale ethnographic study in a suburban area of Oslo, with a large percentage of families with immigrant backgrounds, but the study focused particularly upon two fifteen-year-old girls. The authors found that students experienced different trajectories and changes when moving schools, which had significant implications for their futures. The authors also illustrated the ways in which digital technologies, educational and traditional ethnographic methods were combined in this study; for example, one participant’s learning identity was informed by her friend’s personal interest in pop music, which he demonstrated to her on school computers and which she then began to explore in her own time. Ethnography may provide a much more comprehensive perspective of the users and their environment than other forms of research, but just being ‘in the field’ and ‘following the thing’ is no longer enough. It is vital that ethnography in the digital age is rigorous and that explorations of online and internet spaces are critiqued. Richardson (2000) provided 5 criteria that researchers might find useful in terms of critiquing ethnography: 1 Substantive contribution: Does the piece contribute to our 2 3

4 5

understanding of social life? Aesthetic merit: Does this piece succeed aesthetically? Reflexivity: How did the author come to write this text?… Is there adequate self-awareness and self-exposure for the reader to make judgments about the (imposed) point of view? Impact: Does this affect me? Emotionally? Intellectually? Does it move me? Expresses a reality: Does it seem ‘true’ – a credible account of a cultural, social, individual or communal sense of the ‘real’? (Richardson 2000: 254)

Such criteria, however, could also seem to raise further questions. For example, it seems problematic that judgements of ‘truth’ in critical ethnographic studies could be evident when the researcher’s experience may be very different from that of participants in

76 Research Methods for Education in the Digital Age

the study. Further, we suggest that immersion in online spaces, particularly virtual worlds, has strong legitimacy, but that the issue of representation is more troublesome. For example, how might data from a virtual world ethnography be represented? Is it better to use film footage rather than text? How obtrusive or unobtrusive were the researchers and how did they explain their research and their position in the group being studied, and how was consent gained? The challenges of ethnographic research for education in the digital age mean that particular issues require consideration: 1 Use of field notes: What are field notes and to whom do

2

3

4

5

they belong? For example, what status does blogging have, and how open, closed or shared should field notes be? Participant observation: How will researchers position themselves and explain what they are doing? Do they risk being dishonest in order to gain data? Further, as Mawer (2014) has noted, observations about what the participants were doing, saying or feeling in these virtual worlds and other internet spaces can be misinterpreted or misrepresented. Interviews: How long is an ethnographic interview, what counts as ‘in-depth’ and how might anonymity be ensured if it is required in open spaces such as virtual world and roleplaying games? Ethics: How can ethics be managed and remain an ongoing, negotiated and reflexive response to changes in the study? How might this be conveyed both to ethics boards and to participants? Generalizability: Invariably, ethnography only examines one culture, setting or organization – this means that conclusions are not easily generalizable to other communities and contexts on the internet, which can be seen as problematic.

Conclusion Longitudinal ethnographic studies are needed that examine the impact of cultures and values within different and diverse internet

ETHNOGRAPHIES FOR THE DIGITAL AGE

77

spaces. However, for many researchers in this tradition, there still seems to be a reluctance to see the internet as an immersive space in its own right. Studies are needed that explore the ways in which boundaries and conventions can be (and often are) reinforced with digital spaces, rather than becoming more liquid and transparent, as researchers might expect. The current approaches to ethnography are many and varied, with different approaches emerging each year. As yet, the field of ethnography for the digital age remains varied and somewhat confusing, and there is still work to be done in finding ways to use it effectively whilst holding onto the values of exploring cultures, practices and everyday lives in ways that are in-depth and representative, and portray people and their communities fairly and accurately. Other research approaches have also been adapted for researching digital spaces, some more successfully than others. It is to these that we turn next, in Chapter 4.

Further reading Hine, C. (2015), Ethnography for the Internet: Embedded, Embodied and Everyday. London: Bloom. Hine’s seminal text achieves both critical engagement with ethnographic methodologies and methods and practical guidance on adaptation of ethnographic approaches in everyday and internet-enabled contexts. It presents examples of ethnographies from social media and television, amongst other settings. Whilst this book is more appropriate for researchers who are familiar with ethnography, newer researchers who have read our summary of Hine’s work in this chapter and in Chapter 1 will find it challenging but essential. Kozinets, R. V. (2015), Netnography: Redefined, 2nd edn. London: Sage. In this text, Kozinets updates netnography to today’s digital context, although he rightly points out that it will swiftly become outdated and thus needs to support researchers to adapt their netnographic practices accordingly. Digitical culture and thus ethnographic practice, Kozinet argues, should be treated as liminal, fluid and unstable. However, there are times when the text appears to conflate collective cultures with individual identities, such as when discussing gender and race, revealing a need for caution when using netnography to explore these issues. Perhaps

78 Research Methods for Education in the Digital Age

most significantly, Netnography: Redefined replaces netnography’s original six steps with twelve phases of research that follow the same basic structure but emphasize the importance of preparation and negotiation in research sites. Madson, M. (2014), ‘Digital Ethnography for Intercultural Professional Communication: Some Best Practice Principles’, Rhetoric, Professional Communication, and Globalization, 5 (1): 67–89. Madson argues here that intercultural professional communication suffers from a lack of attention to the subjectivity of real-life experiences. He perceives digital ethnography as a methodology that can help to navigate these challenges, and in doing so, he offers three significant needs that digital ethnography might help the intercultural professional communication community to reach. These needs are the need for more good theory, the need for more sound practice and the need for best practice principles. In order for these challenges to be met, Madson argues, digital ethnography must be undertaken in a manner that allows for both etic and emic research approaches. Roth, S. and O. Erstad (2016), ‘Positional Identities in Educational Transitions: Connecting Contemporary and Future Trajectories Among Multiethnic Girls’, Ethnography and Education, 11 (1): 57–73. This article used ethnography to collect data over 2 years to examine how young people viewed themselves as learners within educational trajectories, in Norway. The authors explored transitions from one level of schooling to another. The findings indicated that students experienced different trajectories and changes when entering upper secondary school. This paper provides a particularly interesting representation of gender, education and digital technologies in culture, and the ways in which in-depth ethnographic studies can illustrate the connections across these fields.

chapter four

Adapting research approaches for educational research in a digital age Introduction This chapter will examine a number of the most popular research approaches that have been adapted for the digital age and are used to explore teaching and learning in digital spaces. Despite their popularity, some of these newer approaches have a better fit philosophically than others, and others have been adopted but tend to have an ill fit with a qualitative standpoint. Examples of key approaches adapted and developed for the digital age include design-based research, which combines both research and practice; design patterns, which is an approach that aims to make explicit a problem or pattern of difficulties that is recurrent, and future technology workshop, which is an approach in which people with knowledge of a particular area of technology envision future activities related to technology design. Actor network theory and activity theory have also become popular methods for research in the digital age and are both used in order to try to understand individual construction of knowledge. Researchers using actor network theory and activity theory see it as their role to understand the ways in which individuals construct meaning, since knowledge, truth and reality are created rather than constructed. However,

80 Research Methods for Education in the Digital Age

many of these approaches remain unexplored philosophically, and this chapter will examine and unpack their relative value as research approaches for education in the digital age.

The challenges of adaptation Over the last 30 years, there have been many debates on the need or possibility for creating and adapting research methodologies (Reason 1988; Whitehead 1993) for the digital age and across educational settings. Many of these arguments have stemmed from the difficulty of attempting to adapt positivist and post-positivist methodologies to settings that really required participatory and flexible approaches. The result was that approaches such as new paradigm research and participatory action research emerged as key methodological shifts. These approaches were based on constructivism and constructionism, with a central focus on inclusivity and seeing participants as central to the research process. Whilst these new approaches challenged traditional notions of positivist research, they have in the main been strongly located philosophically. By this we mean that the methods suggested for these studies have produced data that are appropriate for the underpinning philosophy of the methodology. The difficulty we are now facing is that many of the new approaches being suggested for the digital age are merely convenient constructions, created by those attempting to bridge a paradigmatic gap, but with relatively little understanding of the value and importance of philosophical positioning or methodological reasoning behind the creation of new methodologies. The consequence is that frameworks and theories developed for other purposes are being overlaid as methodologies in simplistic and unhelpful ways, such as actor network theory, which is being used and argued for as a methodology for the digital age. Yet, actor network theory, whilst being presented as a cogent methodology appropriate for qualitative research, is, in fact, underpinned by the positivist paradigm (or, at best, post-positivism), and is then overlaid onto methods that are constructionist in stance, such as interview, storytelling and focus groups. The result is that interview data are made neat and tidy and even turned into numerical data, which does little justice to people’s lives and stories as told in the

Adapting Research Approaches in a Digital Age

81

research (such an example is Rose, Degen and Melhuish 2014). For us new methodologies are important, but it is vital that there is cogency between the ways methodologies are created and practised in relation to their philosophical roots.

Design-based research Design-based research has gained considerable attention in the educational research field, particularly in relation to digital technology use. This research approach, which blends empirical research with theory-driven design of learning environments, begins from a positivist standpoint (Brown 1992). Sandoval and Bell (2004) argued that Brown’s early approach to design-based research was an attempt to bridge laboratory studies of learning and studies of complex instructional interventions based on such insights. What was important about Brown’s work was the way in which she illustrated how findings from the laboratory were limited in their ability to explain learning in the classroom, because of the complexity of classroom interactions. Collins (1992) also argued for educational research as a design science, but in contrast to Brown’s approach, his version required a methodology that tested design variants for effectiveness. In practice, Collins’ focus was on locating the underlying variables of complex social systems such as schooling, and determining the effectiveness of these variables based upon student actions. This in many ways is a more troublesome form of design-based research, since the focus is inherently based on classical pragmatism, rather than the instructional approach seen in the work of Brown. Thus designbased research was developed initially in order to create theories of learning that reflected the complex interaction of diverse learning settings, but which were, and remain to a large degree, located in classical pragmatism. Pragmatism was developed in the late 1800s by a group of sociologists at Johns Hopkins University, United States of America, who challenged the notion of traditional empiricism and argued for the importance of the subjective experience of the social world. The arguments they put forward were that research methods should be strongly linked to the research question, and that research should be undertaken in natural contexts. Pragmatism has developed over

82 Research Methods for Education in the Digital Age

time but the three approaches that are still used today are classical pragmatism, Chicago School interactionism and neopragmatism: 1 Classical pragmatism: Those in this field proposed that

the meaning of conceptions should be sought and defined in their practical applications. The tenets of this classical pragmatism are formulated on the basis that the function of thought is to guide action so that truth must be tested based upon the practical consequences of that ‘truth’. 2 Chicago School interactionism: In the 1930s, at the Chicago School, sociologists developed a new approach to research that focused on the social and interactional nature of reality, as opposed to genetic and personal characteristics. These qualitative researchers argued that the emphasis should be upon understanding people and situations, rather than upon measuring observably defined facts and actions, as in classical pragmatism. 3 Neopragmatism: Pragmatism received renewed attention in the late twentieth century, driven by the linguistic turn in Western philosophy. Rorty (1979), often seen as a deconstructionist, developed his own brand of pragmatism: neopragmatism. Neopragmatism emerged from the belief that classical pragmatism’s aim to test ‘truth’ was ultimately irrelevant, because notions of truth and falsity were not intrinsic to the world, but rather belonged to the human realm of description and language. Thus for Rorty, all language, including the language of research, was contingent. His account considered scientific methods as contingent vocabularies, or, put differently, as a set of beliefs that are neither always true nor always false. Scholars adopted or abandoned these scientific methods based upon social convention. Rorty believed that people are aware of this contingency of vocabulary and that ideas of representation stood in between the mind and the world (Rorty 1979). Current work in the field of design-based research argues predominantly for a synergy between research, design and action, and we suggest is still rooted in pragmatism. Design-based methods and actor network theory, discussed later in the chapter, are

Adapting Research Approaches in a Digital Age

83

perhaps most strongly linked with Rorty’s neopragmatism. Recent construction of design-based research is defined by Wang and Hannafin as a systematic but flexible methodology aimed to improve educational practices through iterative analysis, design, development, and implementation, based on collaboration among researchers and practitioners in real-world settings, and leading to contextually-sensitive design principles and theories. (Wang and Hannafin 2005: 6–7) There are a number of design-based approaches that can be located across the spectrum of pragmatism. For example, Hoadley (2004) explored the notion of methodological alignment. He suggested that as unpredicted observations arise among predicted ones, a design-based research team’s methodological approach needs to change, in order to provide intervention designs that have a better fit with the setting or their intended setting, and that produce better explanations of how they work. Tabak (2004) described how intangible aspects of interventions, such as issues that arise in classroom discussions, tend to blur the relationship between classroom intervention and classroom context. She argued that this is not a methodological disadvantage, but that these kind of emerging designs and issues facilitate the development of theory. Her work, then, exemplifies the shift from the classical pragmatism of Brown to the neopragmatism of Rorty. However, the approach that would seem to fit with educational research for the digital age is that developed by the Design-Based Research Collective (2003). It adopts the following methods: 1 Conducted within a single setting over a long time. 2 Uses iterative cycles of design, enactment, analysis and 3 4 5 6

redesign. Acknowledges contextually dependent interventions. Seeks to document and connect outcomes with development process. Ensures collaboration between practitioners and researchers. Results in the development of knowledge that can be used to inform practitioners and designers.

84 Research Methods for Education in the Digital Age

The reason this fits well with digital educational research is that this approach focuses on characteristics such as being pragmatic, interactive, flexible, integrative and contextual, so in many ways it has a similar feel to other liquid approaches to research. Linked to this approach are developments in the use of design patterns research.

Design patterns This research approach aims to make explicit a problem or pattern of difficulties that is recurrent and in the main relates to a given context. In practice, patterns in problems are located and organized into patterns languages, in which the patterns are related to one another. In the original work by Alexander (1979; Alexander et al. 1977), he argued that patterns could be seen as normative. The difficulty here is that there is an assumption that people can easily be categorized. We argue against such a positivist position because people are always located in contexts that have at their heart issues of structure, power, agency and control, which will affect and change environments and patterns. However, the value of this approach to research is in the move towards the use of pedagogical design patterns and its associated methodological changes away from a positivist position. This can be seen in the JISC-funded project Planet (Pattern Language Network for Web 2.0 in Learning), which used pattern language to explore the use of Web 2.0 technologies in their assessment, learning and teaching. In practice, the project aimed to develop and demonstrate an effective community-based mechanism for capturing and sharing successful practice, based on the pattern approach. The authors (Finlay et al. 2009) developed the participatory pattern workshop methodology, which involved four stages: 1 Sharing and exploring case stories from practice. 2 Eliciting and elaborating patterns across the case stories. 3 Mapping the relationship between patterns and learning

design processes. 4 Applying patterns to new problem scenarios.

Adapting Research Approaches in a Digital Age

85

The idea is that the workshops are guided by facilitation activities. Since the initial development of this work, its use has grown and participatory pattern workshops are seen by the authors as a methodology in its own right, which pays attention to individual contexts. We suggest, instead, that it is a set of methods and ideas that broadly draw on the narrative inquiry tradition, and perhaps would be better located here in terms of the wide range of narrative approaches now used in social research that have emerged from the fields of anthropology, psychology and studies of experiences in education, as well as humanities, and have been driven by many who adhere to a social constructionist position. Mor, Warburton and Winters (2012) argued that the complexity of learning design requires the development of new ways of sharing and applying design knowledge, that is, experience of the learning design process. In particular, they suggested that design patterns are no longer just to be seen as solutions to problems, but, instead, are to be viewed as a means to support theory-praxis conversations: In order to enable a culture of critical, informed and reflective design practice we need a linguistic framework for communicating design knowledge: the knowledge of the characteristic features of a domain of practice, the challenges which inhabit it and the established methods of resolving them. (Mor, Warburton and Winters 2012: 164) In practice, they proposed the use of design narratives and design patterns, which can then be used to form design scenarios. Design narratives are accounts of critical events from a personal, phenomenological perspective, but are also presented by the authors as being based on Bruner’s narrative inquiry. The difficulty here is that it is not clear what kind of phenomenology they are drawing on or how this relates specifically to narrative inquiry. Design patterns draw on the original work of Alexander (1979; Alexander et al. 1977), who argued that the context of discussion, the nature of the problem and the means of addressing these needed to be specified. The difficulty here is that this is a highly analytical model and focuses on solutions that in many ways would seem to reflect classical pragmatism, rather than the social constructionism that is seen as being argued for in the use of design narratives.

86 Research Methods for Education in the Digital Age

Design scenarios appear to be an attempt to bring together design narratives and design patterns, since they ‘offer a suitable representation for projecting design claims into the future, posing hypothetical statements regarding potential challenges and possible solutions’ (Mor, Warburton and Winters 2012: 165). The difficulty with participatory pattern workshops, then, is that they attempt to combine toolkits, pattern mapping and solutions in ways that are outcome-focused, with narratives and stories, which are constructionist. Mor, Warburton and Winters (2012) argued that this is now a robust methodology, but we suggest that this needs more work and that there is a need to develop it away from its objectivist position, towards a more creative means of mapping stories, exploring disjunction and examining the impact of liminal spaces in the context of design narratives. For example, a more sociological perspective could be adopted by using some of the newer forms of ethnography suggested by Hine (2015), as discussed in Chapter 3.

Future technology workshop This is an approach in which people with knowledge of the use of a particular area of technology envision future activities related to technology design. In practice, they envision future activities related to technology design, build models of the contexts of use for future technologies, act out scenarios for use for their models, re-conceive their scenarios in relation to present-day technologies, list problems with implementing the scenarios, explore the gap between current and future technology and activity, and end by listing requirements for future technology. (Vavoula and Sharples 2007: 393) The future technology workshop approach was developed initially from a need to examine possible technological futures in research studies. These two projects were the Children as Photographers project, which focused on how children might use and share images in the future, and the MOBIlearn project, which explored relationships between computers and mobile technologies. Both

Adapting Research Approaches in a Digital Age

87

projects, which commenced in the early 2000s, sought to inform the design of new technologies with reasoned justifications for their future uses. Future technology workshops are designed to meet the following criteria: 1 Minimal participant training 2 Collaborative 3 Direct input to design 4 Cost-effective to run 5 Relates people and technology 6 Open-ended 7 Pragmatic

The advantage of this approach to educational research is that it mirrors a participatory action research approach, while also fitting with and informing other approaches such as human-centred systems design. Future technology workshop enables creation and refinement when exploring the relationship between technology and activity and seems to be an approach that is flexible and adaptable and can be used in a variety of contexts and disciplines.

Actor network theory Actor network theory is a means of exploring the relational ties within a network, although it is more of a method than a theory. What is central to this approach is that actors may be both human and non-human; thus, for example, supermarket products and digital devices are seen as actors that have influence. Actor network theory focuses on exploring networks, and on the impact between networks and actors, and the controversies inherent in them. It was originally created by Callon (1986) and Latour (1987), in an attempt to understand processes of technological innovation and scientific knowledge-creation. This approach (Latour 2005) seeks to explain the interaction between the material and the semiotic, but carries with it a sense of precariousness, since the focus is on nodes that have as many dimensions as connections. One of the central

88 Research Methods for Education in the Digital Age

arguments of this theory is that knowledge is a social product, rather than something developed through a scientific method. Actor network theory focuses on three key principles: 1 Symmetrical analysis: The idea that the material and non-

human elements of any network should be treated analytically in the same way as the social and human elements (Law 1992). 2 Heterogeneous network: The importance of elucidating how any network grows in influence and/ or contracts, rather than explaining end results, such as the size of a network at any point in time (Callon and Latour 1981). 3 Translation: In translation, one element stands in for another or many others (Callon and Law 1982). Translation is seen as implying transformation ‘and the possibility that one thing (for example an actor) may stand for another (for instance a network)’ (Law 1992: 386). It does not typically attempt to explain why networks exist, but rather how they are formed and why they fail. Law argued: This, then, is the core of the actor-network approach: a concern with how actors and organisations mobilise, juxtapose, and hold together the bits and pieces out of which they are composed; how they are sometimes able to prevent those bits and pieces from following their own inclinations and making off; and how they manage, as a result, to conceal for a time the process of translation itself and so turn a network from a heterogeneous set of bits and pieces each with its own inclinations, into something that passes as a punctualized actor. (Law 1992: 386) Thus it is not a pedagogy or a theory, or, indeed, a research methodology, but, rather, a method for mapping networks and relationships, and so its limitations in exploring learning, social networks and online environments must be acknowledged when using this method. This is because actor network theory decontextualizes action and experiences, and those using it tend to adopt post-positivist stances, when much of what occurs in these spaces is constructionist and should be examined as such. Constructionists believe that hidden or private phenomena such as

Adapting Research Approaches in a Digital Age

89

emotions gain their meaning through social settings and practice, and are therefore socially constructed. Reality is, therefore, not entirely external and independent of individual conceptions of the world. Thus signs and systems play an important part in the social construction of reality, as individuals make and experience meaning together (Savin-Baden and Major 2013). Despite many of the arguments posited by Law (1992), in practice, actor network theory operates with the hallmarks of postpositivism, as exemplified in the seminal post-positivist work of Popper (1959). Popper explored the relationship between scientific belief and the guarantees for such beliefs, and argued that the essence of science was the extent to which it could be refuted. Underpinning this argument was the belief that an objective reality existed. Researcher biases or mistakes could mean that understanding of the world, shaped by research, was flawed, but these mistakes could ultimately be managed through continued testing and focus upon refuting research. This stance is a challenge to those who argue that actor network theory is qualitative, since it could also be seen to be located in cognitive theories that are directly concerned with mental processes (which include insight, information processing, memory and perception) rather than products (behaviour). Cognitive theorists seek to understand how individuals learn and what goes on inside the mind when learning occurs. This kind of education focuses on cognitive structuring, which is essential for developing the capacity and skills for better learning, or to learn how to learn. However, what is perhaps more of a concern is that those adopting actor network theory invariably argue that they are using this approach, yet document neither how they do so, nor how they analyse data using this approach. Take the example by Rose, Degen and Melhuish (2014):

Example Rose, G., M. Degen and C. Melhuish (2014), ‘Networks, Interfaces, and Computer-Generated Images: Learning from Digital Visualisations of Urban Redevelopment Projects’, Environment and Planning D: Society and Space, 32 (3): 386–403.

90 Research Methods for Education in the Digital Age

Example Over the past five years, computer-generated images (CGIs) have become commonplace as a means to market urban redevelopments. To date, however, they have been given relatively little attention as a new form of visualizing the urban. This paper argues that these CGIs deserve more attention, and attention of a particular kind. It argues that, instead of approaching them as images situated in urban space, their digitality invites us to understand them as interfaces circulating through a software-supported network space. The paper uses an Actor-Network-Theory understanding of ‘network’ and argues that the action done on and with CGIs as they are created takes place at a series of interfaces. These interfaces – between and among humans, software and hardware – are where work is done both to create the CGI and to create the conditions for their circulation.

This is a fascinating study and appears to have been based on ethnographic data collection. The authors argued that they used actor network theory and Law’s (1992) work to help them understand ‘network’, but there is no indication of how this was undertaken. However, Fox (2005) does illustrate well how actor network theory can be used to critique ideas on community that are influential in higher education. For example, he used actor network theory to examine the interrelationship of dependence between human meanings and mundane technologies, asking: ‘If there is a growing post-national community emerging in the public-cybersphere, then how do nations, national governments and higher education institutions benefit?’ (Fox 2005: 108). It is important that ideas such as actor network theory are not used in behavioural ways separating learner, learning methods, learner identity and learning context, since such uses result in decontextualization of learner and context and a tendency towards overgeneralization. Many researchers who adopt actor network theory suggest that their work is built on a constructivist stance. This seems more a utopian stance than a reality, since constructivism is the notion that knowledge lies in the minds of individuals, who construct what they know on the basis of their own experiences. It suggests that the process of knowledge construction is an active, rather than a passive, one. Researchers who adopt this approach believe both that

Adapting Research Approaches in a Digital Age

91

research involves an attempt to understand individual construction of knowledge and that it is their role to understand the ways in which individuals construct meaning, since knowledge, truth and reality are created rather than constructed. There have been many criticisms of actor network theory, some suggesting that it is absurd to analyse human and non-human ‘actors’ together. Perhaps the most problematic issue with this method is that many researchers using actor network theory masquerade it as constructivism when, in fact, it is far from this: it is in reality a post-positivist approach.

Activity theory Activity theory is a conceptual framework or descriptive meta-theory that covers an entire activity system. It is an attempt to theorize the individual and collective dimensions of the human life forms together, without reducing the social to the psychological or the psychological to the social. The underlying idea of the framework is purposeful activity and the framework is used to examine the interactions between actors (subjects) and the world (objects). The original formulation of activity theory was developed by Leontiev ([1947] 1981), and was later developed and popularized by Engeström (1987). Leontiev’s work began with animals and moved on to humans. He argued for a distinction between activities, which satisfy a need, and the actions that constitute the activities. Leontiev’s activity theory tended to focus on the disciplines of social sciences and organizational study, rather than psychology, which is more central in Engeström’s work and more recent iterations of activity theory. Engeström developed activity theory in the late 1980s into a scheme comprising three interacting entities, the individual, the object and the community, adding community to Leontiev’s two components of the individual and the object. Through Leontiev’s work and Engeström’s subsequent adaptations, activity theory was developed into a set of basic principles that constitute a general conceptual system. These included the hierarchical structure of activity, object-orientedness, ­internalization/ externalization, mediation and development. 1 Hierarchical structure of activity: In activity theory, the unit

of analysis is an activity directed at an object that motivates

92 Research Methods for Education in the Digital Age

2

3

4

5

activity. This activity comprises goal-directed actions that must be undertaken to fulfil the object. It is argued that in activity theory, the components of activity are not fixed, but change as conditions change. Object-orientedness: This principle states that human beings live in a reality that is objective according to both natural sciences and socially and culturally defined properties. Internalization/externalization: Internalization enables people to try potential interactions with reality without performing actual manipulation with real objects (mental simulations, imaginings and considering alternative plans). Externalizations transform internal activities into external ones and are used for an external collaboration between people. It is argued that internal activities cannot be understood if they are analysed separately from external activities, because they affect one another. Mediation: Human activity is seen as being mediated by tools that are created and transformed during the development of the activity, and it is argued that the use of tools helps in the accumulation and transmission of social knowledge. Development: The research method of activity theory is the formative experiment that combines active participation with an examination of developmental changes in the participants of the study, rather than the use of traditional laboratory experiments.

However, since his early work, Engeström’s schemas have developed and become more complex, first with a focus on the relationship (mediation) between components in an activity system. He then later developed a position where he argued for joint activity being the unit of analysis for activity theory, rather than individual activity. In what he termed third generation activity theory, Engeström argued that instability and contradiction are a ‘motive force of change and development’ (Engeström 1999: 9). Thus the shifts and transitions between activity systems are modified through mediated activity, so that ways out of internal contradictions result in new activity systems. He explained:

Adapting Research Approaches in a Digital Age

93

This refers to theorizing and empirical studies that expand the unit of analysis from a single activity system to multiple, minimally two, interacting activity systems. In such a framework, for example schooling is analysed as dynamics within and interplay between the activity systems of the student and the teacher, possibly also including other relevant activity systems. This expansion is accompanied with increased attention to the dynamics of the subject, with new important openings into the analysis of agency, experiencing, and emotion. (Engeström and Glaveanu 2012: 516) Activity theory is widely used in education and psychology, and more recently in professional learning, as in the example of Markauskaite and Goodyear (2014). Those adopting activity theory see cultural practice as moments of activity that are not reducible to small units of analysis. For Nardi (1995), activity theory ‘focuses on practice, which obviates the need to distinguish “applied” from “pure” science – understanding everyday practice in the real world is the very objective of scientific practice.’ Sometimes, activity theory is also referred to as culturalhistorical activity theory (CHAT), which focuses on events or activities rather than objects or tools. The argument for CHAT is that it is not possible to understand events as independent from the culture in which they occur. Similarly, the event or activity is set in an historical context; thus history introduces a further dimension. However, as Roth, Radford and LaCroix have noted: Activity theory has been developed in different directions and I think that there are two results that come out of that. On the one hand, I think that because activity tries to encompass many, many things, it is very, very generic as a theory. It becomes very generic, very general in order to encompass the diversity of activities that human beings carry out in everyday life. This is a strength because you can apply activity theory to almost everything that human beings do. But on the other hand, it is its weakness because when you try to apply it to a very particular research field you find yourself in a situation in which you can’t find the theoretical tools that you need to tackle your specific research question. (Roth, Radford and LaCroix 2012: para. 25)

94 Research Methods for Education in the Digital Age

Example Markauskaite, L. and P. Goodyear (2014), ‘Tapping into the Mental Resources of Teachers’ Working Knowledge: Insights into the Generative Power of Intuitive Pedagogy’, Learning, Culture and Social Interaction, 3 (4): 237–51. This paper provides novel insights into the kinds of mental resources on which teachers draw in their pedagogical sensemaking (about everyday teaching decisions), and into the origins of these mental resources. The paper examines how teachers’ knowledge is grounded in diverse social, cognitive and metacognitive experiences of learning and teaching phenomena. It contributes to the development of new ways of theorizing the links between (a) experiential knowledge resources, which originate in specific activities and interactions, and (b) an integrated conceptual understanding that organizes professional sense-making across diverse situations and contexts. By combining conceptual ideas about knowledge fragmentation with original empirical observations from a study of the form and functioning of teachers’ working knowledge in higher education, the paper advances two lines of theoretical argument. Firstly, teachers’ working knowledge is better seen as contextualized and fragmented rather than as a systematic personal theory. There are advantages to pedagogical ‘knowledge-in-pieces’ that can be activated and combined in different ways in interaction with various contexts. Secondly, pedagogical ideas and ways of knowing that originate in one’s personal experience (‘intuitive pedagogy’) can be a productive resource in teacher thinking, action and professional learning. The paper suggests that the view of professional learning in, and through, practice should be expanded from its traditional focus on social and material interactions to also include the consideration of simultaneous interactions with one’s mind.

Thus, in many ways, activity theory, and particularly CHAT, are problematic, because these approaches are generic, broad and rather dislocated from any distinct philosophical stance or methodological position.

Adapting Research Approaches in a Digital Age

95

Troublesome trajectories The difficulty with many of the approaches presented here is that they are methods rather than methodologies, and several of them are complex and difficult to implement. The result is that researchers tend to argue that they are using, for example, actor network theory or activity theory whilst making little in-depth use of the original theory, framework and principles. Few, it seems, are able to explicate the use of the theory in terms of a conceptual framework, so that whilst there is increasing uptake of these methods, it is unclear as to how the framework has been used to collect, analyse and interpret data. Alongside this, there are also arguments for the agency of objects and affordances, as well as the need to reassemble social science methods (Ruppert, Law and Savage 2013). The notion of agency of objects centres on the argument that digital devices mediate social relations and that they are implicated in the formation of human subjects. Yet, this idea seems to imply that humans lack agency, and have either little choice in the ways in which they operate or little ability to critique the media put before them. This idea is similar to the concept of affordances, which has been increasingly used in research and technology since the late 1980s. The term originated from Gibson, who developed the ecological approach to visual perception in which he argued: When no constraints are put in the visuals system, we look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision. (Gibson 1979: 1) Thus it is possible to see how this term has been (mis-) appropriated when we realize that he argued: ‘The affordances of the environment are what it offers, the animal, what it provides or furnishes, for good or ill’ (115, original italics). The use, then, of ‘affordances’ seems at one level to have provoked an overemphasis on what particular technologies prompt or allow us to do, bringing with it a sense of covert control. On the other hand, there is a sense that the term is used because it offers a linguistic position

96 Research Methods for Education in the Digital Age

and format through which it is possible to discuss the complexity, interactions and impact of technologies on higher education. Thus, as Oliver (2005: 412) argued, the notion of affordances has little value unless ‘we are willing to abandon constructivist values in order to explore “inherent properties” in a positivistic sense’. One option here that might help to counter Oliver’s concerns might be to adopt actor-network theory. Such a stance allows for technology to be seen as an iterative component of the technologypedagogy narrative, rather than something that is reductionist. However, as we have noted, actor network theory does not allow for explanation to be made about why particular networks take the forms they do, which may, in fact, underline further Oliver’s concerns about affordances. In terms of the need to reassemble social science methods, Ruppert, Law and Savage argued that there is a need to explore ‘how digital devices themselves are materially implicated in the production and performance of contemporary sociality’ (2013: 2). They suggested nine propositions about the implications of digital data and devices, which are transactional actors; heterogeneity; visualization; continuous time; whole populations; granularity; expertise; mobile and mobilizing; and non-coherence. They suggested that there is a need to rethink the theoretical assumptions of social science methods because they believed that a return to an older, observational kind of knowledge economy, based on the political power of the visualization and mapping of administratively derived data about whole populations, is what is actually occurring. At the same time, they also perceived a need to examine the ways in which observation is ‘being performed by the digital and its material and productive effects’ (2013: 11). Yet, government surveillance and the monitoring of our shopping habits has been taking place for many years; it is the sophistication that has changed. However, we propose that this focus is too narrow, and, instead, we suggest that the notion of tethered integrity is important here (Savin-Baden 2015). Tethered integrity captures the idea that many of those who are always on, who are digitally tethered, do, in fact, have a degree of integrity about their use of social networking sites. Whilst some studies indicate that youth have more integrity in this area than adults (Brandtzæg, Lüders and Skjetne 2010), there is also an increased recognition of the power of such sites to manipulate what is shared, what is bought and how people behave.

Adapting Research Approaches in a Digital Age

97

Conclusion The danger with many of the current instantiations of design for learning, actor network theory and activity theory is that they can be oversimplified and/or misused. What we have seen in the chapter is a range of approaches and methods that have been developed for the digital age with varying degrees of success. We suggest that activity theory lacks theoretical underpinning and that actor network theory appears to be based too much in post-positivism to be used effectively in qualitative studies, and that those who do use them appear to do so less than well. Other approaches such as design-based research, future technology workshop and design patterns appear to be methods that are offshoots of pragmatism more than anything else. Perhaps what is most apparent in this chapter is that there are many scientists researching the digital who are struggling to move away from what is essentially a positivist standpoint, instead of embracing approaches that do justice to human action and experience in the digital realms.

Further reading Engeström, Y. and V. P. Glaveanu (2012), ‘On Third Generation Activity Theory: Interview with Yrjö Engeström’, Europe’s Journal of Psychology, 8 (4): 515–18. This interview with Yrjö Engeström is essential reading for anyone interested in activity theory’s history. It outlines the development of the theory from his initial dissatisfaction with positivism, to influences from Soviet-Russian cultural-historical activity theory, to current exciting practices in third generation activity theory. Engeström concludes by stating that activity theory ‘is not a superficial fad you can pick and use as theoretical decoration … but it can make a difference, it can influence and change people’s lives.’ Fox, S. (2005), ‘An Actor-Network Critique of Community in Higher Education: Implications for Networked Learning’, Studies in Higher Education, 30 (1): 95–110. In this paper, Fox suggests that networked and online learning challenge what is traditionally thought of as ‘community’. Instead, the internet

98 Research Methods for Education in the Digital Age

opens up a public-cyber-sphere, which is inherently international and impossible to censor. Thus higher education systems are and continue to be internationalized as post-national communities emerge. The question, he asks, is how this might influence nations, governments and higher education institutions. For example, Fox draws attention to the issue of language and the dominance of American-English in online spaces. In doing so, he argues that networks and networked learning have not only changed community but also potentially encouraged translation of other languages and cultures into American-English language culture. Thus it is the responsibility of networked learning to recognize its own influence and take steps to counter that influence. Ruppert, E., J. Law and M. Savage (2013), ‘Reassembling Social Science Methods: The Challenge of Digital Devices’, Theory, Culture and Society, 30 (4): 22–46. Ruppert, Law and Savage take an actor network approach to the interpretation of digital devices in research settings, suggesting that such devices are shaped by the social world and in turn can become agents that shape that world. The paper calls for a post-humanist re-interpretation of methods that can support a heterogeneous understanding of the digital. Such a re-interpretation, they argue, might be achieved through the application of Bourdieu’s concept of field analysis. In the use of field theory, technologies do not possess intrinsic affordances but are defined in relation to one another, as competitors or complementary technologies. This paper thus offers an interesting conceptualization of ‘the digital’ through actor network theory, although their ambiguous definition of ‘the digital’ does challenge practical application.

chapter five

Quantitative data in digital contexts Introduction Until this point, this book has focused predominantly upon qualitative research data in digital contexts. By ‘qualitative’ we mean data in non-numerical format, such as visual data and textual data, whilst ‘quantitative’ data is that which is represented in a numerical format. Whilst these methodological terms have often been contrasted with one another, we suggest that hard binary distinctions of qualitative/quantitative are less relevant in the digital age than in former years. For instance, there are examples now of numerical data being presented visually, and, increasingly, methods overlap, with non-numerical data being recategorized and subsequently quantified. It is for this reason that in this chapter we refer predominantly to qualitative and quantitative data, as opposed to qualitative and quantitative methods. We begin by examining what is meant by quantitative data in the digital age and presenting the different types of quantitative data available to researchers, as illustrated in Table 5.1. In the second section of the chapter, some of the key methods employed for gathering quantitative data are presented, along with examples of innovative educational research practice using digital methods.

100 Research Methods for Education in the Digital Age

Quantitative data in the digital age Quantitative educational data for the digital age seems to fall into three broad categories: 1 Individual data provide information about the student’s

identity, characteristics and portrayal within the learning situation, such as demographic data (e.g. name, age, gender, race), educational data (e.g. topic of study, modules chosen) or personal data (e.g. avatar appearance in a virtual world). Such data are rarely the central focal topic in educational research, but they are, rather, collected and used alongside engagement and learning data. 2 Engagement data provide information about the student’s activities and engagement at a variety of levels. Most data collected about students, particularly by the institutional administration, fall into the category of engagement data. Engagement data can be action-oriented, network-oriented or content-oriented. These labels are not attributed to the data itself, but, rather, to the purposes for its collection and use for educational research: ●●

●●

Action-oriented data provide information about user actions in digital environments or digital applications. Network-oriented data provide information about connections across and between individuals.

Content-oriented data provide information about user interaction with content in digital environments. The distinction between action-oriented data and contentoriented data is that the content itself can provide an additional source of data. 3 Learning data represent data that are designed to provide information on the student’s level of knowledge, such as formative and summative assessment scores for courses of study or research projects. ●●

QUANTITATIVE DATA IN DIGITAL CONTEXTS

101

Table 5.1  Types of quantitative educational data Data type

Description

Methods for collection and examples

Individual

Provide information about the student’s identity, characteristics and portrayal within the learning situation, such as demographic data, educational data or personal data

Typically collected at an institutional level or embedded into whichever method is used to collect engagement and learning data

Engagement

Provide information about the student’s activities and engagement at a variety of levels

Survey methods, for example, ‘clicker’ responses to digital surveys, webbased survey-specific software, virtual learning environment surveys/polls, social media polls

Activity-oriented

Provide information about user actions in digital environments or digital applications

Paradata, which is the automated data captured during online data collection such as keystrokes (Couper 1998) In-application data such as number of clicks (Wolff et al. 2013)

Network-oriented Provide information about connections across and between individuals

Connections across social networks such as number of friends/followers Communication content such as number of private messages

Content-oriented Content-oriented data provide information about user interaction with content in digital environments

Participation logs in online forums (Hakkinen 2015) Video data on student actions in class (Lee, Arthur and Morrone 2015)

Learning

In-application assessment data, for example in serious games

Provide information on the student’s level of knowledge

102 Research Methods for Education in the Digital Age

Big data, learning analytics and educational data mining As discussed in Chapter 1, educational research, and particularly digital educational research, is shaped by the current age of data and big data (Elliot, Purdam and Mackey 2013). Definitions of big data are wide and varied; for instance, there are definitions that concentrate on scale or diversity, and others that focus on the economics of big data. In an educational setting, the term ‘big data’ is complicated by the use of the terms ‘learning analytics’, ‘academic analytics’ and ‘educational data mining’, often with no clear distinction between the terms. It is therefore worth briefly describing the differences between them and their relevance to educational research. Einav and Levin (2014) have argued for three main features of big data: 1 Sources are usually available in real time. 2 The scale of the data makes analysis more powerful and

potentially more accurate. 3 Data often involve human behaviours that have previously been difficult to observe. Kitchin (2014: 1–2) has delineated big data in the following terms: ●●

Huge in volume, consisting of terabytes or petabytes of data.

●●

High in velocity, being created in or near real-time.

●●

Diverse in variety, being structured and unstructured in nature.

●●

●●

●●

●●

Exhaustive in scope, striving to capture entire populations or systems (n = all). Fine-grained in resolution and uniquely indexical in identification. Relational in nature, containing common fields that enable the conjoining of different data sets. Flexible, holding the traits of extensionality in that it is possible to add new fields easily, as well as expand in size.

QUANTITATIVE DATA IN DIGITAL CONTEXTS

103

There is little consensus about what counts as big data, but many across the higher education sector see it as worthy of attention. Conceptions of big data tend to fuse across the realms of collecting large data sets and the processes of managing such data sets, as well as examining how, by whom and for whom the data sets might be used. For scientists, Kitchin’s stance (Kitchin, 2014) seems a good fit, but those in social sciences and humanities tend to use the term ‘data’ differently. For example, researchers in the social sciences see big data as encompassing not just large datasets, but also the complexity of how data are synthesized, the ways in which tools are used, and who makes which decisions about management of possible imbalances between data collection, management and synthesis. Big data and learning analytics, therefore, challenge the segregation between data collection, data analysis and data representation. Learning analytics has been described as ‘big data applied to education’ in the 2013 Horizon Report. More specifically, it has been defined as ‘the measurement, collection, analysis and reporting of data about learners and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs’ (Long and Siemens 2011: 32). Thus the term ‘learning analytics’ does not just encompass the characteristics of the data, as discussed above, but also the purposes for which the data is collected. Implicit also in this definition is that the reporting of data about learners should ultimately be reported to learners where possible. Similarly, educational data mining has been defined as the search for patterns in educational data (Baker and Yacef 2009). There are a number of similarities between educational data mining and learning analytics, in that they both prioritize the improvement of education (particularly through the identification of problems and the subsequent development of interventions) through the use of big data. However, the fields emerged from different disciplines, and both have different priorities. Siemens and Baker (2012: 253) have outlined the ways in which educational data mining can be distinguished from learning analytics with regard to their discovery approaches, approaches to holistic/reductionist analysis, personalization and analysis methods. Educational data mining, they have suggested, is derived from educational software, modelling and outcome prediction approaches, and focuses upon

104 Research Methods for Education in the Digital Age

reducing and analysing individual components and relationships between them. It prioritizes automated discovery and adaption, although human judgement is also valued in support of these goals. In terms of its capture and analysis methods, it tends to draw upon classification, clustering, Bayesian modelling, relationship mining and discovery with models. Learning analytics, Siemens and Baker suggested, is, on the other hand, derived from work on the semantic web, intelligent curriculum, outcome prediction and systemic interventions. It takes a holistic approach to understanding whole systems, and prioritizes the use of human judgement with automated discovery as a tool to achieve this. Whilst there are a number of different data capture and analysis methods, they often include statistics and visualizations, social network analysis, discourse analysis and sense-making models. In particular, learning analytics has been positioned as a data collection, analysis and representation method that can empower learners through the personalization of data. Whilst this usually refers to the individual student, Welles, for example, has argued for ‘making Big Data small’, contending that in the age of data, it is now possible to focus upon the experiences of those who have been statistically underrepresented in scientific research (Welles 2014). However, there are a number of limitations associated with big data research, particularly for the individual researcher. Papamitsiou and Economides (2014) have noted that the huge volume of data produced can result in an ‘information overload’ and make analysis problematic for many researchers. A similar argument has been advanced by Raffaghelli, Cucchiara and Persico (2015) in relation to learning analytics for MOOCs, based upon a review of sixty research papers published between 2008 and 2014. Learning analytics techniques … integrate tools for data collection as well as data analysis, showing that the border between the two is becoming more and more blurred. We note, however, that most research analysed did not unfold the whole potential of learning analytics as the data tracked were visualised in terms of descriptive statistics to ‘show’ trends of participation, whereas more refined analysis for modelling and predicting learning processes was hardly used. (Raffaghelli, Cucchiara and Persico 2015: 502)

QUANTITATIVE DATA IN DIGITAL CONTEXTS

105

This would seem to support our own observations, as well as those of a number of other researchers in the field, that whilst there has been significant attention paid to the use of ‘big data’ on an institutional scale, few smaller research studies have addressed its use and appropriate methodological frameworks. Additionally, whilst learning analytics and educational data mining are useful terms for describing the wholesale use of data at an institutional level, they comprise so many different types of data that they are of little use to the individual researcher who must determine the best methods and data to use to address a particular research question. The remainder of the chapter, therefore, outlines a number of sources of, and methods for collecting, quantitative educational data. This chapter is, however, necessarily restricted – as we noted in Chapter 1, we are undertaking educational research in the age of the internet of things. At a wholly pragmatic level, everything and anything is a possible source of quantitative data for digital educational research, but as noted already, large data sets are troublesome and difficult to manage.

Survey data Surveys (mixed-method and quantitative) remain one of the most commonly used methods in educational research. In some cases, they are considered to be synonymous with quantitative data. Quantitative surveys tend to include the following features: ●●

Multiple-choice questions (single and multiple answers).

●●

Selection list questions.

●●

Scale/ranking questions (Unipopular, Likert, Semantic differential).

Online surveys are generally considered to be a new mode of data collection rather than a new data collection method (Ilieva, Baron and Healey 2002). This means that the method of data collection (surveys) remains largely unchanged. What is different, however, are the modes of data collection, referring to how the survey is administered or delivered. Yet, as technology has developed, it would seem that there is no longer one mode of online data collection but, rather, a number of different modes, as outlined in table 5.2 overleaf.

106 Research Methods for Education in the Digital Age

Table 5.2  Modes of online survey delivery Mode

Description

Webdelivered surveys

In this mode, survey-specific software, such as Qualtrics, Survey Monkey and Bristol Online Surveys, are used to administer surveys. These are similar in format to paper-based surveys, and tend to be formal in appearance. There have been attempts to de-formalize software through the use of images (e.g. the Typeform survey software) but the format remains similar.

Mobiledelivered surveys

The administering of surveys can vary across mobile applications. Most web-based survey sites have mobilespecific applications, which tend to be similar, but are less formal in appearance. However, other applications such as SurveySwipe ask respondents to take and upload photographs, videos, or audio recordings in response to survey questions, thus providing opportunities for new kinds of responses to survey questions and challenging what we mean by ‘survey’. The difficulty with mobile surveys, however, is that they require participants to download the applications and go through the process of creating accounts.

Social media polls

Polls are often designed into social media applications such as Twitter, or asked on newspaper websites, for example. Whilst polls are often used in survey research alongside other survey question types, social media polls would seem to fill the online equivalent of asking respondents in a large room to raise their hands in response to a question. In social media polls undertaken in the public domain, it is often difficult, if not impossible, to collect demographic data and account for survey errors.

Avatardelivered surveys

In this mode, surveys are administered by individuals to other users present in 3D virtual environments such as virtual worlds or online games. Users in these environments are represented by an avatar, which is an embodied tool used to navigate the environment. These can usually be adapted in accordance with individual preferences.

Chatbotdelivered surveys

Here, surveys are administered by intelligent computer programmes known as ‘chatbots’, which are programmed to ask and respond to questions on particular topics. Chatbots are sometimes represented by characters with embodied life-like behaviours, such as gestures and eye gaze/movement. Although chatbot-delivered surveys are relatively new in educational research, they present interesting opportunities for data collection.

QUANTITATIVE DATA IN DIGITAL CONTEXTS

107

There are a number of different ways in which these modes can be blended. For example, virtual learning environments such as Moodle can provide both individual polling questions and detailed surveys similar to web-based survey applications. Yet, there are also clear distinctions across these different modes, which may inform how data are collected. In 2003, Tourangeau, Couper and Steiger found that delivering surveys digitally reduced or eliminated social desirability effects, which are common when surveys are delivered in person by researchers. Social desirability effect means that individuals may be more likely to respond with answers that will make them look good, or answers that they believe the researcher wants to hear; these answers may not necessarily be the ‘truth’. Other studies have also found that online surveys are more effective in eliciting responses from university students. For example, Kays, Gathercoal and Buhrow (2012) examined responses to the American College Health Association’s National College Health Assessment from a random sample of 2,400 students. A total of 1,200 students responded to a web-based survey, whilst the rest of the students responded to the same survey in paper format. Findings revealed that there were higher rates of non-response in the paper-based condition, and that rates of non-response on the most sensitive items were highest in both the web-based and paper-based conditions. The generalizability of these studies, however, may become limited as new modes of online surveys are developed. For example, do the same effects occur in avatar-delivered or chatbot-delivered surveys? Additionally, the mobile-delivered survey mode raises interesting questions about the impact of potential distractions upon survey findings. For example, if research participants receive multiple push notifications on their smartphones whilst completing a survey, how might this impact upon research findings? Thus each of these different modes raises new questions about the trustworthiness of participant responses and the possible competing demands for their attention. These competing demands for attention, and the uncertainties associated with new modes of survey delivery, might perhaps be one of the reasons why polls such as social media polls have gained traction, although they do not necessarily provide sufficient data to be useful in educational research. Whilst surveys often ask a number of questions and use a variety of question types, both qualitative and quantitative, polls typically ask only one or a few questions. Polls are increasingly popular in education as lecturers

108 Research Methods for Education in the Digital Age

and presenters use them to gain instant feedback, and they can be delivered through a wide variety of means, from raising hands to ‘clicker’ technologies. These are Classroom Communication System (CCS) digital technologies, which use mobiles or keypads for instant responses to surveys posed in class; they are one of the most widely used technologies in education (White, Syncox and Alters 2011) and are often evaluated using data from the clickers themselves (Han 2014).

Social media data Social media, as a part of most students’ everyday lives, is increasingly integrated into research settings. Whilst there are countless popular social media applications and platforms (e.g. WhatsApp, Snapchat, Instagram, Kik, Tumblr, Pinterest), the significant market share attained by social media platforms Facebook and Twitter has provided them with a privileged position in educational research studies. Twitter is a microblogging platform that allows users to release messages of 140 characters or less. As of March 2016, it had approximately 310 million monthly active users sending an average of 500 million tweets per day. These tweets can be published publicly (meaning that anyone with an internet connection can see them) or privately (meaning that only those accounts approved by the user can see the tweets). Unlike Facebook, there are no privacy levels between public or private. However, it is possible for users to tag other users in the tweet (@reply) in order to specify that the tweet may be of particular interest to those users, whilst hashtags (#) denote the key topic or topics of the message. They also function as searchable markers, enabling users to view messages sharing the same subject. It is also possible to direct messages (DM) only between two users, although DMs are rarely available for research purposes because of the private nature of the messages. Twitter analytics, however, have rarely been used as the sole method in a study. For example, Junco, Elavsky and Heiberger (2013) explored Twitter as a possible impetus to increased participation in science. The average number of tweets was captured, but an engagement scale and qualitative analysis of the tweets were prioritized in the analysis. Similarly, Gao, Luo and Zhang

QUANTITATIVE DATA IN DIGITAL CONTEXTS

109

(2012), in a review of Twitter research studies published between 2008 and 2011, found fourteen studies that used the amount of student tweets as an indication of participation and engagement in education. Most of these studies also drew upon additional methods such as qualitative surveys (e.g. Ebner et al. 2010) and focus group interviews (e.g. Rinaldo, Tapp and Laverie 2011). Whilst this might be attributed to a decision not to focus on analytical data, it might also be attributed to problems inherent in the collection of the data. Data can be downloaded using an application programming interface. An application programming interface (API), at its simplest, is a set of rules that dictates how one digital application can relate to another. It makes certain functions accessible to other applications, facilitating cross-application connections. For example, websites that offer a ‘login via Facebook’ option have enabled this option by connecting to Facebook’s API. Similarly, programmes that facilitate the downloading or sharing of data with other programmes are enabled using APIs. It should not be assumed, however, that such tools are always reliable. GonzálezBailón et al. (2014), for example, examined sampling bias in Twitter by comparing samples of tweets collected using two different methods. The methods were a search API (which collected tweets published up to seven days prior to the search), and a streaming API (which collected tweets as they were published, but only a selection of them). The authors used both methods to identify and download tweets using one or more hashtags from a total list of six hashtags, and compared the results. Findings revealed that the search API collected fewer tweets than the streaming API, collecting 273,000 tweets compared to 435,000 tweets, respectively, over thirty days. Additionally, whilst findings supported previous work suggesting that larger samples were more accurate than smaller samples (Morsatter et al. 2013), neither method could offer a random sample of the full selection of tweets. Such limitations, therefore, cannot guarantee accurate methodological approaches, and this should be taken into consideration when choosing whether to adopt such methods. Additionally, as Tufekci (2014) has noted, it is not possible to use such methods to respond to practices such as sub-tweeting (tweets that refer to an unnamed but implicitly identifiable individual) and ‘hate-linking’, which denounces, rather than endorses, links and re-tweets. It is also important to note that data sets from

110 Research Methods for Education in the Digital Age

social media are not particularly representative and while an issue or view might seem accurate or generalizable, often it is not.

Mobile application data The notion of ‘mobile’ is, to some extent, becoming obsolete as a term and idea, since these days most devices are portable. Here, we refer to ‘mobile’ in the sense of mobile smartphones or smartwatches, which most users have with them at all times, providing a means by which they can be ‘always on’ the internet; Savin-Baden has referred to this as being digitally tethered (Savin-Baden 2015). More specifically, we refer to mobile application data as those data that are gathered by mobile applications for specific purposes, and are usually shared with the developers of those particular applications. As mobile devices have increased in variety and reach, there has been a focus on the ways in which tablets and phones are used for research purposes. For example, Couper (2013: 150) has identified three different types of mobile use for research purposes: 1 The use of mobile devices (tablets, smartphones, mobile

web) as a tool to conduct surveys and collect data: Here, the researcher is present with the participants and provides them with the mobile device to use. 2 The completion of surveys through mobile devices: Here, the participants use their own mobile devices to complete the survey remotely. 3 The use of mobile devices for enhanced data collection (e.g. GPS, photos, diary studies, food consumption measures, health monitoring). The first two types have been addressed in the preceding section, as essentially survey methods, but the third requires further delineation.

Enhanced mobile data collection The development of innovative educational applications for devices such as tablets, smartphones and smartwatches has almost been matched by the number of studies examining their use in education. However, it would seem that technological innovation has not been

QUANTITATIVE DATA IN DIGITAL CONTEXTS

111

matched by methodological innovation, particularly in quantitative settings. This may be attributed to the following reasons: ●●

●●

●●

●●

Ownership of data: Data from mobile applications are accessed by the operating system and application itself, and this raises numerous challenges in relation to data protection, privacy and ownership of research data. Overload of data: Mobile applications may gather data that may not be necessary for research purposes, and are inappropriate for the researcher to see. In order to protect the student, researchers may determine that the application is not an appropriate method for data collection. Access to data: Whilst the mobile application may gather the information needed for the research study, this may not be directly accessible to the user or to the researcher. Protection of data: This does not refer to the legal protection of data, but, rather, to the user’s and the researcher’s attitude towards the use of mobile devices for data collection and protectiveness over students’ personal data, respectively. In many cases, educational studies exploring the use of mobile devices require students to bring their own devices.

Thus what is needed is a critical and collaborative approach to enhanced data collection for mobile devices. Researchers, research participants and application developers should work together to identify the kinds of data that should be collected and the appropriate protections for research participants and data. This does not necessarily mean that new applications need to be developed for each research study. Instead, researchers should seek out preexisting work in the area and potential open source applications, and consider the ways in which collaborative studies can support enhanced data collection. One possible type of enhanced mobile data is geo-location data, as discussed in the following section.

Geo-location data Whilst mobile applications have long been used as a source of data for educational research, it is only recently that their ‘mobile’

112 Research Methods for Education in the Digital Age

characteristics have begun to be explored in full, through the use of geo-location data in research. For example, the StudentLife study at Dartmouth College in the United States has used geo-location data, amongst other data, to examine students’ mental health, academic performance and behavioural trends. Other similar studies are emerging in educational settings with the advent of wearable technologies, such as those at the Quantified Self Institute in Groningen, the Netherlands. As the locations in which students undertake learning continue to diversify, and educational institutions continue to explore innovative learning spaces,

Example StudentLife (n.d.), ‘StudentLife Study’, Dartmouth College. Available at http://studentlife.cs.dartmouth.edu/ (accessed 17 January 2016). The StudentLife app that ran on students’ phones automatically measured the following human behaviours 24/7 without any user interaction: ●●

Bed time, wake-up time and sleep duration

●●

The number of conversations and duration of each conversation per day

●●

Physical activity (walking, sitting, running, standing)

●●

Where they were located and how long they stayed there (i.e. dorm, class, party, gym)

●●

The number of people around a student through the day

●●

Outdoor and indoor (in-campus) mobility

●●

Stress level throughout the day, across the week and term

●●

Positive affect (how good they felt about themselves)

●●

Eating habits (where and when they ate)

●●

App usage

●●

In situ comments on campus and national events

QUANTITATIVE DATA IN DIGITAL CONTEXTS

113

geo-locational data is likely to become increasingly important in educational research studies. Such methods would seem to be particularly important in light of Facer’s (2014) call for a focus upon the disruption of space through the use of technology: We need something to help make our spaces strange to us in order to make them an object of inquiry and attention. And this is where new technologies play a distinctive role in feeding the imagination of alternative spatial arrangements, in opening up our imagination to the possibility of space as subject to change. These new tools ask questions of us about how we wish to create space, about how we wish to live … As with good science fiction, technological change serves as a powerful prompt to the imagination. It acts as a good ethnomethodologist, encouraging us to make strange our current realities to ourselves. [Technology] encourages us to ask questions such as ‘Why are these the boundaries of the school or university?’ (Facer 2014: 123–4) In addition to highlighting the importance of geo-location data, Facer’s argument would seem to demonstrate the importance of collecting data from virtual worlds. Yet, unlike mobile technologies, which capture data on the movements of the physical self, virtual world data are concerned with movements of the virtual self.

Virtual worlds application data Virtual worlds are computer-based simulated 3D environments, which users navigate using an avatar and can, therefore, meet other avatars in-world. These environments, such as Second Life, OpenSim and ReactionGrid, allow users to purchase virtual land and construct objects on that land using inbuilt programming tools. In doing so, developers and/or users are able to depict virtual representations of particular settings, such as classrooms, historical buildings and geographical landscapes. This land can then be accessed by any avatar who has permission to do, depending upon the settings of the land and the virtual world used. Whilst these environments may also have gaming elements (e.g. World of Warcraft is both a virtual world and a multi-user role-playing game), not all virtual worlds are games.

114 Research Methods for Education in the Digital Age

Virtual worlds have been popular in education since the late 2000s, and particularly so in distance learning educational contexts. For example, the Open University in particular has examined the pedagogical and methodological opportunities of such environments, as in the example below. Virtual Skiddaw is a virtual representation of a section of landscape in northern England, with six detailed sites designed for geological field visit explorations. Users use avatars to navigate the environment, working with virtual tools designed to support fieldwork in scientific disciplines and thus gaining additional practical experience. Such ‘field trips’ have also been used in a number of other disciplines. For example, Grant and Clerehan (2011) have used virtual worlds to provide language students learning Mandarin with ‘real life’ experiences such as ordering a meal in an in-world Chinese restaurant. As we noted in Chapter 1, using virtual worlds for education thus offers interesting opportunities and challenges for educational research. One of the key areas of interest is in the impact of virtual worlds on observational methods. Yet, they would also seem to pose new opportunities for quantitative research. Table 5.3 considers how our typology of quantitative educational data might be applied in virtual world settings. We then go on to consider the use of digital tools in non-digital research settings.

Example Argles, T., S. Minocha and D. Burden (2015), ‘Virtual Field Teaching has Evolved: Benefits of a 3D Gaming Environment’, Geology Today, 31 (6). Conceived as a means of fostering practical fieldwork skills for distance learning students at the Open University, Virtual Skiddaw represents a new breed of virtual field trip. Based in a 3D virtual world modelled on real topographic and geological data, the application offers multi-user functionality, opportunities for detailed observation and a wealth of interactive features. Far from replacing physical field courses, it is intended to complement, enhance, extend and provoke reflection on existing field teaching. However, such virtual field trips could prove ideal for introducing fieldwork, either at undergraduate level or in schools, and across a range of subject areas.

Provide information about the student’s identity, characteristics, and portrayal within the learning situation

Provide information about the student’s activities and engagement at a variety of levels

Provide information about user actions in digital environments or digital applications

Individual

Engagement

Activity-oriented

Provide information about user interaction with content in digital environments

Provide information on the student’s level of knowledge

Content-oriented

Learning

Network-oriented Provide information about connections across and between individuals

Description

Data type

Learning data is highly dependent upon the virtual world used, whilst some virtual worlds may be designed to include assessment objectives. For example, the Circuit Warz (Callaghan et al. 2013) virtual world game was designed to include assessment metrics, whilst the SLOODLE project (Bloomfield and Livingstone 2009) added additional programming capabilities to Second Life to facilitate collection of quantitative assessment data.

In virtual worlds, engagement data often overlaps and is collected using the same tools. For example, Chodos et al. (2014) developed the Second-Life Simulation Capture and Analysis (SCA) tool, which captured avatar movement, avatar experiences with objects and social interaction. Activity-oriented data might include avatar movements within particular in-world locations, and also movement across in-world locations such as the number of times a particular location is visited Network-oriented data might include number of friends in a virtual world, or membership of interest groups in the virtual world Content-oriented data might include the number of times that a particular object in the virtual world is clicked upon, or the objects that are not clicked upon when visiting a location

Avatar name and avatar appearance

Methods for collection and examples

Table 5.3  Quantitative educational data for virtual worlds research

QUANTITATIVE DATA IN DIGITAL CONTEXTS 115

116 Research Methods for Education in the Digital Age

Capturing data and data capture Digital research is not always undertaken in online settings. Digital research is also about the use of digital methods in a variety of research settings. At its most basic level, this can include the use of digital audio recorders for interviews, yet as our lives become increasingly quantified, almost every educational tool is a source of quantitative data. For example, one study explored student actions in a lecture setting through the use of video cameras and digital pens (Maltese et al. 2015). In this study, Maltese et al. captured the amount of time spent drawing diagrams, annotating diagrams, and writing and revising

Example Maltese, A. V., J. A. Danish, R. M. Bouldin, J. A. Harsh and B. Bryan (2015), ‘What Are Students Doing During Lectures? Evidence from New Technologies to Capture Student Activity’, International Journal of Research and Method in Education. Available at http:// dx.doi.org/10.1080/1743727X.2015.1041492. Engaging students in class is paramount if they are to gain a deep understanding of class content. Student engagement is manifested by attention to the various components of instruction. However, there is little research at the tertiary level focusing on what aspects of instruction are related to changes in student attention during class. To address this gap, we collected multiple streams of data that provide a measure of student attention during instruction. We had students in an organic chemistry class who wear hats with a camera mounted on the brim, to provide a record of student gaze (i.e. looking at the board, notes and friends). We also had students who use electronic pens that allowed us to record what information students transferred into their notes (pencasts). Based on our initial results, we believe the data provided by the point-ofview cameras and electronic pens hold great promise for using these technologies as viable research tools in educational settings to address various research questions.

QUANTITATIVE DATA IN DIGITAL CONTEXTS

117

notes. This was then examined in relation to students’ eye gaze and presumed focus of attention. Other studies have utilized digital methods such as facial recognition technologies. For example, Chiu et al. (2014) undertook a study that examined student understanding of complex conceptual learning through the use of facial recognition technologies. The studies by Maltese et al. (2015) and Chiu et al. (2014) bear some similarities to motion capture, an increasingly popular method in digital data collection. Motion capture involves recording the movement of humans or objects, and mapping these movements against digital models in 3D virtual environments, which thus replicate these movements. In these two studies, motion (eye gaze and facial movement) was captured, but not mapped against digital models. Many other studies, however, have chosen to do so. It is particularly popular in filmmaking and gaming; for example, the Nintendo Wii and Microsoft Kinect devices, along with other devices in the same generation, use motion capture technology. The development of such technologies in the late 2000s–early 2010s extended motion capture to a wider audience, when it had previously been restricted to high-budget films. Its integration into educational settings is more recent, but motion capture is increasingly popular in dance, music, sign language, sports, health, digital animation and other subjects in which bodily movements are particularly important. As such technologies become more accessible, it is likely that they may come to be used to support observational research methods. In most of the studies discussed in this chapter, data are captured using a digital tool. It is, therefore, important to remember that the types of data that can be collected and used for research purposes are partially determined by the application that contains or collects the data. With bespoke applications, researchers can usually work with developers to specify what data needs to be collected, how it can be made available for research purposes and who else has access to the data. Similarly, with many open source applications, researchers can have some degree of freedom in the same three areas of collection, availability and accessibility. However, with off-theshelf applications, such as those available to the public in the Apple Store, Google Play, Blackberry World and Windows Store mobile applications, researchers often have limited control over what data

118 Research Methods for Education in the Digital Age

are collected, how they might be used for research purposes and who else has access to the data.

Conclusion This chapter has provided an overview of some of the key and innovative quantitative methods available to educational researchers in the digital age. Quantitative methods, as the rapidly growing learning analytics field has shown, offer opportunities for enhancing teaching, learning and research, and it seems that we have not even begun to explore the methodological opportunities inherent in the internet of things. Whilst it is essential that research methods are grounded in a clear understanding of the needs of the study, we suggest that we can learn here from digital métissage by exploring the ways in which qualitative and quantitative research methods can overlap and interrelate, and thus open up valuable opportunities for further digital educational research. Yet, in exploring these opportunities, we also encounter issues about data protection, privacy and the question of when it is better to not research, and we examine these concerns in Chapter 6.

Further reading Couper, M. P. (2013), ‘Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys’, Survey Research Methods, 7 (3): 145–56. In this paper, Couper reviews three key technology-related trends: big data; nonprobability samples; and mobile data collection. His focus in this paper is upon how the survey research profession has largely failed to adapt to these new trends, and his belief that survey research is essential to their success. In order to meet these new needs, he argues, survey research needs to shift away from overly long surveys and recognize that users who respond to such surveys are likely to be very different from those who do not. Couper contends that survey methodologists should remember that surveys are tools to be used alongside other methods, and that they must be adapted to work with metrics, with digital statistical tools, with computers and with mobile devices, in order to be useful in a

QUANTITATIVE DATA IN DIGITAL CONTEXTS

119

digital age. This paper is particularly accessible for qualitative or newer researchers who are unfamiliar with quantitative research. Raffaghelli, J. E., S. Cuccharia and D. Persico (2015), ‘Methodological Approaches in MOOC Research: Retracing the Myth of Proteus’, British Journal of Educational Technology, 46 (3): 488–509. Raffaghelli, Cuccharia and Persico explore the methodological approaches to MOOC research based upon sixty peer-reviewed journal articles published between January 2008 and May 2014. This paper thus provides a particularly helpful overview of current methodological practices in this area and, in doing so, reveals some of the limitations of the current research field. For example, the theoretical positioning of nine papers was unclear, whilst qualitative papers rarely mentioned the data collection and analysis methods used in the studies. The papers conclude by arguing that what is perhaps most important in every emerging research field is identifying how research is being undertaken; thus there is significant work to be done in the MOOC field of research. Tufekci, Z. (2014), ‘Big Questions for Social Media Big Data: Validity and Other Methodological Pitfalls’, in ICWSM ’14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 505–14. Ann Arbor: AAAI Press. This paper takes big data and social media research into the practical domain. Whilst these methodological approaches are often discussed in the abstract, Tufekci makes explicit the ways in which algorithms can exclude data from social media, or present a skewed version of the dataset. Tufekci provides a timely reminder, with examples, of the ways in which counting sub-tweets, re-tweets or mentions can mask important subtextual information. It is therefore essential reading for any researcher working with these datsets.

120

chapter six

Digital ethics Introduction As educational practices and research practices are adapted for a digital age, new ethical questions are brought to light. Changing practices such as the open education agenda, and the availability of big data, for example, open up new research opportunities, yet they also require researchers to reconsider participant–researcher relationships and what it means to undertake ethical research. This chapter commences with a definition of ethics in digital settings. It examines the ethical issues and conflicts that tend to arise when undertaking research in and for a digital age. We deal with these by considering the broad areas of privacy, the human subject and anonymity. In particular, we address often neglected ethical issues that emerge when creating, collecting and analysing educational data in a digital age. We conclude by examining the various frameworks that need to be considered when undertaking ethical research.

Defining ethics It is broadly accepted that there is not and cannot be a ‘one size fits all’ set of guidelines for undertaking ethical research in digital settings. Despite this, the issue of ethics is often unaddressed in the reporting of educational research findings, and is summarized in a short sentence stating that ‘ethical approval was received from the relevant Institutional Review Board’ (sometimes known as

122 Research Methods for Education in the Digital Age

the Ethics Review Board), or addressed in separate ethics-focused papers or reports. We argue, however, that ethics is too important to research to be simplified in this manner. Ethical practice means respecting the needs of the individuals involved in research – whether participant or researcher – yet, ‘participants’ and, indeed, ‘researchers’ are not homogenous groups but, rather, individuals with different beliefs about their needs in research. Therefore, there is a need for the ethical dilemmas associated with digital research to be made explicit, and for the challenging of ethical decisions in research to be not only accepted, but also commonplace. It is only in doing so and thus opening oneself up to new points of view, we suggest, that truly ethical work can be undertaken. At its most fundamental level, ethical research that includes human subjects or their data is considered to be research that acknowledges the fundamental right of human dignity, autonomy, protection, safety, maximization of research benefits and minimization of harms (UN Declaration of Human Rights 1948; Nuremberg Code 1949; Declaration of Helsinki 1964; Belmont Report 1978). Potential harms are taken to include physical harms (e.g. pain, injury), psychological harms (e.g. depression, guilt, embarrassment, loss of self-esteem), and social and economic harms (e.g. loss of employment or criminal charges) (Savin-Baden and Major 2013). These rights have traditionally been assured through the following procedures:

Anonymity means that no identifiable data (e.g. name, address, Social Security number) is collected as part of the research study, and therefore no identifiable data can be shared. Confidentiality involves the collection of identifiable data about research participants, which is then restricted to particular individuals depending upon the confidentiality agreement. Informed consent involves individuals consenting to participate in research when fully informed of the processes, benefits and harms involved. Inherent in the principle of informed consent is the understanding that the participant can revoke consent, usually up until the study is completed and findings are published.

DIGITAL ETHICS

123

Minimal risk means that the risk associated with the study is similar to that typically encountered in everyday life. Participant validation involves returning data (such as interview transcripts) to participants to check that they represent accurately what was said. Member checking moves beyond participant verification and seeks affirmation of the ways data are interpreted and presented. Reflexivity involves the ongoing reflection upon one’s research practice in order to engage in a process of continual learning. Validity ensures that the study is appropriately designed in order to achieve the original objectives.

Research that seeks to protect these rights tends to align to one of two particular approaches. Consequentialist ethical perspectives focus on the possible consequences of a particular action in research terms, and lead researchers to make decisions based upon those consequences. It is on this basis that participants may be refused the right to withdraw from research, for example, based upon the belief that the maximization of benefits to the many can sometimes outweigh the possible harms to individuals. Non-consequentialist ethical perspectives focus on rules-based approaches to research, which are often those rules set out by Institutional Review Boards (IRBs) or university Ethics Committees, and lead researchers to make decisions based upon the stated regulations. It is on this basis that participants may not be given the opportunity to be identified in research outputs, for example, based upon enforcement of the principle of anonymity as a means of protecting participants. Thus, neither perspective is without its dilemmas, and most approaches to research tend to draw on aspects of both. Definitions of ‘ethics’ differ according to the ethical perspectives adopted by individuals. Non-consequentialist approaches to research tend to suggest that ethical research is research that adheres to principles of correct behaviour. For example, Bogdan and Biklen (1992: 49) have defined ethics as ‘the principles of right and wrong

124 Research Methods for Education in the Digital Age

that a particular group accepts’. However, such approaches imply that principles of correct behaviour are shared across researchers, participants and other stakeholders unless it is specified that that particular group comprises all stakeholders in a particular research project. Definitions of ethics from consequentialist perspectives tend to focus on ethics as philosophical deliberations and moral dilemmas encountered by the researcher(s). For example, Busher and James (2015: 173) have defined ethics as ‘the moral deliberation, choice and accountability on the part of researchers throughout the research process’, as researchers determine the best way to support the fundamental rights laid out for research participants. Whilst this approach does not privilege context-independent rules set out by IRBs or other ethical bodies (as in some non-consequentialist approaches), it does tend to privilege the researcher as the authority on ethical decision-making. We suggest that both consequentialist and non-consequentialist approaches can be useful as guiding frameworks for ‘ethical research’, but that they should take account of individual contexts/research projects and involve stakeholders in ethical decision-making as far as pragmatically possible. It is this understanding of individual contexts, and identification of/involvement of stakeholders, that is most challenging, and also most interesting, about undertaking ethical research for a digital age.

Undertaking ethical research in digital contexts In Chapter 1, we noted that ‘the digital age’ is in itself problematic, with Horst and Miller (2012) arguing that everyone has a different conception of ‘digital’. There are also very few, if any, areas of agreement as to what constitutes ethical research across disciplines, methodologies and research sites, including digital research sites or contexts. Consequently, no official frameworks have been adopted at national or international levels; nor does it seem possible that any such frameworks can (or should) be adopted. However, there have been some attempts to develop suggested guidelines for ethical research in digital contexts. The Association for Internet Researchers, for example, has developed a list of sixtythree questions specific to online research that digital researchers

DIGITAL ETHICS

125

should ask themselves before beginning any research project (Markham and Buchanan 2012: 8–11). A number of educational funding bodies and societies have also developed additional ethical guidelines for online studies, typically adopting consequentialist approaches to online research and encouraging researchers to return to and consider the fundamental principles of research and their application in different digital domains (e.g. British Psychological Society 2013). Institutional Review Boards’ ethics guidelines differ across institutions but tend to be a mix of non-consequentialist and consequentialist approaches, as will be discussed later in this chapter. This lack of guidance across the higher education sector about how to deal with ethical concerns is a direct consequence of the general agreement on the ethical instability encountered in everyday research practice, which represents the situational, negotiable, uncertain and temporal nature of ethical research practice (Whiteman 2010). In essence, the concept of ethical instability highlights that whilst researchers may adopt practices that seem appropriate for a particular research project, the parameters of the project are constantly shifting and potentially raising new ethical issues. In the remainder of this chapter, we address three key issues that are particularly ‘unstable’ and are therefore critical to making ethical decisions in digital research. These are privacy, consent and analytics in digital spaces; the nature of the human subject in digital contexts; anonymization of and attribution to participants.

Privacy, consent and analytics in digital spaces One of the most challenging issues in undertaking ethical educational research in digital spaces is working with ‘public’ and ‘private’ data. There is significant uncertainty around what is a public or private domain online, and whether data being in the public domain entitles researchers to use that data. Whilst these issues are not exclusive to digital domains – for example, the issue of photographing individuals in public spaces has long been discussed – these questions have significant consequences for privacy and informed consent in digital contexts. Users are considered to have a greater

126 Research Methods for Education in the Digital Age

entitlement to privacy in private spaces, and consequently informed consent must be negotiated under different terms.

Privacy and the public/private binary Privacy, which refers to acceptable practices with regard to accessing and disclosing personal and sensitive information, has become one of the most polarizing debates of the digital age (Elwood and Leszcynski 2011). In an age of big data, where surveillance and public awareness of it is increasing, digital research is fraught with tensions regarding the collection, processing, dissemination and use of data in both ‘public’ and ‘private’ domains. Rosenberg (2010) has suggested that online data tends to be characterized as public if it is accessible to anyone with an open internet connection, and is perceived to be public by participants in the space. Often, data in the public domain are considered to be freely available for use for research purposes, without informed consent, so long as the individual providing the data is protected from harm as far as possible. In education, freely available data is often seen as Twitter feeds, education blog posts, and data from MOOCs, for example. It is for this reason that data collected from the public domain for research purposes, without informed consent, have typically been anonymized. The ethical implications of this stance are addressed later in this chapter. By contrast, data that are confined to, or intended for, particular individuals, are perceived to exist on a ‘private’ continuum (BenZe’ev 2003). However, the ‘public/private’ dichotomy has long been debated, and is increasingly considered to be fundamentally flawed. Lee (2009), for example, has found that mobile devices used for taking and sharing photographs amongst Koreans in their early twenties facilitate a blending of public/private boundaries. As personal pictures are easily circulated via networks, they tend to have a semi-public nature. … Individuals set up the gradation of the public openness of their private images and decide the range of image access and control for self-presentation and social interaction. As they continuously self-regulate the pertinence of accessibility to their images, they cultivate skills of self-impression management, caring about others’ gazes. In this process, private/

DIGITAL ETHICS

127

public domains are interpenetrated and overlapped, challenging the conventional dichotomy of contrasting the visible public domain with the invisible private domain. (Lee 2009: 170) Whilst alternative framings have been suggested, such as privately public, publicly private, or semi-private (Anderson and Kanuka 2009), such framings have not achieved traction in the educational research domain or at Institutional Review Boards. Public/private distinctions are thus particularly difficult to operationalize in online educational research settings, with few spaces considered either completely public or completely private. One possible exception is the university Virtual Learning Environment (VLE), which requires students to be enrolled within that institution and often on particular courses in order to access course-related content, and is therefore accepted as private. Consequently, research undertaken in and on the VLE often requires informed consent from research participants. Yet, with the rise of learning analytics, and of open education blending public/private educational spaces, such assumptions may no longer hold true.

‘Found data’ in education The most pressing challenge to digital educational researchers, then, seems to be how to manage the reductionist approach of public/ private binaries, for example, when spaces perceived as private by users are perceived as public by researchers. Open groups and profiles, comments, or photographs on Facebook, for example, are typically considered to be in the public domain. Research undertaken without seeking informed consent assumes that individuals are aware of the Facebook terms and conditions and are cognizant of the ‘public’ nature of the spaces and, therefore, their data. Yet, there are a number of problems with this approach. First, depending upon the levels of access of the individual downloading data, it is possible for non-public data to be inadvertently downloaded and used for research purposes. For example, Zimmer (2010) has noted that undergraduate or postgraduate research assistants hired to download data in Facebook studies may have different levels of access to student profiles, depending upon whether they know

128 Research Methods for Education in the Digital Age

participants in the study. Second, private data can be made public and public data made private with relative ease by amending the privacy settings of digital accounts. If a student shares a public status or tweet that is captured for research purposes but the account is deleted, or made private, then the use of the data can no longer be justified based upon arguments that it is public. Thus public data is not segregated into public/private binary domains, but, rather, enables individuals to share data at different levels and across personalized networks. The use of ‘found data’ is often justified based upon the transparency-and-choice concept, or the belief that the right to privacy is actually the right to control which information is released – in essence, the belief that if individuals put data out in the public domain, they relinquish control of that data. We use the term ‘found data’ here to describe data that are ‘found’ in the public domain. Data perceived to be ‘public’ have been used for research purposes based on the justification that such research does not include human subjects but, rather, involves the study of secondary data already available in the public domain – the ‘found data’ discussed in Chapter 1. Nissenbaum (1998, 2010) has suggested that a contextual integrity framework might be most appropriate for determining how to manage ‘found data’. This framework is designed to evaluate flows of information, and examine why certain uses of data for research purposes are received unfavourably: One withholds, makes public, or ‘commits’ information ‘only to the sight of his friends’. This nuance, I believe, accounts for some of the surprise and indignation these controversial activities stir and can be captured within the analytical framework of contextual integrity … Were we to investigate cases in which people have experienced nasty surprises of discovery, we would find that they have understood themselves to be operating in one context and governed by the norms of that context, only to find that others have taken themselves to be operating in a different one. (Nissenbaum 2010: Kindle location 4291) Bakardjieva and Feenberg (2001), however, have suggested that non-alienation is a better way of reframing approaches to the use of data in the ‘public’ domain. Non-alienation takes account of the fact that data taken out of context can mean something entirely

DIGITAL ETHICS

129

different from its original intent, and highlights the ways in which the use of data for unanticipated uses can be appropriative and disempowering: We believe that alienation, not privacy, is the actual core of the ethical problem of most virtual community research. While practically everybody is allowed and often welcome to join online communities (which undermines the claim to privacy), participants seem generally to take it for granted that members are not authorized to use, or ‘harvest’ or sell the product of the group communication. To do that, they would be expected to ask for permission preferably before the content has been produced, thus granting participants’ right to control their own product. This ‘non-alienation principle’ should be the basis of emergent social conventions in cyberspace. It would apply to researchers as to anyone else. (Bakardjieva and Feenberg 2001: 236–7) The question then emerges – if, as Bakardjieva and Feenberg, and Nissenbaum argue, the public/private dichotomy is impractical for determining when informed consent is necessary in order to undertake research, what should be the approach for researchers to adopt instead? We argue that, ultimately, there is no straightforward response to this, and researchers need to work with possible research participants in order to determine the most respectful and appropriate approach. However, we suggest that there are three key principles that should be considered: 1 The need for informed consent should be discussed with

representatives of the possible participant group; for example, student views should be prioritized in determining whether informed consent is necessary for big data analysis. 2 If non-alienation is accepted as a principle for undertaking good ethics, the representation and portrayal of research must be considered when seeking informed consent. These issues are discussed in detail in Chapter 9. 3 Informed consent in a digital age requires researchers to consider the longevity of data and research outputs, which are likely to be in the public domain for decades to come as research data and outputs are published openly.

130 Research Methods for Education in the Digital Age

Thus informed consent is no longer (if it ever was) about consenting not only to the study and immediate use of the data, but also to the widespread presence of data in the public domain beyond the lifetime of the researcher and participant. These issues are discussed in Chapter 10.

Consent and learning analytics There is currently a range of types of analytics in education, the most notable being learning analytics. Learning analytics in education and educational research focuses on the process of learning and all that is inherent in that (measurement, collection, analysis and reporting of data about learners and their contexts), and academic analytics reflects the role of data analysis at an institutional level. Gaševic´, Dawson and Siemens (2015: 64) argue that learning analytics is ‘a bricolage field drawing on research, methods, and techniques from numerous disciplines such as learning sciences, data mining, information visualization, and psychology’. This bricolage is perhaps reflected in the range of approaches being used across different institutions. Furthermore, it is evident that different institutions in the United Kingdom are using different approaches to collecting and analysing data. These range from Oracle data warehouse and business intelligence software, to the use of QlikView to analyse data held in Microsoft SQL Server, as well as Google Analytics, Google Charts and Tableau. Whilst there are a number of ethical issues that require attention in relation to the use of ‘public’ data, there are also questions around privacy and consent with regard to ‘private’ data such as learning analytics data. As discussed in the previous chapter, Long and Siemens (2011) have defined learning analytics as the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. Data can be gathered from multiple sources such as the VLE, university library, attendance registers and assessment data. In 2013, twentysix institutions in the United Kingdom were using learning analytics to provide students with information on their performance; at the time of writing, usage is increasing worldwide.

DIGITAL ETHICS

131

Sclater, in a JISC-funded study, has provided a review of institutional codes of practice and legal frameworks in a UK context, and subsequently offered a JISC code of practice for the use of learning analytics (2014a, 2014b). Similarly, Slade and Prinsloo (2013) have suggested six ethical principles to guide the use of analytics in education. These are as follows: 1 Learning analytics as moral practice: Research using

2 3

4

5

6

learning analytics should be used to enhance understanding of students rather than facilitating measurement of students. Students as agents: Students should be engaged as collaborators in the collection and analysis of learning analytics data. Student identity and performance are temporal dynamic constructs: Learning analytics data should be understood as temporal and transient. Student success is a complex and multidimensional phenomenon: Learning analytics data is fundamentally incomplete. Transparency: Institutions and researchers should make clear the purposes and restrictions behind their use of learning analytics data. Higher education cannot afford to not use data: Learning analytics data collection and research should not be optional, and institutions have a moral responsibility to collect and utilize as much data as possible in order to achieve their goals.

The ethical implications of learning analytics require particular attention, predominantly if these or other similar guidelines in the field are adopted at national or international levels. Slade and Prinsloo (2013) have distinguished between students’ consent to the use of data at identified, personalized and teaching levels, and at de-anonymized research levels. For example, they – along with most others in the field – have argued that there is a clear need for students to be informed of how their data might be used, so that they can opt out of viewing the results of the analytical analysis, an option that is becoming increasingly popular. However, Slade and Prinsloo (2013: 1522) have also argued that ‘it seems reasonable to distinguish between analyzing and using anonymized

132 Research Methods for Education in the Digital Age

data for reporting purposes … in the context of reporting purposes, we support the notion that the benefit of the majority supersedes the right of the individual to withhold permission for use of his or her data’. The argument not to seek informed consent, therefore, is based upon the belief that the potential risk to participants is particularly low, and that allowing individuals to opt out would jeopardize the likely benefit to those participants as well as others. However, low risk does not equal no risk. In 2014, Oxford University unintentionally released a list of its fifty worst performing students in an email to all students. In the same year, the University of Maryland’s records were hacked, leading to the release of 300,000 students’ personal information. Numerous other data breaches have occurred in financial and healthcare settings, amongst others. Guidelines to the use of learning analytics have also tended to take an institutional slant, examining the ethical implications of large-scale data collection through the VLE and other methods, and the use of this data to provide information on student behaviour and achievement. In doing so, they also serve to further confuse the issue for individual researchers. For example, if one individual is responsible for all learning analytics data gathered in the VLE, as recommended by Sclater, what does this mean for individual practitioner/researchers using learning analytics from the VLE? Who is responsible for managing usage and ultimate destruction of the data? If the institution has a moral responsibility to collect and use as much data as possible, as Slade and Prinsloo suggested, does the individual researcher then have a responsibility to use as much data in research studies as possible? Underpinning these arguments is the belief that collecting and using more data on students is inherently beneficial on both ethical and methodological levels, an assumption that researchers may wish to critique.

The ‘human subject’ in digital contexts The notion of the human subject emerged from early ethical guidelines that were designed as a response to harmful bio-medical experiments on humans (e.g. the Nuremberg Trials and the Tuskegee syphilis experiment). Walther (2002) has defined human subjects research as research that occurs when there is any intervention or interaction with another person for the purpose of gathering

DIGITAL ETHICS

133

information, or in which information is recorded by the researcher in such a way that a person can be identified directly or indirectly with it. The human subject model persists as a guiding model today, yet it is increasingly impractical for digital research; Markham and Buchanan (2012), for example, have argued that it is still unclear what is meant by ‘interaction’ in digital settings. There are two key premises upon which the notion of the human subject in digital educational research has been challenged. These are the human subject as textual data, and the human subject as avatar. Bassett and O’Riordan (2002) have argued that treating all digital research as human subjects research ultimately conflates the internet as a space in which interactions take place and the internet as text, arguing that greater criticality is needed: Academic research that positions the internet as a social space containing cultural activity ripe for observation ignores the range of textual applications that the internet supports … As internet researchers, we need to develop models that acknowledge both spatial and textual understandings of the internet … We should attempt to find a way of acknowledging the hybridity of the internet, acknowledging that the texts it supports are neither virtual selves nor objects completely distinct from those who write them. (Bassett and O’Riordan 2002: 11) Yet others, like Žižek (1999), have argued that the avatar is, in fact, a simulacrum, which fails to represent interactions or interventions between the researcher and the participant. The inference from this is that informed consent is unnecessary when engaging with avatars, because the avatar does not sufficiently represent digital interactions. Whilst there are a number of issues associated with researching in virtual worlds, however, Hill (2013) has argued that the avatar can be a useful reminder of the importance of ethics in digital research. Whilst he agrees with Žižek that the avatar does not fully represent the individual and thus reveals interaction in-world as a messy and complicated concept, Hill also suggests that the avatar can be viewed as a stand-in for the user and thus ethical practice requires informed consent to be sought when engaging with the avatar. The images that constitute virtual environments should be understood as morally evocative, as gesturing towards the

134 Research Methods for Education in the Digital Age

concrete reality they represent. By bringing to the mind the sublime otherness of the other person, they are at the same time a moral lesson: they remind us that the other person ought to be beyond our unfettered desires, that we must limit our own actions so as not to do injustice to them … The avatar must also signify the other person it stands in for; avatars need to be understood as kinds of signs of specific other people. So, I have further argued that the user is extended forward by the avatar such that she can be encountered despite the fact that she is not physically present. (Hill 2013: 83) Yet, the notion of the human subject online is further complicated by work in the literacies field, and particularly around posthumanism, which seeks to break down binary distinctions between ‘human’, ‘machine’ and ‘text’ (Hayles 1999, 2012). Such developments are particularly interesting in relation to the non-human researcher, an issue that has been significantly neglected. We attribute this neglect to the increasing, and welcomed, focus upon researcher stances and the influence of the researcher on the study and data; yet, the issue of non-human researchers would seem to be equally important. In recent years, there have been many developments in the area of artificial intelligence (AI). In particular, pedagogical agents are being used as virtual mentors both within and beyond the academy. Such pedagogical agents are designed to simulate interaction with humans through discussion, including the use of appropriate conversational norms, and are used to achieve specific purposes such as supporting online shopping, promoting services or goods, or supporting student learning. There are two key ethical issues at hand here. First, there is the issue of whether research participants are aware of who is ‘behind the computer’ – whether they believe that they are engaging with a human, and not with an artificially intelligent technology. In a virtual world (Second Life) setting, Hasler, Tuchman and Friedman (2013) compared interviews with an unintelligent pedagogical agent to an avatar controlled by a human interviewer. This study revealed that individuals were significantly less willing to participate in pedagogical agent-facilitated interviews than human-facilitated interviews, but also that the pedagogical agent and human interviewers were equally successful in gathering

DIGITAL ETHICS

135

information on participants’ backgrounds when participants had agreed to be interviewed. Hasler, Tuchman and Friedman thus noted that workloads could be significantly reduced on large-scale studies by using pedagogical agent interviewers. However, this study and others like it also raise a number of interesting questions – for example, whether informed consent needs to involve informing participants whether they are being interviewed by a human or by a technology, and whether the use of pedagogical agents might increase or minimize the potential harm to the participant. This leads to the second ethical issue of participant willingness to disclose information to non-human researchers such as pedagogical agents. Research into pedagogical agents is presently focusing upon the effect of anthromorphism and increased length of engagement on user responses to pedagogical agents, with significant implications for research purposes. For example, Savin-Baden et al. (2015) used a 2D website with a variety of avatar choices to explore student responses to pedagogical agent-mediated surveys on sensitive topics (finances, alcohol, plagiarism, drugs and sexual health) over varying periods of time. Either the survey was delivered in one sitting (short-term sitting), or three questions were delivered every three days over a period of two weeks (long-term sitting). Findings revealed that participants disclosed more information on the most sensitive topics when engaging with the pedagogical agent over the longer time period, emphasizing a clear need for further study to be undertaken. Thus there is presently very little understanding of exactly how pedagogical agents might be used in research settings, or how their use impacts upon participant willingness to disclose information or the quality of that information. What is certain, however, is that discussions around ethics must be at the forefront as artificially intelligent pedagogical agents continue to become ‘researchers’.

Anonymization and attribution of research participants We take anonymity here to mean the removal of names of participants and research sites, as well as the removal of information that might lead to the identification of participants or research

136 Research Methods for Education in the Digital Age

sites. Data are anonymized based upon the belief that to protect research participants from harm means to ensure that their data cannot be attributed to them, and is standard practice when data has been gathered from the public domain without consent. It is suggested as standard practice in a number of research codes (British Psychological Society 2014; Social Research Association 2003; American Sociological Association 1999), although others state that anonymity should be decided by the research participant (Economic and Social Research Council 2015).

Anonymization of data It is generally assumed by researchers that it is possible to anonymize participants who have provided data in the forms of interview quotations, tweets, Facebook status updates, message board postings and other forms of textual data. Researchers achieve this anonymity through the removal of identifying information such as Twitter handles, location data and the de-contextualization of data as far as possible. Yet, it is increasingly possible to identify participants from direct quotations using Google or other similar search engines, even when researchers have made attempts to maintain anonymity by changing names and revealing little identifying data. For example, in a review of seventy-one articles and doctoral theses on educational technologies that included direct quotes from participants, Dawson (2014) was able to identify the source of the quotes from ten articles through the use of a search engine. Facebook status updates cannot currently be searched using Google, at least by the standard computer user, but the full content of tweets can be searched and Twitter handles re-attached to updates even when the researcher has chosen to remove them. Visual data – such as photographs or video – are rather more difficult to anonymize and this has significant implications for the trustworthiness of the data; altering visual data, Wiles et al. (2012) have argued, fundamentally alters how the data might be analysed and interpreted. The implications of this are examined in detail in Chapter 9. Whilst there are few relevant guidelines, studies with visual digital data have tended to prioritize the opinions of the participants in determining whether to share identifying photographs (Pink 2007). Yet, this is not always possible. For

DIGITAL ETHICS

137

example, Blanco Ramírez and Palu-ay (2015) explored university identity construction through examination of promotional photographs of students at the institution. Whilst consent to publish one photograph was obtained from the universities participating in the studies, it was not possible to gain individual consent from the student in the photographs. The student name was removed from the image presented, and the student’s image is now available in a published journal article. The authors highlighted this as a particular ethical dilemma: did they have a responsibility to the institutions involved in the study (who agreed to the use of the photograph), or the student, whose image was portrayed but who did not agree to the use of the photograph for research purposes? In agreeing to the use of the image in the public domain, did the student forfeit the right to opt out of the research study? And did the benefits of the study, which highlighted the ways in which students from racially minoritized groups were exploited by universities for promotional purposes, outweigh the possible harm to the student? For these authors, this decision raised questions about the value of ‘evidence’ when reporting research findings: Perhaps the best way to promote ethical research in a changing environment, like the online realm, is to adjust our own expectations as a scholarly community about what constitutes evidence. The temptation to present hard evidence – such as quotes, verifiable participant names, and photographs – can be substantially reduced as we broaden our perspectives about evidence. (Blanco Ramírez and Palu-ay 2015: 151) Whilst we agree that there is a need to reconsider what we mean by ‘evidence’ – an issue which will be discussed in Chapter 9 – we argue that there is also a significant need to continue to question the ethics of anonymity as default, as discussed below.

Attribution of data With the exception of a minority of studies, particularly those using visual data (such as Blanco Ramírez and Palu-ay 2015), educational research has tended to anonymize data that is gathered without

138 Research Methods for Education in the Digital Age

consent from the ‘public’ domain. As participants are unable to determine for themselves the level of risk of participating, those responsible assume that participants are best protected by the enforcement of anonymity. Yet, there are also a number of ways in which choosing to anonymize participants – with or without consent – can be viewed as equally harmful and disempowering, refusing participants the voice that can come with named attribution to data. For example, Allen (2015) used visual digital research methods to explore issues pertaining to gender and sexuality in educational environments. Many of the students in Allen’s study were marginalized or excluded within the school and viewed the study as a means by which to express their identities and position themselves within their school environment. This raised questions for Allen about the extent to which she or the IRB had the right to insist upon students’ anonymity in the photographs: Lesbian students continue to constitute a marginalized group. Part of the politics of the gay liberation movement historically has been the importance of claiming visibility. … Anonymisation of photographs [by blurring faces] undermines this politics of naming, thwarting these young womens’ right to identify as lesbians. In posing for the photograph and consenting to its use in research, they wanted to be known as a ‘happy couple’. (Allen 2015: 304) With regard to textual research methods, there are similar issues of visibility and exclusion to consider. Kim and Kim (2014), amongst others, have highlighted the ways in which Twitter and other public digital spaces can become exploitative in failing to attribute work appropriately, particularly in the case of women, people of colour and those who are often ‘invisible’ in research. Whilst it is increasingly common for participants to be able to choose whether they wish to be anonymized, when informed consent is not sought and discussed, the participant is silenced. Ultimately, the ethical dilemmas associated with undertaking educational research in digital environments need to be managed with understanding of the context, participants, and methodology. In naturalistic research, for example, the development of alternate approaches that do not require informed consent may be the most ethical stance.

DIGITAL ETHICS

139

Ethical frameworks for a digital age? Throughout this chapter, we have examined what ethical educational research means and looks like in a digital age. In doing so, we have raised a number of questions; we have perhaps asked more questions than we have offered answers. Nind et al. (2013) have suggested that ethical research for a digital age can only ever be undertaken when researchers are as mindful of their reflexivity as they are of the codes of practice set out by institutions and IRBs. The ethical issues raised in this chapter are all pertinent, yet Farrow (2014) has also questioned whether ethical practice requires the exploration of innovative methodologies that are deemed incompatible with existing understandings of ‘ethics’. However, there are a number of frameworks that also need to be taken into account. In some cases, these frameworks can or will need to be negotiated depending on contradictions across guidelines or the needs of individual studies. We identify four key types of frameworks: legal, disciplinary, institutional and study. These are discussed below.

Legal frameworks Legal frameworks exist at local, national and international levels, which Institutional Review Boards will be able to advise on. One particularly important framework, for example, is the European Union’s (2014) ruling that search engines must respond to user requests for the removal of search results related to their names. This is otherwise known as the Right To Be Forgotten ruling. It remains to be seen how this ruling and others like it might influence the dissemination and longevity of research findings. What is most important here is that the legal context is ever-changing, and thus research for a digital age, in some respects, requires researchers to be aware of current debates and to plan research projects and data management accordingly.

Disciplinary frameworks Disciplinary frameworks include those such as the British Psychological Society’s (2013) framework. Unlike legal context

140 Research Methods for Education in the Digital Age

frameworks, many disciplinary guidelines for the digital age seem to be outdated or do not address digital contexts at all. Thus there may be circumstances in which the public/private binary is reinforced, problematizing innovative digital research as researchers struggle to determine appropriate stances on informed consent and anonymity. We suggest that there is an urgent need for disciplinary bodies to update guidelines, and, perhaps more importantly, to encourage discipline-specific and cross-disciplinary discussions about ethical practice in a digital age.

Institutional frameworks Institutional frameworks include Institutional Review Board procedures, as well as data protection and retention policies and open access policies. It is important to understand that there is often a ‘hidden curriculum’ behind institutional contexts, meaning that which is unsaid and unwritten, yet is somehow known to be true. This might mean that there are certain institutional conventions around research practice that are not codified, but still inform ethics applications. For example, institutional ethics applications are usually reviewed by subject experts, which presents challenges in relation to disciplinary differences. How do disciplinary context frameworks and institutional frameworks co-exist, and in what ways do practices collide?

Study frameworks Study frameworks can include terms and conditions (such as Facebook Terms of Service) or written rules of behaviour (such as group-agreed conditions for particular websites). Knowledge of a research context, informed by existing experience in the area and literature reviews, for example, shapes much of how research is designed, but often goes unacknowledged. These contexts inform how ethical practice is undertaken, because participant perceptions of ethics are constituted within these contexts. James and Busher (2007) have suggested that ethics is the moral deliberation, choice and accountability on the part of the researcher throughout the research process. As this chapter has illustrated, educational research in digital contexts produces many issues on

DIGITAL ETHICS

141

which researchers must deliberate, make choices and ultimately hold themselves accountable and be held accountable for.

Conclusion This chapter has examined some of the critical ethical debates for educational research in digital settings and offered questions for researchers to consider when determining ‘ethical practice’ in context. However, we have not sought to suggest which choices are ‘correct’ or ‘incorrect’, nor is it our intent to muddy already murky waters. Yet, these moral deliberations are too important to respectful, excellent and innovative educational research to be simplified in a short sentence stating that ethical approval has been received from the IRB. It is only by making explicit the moral deliberations encountered throughout the research process – and by questioning the assumptions underpinning those deliberations – that ethical research can truly be undertaken.

Further reading Kim, D. and E. Kim (2014), ‘The #TwitterEthics Manifesto: You Don’t Need to Speak for Us – We are Talking’, Model View Culture, 7th April. Available online: https://modelviewculture.com/pieces/thetwitterethics-manifesto (accessed 18 January 2016). This paper provides a critical and essential examination of the ways in which ‘public’ spaces such as Twitter are experienced and perceived differently. In recognition of the title of this piece, we will say nothing else beyond suggesting that we consider this essential reading for researchers working in digital environments. Whiteman, N. (2010), ‘Control and Contingency: Maintaining Ethical Stances in Research’, International Journal of Internet Research Ethics, 3: 6–22. Whiteman draws upon her experience of researching two online fan communities, using textual analysis of written exchanges. This paper discusses her approach to informed consent, particularly when the previously public communities closed off to the public by requiring

142 Research Methods for Education in the Digital Age

users to register and log in. It also discusses a number of additional challenges encountered which destabilized both her original approach and the guidelines she believed she should follow. Whiteman argues, based upon her experiences, that whilst such challenges are unsettling and destabilizing for the researcher, they can also provoke methodological and ethical learning. Wiles, R., A. Coffey, J. Robinson and S. Heath (2012), ‘Anonymisation and Visual Images: Issues of Respect, “Voice” and Protection’, International Journal of Social Research Methodology, 15 (1): 41–53. Wiles et al. report upon a study comprising thirty-nine researchers with experience of visual methods, based upon qualitative focus group and interview data. Participants had varying levels of experience, with both professors and doctoral students involved in the focus groups, yet most, if not all, participants stated that anonymity in visual research was particularly challenging. Findings from this study convey the ways in which researchers grappled with respecting participants’ rights to be heard and researchers’ responsibilities to protect them, which often translates into enforced anonymity. This paper therefore presents a concise and accurate summary of the particular challenges of balancing paternalism and agency in research, which is relevant to all researchers working with qualitative data.

chapter Seven

Digital data creation and collection Introduction This chapter examines how data are created and collected in a digital age. It suggests that the stance of the researcher plays an integral role in data creation and argues that the binary divisions between ‘researcher’ and ‘researched’ are increasingly becoming blurred. The chapter explores how research observations might be undertaken in digital spaces in which the notion of presence is located and constituted, and experienced differently. This chapter also reviews the relationship between theories for research in digital spaces (as addressed in Chapter 4) and digital creation and collection methods. It considers how the qualitative ‘staples’ of interviews and focus groups are being, and might be, altered by the shift into a digital age and provides guidance for ensuring that theory is soundly embedded in research practice.

Researcher roles As criticality and reflexivity have gained increasing attention in educational research, more attention has been paid to the terms used in research and their theoretical implications. One such term is ‘data collection’; information that is collected and examined for the purposes of decision making, which in the main is seen as

144 Research Methods for Education in the Digital Age

relatively unproblematic. Yet, researchers, like Thomson (2013), have questioned whether those who use this term believe that data exists ‘out there’ in the world for researchers to gather. Similarly, Gitelman has noted: The phrase raw data has understandable appeal. At first glance data are apparently before the fact: they are the starting point for what we know, who we are, and how we communicate. This shared sense of starting with data often leads to an unnoticed assumption that data are transparent, that information is selfevident, the fundamental stuff of truth itself. If we’re not careful, in other words, our zeal for more and more data can become a faith in their neutrality and autonomy, their objectivity. Think of the ways people talk and write about data. Data are familiarly ‘collected’, ‘entered’, ‘compiled’, ‘stored’, ‘processed’, ‘mined’ and ‘interpreted’. (Gitelman 2014: 1–2) Yet, Gitelman’s final sentence raises a more relevant issue: that of researcher roles in education in the digital age. Whether data are created, collected, entered, compiled, stored, processed, mined or interpreted, they are done so by someone. Even if all of the data from a study are collected using a computer program, there are human judgements made as to what data should be collected and how it should be collected. This may be the judgements of a researcher, or a research participant or someone who blends those roles. In some cases, the term ‘data collection’ might seem to be appropriate, such as in a study examining the amount of student logins to a particular message board, when there may be minimal interaction between the researcher and the researched. In other cases, the term ‘data collection’ would seem to be completely inappropriate, for example, in a participatory action research study in which teachers-asresearchers and students engage in an online focus group designed to gain insight into the effectiveness of a particular teaching approach. Focusing on whether digital data are collected, created or other, then, risks neglecting researcher roles, as well as the distinctions across multi-researcher projects. One example might be seen in the creation of digital stories. Perhaps one researcher on a research project has very little involvement with the participants. Another researcher working with different participants, yet on the same project, might, however, suggest ways in which the story might be structured, or

DIGITAL DATA CREATION AND COLLECTION

145

remind participants of topics they have mentioned but subsequently forgotten to include. Both researchers might perceive the data as being collected or created, yet researcher roles clearly differ across these two examples. We suggest, therefore, that whilst researchers should consider the implications of the term ‘data collection’, what is most important is reflexivity around researcher stances and roles in the creation/collection of data. Gemma’s philosophical stance below sheds more light on the complexities discussed above:

Gemma’s philosophical stance

M

y philosophical stance varies depending upon the research context and research goals, and Maggi and I share a belief in the importance of critical pedagogy, educational research and educational practice. As a researcher with a background in historical research, I believe that educational research can only benefit from a critical stance that pays attention to the historical complexities of education and society. Much of my research has been underpinned by a constructionist philosophical stance, with aspects of post-structuralism. I see the researcher as an integral part of the research process and as influential in the findings as research participants, and believe that to undertake good research involves criticality about one’s stance, priorities, privilege and the impact of these on data collection, interpretation and portrayal. My constructionist stance means that I have tended to avoid terms such as data collection in my own research, as I share Thomson’s (2013) belief that the term implies data are ‘out there’ waiting to be gathered. Instead, I have always felt that in interviews and focus groups, in particular, as well as participatory methodologies, the researcher is an active participant in the coconstruction of data. As I write this, I wonder if new terms are needed to describe the researcher’s role in the collection, creation and construction of data. Whilst we have tended to refer to data collection in this book as it has become a ‘catchall’ term for the fieldwork phase or interaction with participants, in whatever form it may take, we have done so with full acknowledgement of the complexities associated with this term.

146 Research Methods for Education in the Digital Age

Table 7.1  Paradigm influences on researcher roles and data collection Paradigms or philosophies

Description

Researcher roles and implications for data collection/creation

Positivism

Positive knowledge To collect and interpret exists and is based upon objective data, which can natural phenomena, their produce positive knowledge. properties and relations, and may be discovered through the scientific method.

Post-positivism

Positive knowledge exists but is imperfectly understandable, and it may be uncovered through falsification (primarily quantitative).

To collect and interpret objective data with the goal of coming as close as possible to reaching factual knowledge.

Critical social theory

In critical social theory, the goal of research is to challenge interpretations and values in order to bring about change.

To work with and privilege the experiences of participants, depending upon the methodology employed and research context. Critical social theory studies often draw upon participatory methodologies.

Pragmatism

Reality exists for individuals, but knowledge is contextually contingent; knowledge may be discovered by examining the usefulness of theory in practice.

In pragmatic studies, the researcher is often also filling another role, such as the educator or professional. The specifics of the researcher role are dependent upon the conditions of practice.

Phenomenology

Reality and knowledge reside in the mind, as the individual perceives and experiences it, and knowledge may be discovered by exploring human experiences.

To prioritize individual experiences of participants, typically using unstructured interviews. Researcher roles are dependent upon the particular methodology employed.

DIGITAL DATA CREATION AND COLLECTION

147

Table 7.1 (Continued ) Paradigms or philosophies

Description

Researcher roles and implications for data collection/creation

Poststructuralism and Postmodernism

Knowledge may be found deeply embedded in structures; a later view is that human agency is problematic since there is no unified truth; rather, there are many truths and systems, and such systems impose linguistic codes and structures. Examining codes and structures can help researchers uncover knowledge.

To pay attention to how structures are formed through language, and focus upon the examination of text, rhetoric or discourse. The attitudes, values and beliefs of participants are important, and both researcher and participant perspectives are considered.

Constructionism

Reality and knowledge are socially constructed; knowledge may be gained by examining the ways in which individuals co-create knowledge.

To recognize that as reality and knowledge are socially constructed, the researcher can be influential in shaping what the data ‘looks’ like, particularly in interviews and focus groups. The extent to which the influence of the researcher is minimized, or acknowledged, depends upon the methodology and method and specific researcher role adopted.

Constructivism

Reality and knowledge reside in the minds of individuals. Knowledge may be uncovered by unpacking individual experiences.

Whilst recognizing that reality and knowledge reside in individual experiences, to also acknowledge that reality is created in relation to the world around us. Researchers are thus ‘passionate participants’ who facilitate ‘multivoice reconstructions’ (Guba and Lincoln 2005: 196).

148 Research Methods for Education in the Digital Age

In Table 7.1 we outline some of the key researcher roles in different paradigms and highlight their implications for data collection/creation. Whilst this table provides a broad overview of the kinds of roles that might be adopted in particular studies, researcher roles are situated and contextual and should ideally be negotiated with participants in qualitative research. Such roles are also likely to change throughout the duration of the research study, as well as when using different methods in the study. They are also influenced by the methodologies and methods employed. For example, as we discussed in Chapter 3, researcher roles in ethnography can fundamentally alter the kind of data that is collected. Similarly, as we illustrated in Chapter 1, there are a number of different researcher roles that can be adopted when using observational methods.

Cooperative research One of the most important developments in educational research in a digital age, and educational research in general, is the blurring of lines between researcher and researched in collaborative and participatory research. This trend has been increasingly popular since the 1970s when Heron (1971) developed cooperative inquiry as a research approach, followed by appreciative inquiry (associated with Gergen 1978 and developed by Cooperrider and Srivastva 1987) and collaborative inquiry (associated with Torbert 1981). Cooperative inquiry is a research methodology in which participants and researchers share similar concerns and are both influential in shaping the design of the project. It uses a research cycle to focus on and explore different forms of knowledge: propositional knowledge (understanding facts and truth); practical knowledge (understanding what works in reality); experiential knowledge (gaining understanding through feedback); and presentational knowledge (the process through which we develop practices). Recent approaches to cooperative inquiry, in the digital arena in particular, have been grounded in human–computer interaction research and participatory design of and research into online environments. An example of cooperative inquiry in digital settings can be observed in Toh et al.’s (2013) three-year study of how six Singaporean

DIGITAL DATA CREATION AND COLLECTION

149

students (aged 9–10) used mobile technologies to engage in seamless learning. The study utilized the following methods: ●●

●●

●●

●●

●●

Experience clips, in which participants and their parents showed researchers video clips of their interactions at home. Participatory interviews, in which participants and their parents engaged in joint interviews with researchers, and specific attempts were made to engage with the participants on their levels. Photo-elicitation method, in which participants were encouraged to share particular photos or videos with the researchers and expand upon their feelings about them. Participant observation in class, in which researchers observed students’ interactions in class. Artefact repository using Quiet Captures, a mobile application developed by Boticki and Hyo-Jeong (2010), which captured quantitative data on student use of particular applications, and took screenshots when students began to use particular applications and/or started to use the device.

The following example expands upon how this approach was used to gain insight into one particular student:

Example Toh et al. (2013), ‘Seamless Learning in the Mobile Age: A Theoretical and Methodological Discussion on Using Cooperative Inquiry to Study Digital Kids On-the-Move’, Learning, Media & Technology, 38(3): 314–15. Using one of our participants, Aaron, as an example, the cooperative inquiry mode enabled us to peer into the learning ecology he had created for himself over the years … [His] family equipped Aaron with rich learning resources such as an encyclopaedia on marine life to facilitate his learning. When triangulated with the artefacts he created on the phone (artefact repository), we found that Aaron had digitized a few pages of his encyclopaedia by capturing the images with his

150 Research Methods for Education in the Digital Age

Example smartphone camera. With this discovery, we deployed participatory interview again to find out Aaron’s motive for digitizing his learning resources. He remarked that he wanted to share knowledge about marine life with like-minded peers when the opportunities arose … The qualitative screenshots retrieved via our Quiet Captures tool indicated that Aaron had visited forums on fishing and searched for YouTube videos on marine life. The smartphone, in this sense, has extended and sustained his learning interests. Aaron’s use of the smartphone to create experience clips on the digestive system also revealed how he had tapped on his family members’ funds of knowledge to complete the filming of the video clip. As his mother is a nurse, she is very familiar with the human anatomy and is able to provide her expertise and add value to Aaron’s curriculum knowledge.

Here, digital methods (artefact repository and experience clips) were used with participatory interviews and observations undertaken in face-to-face settings. Since the purpose of the study was to understand Aaron’s digital practices, these methods were employed specifically because they provided the opportunity of gaining insight into Aaron’s daily practices, and prompted Aaron and his family to discuss them. In a sense, these approaches could be considered to be a form of observation. Rather than for observing Aaron’s interaction with his family, or his use of his mobile phone, the smartphone was used as tool to collect these observations. The smartphone also functioned as a mediating device to facilitate the cooperative inquiry approach, since Aaron chose to share certain experience clips with the researchers. Cornwall and Jewkes (1995: 1669) have noted that ‘participatory research consists less of modes of research which merely involve participation in data collection, than those which address issues in the setting of agendas, ownership of results, power and control’. Collaborative approaches such as cooperative inquiry, which prioritize the shared setting of research agendas, would seem to facilitate this. In the educational sector, student involvement in the setting of agendas can be seen in the funding of student projects

DIGITAL DATA CREATION AND COLLECTION

151

such as the UK-based Joint Information Systems Committee (JISC) Summer of Student Innovation, which ran for three years and concluded in July 2016. Here, students were invited to pitch ideas for new educational technologies and winners received a grant to support the technology’s development. Many national and international research funding bodies require student involvement in projects in order to receive funding. Yet, there has been relatively little sustained discussion of the ways in which educational research in digital spaces has an impact on ownership of research data, except in ethical discussions questioning researchers’ right to use data in the ‘public domain’. Instead, such discussion is restricted largely to educational policy, privacy and intellectual property domains. We suggest that it is essential for researchers to engage fully with these discussions and approach data collection with a clear understanding of how data ownership might be negotiated and respected in particular research settings. The remainder of this chapter outlines some of the key methods used for qualitative data collection in educational research in a digital age. There are a number of factors that need to be taken into account when collecting data in digital spaces, particularly when using synchronous methods of communication. These are as follows: 1 The quality and reliability of the researchers’ and

participants’ internet connections. If the participants’ internet connection is unreliable, then an asynchronous method of data collection might be more appropriate. 2 The tools being used for communication, for example, computer, tablet or mobile phone. Their compatibility with the software might need to be considered, and participants and researchers should download and test any necessary software several days in advance of the data collection, if possible. If a particular application is required and cannot be downloaded in advance, researchers should consider and mitigate for possible risks such as the Google App Store or iTunes downtime on the day of data collection. 3 The ability of the researcher and participant to use the software. This might require determining a participant’s ability to navigate mobile applications or their typing skills in advance of the data collection. Researchers should also consider the specific needs of the participants and

152 Research Methods for Education in the Digital Age

the accessibility of software for any participants with disabilities. For example, if a research participant has a visual impairment, can the software increase the font size or be used with a screen reader? 4 Whether the software or device being used by the researcher suits the preferences of the participants. For example, there is little point in studying students’ use of tablets for research when most of them use their mobile phones. A number of research methods are specific to arts-based research, or exist in the quantitative paradigm, and they have been discussed in Chapters 2 and 5. However, methods that are used mainly in qualitative methodologies are observations, and interviews as discussed in the following sections.

Observations Observations, which were discussed briefly in Chapter 1, are methods that are designed to take account of the context in which data are collected. They are ‘fieldwork descriptions of activities, behaviour, actions, conversations, interpersonal interactions, organizational or community processes, or any other aspect of observable human experience’ (Patton 2002: 4). The purpose of observations is to help the researcher make sense of the research context, and they are used across a wide variety of methodologies. What is important to take note of in the observation, then, is determined by the research context. Undertaking observations in a digital context, therefore, disrupts traditional notions of what it means to ‘observe’, in which attention has been paid to interpersonal aspects such as body language, dress and tone of voice. We present a typology of observations in digital spaces in Table 7.2.

Video and audio observations The use of video as a tool by which to collect observational data is not a new development, having been a staple of ethnographic research since the mid-1990s. Alongside the rapid growth in the use of video technologies, however, have been discussions about

DIGITAL DATA CREATION AND COLLECTION

153

Table 7.2  Observational approaches in digital spaces Type of observation Description

Examples

Visual and/or audio observations

The researcher is able to see and hear what participants are doing as it happens in the research space, for example through streaming of synchronous video/audio or asynchronous video recordings, which are later uploaded.

Liang (2015) undertook live video observations to support teacher development in China, using videos to reduce student reactions to researchers’ presence.

Avatar observations

The researcher is able to see participants’ actions as mediated through online avatars, for example in virtual worlds or online games.

Popular in ethnographic/ anthropological studies, for example Fitzimons (2013), a teacher who explored experiences of new users in Second Life using participant observation.

Textual and visual observations

The researcher is able to observe participants’ textual communications, for example through ongoing synchronous Twitter discussions, or asynchronous discussion forums. These communications often include visual artefacts as well, such as images, emojis or GIFs.

Jones and Gallen (2015) undertook textual, visual and video peer observations of synchronous online tutorials, focusing on staff experiences of participating in the peer observations.

the extent to which the use of video might impact upon the context and data. Bogdan and Biklen (1998) argued that the presence of a video camera in a classroom caused students to behave differently, paralleling long-standing debates about the influence of human observers in research settings. Yet, as Delamont (2012) has noted, methods for collecting data using video are increasingly unobtrusive. This is particularly so with the advent of wearable technologies such as Google Glass and the presence of webcams on all new computers.

154 Research Methods for Education in the Digital Age

Criticisms of video-based observations as partial, because of limited mobility and no peripheral vision, have been addressed with the use of panoramic videos as well as multi-perspective wearable cameras and the use of multiple video cameras. One particularly interesting example can be seen in the Tippie College of Business, United States, as shown below.

Example Snee, T. (2015), ‘Academic Panoramic: Research, at a glance’, IowaNow. Available at http://now.uiowa.edu/2015/11/academicpanoramic-research-glance (accessed 17 January 2016). Anyone who has walked through the Pappajohn Business Building in recent months has been a part of the Tippie College of Business’s Contemporary Topics in Marketing class. The building has been outfitted with a device that anonymously reads the facial expressions of people as they pass through, using software that can tell a person’s mood by scanning and analysing the shape and position of his or her mouth, eyes, eyebrows and other features without displaying or recording video. The class teaches students how to ethically gather, interpret and analyse data and find a useful function for it, and the facial expression software provides a wealth of data for them to mine.

Goldman (2007) has developed ten criteria for the evaluation of video-based research projects, from which video products are ultimately produced: 1 Wholeness/particularity refers to the need to ensure that the

video record is sufficiently detailed and fully presented to capture the essence of a particular event. 2 Being there/being with refers to the need to ensure that the video record is detailed enough to enable the viewer to feel present in the recorded setting. 3 Perspectivity refers to the use of video to make clear the videographer’s point of viewing, or multiple points of viewing.

DIGITAL DATA CREATION AND COLLECTION

155

4 Genre consistency/breach refers to the form of the video

research product and the style of presentation. 5 Authenticity refers to the use of video data to support new interpretations, shaped by both the content of the video and the innovation of the video approach. 6 Chronological verisimilitude refers to the use of the video to represent the order of events, not necessarily chronological but ‘truth-like’; the viewer is able to make sense of the video. 7 Conviviality refers to the ease of use of the video, and the extent of the video content’s contribution to a public good. 8 Resonance refers to the extent to which users are able to make connections from the video content to their own lives. 9 Immersion refers to the extent to which the video demonstrates engagement and involvement with the topic – viewers of the video should be ‘lost’ or immersed when watching. 10 Commensurability refers to the extent to which the video can help to share concerns and practices, improving understanding across individuals and cultures, and inspiring others. Whilst these criteria seem to recognize the ways in which users will perceive and interpret data differently, authors like Jewitt (2012) contest that they also make certain assumptions about the nature of the study and the purpose of the observational method: The pre-occupation with whether or not video data has accurately captured reality or distorted it is rejected by some social scientists who use video data as naïve: notably ethnographers and visual anthropologists. They shift attention away from this debate to the question of how video can be employed to understand the perspectives, values, practices and experiences that underpin social interactions. From this perspective there is value in taking account of the role of the researcher and the social (and technical) significance of video technology itself: in particular, the ability of video to ‘preserve the interaction for re-presentation and participants’ awareness of that ability’. From this perspective,

156 Research Methods for Education in the Digital Age

whether (and how) the camera is ‘made at home’ or brought into the interaction are not understood in terms of ‘good data’ or ‘bad data’ but rather they become points of interaction. (Jewitt 2012: 10) Jewitt’s argument above is a useful reminder to consider the epistemologies underpinning particular methods, and to determine what each method should achieve in a research study. Her argument is also particularly interesting when considering the second method of observation in digital settings, as discussed in the following section.

Avatar observations As noted in Chapter 1, Mawer (2014) has argued that it is important to exercise caution when undertaking observations in virtual worlds. Avatar observations, which are popular in ethnographic and anthropological studies, but relatively rare in educational research, pose significant challenges as well as opportunities to researchers. This method requires researchers to consider whether the avatar itself, or the user controlling the avatar, is the object of interest; put differently, is the researcher interested in the avatar’s actions, or in how the avatar’s actions might reveal the actions and interests of the user controlling the avatar? In most cases, the object of interest is the user controlling the avatar. However, avatar actions can be the result of intentional acts, unintentional failures in control and technical failures, and it is not always possible to discern the difference between them. Thus, Jewitt’s argument as applied to virtual worlds suggests that a key advantage of virtual world observations might be the ability to film avatar actions and show them to participants, thus allowing participants to reflect upon their avatars’ actions. This approach might also take account of a neglected issue in virtual worlds research, namely, the impact of spatial design upon interaction and observation. For example, Papachristos et al. (2014) used avatar observations to examine student responses to spatial design in virtual worlds. Two learning environments were designed in Second Life. One of these replicated an auditorium with multi-level seating for student avatars to sit in, a whiteboard and a lecturer’s podium,

DIGITAL DATA CREATION AND COLLECTION

157

whilst the other provided an open air setting in which the avatars stood in front of a whiteboard. This latter setting included nothing but the whiteboard and avatars, standing upon a depiction of browning grass. The study sought to compare students’ experiences, sense of presence and learning outcomes, in the auditorium setting (28 students) and the open air setting (23 students) in Second Life. Findings from this study indicated that student experiences, sense of presence and learning outcomes were not affected by the two different settings. However, it is notable that in the auditorium setting, students were shown to be sitting in seats and utilizing a variety of avatar gestures, such as leaning back in the chair with arms behind one’s head, or in the case of one student, leaning forward to ‘look’ more closely at the Second Life whiteboard, making explicit their interest in the topic at hand. In the open air setting, there were no chairs available for avatars to sit in, and no objects available for the avatars to interact with, other than the whiteboard. Instead, the avatars stood gathered together facing the whiteboard. Observation of student actions in virtual worlds, therefore, needs to pay attention to the design of the space as much as the actions of the avatar.

Textual and visual observations The collection of data from discussion forums, or email mailing lists, draws upon both observational methods and documentarybased methods of data collection, disrupting traditional notions of what it means to ‘observe’ in digital spaces. Much of the work in this area has come from ethnographic researchers, as discussed in detail in Chapter 3. Rather less attention has been paid to the notion of textual spaces as documents in the educational research domain. As a consequence, there has been a tendency to undertake observations of online communities without fully comprehending the methodological implications of this choice. Moreover, visual methods are often undertaken in a way that is dislocated from any kind of methodological approach, for example, Kress and van Leeuwen (1996). Furthermore, Nørskov and Rask (2011) undertook observations of group emailing lists whilst working with a community that utilized a number of different communication methods. The observations were considered to provide inadequate

158 Research Methods for Education in the Digital Age

data because the research participants often referred to discussions undertaken in other contexts. Since observational methods are usually employed specifically to provide insight into the research context, the adoption of an observational approach for only one means of communication out of many would seem to be methodologically problematic. Instead, perhaps, the emails might have been treated as documents; whilst documents are ‘constructed in particular contexts, by particular people, with particular purposes, and with consequences – intended and unintended’ (Mason 2002: 110), they are only expected to provide clues about the context (Savin-Baden and Major 2013: 404). Considering the emails as documents collected rather than observances collected, then, would fundamentally alter the researchers’ expectations and analytical approaches associated with this data source. A similar issue arises in relation to the use of blogs as sources of data for educational research. The use of blogs as tools for data collection can be likened to the use of the diary method, a method long used in social science and educational research (AshtonWarner 1963), and which bears similarities to documentary analysis (Arosio 2010). Yet, digital diaries, in particular, would seem to embody digital literacies that blend text and image and present new opportunities for data collection, analysis and interpretation. Digital diaries are rarely text-only, but draw upon a mixture of text, userfilmed videos, collated videos (made from a compilation of already existing videos), edited and unedited photographs, GIFs, memes, audio files and more. For example, Harricharan and Bhopal (2014) used online private blogs, accessible only to the researchers and the participant, to examine international students’ experiences in UK universities. These blogs were multi-modal, and students provided videos, images and personal reflections. Whilst the researchers chose to focus exclusively upon the textual data provided by the participants, the multitude of data sources provided required participants to adapt their data collection approach accordingly: The capabilities of the blog for facilitating multi-modal expression were capitalized on in the research. Any mode of expression that the technology facilitated was admitted on the blog. However, as only written texts were to be analysed for this research, nontext-based posts were used to stimulate written exploration and discussion about the meaning of visual or auditory messages for

DIGITAL DATA CREATION AND COLLECTION

159

the participant/s. Follow-up questions were asked to probe the impact of the piece for the participant so that the visual/auditory piece itself would not have to be analysed separately. (Harricharan and Bhopal 2014: 335) Others, however, have chosen to analyse visual diaries alone. Roberts (2011) has summarized the key advantages associated with using video diaries as a method of data collection: Renov (1996), citing Jean Rouch as quoted in Eaton (1979), notes the dual role of the video camera, as both a mirror of the subject and window to a wider world. The relationship between the subject and the camera is often characterized as intimate (Holliday 2000). In a research context, the potential audience, and therefore the camera itself, can be imagined by the subject as either their future self, the researcher, an unknown observer or an impersonal object (Noyes 2004). In the first of these roles the impulse towards confidential discourses is great – as Holliday (2000) notes, one cannot keep secrets from oneself. (Roberts 2011: 680) In this sense, then, video diaries would seem to blend observational and interview methods. Cooley et al. (2014), for example, used a diary room, in which undergraduate students shared their diaries in a manner akin to the television show Big Brother. Students were seated in front of a camera in a comfortable room, and asked questions via microphone by a researcher who was not present in the room. Whilst the authors referred to this approach as a diary room methodology, it would seem that they were, in fact, utilizing an interview method situated within a narrative inquiry methodology; the presence of a researcher to direct questions fundamentally altered the method of data collection and, thus, the type of data that was collected.

Interviews Interviews are the most common method used in qualitative research, in both digital and non-digital settings, and allow researchers to gain in-depth information about research participants (Wengraf

160 Research Methods for Education in the Digital Age

2001). They are intended to be a conversation between researcher and research participant, in which the researcher asks questions and the participant responds. However, the particular dynamics of the interview vary in accordance with researcher stances, the power relationship between the researcher and participant, the research topic and the interview approach adopted (structured, semistructured or unstructured). ●●

●●

●●

Structured interviews: In structured interviews, questions are read in a specific order and are not altered from the original interview schedule. Questions can be open-ended or closed. The goal of structured interviews is to reduce the influence of the researcher on the interview answers and enable comparison across interview data. Semi-structured interviews: In semi-structured interviews, questions or themes are provided, usually in a particular order, and are always open-ended. The goal of semistructured interviews is to cover a number of themes that are of relevance to the study and enable comparison across interview data, but they allow the researcher to add in additional questions depending upon the interview responses. Unstructured interviews: In unstructured interviews, a broad list of themes may or may not be provided. The goal of unstructured interviews is to allow the interviewee to guide the discussion, and help the researcher to gain a deep understanding of the interviewee and his or her experiences.

A number of methodology-specific interview approaches also exist within these three broad methods – for example, ethnographic unstructured interviews can include oral histories, creative interviews and postmodern interviews (Fontana and Frey 1994). Other approaches have included photo-elicitation interviews (e.g. Epstein et al. 2006) and concept-mapping interviews (e.g. Kinchin, Streatfield and Hay 2010). Both of these studies used approaches in which the purpose of the interview was to create or respond to particular artefacts. All of these approaches to interviews can be undertaken in digital settings. What is different about research in digital spaces, however, is that there are a number of different ways in which these interviews might be undertaken. Table 7.3 outlines

161

DIGITAL DATA CREATION AND COLLECTION

Table 7.3  Interview approaches in digital spaces Interview approach

Advantages

Disadvantages

Email

Little specialist knowledge Slow response rate or digital skills required No visual/auditory allows longer responses and connection reflection Asynchronous communication removes time zone/scheduling as well as internet connectivity difficulties

Instant messaging and chat rooms

Synchronous communication enables quick response rate Emojis and other similar features can be used to provide additional insight No transcription required

Shorter responses No visual/auditory connection Can be harder to elicit rich data because of short responses

Comment-based interviewing (for example, comments on a forum)

Little specialist knowledge or digital skills required Allows for longer responses and reflection Asynchronous communication removes time zone/scheduling as well as internet connectivity difficulties Can be undertaken in ‘public’ or ‘private’

No visual or auditory connection May result in shorter responses

Telephone

Synchronous communication enables quick response rate Auditory connection enables observation of tone of voice Familiar technology requiring no specialist skills May be the only means of access to some groups without or with limited internet access

No visual connection Requires additional hardware in order to record the interview

(Continued)

162 Research Methods for Education in the Digital Age

Table 7.3 (Continued ) Interview approach

Advantages

Disadvantages

Video

Visual/auditory connection enables observation of body language and tone of voice Synchronous communication enables quick response rate Does not require typing/ literacy skills, important for younger or illiterate participants

Requires applications to be downloaded, and additional recording software Requires a reasonably good internet connection

Virtual worlds/ online multi-player games

Synchronous communication enables quick response rate Enables choice between audio/instant message communications Visual cues can be observed through avatar appearance, placement and gestures Can enable contextual cues when the virtual world/ online game is the topic of discussion Can be a familiar/comforting environment for some research participants

Cannot directly observe facial expressions or body language Requires applications to be downloaded, and recording software Requires a reasonably good internet connection Requires both researcher and participant to be confident in using the application

some of the most common media and their possible advantages and disadvantages. Whilst we have distinguished the ‘disadvantages’ and ‘advantages’ of these approaches, it is important to note that they are dependent upon the research context. For example, when working with students with hearing difficulties, or students who struggle with eye contact, the use of text-based interviews such as instant messaging or email may be a significant advantage. James (2015) argues for the importance of acknowledging the needs of participants and the advantages of particular mediums.

DIGITAL DATA CREATION AND COLLECTION

163

Example James, N. (2015), ‘You’ve Got Mail! Using Email Interviews to Gather Academics’ Narratives of Their Working Lives’, International Journal of Research and Method in Education. This paper explores how computer-mediated communication offers space for academics to think and make sense of their experiences in the qualitative research encounter. It draws on a research study that used email interviewing to generate online narratives to understand academic lives and identities through research encounters in virtual space. The paper discusses how email can provide a site where the self can be viewed reflexively and re-negotiated through a process of interaction. The paper demonstrates that the asynchronous nature of email helps to facilitate this, by allowing participants to contribute to research in their space and according to their own preference in time. However, it also argues for the construction of more collaborative approaches to research that acknowledge the right of participants to use the temporal nature of space and time that email offers to construct, reflect upon and learn from their stories of experience in their own manner, and not merely to the researcher’s agenda. It concludes by recognizing the importance of email as a research tool for capturing the complexity of social interaction online.

Conclusion Despite – or perhaps because of – the rapid growth of learning using digital methods, there is still much that is not understood about research in these spaces. Educational research in a digital age blends methods, methodologies and analytical approaches in ways that seem both uncomfortable and exciting, challenging our understanding of what it means to observe in digital spaces and prompting us to consider whether we can truly observe in digital spaces. It is important that researchers consider the methodological implications of the methods employed in their studies, and the underpinning epistemological assumptions. Further, studies are

164 Research Methods for Education in the Digital Age

needed that examine the distinctions between documentary research methods and observational research methods in educational research in a digital age, taking account of new theories and the rapid development of online ethnographic observational approaches. These differing methods have significant implications for data analysis and data management, as will be discussed next in Chapter 8.

Further reading Fitzimons, S. (2013), ‘The Road Less Travelled: The Journey of Immersion into the Virtual Field’, Ethnography and Education, 82 (2): 162–76. Fitzsimons presents a rich ethnography of her experiences as she learned to use the virtual world Second Life, first as a user and then as a teacher. She identifies three distinct stages of her journey in learning to use it and be present in the virtual world: separation, transition and transformation. This paper provides a particularly interesting account of the journey of a teacher learning how to be in a virtual world, as well as an example of virtual ethnographic research methodologies. However, Fitzsimmons does not explain which particular ethnography or data analysis approach she is using, problematizing any critique of her methodological choices. Goldman, R. (2007), ‘Video Representations and the Perspectivity Framework’, in R. Goldman, R. Pea, B. Barron and S. J. Derry (eds), Video Research in the Learning Sciences, 3–38. New York: Routledge. Goldman, a pioneer in the use of video-based research methods, provides an accessible and substantial introduction to video research in educational research. This chapter, the first in her co-edited collection on the same topic, begins by outlining the history of video research, allowing readers to understand how this method has been developed and adapted. It then explores the theoretical and practical considerations involved in using such research methods, before presenting the perspectivity framework. Whilst responses to the framework will differ depending upon researchers’ philosophical stances, it is adaptable enough to be practically applicable in most video research contexts. Roberts, J. (2011), ‘Video Diaries: A Tool to Investigate SustainabilityRelated Learning in Threshold Spaces’, Environmental Education Research, 17 (5): 675–88.

DIGITAL DATA CREATION AND COLLECTION

165

Roberts presents a case study of an undergraduate field class from a UK university to rural Uganda, in which video diaries were used to gather reflections from UK students on the field trip. These diaries were found to be more successful than previous written diaries in eliciting in-depth reflections. In particular, Roberts suggested, the diaries seemed to provide unstructured responses, which revealed that students were on the threshold of a higher level of learning.

166

chapter eight

Data management Introduction This chapter examines the ways in which digital data are managed, analysed and interpreted, addressing the advantages and disadvantages associated with the use of data software. We propose ways of integrating increasingly disparate forms of digital data, such as the integration of student YouTube videos with online forum analyses, with learning analytics. The chapter then offers suggestions for analytical approaches, concluding by identifying innovative and creative theories for interpreting data in the digital age. Whilst this chapter has been treated as distinct from Chapter 7 for ease of understanding and reference, it will also address the interrelated natures of data creation and collection, and data analysis and interpretation.

Defining data management Data management is generally taken to refer to the security, storage, access and archiving of data. Whilst this has been an issue of importance for as long as research has been undertaken, the rapid growth in digital data availability, and the rise of the open data movement, has pushed data management to the forefront of research scholarship. Organizations such as the international Data Documentation Initiative and the UK-based Digital

168 Research Methods for Education in the Digital Age

Curation Centre offer guidelines for data management, whilst librarians, archivists, managers, researchers and administrators are engaged in discussions about how to manage the challenges associated with digital data. These include (but are not restricted to) the following: ●●

●●

●●

Data access: Who has access to data, and how they should be accessed. This includes issues not only about open access, but also about technological challenges such as changing digital formats. For example, digital data saved in 2015 software may not be accessible in 2030 software, let alone 2065 software. Data access also includes issues relating to corruption of data files and technological failures. Data protection: How data are protected, and how ethical promises are maintained, in a digital age. Data placed in open access repositories can or already have outlived the researchers and participants involved in its creation. Data protection also includes issues around data hacking and accidental release of confidential datasets. Data compilation: Data, as the plural for the Latin term datum, does not refer to a single participant’s survey, for example, but, rather, to the data gathered throughout a whole study. The difficulty currently is that many researchers using big datasets and learning analytics, for example, do assume data have been coded and managed in the same ways, when often they have not. Qualitative and mixed-method studies often draw upon a large variety of methods, all of which overlap in various ways in order to address the research questions. This means that data need to be managed and stored in such a way that it represents the interrelationship of participant mind maps, with video diaries, with digital images and with virtual world interviews. Segregating data according to its format, and thus not representing the relationships between different methods in a dataset, decontextualizes the data from the participant and study framing. Whilst this may be advantageous in some respects – and we will address the question of recontextualizing data later – it is an issue that must be considered.

DATA MANAGEMENT

169

Thus data management, although often discussed in purely administrative terms, has significant implications for the analysis and interpretation of data. Such implications should include checking on data security before using a web-based analysis tool, and also ensuring that data stored in open access repositories are collated in ways that do justice to the original data collection approach. The impact of the open access agenda on research data is discussed in more detail in Chapter 10. In this chapter, we refer to data management as a form of control. The controlling of data in a digital age includes its storage and protection, and also its movements and manipulation through analysis and interpretation, as this chapter will illustrate. First, however, it is important to clarify what we mean by digital data in the context of this chapter. There are four key ways in which we consider research data to be ‘digital’, as shown in Table 8.1. We also suggest some questions that need to be considered when analysing each of these types of data. In this respect, then, almost all data are digital; even if data are collected using non-digital tools in non-digital settings, they are almost always subsequently refashioned digitally. Each of these four types of data raise different questions for data management and, thus, for data analysis and interpretation. In the following sections, we discuss these issues in more detail.

Analysing digital data Data analysis is the process of breaking a dataset down into its constituent parts and comparing these parts in different ways. It involves the application of particular analytical techniques or approaches, which determine exactly how the dataset is broken down and the connections and distinctions between its component parts. Whilst this approach sounds strategic and organized, it depends largely upon the methodology, methods and analytical approaches employed, which we now discuss.

Mixing digital methods Throughout this text, we have argued for the importance of acknowledging the epistemological and ontological underpinnings

170 Research Methods for Education in the Digital Age

Table 8.1  Types of digital data Type of data

Description

Questions for analysis

Refashioned digital data

Data that are collected without the use of digital tools but are subsequently digitized. For example, written concept maps that are photographed, or inputted into mindmapping software, and subsequently recorded as digital files.

Making data digital is an analytical process and often involves adding to the data and altering the data. For example, if data are photographed, what angles are chosen and how are they arranged? This adds new aspects, which might influence analysis. Or if a participant creates a concept map in an interview, how does inputting it into mind-mapping software alter the data?

Re-created digital data

Data that are collected in non-digital settings using digital tools – for example, face-to-face focus groups that are video-recorded.

The most common type of data in qualitative educational research. What is most important to consider here is how the presence of digital technologies might influence the data, discussed later in this chapter.

Digitally Data in which the collected data content, but not necessarily the format, is digital – for example, face-to-face interviews undertaken on a student’s experience of learning in a virtual world. This kind of data, however, is usually also refashioned digitally.

Using digitally collected data raises some important questions: Why have non-digital methods been chosen to evaluate digital topics? How might the use of digitally collected data and digitally created data in the same study impact on data collection and analysis?

Digitally created data

Whilst both the content and format of the data are digital here, the content is not necessarily the same as the format. How do the content and format of the data overlap and/or conflict? Storytelling data, for example, might use narrative analysis, whilst social media data might use social network analysis.

Data in which both the content and the format are digital – for example, digital storytelling of a students’ experience of learning using social media.

DATA MANAGEMENT

171

of the methodologies and methods used in digital research. Such a stance, however, raises questions about the use of mixed methods in research. Mixed methods traditionally refer to a mix of qualitative and quantitative methods and are most often used in pragmatic and case study research approaches, yet they are increasingly common in other methodologies. Bazeley (2012) has written extensively on the integration of qualitative and quantitative methods in mixed-methods research. She has suggested that there are five possible strategies for integrating data. These five strategies, and Bazeley’s recommendations for each strategy, are summarized below. 1 Strategies that integrate results from analyses of separate

data components. This approach refers to the integration of data from different sources, such as surveys and interviews. In this case, the results should be integrated as early in the interpretation phase as possible. However, this approach also raises challenges when the separate data seem to reveal different things. 2 Strategies where one form of data informs the design or analysis of another. In this approach, the use of the different data sources is usually sequential. For example, findings from interviews or focus groups may be used to inform the development of a quantitative survey. In this strategy, it is important to ensure that the first form of data is not discarded in the final analysis. 3 Strategies that integrate multiple data components or

sources during the process of analysis. Here, the analytical approach employed draws upon two or more sources of data. Bazeley noted that this approach is used when the intent is to compare the data; however, we suggest that it might be equally beneficial when seeking to complement and enhance data from other sources. 4 Strategies where the data invite integration of more than one strategy for analysis. In this approach, data are analysed in one form and then manipulated into a different form. Most often, this involves quantifying qualitative data, by coding and counting. However, it can also involve ‘qualitizing’ quantitative data, by building detailed histories based upon extensive quantitative data.

172 Research Methods for Education in the Digital Age

5 Strategies where methods are ‘inherently mixed’. When

studies use ‘inherently mixed’ methods (Teddlie and Tashakkori 2009: 273), the same data source is used to provide both qualitative and quantitative information. This information is then used to answer interlinked questions. Bazeley suggested that studies using social network analysis often use inherently linked methods. Whilst Bazeley’s (2012) recommendations are helpful for thinking about how researchers might integrate different methods, they also raise questions about qualitative and quantitative distinctions: Is it truly possible to ‘quantify’ and ‘qualitize’ data? For many researchers, the proliferation of mixed-methods research has resulted in calls for the breaking down of barriers between the three perceived paradigms of quantitative methods (numbers), qualitative methods (other) and a mix of the two. For example, Symonds and Gorard (2010) have argued that numbers should not be considered a paradigm of their own, noting that qualitative data can easily be quantified throughout the analysis procedure. Perhaps the real difference lies in the formality of systems that are generally used to sort and categorise units of data such as numbers, words and visual observations. Numerical research tends to use a highly developed formal system such as the application of mathematical logic, whilst thematic analysis of word-based data generally takes a looser, more inductive approach. However, this is not always the case as interview data can be subject to formal systems such as discourse analysis which makes use of particular semantic structure. … One consequence of the current paradigmatic classification is that mixed-methods work must involve quantitative elements. (2010: 128) This argument would seem to have particular implications for digital educational research in light of the popularity of learning analytics. Whilst there have been critiques of learning analytics’ ability to provide in-depth information about students’ experiences, there appears to be considerable uncertainty about which methods are best used alongside learning analytics and how these methods should be analysed together. Additionally, as digital contexts open up new opportunities for data collection, the

DATA MANAGEMENT

173

definition of the ‘qualitative’ data paradigm seems increasingly ineffectual. There is a tendency for newer researchers, in particular, to believe that qualitative data generated from different methods such as virtual world observations, Facebook statuses and survey responses can be analysed together. However, this may not be the case, and this is often a challenge that is encountered only once the data has been collected. We therefore offer the following questions to consider when undertaking analysis of digital data and using digital tools: ●●

●●

●●

●●

What are the questions guiding the research? The research design should begin from the question, and identify an appropriate methodology, and methods, to fit this research question. Figure 1 in Chapter 1 provides guidelines on this. What are the theoretical underpinnings of the methodologies and methods employed, and do they complement each other? Liquid methodologies, as we have discussed in Chapter 1, allow for flexibility, adaptation and co-creation of research design. We do not seek to suggest that methods cannot be adapted to work within different research paradigms, but, rather, that researchers must be aware of their histories before adopting them. Before commencing data collection, researchers should consider the type of data that their methods will produce, and the analytical approach that they intend to use. If they intend to use a mixed-methods approach, at what stage do they plan on integrating their data? We suggest that researchers should seek to integrate their data as early as possible in the data collection and analysis phases. As we have noted above, there are four types of digital data: those made digital, those re-created digitally, those digitally collected and those digitally created. If multiple types of data are being used in this study, how might this influence the processes of analysis and interpretation, and the outcomes of analysis and interpretation? What are the challenges associated with using these types of data within individual studies? These aspects should be considered at the outset of the research project.

174 Research Methods for Education in the Digital Age

Analysing using digital tools Data analysis software is increasing in popularity across all disciplines, yet it is still unclear exactly how it is being used in educational research and how different methods are combined in the analysis. This is best exemplified by a study from Woods et al. (2015), who reviewed journal papers using ATLAS.ti and NVivo for qualitative research between 1994 and 2013. Woods et al. found that of the 763 papers analysed, less than 5 per cent were from the education discipline. Most of these papers were from the United Kingdom, the United States, Canada and Australia. These findings would seem to suggest that despite the increasing popularity of data analysis software, its use and implications are significantly underdiscussed in educational research publications. Data analysis software can be simplified into two categories: automation of analysis and facilitation of analysis. When researchers consider data analysis software, they tend to think of the first category in which the software automates the analysis. Most quantitative software, such as SPSS, falls into this category. In the second category, the software facilitates the analysis. Most qualitative software falls into this category. The distinction between the two can be understood in terms of the researcher’s role, the kind of data analyzed and the extent to which analysis and interpretation overlap. Quantitative data tends to be automated, whilst qualitative data is predominantly facilitated using data analysis software and overlaps analysis. Yet, whilst such a categorization is helpful in terms of determining whether and how to use data analysis software, it should be recognized as a simplistic categorization. Software that automates the analysis only does so because the researcher decides which data to input, which data to exclude and which tests to run. The researcher must then interpret the outputs of the automated analysis. Importantly, the researcher must also decide upon the usefulness of the software’s analytical outputs and determine if further tests should be run, perhaps using an alternate software. Thus, whilst it might be easy to assume that the software is key in the analysis – which it is – it should also be recognized that the software is directed by the researcher, thus making them integral to the analytical procedure. In contrast, it is also often easy to assume that analysis facilitated through data analysis software is directed solely by the researcher,

DATA MANAGEMENT

175

with the tool viewed as ‘invisible’ in the analytical procedure. Such software, however, may have an impact upon the analysis in ways that are often not discussed, and therefore questions perhaps need to be asked about the ways in which particular features of the software influence both the data and the researcher. This might include the length of notes allowed in the software, the number of notes or tags that can be attached to certain sections of data, the placement of connecting lines on mind maps and even the quality of images that can be integrated into the analysis software. One example might include the importing of an image, which reduces size and quality and, in doing so, hides or distorts something important to the analysis. Conversely, the use of audio software to enhance background noise recorded in a classroom environment might reveal something unheard by an observer not using digital tools. Whilst software such as AtlasTI and NVivo are used for a variety of analytical approaches from all methodologies, others are used to automate or facilitate particular analytical approaches. Table 8.2 and the following sections provide some examples of these, and their associated analytical approaches.

Social network analysis As social networks such as Facebook and Twitter have become mainstream, interest in social network analysis has grown. It is unclear, however, exactly what social network analysis is. It has been described as a theory, a strategy, an approach, a set of techniques, a methodology and a paradigm. Here, we refer to it as a set of techniques for analysis, predominantly of quantitative data. Social network analysis (SNA) is rooted in the work of sociologists like Durkheim, who wrote in the late nineteenth century and contended that sociology should study the holistic society and networks between social actors. Durkheim’s work was brought to Britain by the anthropologist Radcliffe-Brown, whose work on social structure underpins current understandings of social network analysis. Broadly, a social network can be defined by a set of nodes (or points) that may have relationships with one another (Wasserman and Faust 1994). These nodes might be individuals, groups, organizations, governments, countries or other distinct social units of analysis. The number of nodes, and numbers and

176 Research Methods for Education in the Digital Age

Table 8.2  Digital tools for analysis Analysis approach

description

Examples of digital tools

Analysis approaches specific to the study methodology Social network analysis (SNA)

SNA is often described as both a methodology and an analysis approach. It is used to examine relationships within social networks, predominantly using quantitative data.

The use of software to automate social network analysis approaches, such as Gephi.

Analytic induction (AI)

Usually associated with ethnography, but has been adapted for other methodologies. Identifies similarities between phenomena in order to develop categories and subcategories of understanding.

The use of multimethod qualitative software for coding and categorization, such as NVivo.

Critical discourse analysis (CDA)

Falls under the broader category of discourse analysis approaches, informed by critical social theory. Focuses on how inequality and power are produced, reproduced and resisted in discourse.

The use of multimethod qualitative software or specialized linguistic software, such as Wordsmith Tools.

Interpretative Commonly used in phenomphenomenological enological studies, IPA uses analysis (IPA) rich data such as visual methods to examine how individuals make sense of a phenomenon in a specific context.

Most often used with multi-method qualitative software that supports coding.

Narrative analysis (NA)

The use of multimethod qualitative data software for note-taking on digital stories (Alston and Ellis-Hervey 2015)

Most often used in narrative research studies, NA is a collection of analysis approaches that treat data as ‘storied’ and interpretive social products. NA emphasizes and embraces subjectivity in individual participant stories.

177

DATA MANAGEMENT

Table 8.2 (Continued) Analysis approach

description

Examples of digital tools

Analysis approaches used across multiple methodologies Content analysis

A set of techniques that involve the categorization and classification of data (textual, visual, spoken, or other) and provision of a quantitative representation of that content.

The use of specialized software to automate analysis, such as natural language processing tools (Mu et al. 2012)

Keyword analysis

Involves the identification of words with meaning, which often means counting words with higher frequency. Keyword analysis is sometimes referred to as content analysis.

The use of Wordle to automate identification of frequency of words in interviews (McNaught and Lam 2010)

Thematic analysis

Tends to blend analysis and interpretation, and relies on researcher and participant intuition. Involves the identification and reviewing of patterns in the data, and emphasizes holistic understanding of the data.

The use of qualitative data software for coding, note-taking and concept mapping, such as MindMup.

strength of relationships, can vary. Social network analysis uses mapping and graphing as methods to represent these relationships, although its usefulness in studying the content and nature of such relationships is limited. It is broadly aligned with the postpositivist tradition, although several studies using SNA seem to be underpinned by positivism. For example, Buch-Hansen (2014) has argued that the positivistic tendencies of social networking analysis manifest in three key ways. These are as follows: 1 The use of theories and models, in which a reductionist

approach is employed and social network analysis methods are combined with approaches such as rational choice theory or formal mathematical models.

178 Research Methods for Education in the Digital Age

2 The aspiration of (some) researchers to use social network

analysis to predict future interactions and relationships within networks. 3 The tendency towards generalization, in which inferential statistics are used to explore the extent to which social network analysis findings are representative of larger populations. Whilst pointing to these tendencies of social networking analysis towards positivism, however, he has also argued that ‘it is perfectly possible to apply social network analysis techniques without using them deductively, without combining them with rational choice theory, without making predictions and generalizations, and without relying on crudely reductionist formal models’ (2014: 312, original emphasis). These arguments would seem to be particularly important in light of recent calls for qualitative data in social network and learning analytics data.

Analytic induction Analytic induction was initially developed by Znaniecki (1934), but it is most commonly associated with Lindesmith (1952), who developed the theory of opiate addiction using analytic induction. It follows a clear process to identify broad categories of understanding, from which it then develops sub-categories, and seeks to develop causal explanations and universal generalizations. Whilst analytic induction is most often associated with ethnographic research, it has been adapted for several other methodological approaches, such as grounded theory. Savin-Baden and Major (2013) have outlined the following process for analytic induction: 1 Examine an event. 2 Develop a hypothetical statement of what happened during

the event. 3 Examine a different but similar event to determine whether the new event fits the hypothesis. 4 If the event does not fit the hypothesis, revise the hypothesis.

DATA MANAGEMENT

179

5 Repeat the process and revise the hypothesis until it explains

all examples encountered. 6 Develop a hypothesis that accounts for all cases. (Savin-Baden and Major 2013: 441) Thus analytic induction uses inductive reasoning (as opposed to deductive reasoning), meaning that it seeks to provide strong evidence for the conclusion, but does not claim to provide absolute proof. Instead, those using analytic induction generally acknowledge that there are always more cases, which might lead to another revision of the hypothesis. This analysis technique is most often used with digital tools that facilitate extensive coding of qualitative data, such as NVivo and Atlas.ti.

Critical discourse analysis Discourse analysis is strongly associated with linguistic analysis, and includes a wide range of analytical techniques, such as rhetorical analysis and conversation analysis. Although these techniques have different focal points, they share a common interest in the social context of discourse. This is particularly evident in critical discourse analysis, which prioritizes the political and examines the ways in which societal power relations are produced, reproduced and resisted through language (text and talk). Critical discourse analysis is most often associated with the work of Fairclough (1989) from the Loughborough school of linguists. For Fairclough, critical discourse analysis does not focus upon discourse alone, but, rather, on the relationship between discourse and its surrounding social and political context. What then is CDA analysis of? It is not analysis of discourse ‘in itself’ as one might take it to be, but analysis of dialectical relations between discourse and other objects, elements or moments, as well as analysis of the ‘internal relations’ of discourse. And since analysis of such relations cuts across conventional boundaries between disciplines (Linguistics, Politics, Sociology and so forth), CDA is an interdisciplinary form of analysis, or as I shall prefer to call it a trans-disciplinary form. What this term entails is that the ‘dialogues’ between disciplines, theories and

180 Research Methods for Education in the Digital Age

frameworks which take place in doing analysis and research are a source of theoretical and methodological developments within the particular disciplines, theories and frameworks in dialogue – including CDA itself. Fairclough (2010: 4) One of the most common ways in which critical discourse analysis has been used in relation to technology has been in the study of language associated with technology itself. For example, Silcock, Payne and Hocking (2015) have examined power and governmentality in relation to technology in play with ten children (aged 10–12) in New Zealand, as the following example shows.

Example Silcock, M., D. Payne and C. Hocking (2015), ‘Governmentality Within Children’s Technological Play: Findings from a Critical Discourse Analysis’, Children and Society. Available online: http:// onlinelibrary.wiley.com/doi/10.1111/chso.12123/abstract (accessed 17 January 2016). In many countries today, digital technology and instant communication are embedded in children’s everyday lives to the extent that their play frequently incorporates smartphones, the Internet and other technologies. In this paper, we explore the recent historical conditions within the New Zealand context that have increased the accessibility of these technologies and imbued them with particular meanings. We suggest that from a Foucauldian perspective, these technologies can be seen as a form of subtle disciplinary power using techniques of governmentality through which children’s ways of thinking are shaped to benefit societal requirements of the current historical era.

However, in this study – as in several other educational research studies – it is unclear exactly what tools or approaches, digital or otherwise, were used for analysis. This neglect would seem surprising in light of the analytical focus of critical discourse analysis, which emphasizes the ways in which discourse is shaped

DATA MANAGEMENT

181

by its surrounding context. We suggest that although critical discourse analysis studies are essential for education in a digital age, it is important to consider and explain how research findings are shaped by the analytical techniques used.

Interpretative phenomenological analysis There are certain analytical approaches, however, which might seem to be incompatible with qualitative data analysis software in its current form. For example, Goble et al. (2012) attempted to use qualitative data software for phenomenological analysis. Whilst they call their approach hermeneutic phenomenology, this is, in fact, a methodology rather than an analysis approach. Hermeneutic phenomenology is based upon the work of Merleau-Ponty ([1945] 1962), a phenomenological philosopher influenced by Husserl’s ([1907] 1964) work on phenomenology and Heidegger’s ([1927] 1962) work on heuristics. It seeks to understand how an individual experiences a given phenomenon and the meanings that emerge from that experience before it is analysed. Goble et al. (2012), instead, seemed to be using interpretative phenomenological analysis, which is a specific analytical technique developed by Smith (1996). It is concerned with an individual’s personal account of an object or event. In this study, the researchers undertook one- to two-hour in-depth interviews with fifty-three participants. They attributed their ability to manage a reasonably large dataset to the use of the software, suggesting that otherwise no more than twenty participants would have been recruited. Goble et al.’s reflections on the study are shared below.

Example Goble, E., W. Austin, D. Larsen, L. Kreitzer and S. Brintnell (2012), ‘Habits of Mind and the Split-Mind Effect: When Computer-Assisted Qualitative Data Analysis Software is Used in Phenomenological Research’, FQS/Forum: Qualitative Social Research, 13 (2): article 2: 16–17 With computer-assisted qualitative analysis software: We become separate and distinct from our research. While we conduct our research, we are no longer part of it, for, like the data

182 Research Methods for Education in the Digital Age

Example itself, we must ‘input’ ourselves (our hunches, interpretations and lines of thinking) as written responses, as ‘memos’ into the computer that holds and that now is our research. … We become certain. For [the software] removes the groping for understanding, the searching and grappling with that which is just beyond understanding, with that which can only be found through writing. [The software] does not allow for ambiguity. Our data is truly ordered, for, in all regards, [the software] embodies our technoscientific understanding of being. It forces order upon messiness. We must make definitive statements; we decide it is this, not that, every time we enter a code. Even the ability to revise, merge, split, and delete codes are definitive moves, not the subtle development of understanding. With [the software], we work with nice discrete categories, data that are only themes waiting to be identified, theories waiting to be generated.

Others, however, have managed this through the use of additional qualitative tools. De Felice and Janesick (2015), for example, also undertook phenomenological analysis of a similar-sized dataset. Like Goble et al. (2012), they expressed concern about the perceived loss of the researcher–data relationship through coding. In order to manage this, they chose to use Microsoft OneNote for researcher diaries, enabling them to link their data to their reflective notes and to related videos and images, which helped support their interpretation and analysis. In some cases, interpretive phenomenological analysis is also used as an analytical tool dislocated from phenomenology.

Narrative analysis Narrative analysis is a collection of analytical approaches associated with narrative inquiry methodologies, although narrative analysis may also be undertaken in other studies such as arts-based research or case study. Whilst narratives are collected in multiple ways – such as through interviews, blogs and audio recordings – perhaps the most popular method for narrative research in digital education

DATA MANAGEMENT

183

is the digital story. Yet, there seems to be considerable uncertainty about how these stories might be analysed for research purposes; instead, there has been a tendency to use the story process for educational purposes whilst paying little attention to the research and educational content of the digital story. Daiute (2014) has proposed three strategies for analysing digital interactive stories. They are as follows: 1 Hyper-plot analysis: Plot is the structural organization of

stories, guiding perception and interpretation of meaning. From the reader’s and the author’s perspectives, story meaning – and a reason for interacting – comes in large part from his/her sense of the evolving plot as integrated with sub-plots, parallel plots and so on as these relate to some personal or collective process. To create and make one’s way through complex narratives, a participant uses a plot structure (often intuitively). 2 Multi-dimensional character mapping: While plots function as structural scaffolds, characters serve as anchors of interactive digital storytelling. Dynamic in their own way, characters enact and/or develop different meanings with their orientations, qualities, goals or relationships over time and spaces in a story world. For this reason, character mapping offers insights about another dimension of how story authors create meaning individually and collectively, to interact with one another, to elaborate or shift plots, and to change or maintain their own involvement over time in the story world. 3 Poly-cultural values analysis: As cultures, interactive digital storytelling environments establish values, which may be temporary, enduring, consistent, conflicting or transforming as participation by different authors augments the story in different expressive modes/spaces and over time. Guiding story values are worth identifying as the basis for author/ interpreter selection of what to express, what not to express, and how to do that, as well as the contribution of implicit and explicit values with plot and other elements to meaning. (Daiute, 2014: 255–7)

184 Research Methods for Education in the Digital Age

Daiute’s work is still in development, and thus it is not entirely clear how these suggested approaches might work in practice, how they map against existing narrative analysis approaches and what tools might be used to analyse digital stories. Other narrative researchers, by contrast, have sought to leverage digital technologies to increase the visibility of narratives. Matthews and Sunderland (2013), for example, have discussed their approach to analysing large numbers of digital stories. Digital storytelling is particularly popular in narrative research, and tends to prioritize the experiences of individuals. In this study, the authors noted that whilst there is considerable research on how digital stories are created, there is significantly less on how they are analysed and how others – for example, educators, researchers, practitioners and policy-makers – listen to them. They suggested a need, therefore, for digital stories to be made accessible to policy-makers familiar with large-scale datasets, drawing upon their own experiences of working with a large-scale digital disability life stories project.

Example Matthews, N. and N. Sunderland (2013), ‘Digital Life-Story Narratives as Data for Policy Makers and Practitioners: Thinking Through Methodologies for Large-Scale Multimedia Qualitative Datasets’, Journal of Broadcasting and Electronic Media, 57 (1): 97–114: 110. [There are] hesitations in the literature in digital storytelling – a hesitation to move away from the individual voice; from citizen to citizen communication; and from individual reflexivity – suggests the acute awareness of many researchers and practitioners of what is lost in the process of recontextualization. However, we have suggested that there is also something to be gained from attempting to reframe life stories so that some of their ‘messages’ may come to be inscribed in the institutions and practices that help to frame and shape these lives … The consequence of recontextualization is that increasingly durable realities – such as a hospital building, a technology or a product – are seen to encapsulate the discourses that have shaped their being and becoming; they are discourse materialities. Such recontextualization

DATA MANAGEMENT

185

Example of life stories presents many pitfalls that those working on digital storytelling have thoughtfully and carefully moved around. We would argue, however, that the potential value of digital life narratives as a source of data makes it worth confronting and interrogating these dangers rather than attempting to slide past them.

What seemed less clear, however, was how the digital stories might be recontextualized, and the methodological implications of this recontextualization. One approach might be to explore thematic rather than holistic analyses of digital story data, or consider how digital narratives might be re-viewed through an arts-based lens, as discussed in Chapter 2.

Content and thematic analysis Content analysis involves the identification of patterns across the data. This is usually textual data (such as a document, or an interview transcript), but can also include videos and photographs. Traditionally, content analysis has been used to code and then count data; however, more recent adaptations seem to draw upon aspects of thematic analysis. Whilst both approaches identify patterns in the data, content analysis examines the frequency of patterns whilst thematic analysis focuses upon the content of patterns and the relationships between them. Whilst content analysis is usually thought to refer to the analysis of textual documents, it is increasingly applied to videos and images, particularly in digital settings. For example, Martinho, Pinto, and Kuznetzova (2012) have examined the YouTube channels of three digital scholars using what they call content analysis but what would seem to be a mix of both content analysis and thematic analysis. Whilst the themes identified were counted, they also discussed the content of the themes and interconnections between the themes. By contrast, Kousha, Thelwall and Abdoli (2012) undertook classic content analysis of 515 YouTube videos, which were cited in journal publications from 2006 to 2011.

186 Research Methods for Education in the Digital Age

Qualitative data analysis software was initially developed to facilitate manual content analysis and thematic analysis. However, as datasets have grown, there have been attempts to develop software that can automate this analysis. In Morris and Ecclesfield’s (2011) study, a new piece of software was used to undertake content analysis of seven years of proceedings for the Association for Learning Technology Conference. The process included the following stages: 1 Knowledge indexing to capture, organize and classify

unstructured text to allow easy navigation. The data was classified according to frequency of occurrence. 2 Creation of thematic indexes, which identified the most important themes within the text. 3 Identification of relationships between the themes through the identification of recurring patterns in the text. These relationships were identified predominantly through proximity in the text, but specifics of the software remained under Cirilab’s proprietary protection. Based upon these processes, the software outputs both mind maps of the content and connections between content, and also what is called a ‘Knowledge Signature’, including a written summary of the content. Yet, one of the primary concerns in this study was the extent to which the tool could understand and interpret context and the intentions of the researcher. This is particularly important in relation to thematic analysis, which relies on iterative and intuitive analysis and interpretation to a greater extent than classical content analysis. Thus newer developments have involved the personalization of research tools, as the following example shows.

Example Nguyen, P. H., K. Xu, A. Wheat, B. L. W. Wong, S. Attfield and B. Fields (2016), ‘SensePath: Understanding the Sensemaking Process Through Analytic Provenance’, IEEE Transactions on Visualization and Computer Graphics, 22 (1): 41–50.

DATA MANAGEMENT

187

Example Sensemaking is described as the process of comprehension, finding meaning and gaining insight from information, producing new knowledge and informing further action. Understanding the sensemaking process allows building effective visual analytics tools to make sense of large and complex datasets. Currently, it is often a manual and time-consuming undertaking to comprehend this: researchers collect observation data, transcribe screen capture videos and think-aloud recordings, identify recurring patterns, and eventually abstract the sensemaking process into a general model. In this paper, we propose a general approach to facilitate such a qualitative analysis process, and introduce a prototype, SensePath, to demonstrate the application of this approach with a focus on browser-based online sensemaking. The approach is based on a study of a number of qualitative research sessions including observations of users performing sensemaking tasks and post hoc analyses to uncover their sensemaking processes. Based on the study results and a follow-up participatory design session with HCI researchers, we decided to focus on the transcription and coding stages of thematic analysis. SensePath automatically captures user’s sensemaking actions, that is, analytic provenance, and provides multi-linked views to support their further analysis. A number of other requirements elicited from the design session are also implemented in SensePath, such as easy integration with existing qualitative analysis workflow, and it is non-intrusive for participants. The tool was used by an experienced HCI researcher to analyse two sensemaking sessions. The researcher found that the tool was intuitive and considerably reduced analysis time, allowing better understanding of the sensemaking process.

The use of digital techniques for analysis Many studies which use digital techniques in educational research are those which are analysing the role of technology in or outside of the classroom. Yet, digital tools are also present in almost every

188 Research Methods for Education in the Digital Age

aspect of research, as has been discussed in Chapters 5 and 7. These include the following: ●●

●●

●●

Digital audio and video recorders, including mobile phones and laptops Digital tools used for note-taking, such as digital pens, tablets, mobile phones, laptops Digital time-recording devices, such as a digital watch, mobile phone or stopwatch

Beyond these tools, it is likely that digital techniques may be present in other interactions with participants. For example, interviews may be scheduled by email, or participants may be sent digital versions of participant consent forms to complete. Depending upon the methodology employed, these interactions may all become part of the analytical foci of the study. Gemma’s reflection below demonstrates the ways in which her own analytical approach and practices changed as a consequence of her use of digital technologies.

Gemma’s reflection

I

n my own research, I began using a digital pen that could record audio, for note-taking in interviews. This pen enabled me to take notes on special paper, and in doing so link the audio to the notes taken at that particular time in the interview. This was extremely helpful in the analysis process for replaying interviews and moving to particular parts of the interview. Yet, I found it to be distracting for the interview participants. As the digital pen was relatively uncommon at the time, interviews were inevitably delayed at the outset as participants were eager to hear about how the pen worked and what its advantages were. In one interview discussing the role of digital technologies in the classroom, the interviewee referred to the digital pen as an example of innovative practice. As this interviewee hadn’t heard of digital pens before, it is likely that my own pen influenced what he considered to be innovative practice. In other interviews, I observed that participants were

DATA MANAGEMENT

189

watching the pen as I was writing, as it seemed to remind them that the interview was being recorded; this did not seem to happen when I took notes with a standard pen. Ultimately, I decided to continue note-taking using a standard pen and digital audio recorder, feeling discomforted at the digital tool infringing on interviews in ways I hadn’t anticipated. Just as an overly cold or noisy interview room might be considered in a qualitative researcher’s analysis of an interview, I realized that I could not disregard my digital tools from my analysis of these interviews.

In this particular example, Gemma chose to use a digital audio recorder; her reasoning for this was that digital recorders have become ‘invisible’ in many qualitative research settings. Yet, the role of digital technologies in data collection, analysis and interpretation also depends upon the researcher’s epistemological framework and the focus of the study. For example, Nordstrom (2015) undertook a qualitative study in which she examined the relationship between objects (such as photographs) and ancestors in family history genealogy. Although the purpose of the study was examining the entangled relationship between family history and objects, she did not originally consider the role of the recorder. Yet, a number of experiences in data collection altered her perspective, such as participants who expressed a wish for the recording device to be turned off, and interviews that began before she could switch the recording device on. Ultimately, Nordstrom argued that in order to remain truthful to the topic and methodological underpinnings of the study, she had to consider the digital recorder as an object of analysis integral to the interview context. Recording devices are both embodied and practiced with an objectivist-realist material-discursive practice in which certain conceptions of stable meanings, knowledge, and reality become intelligible … The recording device has remained unquestioned. As a result, even for the most critical qualitative researcher, recording devices are normalized and unquestioned scientific tools that operate within objectivism and realism in qualitative interviews. (Nordstrom 2015: 398)

190 Research Methods for Education in the Digital Age

As this section has shown, data are informed by the digital without the use of digital environments or the study of digital topics in research undertaken in a digital age. Thus analysis and interpretation, as discussed in the following section, must take account of the impact of digital technologies upon the data.

Interpreting digital data Thus far we have discussed data collection, data analysis and data creation as relatively separate activities. In practice, this is rarely the case. Data analysis involves breaking data apart and ‘making sense’ of it by identifying patterns. These patterns, however, are often identified with a first literature review. Semi-structured interviews, in particular, involve identifying patterns and asking additional questions in order to seek further meaning; this may subsequently alter how further interviews are undertaken. Data interpretation, which is closely interrelated with data analysis, is the search for meaning within those patterns. Interpretation, Savin-Baden and Major explain, ‘is the act of explication, explanation and elucidation,’ requiring both logic and intuition. The previous section has shown that analysis can be undertaken in very structured ways, and also that digital software can impose structure on that data, as in Goble et al.’s (2012) example. Interpretation is often messy and always incomplete. New meanings can always be found, and it is here that interpretation overlaps with representation and portrayal, as will be shown in Chapter 9. There are a number of strategies used for undertaking interpretation of data, such as examining oppositional talk and subtext. The issue of most relevance to educational research for a digital age, however, is the use of prior theory to seek meaning in data. In Chapter 4, we addressed a number of key theories used in educational research for the digital age, and outlined the advantages and challenges associated with their adaptations. However, there are a number of theories that are used only on the boundaries of educational research and yet proliferate in digital society research; or which are popular in educational research but are only rarely considered in relation to educational technology. As cross-disciplinary cooperation continues to increase, it seems worth outlining some of the other key theories used for interpreting social science and educational research, which are shown in Table 8.3.

191

DATA MANAGEMENT

Table 8.3  Theories for interpreting educational research data in a digital age Theory and theorists

Description

Examples in digital educational research

Cyborg theory (Haraway 1991)

Anti-essentialist theory, which rejects rigid divisions between ‘human’ and ‘machine’, suggesting that ‘we are all cyborgs’.

Bayne and Ross (2013)

Rhizome theory (Deleuze and Guattari 1987)

Derived from the botanical term ‘rhizome’, rhizome theory is used to represent non-hierarchical and interconnected ideas. It is rootless, with no beginning and no end.

Drumm (2015)

Network society (Van Dijk 1999; Castells 1996)

Network society is used to represent the idea that modern society is interconnected through technological networks that alter politics, society, economy and culture.

Bennett and Maton (2010)

Supercomplexity (Barnett 2000)

Influenced by postmodern theory, supercomplexity represents the idea that the future is fundamentally unknown and unknowable, and that knowledge of this shapes society and education.

Tombs (2017)

Digital tethering (Savin-Baden 2015)

Defined as a way of being and a set of practices associated with it. Associated with carrying, wearing or using a device that enables one to be ‘always connected’ with digital media.

Savin-Baden (2017)

192 Research Methods for Education in the Digital Age

Conclusion In this chapter, we have examined issues around data management for a digital age, distinguishing between the different ways in which data are made digital and their impact upon analysis and interpretation. We have suggested that perhaps there is a need for greater criticality around the distinction between ‘qualitative’ and ‘quantitative’ data, considering the rapid growth in methods for both paradigms and the tendency to ‘quantify’ qualitative data without consideration of the theoretical underpinnings. Perhaps most importantly, we have demonstrated that digital tools can shape data collection, analysis and interpretation, in a variety of ways that we as researchers have perhaps not yet begun to make sense of.

Further reading Bennett, S. and K. Maton (2010), ‘Beyond the “Digital Natives” Debate: Towards a More Nuanced Understanding of Students’ Technology Experiences’, Journal of Computer Assisted Learning, 26, 321–31. In this paper, Bennett and Maton provide a critique of the digital natives theory, and others like it. This theory contended that a fundamental age-related gap existed between the technologically skilled and the technologically unskilled, thus creating an unbridgeable divide between most educational practitioners and most students. Bennett and Maton suggest that whilst the theory has been debunked even by its originator, Marc Prensky, its impact is still felt across many educational settings. They therefore offer an alternative framework for researching young people’s technology experiences. Whilst it is unclear how the framework might be conceptualized in practice, Bennett and Maton’s considered and pragmatic response to the digital natives ‘moral panic’ remains essential reading. Buch-Hansen, H. (2014), ‘Social Network Analysis and Critical Realism’, Journal for the Theory of Social Behavior, 44 (3): 306–25. This paper provides a particularly interesting conceptualization of social network analysis within the theoretical framework of critical realism. Buch-Hansen distinguishes between two approaches to social network

DATA MANAGEMENT

193

analysis: one which is applied, methods-oriented and mostly positivist or post-positivist; and another which explores the social-theoretical nature and implications of networks, and can be considered to be compatible with critical realism. Whilst this paper prompts much-needed consideration of the theoretical underpinnings of social network analysis, it is most appropriate for researchers who already have some familiarity with social network analysis and critical realism. Symonds, J. E. and S. Gorard (2010), ‘Death of Mixed Methods? Or the Rebirth of Research as a Craft’, Evaluation and Research in Education, 23 (2): 121–36. Symonds and Gorard begin from the premise that data collection tools, types of data and analysis techniques are not necessarily paradigmatic – meaning that they do not inherently belong to either ‘qualitative’ or ‘quantitative’ research paradigms. In this paper, they trace the history of ‘mixed-methods’ research, examining its various definitions and considering the implications for the binary division of qualitative and quantitative data. Such divisions, they argue, are inherently flawed; qualitative methods such as interviews can produce quantitative data as a consequence of the analysis technique employed (for example, keyword or content analysis). They conclude by calling for researchers to reconsider their stances on qualitative, quantitative and mixed-methods research paradigms, and, instead, move towards the rebirth of plain ‘methods’ and ‘research’.

194

chapter Nine

Representation and portrayal in qualitative research Introduction This chapter begins by examining understandings and conceptualizations of representation and portrayal in the context of current qualitative education studies in the digital age. Representation is explored in terms of different representation claims, and arguments are made about how these might be implemented in practice. It is then suggested that there should be new perspectives about portrayal, and we offer new concepts and ideas that offer a different view, namely the notions of mustering, folding, cartography and assemblage. The final section of the chapter considers the interfaces between portrayal and representation as spaces of friction that need to be explored further in educational research.

Issues of representation and portrayal Representation and portrayal are often seen by qualitative researchers as issues that are relatively straightforward. These

196 Research Methods for Education in the Digital Age

are terms used easily, often interchangeably. They invariably refer to putting the findings of the study together with excerpts from participants and usually, but not always, some interpretation. There is often a perceived overlap between representation and portrayal in qualitative studies, and in many cases researchers do not make any distinction between the two. However, we believe it is essential to both distinguish between these acts and consider their differing influences upon educational research. 1 Representation: This tends to refer to the way in which a

researcher provides warranted accounts of data collected. Thus the main way the term ‘presentation’ is used is in the sense of a proxy – the researcher is (re)presenting the views of the participants, as it were, speaking for them. This is often seen or presented by the researcher as being unproblematic. Yet, researchers need to acknowledge and voice that the research account they are providing does, in fact, reflect their own stance and position. 2 Portrayal: This notion is invariably seen as the means by

which the researcher has chosen to position people and their perspectives in terms of the use of images, quotations and positioning in a social and political context. Portrayal tends to be imbued with a sense of not only positioning but also a contextual painting of a person in a particular way. There are an array of issues and challenges related to what representation and portrayal can or might mean in digital spaces. Most qualitative data are textually presented and in most cases, words are seen as more important than images. In Chapter 2, we argued that the way Kress and van Leeuwen (2001) adopted multi-modality theory was misplaced. To recap: Is the idea that there has been a recent shift away from monomodality (text-based versus image) towards multimodality (text and image), with the screen rather than the book becoming the most common form of representation, as Kress and van Leeuwen have suggested? Instead, we argue that researching education in a digital age provides greater or different opportunities to represent and portray data differently, and suggest that these ways are underutilized.

REPRESENTATION AND PORTRAYAL

197

For many qualitative researchers, legitimacy comes through the use of participants’ voices in the form of quotations. This is because the use of the quotations, the ‘raw’ data, is often seen as the means of validation since it is the participant speaking and there is a tendency to overlook the point that the quotation has already been mediated by the researcher. We argue, therefore, that this stance towards plausibility and legitimacy is problematic and needs to be reconsidered in terms of the following: 1 Understanding differences in types of representation and

portrayal. 2 Recognizing the different ways in which researchers choose to position themselves in relation to representation and portrayal. 3 How decisions are made about the way data are represented and portrayed. 4 The ways in which quality is understood and effected in relation to representation and portrayal.

Representation One of the central difficulties in qualitative research is how claims in representation are managed between researcher and participants. It is often the case that researchers alone collect data, and they may undertake participant validation but not member checking. These two terms are often confused and used interchangeably. The basic premise is that researchers need to check the trustworthiness of data with participants. Participant validation involves returning data, such as interview transcripts, to participants to check that they represent what was said accurately. Member checking moves beyond mere verification and seeks affirmation of the ways data are interpreted and presented. This strategy involves checking with participants for feedback or verification of interpretation. As a result, research is thought to be more credible. This approach allows participants a voice in what the findings say and the opportunity to correct any possible misinterpretations by the researcher. Without member checking, the result can be that the individualistic stance and voice of the researcher will prevail in representation, and that

198 Research Methods for Education in the Digital Age

researcher ideas will tend to be dominant instead of those of the participants. Thus, embedded within the notion of representation is the idea of ‘claim’, what it is the researcher is claiming, for example: 1 Claims to represent the interests of a person. It might be

2

3

4

5

that the researcher represents the interest of a person, but unless in-depth member checking and rigorous discussion has occurred about how someone’s data has been interpreted, the researcher cannot make this kind of claim. Claims to embody the needs of a group of people. Given that data from different people cannot be compared and contrasted per se, but only explored in light of one another’s perspectives, claims cannot be made about data being portable. Data should be specifically understood in the light of what is being claimed by one participant about the issue under study, in the context of his or her story and the stories of others in the project. Claims to stand for the views of those in the study. This kind of claim is often made by those using qualitative and mixed-methods studies, as if the truth is out there and can be claimed, managed and subjugated by the researcher. Researchers cannot stand for those in the study; they may be able to offer a partial snapshot or a small representative perspective of key issues from the study, but that is all. Claims to know how data might best represent those involved in the study. Researchers often claim or feel they ‘know best’, but to take such a stance is to position the researcher as better, more knowing and possibly even the privileged knower – this is a dangerous stance. Researchers may know how to reach complex audiences, and write and publish in ways that enable participants’ voices to be heard, but the idea of laying claim to knowing how data might best represent those involved in the study is a mistake. Claims to symbolize the data in ways that are honest. This is a vital stance and claim for researchers in qualitative inquiry. This requires that researchers both situate themselves in relation to participants and

REPRESENTATION AND PORTRAYAL

199

acknowledge power relationships during data collection and interpretation. Researchers need to demonstrate their honesties in disclosing their positionality with participants, voicing mistakes as well as discussing data ownership and how it is to be used. We advocate here that researchers need to move away from the notion of forms of representation that are about speaking for participants and, instead, examine what is going on in representation (and even whether portrayal might be seen as central to what is going on in representation). The focus in qualitative research tends to be on the representative (the researcher) rather than the represented. The result is that the represented become constructed by the researcher in particular ways, invariably with little or no voice in the construction and subsequent portrayal. For example, how many participants are asked if they would like to be presented through, text, pictures, art or other forms of media? Perhaps researchers need to ask themselves when they are mapping findings whether they are ‘acting for’ or ‘standing (in)’ for the participants – whether their role is active or passive representation. Or instead of operating from such a polarized either/or stance, should researchers position themselves and their claims about representation ‘in relation’ to their participants and audience? The latter, we suggest, is a more plausible path even though the polarized position remains common. Central, then, to honest and plausible representation is the claimmaking within the research process as well as the representation and portrayal of research. Participants, and their data, are presented as representing something – a view, position, a finding; and in that process they are constructed and reconstructed by the researcher, yet it is not always clear what the researcher is laying claim to, and if, indeed, one can even speak for more than oneself, as Alcoff reflected: Adopting the position that one should only speak for oneself raises ... difficult questions. If I do not speak for those less privileged than myself, am I abandoning my political responsibility to speak out against oppression, a responsibility incurred by the very fact of my privilege? If I should not speak for others should I restrict myself to following their lead uncritically? Is my greatest contribution to move over and get out of the way?

200 Research Methods for Education in the Digital Age

And if so what is the best way to do this – to keep silent or to deconstruct my own discourse? (Alcoff 2009: 119) What is often missing from claim-making debates is how power is used, created or ignored in the management and representation of data, or where voices are heard or ignored, privileged or taken for granted. Should researchers be more honest about what is meant by representation? Perhaps, instead of seeing something as unproblematic, the complexities of it should be unpacked and the claims about who is being represented, by whom and in what ways, should be explored. One such way would be to invoke the notion of representation claims following Saward’s (2006) work, albeit by adapting them for qualitative work. Whilst Saward’s work focused on political representation, we suggest his stance is a prompt to qualitative researchers to consider how representation should and might be reconstituted. Using Saward’s lines of variation about representative claims is one means of reviewing notions of representation critically. However, instead of using representative claims, we argue that representation claims are more accurate, since it is both the voices and the findings that are being presented, rather than the stance of a political party representative. Below we present representation claims, based on Saward’s work: 1 Singular-multiple: This is where the researcher adapts the

representative messages of the findings so that they can be used to reach multiple audiences – but invariably with little reference to or discussion with the participants of the study. 2 Particular-general: This is where the claims of a particular set of findings are used to inform, guide or influence larger scale issues or projects, often using strategic claims. 3 Implicit-explicit: Most representative claims in research tend to be explicit, particularly when the findings are new, unfamiliar or controversial. Implicit representation claims are more often made when the findings are familiar and build on accepted codes or rhetoric. Implicit representation claims are troublesome because they tend to be accepted unthinkingly – based on particular values that are held. For example, research findings that indicated that young

REPRESENTATION AND PORTRAYAL

201

children’s use of mobile devices affected their concentration in the classroom are likely to be accepted implicitly, since that is what many adults already assume as a result of perceived and espoused cultural norms. Thus cultural context and assumptions affect what is seen and accepted, and what is ignored. In the debates about representation, it is important to recognize that participants are produced, located, positioned and even silenced through acts of representation and representation claims. As Bourdieu has argued: In appearance the group creates the man who speaks in its place – to put it that way is to think in terms of delegation – whereas in reality it is more or less just as true to say that it is the spokesperson who creates the group. It is because the representative exists, because he represents (symbolic action), that the group that is represented and symbolized exists and that in return it gives existence to its representative as the representative of a group. (Bourdieu 1991: 204) Representation in research needs to be seen as a dynamic process centred on multiple claims, actions and activities. Butler-Kisber (2008) suggested four forms of representation, which we have summarized and supplemented: 1 Thematic: In this form of representation, components of the

texts (usually interview data) are compared and contrasted and woven together so as to tell the story of the research. The findings are told and represented from the researcher’s point of view with participants’ quotations used to render the story plausible. 2 Narrative: In narrative representation, a variety of ways are used to tell stories – some researchers may use a thematic approach with a more narrative element, others may adopt a literary style. For example, Davies (2009) used a radio play format to experiment with a Deleuzian fictional approach to writing, in order to shake up more traditional habits and writing in the representation process.

202 Research Methods for Education in the Digital Age

3 Performance: Representation that uses performance tends

to overlap to a degree with portrayal, as do some narrative approaches, as mentioned above in relation to Davies’ (2009) work. Performances are usually in the form of drama and are used to incorporate gestures, voice, word, image and sound. Performance is used to enable participants to have a voice and to be actors in the research process, as well as to represent their stories and collected and collated findings. 4 Visual: This form of representation involves the use of images, generally film and photographs, as a means of recording as well as representing data. More recently, other arts-based methods have been used, such as collage, painting and computer-generated imagery, to represent findings. However, these forms of representation do tend to relate to the specific research approach adopted, since what is central to the issues of representation is the positioning of the researcher and the research. Thus another way of examining representation is to consider the way in which conceptual frameworks and researcher stances can be used to ensure rigour in the representation process, as presented in Table 9.1. An example of this is in representing big visual data. Rose (2015) suggested that images represent things but that they also create, make and prompt performance: One challenge, though, is grasping the performativity of these doings with images. These are images that represent things, but, as importantly, they performatively create sociality – through links, sharing, networks, likes – as they are taken and uploaded and glanced at. … I think we need to be thinking about images as not only representational but also as performative, and thus we also need to think about production, immersion or intensity as analytical terms to understand their relation to the social. (Rose 2015: paragraph 9) Performance and performativity do not involve just communication and (re)presentation of research, but also the construction and presentation of an identity. The idea of representation and performance has thus become intertwined. For example, Manovich (2013)

Phenomenology

Grounded theory

Narrative inquiry

Knowledge resides in the mind, as the individual perceives and experiences it, and may be discovered by exploring human experiences.

Knowledge is constructed by the researcher and not discovered in the world.

Knowledge is constructed through dialogue and negotiation.

Narrative interviews Storytelling Personal artefacts

Structured or semistructured interviews Documents Observation

Narratives

(Continued )

Coded analysis and unmediated quotations Themes

Coded analysis and unmediated quotations Themes

Coded analysis and unmediated quotations

Structured or semistructured interviews Documents Observation

Pragmatic qualitative research

Knowledge may be discovered by examining the usefulness of theory in practice.

Structured or semistructured interviews Observation Documents

Graphs charts and statistics Coded analysis and unmediated quotations

Surveys, structured or semi-structured interviews

Mixed-methods research

Knowledge exists but is imperfectly understandable, and it may be uncovered through falsification.

Forms of representation Graphs charts and statistics

Methods Measurement: questionnaires, surveys

Methodology

Knowledge exists and is based upon natural Quantitative phenomena, their properties and relations and research may be discovered through the scientific method.

Researcher stance

Table 9.1  Researcher stance in representation

REPRESENTATION AND PORTRAYAL 203

Collection of cases (stories, interviews) Documents Observation Observation Documents Semi-structured interviews

Case study

Ethnography

Action research

Collaborative research Evaluation

Arts-based research

Knowledge is constructed through dialogue and negotiation.

Knowledge is constructed by the researcher and also constructed through dialogue and negotiation.

Knowledge is constructed through reflection, discussion and negotiation.

Knowledge is constructed discussion and sharing ideas.

Knowledge is constructed by the researcher and also through dialogue and debate.

Knowledge may be gained through the deconstruction of social products, including language, media and institutions.

Art Poetry Collage Ethnodrama Performance

Structured or semistructured interviews Documents Observation Focus groups

Observation Unstructured interviews Focus groups

Observation Unstructured interviews Focus groups

Methods

Methodology

Researcher stance

Table 9.1 (Continued )

Narratives Performance and Visual

Themes

Narratives and Performance

Themes and Narratives

Themes and Narratives

Narratives

Forms of representation

204 Research Methods for Education in the Digital Age

REPRESENTATION AND PORTRAYAL

205

s­ uggested that we no longer have documents, but software performances, because engaging with web-based media means that our experiences are constructed in real time. From this perspective, we suggest that representation can be seen as liquid since software is always in flux. For example, as Manovich explained, anyone ­using Google Earth will experience a different earth each time he or she uses it, since the software is likely to have updated street view, ­photographs and buildings from the satellite. Thus he argued: Google Earth is not just a ‘message’. It is a platform for users to build on. And while we can find some continuity here with users’ creative reworking of commercial media in the 20th century – pop art and appropriation, music, slash fiction and video, and so on – the differences are larger than the similarities. (Manovich 2013: para. 12) Performance overlaps not only with representation but also with the notion of portrayal.

Portrayal Although portrayal is defined here as the contextual painting of a person or data set in a particular way, many research studies use the terms of representation and portrayal interchangeably. Those who do use ‘portrayal’ are invariably referring to media (mis)representation of particular groups: women, Muslims, black youth. We argue that in qualitative research, portrayal should centre not on how something is restated but on how it is depicted. Portrayal is not just the data that are presented, but the additional contextual information, such as the literature that is selected in a literature review and the demographic data that are included or excluded. Thus what is central to portrayal is in-depth interpretation. There is no sense of quick coding and analysis in this process. Instead, as St Pierre has argued: I believe we have burdened the voices of our participants with too much evidentiary weight. I suggest we put voice in its place as one data source among many from which we produce evidence to warrant our claims and focus for a time on other

206 Research Methods for Education in the Digital Age

data we use to think about our projects that we’ve been ignoring for decades. (St Pierre 2009: 221) Thus other data sources might include an exploration of the theoretical frameworks in which data have been positioned, a critical stance towards the literature that has been included, and exploration of the hidden curriculum of the subject under discussion. Jackson and Mazzei (2012) suggest that in the analytical process, the researcher and the researched are both subject to change, as is the audience or viewer, so that as the research data become transformed and offer something else, something new is made available; a new portrayal of the phenomena. This stance places portrayal as somehow less static and acknowledges the importance of the interaction between researcher and participants. Portrayal, then, needs to be seen as a process rather than an ending, as Butler-Kisber suggested: A portrayal presents the essence of a phenomenon at a certain time while retaining the signature of the creator. Artful portrayals mediate understanding, our own and that of others. (Butler-Kisber 2002: 238) Yet, space is also an important fact in portrayal. Lefebvre (1991) has suggested that social space might be seen as comprising a conceptual triad of spatial practice, representations of space and representational spaces. Spatial practice represents the way in which space is produced and reproduced in particular locations and social formations and we suggest it has strong links with portrayal. Research is portrayed in particular spaces, yet the portrayal of research might also produce space. For example, Rose et al. (2014) argued that computer-generated images (CGI), particularly those used to market urban development, should be seen not as images in an urban space but, rather, as ‘interfaces circulating through a software-supported network space’ (2014: 386). The authors propose that software is an infrastructure for the production of space. The term ‘software supported spatiality’ was coined by Sheller (2009) to describe the spaces structured by software codes that she believes are increasingly shaping place, territories and mobility. Her work essentially examines how software is used to create and portray space. Thus in terms of portrayal, software can be seen as an infrastructure for

REPRESENTATION AND PORTRAYAL

207

the production of space. The work of Harrison (2013) is a useful example of a moving portrayal of space, in his creation of a circus tent as a means of representation, performance and portrayal. As an Artist-in-Residence in a Toronto District School Board high school I began my research. This involved setting up an open door studio through which students could come and go ongoing through the process of the research. An autoethnographic, arts informed project was begun in which I would explore the narratives of my own life as a lens into growing up gay in rural Ontario in the 1960s and 1970s. The dissemination of the findings was achieved through painting on the walls of a small circus or freak show tent. Images on the outside of the tent were appropriated from Ringling and Barnum Bailey’s circus and freak show advertisements and historical photographs (Jando, Sabia and Daniel 2008) intertwined with self-portrait images of the more negative ways I am imagined as a gay man. On the inside walls of the tent autoethnographic images were painted which explore the formative years of my life and how I imagined myself. The painted freak show tent is the dissertation. An artist’s catalogue was created documenting the studio, the research conducted to produce the narratives, the creation of the tent and the tent itself. It became the document that with the tent itself could be defended to conclude my doctoral research, for it both documented and contextualized the cultural artifact (Lyman and Kahle 1998) of the tent. (Harrison 2013: n.p.) What is significant about Harrison’s work is that it is used to enhance understanding, and to reach multiple audiences. The interfaces of representation and portrayal interrupt ideas of data presentations as well as using media to make research findings accessible to a variety of people.

Concepts of portrayal However, whilst space is a significant consideration, the processes involved in ‘portrayal creation’ are also important. We suggest that portrayal can be delineated as four overlapping concepts: Folding, Mustering, Cartography and Assemblage, as shown in Figure 9.1.

208 Research Methods for Education in the Digital Age

Figure 9.1  Concepts of portrayal

Mustering ‘Mustering’ is a term often used to mean gathering troops for battle and this has resonance here, in that researchers gather themselves, gird their thoughts and ideas and begin the portrayal process. Mustering then is the part of the portrayal process where data are brought together and decisions are made about how they will be used in the act of portrayal. It involves making decisions about voice, colour, text, what is to be included and how to account for what is to be. There is a sense of living and working with order and chaos simultaneously. What emerges is an appreciation that what was once frayed meaning becomes an holistic depiction that is both fragile and portrayed. This mustering is influenced to a degree by the folding process.

Folding The notion of folding (Deleuze 1993) disrupts the idea of data being portrayed as straightforward and one-dimensional. The idea of a fold helps us to see portrayal as a means of being and becoming part of the data and its endings. Folding allows for a multiplicity

REPRESENTATION AND PORTRAYAL

209

of portrayals whilst helping readers see some kind of sense in the findings, as well as possible continuities and labyrinths with other research: Thus a continuous labyrinth is not a line dissolving into independent points, as flowing sand might dissolve into grains, but resembles a sheet of paper divided into infinite folds or separated into bending movements, each one determined by the consistent or conspiring surrounding ... . A fold is always folded within a fold, like a cavern in a cavern. The unit of matter, the smallest element of the labyrinth, is the fold, not the point which is never a part, but a simple extremity of the line. (Deleuze 1993: 6) Folding means there is disruption between the idea of an inside and an outside so that inside and outside are both inside and outside – to reiterate: ‘a fold is always fold within a fold’ (Deleuze 1993: 6). Thus there is recognition by the researcher that data, findings and interpretations are neither stable nor do they offer a singular view. In the context of portrayal, using the concept of folding imbues the portrayal of findings with the idea that there are necessary complexities and that complexities are necessary. What is seen and portrayed is not distinct or fixed but is complex, disrupting, changing and fluid.

Cartography Cartography is defined as the study and practice of making maps. The process and action involved in cartography has similarities to the ways data are managed and, especially, portrayed in qualitative research. The changes in technology have meant that cartography has a role both in the creation of physical maps and in the graphical presentation of geospatial information about the environment and people. We suggest that, at a number of different levels, researchers are cartographers on tour who collect, represent and then portray data – sometimes factually, sometimes in ways that are troublesome and messy, and at other times are tidy, manageable and managed. One way of viewing cartography in relation to portrayal in research is exemplified in the work of Lammes (2008). Lammes reviewed

210 Research Methods for Education in the Digital Age

real-time strategy games in which players explored and mastered environments through digital mapping. The player becomes an imaginary cartographer while creating a spatial story around environments. According to de Certeau these two conceptions of spatiality (maps and tours) are both incongruous dimensions of contemporary culture: we are confronted with a static representation of the world we live in, while at the same time sensing our space in a dynamic and more personal way. As place and space, maps and tours necessitate one another and come into being through twoway movement. Even more, a map always presupposes a tour, since one first needs to go somewhere to give an objective spatial account of it (de Certeau 1984: 117–21). (Lammes 2008: 87–8; piece in parenthesis added) Lammes suggested that these games help to improve spatial awareness, and drew on the work of Fuller and Jenkins (1995), who suggested that digital games and new media should be seen as spatial stories, since players construct a narrative by travelling through space. We suggest that games portray space, positioning it in particular ways. In the past, spaces and tours have been seen as relatively fluid and changeable, whereas places and maps are seen as stable. Thus portrayal extends not only to the way data are assembled and folded, but also to the ways in which they are seen in terms of mobilities. For example, there is invariably an assumption that data and meaning are ‘portable property’ (Spivak 1974: lvii) and that meaning can necessarily be transported and transposed across contexts, interviews and continents. The notion of ‘communication’ (a ‘function’ of human structures), important to structuralism as a tool of investigation, also carries with it the notion of unified subjects, of meaning as portable property: communication, which, in fact, implies the transmission charged with passing, from one subject to the other, the identity of a signified object of a meaning or a concept in principle separable from the process of passage and of the signifying operation. (Spivak 1974: lvii)

REPRESENTATION AND PORTRAYAL

211

Texts are like maps: they can be interpreted in a myriad of ways and researchers must not ‘just map’ but also understand who, what and how the mapping portrays in particular ways, ways that might be, sometimes, portable across contexts and cultures, but often may not.

Assemblage This notion of portrayal is the idea that data are collected together from different sources and points in time in order to assemble whole (rather than partial) depictions of participants and their lives, contexts and stories. Assemblage then is not some kind of snapshot, something that is cut from data and recreated from data. Rather, assemblage is the creation of an holistic description of the research and the people involved as possible. Assemblage includes the assembling of words, pictures, reflections from theorists, friends, tweets, ideas. Portrayal in this sense is the bringing together of all the influences that have an impact on the researcher as he or she sees, interprets and creates the portrayal of the findings of the study.

Reflections on portrayal Portrayal has often been seen as unproblematic, yet authors such as St Pierre (2008, 2009) and Butler-Kisber (2002, 2008, 2010) indicate that it is invariably much more troublesome than most researchers acknowledge. Portrayal, we suggest, is often a space of friction between the interfaces of analysis and interpretation. Galloway (2012) suggested that it is difficult to see friction at the interfaces – since for the most part they are designed to be invisible. Doing research at an interface can often render the work itself invisible. As the interfaces are made invisible, they cast what is called ‘the glow of unwork’ (Galloway 2012: 25). An example of this would be the spaces and transitions that occur in the shift from analysing data to in-depth interpretation of data when the interfaces merge and the complex work that has been undertaken is barely visible. Perhaps when undertaking educational work in a digital age, we need to give greater attention to what is occurring at the interfaces, particularly between representation and portrayal. There is a need to recognize that students and young people centre

212 Research Methods for Education in the Digital Age

their lives on networked publics – spaces that are created, structured and restructured around networked technologies, and which are further sets of fractioned fractures and swirling interfaces that affect representation and portrayal of findings. Thus we need to explore what is privileged and what is missing in such spaces, to examine what has been created and crafted, and to recognize how frictions and fractures at these interfaces can improve our understandings and make us better, braver researchers.

Conclusion The chapter has examined the complex but interrelated issues about the nature of representation and portrayal. Whilst there are differences between them, there are also areas of overlap. Representation and portrayal are processes and practices that tend to leave behind trails of earlier versions. Most of these are hidden in the dustbins of our homes and computers, and ignored as no longer valuable, even if they have been central to the mustering and assemblages of our findings. If we are to be researchers who wish to present plausible accounts of our findings, we need to examine these trails, particularly exploring what has been cast aside or missed. At the same time, researchers need to be aware of the importance of the interfaces between interpretation of data and the ways they are subsequently (over) managed, represented and portrayed. Perhaps we have to ask, too, what essence we are portraying with data and findings that are constantly moving and shifting, and how we can share these in meaningful ways with our various publics, whether networked – or not.

Further reading Butler-Kisber, L. (2010), Qualitative Inquiry: Thematic, Narrative and Arts-Informed Perspectives. London: Sage. Butler-Kisber’s text provides an excellent introduction for those who are newer to thematic, narrative and arts-informed research, presenting approaches such as poetic inquiry, collage inquiry, photographic inquiry and performative inquiry. Perhaps more importantly, though, Butler-

REPRESENTATION AND PORTRAYAL

213

Kisber’s work provides a timely reminder for researchers to consider the impact of their research representations and portrayals. In her discussion of different forms of representation, she makes clear the ways in which ‘findings’ are influenced by researcher choices, a discussion that will be useful for both newer and experienced researchers. Jackson, A. Y. and L. Mazzei (2012), Thinking with Theory in Qualitative Research: Viewing Data Across Multiple Perspectives. London: Routledge. As Butler-Kisber uses the arts to highlight the importance of portrayal and representation, Jackson and Mazzei do likewise with theory. They explore one dataset through a variety of theoretical perspectives, such as deconstruction, desire, postcolonial marginality and power/knowledge. In doing so, they represent the ways in which theoretical positions and interpretations shape data and ‘findings’, and thus highlight the importance of making explicit analytical decisions in a research project. Whilst this text draws upon complex theories, readers are guided through the interpretation in a manner that makes it accessible for all researchers. St Pierre, E. and A. Y. Jackson (2014), ‘Qualitative Data Analysis After Coding’, Qualitative Inquiry, 20 (6): 715–19. St Pierre and Jackson, writing an editorial for a special issue in Qualitative Inquiry, ask a seemingly simple question: What does postcoding qualitative analysis look like? Coding qualitative data through labelling, counting and modelling, they argue, is ‘thinkable and doable only in a Cartesian ontological realism that assumes data exist out there somewhere in the real world to be found, collected, and coded’. Their paper thus provides a considered critique of the epistemological difficulties inherent in coding qualitative data, and presents a number of examples of data analysis after coding, discussed in the remaining papers in this Qualitative Inquiry special issue.

214

chapter Ten

Digital impact Introduction The final chapter of this book examines the impact of educational research in and for a digital age. It begins by exploring what is meant by impact, the political underpinnings of the term and the most common approaches to measuring impact. We then address critical issues such as the open education movement and the sharing of academic research via blogs, wikis and social media. We recognize that dissemination of research does not automatically result in impact, and examine the nuanced relationship between portrayal, representation, dissemination and impact. The chapter concludes by turning to the issue of teaching research methods in a digital age, arguing that current approaches need to be radically overhauled in order to support new researchers in education.

The impact agenda in education and research The unpredictable definition of impact has emerged as a result of the ephemeral nature of the university in the twenty-first century, in which historical visions of the university as a means of furthering egalitarian public society conflict with neoliberal capitalist visions of university education as a private good. For example, Nussbaum (2010: 128) has argued that ‘impact’ is an increasingly prominent

216 Research Methods for Education in the Digital Age

buzzword in government and, therefore, in education. As with many other buzzwords, it is particularly difficult to define. Instead, Vincent (2015: 474) has argued that impact is ‘part of an anomalous ideological hybrid … which remains troublingly unpredictable’. From a neoliberal capitalist perspective, universities are entrepreneurial businesses, students are consumers or customers, and research funders purchasers of research findings. Research in this context, particularly publicly funded research, therefore must produce findings to justify its value and existence, and do so by demonstrating impact beyond the academy. The reasons for this lie in the knowledge economy ethos, which as Bastalich (2010) has noted, posits that causal relations exist between the commercialization of research, and national prosperity. Put simply, impact in governmental terms, particularly in Western governments, equates to economic progression. This is supported by the UK Research Excellence Framework’s (2014: 6) definition of impact as ‘an effect on, change or benefit to the economy, society, culture, public policy or service, health, the environment or quality of life, beyond academia’. In the years since Nussbaum’s (2010) seminal Not for Profit: Why Democracy Needs the Humanities, which criticized the neoliberal systemic equation of ‘impact’ with ‘economic impact’, the impact agenda has become increasingly entrenched in higher education policy. Research quality measurement exercises, such as Excellence in Research for Australia (to be held in 2018) and the United Kingdom’s Research Excellence Framework (to be held in 2022), are focused upon measuring impact from research, whilst academic journals are often judged solely by their journal impact factors; papers published in these journals are judged similarly. Research impact measurements, such as the h-index, are used to determine funding, promotion, tenure, hiring and firing. It should be noted, however, that whilst countries such as the United Kingdom and Australia are leading the way, this increase in measurement and evaluation of research is by no means exclusive to these countries (Ab Iorwerth 2005).

Measuring impact through the h-index The h-index, proposed by Hirsch in 2005, is one of the most prominent methods of measuring individual researchers’ impact

DIGITAL IMPACT

217

through research publications. It was created based upon the hypothesis that higher citations mean a higher impact: A scientist has index h if his or her Np papers have at least h citations each and the other (Np-h) papers have    h citations each. (Hirsch 2005: 16569) In short, in order for a researcher to have an h-index of ten, he or she must have published ten papers receiving at least ten citations each. The amount of other papers that they have published does not factor into the h-index, nor does the amount of citations over ten. For example, if an early career researcher has published ten papers and received hundred citations for each, his or her h-index would be the same as that of a researcher with forty publications of which only ten received ten citations. There have been a number of criticisms of the h-index, such as its disregard for the quality of the papers, bias against early career researchers, inability to facilitate crossdisciplinary comparisons, inconsistent application of the h-index, and lack of evidence to support its value in measuring impact. There have been a few attempts to counter these problems. In his paper introducing the h-index, Hirsch (2005) also developed the m-value, which divides a researcher’s h-index by the number of years he or she has been active in research and thus attempts to counter some of the challenges in comparing researchers at different career stages. Others like Schreiber (2015) have restricted the h-index to papers published after a particular period, excluding older papers, which tend to favour established researchers. Yet, this does not help in addressing problems with self-citation, or the ways in which established, international journals are preferred, as Dong, Johnson and Chawla (2015) have found. Perhaps most importantly, however, the h-index is unsuitable as a single method to measure impact because it assumes that journal articles are the only means of achieving impact in a digital age. It is for this reason that altmetrics have emerged.

Measuring impact through altmetrics Altmetrics (article-level metrics) are metrics that offer an alternative to traditional journal or book citation metrics. The term was originally coined by Priem (2010), and is defined as ‘the creation

218 Research Methods for Education in the Digital Age

and study of new metrics based on the Social Web for analyzing, and informing scholarship’ (Priem et al. 2010: para. 1). Examples of altmetrics include article clicks, time spent on blog pages, YouTube views, retweets or favourites on Twitter, Facebook comment amounts and more. Thus, altmetrics are not new; data about our online engagement has been captured from the beginning of the internet. What is comparatively new, however, is the exploration of analytics as a method to measure impact in educational research. A number of tools have been developed to support researchers and institutions in using altmetrics. These tools act as a ‘hub’ for collecting and presenting a variety of altmetrics related to a particular research output, collection of outputs or individual researchers. The web-based tool Altmetric, for example, provides a quantitative score of an audience’s online attention to a particular research output: the journal article. As of January 2016, it derived this score from three key factors. First, it explores the volume of attention (amount of mentions on various platforms), and then the sources of attention (e.g. blog posts, newspaper articles, tweets). It then looks at the authors of that attention and, particularly, ‘at how often the author of each mention talks about scholarly works, at whether or not there’s any bias towards a particular journal or publisher and at who the audience is’ (Altmetric n.d.: para. 7). Thus, someone who is not the author sharing a link within their professional circle has a greater influence on the Altmetric score than a journal account tweeting recent articles. The altmetrics that contribute to these three factors (volume, source, author) are also weighted, which means that a newspaper article contributes more to the final score than a blog post. It is unclear how exactly these altmetrics are weighted, which is disregarded entirely, or whether there is any geographical, gender, language or other bias involved in weighting scores, and thus the overall Altmetric score. It is important to note, however, that the tool Altmetric seeks to suggest that the scores do not represent impact, but, rather, attention. This is particularly important, since the definition of impact requires some kind of change as a direct consequence of the research product being evaluated for impact. Other digital tools such as Impactstory, however, have suggested that attention can be conflated with impact, or is roughly equivalent to impact: Impactstory is an open-source, web-based tool that helps scientists explore and share the diverse impact of all their

DIGITAL IMPACT

219

research products – from traditional ones like journal articles, to emerging products like blog posts, datasets, and software. By helping scientists tell data-driven stories about their impact, we’re helping to build a new scholarly reward system that values and encourages web-native scholarship. (Impactstory n.d.: paragraph 1) Impactstory draws upon a significant number of altmetrics, including Twitter mentions and retweets, YouTube views, Mendeley downloads, slideshow views, comments, downloads, and favourites, Citeulike bookmarks and Wikipedia mentions. It, therefore, takes account of video abstract views and video journal views, and also presents an interesting challenge: If a journal article is published with a video abstract and the video is watched, but the viewer does not then download or read the article itself, what impact does this have on altmetrics? Or how can the number of views be accounted for when, for example, researchers re-watch their own videos, or use a ‘flipped classroom’ approach in which 300 students per semester are told to view their lecturer’s video article before a lecture, which would seem to skew the data? Thus the value of altmetrics for measuring impact remains unclear. Early work in this field examined possible relationships between altmetric citations and traditional citations, such as in the example by Shema, Bar-Ilan and Thelwall (2014).

Example Shema, H., J. Bar-Ilan and M. Thelwall (2014), ‘Do Blog Citations Correlate with a Higher Number of Future Citations? Research Blogs as a Potential Source for Alternative Metrics’, Journal of the Association for Information Science and Technology, 65 (5): 1018–27. Journal-based citations are an important source of data for impact indices. However, the impact of journal articles extends beyond formal scholarly discourse. Measuring online scholarly impact calls for new indices, complementary to the older ones. This article examines a possible alternative metric source, blog posts

220 Research Methods for Education in the Digital Age

Example aggregated at researchblogging.org, which discuss peer-reviewed articles and provide full bibliographic references. Articles reviewed in these blogs therefore receive ‘blog citations’. We hypothesized that articles receiving blog citations close to their publication time receive more journal citations later than the articles in the same journal published in the same year that did not receive such blog citations. Statistically significant evidence for articles published in 2009 and 2010 support this hypothesis for seven of ten journals (58 per cent) in 2009 and thirteen of nineteen journals (68 per cent) in 2010. We suggest, based on these results, that blog citations can be used as an alternate metric source.

Similarly, in 2011, Eysenbach examined 4208 tweets that cited 286 articles in the Journal of Medical Internet Research. Findings from this study showed that highly tweeted articles were eleven times more likely to be highly cited than less-tweeted articles and that tweets in the first three days after publication could be used to predict citation numbers. Haustein et al. (2014), however, found only a weak correlation between tweets and citations in the biomedical literature. Additionally, Ortega (2015) has examined the relationship between social media use and citations for researchers at the Consejo Superior de Investigaciones Científicas in Spain.

Example Ortega, J. (2015), ‘Relationship between Altmetric and Bibliometric Indicators across Academic Social Sites: The Case of CSIC’s Members’, Journal of Informetrics, 9: 39–49. This study explores the connections between social and usage metrics (altmetrics) and bibliometric indicators at the author level. It studies to what extent these indicators, gained from academic

DIGITAL IMPACT

221

Example sites, can provide a proxy for research impact. Close to 10,000 author profiles belonging to the Spanish National Research Council were extracted from the principal scholarly social sites: ResearchGate, Academia.edu and Mendeley, and academic search engines: Microsoft Academic Search and Google Scholar Citations. Results describe little overlapping between sites because most of the researchers only manage one profile (72 per cent). Correlations point out that there is scant relationship between altmetric and bibliometric indicators at author level. This is due to the altmetric ones being site-dependent, while the bibliometric ones are more stable across websites. It is concluded that altmetrics could reflect an alternative dimension of the research performance, close, perhaps, to science popularization and networking abilities, but far from citation impact.

Perhaps, then, the question should not be whether there is a relationship between altmetrics and traditional metrics, considering the problems associated with the h-index and citation levels in general, but rather what value altmetrics alone provide. Like the h-index, altmetrics capture the attention surrounding a research product, and they can include new forms of research products, but they cannot assess the quality of those products. Similarly, altmetrics can be used to evaluate the popularity of researchers, as Ortega (2015) showed, but not necessarily the quality of their research outputs. A number of online tools in addition to ImpactStory also proclaim to measure influence and impact via Twitter, Facebook, LinkedIn and other sites, but the detailed metrics used to formulate ‘impact levels’ are often hidden, problematizing any useful application of such tools. We suggest that whilst altmetrics provide a useful means of examining the reach of educational research, it is essential that the methods used are both qualitative and quantitative, rigorous and transparent. Understandings of impact vary across countries, institutions, disciplines, stakeholders, governing bodies and individuals, and current measures of impact are almost exclusively quantitative.

222 Research Methods for Education in the Digital Age

Further, the definitions of impact that prioritize practice beyond the academy pose significant challenges to educational researchers whose work is often about improving educational practices within the academy, work which, of course, ultimately, but not directly, contributes to impact beyond the academy. It is important, therefore, that educational researchers engage with critical discussions on impact measurement, and challenge existing notions of impact, in order to ensure that their work is acknowledged fully. One of the ways in which this can be done is by exploring new ways of presenting research findings to increase their accessibility within and beyond the academy.

New ways of presenting research findings Impact beyond the academy has become increasingly important in recent decades, and particularly in the 2000s; consequently, the attention of researchers, managers and research funders has turned to the ways in which research might be communicated to increase accessibility. For example, a number of UK Research Councils (Natural Environment Research Council, Biotechnology and Biological Sciences Research Council, and Economic and Social Research Council) held ‘impact awards’ in 2016, in which research projects competed to determine which one was most effective at demonstrating impact in an accessible and creative manner. In this section, we outline some of the key forms of portraying and sharing research findings in a digital age, continuing from our discussion of portrayal and representation in Chapter 9. These new ways of presenting research findings include the following: 1 Institutional websites and blogs 2 Personal, collective and community websites and blogs 3 Video abstracts and video articles 4 Data visualizations

We use blog here to refer to individual pieces of writing reporting research findings, which can include images, videos and other

DIGITAL IMPACT

223

media. Websites, by contrast, are sets of interconnected pages that can host blogs but also include other information.

Institutional websites and blogs Alongside journal articles, institutional websites and blogs are the most common means of presenting research findings in the digital age. These include the following: ●●

●●

●●

Research findings presented by the educational institutions and departments undertaking the research Research findings presented by the funding bodies supporting the research Research findings presented on project-specific websites

Research findings presented in this manner are often short and output-focused, and they prioritize high-impact studies. Such findings may be written by researchers, community organizers, marketing departments or others involved in disseminating research.

Personal, collective and community websites and blogs Personal blogs are written by individual researchers, and may be hosted on a variety of websites. These include the following: ●●

●●

●●

Individual websites, created by researchers to share publications, thoughts on current research projects, research findings and personal profile information. Collective websites, or hubs, in which research findings or discussions organized around a particular theme are contributed by a variety of invited researchers. One example is the London School of Economics and Political Science’s Impact Blog (London School of Economics and Political Science n.d.). Community websites, in which research findings or discussions, usually contributed by an individual researcher,

224 Research Methods for Education in the Digital Age

are hosted on a website that allows universal contributions. Examples include Medium, Svbtle, Tumblr, YouTube and Pinterest. Research findings presented in this manner vary in content and format, but may take a less formal approach than institutional websites or blogs. These websites prioritize community engagement with research findings and discussion. For example, the London School of Economics and Political Science’s Impact Blog invites a variety of educators, managers, researchers, librarians and others involved in the social sciences, to contribute to the blog on the topic of impact. It is important to note, however, that these discussions are not necessarily textual. For example, the Speaking Openly (2015) project involved conversation between a number of different speakers on privacy and trust in education in the digital age, through asynchronous video discussion. Six key researchers in this area were invited to contribute to an ongoing discussion; the first participant created a video, which the second participant viewed and then created a video with a response and his or her own thoughts. In total, eight videos were created in which the participants reflected on the videos that had come before theirs. Those interested in the topic were then invited to share their own videos, tweets, podcasts and blogs in response, which were all hosted on the same website. This website, therefore, would seem to be a mix of a collective and community website focused upon facilitating discussion around research into privacy and trust in education in a digital age. It is also an example of representation through video dialogue.

Video abstracts and video articles In the past decade, video abstracts – and more recently, video articles – have gained increasing prominence as a form of dissemination in the digital age. The 2006 Journal of Visualized Experiments was the first peer-reviewed scientific video journal, whilst the first peerreviewed video journal in education, the Springer Video Journal of Education and Pedagogy, was launched in 2016, illustrating that video journals and video abstracts are in their infancy. There has been little research into the possible impact of these forms of dissemination, and those studies that have been undertaken have tended to be in scientific and medical research communities.

DIGITAL IMPACT

225

For example, Spicer (2014) has examined video abstracts across disciplines: While this paper explored the use of video abstracts within the science discipline, this was primarily due to the early adoption of video abstracts in fields such as medicine, biology, chemistry, math, and physics. As authors have suggested (Berkowitz 2013), one of the greatest arguments in favor of video abstracts is that they provide an amazing opportunity to communicate complex information in aurally and visually stimulating ways that would otherwise be impossible to communicate through print alone … Though the 926 videos, 20 journals (six distinct publishers), and roughly 866,000 YouTube videos identified in this paper over the past five years may suggest that video abstract publication trends are still relatively young, it is clear that this form of scholarship has experienced consistent annual growth and will likely continue to do so in the future. Indeed, the emergence of the video abstract offers a low-barrier opportunity for researchers to leverage the video medium to communicate their research more effectively. (Spicer 2014: 11) Websites such as WeShareScience and Thinkable.org also aim to increase access to and impact from research through the creation and sharing of videos to disseminate research findings. On Thinkable. org, users create and upload videos, and are then able to vote on others’ videos to create a leaderboard of popular research. The approach by Thinkable.org, which engages the viewers by asking them to vote on videos, highlights the importance of recognizing audiences for research publications, as we discussed in Chapter 9.

Data visualizations There are many different forms of data visualization, such as graphs and charts, infographics, photographs, collages, maps and network diagrams. What is particularly interesting about newer forms of representation is the way in which they allow audiences to re-present data for themselves. For example, tools such as Datascapes, created by the UK company, Daden (2014), support the visualization and presentation of data in 3D form. Datascapes is designed to maximize

226 Research Methods for Education in the Digital Age

human analysis by allowing data to be viewed, adapted and presented from multiple angles, whether structured or unstructured, enabling a wide variety of viewpoints to be taken from both inside and outside the data. The OECD Better Life Index can also be personalized for the audience as they request information on areas that are particularly important to them, challenging the notion that data is static and immovable and highlighting the mutable, folded and situated nature of research findings. As these new forms of representation are employed, researchers have begun to pay greater attention to the ways in which audiences perceive and interpret these representations, such as in the University of Sheffield Seeing Data project.

Example University of Sheffield (n.d.), ‘About Seeing Data Research’, Seeing Data: Making Sense of Data Visualisations. Available online: http:// seeingdata.org/about-seeing-data/ (accessed 23 January 2016). Seeing Data is a research project that aims to understand how people make sense of data visualizations. There are more and more data around us, and data are increasingly used to explain our social world. One of the main ways that people get access to data (big and small) is through visualizations, such as those on the pages of this website. Visualizations are visual representations of data. They are used to help people make sense of data or to allow people to explore data. They take the form of graphs, charts and other more complex or less familiar diagrams. Visualizations appear in newspapers, on television (especially in documentaries and news programmes) and on the internet in social media such as Facebook. What we don’t know is how people make sense of visualizations. How do we interact with them? How do we interpret them? Do they help us make sense of data? Do different people interact with visualizations in different ways? What messages do we take away from visualizations? On the Seeing Data project, we have been exploring these questions and finding out what skills people need to help them to make sense of visualizations.

DIGITAL IMPACT

227

Preliminary research findings from the Seeing Data project, based upon focus group data with forty-six participants, have illustrated ‘the profound way[s] that a range of social and human factors affect people’s encounters with visualizations, and the importance of emotions when engaging with data visualizations’ (Kennedy 2015: para. 3). The social and human factors included participants’ interest in the subject matter, their opinions on the source, their beliefs and opinions, their time available and their confidence in understanding visualizations. Perceptions of the data were also informed by participants’ skills, such as interpretative skills, language skills, mathematics and statistics skills, visual literacy skills, computer skills and critical thinking skills. All of these factors resulted in emotional responses, such as fear in response to a visualization demonstrating crime rates, frustration when faced with visualizations that were felt to be irrelevant, and enjoyment when viewing a particularly attractive visualization. As even more new forms of data visualization are developed, then, researchers must be aware of the ways in which their representations and portrayals of digital data can impact upon the audience, and how audiences will perceive research findings differently. This is particularly so for researchers seeking to ‘open’ their data to wider audiences, through the open education movement.

The open agenda in education and research Perhaps the most significant impact on education in the digital age has been the dramatic shift towards forms of open education in the late twentieth century. Prior to the integration of digital technologies in education, pioneering programmes such as the Open University in the United Kingdom (established in 1969) and Athabasca University in Canada (established in 1970) sought to reduce barriers to entry by providing distance education and removing entry requirements in many cases. Technological advancement in the late twentieth and early twenty-first centuries, however, has resulted in a rapid advancement of the open agenda in education and research. The educational sector has a proliferation of terms such as ‘open source’, ‘open education’, ‘open data’, ‘open

228 Research Methods for Education in the Digital Age

content’, ‘open educational resources’, ‘open educational practices’, ‘open access’, ‘open repositories’ and ‘open knowledge’, yet there is little clarity about what the terms mean, how they differ and how these practices might impact educational researchers. Table 10.1 provides a summary of the key terms used in this area. In the following sections, we explore the concepts that are most relevant to educational researchers: open access (literature) and open data.

Table 10.1  Key terms in open education and open research Key term

Description

Open access

Generally refers to open access research literature, and refers to literature that is freely used, adapted and redistributed by anyone, subject to attribution requirements.

Open content

Refers to copyrightable work (excluding software, covered under open source) that is licenced in a manner that provides users with free and perpetual permission to retain, reuse, revise, remix and redistribute content (OpenContent.org).

Open data

Data that are freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and share-alike (Open Knowledge Foundation).

Open educational resources

Teaching, learning, and research resources that reside in the public domain or have been released under an intellectual property licence that permits their free use and re-purposing by others (William and Flora Hewlett Foundation)

Open repositories

Digital repositories for storing and organizing open content such as open access publications, open data, and open educational resources.

Open source (software)

Computer programmes in which the programme and its source code can be freely used, adapted, reused and redistributed by anyone (Open Source.org).

DIGITAL IMPACT

229

Open access Open access has traditionally been used to refer to research literature and findings, although it has also been used as a collective term for open source, open educational resources and open data. The first definition of open access was created at the Budapest Open Access Initiative (Open Society Institute 2002), with subsequent definitions presented in the Bethesda Statement on Open Access Publishing (Howard Hughes Medical Institute 2003) and the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities (Max Planck Society 2003); collectively, these definitions are often termed as the BBB definition of open access. The Budapest Open Access Initiative statement defined open access literature as that with: free availability on the public internet, permitting any users to read, download, copy, distribute, print, search, or link to the full texts of these articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose, without financial, legal, or technical barriers other than those inseparable from gaining access to the internet itself. The only constraint on reproduction, and the only role for copyright in this domain, should be to give authors control over the integrity of their work and the right to be properly acknowledged and cited. (Open Society Institute 2002: para. 3) The subsequent definitions in Bethesda and Berlin added to this by emphasizing that users should be able to adapt and distribute derivative copies in order for work to be considered open access. All three meetings suggested self-archiving and open access journals as the best means by which to support open access to research literature. Later definitions such as that by Suber (2008), however, sought to add clarity to this definition by distinguishing between the removal of price barriers (gratis open access), and the removal of some permission barriers to enable derivative works (libre open access). This distinction has been necessary, as there has been little uptake of libre open access in educational research publications. Indeed, Weller (2014) has argued that early proponents are disheartened and frustrated by the lack of uptake of libre open access. It seems

230 Research Methods for Education in the Digital Age

likely, however, that libre open access will have more success with regard to educational research data, as will be discussed shortly. Gratis open access, however, has had significant success with regard to educational research publications. It is no longer a niche practice, but is accepted and often demanded, as in recent years pressure has been placed upon research funders to require open access research findings. Additionally, student-led organizations such as the Right to Research Coalition have emerged that demonstrate the commitment of students to open access.

Example Right to Research Coalition (n.d.), Right to Research: Access to Research is a Student Right. Available online: http://righttoresearch. org/ (accessed 23 January 2016). The Right to Research Coalition was founded by students in the summer of 2009 to promote an open scholarly publishing system, based on the belief that students should not be denied access to the articles they need because their institution cannot afford the often high cost of access. Since its launch, the Coalition has grown to represent nearly 7 million students internationally and counts among its members the largest student organizations in both the United States and Canada. While the Coalition currently has a strong base in North America, it is by no means solely a North American organization and is expanding to incorporate student organizations from around the world.

Gratis open access to research publications can be categorized into three distinct approaches: 1 Green: The author places the article, usually a pre-print, in

an institutional repository. 2 Gold: The publisher charges an Application Processing Fee to make the article openly available. Institutions or funding bodies may cover this cost. 3 Platinum: The journal operates for free, and articles are made freely and publicly available.

DIGITAL IMPACT

231

The green approach, in which articles are placed into open access repositories, is most common. The Directory of Open Access Repositories, which is run by the Centre for Research Communications, provides and updates a list of worldwide open access repositories. As of July 2016, nearly 300,000 repositories were listed. The UK Research Excellence Framework requires that publications be placed into an open access repository, as done by a number of key research funders such as the Bill and Melinda Gates Foundation. Yet, the green approach is in many ways problematic; for example, some publishers insist upon embargo periods before pre-print publications can be released in repositories. Citing pre-print publications without final page numbers and citation information can also be challenging, and raises particular issues around the measurement of impact: How might citations of preprint articles be combined with citations of published articles, for example? The green standard is also considered sufficient to justify the publication of articles on open access in closed journals and then released in repositories, instead of using gold or platinum standard approaches. This, ironically, means that research published on open access is often inaccessible. Despite this, there is significant progress being made in the field of open access. The TCRE project Towards the Collaborative Repository for Ethiopian Academic and Research Institutions (ADLSN n.d.), which is a collaboration between the Consortium of Ethiopian Academic and Research Libraries and the African Digital Library Support Network, implements digital repositories and provides training on their use. Similar projects are undertaken by bodies such as Research4Life and EIFL. What is not always addressed in open access discussions, though, is the access in open access. For example, Fox and Hanlon (2015) have addressed the ways in which the impact and open agendas can exclude non-Western countries from research findings, discussions and, thus, research funding: The effect of western journal impact requirements was to steer research away from the areas where it is needed most and to drive a culture of publishing only in prestigious journals. Even where open access is an option, there are prohibitive cost implications through the application of application processing

232 Research Methods for Education in the Digital Age

charges. Perhaps the greater concerns are barriers to open access, unintentionally implemented by the West, while still in pursuit of financial gain at Africa’s expense. For example, there are language issues in that English is so often the preferred language for publication. A further issue is the dependence on written knowledge sources, which creates access barriers to vital orally delivered indigenous knowledge. Also identified is the fear of exploitation of such knowledge, if it were made visible to the international research community, due to unclear and weak intellectual property protection policies for the creators and owners of that knowledge. (Fox and Hanlon 2015: 710) Whilst application processing fees are often covered by funding bodies or institutions, this is not always possible. Those who cannot access the gold model, then, may be restricted to either the green or platinum models or excluded from open access opportunities entirely. Additionally, new forms of visualizations such as infographics, big data visualizations and video articles are rarely developed with consideration for users who access the internet using a screen reader. We argue, therefore, that educational researchers who support open access publishing should ensure that there is as much focus on access as there is on openness.

Open data Open data is defined thus by the Open Knowledge Foundation: Data that are freely used, reused and redistributed by anyone – subject only, at most, to the requirement to attribute and sharealike. The most important features of open data are: ●●

●●

Availability and access: The data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form. Re-use and redistribution: The data must be provided under terms that permit re-use and redistribution including the intermixing with other datasets.

DIGITAL IMPACT

●●

233

Universal participation: Everyone must be able to use, re-use and redistribute – there should be no discrimination against fields of endeavour or against persons or groups. For example, non-commercial restrictions that would prevent ‘commercial’ use or restrictions for certain purposes (e.g. only in education) are not allowed. (Open Knowledge n.d.: paras. 4–5)

In 2013, the G8 leaders signed the Open Data Charter, which stated that all government data would be released openly by default. The European Commission made a similar request in 2012 in the Commission Recommendation on Access and Preservation of Scientific Information, whilst the Organisation for Economic Co-Operation and Development has been including commitments to open data in its policies since 2007. It seems likely, therefore, that open data is here to stay, although the discussion has focused upon governmental open data rather than on publishing research data. However, a significant number of national and international research funders now require that research data is openly available in data repositories. This includes the European Union Horizon2020 programme (2014–20), which has made available nearly €80 billion in funding and led to the creation of the OpenAIRE network of Open Access repositories. Similar movements towards open data can be observed in other countries, such as in the United States FASTR Act (Fair Access to Science and Technology Research), and open access data and research policies in funding bodies and universities across Africa, Asia Pacific, Europe, the Middle East and North Africa (EIFL 2016). In 2015, the European Union RECODE Project (Recommendations for Open Access to Research Data in Europe) identified two overarching issues that are inhibiting take-up of policies related to open access to research data in Europe: a lack of a coherent open data ecosystem; and a lack of attention to the specificity of research practices, processes and forms of data collections. (Tsoukala et al. 2015: 5) It is this last point that is most challenging, we suggest. For example, one of the most significant issues in sharing data openly

234 Research Methods for Education in the Digital Age

is the silencing of participant voices in debates around openness. Research funder or institutional enforcement of open data adds another dimension to participant-researcher discussions around anonymity, attribution and ongoing informed consent. Researchers and funders must remember that qualitative research participants are not passive sources of data but, rather, active co-creators whose perspectives in this matter should be acknowledged. We therefore offer the following suggestions for researchers to consider in relation to the sharing and use of research data: 1 It is essential that researchers are aware of the guidelines

associated with individual research projects. Data management and sharing policies should be considered at the outset of any project, and compared across institutional, funding body and national guidelines. 2 Individual researchers should also ensure that they are working with those who can support them across their institutions, for example librarians and data management officers. 3 Researchers should pay particular attention to the negotiation of informed consent with participants and management of anonymity and attribution, as discussed in detail in Chapter 6. It is important to make this clear when sharing data, and to respect this when using open data. 4 Challenges may be encountered in analysis, as a result of the lack of knowledge of informed consent and participantresearcher dynamics, and merging interpretations. However, these challenges are not unique to open data, and mixedmethod syntheses and meta-analyses (e.g. Major and SavinBaden 2010) can offer guidelines here. The challenges we have raised here would seem to encourage a risk-averse approach to the sharing of open data. However, others, like Atenas, Havemann and Priego (2015) have argued that ethical educational practice requires the use of open data sources: There is much research still to be done to join the dots between open data, open educational resources and the development of transversal skills and discipline-specific competencies. There is a world of potential to be explored, particularly in relation to the

DIGITAL IMPACT

235

development and adoption of methods of assessment that identify how the use of open data can feed into specific competences. … If we understand learning as a transformation of knowledge, mere field-specific ‘competences’ may not be enough; it is necessary to ‘cross boundaries’ (Wenger 1998: 140) in order to be exposed to different modes of behaviour, processes and outcomes. So far, it seems that engagement with open data has been driven by experience, rather than competence, but competences have been developed through the exposure (experience with) to open datasets. In order to formalise more objective mechanisms of assessment, it is necessary to engage critically with, and foster further interconnections between, those engaged in open data and those engaged in education. (Atenas, Havemann and Priego 2015: 385) We agree that open data offer significant opportunities for researchers in terms of meta-analyses and syntheses. For example, one approach might be to combine data from studies exploring the experiences of transgender students across multiple schools, nationally or internationally. Such an approach would support greater national and international understanding of students who are often under-represented in research. Open data can also allow greater cross-disciplinary collaboration, allow research participants to follow the progress of their data and support researchers in working more effectively and efficiently.

Undertaking research in the digital age: Current and future practice As the preceding chapters have shown, research in a digital age offers a wealth of opportunities in terms of research approaches, methods, dissemination, portrayal and representation. These opportunities bring with them challenges related to appropriate ethical stances, the relationship between research and theory, and the nature of research and data. Researchers in a digital age must plan for both the permanence and transience of their data and findings. Data and publications will outlive participants and researchers in open repositories, yet these data are likely to be

236 Research Methods for Education in the Digital Age

re-viewed and re-analysed in unexpected ways, fundamentally altering the context in which data are portrayed and represented. Data should be stored in formats that ensure the permanence of the data, since one technical failure can result in its destruction. There is still much work to be done on research methods, for example, in developing different types of digital stories, in understanding the possibilities for and advantages of big data and in undertaking digital métissage. It is also important, then, to turn our attention to the teaching of research methods in a digital age. There appears to be little discussion about how research methods are taught, suggesting that there is either little innovation in this area, or that, more likely, the subject appears so overwhelming that those teaching digital methods are not quite sure where to start; in terms of the content to be addressed, and the pedagogies that might be used. Nind, Kilburn and Wiles (2015) have explored the pedagogical challenges associated with teaching innovative social science research methods by using video-stimulated focus groups. They observed and recorded a particular teaching and learning event, with one camera angle focused on the teacher and another focused on the students. These angles were then combined into a single video and used as a prompt for a focus group with both staff and students after the teaching and learning event. The focus group raised interesting challenges and successes, in which certain actions that appeared exciting during the observation seemed less interesting on the video. There were also occasions when the researchers seemed to attribute more importance to an action than the staff or students did. This study, therefore, provides an interesting example of how data collection and analysis can be combined, and how participants’ perceptions can be included in the analytical process. Corti and Van der Eynden (2015), in contrast, have explored how data management skills for a digital age might be taught by developing a modular approach. They undertook seventyfive workshops with doctoral students, researchers and research support staff, adapting the workshops to suit their audience and undertaking group discussions, quizzes and exercises, although it is not entirely clear what these exercises were. For these authors, it was important that attention be paid to the rules and regulations that researchers were working within, and facilitating crossdepartmental collaboration in institutions. Yet, this approach did seem to set out particular rules of ‘good research’ regardless

DIGITAL IMPACT

237

of the research context, which we suggest are problematic in a digital age. Other pedagogical approaches, therefore, might include the following: ●●

●●

●●

Problem-based learning approaches with open datasets using disparate methods, such as video, poetry, photography and diaries. Students working in groups should be provided with preliminary contextual information and the datasets, and asked to determine what additional contextual information they need to develop an analytical strategy, before they undertake the analysis and interpretation. Students should then be encouraged to compare the research outputs across groups and reflect upon the ways in which their different approaches (likely) produced very different research outputs. Experiential learning approaches with online interviews. Students should role-play a number of different interview contexts (for example, virtual worlds, messenger, face-toface, video) to explore the challenges and opportunities present in each approach, and reflect upon their experiences and preferences. They should then be required to undertake analysis across all interviews and consider how each individual context can be represented. Supercomplexity and flexible pedagogies focus upon the ethics of undertaking research in digital environments. Students should be provided with complex ethical challenges, such as determining whether informed consent is necessary in a particular study, working with a group of participants in which half wish to be anonymized and half want their data attributed to them, or being told to analyse open datasets with little contextual information about methodologies or methods (for example, interviews in which the questions asked by the researcher are not provided). The goal of this approach is to present students with questions that are troublesome and inexplicable and rely solely on the researcher’s own ethical stance; to find the challenges that fall between ethical guidelines. It is only in doing so that researchers can be properly prepared for researching in a digital age.

238 Research Methods for Education in the Digital Age

Conclusion This chapter has explored digital impact through the measurement of impact and the dissemination of research. Yet, it is important to remember that dissemination (even creative and digital forms of dissemination) does not automatically lead to impact. Instead, impact requires engagement with scholarly and non-scholarly communities, such as in the School of Data project, or in the adaptation of pedagogies to teach newer researchers. Newer forms of dissemination, however, can open up research and potential impact to previously excluded communities. What seems to be most important is exploring these new opportunities, yet at the same time paying attention to the unanticipated and unwelcome consequence of the impact and open agendas. One such effect is the marginalization of some scholars in research discussions, as has been discussed throughout this chapter. It is also likely that a researcher’s personal definition of impact will differ from that of funding bodies and evaluation schemes such as the UK REF, just as definitions of impact – and of open – will differ across researchers.

Beginning and endings We began this book with a Table (Table 0.1) that offered an overview of terms and practices used in educational research, yet as we close we are aware that many such terms are used less frequently and some have even become obsolete. Researching Education in a Digital Age demands that as researchers, we are flexible and critical, always examining what it is we are really seeking to find out. For many, in the United Kingdom at least, ‘blended learning’ and ‘eLearning’ are terms that are passé, and digital fluency is the new genre. At a time when accountability and impact are high on worldwide government agendas, and the norms and procedures in education are not questioned enough, it is vital that researchers seek new beginnings for research. What, for example, do we mean by ‘safeguarding’? Who decides what is ‘safe’, and whom are we keeping safe really? Asking troublesome questions is central to our work. Harnessing in-depth and complex data is vital to ensure

DIGITAL IMPACT

239

credibility in research, yet often it seems to easier to harvest the readily and easily available data, and we need to be aware of the danger of this. Working in hidden and difficult places is where educational researchers must be. This is not easy work, but it is vital. Questioning and criticality must be at the heart of research in a digital age. As research in a digital age moves and develops, it is vital that researchers work institutionally, nationally and internationally, both within and across disciplines, in creative and sustainable ways. In this text, we have sought to identify current challenges and offer suggestions for practice, but ultimately these challenges and practices are contextually based and ever-changing. The challenges of government initiatives, neoliberal capitalist values and constant change can be managed only by engaging with these challenges, recognizing the values that underpin them and developing ethical responses. By ethical, here, we mean acknowledging one’s researcher stance and responding with integrity. This book has sought to offer and suggest ideas in and for the future, for as Abraham Lincoln said, ‘The best way to predict the future is to create it.’

Further reading Bartling, S. and S. Friesike (eds) (2013), Opening Science: The Evolving Guide on How the Internet Is Changing Research, Collaboration and Scholarly Publishing. London: SpringerOpen. Bartling and Friesike’s edited collection begins by outlining the history of the open movement, examining open education, open access literature, open source technologies and open data. It then goes on to discuss tools to support openness, addressing practical how-tos as well as the ultimate vision of the open agenda. Whilst practice is moving on particularly quickly in this area, this text (published openly) provides valuable insight into open practices across a number of areas, and is designed for those trying to determine how ‘the open agenda’ might work in practice. Weller, M. (2014), The Battle for Open: How Openness Won and Why It Doesn’t Feel Like a Victory. London: Ubiquity Press. In contrast to Bartling and Friesike’s collection, which focuses upon the practicalities of the open movement, Weller’s text traces its history,

240 Research Methods for Education in the Digital Age

successes and failures, from the perspective of an academic involved in online open education from an early stage. Weller engages with the concept and the heart behind open education and open research, questioning the extent to which it has succeeded and the ways in which the open education movement has been co-opted for other, political, purposes. Whilst Weller recognizes the successes of the open education and research movement, he also notes the contradictions in practice, for example, journals publishing special issues about open research, which are not published openly.

References

ab Iorwerth, A. (2005), ‘Methods of Evaluating University Research Around the World’, Working Paper 2004-2005, Department of Finance, Canada. Available online: http://www.fin.gc.ca/pub/pdfs/ wp2005-04e.pdf (accessed 20 January 2016). Adali, S. and J. Golbeck (2014), ‘Predicting Personality with Social Behavior: A Comparative Study’, Social Network Analysis and Mining, 4 (1): 159–79. ADLSN (n.d.), ‘Towards the Collaborative Repository for Ethiopian Academic and Research Institutions (TCRE) Project’, African Digital Library Support Network. Available online: http://adlsn.org/projects/ elsevier-grant/ (accessed 23 January 2016). Alcoff, L. M. (2009), ‘The Problem of Speaking for Others’, in A. Y. Jackson and L. A. Mazzei (eds), Voice in Qualitative Inquiry, 221–36, London: Routledge. Aldrich, C. (2009), Learning Online with Games, Simulations and Virtual Worlds: Strategies for Online Instruction, San Francisco: Jossey-Bass. Aleshidi, H. A. and A. M. Zeki (2011), ‘Virtual Learning Environment’, in Akram. M. Zeki, M. Kartiwi and Ahmed. M. Zeki (eds), Advancement in E-Learning, 5–9, Malaysia: IIUM Press. Alexander, C. (1979), The Timelessway of Building, New York: Oxford University Press. Alexander, C., S. Ishikawa, M. Silverstein, M. Jacobson, I. FiksdahlKing and S. Angel (1977), A Pattern Language: Towns, Buildings, Construction, New York: Oxford University Press. Allen, L. (2015), ‘Losing Face? Photo-Anonymisation and Visual Research Integrity’, Visual Studies, 30 (3): 295–308. Altmetric (n.d.), ‘The Donut and Score’. Available online: http://www. altmetric.com/about-altmetrics/the-donut-and-score/ (accessed 23 January 2016). Alston, G. D. and N. Ellis-Hervey (2015), ‘Exploring the Nonformal Adult Educator in Twenty-First Century Contexts using Qualitative Video Data Analysis Techniques’, Learning, Media and Technology, 40 (4): 502–13.

242

REFERENCES

American Sociological Association (1999), Code of Ethics and Politics and Procedures of the ASA Committee on Professional Ethics, Washington: American Sociological Association. Anderson, T. D. and H. P. Kanuka (2009), ‘Ethical Conflicts in Research on Networked Education Contexts’, in U. Demiray and R. Sharma (eds), Ethical Practices and Implications in Distance Learning, 108–24, Hershey, PA: Information Science Reference. Argles, T., S. Minocha and D. Burden (2015), ‘Virtual Field Teaching Has Evolved: Benefits of a 3D Gaming Environment’, Geology Today, 31 (6): 222–6. Arosio, L. (2010), ‘Personal Documents on the Internet: What’s New and What’s Old’, Journal of Comparative Research in Anthropology and Sociology, 1 (2), 23–38. Ashton, K. (2009), ‘That “Internet of Things” Thing’, Radio Frequency Identification Journal, 22 July. Available online: http://rfidjournal.com/ article/view/4986 (accessed 19 December 2015). Ashton-Warner, S. (1963), Teacher, New York: Touchstone, Simon and Schuster. Atenas, J. and L. Havemann, eds (2015), Open Data as Open Educational Resources: Case Studies of Emerging Practice, London: Open Knowledge, Open Education Working Group. Available online: http://education.okfn.org/files/2015/11/Book-Open-Data-as-OpenEducational-Resources1.pdf (accessed 1 January 2016). Atenas, J., L. Havemann and E. Priego (2015), ‘Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship’, Open Praxis, 7 (4): 377–89. Atkinson, P. (2005), ‘Qualitative Research-Unity and Diversity’, FQS/ Forum: Qualitative Social Research, 6 (3). Available online: http:// www.qualitative-research.net/index.php/fqs/article/view/4/10 (accessed 19 December 2015). Bakardjieva, M. and A. Feenberg (2001), ‘Involving the Virtual Subject: Conceptual, Methodological and Ethical Dimension’, Journal of Ethics and Information Technology, 2 (4): 233–48. Baker, R. S. J. D. and K. Yacef (2009), ‘The State of Educational Data Mining in 2009: A Review and Future Visions’, Journal of Educational Data Mining, 1 (1): 3–17. Barnett, R. (2000), Realizing the University in an Age of Supercomplexity, Buckingham: Society for Research into Higher Education and Open University Press. Bartling, S. and S. Friesike, eds (2013), Opening Science: The Evolving Guide on How the Internet is Changing Research, Collaboration and Scholarly Publishing, London: SpringerOpen.

REFERENCES

243

Bassett, E. H. and K. O’Riordan (2002), ‘Ethics of Internet Research: Contesting the Human Subjects Research Model’, Ethics and Information Technology, 4 (3): 233–47. Bastalich, W. (2010), ‘Knowledge Economy and Research Innovation’, Studies in Higher Education, 35 (7): 845–57, doi: 10.1080/03075070903406533. Bauman, Z. (2000), Liquid Modernity, Cambridge: Polity Press. Bayne, S. (2008), ‘Uncanny Spaces for Higher Education: Teaching and Learning in Virtual Worlds’, Association for Learning Technology Journal, 16 (3): 197–205. Bayne, S. (2014), ‘What’s wrong with “technology enhanced learning”?’, in S. Bayne, C. Jones, M. de Laat, T. Ryberg and C. Sinclair (eds), Proceedings of the 9th International Conference on Networked Learning, 347–50. Bayne, S. and J. Ross (2013), ‘Posthuman Literacy in Heterotopic Space: A Pedagogic Proposal’, in R. Goodfellow and M. Lea (eds), Literacy in the Digital University: Critical Perspectives on Learning, Scholarship and Technology, 95–110, London: Routledge. Bazeley, P. (2012), ‘Integrative Analysis Strategies for Mixed Data Sources’, American Behavioral Scientist, 56 (6): 814–28. Becker, H. S., B. Greer, E. Hughes and A. L. Strauss (1961), Boys in White, New Brunswick: University of Chicago Press. Ben-Ze’ev, A. (2003), ‘Privacy, Emotional Closeness, and Openness in Cyberspace’, Computers in Human Behavior, 19 (4): 451–67. Beneito-Montagut, R. (2011), ‘Ethnography Goes Online: Towards a User-Centred Methodology to Research Interpersonal Communication on the Internet’, Qualitative Research, 11 (6): 716–35. Benford, S., J. Bowers, L. E. Fahlén, C. Greenhalgh and D. Snowdon (1995), ‘User Embodiment in Collaborative Virtual Environments’, in I. R. Katz, R. Mack, L. Marks, M. B. Rosson and J. Nielsen (eds), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 242–9, New York: ACM Press. Bengry-Howell, A., R. Wiles, M. Nind and G. Crow (2011), A Review of the Academic Impact of Three Methodological Innovations: Netnography, Child-Led Research and Creative Research Methods, Southampton, UK: ESRC National Centre for Research Methods. Available onlinw: http://eprints.soton.ac.uk/194895/ (accessed 16 January 2016). Bennett, S. and K. Maton (2010), ‘Beyond the “Digital Natives” Debate: Towards a More Nuanced Understanding of Students’ Technology Experiences’, Journal of Computer Assisted Learning, 26, 321–31.

244

REFERENCES

Berger, P. L. and T. Luckmann (1966), The Social Construction of Reality: A Treatise in the Sociology of Knowledge, Garden City, NY: Anchor Books. Berkowitz, J. (2013), ‘Video Abstracts, the Latest Trend in Scientific Publishing: Will “Publish or Perish” soon include “Video or Vanish”?’, University Affairs, 6th February. Available online: http://www. universityaffairs.ca/video-abstracts-the-latest-trend-in-scientificpublishing.aspx (accessed 20 December 2015). Blanco Ramírez, G. and L. Palu-ay (2015), ‘“You Don’t Look Like Your Profile Picture”: The Ethical Implications of Researching Online Identities in Higher Education’, Educational Research and Evaluation, 21 (2): 139–53. Bloomfield, P. R. and D. Livingstone (2009), ‘Immersive Learning and Assessment with QuizHUD’, Computing and Information Systems Journal, 13 (1): 20–6. Boellstorff, T. (2008), Coming of Age in Second Life: An Anthropologist Explores the Virtually Human, Princeton: Princeton University Press. Boellstorff, T. (2010), ‘A Typology of Ethnographic Scales for Virtual Worlds’, in W. S. Bainbridge (ed.), Online Worlds: Convergence of the Real and the Virtual, 123–33, London: Springer-Verlag. Bogdan, R. C. and S. K. Biklen (1992), Qualitative Research for Education, Needham Heights, MA: Allyn and Bacon. Bogdan, R. C. and S. K. Biklen (1998), Qualitative Research for Education, 3rd edn, Needham Heights, MA: Allyn and Bacon. Boticki, I. and S. Hyo-Jeong (2010), ‘Quiet Captures: A Tool for Capturing the Evidence of Seamless Learning with Mobile Devices’, in S. R. Goldman, J. Pellegrino, K. Gomez, L. Lyons and J. Radinsky (eds), Proceedings of the International Conference of the Learning Science 2010 (1): 500–7, Illinois, USA: International Society of the Learning Sciences. Bourdieu, P. (1991), Language and Symbolic Power, trans. J. B. Thompson, Cambridge: Harvard University Press. Brandtzæg, P. B., M. Lüders and J. H. Skjetne (2010), ‘Too Many Facebook “friends”? Content Sharing and Sociability Versus the Need for Privacy in Social Network Sites’, Journal of Human-Computer Interaction, 26 (11–12): 1006–30. British Psychological Society (2013), Ethics Guidelines for Internetmediated Research, INF206/1.2013, Leicester: British Psychological Society. Available online: http://www.bps.org.uk/system/files/ Public%20files/code_of_human_research_ethics_dec_2014_inf180_ web.pdf; http://www.bps.org.uk/system/files/Public%20files/inf206guidelines-for-internet-mediated-research.pdf (18 January 2016). British Psychological Society (2014), Code of Human Research Ethics, INF180/12.2014, Leicester: British Psychological Society. Available

REFERENCES

245

online: http://www.bps.org.uk/system/files/Public%20files/code_of_ human_research_ethics_dec_2014_inf180_web.pdf (18 January 2016). Brown, A. (1992), ‘Design Experiments: Theoretical and Methodological Challenges in Creating Complex Interventions in Classroom Settings’, The Journal of the Learning Sciences, 2 (2): 141–78. Bruner, J. S. (1990), Acts of Meaning, Cambridge, MA: Harvard University Press. Bruner, J. S. (1996), The Culture of Education, Cambridge, MA: Harvard University Press. Buch-Hansen, H. (2014), ‘Social Network Analysis and Critical Realism’, Journal for the Theory of Social Behavior, 44 (3): 306–25. Buck, S. (2013), ‘How 1 Billion People are coping with Death and Facebook’, Mashable, 13 February. Available online: http://mashable. com/2013/02/13/facebook-after-death/ (accessed 19 December 2015). Burgess, J. (2006), ‘Hearing Ordinary Voices: Cultural Studies, Vernacular Creativity and Digital Storytelling’, Journal of Media and Cultural Studies, 20 (2): 201–14. Busher, H. and N. James (2015), ‘In Pursuit of Ethical Research: Studying Hybrid Communities using Online and Face-to-Face Communications’, Educational Research and Evaluation, 21 (2): 161–81. Butler-Kisber, L. (2002), ‘Artful Portrayals in Qualitative Inquiry: The Road to Found Poetry and Beyond’, The Alberta Journal of Educational Research, 48 (3): 229–39. Butler-Kisber, L. (2008), ‘Representational Forms of Dissemination’, in L. M. Given (ed.), The Sage Encyclopaedia of Qualitative Research, vol. 2, 756–60, CA: Sage. Butler-Kisber, L. (2010), Qualitative Inquiry: Thematic, Narrative and Arts-Informed Perspectives, London: Sage. Callaghan, M., K. McCrory, J. L. Losada, J. Harkin and S. Wilson (2013), ‘Using Game-Based Learning in Virtual Worlds to Teach Electronic and Electrical Engineering’, IEEE Transactions on Industrial Informatics, 9 (1): 575–84. Callon, M. (1986), ‘The Sociology of an Actor-Network: The Case of the Electric Vehicle’, in J. Law and A. Rip (eds), Mapping the Dynamics of Science and Technology, 19–34, London: Macmillan. Callon, M. and B. Latour (1981), ‘Unscrewing the Big Leviathan’, in K. D. Knorr-Cetina and M. Mulkay (eds), Advances in Social Theory and Methodology, 277–303, London: Routledge. Callon, M. and J. Law (1982), ‘On Interests and their Transformation: Enrolment and Counter-Enrolment’, Social Studies of Science, 12 (4): 615–25. Campbell, L. H. and D. McDonagh (2009), ‘Visual Narrative Research Methods as Performance in Industrial Design Education’, Qualitative Inquiry, 15 (3): 587–606.

246

REFERENCES

Cancienne, M. B. and C. Snowber (2003), ‘Writing Rhythm: Movement as Method’, Qualitative Inquiry, 9 (2): 237–53. Castells, M. (1996), The Rise of the Network Society, The Information Age: Economy, Society and Culture Vol. 1, Oxford: Blackwell. Chambers, C., E. Hasebe-Ludt, D. Donald, W. Hurren, C. Leggo and A. Oberg (2008), ‘Métissage: A Research Praxis’, in G. Knowles and A. Cole (eds), Handbook of the Arts in Qualitative Research: Perspectives, Methodologies, Examples and Issues, 141–53, CA: Sage. Chen, H. M. (2014), ‘From Commercial Film to Micro-Movie: Innovative Thinking on Cinematographic Production in the New Media Age’, MA diss., Shih Hsin University, Taiwan. Chiu, M. H., C. C. Chou, W. L. Wu and H. Liaw (2014), ‘The Role of Facial Microexpression State (FMES) Change in the Process of Conceptual Conflict’, British Journal of Educational Technology, 45 (3): 471–86. Chodos, D., E. Stroulia, S. King and M. Carbonaro (2014), ‘A Framework for Monitoring Instructional Environments in a Virtual World’, British Journal of Educational Technology, 45 (1): 24–35. Clifford, J. and G. Marcus, eds (1986), Writing Culture: The Poetics and Politics of Ethnography, Berkeley, CA: University of California Press. Coffey, A., E. Reynold, B. Dicks, B. Soyinka, and B. Mason (2006), ‘Hypermedia Ethnography in Educational Settings: Possibilities and Challenges’, Ethnography and Education, 1 (1): 15–30. Collins, A. (1992), ‘Toward a Design Science of Education’, in E. Scanlon and T. O’Shea (eds), New Directions in Educational Technology, 15–22, Berlin: Springer-Verlag. Connelly, F. M. and D. J. Clandinin (2005), ‘Narrative Inquiry’, in J. Green, G. Camilli and P. Elmore (eds), Handbook of Complementary Methods in Education Research, 477–89, Washington, DC: American Educational Research Association. Constable, N. (2003), Romance on a Global Stage: Pen Pals, Virtual Ethnography and ‘Mail Order’ Marriages, Berkeley: University of California Press. Cooley, S. J., M. J. G. Holland, J. Cumming, E. G. Novakovic and V. E. Burns (2014), ‘Introducing the Use of Semi-Structure Video Diary Room to Investigate Students’ Learning Experiences During an Outdoor Adventure Education Groupwork Skills Course’, Higher Education, 67: 105–21. Cooperrider, D. L. and S. Srivasta (1987), ‘Appreciative Inquiry in Organizational Life’, in R. W. Woodman and W. A. Pasmore (eds), Research in Organizational Change and Development, 129–69, Samford, CT: JAI Press. Cornwall, A. and R. Jewkes (1995), ‘What is Participatory Research?’, Social Science and Medicine 41 (2): 1667–76.

REFERENCES

247

Corti, L. and V. Van den Eynden (2015), ‘Learning to Manage and Share Data: Jump-Starting the Research Methods Curriculum’, International Journal of Social Research Methodology, 18 (5): 545–59. Couper, M. P. (1998), ‘Measuring Survey Quality in a CASIC Environment’, in Proceedings of the Survey Research Methods Section, 41–9, American Statistical Association. Available online: http://www. amstat.org/sections/srms/Proceedings/papers/1998_006.pdf (accessed 17 January 2016). Couper, M. P. (2013), ‘Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys’, Survey Research Methods, 7 (3): 145–56. Craig, C. (2013), ‘Opportunities and Challenges in Representing Narrative Inquiries Digitally’, Teachers College Record, 115 (4): 1–45. Daden (2014), ‘Datascape: 3D Immersive Data Visualisation’, Daden. co.uk. Available online: http://www.daden.co.uk/conc/datascape (accessed 23 January 2016). Daiute, C. (2014), ‘Narrative Analysis of Interactive Digital Storytelling’, in A. Mitchell, C. Fernández-Vara and D. Thue (eds), Interactive Digital Storytelling Conference, 254–7, Switzerland: Springer International Publishing. Dalgarno, B., M. Lee, L. Carson, S. Gregory and B. Tynan (2011), ‘An Australian and New Zealand Scoping Study on the Use of 3D Immersive Virtual Worlds in Higher Education’, Australasian Journal of Educational Technology, 27 (1): 1–15. Davies, B. (2009), ‘Life in Kings Cross: Play of Voices’, in A. Y. Jackson and L. A. Mazzei (eds), Voice in Qualitative Inquiry, 221–36, London: Routledge. Dawson, P. (2014), ‘Our Anonymous Online Research Participants Are Not Always Anonymous: Is This a Problem?’, British Journal of Educational Technology, 45 (3): 428–37. de Certeau, M. (1984), The Practice of Everyday Life, trans. S. Rendall, Berkeley: University of California Press. de Felice, D. and V. J. Janesick (2015), ‘Understanding the Marriage of Technology and Phenomenological Research: From Design to Analysis’, The Qualitative Report, 20 (10): 1576–93. de Freitas, S. and T. Neumann (2009), ‘The Use of ‘Exploratory Learning’ for Supporting Immersive Learning in Virtual Environments’, Computers and Education, 52 (2): 343–52. Dede, C. (1995), ‘The Evolution of Constructivist Learning Environments: Immersion in Distributed, Virtual Worlds’, Educational Technology, 35 (5): 46–52. Delamont, S., ed. (2012), Handbook of Qualitative Research in Education, Abingdon: Edward Elgar Publishing.

248

REFERENCES

Deleuze, G. (1993), The Fold: Leibniz and the Baroque, trans. T. Conley, New York: Continuum. Deleuze, G. and F. Guattari ([1980] 1987), ‘A Thousand Plateaus: Capitalism and Schizophrenia’, Minneapolis: University of Minnesota Press. Design-Based Research Collective (2003), ‘Design-Based Research: An Emerging Paradigm for Educational Inquiry’, Educational Researcher, 32 (1): 5–8. Dewey, J. (1938), Experience and Education, New York: Collier Books. Dicks, B., B. Soyinka and A. Coffey (2006), ‘Multimodal Ethnography’, Qualitative Research, 6 (1): 77–96. Dittmer, J. (2010), ‘Immersive Virtual Worlds in University-Level Human Geography Courses’, International Research in Geographical and Environmental Education, 19 (2): 139–54. Dominguez Figadero, D., A. Bealieu, A. Estalella, E. Gómez, B. Schnettler and R. Read (2007), ‘Virtual Ethnography’, FQS/Forum: Qualitative Social Research, 8 (3). Available online: http://nbn-resolving.de/ urn:nbn:de:0114-fqs0703E19 (accessed 19 December 2015). Dong, Y., R. A. Johnson and N. V. Chawla (2015), ‘Will this Paper Increase Your h-Index?’, Machine Learning and Knowledge Discovery in Databases: Lecture Notes in Computer Science, 9286: 259–63. Downes, S. (2005), ‘E-learning 2.0’, E-Learning Magazine, 1 October. Available online: http://elearnmag.acm.org/featured.cfm?aid=1104968 (accessed 19 December 2015). Drumm, L. (2015), ‘Is There Theory Behind Practice? Theorising University Teaching with Digital Technologies’, 14th European Conference on E-Learning. Available online: https://www.researchgate. net/publication/283373448_Is_There_Theory_Behind_Practice_ Theorising_University_Teaching_With_Digital_Technologies (accessed 21 January 2016). Durkheim, E. (1955), Pragmatism and Sociology, Cambridge University Press: Cambridge. Eaton, M. (1979), Anthropology-Reality-Cinema: The Films of Jean Rouch, London: British Film Institute. Economic and Social Research Council (2015), Framework for Research Ethics. Available online: http://www.esrc.ac.uk/funding/guidance-forapplicants/research-ethics/ (18 January 2016). Ebner, M., C. Lienhardt, M. Rohs and I. Meyer (2010), ‘Microblogs in Higher Education––A Chance to Facilitate Informal and ProcessOriented Learning?’, Computers and Education, 55 (1): 92–100. EIFL (2016), ‘Open Access Policies and Theses Mandates Implemented in EIFL Partner Countries’, EIFL.net, 15th January. Available online: http://www.eifl.net/programme/open-access-programme/open-accesspolicies (accessed 24 January 2016).

REFERENCES

249

Einav, L. and J. Levin (2014), ‘The Data Revolution and Economic Analysis’, NBER Working Paper Series No. 19035. Available online: http://www.nber.org/papers/w19035 (accessed 17 January 2016). Elliot, M., K. Purdam and E. Mackey (2013), Data Horizons, London: ESRC. Elwood, S. and A. Leszcynski (2011), ‘Privacy, Reconsidered: New Representations, Data Practices, and the Geoweb’, Geoweb, 42 (1): 6–15. Engeström, Y. (1987), Learning by Expanding: An Activity-Theoretical Approach to Developmental Research, Helsinki: OrientaKonsultimark. Engeström, Y. (1999), ‘Innovative Learning in Work Teams: Analyzing Cycles of Knowledge Creation in Practice’, in Y. Engeström, R. Miettinen and R. L. Punamaki (eds), Perspectives on Activity Theory, 377–406, New York: Cambridge University Press. Engeström, Y. and V. P. Glaveanu (2012), ‘On Third Generation Activity Theory: Interview with Yrjö Engeström’, Europe’s Journal of Psychology, 8 (4): 515–18. Epstein, I., B. Stevens, P. McKeever and S. Baruchel (2006), ‘Photo Elicitation Interview: Using Photos to Elicit Children’s Perspectives’, International Journal of Qualitative Methods, 5 (3): 1–11. European Union Directive 95/46/EC of 13 May 2014 on personal data – the protection of individuals with regard to the processing of such data. Eysenbach, G. (2011), ‘Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional metrics of Scientific Impact’, Journal of Medical Internet Research, 13 (4): e123. Facer, K. (2014), ‘What is Space for? Towards a Politics and a Language for the Human in Education’, Technology, Pedagogy and Education, 23 (1): 121–6. Fairclough, N. (1989), Language and Power, London: Longman. Fairclough, N. (2010), Critical Discourse Analysis: The Critical Study of Language, 2nd edn, London: Routledge. Farrow, R. (2014), ‘Ethical Use of New Technology in Education’, Philosopher1978 | Dr. Robert Farrow, 11th March. Available online: https://philosopher1978.wordpress.com/2014/03/11/ethical-use-ofnew-technology-in-education/ (18 January 2016). Fields, D. and Y. B. Kafai (2009), ‘A Connective Ethnography of Peer Knowledge Sharing and Diffusion in a Tween Virtual World’, Computer-Supported Collaborative Learning, 4 (1): 47–68. Fine, M., L. Weis, S. Weseen and M. Wong (2000), ‘For Whom? Qualitative Research, Representations and Social Responsibilities’, in N. K. Denzin and Y. S. Lincoln (eds), Handbook of Qualitative Research, 167–207, Thousand Oaks, CA: Sage.

250

REFERENCES

Finlay, J., J. Gray, I. Falconer, J. Hensman, I. Mor and S. Warburton (2009), PLANET: Pattern Language Network for Web 2.0 in Learning Final Report, Bristol: JISC. Available online: https://hal.archivesouvertes.fr/hal-00592752/document (accessed 19 December 2015). Fitzimons, S. (2013), ‘The Road Less Travelled: The Journey of Immersion into the Virtual Field’, Ethnography and Education, 82 (2): 162–76. Flewitt, R. S. (2011), ‘Bringing Ethnography to a Multimodal Investigation of Early Literacy in a Digital Age’, Qualitative Research, 11 (3): 293–310. FoldingStory (n.d.), Folding Story. Available online: http://foldingstory. com/ (accessed 19 December 2015). FoldThatStory (n.d.), Fold That Story. Available online: http:// foldthatstory.com/ (accessed 19 December 2015). Fontana, A. and J. H. Frey (1994), ‘Interviewing: The Art of Science’, in N. K. Denzin and Y. S. Lincoln (eds), Handbook of Qualitative Research, 361–76, Thousand Oaks: Sage Publications. Ford, H. (2013), ‘Christine Hine on Virtual Ethnography’s E3 Internet’, Ethnography Matters, November 29. Available online: http:// ethnographymatters.net/blog/2013/11/29/christine-hine-on-virtualethnographys-e3-internet/ (accessed 19 December 2015). Fox, M. and S. M. Hanlon (2015), ‘Barriers to Open Access Uptake for Researchers in Africa’, Online Information Review, 39 (5): 698–716. Fox, S. (2005), ‘An Actor-Network Critique of Community in Higher Education: Implications for Networked Learning’, Studies in Higher Education, 30 (1): 95–110. Freire, P. (1970), Pedagogy of the Oppressed, New York: Continuum. Frost, M. (2014), ‘The Grief Grapevine: Facebook Memorial Pages and Adolescent Bereavement’, Australian Journal of Guidance and Counselling, 24 (2): 256–65. Fuller, M. and H. Jenkins (1995), ‘Nintendo and New World Travel Writing: A Dialogue’, in S. G. Jones (ed.), Cybersociety: ComputerMediated Communication and Community, 57–72, Thousand Oaks, CA: Sage. Furlong, J. and C. Davies (2012), ‘Young People, New Technologies and Learning at Home: Taking Context Seriously’, Oxford Review of Education, 38 (1): 45–62. Galloway A. R. (2012), The Interface Effect, Cambridge: Polity Press. Gao, F., T. Luo and K. Zhang (2012), ‘Tweeting for Learning: A Critical Analysis of Research on Microblogging in Education Published in 2008–2011’, British Journal of Educational Technology, 43 (5): 783–801. Gaševic, � D., S. Dawson and G. Siemens (2015), ‘Let’s Not Forget: Learning Analytics Are About Learning’, TechTrends, 59 (1): 64–71. Geertz, C. (1973), The Interpretation of Cultures, New York: Basic Books.

REFERENCES

251

Geiger, R. S. and D. Ribes (2011), ‘Trace Ethnography: Following Coordination Through Documentary Practices’, in The 44nd Annual Hawaii International Conference on System Science (HICSS-44), 1–10, Hawaii, HI: IEEE Computer Society Press. Gergen, K. J. (1978), ‘Toward Generative Theory’, Journal of Personality and Social Psychology, 36 (11): 1344–60. Gibbons, M. and H. Simons (2002), ‘In Four Movements: Using the Creative Arts in Evaluation’, United Kingdom Evaluation Society’s Annual Conference: The Art of Evaluation - Artistry, Discipline, and Delivery, Paper presented at South Bank Centre, London, 12–13 December. Gibson, J. (1979), The Ecological Approach to Visual Perception, Boston: Houghton Mifflin. Girvan, C. and T. Savage (2010), ‘Identifying an Appropriate Pedagogy for Virtual Worlds: A Communal Constructivism Case Study’, Computers and Education, 55 (1): 342–9. Gitelman, L., ed. (2014), Raw Data is an Oxymoron, Massachusetts: Massachusetts Institute of Technology. Goble, E., W. Austin, D. Larsen, L. Kreitzer and S. Brintnell (2012), ‘Habits of Mind and the Split-Mind Effect: When Computer-Assisted Qualitative Data Analysis Software is Used in Phenomenological Research’, FQS/Forum: Qualitative Social Research, 13 (2): article 2. Goldman, R. (2007), ‘Video Representations and the Perspectivity Framework’, in R. Goldman, R. Pea, B. Barron and S. J. Derry (eds), Video Research in the Learning Sciences, 3–38, New York: Routledge. González-Bailón, S., N. Wang, A. Rivero, J. Borge-Holthoefer and Y. Moreno (2014), ‘Assessing the Bias in Samples of Large Online Networks’, Social Networks, 38: 16–27. Goodbrey, D. M. (2015), ‘A Duck Has an Adventure’, Daniel Merlin Goodbrey’s New Experiments in Fiction. Available online: http://e-merl.com/stuff/duckadv.html (accessed 19 December 2015). Grant, S. and R. Clerehan (2011), ‘Finding the Discipline: Assessing Student Activity in Second Life’, Australasian Journal of Educational Technology, 27 (5): 813–28. Guba, E. G. and Y. S. Lincoln (2005), ‘Paradigmatic Controversies, Contradictions, and Emerging Confluences’, in N. K. Denzin and Y. S. Lincoln (eds), The SAGE Handbook of Qualitative Research, 3rd edn, 191–215, London: Sage. Hakkinen, P. (2015), ‘Multiphase Method for Analysing Online Discussions’, Journal of Computer Assisted Learning, 29: 547–55. Hammersley, M. (1991a), What’s Wrong with Ethnography?, London: Routledge. Hammersley, M. (1991b), Reading Ethnographic Research: A Critical Guide, London: Longman.

252

REFERENCES

Han, J. H. (2014), ‘Closing the Missing Links and Opening the Relationships among the Factors: A Literature Review on the Use of Clicker Technology Using the 3P Model’, Educational Technology and Society, 17 (4): 150–68. Haraway, D. (1991), Simians, Cyborgs and Women: The Reinvention of Nature, London: Routledge. Harricharan, M. and K. Bhopal (2014), ‘Using Blogs in Qualitative Educational Research: An Exploration of Method’, International Journal of Research and Method in Education, 37 (3): 324–43. Harrison, B. (2002), ‘Photographic Visions and Narrative Inquiry’, Narrative Inquiry, 12 (1): 87–111. Harrison, S. J. (2013), ‘Not a Freak Show: Growing up Gay in Rural Ontario in the 1960s without Narratives, Images or Role Models Upon Which to Formulate an Identity’, Paper presented at AERA Annual Meeting and Exhibition, San Francisco, CA, 27th April to 1st May. Hasebe-Ludt, E., C. Chambers and C. Leggo (2009), Life Writing and Literary Métissage as an Ethos for Our Times, New York, NY: Peter Lang. Hasler, B. S., P. Tuchman and D. Friedman (2013), ‘Virtual Research Assistants: Replacing Human Interviewers by Automated Avatars in Virtual Worlds’, Computers in Human Behavior, 29 (4): 1608–16. Haustein, S., I. Peters, C. R. Sugimoto, M. Thelwall and V. Larivère (2014), ‘Tweeting Biomedicine: An Analysis of Tweets and Citations in the Biomedical Literature’, Journal of the Association for Information Science and Technology, 65 (4): 656–69. Hayles, K. (1999), How We Became Posthuman: Virtual Bodies in Cybernetics, Literature and Informatics, Chicago: The University of Chicago Press. Hayles, K. (2012), How We Think: Digital Media and Contemporary Technogenesis, Chicago: The University of Chicago Press. Heid, S. and T. Kretschmer (2009), ‘LLL3D’s Contribution to Teaching and Learning with 3D-MUVEs in Higher Education’, British Journal of Educational Technology, 40 (3): 568–71. Heidegger, M. ([1927] 1962), Being and Time, trans. J. Macquarrie and E. Robinson, New York: Harper. Heron, J. (1971), Experience and Method, Guildford: University of Surrey. Herman, L. (2005), ‘Researching the Images of Evil Events: An Arts-Based Methodology in Liminal Space’, Qualitative Inquiry, 11 (3): 468–80. Hill, D. W. (2013), ‘Avatar Ethics: Beyond Images and Signs’, Journal for Cultural Research, 17 (1): 69–84. Hine, C. (2000), Virtual Ethnography, London: Sage. Hine, C. (2007), ‘Connective Ethnography for the Exploration of E-Science’, Journal of Computer-Mediated Communication, 12 (2): 618–34.

REFERENCES

253

Hine, C. (2015), Ethnography for the Internet: Embedded, Embodied and Everyday, London: Bloom. Hirsch, J. E. (2005), ‘An Index to Quantify an Individual’s Scientific Research Output’, Proceedings of the National Academy of Sciences of the United States of America 102 (46): 16569–72. Hoadley, C. (2004), ‘Methodological Alignment in Design-Based Research’, Educational Psychologist, 39 (4): 203–12. Holliday, R. (2000), ‘We’ve Been Framed: Visualising Methodology’, The Sociological Review, 48 (4): 503–21. hooks, b. (1994), Teaching to Transgress, London: Routledge. Horkheimer, M. (1982), Critical Theory, New York: Seabury Press. Horkheimer, M. ([1972] 2002), Traditional and Critical Theory, in M. Horkheimer (ed.), Critical Theory: Selected Essays, 188–243, trans. M. J. O’Connell et al., New York: Continuum. Horkheimer, M. and T. W. Adorno (1972), Dialectic of Enlightenment, New York: Herder and Herder. Horst, H. and D. Miller, eds (2012), Digital Anthropology, London: Berg. Howard Hughes Medical Institute (2003), Bethesda Statement on Open Access Publishing. Available online: http://legacy.earlham.edu/~peters/ fos/bethesda.htm (accessed 23 January 2016). Huizing, A. and J. A. van der Wal (2014), ‘Explaining the Rise and Fall of the Warez MP3 Scene: An Empirical Account from the Inside’, First Monday, 19 (10). Available online: http://firstmonday.org/ojs/index. php/fm/article/view/5546/4125 (accessed 19 December 2015). Husserl, E. ([1907] 1964), The Idea of Phenomenology, trans. W. Alston and G. Nakhnikian, The Hague: Nijhoff. Ilieva, J., S. Baron and N. M. Healey (2002), ‘Online Surveys in Marketing Research: Pros and Cons’, International Journal of Market Research, 44 (3): 361–76. ImpactStory (n.d.), ‘About ImpactStory’, ImpactStory.org. Available online: https://impactstory.org/about (accessed 23 January 2016). Jackson, A. Y. and L. A. Mazzei (2012), Thinking with Theory in Qualitative Research: Viewing Data Across Multiple Perspectives, London: Routledge. James, N. (2015), ‘You’ve Got Mail! Using Email Interviews to Gather Academics’ Narratives of Their Working Lives’, International Journal of Research & Method in Education. Available online: http://www. tandfonline.com/doi/abs/10.1080/1743727X.2015.1056136?journalC ode=cwse20 (accessed 17 January 2016). James, N. and H. Busher (2007), ‘Ethical Issues in Online Educational Research: Protecting Privacy, Establishing Authenticity in Email Interviewing’, International Journal of Research and Method in Education, 30 (1): 101–13.

254

REFERENCES

Jando, D., R. F. Sabia and N. Daniel (2008), The Circus: 1870–1950, Köln, Germany: Taschen. Jewitt, C. (2012), ‘An Introduction to Using Video for Research’, National Centre for Research Methods Working Paper. Available online: http:// eprints.ncrm.ac.uk/2259/4/NCRM_workingpaper_0312.pdf (accessed 20 January 2016). Jones, M. H. and A.-M. Gallen (2015), ‘Peer Observation, Feedback and Reflection for Development of Practice in Synchronous Online Learning’, Innovations in Education and Teaching International. Available online: http://dx.doi.org/10.1080/14703297.2015.1025808 (accessed 10 January 2016). Junco, R., C. M. Elavsky and G. Heiberger (2013), ‘Putting Twitter to the Test: Assessing Outcomes for Student Collaboration, Engagement and Success’, British Journal of Educational Technology, 44 (2): 273–87. Kays, K., K. Gathercoal and W. Buhrow (2012), ‘Does Survey Format Influence Self-Disclosure on Sensitive Question Items?’, Computers in Human Behavior, 28 (1): 251–6. Kennedy, H. (2015), ‘Part 1: What We Found and What This Means for How We Think About “Effective” Visualisations?’, Visualising Data, 12 October. Available online: http://www.visualisingdata.com/2015/10/ views-from-seeing-data-research-part-1/ (accessed 23 January 2016). Ketelhut, D. J., B. C. Nelson, J. Clarke and C. Dede (2010), ‘A Multi-User Virtual Environment for Building and Assessing Higher Order Inquiry Skills in Science’, British Journal of Educational Technology, 41 (1): 56–68. Kim, D. and E. Kim (2014), ‘The #TwitterEthics Manifesto: You Don’t Need to Speak for Us – We are Talking’, Model View Culture, 7th April. Available online: https://modelviewculture.com/pieces/thetwitterethics-manifesto (accessed 18 January 2016). Kinchin, I. M., D. Streatfield and D. B. Hay (2010), ‘Using Concept Mapping to Enhance the Research Interview’, International Journal of Qualitative Methods, 9 (1): 52–68. Kitchin, R. (2014), ‘Big Data, New Epistemologies and Paradigm Shifts’, Big Data and Society, 1–12 June. Available online: http://bds.sagepub. com/content/spbds/1/1/2053951714528481.full.pdf (accessed 17 January 2016). Kousha, K., M. Thelwall and M. Abdoli (2012), ‘The Role of Online Videos in Research Communication: A Content Analysis of YouTube Videos Cited in Academic Publications’, Journal of the American Society for Information Science and Technology, 63 (9): 1710–27. Kozinets, R. V. (2002), ‘The Field Behind the Screen: Using Netnography for Marketing Research in Online Communities’, Journal of Marketing Research, 39 (1): 61–72.

REFERENCES

255

Kozinets, R. V. (2010), Netnography: Doing Ethnographic Research Online, London: Sage. Kozinets, R. V. (2012), ‘Marketing Netnography: Prom/ot(ulgat)ing a New Research Method’, Methodological Innovations Online, 7 (1): 37–45. Kozinets, R. V. (2015), Netnography: Redefined, 2nd edn., London: Sage. Kress, G. and T. van Leeuwen (2001), Multimodal Discourse: The Modes and Media of Contemporary Communication, London: Hodder Arnold. Lambert, J. (2006), Digital Storytelling: Capturing Lives, Creating Community, Berkeley, CA: Digital Diner Press. Lammes, S. (2008), ‘Spatial Regimes of the Digital Playground: Cultural Functions of Spatial Practices in Computer Games’, Space and Culture, 11 (3): 260–72. Latour, B. (1987), Science in Action: How to Follow Scientists and Engineers Through Society, Milton Keynes: Open University Press. Latour, B. (2005), Reassembling the Social, Oxford: Oxford University Press. Law, J. (1992), ‘Notes on the Theory of the Actor-Network: Ordering, Strategy, and Heterogeneity’, Systems Practice, 5 (4): 379–93. Leander, K. M. and K. K. McKim (2003), ‘Tracing the Everyday “Sitings” of Adolescents on the Internet: A Strategic Adaptation of Ethnography Across Online and Offline Spaces’, Education, Communication and Information, 3 (2): 211–40. Leander, K. M. and J. F. Lovvorn (2006), ‘Literacy Networks: Following the Circulation of Texts, Bodies, and Objects in the Schooling and Online Gaming of One Youth’, Cognition and Instruction, 24 (3): 291–340. Lee, D. H. (2009), ‘Mobile Snapshots and Public/Private Boundaries’, Knowledge, Technology and Policy, 22 (3): 161–71. Lee, D., I. T. Arthur and A. S. Morrone (2015), ‘Using Video Surveillance Footage to Support Validity of Self-Reported Classroom Data’, International Journal of Research and Method in Education. Available online: http://dx.doi.org/10.1080/1743727X.2015.1075496 (accessed 10 January 2016). Lefebvre, H. (1991), The Production of Space, 15th edn, Oxford: Blackwell. Leggo, C. (2008), ‘Narrative Inquiry: Attending to the Art of Discourse’, Language and Literacy, 10 (1): 1–21. Leontiev, A. ([1947] 1981), Problems of the Development of Mind, Moscow: Progress Press. Liang, J. (2015), ‘Live Video Classroom Observation: An Effective Approach to Reducing Reactivity in Collecting Observational Information for Teacher Professional Development’, Journal of Education for Teaching, 41 (3): 235–53.

256

REFERENCES

Lincoln, Y. S. and E. G. Guba (1985), Naturalistic Inquiry, Newbury Park, CA: Sage. Lindesmith, A. R. (1952), ‘Two Comments on W. S. Robinson’s “The Logical Structure of Analytic Induction”’, American Sociological Review, 17: 492–93. Lives On (n.d.), Lives On: Your Social Afterlife. Available online: http:// www.liveson.org/. (accessed 19 December 2015). London School of Economics and Political Science (n.d.), The Impact Blog. Available online: http://blogs.lse.ac.uk/impactofsocialsciences/ (accessed 23 January 2016). Long, P. and G. Siemens (2011), ‘Penetrating the Fog: Analytics in Learning and Education’, EDUCAUSE Review, 46 (5): 30–40. Lund, K. (2005), ‘Seeing in Motion and the Touching Eye: Walking over Scotland’s Mountains’, SENSES, 18 (1): 27–42. Lyman, P. and B. Kahle (1998), ‘Achieving Digital Cultural Artifacts: Organizing an Agenda for Action’, D-Lib Magazine, July/August. Available online: http://www.dlib.org/dlib/july98/07lyman.html (accessed 19 December 2015). McAlear, A. (2011), ‘Grappling with Tradition’, Death and Digital Legacy, 17 May. Available online: http://www.deathanddigitallegacy. com/2011/05/17/grappling-with-tradition (accessed 19 December 2015). McKelvey, N., K. Curran and N. Subaginy (2015), ‘The Internet of Things’, in M. Khosrow-Pour (ed.), Encyclopedia of Information Science and Technology, 3rd edn, 5777–83, Hershey, PA: Information Science Reference. McNaught, C. and P. Lam (2010), ‘Using Wordle as a Supplementary Research Tool’, The Qualitative Report, 15 (3): 630–43. McNiff, S. (2008), ‘Arts-Based Research’, in J. G. Knowles and A. L. Cole (eds), Handbook of Education and the Arts in Qualitative Research, 29–40, Thousand Oaks, CA: Sage. Madison, S. (2005), Critical Ethnography: Method, Ethics, and Performance, Thousand Oaks, CA: Sage. Madson, M. (2014), ‘Digital Ethnography for Intercultural Professional Communication: Some Best Practice Principles’, Rhetoric, Professional Communication, and Globalization, 5 (1): 67–89. Major, C. H. and M. Savin-Baden (2010), An Introduction to Qualitative Research Synthesis: Managing the Information Explosion, London: Routledge. Malinowski, B. ([1922] 1984), Argonauts of the Western Pacific, Long Grove: Waveland Press. Maltese, A. V., J. A. Danish, R. M. Bouldin, J. A. Harsh and B. Bryan (2015), ‘What are Students Doing During Lectures? Evidence from New Technologies to Capture Student Activity’, International Journal

REFERENCES

257

of Research and Method in Education. Available online: http://dx.doi. org/10.1080/1743727X.2015.1041492 (accessed 10 January 2016). Manovich, L. (2013), ‘The Algorithms of Our Lives’, The Chronicle of Higher Education, 16 December. Available online: http://chronicle. com/article/The-Algorithms-of-Our-Lives-/143557/ (accessed 19 December 2015). Margolis, E. (2005), ‘Liberal Documentary Goes to School: Farm Security Administration Photographs of Students, Teachers and Schools’, in D. Holloway and J. Beck (eds), American Visual Cultures, 107–15, London and New York: Continuum. Markauskaite, L. and P. Goodyear (2014), ‘Tapping into the Mental Resources of Teachers’ Working Knowledge: Insights into the Generative Power of Intuitive Pedagogy’, Learning, Culture and Social Interaction, 3 (4): 237–51. Markham, A. and E. Buchanan (2012), Ethical Decision-Making and Internet Research: Recommendations for the AOIR Ethics Working Committee Version 2.0, Association of Internet Researchers. Available online: http://aoir.org/reports/ethics2.pdf (accessed 17 January 2016). Martin, V. (2008), A Narrative Inquiry into the Effects of Serious Illness and Major Surgery on Conceptions of Self and Life Story, PhD thesis, University of Bristol. Available online: http://ethos.bl.uk/OrderDetails. do?uin=uk.bl.ethos.495891 (accessed 19 December 2015). Martinho, M., M. Pinto and Y. Kuznetzova (2012), ‘Scholars’ YouTube Channels: Content Analysis of Educational Videos’, Internet Latent Corpus Journal, 2 (2): 76–90. Mason, J. (2002), Qualitative Researching, 2nd edn, London: Sage. Matthews, N. and N. Sunderland (2013), ‘Digital Life-Story Narratives as Data for Policy Makers and Practitioners: Thinking Through Methodologies for Large-Scale Multimedia Qualitative Datasets’, Journal of Broadcasting and Electronic Media, 57 (1): 97–114. Mawer, M. (2012), ‘Virtual Worlds in UK Higher Education: Students’ Perspectives’, PhD diss., Coventry: Coventry University. Available online: http://mattmawer.co.uk/wp-content/uploads/2013/12/MattMawer-Virtual-worlds-in-HE-Students-perspectives.pdf (accessed 18 January 2016). Mawer, M. (2014), ‘Observational Practice in Virtual Worlds: Revisiting and Expanding the Methodological Discussion’, International Journal of Social Research Methodology. Available online: http://www. tandfonline.com/doi/full/10.1080/13645579.2014.936738 (accessed 19 December 2015). Max Planck Society (2003), Berlin Statement on Open Access to Knowledge in the Sciences and Humanities. Available online: http:// openaccess.mpg.de/Berlin-Declaration (accessed 23 January 2016).

258

REFERENCES

Mead, M. ([1928] 2001), Coming of Age in Samoa: A Psychological Study of Primitive Youth for Western Civilisation, New York: HarperCollins. Merleau-Ponty, M. ([1945] 1962), Phenomenology of Perception, London: Routledge. Mezirow, J. (1991), Transformative Dimensions of Adult Learning, San Francisco: Jossey Bass. Mikropoulos, T. A. and A. Natsis (2011), ‘Educational Virtual Environments: A Ten-Year Review of Empirical Research (1999–2009)’, Computers and Education, 56 (3): 769–80. Moore, J. L., C. Dickson-Deane and K. Gaylen (2011), ‘E-Learning, Online Learning, and Distance Learning Environments: Are They the Same?’, Internet and Higher Education, 14: 129–35. Mor, Y., S. Warburton and N. Winters (2012), ‘Participatory Pattern Workshops: A Methodology for Open Learning Design Inquiry’, Research in Learning Technology, 20. Available online: http://www. researchinlearningtechnology.net/index.php/rlt/article/view/19197/html (accessed 18 January 2016). Morris, D. and N. Ecclesfield (2011), ‘A New Computer-Aided Technique for Qualitative Document Analysis’, International Journal of Research and Method in Education, 34 (3): 241–54. Morsatter, F., J. Pfeffer, H. Liu and K. M. Carley (2013), ‘Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose’, in ICWSM’13: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, 400–8, Boston: AAAI Press. Mu, J., K. Stegmann, E. Mayfield, C. Rosé and F. Fischer (2012), ‘The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussion’, Computer-Supported Collaborative Learning, 7: 285–305. Murthy, D. (2011), ‘Emergent Digital Ethnographic Methods for Social Research’, in S. N. Hesse-Biber (ed.), The Handbook of Emergent Technologies in Social Research, 158–79, Oxford: Oxford University Press. Nardi, B. (1995), Context and Consciousness: Activity Theory and Human-Computer Interaction, MA: MIT Press. National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978), Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research. Available online: http://www.fda.gov/ohrms/dockets/ac/05/briefing/2005– 4178b_09_02_Belmont%20Report.pdf (18 January 2016). National Institute of Health (1949), Nuremberg Code. Available online: https://history.nih.gov/research/downloads/nuremberg.pdf (18 January 2016). Neergaard, M. A., F. Olesen, R. S. Andersen and J. Sondergaard (2009), ‘Qualitative Description – The Poor Cousin of Health Research?’, BMC Medical Research Methodology, 9: 52–6.

REFERENCES

259

Nguyen, P. H., K. Xu, A. Wheat, B. L. W. Wong, S. Attfield and B. Fields (2016), ‘SensePath: Understanding the Sensemaking Process Through Analytic Provenance’, IEEE Transactions on Visualization and Computer Graphics, 22 (1): 41–50. Nind, M., R. Wiles, A. Bengry-Howell and G. Crow (2013), ‘Methodological Innovation and Research Ethics: Forces in Tension or Forces in Harmony?’, Qualitative Research Journal, 13 (6): 650–67. Nind, M., D. Kilburn and R. Wiles (2015), ‘Using Video and Dialogue to Generate Pedagogic Knowledge: Teachers, Learners and Researchers Reflecting Together on the Pedagogy of Social Research Methods’, International Journal of Social Research Methodology, 18 (5): 561–76. Nissenbaum, H. (1998), ‘Protecting Privacy in an Information Age: The Problem of Privacy in Public’, Law and Philosophy, 17: 559–96. Nissenbaum, H. (2010), Privacy in Context: Technology, Policy, and the Integrity of Social Life, Amazon Kindle: Stanford Law Books. Available online: http://www.amazon.co.uk/gp/product/B003E3LBIA?psc=1&redi rect=true&ref_=oh_aui_d_detailpage_o05_ (accessed 15 October 2015). Nordstrom, S. N. (2015), ‘Not So Innocent Anymore: Making Recording Devices Matter in Qualitative Interviews’, Qualitative Inquiry, 21 (4): 388–401. Nørskov, S. and M. Rask (2011), ‘Observation of Online Communities: A Discussion of Online and Offline Observer Roles in Studying Development, Cooperation and Coordination in an Open Source Software Environment’, FQS/Forum: Qualitative Social Research, 12 (3): article 5. Noyes, A. (2004), ‘Video Diary: A Method for Exploring Learning Dispositions’, Cambridge Journal of Education, 34 (2): 193–209. Nussbaum, M. C. (2010), Not for Profit: Why Democracy Needs the Humanities, Princeton: Princeton University Press. OECD (n.d.), Better Life Index. Available online: http://www. oecdbetterlifeindex.org/ (accessed 23 January 2016). Ohler, J. (2009), Digital storytelling in the Classroom: New Media Pathways to Literacy, Learning, and Creativity, Thousand Oaks, CA: Sage. Oliver, M. (2005), ‘The Problem with Affordance’, E-Learning, 2 (4): 402–13. Oliver, M. and K. Trigwell (2005), ‘Can “Blended Learning” be Redeemed?’, E-Learning, 2 (1): 17–26. OpenContent (n.d.), ‘Defining the “Open” in Open Content’, OpenContent.org. Available online: http://www.opencontent.org/ definition/ (accessed 23 January 2016). Open Knowledge Foundation (n.d.), ‘What is Open Data?’, Open Data Handbook. Available online: http://opendatahandbook.org/guide/en/ what-is-open-data/ (accessed 1 January 2016). Open Society Institute (2002), Budapest Open Access Initiative, Budapest: Open Society Institute. Available online: http://www. budapestopenaccessinitiative.org/read (accessed 23 January 2016).

260

REFERENCES

Open Source Initiative (2007), ‘The Open Source Definition’, OpenSource.org. Available online: http://opensource.org/osd (accessed 23 January 2016). Ortega, J. (2015), ‘Relationship between Altmetric and Bibliometric Indicators across Academic Social Sites: The Case of CSIC’s Members’, Journal of Informetrics, 9: 39–49. Papachristos, N. M., I. Vrellis, A. Natsis and T. A. Mikropolous (2014), ‘The Role of Environment Design in an Educational Multi-User Virtual Environment’, British Journal of Educational Technology, 45 (4): 636–46. Papamitsiou, Z. and A. A. Economides (2014), ‘Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence’, Educational Technology and Society, 17 (4): 49–64. Patton, M. (2002), Qualitative Research and Evaluation Methods, Thousand Oaks, CA: Sage Publications. Perez-Garcia, M. (2009), ‘MUVEnation: A European Peer-to-Peer Learning Programme for Teacher Training in the Use of MUVEs in Education’, British Journal of Educational Technology, 40 (3): 561–7. Peterson, M. (2012), ‘Learner Interaction in a Massively Multiplayer Online Role Playing Game (MMORPG): A Sociocultural Discourse Analysis’, ReCALL, 24 (3): 361–80. Pink, S. (2001), Doing Visual Ethnography: Images, Media and Representation in Research, London: Sage. Pink, S. (2007), Doing Visual Ethnography: Images, Media and Representation in Research, 2nd edn, London: Sage. Pink, S. (2009), Doing Sensory Ethnography, London: Sage. Pink, S. (2011), ‘Multimodality, Multisensoriality and Ethnographic Knowing: Social Semiotics and the Phenomenology of Perception’, Qualitative Research, 11 (1): 261–76. Pink, S., ed. (2012), Advances in Visual Methodology, London: Sage. Polkinghorne, D. E. (1995), ‘Narrative Configuration in Qualitative Analysis’, in J. A Hatch and R. Wisniewski (eds), Life History and Narrative, 5–24, London: The Falmer Press. Popper, K. (1959), The Logic of Scientific Discovery, London: Hutchinson. Priem, J. (2010), I like the term #articlelevelmetrics, but it fails to imply *diversity* of measures. Lately, I’m liking #altmetrics, Twitter, 29 September. Available online: https://twitter.com/jasonpriem/ status/25844968813 (accessed 8 January 2016). Priem, J., D. Taraborelli, P. Groth and C. Neylon (2010), ‘About Altmetrics’, Altmetrics. Available online: http://altmetrics.org/about/ (accessed 23 January 2016). QuestionBridge.com (n.d.), Question Bridge: Black Males. Available online: www.questionbridge.com (accessed 19 December 2015).

REFERENCES

261

Raffaghelli, J. E., S. Cuccharia and D. Persico (2015), ‘Methodological Approaches in MOOC Research: Retracing the Myth of Proteus’, British Journal of Educational Technology, 46 (3): 488–509. Reason, P. (1988), Human Inquiry in Action, London: Sage. Renov, M. (1996), ‘Video Confessions’, in M. Renov (ed.), Resolutions, Minneapolis, MN: University of Minnesota Press. Research Excellence Framework (2014), Research Excellence Framework 2014: The Results, Higher Education Funding Council for England. Available online: http://www.ref.ac.uk/media/ref/content/pub/REF%20 01%202014%20-%20full%20document.pdf (accessed 20 January 2016). Richardson, L. (2000), ‘Evaluating Ethnography’, Qualitative Inquiry, 6 (2): 253–5. Right to Research Coalition (n.d.), Right to Research: Access to Research is a Student Right. Available online: http://righttoresearch.org/ (accessed 23 January 2016). Rinaldo, S. B., S. Tapp and D. A. Laverie (2011), ‘Learning by Tweeting: Using Twitter as a Pedagogical Tool’, Journal of Marketing Education, 33 (2): 193–203. Roberts, J. (2011), ‘Video Diaries: A Tool to Investigate SustainabilityRelated Learning in Threshold Spaces’, Environmental Education Research, 17 (5): 675–88. Robinson, L. and J. Schulz (2009), ‘New Avenues for Sociological Inquiry: Evolving Forms of Ethnographic Practice’, Sociology, 43 (4): 685–98. Rodriguez, S. (2014), ‘I Do Activist Things Even Though I’m Nothing: A Critical Ethnography of Immigrant Youth Identity Formation in an Urban Community-School’, Dissertations, PhD diss., Loyola University Chicago. Available online: http://ecommons.luc.edu/luc_diss/1110 (accessed 19 December 2015). Rogers, C. (1983), Freedom to Learn for the ‘80’, Columbus, Ohio: Merrill. Rorty, R. (1979), Philosophy and the Mirror of Nature, Princeton, NJ: Princeton University Press. Rose, G. (2014), ‘On the Relation Between “Visual Research Methods” and Contemporary Visual Culture’, Sociological Review, 62 (1): 24–46. Rose, G. (2015), ‘Some Early Thoughts on the Fourth Edition of Visual Methodologies’, 6 January. Available online: https://visualmethodculture. wordpress.com/2015/01/06/some-early-thoughts-on-the-fourth-editionof-visual-methodologies/ (accessed 19 December 2015). Rose, G., M. Degen and C. Melhuish (2014), ‘Networks, Interfaces, and Computer-Generated Images: Learning from Digital Visualisations of Urban Redevelopment Projects’, Environment and Planning D: Society and Space, 32 (3): 386–403.

262

REFERENCES

Rosenberg, A. (2010), ‘Virtual World Research Ethics and the Private/ Public Distinction’, International Journal of Internet Research Ethics, 3 (1): 23–37. Roth, S. and O. Erstad (2016), ‘Positional Identities in Educational Transitions: Connecting Contemporary and Future Trajectories Among Multiethnic Girls’, Ethnography and Education, 11 (1): 57–73. Roth, W., L. Radford and L. LaCroix (2012), ‘Working with CulturalHistorical Activity Theory’, FQS/Forum: Qualitative Social Research, 13 (2). Available online: http://www.qualitative-research.net/index.php/ fqs/article/view/1814/3379 (accessed 19 December 2015). Ruby, J. (1996), ‘Visual Anthropology’, in D. Levinson and M. Ember (eds), Encyclopedia of Cultural Anthropology, 1345–51, New York: Henry Holt and Company. Ruppert, E., J. Law and M. Savage (2013), ‘Reassembling Social Science Methods: The Challenge of Digital Devices’, Theory, Culture and Society, 30 (4): 22–46. Rotman, D., J. Preece, Y. He and A. Druin (2012), ‘Extreme Ethnography: Challenges for Research in Large Scale Online Environments’, in Proceedings of the 2012 iConference, 207–14, Toronto, Canada: ACM. Sade-Beck, L. (2004), ‘Internet Ethnography: Online and Offline’, International Journal of Qualitative Methods, 3 (2): 45–51. Saied, B., A. Olivereau, D. Zeghlache and M. Laurent (2014), ‘Lightweight Collaborative Key Establishment Scheme for the Internet of Things’, Computer Networks, 64 (8): 273–95. Saldaña, J. (2011), ‘Writing Ethnodrama: A Sampler from Educational Research’, in M. Savin-Baden and C. Major (eds), New Approaches to Qualitative Research: Wisdom and Uncertainty, 61–79, London: Routledge. Salmon, P. (1989), ‘Personal Stances in Learning’, in S. W. Weil and I. McGill (eds), Making Sense of Experiential Learning: Diversity in Theory and Practice, 230–41, Buckingham: Open University Press. Sandoval, W. A. and P. L. Bell (2004), ‘Design-Based Research Methods for Studying Learning in Context: Introduction’, Educational Psychologist, 39 (4): 199–201. Sarbin, T. R. (1986), Narrative Psychology: The Storied Nature of Human Conduct, New York, NY: Praeger. Savin-Baden, M. (2010), A Practical Guide to Using Second Life in Higher Education, Maidenhead: Open University Press. Savin-Baden, M. (2013), ‘Spaces In-Between Us: Understanding Spatial Practice in Second Life’, London Review of Education, 11 (1): 59–75. Savin-Baden, M. (2015), Rethinking Learning in an Age of Digital Fluency: Is being Digitally Tethered a New Learning Nexus?, London: Routledge.

REFERENCES

263

Savin-Baden, M. (forthcoming), ‘“Always Connected”: Transforming Teaching and Learning in Higher Education’, in K. Sheehy and A. Holliman (eds), Education and New Technologies: Perils and Promises for Learners, London: Routledge. Savin-Baden, M. and C. Tombs (2010), ‘Provisionality, Play and Pluralism in Liminal Spaces’, in R. Sharpe, H. Beetham and S. de Freitas (eds), Rethinking Learning for a Digital Age: How Learners are Shaping their Own Experiences, 73–82, London: Routledge. Savin-Baden, M., L. Gourlay, M. Mawer, N. Steils, C. Tombs and G. Tombs (2010), ‘Situating Pedagogies, Positions and Practices in Immersive Virtual Worlds’, Educational Research, 52 (2): 123–33. Savin-Baden, M. and C. Major (2013), Qualitative Research: The Essential Guide to Theory and Practice, London: Routledge. Savin-Baden, M. and K. Wimpenny (2014), A Practical Guide to Artsrelated Research, Rotterdam: Sense. Savin-Baden, M., G. Tombs and R. Bhakta (2015), ‘Beyond Robotic Wastelands of Time: Abandoned Pedagogical Agents and New Pedalled Pedagogies’, E-Learning and Digital Media, 12 (3–4): 295–314. Saward, M. (2006), ‘The Representative Claim’, Contemporary Political Theory, 5 (3): 297–318. Schembri, S. and M. V. Boyle (2013), ‘Visual Ethnography: Achieving Rigorous and Authentic Representations’, Journal of Business Research, 66 (9): 1251–4. Schneider, M. A. (1987), ‘Culture-as-Text in the Work of Clifford Geertz’, Theory and Society, 16 (6): 809–39. Schreiber, M. (2015), ‘A Variant of the h-Index to Measure Recent Performance’, Journal of the Association for Information Science and Technology, 66 (11): 2373–80. Schroeder, R. (2008), ‘Defining Virtual Worlds and Virtual Environments’, Journal of Virtual Worlds Research, 1 (1): 1–3. Schurr, C. (2012), ‘Visual Ethnography for Performative Geographies: How Women Politicians Perform Identities on Ecuadorian Political Stages’, Geographica Helvetica, 67 (4): 195–202. Sclater, N. (2014a), Learning Analytics: The Current State of Play in UK Higher and Further Education, JISC. Available online: http:// repository.jisc.ac.uk/5657/1/Learning_analytics_report.pdf (accessed 17 January 2016). Sclater, N. (2014b), Code of Practice for Learning Analytics: A Literature Review of the Ethical and Legal Issues, JISC. Available online: http:// repository.jisc.ac.uk/5661/1/Learning_Analytics_A-_Literature_Review. pdf (accessed 17 January 2016). Selwyn, N. (2013), ‘Digital Technologies in Universities: Problems Posing as Solutions?’, Learning, Media and Technology, 38 (1): 1–3.

264

REFERENCES

Selwyn, N. (2014), ‘Education and “the Digital”’, British Journal of Sociology of Education, 35 (1): 155–64. Sheller, M. (2009), ‘Infrastructures of the Imagined Island: Software, Mobilities, and the Architecture of Caribbean Paradise’, Environment and Planning A, 41: 1386–403. Shema, H., J. Bar-Ilan and M. Thelwall (2014), ‘Do Blog Citations Correlate with a Higher Number of Future Citations? Research Blogs as a Potential Source for Alternative Metrics’, Journal of the Association for Information Science and Technology, 65 (5): 1018–27. Siemens, G. and R. S. J. D. Baker (2012), ‘Learning Analytics and Educational Data Mining: Towards Communication and Collaboration’, Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–4, New York: ACM. Simons, H. and B. McCormack (2007), ‘Opportunities and Challenges Integrating Arts-Based Inquiry in Evaluation Methodology’, Qualitative Inquiry, 13 (2): 292–311. Silcock, M., D. Payne and C. Hocking (2015), ‘Governmentality Within Children’s Technological Play: Findings from a Critical Discourse Analysis’, Children and Society. Available online: http://onlinelibrary. wiley.com/doi/10.1111/chso.12123/abstract (accessed 17 January 2016). Skelton, A. (2012), ‘Facebook After Death: What Should the Law Say?’, Mashable, 26 January. Available online: http://mashable. com/2012/01/26/digital-assets-after-death/ (accessed 19 December 2015). Skwarek, M. and W. Pappenheimer (2011), ‘A Curatorial Project by Mark Skwarek and Will Pappenheimer at Devotion Gallery, Brooklyn’, Gradually Melt the Sky, 28 January. Available online: https:// graduallymeltthesky.wordpress.com/2011/01/28/hello-world/ (accessed 19 December 2015). Slade, S. and P. Prinsloo (2013), ‘Learning Analytics: Ethical Issues and Dilemmas’, American Behavioral Scientist, 57 (10): 1510–29. Smith, J. A. (1996), ‘Beyond the Divide Between Cognition and Discourse: Using Interpretative Phenomenological Analysis in Health Psychology’, Psychology & Health, 11 (2): 261–71. Snee, T. (2015), ‘Academic Panoramic: Research, at a Glance’, IowaNow, 11 November. Available online: http://now.uiowa.edu/2015/11/ academic-panoramic-research-glance (accessed 17 January 2016). Social Research Association (2003), Ethical Guidelines. Available online: http://the-sra.org.uk/wp-content/uploads/ethics03.pdf (18 January 2016). Speaking Openly (2015), Speaking Openly. Available online: http:// speakingopenly.co.uk/ (23 January 2016).

REFERENCES

265

Spicer, S. (2014), ‘Exploring Video Abstracts in Science Journals: An Overview and Case Study’, Journal of Librarianship and Scholarly Communication, 2 (2): 1–13. Spivak, G. C. (1974), ‘Translator’s Preface’, in J. Derrida (ed.), Of Grammatology, lx-lxxxvi, trans. G. C. Spivak, Baltimore: Johns Hopkins University Press. Springgay, S., R. Irwin and S. Kind (2005), ‘A/r/tography as Living Inquiry Through Art and Text’, Qualitative Inquiry, 11 (6): 897–912. St Pierre, E. A. (2008), ‘Decentering Voice in Qualitative Inquiry’, International Review of Qualitative Research, 1 (3): 319–36. St Pierre, E. (2009), ‘Afterword: Decentering Voice in Qualitative Inquiry’, in A. Y. Jackson and L. A. Mazzei (eds), Voice in Qualitative Inquiry, 221–36, London: Routledge. Steils, N., G. Tombs, M. Mawer, M. Savin-Baden and K. Wimpenny (2014), ‘Implementing the Liquid Curriculum: The Impact of Virtual World Learning on Higher Education’, Technology, Pedagogy and Education, 24 (2): 155–70. Stenhouse, R, J. Tait, P. Hardy and T. Sumner (2013), ‘Dangling Conversations: Reflections on the Process of Creating Digital Stories During a Workshop with People with Early Stage Dementia’, Journal of Psychiatric and Mental Health Nursing, 20 (2): 134–41. Stogner, M. (2014), ‘Searching for Aristotle in the Digital Age: Creating Cultural Narrative with 21st Century Media’, The International Journal of New Media, Technology and the Arts, 8 (1): 11–21. Story, D. (n.d.), ‘The Digital Story Podcast Archive’, The Digital Story. Available online: http://thedigitalstory.com/podcasts/archive/ (accessed 19 December 2015). StudentLife (n.d.), ‘StudentLife Study’, Dartmouth College. Available online: http://studentlife.cs.dartmouth.edu/ (accessed 17 January 2016). Suber, P. (2008), ‘Welcome to the SPARC Open Access Newsletter, Issue #124’, Scholarly Publishing and Academic Resources Coalition, 2nd August. Available online: http://legacy.earlham.edu/~peters/fos/ newsletter/08-02-08.htm#gratis-libre (accessed 23 January 2016). Sullivan, G. (2006), ‘Research Acts in Practice’, Studies in Art Education, 48 (1): 19–35. Symonds, J. E. and S. Gorard (2010), ‘Death of Mixed Methods? Or the Rebirth of Research as a Craft’, Evaluation and Research in Education, 23 (2): 121–36. Tabak, I. (2004), ‘Reconstructing Context: Negotiating the Tension Between Exogenous and Endogenous Educational Design’, Educational Psychologist, 39 (4): 225–33. Teddlie, C. and A. Tashakkori (2009), Foundations of Mixed Methods Research, Thousand Oaks, CA: Sage.

266

REFERENCES

Thomas, J. (1993), Doing Critical Ethnography, Newbury Park, CA: Sage. Thomson, P. (2013) ‘Do We “Collect” Data? Or – Beware the Ontological Slip …’, Patter, 11th March. Available online: http://patthomson. net/2013/03/11/do-we-collect-data-or-is-your-ontological-slip-showing/ (accessed 20 January 2016). Toh, Y., H.-J. So, P. Seow, W. Chen and C.-K. Looi (2013), ‘Seamless Learning in the Mobile Age: A Theoretical and Methodological Discussion on Using Cooperative Inquiry to Study Digital Kids Onthe-Move’, Learning, Media & Technology, 38(3): 314–15. Tombs, G. (2017 to be submitted), ‘Teaching with Uncertainty: Supercomplexity in Virtual World Pedagogy’, International Journal of Learning and Teaching in Higher Education. Torbert, W. R. (1981), ‘Why Educational Research Has Been So Uneducational: The Case for a New Model of Social Science Based On Collaborative Inquiry’, in P. Reason and J. Rowan (eds), Human Inquiry: A Sourcebook of New Paradigm Research, Chichester: Wiley. Tourangeau, R., M. P. Couper and D. M. Steiger (2003), ‘Humanizing Self-Administered Surveys: Experiments on Social Presence in Web and IVR Surveys’, Computers in Human Behavior, 19: 1–24. Tsoukala, V., M. Angelaki, V. Kalaitzi, B. Wessels, L. Price, M. J. Taylor, R. Smallwood, P. Linde, J. Sondervan, S. Reilly, M. Noorman, S. Wyatt, L. Bigagli, R. Finn, T. Sveinsdottir and K. Wadhwa (2015), Policy Guidelines for Open Access and Data Dissemination and Preservation. Available online: http://recodeproject.eu/wp-content/uploads/2015/02/ RECODE-D5.1-POLICY-RECOMMENDATIONS-_FINAL.pdf (accessed 24 January 2016). Tufekci, Z. (2014), ‘Big Questions for Social Media Big Data: Validity and Other Methodological Pitfalls’, in ICWSM’14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 505–14, Ann Arbor: AAAI Press. Turkle, S. (2005), The Second Self: Computers and the Human Spirit, 2nd edn, Cambridge, MA: MIT Press. United Nations (1948), Universal Declaration of Human Rights. Available online: http://www.un.org/en/universal-declaration-human-rights/ (accessed 18 January 2016). University of Sheffield (n.d.), ‘About Seeing Data Research’, Seeing Data: Making Sense of Data Visualisations. Available online: http:// seeingdata.org/about-seeing-data/ (accessed 23 January 2016). Upton, I. (2012), Extract/Insert. Available online: http://www.ianupton. info (accessed 16 January 2016). Ushmm.org (n.d.), From Memory to Action: Meeting the Challenge of Genocide. Available online: http://www.ushmm.org/information/

REFERENCES

267

exhibitions/museum-exhibitions/from-memory-to-action-meeting-thechallenge-of-genocide (accessed 19 December 2015). van Dijk, J. ([1991] 1999), The Network Society, Thousand Oaks, CA: Sage. Vavoula, G. N. and M. Sharples (2007), ‘Future Technology Workshop: A Collaborative Method for the Design of New Learning Technologies and Activities’, International Journal of Computer Supported Collaborative Learning, 2 (4): 393–419. Vidich, A. J. and S. M. Lyman (2000), ‘Qualitative Methods: Their History in Sociology and Anthropology’, in N. K. Denzin and Y. S. Lincoln (eds), Handbook of Qualitative Research, 37–84, 2nd edn, Thousand Oaks: Sage. Vincent, A. (2015), ‘The Ideological Context of Impact’, Political Studies Review, 13 (4): 474–84. von Glasersfeld, E. (1989), ‘Cognition, Construction of Knowledge and Teaching’, Synthese, 80 (1): 121–40. Walther, J. B. (2002), ‘Research Ethics in Internet-Enabled Research: Human Subjects Issues and Methodological Myopia’, Ethics and Information Technology, 4 (3): 205–16. Wang, F. and M. J. Hannafin (2005), ‘Design-Based Research and Technology-Enhanced Learning Environments’, Educational Technology Research and Development, 53 (4): 5–23. Warschauer, M. (2007), ‘The Paradoxical Future of Digital Learning’, Learning Inquiry, 1: 41–9. Wasserman, S. and K. Faust (1994), Social Network Analysis: Methods and Applications, Cambridge: Cambridge University Press. Weller, M. (2014), The Battle for Open: How Openness Won and Why It Doesn’t Feel Like a Victory, London: Ubiquity Press. Welles, B. F. (2014), ‘On Minorities and Outliers: The Case for Making Big Data Small’, Big Data and Society, 1 (1): 1–2. Wenger, E. (1998), Communities of Practice: Learning, Meaning and Identity, Cambridge: Cambridge University Press. Wengraf, T. (2001), Qualitative Research Interviewing: Biographic Narrative and Semi-Structured Method, London: Sage. White, P., D. Syncox and B. Alters (2011), ‘Clicking for Grades? Really? Investing the Use of Clickers for Awarding Grade-Points in PostSecondary Education’, Interactive Learning Environments, 19 (5): 551–61. Whitehead, J. (1993), The Growth of Educational Knowledge: Creating Your Own Living Educational Theories. Bournemouth: Hyde Publications. Available online: http://www.actionresearch.net/writings/ writing.shtml (accessed 19 December 2015). Whiteman, N. (2010), ‘Control and Contingency: Maintaining Ethical Stances in Research’, International Journal of Internet Research Ethics, 3: 6–22.

268

REFERENCES

Wiles, R., A. Coffey, J. Robinson and S. Heath (2012), ‘Anonymisation and Visual Images: Issues of Respect, “Voice” and Protection’, International Journal of Social Research Methodology, 15 (1): 41–53. William and Flora Hewlett Foundation (n.d.), ‘Open Educational Resources’, William and Flora Hewlett Foundation. Available online: http://www.hewlett.org/programs/education/open-educationalresources (accessed 23 January 2016). Wimpenny, K. and M. Savin-Baden (2012), ‘Exploring and Implementing Participatory Action Synthesis’, Qualitative Inquiry, 18 (8): 680–9. Wimpenny, K. and M. Savin-Baden (2014), ‘Using Theatre and Performance for Promoting Health and Well Being amongst the 50 + Community: An Arts-Informed Evaluation’, The International Journal of Social, Political, and Community Agendas in the Arts, 8 (1): 47–64. Wolff, A., Z. Zdrahal, A. Nikolov and M. Pantucek (2013), ‘Improving Retention: Predicting At-Risk Students by Analysing Clicking Behaviour in a Virtual Learning Environment’, in D. Suthers, K. Verbert, E. Duval and X. Ochoa (eds), Proceedings of the Third International Conference on Learning Analytics and Knowledge, 145–9, New York: ACM. Woods, M., T. Paulus, D. P. Atkins and R. Macklin (2015), ‘Advancing Qualitative Research Using Qualitative Data Analysis Software (QDAS)? Reviewing Potential Versus Practice in Published Studies Using ATLAS.ti and NVivo, 1993–2013’, Social Science Computer Review. Available online: http://ssc.sagepub.com/cgi/ doi/10.1177/0894439315596311 (accessed 20 January 2016). World Medical Association (1964), Declaration of Helsinki. Helsinki: World Medical Association. Zimmer, M. (2010), ‘“But the Data is Already Public”: On the Ethics of Research in Facebook’, Ethics and Information Technology, 12 (4): 313–25. Žižek, S. (1999), ‘Is it Possible to Traverse the Fantasy in Cyberspace?’, in S. Žižek, Elizabeth Wright and Edmond Wright (eds), The Žižek Reader, 102–24, Oxford: Blackwell. Znaniecki, F. (1934), The Method of Sociology, New York: Farrar and Rinehart.

Index

activity theory (AT)  xvi, 6, 79, 91–5, 97 example 94 further reading  97 actor network theory (ANT)  xvi, 6, 79–80, 82, 87–91, 95–7 example 89–90 further reading  97–8 affordances 95–8 further reading  98 age of data  xvi, 10–11, 15, 102, 104 altmetrics  xvi, 217–22 example 219–21 analytic induction  176, 178–9 analytics, see big data, learning analytics, twitter analytics anonymity  23, 76, 122–3, 135–40 further reading  142 appreciative inquiry  11, 148 artificial intelligence (AI)  14–15, 134 a/r/tography 43–4 arts-based inquiry  41 arts-engaging inquiry  42 arts-informed inquiry  37, 41 arts-informing inquiry  42 arts-inquiring pedagogy  41 arts-related evaluation  42 arts-related research  xvii, 40–4, 212 further reading  54, 212–13

assemblage  xvii, 207, 211–12 Association of Internet Researchers (AoIR) 124–5 autoethnography  xviii, 35, 56, see also ethnography avatar  xviii, 3, 22, 58, 106–7, 113–15, 132–5, 153, 156–7, 162, see also virtual world big data  xviii, 102–3, 126, 129, 168, see also learning analytics further reading  118–19 blackboard 2, see also virtual learning environment blended learning  2, 4, 238 blogs  10–11, 126, 158, 182, 215, 219–20, 222–4 cartography  xviii, 209–11 chatbot  xviii, 106, see pedagogical agent Chicago School  82 collaborative inquiry  148 conceptual framework  xix, 19, 34, 91, 95, 202 confidentiality  122, 168 consent, see informed consent constructionism  6, 16, 18, 42, 55, 80, 145, 147 and actor network theory 88–9

270

Index

definition 18 and design patterns  85–6 and ethnography  6, 60, 68 constructivism  6, 7, 35, 41–2, 55, 80, 147 and actor network theory 90–1 definition 18 and ethnography  6, 60, 68 content analysis  64, 177, 185–7 example 186–7 cooperative inquiry  148–52 example 149 critical discourse analysis (CDA)  176, 179–81 example 180 critical social theory  17, 60, 146 design-based research  79, 81–4, 97 design patterns  79, 84–6, 97 digital data analysis  xvi, 169–90 collection tools  116–18, 188 creation 144–5 example 116 found 127–30 further reading  30, 193 geo-location  10, 111–13 interpretation 190–2 social media  108 typology  11–12, 100–1, 170 digital immortality  xix, 14–15 digital learning  2 digital métissage  xix, 28, 50–3, 118 digital natives  27 further reading  31, 192 digital tethering  xix, 13–15, 191 further reading  31 dissemination  8, 20, 58, 126, 139, 207, 215, 238, 224

educational data mining  102–5, see also learning analytics educational virtual environment  3 e-learning  2, 238 embodiment  43, 69 ethics  7, 121–42, see also anonymity, informed consent, privacy definitions 121–4 frameworks 139–41 further reading  53–4 Ethics Review Board, see Institutional Review Board ethnography  xx, 5–6, 17, 55–9, 73–7, see also autoethnography, netnography connective ethnography  67–8 critical ethnography  57, 71–3 cyber ethnography  58 example  66–7, 71 further reading  77–8, 164 immersive ethnography  68–9 sensory ethnography  65–6 virtual ethnography  xxiv, 62–3 visual ethnography  37–8, 69–71 Facebook  12, 14, 108–9, 127, 136, 140, 175, 218, 221, see also social media focus groups  11, 23–4, 80, 143–4, 170–1, 204, 236 folding  xx, 207–9 future technology workshop  6, 86–7, 97 grounded theory  24, 37, 73, 178, 203 highly interactive virtual environment (HIVE)  2 h-index 216–17

INDEX immersion  43, 62, 68–9, 74–6, 155, 202 example 164 immersive virtual world (IVW)  2, 4, see also virtual world impact  xx, 8, 215–22 informed consent  xx, 122, 125–32, 135, 140–1, 234 further reading  141–2 Institutional Review Board  122–4, 127, 139 internet of things  xxi, 12–13, 105 further reading  30 interpretative phenomenological analysis (IPA)  176, 181–2 example 181–2 interpretivism  5, 55 interviews  22–3, 159–63 example 163 learning analytics  xxi, 103–5, 130–2, see also big data, educational data mining liquid methodologies  30, 34–5 massively multiplayer online role-playing game (MMORPG) 3 massive open online course (MOOC)  104, 126 further reading  119 member checking  xxi, 123, 197–8 mobile  110–13, 148–9 example  112, 149–50 further reading  118 Moodle  2, 107, see also virtual learning environment multi-user virtual environment (MUVE) 3 mustering  xxi, 207–8

271

narrative analysis  176, 182–5 example 184 narrative inquiry  37, 44–9, 51, 85, 159, 182, 203, 212 example 47 netnography  59, 60, 61, 63–5, see also ethnography further reading  53–4, 77–8 observations  20–2, 59, 61–5, 76, 133, 150–9 example 149 online learning  2 further reading  98 open research  xxii, 76, 227–38 access 227–32 data  126–9, 227, 232–8 further reading  239–40 participant voice  141–3, 234 participatory action research (PAR)  11, 80, 87, 144 participatory action synthesis  29 participatory research  xxii, 11, 28–9, 80, 150 pedagogical agent  106, 134–5, see also avatar peer review  224 personal stance, see researcher stance phenomenology  18, 85, 146, 181–2 podcast 52 portrayal  xxii, 196, 205–12 further reading  212–13 positivism  xxii, 17, 69, 80–1, 84, 96, 97, 146, 177–8, 193 posthumanism  134, 191 postmodernism  18, 41, 147, 160, 191 post-positivism  xxii, 17, 60, 64, 80, 88–9, 91, 97, 146, 193

272

Index

post-structuralism  18, 41, 145, 147 pragmatism  17, 33–4, 81–5, 97, 146 privacy  13, 108, 111, 125–30, 224 public/private spaces, see privacy reflexivity  xxii, 18, 28, 123 representation  xxii, 196–205 further reading  212–13 researcher role  143–8 researcher stance  xxiii, 16–17, 19, 35, 145, 160, 203–4, 239 research question  16, 19–20, 28–9, 34, 38, 65, 81, 173 social media  12, 63, 101, 106–10, 170, 220, see also Facebook, Twitter example 220–1 further reading  77, 119 social network analysis (SNA) 175–8 further reading  192 storytelling 49–50 supercomplexity  xxiii, 191, 237 surveys 105–8 further reading  118 tethered integrity  xxiii, 96 thematic analysis  172, 177, 185–7 thick description  xxiii, 25

three-dimensional immersive virtual world (3D IVW)  3, see also virtual world trustworthiness  xxiv, 107, 136, 197 Twitter  10, 12, 21, 106–10, 126, 136, 138, 141, 153, see also social media further reading  119, 141 validity  xxiv, 12, 123 virtual learning environment (VLE)  2, 4, 28, 101, 107, 127, 130, 132 virtual world (VW)  2–4, 67, 133, see also avatar ethnography  61, 68–9, 75–6, 164 example 114 focus groups  23–4 interviews 22–3 observations  21–2, 153, 156–7, 162 quantitative data  113–16 surveys 106 visual methods  35–9 further reading  53–4, 142, 164–5 Wikipedia (wiki)  219 YouTube  23, 150, 185, 218–19, 224–5