Learning, Teaching, and Assessment Methods for Contemporary Learners: Pedagogy for the Digital Generation (Springer Texts in Education) 9811967334, 9789811967337

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Springer Texts in Education

K G Srinivasa Muralidhar Kurni Kuppala Saritha

Learning, Teaching, and Assessment Methods for Contemporary Learners Pedagogy for the Digital Generation

Springer Texts in Education

Springer Texts in Education delivers high-quality instructional content for graduates and advanced graduates in all areas of Education and Educational Research. The textbook series is comprised of self-contained books with a broad and comprehensive coverage that are suitable for class as well as for individual self-study. All texts are authored by established experts in their fields and offer a solid methodological background, accompanied by pedagogical materials to serve students such as practical examples, exercises, case studies etc. Textbooks published in the Springer Texts in Education series are addressed to graduate and advanced graduate students, but also to researchers as important resources for their education, knowledge and teaching. Please contact Yoka Janssen at yoka.janssen@ springer.com or your regular editorial contact person for queries or to submit your book proposal.

K. G. Srinivasa · Muralidhar Kurni · Kuppala Saritha

Learning, Teaching, and Assessment Methods for Contemporary Learners Pedagogy for the Digital Generation

K. G. Srinivasa Department of Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur, Chhattisgarh, India

Muralidhar Kurni Department of Computer Science and Engineering Anantha Lakshmi Institute of Technology and Sciences Ananthapuramu, Andhra Pradesh, India

Kuppala Saritha Department of Computer Science Engineering, School of Engineering Presidency University Bengaluru, Karnataka, India

ISSN 2366-7672 ISSN 2366-7680 (electronic) Springer Texts in Education ISBN 978-981-19-6733-7 ISBN 978-981-19-6734-4 (eBook) https://doi.org/10.1007/978-981-19-6734-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Contemporary learners are looking for new ways to learn and assess. The instructors have to apply various teaching, learning, and assessment methods (“Learning Strategies”) to meet their needs. Also, contemporary learners must be aware of these learning strategies. As long as students believe that their learning strategies are helping them achieve their academic goals, they should focus on improving them. Students need to consider various factors when implementing learning strategies, such as the subject matter, concepts, and academic goals. Consequently, it can be said that modern, scientific, and innovative learning strategies for contemporary learners are required over time. However, students must be aware of various learning strategies and put them to good use. The Textbook “Learning, Teaching, and Assessment Methods for Contemporary Learners: Pedagogy for the Digital Generation” is designed to tackle the contemporary learners’ needs. To cope with the modern world and the knowledgedriven era of technology, adopting modern ways is the only means to survive. This book aims to introduce modern learning, teaching, and assessment methods and provide a deeper understanding of them so that the students/teachers can create opportunities for themselves and others. This book also offers a good deal about what pedagogy is, why it is essential, and how pedagogy has evolved to consider 21st-century skills and learning. Readers will learn about various modern learning, teaching, and assessment methods for contemporary learners; each chapter will provide greater insight into how you can apply these modern methods to your classroom. Readers will understand why and how these modern methods can support the curriculum. This book also touches on some case studies. This book provides students, educators, and researchers alike with how to effectively make sense of and use modern learning, teaching, and assessment methods in their everyday practice. This book will be a valuable addition to researchers’ bookshelves.

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Organization of the Book Chapter 1 explains what ‘pedagogy’ is, why it is essential, and how pedagogy has evolved to take 21st-century skills and learning into account. This chapter also introduces various modern learning, teaching, and assessment methods. Chapter 2 introduces Crossover learning, a new teaching method that aims to bridge formal and informal education. Using this method, students can learn in both formal and informal settings. Further, this chapter concentrates on the implementation of crossover learning and its effectiveness. Chapter 3 introduces the concept of Collaborative learning and its benefits, argumentation in learning environments, preparing the classroom environment for collaborative learning, and evaluating argumentation. Chapter 4 introduces the concepts of incidental learning and its impact on the learners, and how to engage and implement it in the classroom. Further, this chapter attempts to differences and similarities between incidental learning, intentional learning, and informal learning. Finally, it presents a case study. Chapter 5 aims to introduce Context-based learning, its impact on students’ learning, and create context-based learning environments. Later, this chapter discusses how the assessment will be done in context-based learning and case studies. Finally, this chapter throws a lite-on Contextual Teaching and Learning. Chapter 6 presents Computational thinking and its principles, learning strategies for developing computational thinking skills, and assessing computational thinking. Chapter 7 presents the Learning-by-doing method, its importance, and how to do it. Also, we present the most critical challenges of applying the learning-bydoing approach. Chapter 8 introduces Embodied learning, its principles for bringing it to the classroom, and how it can help the students. Chapter 9 introduces the Adaptive Teaching/Learning method, presents various development tools, and discusses how to adapt and/or apply the adaptive teaching/learning strategies to students in practice. Chapter 10 discusses the ubiquity and importance of emotions in learning. Chapter 11 describes the problems with the current assessment techniques and how the stealth assessment can be effective. Chapter 12 aims to provide the basis by which teachers can comprehend the strategies for developing effective online courses. Chapter 13 examines the potential impact of utilizing AI in education.

Preface

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Key Features of the Book • Describes how modern learning, teaching, and assessment methods could bring remarkable benefits for contemporary Learners. • Role and importance of modern learning, teaching, and assessment methods in the 21st Century. • Presents various modern learning, teaching, and assessment methods.

Naya Raipur, India Ananthapuramu, India Bengaluru, India

K. G. Srinivasa Muralidhar Kurni Kuppala Saritha

Acknowledgements

Srinivasa would like to thank Dr. Pradeep K. Sinha, Vice Chancellor and Director, IIIT Naya Raipur, for his kind encouragement for publishing this book. He also would like to thank all faculty members of IIIT Naya Raipur, for their whole hearted support for publishing this book. Muralidhar would like to thank his mother, Smt. P. Sanjeevamma and friends Mr. Thanooj, Dr. Mujeeb Shaik Mahammed, Miss Nandini Sahani, and Mr. K. Somasena Reddy for their wholehearted support in completing this book. Saritha would like to express heartful thanks to her father, K. Jayaram, her daughter K. Manushri and all her friends for their encouragement and support in completing this book.

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Contents

1

2

3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 What is Pedagogy? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Why is Pedagogy Important? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Different Types of Pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 How is Pedagogy Changing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 How Does Pedagogy Impact the Learner? . . . . . . . . . . . . . . . . . . . . 1.6 Learning, Teaching, and Assessment Methods for Contemporary Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Structure of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Crossover Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Crossover Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Crossover Learning: Connecting Formal and Informal Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 The Importance of Crossover Learning . . . . . . . . . . . . . . . . . . . . . . . 2.5 Strengths of Crossover Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Why is It Good to Implement Crossover Learning? . . . . . . . . . . . 2.7 How to Implement Crossover Learning? . . . . . . . . . . . . . . . . . . . . . . 2.8 Why is Crossover Learning Working in the Classroom? . . . . . . 2.9 Try Out Crossover Learning with Your Students . . . . . . . . . . . . . . 2.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Learning Through Collaborative Argumentation . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 What is Collaborative Argumentation? . . . . . . . . . . . . . . . . . . . . . . . 3.3 Reasons for Using Argumentation in Learning Environments or Instruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Benefits of Argumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Kinds of Arguments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 The Skills of Argumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Learning to Argue and Arguing to Learn . . . . . . . . . . . . . . . . . . . . .

1 2 2 2 4 5 6 14 14 15 17 17 19 20 22 22 22 23 24 25 26 26 27 27 29 35 36 40 42 43 xi

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3.8

Preparing Classroom Environments for Collaborative Argumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9 Methods for Engaging and Supporting Argumentation in Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.10 Evaluating Argumentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.11 Factors Affecting Collaborative Argumentation . . . . . . . . . . . . . . . 3.12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

44 46 50 53 54 54

4

Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 The Premise of Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 The Opportunity of Incidental Learning . . . . . . . . . . . . . . . . . . . . . . 4.5 How to Engage with Incidental Learning . . . . . . . . . . . . . . . . . . . . . 4.6 Using Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Incidental Learning in the Classroom . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Informal and Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Intentional and Incidental Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.10 Incidental Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.11 Case Studies on Incidental Teaching . . . . . . . . . . . . . . . . . . . . . . . . . 4.12 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 61 62 63 63 63 64 64 69 77 83 84 84

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Context-Based Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 What is Context-Based Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Why Context-Based Learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Increasing Performance with Context-Based Learning . . . . . . . . 5.5 Context-Based Learning Environments . . . . . . . . . . . . . . . . . . . . . . . 5.6 Assessment in Context-Based Teaching and Learning . . . . . . . . . 5.7 Contextual Teaching and Learning (CTL) . . . . . . . . . . . . . . . . . . . . 5.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87 87 89 96 100 100 104 106 112 112

6

Computational Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 What is Computational Thinking? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Thinking Computationally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 The Computational Thinking Process . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Key Skills for Computational Thinking . . . . . . . . . . . . . . . . . . . . . . . 6.6 Principles of Computational Thinking . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Computational Thinking in the Classroom . . . . . . . . . . . . . . . . . . . . 6.8 Why is Computational Thinking an Essential Tool for Teachers and Students? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.9 Integrating Computational Thinking into Your Classroom . . . . .

117 117 118 121 122 130 131 131 133 135

Contents

6.10 6.11 6.12 6.13

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Examples of Computational Thinking in the Classroom . . . . . . . Computational Thinking Through Classroom Assessment . . . . . Integrating into Existing Routines and Curricula . . . . . . . . . . . . . . Differences Between CT and Other Types of Thinking Skills . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

136 137 140

Learning by Doing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 What is Learning by Doing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Why Should We Learn by Doing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Why Learning by Doing is Effective . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 When Does Learning by Doing Work? . . . . . . . . . . . . . . . . . . . . . . . 7.6 Why Does Learning by Doing Work? . . . . . . . . . . . . . . . . . . . . . . . . 7.7 How to Use Learning by Doing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 What are the Benefits of Learning by Doing? . . . . . . . . . . . . . . . . 7.9 How to Get Started? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.10 Developing Learning by Doing Approach (How to Do It?) . . . . 7.11 Drawbacks to Learning by Doing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.12 The Challenges of Learning by Doing . . . . . . . . . . . . . . . . . . . . . . . . 7.13 Learning by Doing Science with Remote Labs . . . . . . . . . . . . . . . 7.14 Realistic Learning Situations Through Simulations (Learning by Doing Through Simulations) . . . . . . . . . . . . . . . . . . . 7.15 Approaches to Learning by Doing/Experiential Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.16 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

147 147 149 150 150 152 153 154 155 157 158 159 160 161

Embodied Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Embodied Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Principles of Embodied Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Pros and Cons of Embodied Learning . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Embodied Learning in Classrooms . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Embodied Learning Through Virtual Reality . . . . . . . . . . . . . . . . . 8.7 Embodied Learning and Technological Developments in Educational Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Role-Playing and Embodied Learning . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Embodied Learning in Varied Disciplines . . . . . . . . . . . . . . . . . . . . 8.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

177 177 180 182 185 186 189

143 144 145

163 165 175 175

191 193 194 197 197

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Adaptive Teaching/Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 What is Adaptive Teaching/Learning? . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Technology and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Development Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.6 Adaptive Learning is the Future of Online Education . . . . . . . . . 9.7 How to Apply Adaptive Learning in Practice? . . . . . . . . . . . . . . . . 9.8 Adapting to Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.9 Adapting Adaptive Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.10 Strengths and Weaknesses of Adaptive Learning . . . . . . . . . . . . . . 9.11 When and How to Apply Adaptive Learning . . . . . . . . . . . . . . . . . 9.12 How is Adaptive Learning Changing Traditional Teaching Methods? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.13 Design Principles in Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . 9.14 Adaptive Classrooms: How Accessible Furniture is Paving the Way to Success for Students with Special Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.15 Intelligent Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.16 Adaptive Learning Systems and Platforms . . . . . . . . . . . . . . . . . . . . 9.17 Benefits of Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.18 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

201 201 202 207 208 209 210 211 213 217 217 220

10 Analytics of Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 The Importance of Emotions in Learning . . . . . . . . . . . . . . . . . . . . . 10.3 Four Emotions of Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 How Children Use Their Emotions to Learn . . . . . . . . . . . . . . . . . . 10.5 How Emotions Affect Learning and Teaching (Emotions in Classrooms) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Emotion-Aware E-learning Platform Architecture . . . . . . . . . . . . . 10.7 Emotion AI in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.8 Emotional Learning Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.9 Examples of Emotion Analytics in the Real World . . . . . . . . . . . 10.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

241 241 243 246 247

11 Stealth Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Assessment: Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problems with Current Assessments . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 What is Stealth Assessment? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Is Stealth Assessment Practical? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5 Stealth Assessment in the Classroom . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Principles and Theories of Stealth Assessment . . . . . . . . . . . . . . . . 11.7 Stealth Assessment and Evidence-Centred Design . . . . . . . . . . . .

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11.8 How to Design and Develop Good Stealth Assessment . . . . . . . 11.9 Stealth Assessments: Success Stories . . . . . . . . . . . . . . . . . . . . . . . . . 11.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Pedagogy for E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 What is E-learning? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Types of E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Advantages of E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Disadvantages of E-learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Contributions of E-learning to Education . . . . . . . . . . . . . . . . . . . . . 12.6 Modern E-learning Pedagogy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.7 Models of E-learning and Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . 12.8 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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13 Harnessing the Power of AI to Education . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 The Need for AI in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 The Role of AI in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 The Impact of AI on Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Technologies for AI in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.6 Best Practices for Incorporating AI in Education . . . . . . . . . . . . . 13.7 Applications of AI in Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.8 Pros and Cons of Using AI in Education . . . . . . . . . . . . . . . . . . . . . 13.9 Companies Using AI in Education to Enhance the Classroom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.10 AI-Driven Solutions in Education/AI Apps and Tools for Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.11 Is AI Replacing Human Teachers, or Does It Assist Teachers? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.12 Usage of AI in Education—Present and Future . . . . . . . . . . . . . . . 13.13 Case Studies: Examples of Successful AI in Higher Education that Can Serve as Inspiration for Our Future . . . . . . . 13.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abstract

Pedagogy describes what is being learned and refers to how teaching theory and practice are carried out. It establishes the connection between learning methods and culture, defined based on an educator’s beliefs about how learning should occur. Pedagogy includes positive classroom interactions and appreciation for educators and learners. The intention is to help students draw on their prior knowledge to develop skills, design, and present educators’ curricula in a meaningful way to students, compatible with their needs and cultures. To make it in the future, today’s students cannot learn in the past pedagogy. Modern society’s growth is exponential. Whatever skills applicable today can quickly become redundant within a half-decade. Thus, current-generation students need to absorb even more knowledge than their previous generations and apply the knowledge and skills in real situations, but they still need an adaptive mentality where the need is to become lifelong learners. Such a standard of education can only come by adopting modern learning, teaching, and assessment methods, and the best schools can accomplish this feat by successfully integrating these modern methods that complement each other at every stage. This chapter aims to explain what ‘pedagogy’ is, why it is essential, and how pedagogy has evolved to take 21st-century skills and learning into account. This chapter also introduces various modern learning, teaching, and assessment methods. Keywords

Pedagogy • Learning strategies • Online learning • Contemporary learners Learning • Teaching • Assessment methods

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_1

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Introduction

What is Pedagogy?

Curriculum and pedagogy are frequently conflated in the public mind. While pedagogy refers to teaching theory and practice, the curriculum explains what is taught (Persaud, 2021). Pedagogy is the relationship between learning methods and culture and is determined by an educator’s beliefs about how learning should occur. For pedagogy to be successful, teachers and students must have mutual respect for one another in the classroom. An educational objective is to help students build on their prior knowledge and develop skills and attitudes, while educators design and present curriculum tailored to students’ needs and cultures. When it comes to pedagogy, teachers must consider the context in which they are teaching and the people they are teaching. It is not about the materials but the process and strategy used to achieve meaningful cognitive learning. In Greek, agogos mean leader, and pedagogue means teacher. Pedagogy is derived from a Greek word that means “the art of teaching children.” Paidagogos were slaves responsible for transporting boys to and from school, teaching them manners, and tutoring them (Persaud, 2021).

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Why is Pedagogy Important?

As a teacher, you can improve your quality of instruction and the way students learn by developing an effective pedagogy. This will help students gain a deeper understanding of the fundamental material. In order to help students learn more effectively, teachers should be aware of how they teach. As a result, students are more likely to collaborate in the classroom. Students can progress from simple memorization and comprehension processes like Bloom’s taxonomy pyramid to more complex learning processes like analysis, evaluation, and creation with the right approach. Students can use their preferred learning methods to their advantage by utilizing a teaching process tailored to their needs and preferences.

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Different Types of Pedagogy

The following are some of the vital pedagogies (Persaud, 2021). What is social pedagogy? According to social pedagogy, education is essential for a student’s social development and well-being, and as such, it must be viewed broadly to support a person’s growth throughout his or her entire life. Social and educational issues must be addressed because students are social beings by definition. However, they must be taught how to communicate effectively in order to do so. Social pedagogy can be approached in various ways in different countries and is influenced by cultural and social norms. For example, educators see it in the

1.3 Different Types of Pedagogy

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same light as social work when it comes to social pedagogy in Germany. Working with children and adolescents is a priority in Norway. An example of social pedagogy. There are many ways to use social pedagogy, such as highlighting the importance of compassion and kindness as well as using dialogue as a means of conveying information; examining concepts in the context of modern lifestyles; or looking at common issues within society, such as social exclusion, its causes, and consequences. What is critical pedagogy? To practice critical pedagogy is to break down and deconstruct common worldviews about subjects and learning in the classroom. Critical theories and even radical philosophies are frequently entwined in this process. The goal is to continually challenge students to question their thoughts and ideas, beliefs, and practices, think critically, and understand. Let go of the conventional wisdom and try to discover things yourself and in your way. To inspire his students to live more freely and “seize the day,” an English teacher in the film Dead Poets’ Society challenges the conventional teaching methods, opting for unorthodox methods. An example of critical pedagogy. Teachers can use critical pedagogy to encourage students to look for more profound meaning and root causes in everything from religion to war and politics or investigate and analyze relationships and power issues in their families. Popular culture and mass media could also be examined for hidden messages or biases. What is culturally responsive pedagogy? Institutional, personal and instructional dimensions work together to recognize and respond to students’ cultural differences and celebrate their different approaches and learning methods in a culturally diverse society. Students in a multicultural classroom have unique needs, and educators must meet those needs by providing a safe and stimulating environment for all students. Using a student-centered approach to teaching, educators use this pedagogical method to cultivate students’ sense of self-worth and ability to achieve their goals by identifying and nurturing their various cultural strengths. To implement culturally responsive pedagogy, teachers must adjust their teaching methods to meet the needs of students from a variety of different backgrounds. Also, an institution may have to change its policies and procedures so that students are more likely to get involved. An example of culturally responsive pedagogy. As part of a culturally responsive teaching approach, educators should be sensitive to students’ backgrounds and encourage students to share their ethnicities, races, and beliefs. This could imply, for example, that a student taking a culinary arts course is required to learn about various ethnic cuisines. Debates and analyses of political topics can occur in various ways in an academic course on politics. Respecting religious and cultural differences in how families view the same legal issues could be an example.

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What is Socratic pedagogy? Socratic pedagogy is based on a more philosophical approach to education that aims to help students develop their social and intellectual abilities to participate more actively in a democratic society. Students are encouraged to think critically about knowledge, seek out alternatives, and create their knowledge through their thoughts, experiences, and meaningful dialogue with others in the classroom. This means that students will be encouraged to test out their ideas against their peers to broaden their perspectives and gain a more in-depth understanding of complex concepts. An example of Socratic pedagogy. It is not uncommon for students taking a science or math course to delve more profound into the surface facts to discover the underlying causes and implications of a given scientific or mathematical principle. The community of inquiry proposed by C.S. Peirce and John Dewey, for example, looks for social context to learn more about a topic rather than relying on fixed scientific facts. Another is Bohm Dialogue, a group discussion where everyone participates without judgment to understand a topic better.

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How is Pedagogy Changing?

Pedagogy has evolved over the years to support better 21st-century skills and ideas and the changing nature of teaching. Teaching traditionally in a classroom has lost much of its effectiveness over time. New methods of instruction, such as interactive and collaborative projects, online and remote curricula, and more flexible class schedules, have all been incorporated into the classroom (Persaud, 2021). Incorporating real-world scenarios and cultural differences allows students to acquire, construct, and organize their learning in new ways. Pedagogy is moving away from rote memorization and using simple procedures to help students develop higher-order thinking skills, effective communication, and a greater sense of self-reliance (Persaud, 2021). Online learning. Teaching and learning in the digital age necessitate understanding how students can find, analyze, and apply knowledge from an ever-expanding array of sources. In order to succeed in this environment, students must be able to think critically, work independently, and collaborate with others in person and online. As educators, we can use technology to improve our course materials. In order to learn, access, share, and create useful information, students must feel comfortable using technology. To that end, teachers should implement innovative teaching strategies such as flipped classrooms, which allow students to access course materials like videos, lecture notes, quizzes, and additional readings after class has ended. This will give students greater access to online resources and experts. Teachers can use videos, animations, simulations, and sources like YouTube channels, iTunes University, clickers, and more to incorporate new forms of technology into their teaching. Textbooks of the modern era can include multimedia content, such as video and audio clips, animations, and rich graphics, which

1.5 How Does Pedagogy Impact the Learner?

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students can access and annotate. This information benefits students, but it is beneficial to those with difficulties. It can also save money because students have access to a wealth of valuable, up-to-date information for free. In the meantime, social media provides students with the opportunity to create communities where they can exchange ideas, discuss theories, and benefit from one another’s experiences. The classroom is not the only place where educators can connect with students. Personalizing pedagogies. It is essential that your teaching be relevant, meaningful, and personalized to the student’s experiences. Student access to information has increased due to the rise in nonformal and self-directed learning methods in schools. Educators can more easily keep track of their digital learning activities. However, it requires more time and effort to guide students to the appropriate resources, adapts lecture content and approaches in light of students’ online activity, and foster collaboration among colleagues. There is a shared power between educators and students in the most recent forms of pedagogy. Teachers can use lecture time more efficiently for discussion and collaborative work, which benefits students in many cases. Students learn more when working independently rather than relying solely on an instructor’s lectures and textbooks. In today’s world, lectures are no longer the only method of delivering information. A new strategy is needed for how students learn and are monitored and assessed. As a result, the educator becomes a critical guide and assessor for students, connecting them to accepted sources of information and stressing the importance of accreditation.

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How Does Pedagogy Impact the Learner?

Teachers and students benefit from clear and concise understandings of how and why the curriculum is structured and what students expect. Everyone is basically on the same page. By applying what they learn in the classroom to real-world scenarios and contexts, students understand concepts and connect what they have learned and what they have experienced. Their cultural knowledge and personal experiences can form their thoughts and opinions. The use of concrete evidence, facts, and data and the exploration of other people’s cultural differences allow students to reflect more objectively on new concepts and open their minds to new approaches (Persaud, 2021). Students can also gain insight into what methods work best for them, which learning activities and learning styles they gravitate toward, and how to develop concepts and build mental models to further their learning through your pedagogical process. As a whole, active learning increases student engagement. Instead of being passive participants in the classroom, students are allowed to participate in their education actively.

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Learning, Teaching, and Assessment Methods for Contemporary Learners

Contemporary learners are looking for new ways to learn and assess (Concordia University, 2018). The instructors have to apply various teaching, learning, and assessment methods (“Learning Strategies”) to meet their needs. Also, contemporary learners must be aware of these learning strategies. It is widely accepted that instructors are responsible for disseminating knowledge to their students, whether in the form of lesson plans, academic concepts, or other activities associated with the learning process. However, students must be aware of various learning strategies and put them to good use. Students need to consider various factors when implementing learning strategies, such as the subject matter, concepts, and academic goals. As long as students believe that their learning strategies are helping them achieve their academic goals, they should focus on improving them. Consequently, it can be said that modern, scientific, and innovative learning strategies for contemporary learners are required over time. Learning strategies for contemporary learners are discussed in this section, along with their benefits. Learning Strategies. Learning strategies are the methods and actions used by language learners to learn and use the language more effectively. Students must be well-versed in various learning strategies and have ample opportunity to put those skills to use throughout their studies. When it comes to methods and approaches that can help students better understand subjects and concepts and accomplish academic goals, students are usually well-versed in this knowledge and are adept at using them. Improved memory techniques, better study habits, and better test-taking strategies are examples of learning strategies (InstructionalDesign.org, 2020). Before an exam, students usually practice test-taking strategies to see how well they are prepared for it. To truly understand what they are learning, students must establish a personal connection to the subject matter. Learners must cultivate the qualities of self-motivation, curiosity, and zeal for knowledge development. Learning strategies involve making changes to the design and instruction of the learning environment. Students must change their approaches to learning if they find that their current methods are not helping them achieve their desired outcomes. When students use test-preparation strategies to boost their grades, the results are often less than stellar. As a result, they will have to undergo some transformation. Most students can grasp the concepts of any subject matter through their reading and oral communication. Despite this, instructors stress the importance of developing their writing skills to grasp the concepts quickly and apply them in their professional and personal lives. It is generally agreed that learning through writing is preferable to learning through oral means. A better understanding of lesson plans and academic concepts is expected due to the change in learning strategies. Memorizing is a rare learning method. Students usually dislike this learning method because they fear they will forget what they have learned. As a result, reading and comprehension have supplanted memorization as a primary method of education. Most of the time, students in elementary, middle, and high school

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must memorize concepts to do well in class. As a result of technological advancement, people today rely on technology to help them better understand various subjects and concepts. From preschool to college, teachers use technology to help students learn about lesson plans and academic concepts in today’s world. On the other hand, students use them to prepare assignments and projects and improve their understanding of studying material. Consequently, it can be said that one of the most widely used learning strategies is to make efficient use of technological resources. Meaning and Significance of Learning Strategies for Contemporary Learners. Learning strategies refer to a student’s methodical approach to mastering academic concepts and lesson plans. In order to achieve their academic goals, students will use effective learning strategies when they are motivated. Students at all levels of education must use their skills and abilities to learn the material and perform other tasks and activities. Rather than using the strategies to achieve educational goals, individuals use them in non-academic contexts. In addition to academics, students in schools and colleges are encouraged to participate in extracurricular and creative activities as part of their overall education experience. Artworks, handicrafts, music, singing, dancing, sports, role-playing, and so on are all examples of these. When students show interest and enthusiasm in one or more of these activities, they may pursue them as a career path. As a result, the significance and meaning of learning strategies are recognized in both academic concepts and extracurricular and creative activities. Education and culture are intertwined in pedagogy. At all levels of education, pedagogy is an effective means of enriching the institution’s cultural identity. It is determined by the educators’ norms and beliefs about how learning should be conducted. The teaching and learning materials used in the classroom have no bearing on the students’ success. The process is what matters. Effective, meaningful, and worthwhile procedures are required. The strategies employed should significantly impact the achievement of academic goals. For students to succeed academically, they must be well-versed in the best ways to learn. Group work is encouraged by teachers at all levels of education. In other words, they encourage students to work together as a team to develop mutual understanding and work together as a team. Therefore, the significance and meaning of learning strategies can be recognized when using efficient procedures and working in groups. Students typically rely on the assistance and support of others to help them learn, whether they attend a nursery school, elementary school, middle school, or high school. These are members of your family, friends, or private tutors. Technology and the internet can help when challenging academic concepts, but students still need assistance. In some cases, students may not grasp the concepts from the reading materials on their own and will need someone else to do so. Consequently, it is widely accepted that students learn best when they can communicate with one another. When it comes to teaching and learning, there are many methods available. Because of this, it can be concluded that students recognize the significance

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Fig. 1.1 Learning strategies for contemporary learners (TeachThought Staff, 2020)

and meaning of learning strategies when they use them following their grade levels and academic goals. Learning Strategies for Contemporary Learners. A wide range of educational institutions is putting to use the learning strategies (Sharples et al., 2015) (as shown in Fig. 1.1) for contemporary learners that have been described in this section. Students can better understand how to use technology, the internet, books, articles, projects, reports, and other reading materials to enhance their learning of subjects and concepts and use learning strategies. For example, engaging in effective communication, interacting with the environment, utilizing tools, devices, and machinery, and making sure the mind and body are working together are just a few of the many learning strategies that are put into action to help students learn faster and more effectively. There are many different learning strategies (Sharples et al., 2015) that educators in educational institutions can use to pass on their expertise to students. These are outlined in the following manner. 1. Crossover Learning: Museums and after-school clubs can help students connect educational content to real-world issues. These connections work both ways. There are many ways to incorporate classroom knowledge into informal learning, such as incorporating classroom questions into everyday experiences. When a teacher asks a question in class, students can then go on a museum or field trip to investigate the issue, collect evidence, and present their findings back in class. Learners benefit from the advantages of both environments when they participate in these cross-curricular learning experiences. Recording, linking, recollecting, and sharing diverse learning experiences is a broader opportunity to support learners because learning occurs throughout a person’s life. These crossover learning experiences pique one’s interest and desire to learn even more. 2. Learning Through Collaborative Argumentation: Students can better understand science and mathematics by arguing like professionals in those fields.

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Students benefit from argumentation by paying attention to opposing viewpoints, enhancing their understanding. It makes technical reasoning accessible to the general public. Students can work together to improve their ideas and better understand how scientists think and collaborate to prove or disprove a claim or theory. Teachers can encourage students to ask open-ended questions, rephrase their remarks more scientifically, and create and use models to explain things in their classes. Students learn to take turns, listen attentively, and respond positively to others when they engage in scientific argumentation. It is possible to help teachers develop these skills and overcome obstacles such as sharing their knowledge with students through professional development appropriately. 3. Incidental Learning: “Incidental learning” refers to unplanned or unintentional learning. It can happen while doing something that seems unrelated to what one has just learned. Early studies focused on how people learn in their daily work routines. Using mobile devices in their daily lives, they have many opportunities for technology-supported incidental learning, which they can access at any given time. No structured curriculum or formal certification is required for a teacher to lead incidental learning. On the contrary, selfreflection may lead to learners rethinking isolated learning fragments as part of longer-term learning journeys, which could motivate students to do so. 4. Context-Based Learning: Learning from experience is made possible because of the context in which it occurs. New information can only be interpreted in the time and place it is presented and about what we already know. The context is typically limited to a fixed location and a set amount of time in a lecture hall or classroom. In addition to classroom instruction, students can gain knowledge by visiting a museum or historical site or reading a wellwritten book. Context can be created by interacting with our surroundings, having conversations, taking notes, and modifying objects in our immediate proximity. With the help of guides and measuring instruments, we can also understand the context by exploring the world around us. In order to create effective learning environments for schools, museums, and websites, designers must have a thorough grasp of how context influences learning. 5. Computational Thinking: An approach to problem-solving based on computational thinking can be powerful. Decomposition, pattern recognition, abstraction, identifying and developing the steps necessary to reach a solution (algorithms), and refinement of these steps (debugging) are all part of this process. Computational thinking skills can be helpful in a wide range of situations, from writing a recipe to share a favorite dish with friends to planning a vacation or an expedition and deploying a scientific team to tackle a complex challenge like a disease outbreak. The goal is to teach children how to structure problems to solve them. There are many ways to teach computational thinking, including math, science, and the arts. In addition to promoting computer coding, the goal is to help children develop the ability to think critically and creatively in all areas of their lives.

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6. Learning By Doing: Building science inquiry skills, improving conceptual understanding, and increasing motivation using essential scientific tools and practices, such as remote laboratory experiments or astronomical telescope monitoring. Remote access to specialized equipment, first developed for scientists and university students, is now being used by trainee teachers and school students. It is common for remote labs to include equipment, robotic arms for operating it, and cameras that record the experiments in progress. Teachers’ professional development and curriculum materials, as well as easy-to-use Web interfaces, can all help lower the bar to participation in a remote lab. Access to remote labs can provide hands-on investigations and direct observation opportunities that complement textbook learning with appropriate support for teachers and students. Remote labs can also be used in the classroom to give students a real-world experience. It is possible to use a telescope to observe the night sky during daytime school science classes. 7. Embodied Learning: Embodied learning is a type of learning in which the learner’s body is used as a tool for learning. In order to learn a new sport, physical movement is an essential component. The goal of embodied learning is that the mind and body work together so that physical feedback and actions reinforce the learning process. Wearable sensors, visual systems that track movement, and mobile devices that respond to tilting and motion actions are examples of technology that can assist. Applying this method to physical sciences like friction, acceleration, and force is excellent for investigating simulated structures like molecules. For more general learning, physical activity provides learners to feel what they are learning. Mindfulness can be enhanced by becoming more in tune with one’s body’s interactions with the environment. 8. Adaptive Teaching/Learning: There is no one-size-fits-all approach to education. However, most educational materials and presentations are the same for everyone. Putting the burden of engagement on the learner creates a learning problem. Many learners are likely to be bored, others will get lost, and only a few will discover the optimal learning paths through the content. This problem can be solved with the use of adaptive teaching. Personalized educational content is created by analyzing a learner’s previous and current educational experiences. New content can be introduced, and old content can be revisited using adaptive systems. In the classroom and online, adaptive teaching can tailor the learning experience to each student’s individual needs. Moreover, they offer a variety of ways to keep track of one’s progress. They build on traditional learning methods, such as reading textbooks and adding computer guidance. Students can be guided through educational materials using data such as the time they spend reading and their self-assessment scores. 9. Analytics of Emotions: Analyzing how students learn with eye-tracking and facial recognition software is a powerful tool for educators. Students’ answers to questions and explaining their knowledge are examples of cognitive aspects

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of learning. Non-cognitive factors include whether a student is irritated, confused, or otherwise unable to concentrate. Students’ mindsets, strategies, and qualities of engagement (such as tenacity) all significantly impact how they learn. Teaching in the classroom can benefit from a promising approach that combines computer-based cognitive tutoring systems with the expertise of human teachers in responding to their students’ emotions and dispositions. 10. Stealth Assessment: When students work in rich digital environments, the automatic data collection in the background can be used to conduct unobtrusive, “stealth” assessments of their learning processes. For example, in World of Warcraft, the system constantly collects data about players’ actions and infers their goals and strategies to present new challenges. Stealth assessment borrows these techniques. Schools in the sciences, history and adult education are now implementing this assessment strategy in their classrooms. Stealth assessment can measure difficult-to-measure characteristics of students’ learning, such as perseverance, creativity, and strategic thinking. It can also gather data about students’ learning states and processes without requiring them to stop and examine. In principle, teachers could use stealth assessment techniques to monitor their students’ progress regularly. Benefits of Learning Strategies for Contemporary Learners. Students need to be aware of the advantages of the learning strategies to benefit from them. Teachers in schools and parents at home assist young students in learning and comprehending subjects and concepts in nursery and elementary school. The students themselves are aware of the importance of effective and meaningful learning strategies in secondary, senior secondary, and higher education institutions. Using learning strategies in a well-organized manner enables contemporary learners to understand the significance and benefits of their use. The advantages of learning strategies for contemporary learners are outlined below (Kapur, 2020). 1. Develops Collaborative Skills: Instructors at all levels of education encourage students to collaborate. In other words, instructors encourage them to work together as a team. The development of collaborative skills is an essential benefit of learning strategies for contemporary learners. When they collaborate, several advantages accrue to students—sharing ideas and viewpoints, developing mutual understanding, accepting each other’s cultures and other factors, and creating an enjoyable and sociable atmosphere in the workplace help alleviate work pressure. Group members are assigned tasks based on their abilities and skills. That is why the importance of collaborative skills is widely acknowledged by instructors and students alike as one significant benefit of learning strategies. 2. Makes the Individuals more Disciplined: Learning academic subjects and concepts and achieving educational goals is usually the primary focus of students’ time and energy. On the other hand, some students cannot focus on their studies and consequently suffer setbacks. This group of students needs to hone their focus and become more disciplined in their academic pursuits. When

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they fail to get good grades on tests and assignments, their teachers tell them to improve their focus and discipline. Using the learning strategies mentioned above, they will learn that it is unacceptable to engage in leisure or recreational activities while studying. In addition, they can put into action all of the elements that would help them become disciplined. As a result, learning strategies are well-understood to help students become more self-disciplined. Encourages Risk-Taking: When students practice various learning strategies, some of them can be risky. When students are conducting experiments in the laboratory, they are required to use machines, tools, and chemicals to complete their work. Their experiments are hampered because they lack the necessary background knowledge. However, if students are well-informed, they will face all kinds of challenges, take risks, and perform their job duties effectively. In order to achieve the desired results and encourage risk-taking, it is necessary to have a thorough understanding of learning strategies. As a result, it can be concluded that students who are well-prepared in terms of learning strategies will grasp lesson plans and deal with risks more effectively. Requires Student Preparation: In order for students to succeed, they must be well-prepared. They must be well-prepared for any tests, competitions, quizzes, or class lectures they must attend. As a result, they must respond adequately to the questions put forth by their instructors. Learning strategies and lesson plans based on academic subjects can help students enhance their knowledge and prepare themselves well at any level of education. Students will be better able to deal with their difficulties if the learning strategies are practiced. Because of this, it can be stated that contemporary learners will be able to generate information and be well-prepared in terms of academic concepts and lesson plans when they are well-equipped in terms of learning strategies. Increases Participation in Tasks and Activities: As part of their education, students use a variety of sources, such as technology, the internet, books, other reading materials, and other tasks and activities to enhance their learning. Participating in seminars, workshops, conferences, competitions, presentations, creative work, and other activities are other tasks and responsibilities. These tasks and activities have given Consideration to improve their knowledge, skills, and abilities. The tasks and activities are organized according to the students’ grade levels, subjects and concepts, and age groups. Because of this, it can be concluded that learning strategies can help students develop the self-confidence and aptitude needed to participate in tasks and activities more effectively. Improves Critical Thinking Skills: Students need critical thinking skills to think critically. What are the skills necessary for critical thinking? These include observation, analysis, interpretation, and evaluation (SkillsYouNeed, 2020). Students must practice critical thinking skills as they learn academic concepts. Students will think critically and creatively if they learn these skills. Critical thinking skills are helpful for their professional lives, such as when they are in school or working. However, they are also crucial and valid in

1.6 Learning, Teaching, and Assessment Methods …

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their personal lives. Students will develop critical thinking skills and a practical grasp of lesson plans and academic concepts if they are dealt with the learning strategies mentioned above. Improves Problem Solving Skills: Solving problems requires thinking creatively and analytically. Students run into issues while grasping academic concepts necessary to achieve their goals. Because of this, they will be able to solve various issues with their problem-solving abilities. Either in a major or minor way, the problems can occur. Various considerations must be taken into account when attempting to solve various issues. Problems, causes, and solutions are all examined in this process. Depending on the situation, they may be able to solve them independently or need help from others. The students who are knowledgeable about learning strategies can develop problem-solving skills and provide solutions to a wide range of issues. Improves Technical Skills: Students rely on technology to learn academic concepts and subjects in the modern era. Students at all levels of education have found technology beneficial and meaningful for learning. Typically, students enroll in computer training centers to learn how to use computers and improve their technical abilities. When it comes to using new technologies, people can get nervous. Then, when they need to use them, they become engaged in regular practice. There is no substitute for practice in order to become a better person. When people use technology regularly, they can improve their technical abilities. Regardless of one’s field, category, or background, one must constantly improve their technical abilities. To sum up, understanding how to use technology in teaching, learning, and assessment is one of the significant advantages of the learning strategies. Facilitates better understanding of Subjects and Concepts: Regardless of their educational level, all students strive to improve their knowledge of the subjects and concepts they study. It is possible to understand some subjects and concepts easily, while others are difficult. Consequently, students who use effective and meaningful learning strategies will better grasp the material and concepts they are studying. Aside from allowing students to grasp concepts and subjects better, the learning strategies are widely recognized for improving students’ comprehension abilities. Instructors must read and understand academic concepts and lesson plans after the instructors have taught the students. To perform well on assignments and tests, they need to practice the exercises that will help them learn the material thoroughly. Facilitates Management of Talents: Individuals need to work on honing and managing their skills early. In order to have a more prosperous life and be able to meet their basic needs, people must have a diverse range of skills. It is no surprise that talent management has evolved over the years, just like all other aspects of the workplace (Ghosh, 2021). Students need to learn more about different learning methods to develop their talents. There are several things that students need to learn in order to improve their artistic abilities. In order to succeed, they must also put in regular practice time. By learning how to manage their talents, students can build on and improve their skills over

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time, achieving tremendous success in school and other areas of their lives. In this regard, learning strategies facilitate the management of talents, which is a significant benefit for contemporary learners.

1.7

Structure of the Book

The textbook ‘Learning, Teaching, and Assessment Methods for Contemporary Learners: Pedagogy for the Digital Generation’ addresses contemporary learners’ needs. Adopting modern practices is the only way to make it in today’s knowledgedriven world. This book provides students, educators, and researchers alike with how to effectively make sense of and use modern learning, teaching, and assessment methods in their everyday practice. This book aims to introduce modern learning, teaching, and assessment methods as a separate chapter and provide a deeper understanding of these methods so that the students/teachers can create opportunities for themselves and others. Each chapter will give greater insight into how you can apply these modern methods to your classroom. You will understand why and how these current methods can support the curriculum. We will also touch on some case studies. This book also provides two separate chapters on E-Learning and the role of Artificial Intelligence (AI) in education.

1.8

Conclusion

The procedures and activities that students at all levels of education use to help them learn and effectively use their communication skills are referred to as learning strategies. “Learning strategies” refers to a person’s methodical approach to acquiring a thorough grasp of academic material. Individuals use learning strategies in their daily lives, not just for educational purposes but also in non-academic situations. For students to practice the learning strategies in a meaningful and useful way, they must be focused on their academic goals. To be successful in school, students at all levels must put their skills and abilities to good use in both academics and extracurricular activities. Learning strategies for Contemporary Learners develop collaborative skills, encourage risk-taking, require student preparation, increase participation in tasks and activities, improve critical thinking skills, improve problem-solving skills, improve technical skills, facilitate a better understanding of subjects and concepts, and aid in the management of talents and abilities. Finally, it can be stated that learning strategies can help contemporary learners improve their skills and abilities and achieve their goals and objectives if they are used effectively by learners.

References

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References Concordia University. (2018). Contemporary learners look for innovative ways to learn. Concordia University. https://www.concordia.edu/blog/faculty-blog-series-teaching-and-learningalexandra-herron.html. Ghosh, P. (2021). What is talent management? Definition, strategy, process and models. ToolBox. https://www.toolbox.com/hr/talent-management/articles/what-is-talent-management/#. InstructionalDesign.org. (2020). Learning strategies. InstructionalDesign.Org. https://www.instru ctionaldesign.org/concepts/learning-strategies/. Kapur, R. (2020). Learning strategies for modern pedagogy. Persaud, C. (2021). Pedagogy: What educators need to know. https://tophat.com/blog/pedagogy/#: ~:text=Howdoespedagogyimpactthelearner%3F,-Asnoted%2Cwith&text=Throughyourpeda gogicalprocess%2Cstudents,modelstofurthertheirlearning. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. SkillsYouNeed. (2020). Critical thinking skills. Skillsyouneed.Com. https://www.skillsyouneed. com/learn/critical-thinking.html. TeachThought Staff. (2020). 10 innovative learning strategies for modern pedagogy. Teachthought.Com. https://www.teachthought.com/the-future-of-learning/innovative-strate gies/.

2

Crossover Learning

Abstract

Learners can learn the information from informal environments, such as museums and after-school clubs, related to their educational content. Experiences from daily life can enrich learning in schools and colleges. This form of learning can be deepened by incorporating classroom queries and content. These related experiences ignite more curiosity and desire to learn. The Teacher can suggest and debate a topic for the learners to explore the topic they learn during an academy visit or field trip, collect the images or records as proof and provide their results back into the classroom as individual group responses. This method provides a reliable environment for students to take advantage of learning opportunities. This method allows learners to record, incorporate, reference, and update their diverse learning experiences because learning occurs throughout a person’s life. This chapter introduces crossover learning. Further, it concentrates on the implementation and its effectiveness. Keywords

Crossover learning • Contemporary learners • Learning strategies • Formal and informal learning • Competency-based learning

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Introduction

According to education, crossover learning is one of the innovations that could change everything, according to an Innovating Pedagogy report published in 2015 (Sharples et al., 2015). crossover learning refers to a comprehensive understanding of learning that spans formal and informal educational environments. Traditional learning environments (school, university, professional development) are expected to become increasingly supportive of learners in connecting diverse learning events that connect the classroom with informal and incidental learning over the next few

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_2

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years. Both ways are possible with these links. There are many ways to incorporate classroom knowledge into informal learning. For example, students in schools and colleges can benefit from incorporating classroom knowledge into their informal learning. These shared experiences pique our curiosity and energize our desire to keep learning new things (Sharples et al., 2015). As Curt Bonk stated, “Anyone can now learn anything from anyone at any time” (Bonk, 2009). Online learning, open courses, web 2.0 tools, and informationsharing through online communities have significantly altered the educational landscape compared to 10 or 20 years ago: Learning is no longer confined to institutions but takes place in the context of personal networks. We learn as we connect. As Mackey and Evans (2011) point out, institutional perspectives of socially constructed learning have a problem in that the area of interaction is typically limited to the online course community: “There is little acknowledgment of the overlapping experiences of participants in communities of practice and other informal learning networks beyond the online course.” It is important to note that informal learning is an integral part of our learning experience, and it occurs in various ways, including through communities of practice and personal networks. There is no longer a distinction between educational and work-related activities and educational and networking activities in professional networks (Siemens, 2005). To George Siemens and other educational technology researchers, the central idea of connectivism’s learning theory is that people learn best when they share and collaborate in networks of people who have similar interests (Siemens, 2005; Verhagen, 2006; Kop & Hill, 2008). According to connectivism, information is stored in various (digital) formats and disseminated through a network of computers. Digital learning environments and the network structure of online interactions are vital considerations in this theory’s development as an educational model. In the digital age, learners must find current information and filter out secondary and extraneous information. When a student connects to a learning community and shares information, this is considered the beginning of the learning process according to connectivism (Kop & Hill, 2008). To learn, you must recognize patterns in your technologically enhanced personal network. A common assumption in instructional design models is to assume a welldefined target audience with presumed learning needs and intrinsic and extrinsic incentives. There is no extrinsic reward mechanism for informal learners in open networks, which means many potential learners with unidentified learning needs (Panke & Seufert, 2013). We no longer need to ask ourselves whether or not informal online learning is taking place, but rather how we can better understand the confluences of meaningmaking that are taking place in diverse social media spaces, according to Gerber et al. (2016). This chapter will look at crossover learning, which refers to a comprehensive understanding of learning that spans both formal and informal environments.

2.2 Crossover Learning

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Crossover Learning

It is a new teaching method that aims to bridge formal and informal education. Is going to a museum a good way for a student to learn about art history? On a field trip, could students identify certain plant species? Can teens use math and statistics to plan social projects? According to crossover learning, answering all these questions is “yes.” To a large extent, this is due to a common theme among all the questions: connecting what students learn in the classroom with real-world experiences that they find pleasurable and engaging outside of school (Youaremom, 2021). Authentic learning can take place this way. What Is Crossover Learning? Using this method, students can learn in both formal and informal settings. As a result, crossover learning links educational material with real-world experiences. Because of this, it aims to connect schoolwork to experiences outside the classroom. Crossover learning is discussed in the Sharples 2015 report (Sharples et al., 2015) as an alternative learning environment outside the classroom. The Institute of Educational Technology of the Open University of the United Kingdom recently researched crossover learning. Researchers examined new methods of teaching that connect the classroom with everyday life in this study. According to the study (Sharples et al., 2015), “Experiences from everyday life can enrich learning in schools and universities can be enriched; Adding questions and knowledge from the classroom can deepen informal learning.” As the name implies, it can be used in various educational contexts. As the name implies, it can be used in various educational contexts. “These connections work in both directions,” according to the report (Sharples et al., 2015). Put another way, it is better for academic and everyday learning to occur simultaneously. Crossover learning to motivate and spark interest in students. Crossover learning is an exciting learning strategy for educators because of its potential to inspire students. Students’ attention and interest in learning can be awakened by tying curricular material to what they enjoy. Kids should learn some subjects outside the classroom. According to crossover learning, it is possible to make many school subjects more enjoyable by connecting them to a real-world application. So, daily experiences can be considered learning opportunities (Youaremom, 2021). Name just a few: field trips to the natural world and museum and theatre visits. With the knowledge they have gained in the classroom, students can apply what they have learned in the real world to make it more enjoyable. Crossover learning and new technologies. Teaching and learning have undergone a significant shift in today’s technologically advanced world (Youaremom, 2021). Thanks to them, many formal and informal learning options have been developed and expanded. The use of technology is prevalent both inside and outside of the classroom. Key tools for enabling and improving education have become virtual environments, educational platforms, and online education. In order to ensure a more comprehensive, complex, and long-lasting education through the use of new technologies, crossover learning makes a great deal of sense.

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The need to connect knowledge with formal and informal educational contexts. It is essential to have access to both formal and informal educational opportunities. Moreover, it is more likely to happen at any time, anywhere. Furthermore, it is a more pleasurable method of education for many people (Youaremom, 2021). In general, informal learning is more closely linked to the environment, experiences, and the way people live. The majority of them cater to individual students’ interests, preferences, and needs. Several learning objectives have been tied to them as well. Formal and non-formal education is necessary for today’s society. In addition, real learning makes use of both academic and everyday knowledge. Finally, the classroom and the rest of one’s life must be intertwined, and vice versa.

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Crossover Learning: Connecting Formal and Informal Learning

When it comes to our educational journeys, we prefer to refer to them as ‘kindergarten, school, university, etc., rather than simply labeling them as ‘educational’ (Sharples et al., 2015). Those distinctions are becoming less relevant as formal and informal learning experiences are intertwined in museum visits, after-school and hobby groups, or internships. Technology, assessment and recognition methods, and new insights into the value of informal learning are all working together to blur the lines between the two types of education that were once distinct. As we will see in this section, formal and non-formal learning can benefit from cross-pollination. Informal education can help students in their academic endeavors and enrich their lives. Learning outside of the classroom helps students develop the skills and dispositions they need to succeed in school (Sharples et al., 2015). In a museum visit, for instance, there are apparent educational aspects, but the structure and purpose of the experience can alter the educational value. An excellent example of this is to set specific goals for student visitors, such as collecting evidence to answer specific questions about the course material. When students are allowed to pursue their topics of interest in the formal curriculum, they can influence curriculum topics and tasks from their own experiences. With the help of organizations like museums, youth and hobby clubs, and schools or university departments, it is possible to develop educational materials aligned with the local curriculum (Sharples et al., 2015). Real-life experiences with subjects, like internships or mentorship, can help students stay motivated to pursue related careers. There has been a shift in the way educators, policymakers, and researchers view learning as taking place in a ‘learning ecosystem.’ There has been a rise in interest and opportunities for this learning regarding crossover learning. It is possible to apply the concept of crossover learning to the way we think about learning as a whole and how formal and informal combine to influence the way people think and feel about learning at all stages of their lives.

2.3 Crossover Learning: Connecting Formal and Informal Learning

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As a result, studies on the intersection of informal and formal learning look at various topics, including how we can better design learning activities, reevaluate assessment and recognition practices, and how methods and technology can facilitate the transfer of knowledge and experience between different contexts. Towards competency-based learning. Non-academic learning practices are being considered as schools and universities move away from education based on “seat-time” and homework toward a competency-based approach that emphasizes achieving goals and acquiring skills rather than the volume of knowledge learned. It may be possible to reduce student workload by recognizing that students’ informal learning contributes to their development of skills and competencies (Sharples et al., 2015). One extreme of incorporating informal learning into the classroom is lower expectations for formal outcomes. Aside from the traditional classroom setting, teachers can create lessons that encourage students to think about their extracurricular activities. Students studying American Literature, for example, built their chairs and learned problem-solving, communication, and collaboration skills as a result (Sharples et al., 2015). They were able to gain a greater appreciation for the craft and structure of written texts by drawing connections between the poetry they were studying and the furniture they were making. As a result of practical activities, students can develop traits and skills like persistence and self-direction that can be applied to any subject. Recognizing achievement. Crossover learning necessitates changes in how we evaluate and recognize success. Recognition of activities that originate from various sources, such as awarding badges for less formal achievements, is an example. Tumblr and Pinterest can help students develop transferable skills like curation, evidence building, and reflective commenting by gathering resources. An item or a collection of items can serve as an entry point for further investigation into a subject because users of these tools record their interactions and paths through online information. Many teachers and non-formal educators are now able to participate in crossover learning. In museums and community centers, informal educators can use the local curriculum to cross over key concepts and create intentional links between their program activities and school-based education. On the other hand, teachers can learn how to facilitate open-ended explorations in their classrooms. For teachers, the Teacher Institute at the Exploratorium guides how to use the museum’s interactive exhibits to teach students about the museum’s educational philosophy (Sharples et al., 2015). In order to ensure that staff strategies for crossover learning are maintained, formal and informal education entities should collaborate. Incorporating informal learning into formal education can enrich knowledge with real-world experiences by providing structure and direction to unstructured activities, increasing the value of non-formal learning in the classroom and workplace. Keeping the established curriculum’s coherence with this integration while allowing for fun and freedom.

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The Importance of Crossover Learning

Crossover learning is becoming increasingly popular (Alice, 2017). The concept of crossover learning encompasses both formal and non-formal education. Internships and after-school activities are essential components of a well-rounded educational experience. Visiting museums and hobby clubs can provide students with hands-on learning opportunities. For students, education in school is essential because it provides them with the theoretical framework needed to understand practical applications. Real-world examples and informal application of knowledge enrich the classroom experience. Students’ academic performance improves when they learn new skills outside the classroom (Alice, 2017). For example, a trip to a museum can teach students how to find and evaluate evidence. Students can also focus on personally exciting topics, adding a personal touch to the educational experience. Students learn to acquire knowledge throughout their lives by concluding various settings and environments (Alice, 2017). Crossover learning has a lot to offer in this regard.

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Strengths of Crossover Learning

Crossover learning provides a link between the classroom and the natural world and an opportunity for students to gain practical experience, feed their curiosity, and learn engagingly and authentically (Bhagi, 2020). It also increases the chances for students to make friends outside of the classroom, participate in various learning activities, and develop their creativity and teamwork skills (Bhagi, 2020).

2.6

Why is It Good to Implement Crossover Learning?

The term “crossover learning” refers to a strategy that combines formal classroom instruction with learning that takes place outside of the traditional school day, such as in museums, after-school programs, and even online. The 2015 Innovating Pedagogy report lists crossover learning as one of the top ten educational innovations of the decade. The advantages are apparent (Popova, 2019). 1. First and foremost, students’ sense of agency grows due to crossover learning. Most teenagers can and should take more responsibility for their education. Students will choose from a wider variety of extracurricular activities outside of the classroom than they would be in a traditional classroom setting. 2. Teenagers will choose educational content outside of the classroom relevant to their lives. If they are interested in something, they will not see it as a burden, especially authentic and fun. Motivation is also boosted as a result.

2.7 How to Implement Crossover Learning?

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3. Third, students’ formal linguistic knowledge can benefit significantly from some hands-on experience gained through crossover learning. Informal learning, on the other hand, can benefit from teacher guidance.

2.7

How to Implement Crossover Learning?

The implementation of crossover learning, like any other innovation, may require a significant amount of time from a teacher. Approximately how much time do we have to devote to this project? When dealing with students who are apprehensive about learning English, what should be done? Do you know how to look into it? How can I avoid falling behind in class? When it comes to such questions, the list can belong. “Start small” (Popova, 2019) is the most important piece of advice. 1. For now, flipped classrooms have been included in some coursebooks, such as Gateway. Crossover learning has begun! This is a huge accomplishment, especially if you do not use flipped classroom videos and instead allow students to conduct independent research on specific topics. Students in a flipped classroom first learn the new material at home, then practice it in the classroom setting to solidify their understanding. It gives students more control over their own time and effort. 2. It is good to take general knowledge quizzes and do internet research to improve your presentation and digital literacy skills. Are you talking about a subject or concept that your teenagers are unfamiliar with? It is up to the students to do their research and then present their findings to the group. 3. Use CLIL in your classroom. Teenagers may benefit from English-based content and language-integrated learning. They can also use this time to concentrate on the subjects they consider for their future careers. If they are interested in physics or chemistry, you can ask them to conduct an experiment and record a short video about it. Geography nerds can host mini-discussion groups in various countries and cultures worldwide. Decide for yourself. 4. Plan a trip to a museum, a cinema, or a cooking class for your child. You can ask students questions or draw KWL diagrams as a starting point. Teens can use them to organize their thoughts and notes in the days leading up to, during, and following a visit. KWL stands for: K—What do you know about the topic? W—What do you want to know? L—What did you actually learn? You have the option of accompanying them or allowing your teenagers to go on their own and report back later on their experience. Students’ interests and motivation will be piqued and their knowledge deepened due to these outings. 5. Discuss online learning platforms like Coursera or FutureLearn with your teenagers. You can take free online courses from some of the world’s most prestigious universities, and there is something for everyone. These classes are

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taught entirely in English means that students have access to a wide range of communication strategies and a wealth of information. Students should choose a course on a platform of their choice and study at their own pace. Set a date and time to meet with them to go over what they learned from the course. On the other hand, higher-level teenagers are better suited to taking an extracurricular course like this because they are already comfortable communicating in English. Crossover learning has the potential to help students improve their skills and competencies. Learning English will be more fun and rewarding if students can see why they are doing it. Isn’t it time to try?

2.8

Why is Crossover Learning Working in the Classroom?

Grades and ages are not the only ways to categorize what you learn. Since learning does not stop when students leave their desks, most traditional curricula ignore this vital point. After school hours, students are still learning and progressing. A growing number of organizations are focusing on educational reforms that encourage lifelong learning due to this reality. This is one of the methods that has been used to help students (BoredTeachers, 2017). 1. Connecting Formal and Informal Learning: Students benefit from the best of both formal and informal learning environments when they participate in crossover learning programs. In order to facilitate cross-curricular learning, educators can pose a question or problem in the classroom that students can then work to solve during museum outings or other school-sponsored excursions. The ability of a child to learn a variety of topics and subjects outside of formal schooling helps them develop their skills and perform better in their school requirements and activities. As a counterpoint, classrooms can benefit from incorporating more informal learning methods. For students in the formal curriculum, crossover learning allows them to pursue their interests and themes related to the subject at hand. Students can retain information better if they actively engage in the pursuit of knowledge, even through simple discussions with their peers. 2. Creativity is Key: The ability to think outside the confines of conventional education necessitates creativity. Making mistakes and learning from them helps students build competence, self-confidence, and self-esteem. Playful games and visual exercises can also pique students’ interest and encourage them to learn. Math can be made more exciting and understandable through the creative use of art. As a result of engaging and fun activities, students can better comprehend and group the various topics in the course and gain a better overall understanding. 3. Innovating Further: Crossover learning is just the beginning. Finland, a global leader in quality education, is taking crossover learning to the next

2.9 Try Out Crossover Learning with Your Students

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level, emphasizing phenomenon-based learning. According to the BBC, Finnish schoolchildren, who consistently demonstrate above-average proficiency in literacy and math, will have fewer “subjects” in their daily education. They have been replaced by inter-disciplinary topics in which teachers can incorporate all of the fundamental subjects used around the world in their classrooms. For example, a lesson on the European Union could include history, geography, languages, and economy. For decades, pedagogical research has stated that children need to learn how to think rather than what they should think. In addition, the approach places less emphasis on getting children to perform better on international standard exams and more on teaching them the knowledge and skills they will need for the rest of their lives.

2.9

Try Out Crossover Learning with Your Students

In order to use crossover learning effectively, teachers should first pose a question/discussion to students in any subject area and then encourage students to explore and return with their findings to discuss in the classroom (Bhagi, 2020). Consider the following: Example 1 When teaching about the Mughal Empire in a history class, ask students questions like, • “What was the sequence of events during the Mughal empire?” • “Who were the rulers during Mughal Empire” or • “Explain the downfall of the Mughal Empire.” Taking students to a museum is a great way to bring classroom learning into the real world. Make small groups of students work together in a museum to find answers to questions or discussions during your time there. Students need to take notes in the museum and back up their answers with creative materials, such as photos or drawings. Reinforce their understanding of the findings in the classroom by discussing them. It is possible to connect any subject to a real-world setting, such as geography, by giving children a map and asking them to find directions, maths by using the playground to form shapes, biology by forest expedition, or moral science by a trip to non-government organizations. Example 2 Music teachers who use crossover learning in their classrooms can take their class of 15 students on field trips to music venues and rehearsals as an extension of their classroom learning. After each event, students can discuss their experiences, leading to new topics of study.

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Conclusion

Incorporating informal learning into formal education can enhance knowledge with real-world experience. Motivation can be increased, and the impact of informal experiences on education and the workplace can be enhanced by giving informal activities a sense of direction. As a teacher, you have the challenge of designing this integration to retain the established curriculum’s coherence while allowing for some fun and freedom in informal exploration.

References Alice, B. (2017). What are the most effective educational reform strategies? Yourstory.Com. https://yourstory.com/mystory/efeae1a099-what-are-the-most-effective-educational-reform-str ategies-/amp. Bhagi, S. (2020). Innovative learning strategies. LinkedIn. https://www.linkedin.com/pulse/innova tive-learning-strategies-sukanya-bhagi/. Bonk, C. J. (2009). The world is open: How web technology is revolutionizing education. BoredTeachers. (2017). Why crossover learning is working in the classroom? Boredteachers.Com. https://www.boredteachers.com/post/crossover-learning. Gerber, H. R., Abrams, S. S., Curwood, J. S., & Magnifico, A. M. (2016). Conducting qualitative research of learning in online spaces. Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open and Distance Learning, 9(3). Mackey, J., & Evans, T. (2011). Interconnecting networks of practice for professional learning. International Review of Research in Open and Distance Learning, 12(3). Panke, S., & Seufert, T. (2013). What’s educational about open educational resources? Different theoretical lenses for conceptualizing learning with OER. E-Learning and Digital Media, 10(2), 116–134. https://doi.org/10.2304/elea.2013.10.2.116. Popova, N. (2019). Crossover learning. Skyteach.Ru. https://skyteach.ru/2019/10/10/crossover-lea rning/. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 1–9. Verhagen, P. (2006). Connectivism: A new learning theory? Surf E-Learning Themasite. https://jor ivas.files.wordpress.com/2009/11/connectivismnewtheory.pdf. Youaremom. (2021). Crossover learning: What is it? Youaremom.Com. https://youaremom.com/ education/crossover-learning-what-is-it/.

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Learning Through Collaborative Argumentation

Abstract

By interacting with experienced scientists and mathematicians, students can able to enrich their knowledge in the field of science and mathematics. They learn to transform, listen critically, and respond to others constructively when students argue scientifically. Argumentation helps learners deal with different ideas that can deepen their learning. This approach will encourage the students to refine the concepts with others, which helps them understand and work together to refute assumptions. In this approach, the students can question the topic restate their comments in more technical terms, which helps them build and create the models for clarification. Teachers may promote constructive dialogue in classrooms. The professional development approach can help the instructor address the possible obstacles, such as communicating with the students during their academic activities. This chapter introduces the concept of collaborative learning and its benefits, argumentation in learning environments, preparing the classroom environment for collaborative learning, and evaluating argumentation. Keywords

Collaborative argumentation • Argumentation-based learning technologies • Benefits of argumentation • Kinds of arguments • Skills of argumentation • Learning to argue and arguing to learn • Argumentation in learning environments • Evaluating argumentation

3.1

Introduction

The transmission of knowledge and procedures to become active and thoughtful learners extends beyond modern education. Argumentation pedagogy prepares students for a world that affects every person and is publicly discussed in the light

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_3

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of scientific consequences such as climate change and genetic enginery (Sharples et al., 2015). Only by participating in the kind of research and communication processes that scientists use, students can understand scientific ideas in detail. These include reasoning and arguing from the evidence to improve, refute ideas and explanations, and communicate understanding using accurate language. The methods of scientific argumentation can only be applied to mathematics, history, language, the arts, and human science, not to traditional sciences (Sharples et al., 2015). The scientific content and science practices must be taught together; neither in isolation nor as the precondition for another should be taught. Students’ involvement in scientific argument contrasts with the traditional science-based pedagogy on lectures and closing questions, which are already known to answer. Argumentation pedagogy calls on students to make their claims, provide supporting proof of such claims, and examine whether the evidence presented by a person is sufficient and justified by discipline standards (Sharples et al., 2015). How can teachers promote productivity argumentation? Students must listen carefully to arguments and talk, justify claims and talk ideas based on reason and evidence to profit from arguments. Most students do not readily have this kind of classroom debate and need to be supported carefully. Learning through argumentation teacher practices include (Sharples et al., 2015): • students to articulate oral and written ideas • to ask questions that encourage students to evaluate and improve their ideas • to reaffirm or revoke comments from students in more scientific or mathematical language • to have students develop and use templates to develop explanations. Teachers can help with professional development using these strategies to conduct dynamic group discussions. Teachers can support a constructive argument by establishing conversation classroom standards, active listening, and a constructive reaction to the ideas of others. Argumentation-based learning technologies. One good way to stimulate a serious discussion of scientific ideas is to pose a question of thinking that does not have an easy answer and requires discussion of theory and evidence (Sharples et al., 2015). Here are a few examples, which range across topics and levels. • • • • •

Why aren’t birds electrocuted on electric cables when they land? Why are we not feeling an aircraft’s weight when it flies over us? Can we measure intellectuals? Is it possible to journey time? How could we know if Jesus was a real person?

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Teachers can ask students to examine the subject in groups and then share and compare their answers. Classroom communication technologies can assist this process. Each student in a classroom is given a ‘clicker’ to indicate their response to a question. A teacher can ask students to provide various answers to a question and vote for the best answer by showing a bar chart of the number of students who choose each answer. The teacher then asks that the class examine the answers and perhaps add additional proof and vote again. Students’ responses usually converge into a more normative understanding of the scientific subject. Students can also use an online scientific argument. The Cohere system, for example, enables argument visualization. The online knowledge forum helps students articulate, link, and think about their own and each other’s ideas. Students took the opportunity of the Knowledge Forum to understand scientific topics such as human body systems and pollution causes. The WISE (Web-based Inquiry Science Environment) platform offers various science projects on middle and secondary schools’ biology, chemistry, earth science, and physics (Sharples et al., 2015). These projects pose an essential question and then lead to an online investigation process that requires students to argue and explain in the investigation, backed up by data collected. Many of the projects lead to a student discussion to further refine student thinking, placing various solutions or conclusions against each other.

3.2

What is Collaborative Argumentation?

The interest in collaborative argumentation has been intense in recent decades. Since Greece’s age, the argument was a philosophical and rhetorical study (Chinn & Clark, 2013). Today, scholars study argumentation in many academic disciplines, from philosophy and rhetoric to communication. Since historical times, much of the work has focused on written arguments or arguments built by one speaker; scholars have analyzed and evaluated how authors or speakers support claims separately with evidence and reasons (e.g., (Toulmin, 2003)). A dialogue, an exchange of statements, questions, or answers involving two or more participants, leads to collaborative argumentation (van Eemeren & Grootendost, 2004). Participants in dialogue usually make claims and provide reasons to support them. The participants often disagree with their views, so their arguments might be directed towards examining this disagreement and resolving it. The definition of collaborative argumentation (Chinn & Clark, 2013) states that people can work together to develop and refine a position (such as a scientific explanation) by using evidence to support their developing ideas, or they can develop and refine a position (such as a scientific explanation) by working together to gather evidence. In these cases, due to the joint participation in building arguments activities, there is collaborative argumentation (reasons and evidence to support claims, entertaining other positions and evidence for those other positions, and so). Due to the dialectic nature, collaborative argumentation can be labeled as dialectical argumentation (Chinn & Clark, 2013).

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Educators have argued for argumentation in educational settings because arguments are prominently present in real-life practices. For example, historians and science philosophers saw argumentation as a discourse that progresses science and mathematics (Duschl & Osborne, 2002; Lakatos, 1976). In many other settings, argumentative disclosure can also be used from city council members who discuss ethical and practical dimensions of policy decisions to families who consider which of several available houses to buy. Educators thus advocate collaborative argumentation as a helpful practice outside educational environments. Recent emphasis on educational standards is a reflection of the significance of argumentation. A second reason to foster collaborative argumentation in education is that it can help students learn core content. Educators who advocate collaboration as an educational method differentiate argument and personal disputes (e.g., (Walton, 1989)) that are not rational but characterized by emotional, personal attacks. Educators need to explain to students that “arguing” does not mean “fighting” because students may assume that “arguing” means “quarreling” when they arrive at the classroom. It may be more productive to introduce argumentation in the classroom as “collaborative knowledge building” and “working together to make sense of data,” rather than using the terms argument and argumentation. Many educational researchers have seen argument as a dialogue on persuasion (Walton, 1989), in which participants use their arguments to try to convince each other by arguing, countering, and refuting others’ counterarguments (e.g., (Chinn et al., 2001); Collaborative discussion can, however, also invoke investigative discussions with participants (Walton, 1989), with which knowledge is constructed using agreed inquiry methods. These dialogues often begin where knowledge is lacking, and discussion is used to develop what is known and make new claims about knowledge (e.g., (Sampson & Clark, 2009)). While these claims are supported by evidence and arguments, they are rarely discussed in depth because there may not be much disagreement. However, they can be referred to as collaborative arguments because students use evidence and reasons to support knowledge claims. Some researchers have described such dialogues without necessarily identifying them as arguments; however, they are, in our view, a form of collaboration. Many researchers argue that elements of persuasion dialogues and research dialogues are combined (e.g., (Mercer et al., 2009)). Even if students disagree, they take each other’s ideas seriously and work to develop the best solutions in the spirit of collaborative research. When they find new evidence and arguments, they are willing instead to simply dig in and try to persuade others of their position to adapt their ideas to new information and evidence. Persuasion can sometimes be a way of understanding the world, but the ultimate goal is to work together to develop an understanding (for example, (Clark & Sampson, 2007; Sampson & Clark, 2008)). Dual-space model of collaborative argumentation. Barron (2003) proposed that collaborative learning should be seen as an area with two problems. The dualspace model defines the two cooperation spaces as the space for content where

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the students address the problem and the relational space where the interpersonal relations of the students are concerned. The space for content and relational interferes constantly and competes for student focus. In addition, research on the topic suggests that related activities permit students to significantly interact in collaboration in the content (Janssen & Bodemer, 2013). Content space and relational space coordination and control activities can be an overwhelming effort (Slof et al., 2010; Janssen et al., 2012). Students in collaborative argumentation often have to deal with the dynamics of the group while simultaneously struggling to learn how to argue and argue for learning (Aufschnaiter et al., 2008). Content space of collaborative argumentation. Students interact with each other in the space of collaborative arguments to co-construct arguments about complex problem resolution scenarios. The objective of interaction depends on the type of collaborative task at hand, i.e., students engage in cognitive activity in both tasks, such as critical data monitoring, discussion, and multiple perspectives exploration (Kirschner et al., 2003). We know that the combinations of structured and unrestricted interactions with educational and computer support for collaborative arguments include strategies for these cognitive activities. These tasks should provide several acceptable solutions, detailed instructions on task requirements and processes, integrated role-playing or predefined positions of conflict, equal sharing of crucial information, the individual preparation phase, and focus on the joint collaborative product (Veerman et al., 2002). Students must also focus on the topic and the discussion processes to achieve effective argumentation and collaborative problem resolution (Kirschner, 2002). Using the best strategy for solving the arguments can help students maintain a shared focus employing metacognitive activities (Ryu & Sandoval, 2015). In argumentative knowledge construction, students often fight with cognitive activities. The latter is described as “the joint construction and the individual acquisition of knowledge through collaborative argumentation” (Stegmann et al., 2012). They have difficulties arguing in class unless they receive some scaffold from the learning environment (Evagorou & Osborne, 2013). In addition, students often find it difficult to gather evidence or provide sufficient evidence to support their claims when they are presented with a topic for argumentation (Bell et al., 2004; Sandoval & Millwood, 2005). Problems with the structure of the argument include difficulties in disproving an argument or claim made by other students (Cavagnetto et al., 2010). In addition to the challenges of argument building, students struggle to coordinate experience, values, and aims. Students also have difficulties understanding the steps necessary for a complex argument (i.e., task identification, data interpretation). These problems relate to the lack of metacognitive capabilities, such as reflecting on one’s arguments, considering the quality of group arguments, and evaluating group arguments (Miller & Hadwin, 2015; Ryu & Sandoval, 2015). Relational space of collaborative argumentation. Students face social and interpersonal challenges in the relational field of collaborative argumentation. Regular communication in the relationship area of collaboration involves the exchange of views and questions of clarification. These activities aim to develop and maintain

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a shared understanding of the concepts discussed in the content space. A common strategy for achieving shared understanding is to discuss all contradictory views of the issue (that is, create the common reference framework) and see whether the opinions and material informing the arguments are appropriate in the standard reference framework (Janssen & Bodemer, 2013). Moreover, students caring for the well-being of group members and group cohesion take part in social work (i.e., exchange compliments, give positive feedback) (Slof et al., 2010). These efforts are influenced by social and interpersonal problems between group members. In particular, students’ performance is affected by their fight against social problems, for example, by regulating dynamics in group interaction (Barron, 2003). About the influence of group dynamics on collaborative argumentation, we know that phenomena of negative collaboration, i.e., the dominance of discussion and lack of common focus, can inhibit the argumentation of substance, while social conflict can promote it (Ryu & Sandoval, 2015). The quality of group output can be detrimental if other members’ contributions are ignored or rejected without group discussion. Power dynamics issues could arise and may affect the interaction between group members, especially when arguing for the solution of unstructured issues (Ryu & Sandoval, 2015). Group members, for example, who are close friends, often display higher levels of agreement and make their arguments quicker. The “free-rider effect” (including the “Free-Loading Effect,” for example) is not frequently but still evident in smaller group collaboration. There is no sufficient contribution of one member in the group discussion, and there is not enough motivation for one member to add to the group effort. Instead, respect for and acceptance of the contributions of other members to the discussion may result in better group results. Collaborative processes in collaborative argumentation. The pedagogical approach to collaborative learning is collaborative argumentation. In order to reach the full potential of the group and attain the learning goals in collaboration, it must care for five essential conditions (Johnson & Johnson, 1999; Kirschner et al., 2015). The following conditions apply (Johnson & Johnson, 1996): • • • • •

positive interdependence Accountability of individuals and groups Promotive interaction adequate use of social competencies group processing.

The efficiency of these conditions in collaborative learning requires specific cognitive processes (i.e., task-related planning), metacognitive processes (i.e., collaboration monitoring and evaluation) as well as socio-cognitive processes (i.e., group formation), and socio-emotional processes (i.e., trust-building and helpmaking). For example, the monitoring of team members can strengthen the links between their actions (i.e., positive interdependence) and promote individual accountability by evaluating cooperation processes. In addition, exhibiting helping

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behavior can provide cohesion and solve group conflict potentials (i.e., promotive interaction). If these five conditions are realized, social interaction will be stimulated on the collaborative content space leading to better cognitive performance (e.g., equality of participation, quality of product) (i.e., high-quality argumentative essays). Also, when social interaction is stimulated in the context of relational collaboration space, social results that manifest as a healthy social area can lead to better social results (e.g., group cohesion, satisfaction) (Kirschner et al., 2015). The active participation of students in these processes is regarded as a precondition for effective social interactions (Kirschner et al., 2015). Continued active participation is also a key factor in collaborative learning success (Chavez & Romero, 2012). When cognitive and social performance are both at a reasonable level, students feel satisfied and motivated to continue their participation. They strengthen each other (P A Kirschner et al., 2015). Collaborative problem-solving and process-enhancing support is needed to ensure high levels of cognitive and social performance. Group awareness for team effectiveness. The team-based sensitization role and task aspects of collaboration (i.e., the mix of social, cognitive, and behavioral awareness) for the effectiveness of the mediating team were conceptualized by Fransen and his colleagues (Fransen et al., 2011). The team efficiency of learning groups was defined as a combination of high-quality learning results and highquality team performance and the satisfaction of the needs of members of the group. This definition is a social-constructivist paradigm that demands a more active commitment to knowledge-building processes (discussion and argumentation etc.) to achieve profound knowledge and conceptual transformation. They formed a team effectiveness conceptual framework based on Salas, Sims, and Burke’s “The Big Five in teamwork” (Salas et al., 2005). The five main factors (e.g., team leadership, team direction, mutual performance monitoring, compatibility, and supporting behavior) were examined in the study groups. They defined the interaction of these factors and the effectiveness of the learning team in a conceptual framework for team effectiveness. They were specifically concerned about team awareness and task-related issues as an intermediate variable for team effectivity, facilitating mutual faith, shared mental models, and monitoring. Mutual performance monitoring (MPM) means “being aware of and keeping track of one’s fellow team members’ work while carrying out one’s work to ensure that everything is running as expected and procedures are followed correctly” (Fransen et al., 2011). This definition requires students to know about tasks and aspects of teams to achieve a shared understanding of tasks and team responsibilities. In order to monitor team performance effectively, they must also update their understanding of the current status of group processes using information from the environment. This information is based on the concept of awareness of situations (Leinonen et al., 2005) and serves both as an initial condition for mutual monitoring of the efficiency and as an efficiency assurance mechanism. The need to exchange information about team members’ activities depends on the awareness of participation (Janssen et al., 2007; Kreijns et al., 2003). When information about

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these kinds of information is provided, effective mutual monitoring of performance should occur and relatively efficient execution of tasks. Therefore, Fransen et al. (2011) hypothesized that mutual monitoring of performance could forecast the effectiveness of the learning team. Mutual monitoring processes can create friction between group members, as it includes feedback and/or criticism on the activities of other members. In addition, student protecting information often leaves little room for constructive cooperation, checking and inspecting each other and the behaviors of each other. When confidence is established, team members can share information freely and less fear for the criticism of team members (Nelson & Cooprider, 1996). Mutual trust (MT) can be achieved by raising awareness about all members’ interests and stressing that their actions are interdependent (i.e., social dimension). In addition, information sharing between group members can be encouraged (i.e., cognitive dimension). Fransen et al. (2011) assume that mutual trust (socially and cognitively), in all stages of teamwork, particularly in the initials of collaboration, is essential for team efficiency. The construction of standard mental models also constitutes a critical support mechanism for effective reciprocal performance monitoring. Shared mental models (SMM), for the allocation of subtask among members in the group, can be distinguished by the team and task-related mental models. Mental models related to teams refer to common knowledge of team processes. They need to know the teamwork and expected behavior individually and as a group of the team members (i.e., team awareness). Task-related mental modeling involves a shared understanding of task processes, for instance, the planning of cooperation steps. They need information about the materials and strategies to carry out the task successfully (i.e., task awareness). “Shared team-related and task-related mental models, or team and task awareness, facilitate task execution by creating a framework that promotes common understanding and action” (Fransen et al., 2011). Fransen et al. (2011) assumed that team and task-related mental models were a prerequisite for efficient mutual monitoring of the performance of learning teams. In a study of students at a Netherlands university (N = 1 16), Fransen and his collaborators (Fransen et al., 2011) tested their conceptual framework as part of a hypothetical scenario in groups designing primary school pedagogical and organizational policy. The tasks provided a mixed communication model (face-toface, online and virtual) and required both team and task skills to develop. The level of mutual trust development, standard mental models, and mutual performance surveillance and their impact on team effectiveness was measured using a questionnaire. The hypothesized connections between the three intermediate variables (SMM, MT, MPM) and the team’s efficiency were tested through regression analyses. Results showed that collaborative development of shared mental models and mutual monitoring is essential for group collaboration. Also, interpersonal trust was demonstrated to be conditional upon the development of sufficient standard mental models but was not linked to the successful completion of the work. In addition, the students concentrated on task-mental models and consciousness, taking a pragmatic position on cooperation. The absence of plenary discussions

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on monitoring and feedback in virtual circles and handling the feedback accordingly explained a lack of appropriate mutual performance monitoring methods. Finally, the study showed that further research is needed to improve team modeling and collaborative awareness and how these affect mutual performance monitoring procedures.

3.3

Reasons for Using Argumentation in Learning Environments or Instruction

Argumentation is a tool for solving and rationally resolving problems. Integrating argumentative work in learning environments promotes productive thinking, conceptual change, and problems resolution (Jonassen & Kim, 2010). 1. Ways of thinking: Many scientists argued that argument is central to scientific thinking. Practitioners use argument to articulate and refine their scientific knowledge. The public uses arguments for discussions about important issues when applied scientifically. For example, issues of environment, health, and food production affect people, who must speak legally to resolve these problems. The purpose of using science as an argument is to connect students’ primary thinking activities with scientists. The social constructivist concept of meaningmaking is closely linked to argumentation because it emphasizes the importance of students engaging in reflective interactions with one another (arguments). Compare this concept of learning with the model for transmitting authentic facts, or with the model of discovery or investigation, by observing and inducing a set of laws and theories. Although science educators universally agree with investigative learning, Duschl and Osborne (2002) argued that “teaching science as a process of inquiry without the opportunity to engage in argumentation, the construction of explanations and the evaluation of evidence is to fail to represent a core component of the nature of science or to establish a site for developing student understanding.” To use science as an argument is to connect the primary thinking activity of scientists to the thinking activity of students. A deeper and more mature epistemological level of learning can be reached through argumentation. When students argue for or against a claim, they investigate the epistemological foundations of the knowledge domains they are studying. Contextual relativism (the student learns the methods of his or her discipline) and even a commitment to relativism (choices made in the face of legitimate alternatives) can result from argumentation. Argumentation is a vital way to reflect on any discipline. Disciplinary truths, not accepted by faith, must be demonstrated. The argument is intended to resolve the issues, questions, and problems rationally. Not only does science learning benefit from arguments, but science educators have focused more broadly than other disciplines on the role of argument. The importance of argument for interpreting history is highlighted in (Wineburg, 2001).

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2. Conceptual change: A growing body of research has demonstrated the role of argumentation in promoting conceptual change. Things change when students understand the concepts they use and the conceptual frameworks they include and reorganize their frameworks to fit new perspectives. Arguments involve conceptual changes due to students’ high level of conceptual involvement— argumentation results in conceptual change. Integrating argument into sciences enhances conceptual and epistemic understanding and contributes to the visualization of scientific reasoning. In the humanities, similar results were found. For example, instructions to write an argumentative essay about a historical subject gave us a better idea than instructions for writing stories, summaries, or explaining essays. 3. Problem-solving:-solving: “All life is problem-solving,” as Popper (1999) said. Problem-solving is the most ubiquitous, engaging, and essential intellectual activity. Although structure, complexity, and context are different, argumentation is a vital learning skill to solve most or all issues and a powerful method to evaluate problem resolution for ill-structured and well-structured issues. It has been shown that arguments support learning to solve structured and ill-structured issues. Nussbaum and Sinatra (2003) have found that the students had better reasoning on the problems when answering well-structured physics problems incorrectly and then built an argument for the correct scientific answer. The quality of their thinking remained strong when the students were examined a year later. On the other hand, Cho and Jonassen (2002) demonstrated a more remarkable ability to produce coherent arguments for solutions and actions to solve ill-structured problems than for well-structured ones. Ill-structured problems are the kinds of problems encountered in everyday practice with (a) alternative solutions, (b) vague or unclear objectives and constraints, (c) multiple ways to resolve them, and (d) multiple assessment criteria. Groups that have solved unstructured economic problems have produced broader arguments. Due to the lack of structured problems, students need to build arguments that justify their solutions. Argumentation (justification) and ill-structured problem solving are now clearly linked.

3.4

Benefits of Argumentation

The section discusses five educational benefits resulting from engagement in argumentation (Chinn & Clark, 2013). 1. Improved Motivation: Improved motivation is one of the possible benefits of argumentation engagement. The literature has some proof of this advantage. More research is still needed on this subject. Chinn et al. (2001) compared students’ speeches in small-scale lecture lessons to students who took part in traditional lecture lessons. Students who took up arguments talked more and were keener to even talk to the extent

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that others were interrupted. Smith et al. (1981) report that sixth grades of argumentation were more ready than students who did not learn through an argumentation method to spend some of their time learning more about the topic they discussed. Several possible mechanisms could explain more motivation. i. Arguments can first be enhanced by students being more autonomous than conventional teacher-driven recitations, where teachers are restricted to answering their numerous questions. Autonomy has been found as a powerful motivator by motivational researchers. ii. Second, an argument can provide for a more motivating interaction with peers. iii. Third, as students argue, they find that their peers have different ideas and might be curious to discover which ideas are more defendable. They can thus take mastery goals to understand the topic better; mastery goals are linked to intrinsically motivated learning, a deeper learning strategy, and positive learning emotions. However, there is a danger that arguments may become quarrels or debates about the argument. Discussions can lead to disagreeable interactions. Debates that focus on winners and losers may promote performance objectives for people considered less conducive to profound learning in the classroom by many motivational researchers. There is, indeed, some evidence that some students do not like the conflict because they argue. Therefore, it is challenging to ensure that collaborative knowledge-building standards replace quarrels or winning and losing debates when discussing instructional methods. 2. Content Learning: Content learning is another advantage of collaborative argumentation. Several researchers on argumentation have distinguished between arguing and learning. If students argue for learning, they argue for mastering the content they are talking about. If students learn to argue, they learn the components of argumentation and how to effectively engage in the argumentative practice. Students who discuss how to explain the results of electric circuits experiments, for example, can learn something general about how arguments, counterarguments, and refutations can be constructed (learning to argue). The focus on learning content, i.e., learning the core concepts and principles of teaching, is to be considered. Several literature studies have shown that collaborative argumentation can promote content learning. Episodes of child argumentation have been found in psychologists, for example, to encourage the ability of children to conserve in non-conservers. In an educational intervention that included discussion, Macarthur et al. (2002) proved a sixth-grade project on U.S. immigration. Similarly, Asterhan and Schwarz (2007) found that undergraduates who debated evolutionary theory better understood the theory’s principles than undergraduates who collaborated without being encouraged to debate.

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Sampson and Clark (2009) examined how students of all ages deal with the concept of melting in argumentation. Students who have collaborated showed a greater knowledge of heat and heat ideas than those who have written individual arguments but have not collaborated. This result is interesting because previous research demonstrated a very effective teaching technique to ask students to write arguments; collaborative arguments appear to have advantages over and above individual arguments. Three processes that can explain why argument can encourage content learning are (Chinn & Clark, 2013): i. Explicit elaborative processing and change of ideas: Students engage in various explicit elaborative processes, such as explanation, when arguing in groups. These processes have been shown to help students learn. So, for example, students may be required to go into greater detail in defending their explanations for why melting occurs when trying to settle a disagreement with another student. Additionally, students’ ability to make connections between evidence and explanations can be enhanced through this process. ii. Learning from others: When students argue, they learn from each other. They might pick up something new from their colleagues, such as a better explanation or a different way of looking at an existing explanation. iii. Reason to believe: Students benefit from providing evidence for their ideas to understand better what they are learning and have more confidence in their claims. Because of their strong belief in the claim, students may be more likely to use it outside of the classroom context. A student may not take the explanation seriously if there are not enough reasons for it to be taken seriously outside of the classroom. 3. Enhanced General Argumentation Skills: Collaborative argumentation can also improve the general skills of argumentation. Students who develop improved general argumentative knowledge will show better articulation fluidity across different situations (positions, reasons, and justification). They should also show more ability to articulate counterarguments against other positions, anticipate counterarguments from others, and reject counterarguments from others. Ideally, they will also be more qualified to consider and discuss both reasons for their positions and against them. Much research has documented the methods of education that encourage students to argue and evaluate arguments better. Some of these studies have documented that collaboration promotes a better ability to write arguments (like persuasive essays), although the instruction in argumentation did not teach how to write. Zohar and Nemet (2002) found that participating in a collaborative argumentation about genetics enables one to write similar and dissimilar arguments. Other studies have shown that instructions on collaborative argument improve the ability to argue in collaboration. In several studies, Deanna Kuhn has developed methods to teach general argumentation components such as

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counterarguments and rebuttals; this instruction is paired with pervasive opportunities for argumentation. According to the findings of these studies, students who learn to argue and practice argumentation become better at arguing, even on new transfer topics. Several researchers have offered Vygotskian explanations. According to Vygotsky (1981), the function of thinking appears first in a social context, and only later do individuals internalize it. This finding demonstrates that engaging in argumentation leads to better writing for individuals. On a social level of collaborative argumentation, for example, students learn that counterarguments can challenge their positions and that in response to those counterarguments, they might have to review their position or develop rebuttals for counterarguments. After many such experiences, students internalize the social process of considering and rejecting counterarguments that have been previously externalized. Even without social interaction, they gradually learn to anticipate these counterparts individually. The social process of looking at other points of view is internalized to participate in the process themselves. In this Vygotsky explanation, Reznikskaya added a schematic explanation. She theorized that internal schemes for the arguments, including counterarguments, rebuttals, and the like, are developed during the internalization process. 4. Improved Specific Argumentation: A wide range of general argumentation skills, including making arguments and counterarguments and predicting counterarguments, can be used in any situation where argumentation is involved, from choosing a movie to discussing alternative explanations for an experiment’s results. However, these abilities can only be used in practice if the user has sufficient domain expertise. Biology students with limited knowledge of microbiology will be unable to comprehend a microbiology experiment, let alone argue for or against alternative explanations of its findings. Effective argumentation, therefore, requires domain knowledge. This might include knowledge of typical evidence of a domain (for example, the possibility to argue evolutionary theory requires domain-specific knowledge of different types of evidence such as fossils, comparisons between DNA, and so forth), knowledge of the theories involved (for instance, knowledge in evolutionary theories). There is evidence that collaborative argumentation promotes domain-specific arguments to be employed and evaluated. For example, Zohar and Nermet (2002) have found that engagement with argumentation on genetically modified ethical problems enhances the ability to build other genetic arguments. Chinn et al. (2000) found it possible for fifth grades to assess better the quality of the conclusions of such experiments with the help of efficient collaborative experiments. Chinn (2006) argued that there is the intermediate level of argument-relevant knowledge that falls within a certain kind of microbiology experiment between very common knowledge of argument elements, such as counterarguments (can be used for each argument) and particular knowledge of common defects (which can be used only in one particular domain of argumentation). For

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instance, in a specific type of microbiology experiment, knowledge of sample size is essential to the reason for common defects (which can be used only in one particular domain of argumentation). For example, knowledge of the sample size is essential for thinking about a wide range of social sciences and biology studies, but it is not relevant to all subjects and situations. Similarly, it is helpful to understand how wording differences can change the responses to questionnaires on a wide range of research—psychological studies, political polls, and internal staff surveys by a company. Another general but not universal form of argument is the Walton (1996) argumentation scheme. The argument from signs can be used in many situations, but many other situations are not usable (e.g., the argument from signs probably plays no role in mathematical arguments). Therefore, there is an intermediate level of knowledge necessary for effective argumentation. While such intermediate knowledge appears to be a promising goal, there is little to date evidence that collaborative argumentation can promote knowledge through such intermediate reasoning skills. 5. Enhanced practices in the field of knowledge building: So far, the role of argumentation for developing certain types of personal knowledge, such as content knowledge and general knowledge, is taken into account. Other scholars emphasized that argumentation is a social practice for building knowledge, and how students participate and contribute to such practices is what they learn. Students learn to build knowledge in communities through a powerful tool for creating social knowledge.

3.5

Kinds of Arguments

Philosophers, logicians, and rhetoricians have been occupied with argumentation for millennia. Aristotle is credited with laying the foundations of argumentation. According to Aristotle, there are three primary or argumentative purposes: apodictic (demonstrative), rhetorical, and dialectical (Jonassen & Kim, 2010). Apodictic arguments aim to show absolute reliance on apodictic evidence, which leaves no doubt about the truthfulness of a claim. From a naturalistic point of view, we can often make claims in daily speech as apodictic truths, but those claims are seldom tested informal educational environments and therefore will not be further examined. Listed below are the kinds of arguments (Jonassen & Kim, 2010) that students can construct. 1. Rhetorical arguments: The most common form of argumentation is rhetorical, as a dialogue between the argument and the public. The purpose of rhetorical arguments, or monological arguments, is to convince or persuade other people to believe in a claim or proposition that the arguer believes in without regard to the positions held by other people. A rhetorical argument is successful if

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the target audience approves it. Therefore, most rhetorical arguments focus on developing effective convincing techniques for argumentation. Toulmine (2003) developed an argument structure, which included claim (C), data (D), warrant (W), as well as elements such as backing (B) and qualifier (Q), and rebuttal (R) as the most prominent model of rhetorical argument. An argument justifies its claim by linking a fact (D) to claim (C) via a warrant (W). The qualifier (Q) transmits the strength of the data, while the refutation conflicts with the claim (R). According to Toulmin’s theory, there are no universal standards for evaluating arguments. Instead, the validity of an argument is determined by its relevance to the problem at hand. While his model was very influential in argumentation theory, Toulmin’s actual application to ordinary arguments is somewhat problematic. Although Toulmin may use multiple-sided arguments for building arguments, his model fails to consider a controversial issue on both sides. In other words, the model only shows the proponent’s side, minimizing the opposition’s role in the process of the argument. As a result, warrants can be difficult to distinguish from the backing, as they are often implicit. Toulmin’s model may be helpful for an individual to evaluate an argument, but it cannot be applied to evaluate an argument involving two and more arguments. Although they are used to persuade others, rhetorical forms of argument are unilateral so that their educational conditions can be restricted. 2. Dialectical arguments: A dialectical argument is a dialogue between supporters of alternative claims in a dialogue game or discussion rather than a monologue between argue and a real or imaginary audience. The purpose of dialectical arguments is also known as dialogical or multilingual arguments to resolve differing views. This resolution could take various forms. Dialectical arguments can be adversarial where the aim is to persuade adversaries that their claim is superior. You can also seek a compromise between several claims. There can be dialectical arguments within individuals (e.g., decision-making) or social groups. Because dialectic arguments can be more applied to educational purposes than rhetorical arguments, there are two prominent patterns of dialectical argument, pragma-dialectics and argumentation schemes for presumptive reasoning, providing valuable insights in the argumentation of discourses. Pragma-dialectics sees argumentation as resolving differences of opinion in critical discussions and suggests a formal model for conducting those discussions. There are four essential phases to criticism: (1) confrontation stage, (2) opening stage, (3) argumentation stage, and (4) concluding stage (van Eemeren & Grootendorst, 1992; Eemeren et al., 1996). People make their different claims during the confrontation phase. There is no argument if there are no differences of opinion. People agree to their roles and rules for the argumentation during the opening phase. People defend their claims and challenge others at the argumentation stage. The participants decide who will win and who will lose.

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A helpful model for conducting classroom or online discussions is pragmadialectics. Walton’s (1996) concept of presumptive arguments is another valuable model of argument for educational purposes. Walton claims that argument is an objective, interactive dialogue in which participants argue that argument is supported or disproved in common. The reasons are timely and open to challenge in arguments based on presumptions. The burden of evidence is transferred to the other party in a dialogue in presumptive arguments. Consequently, counterarguments are as crucial in dialectical argument as the original argument. Walton (1996) identified 25 presumable arguments and provided the interviewees with a combination of critical questions. These schemes offer models for organizing classroom discussions and online discussions. When designing a learning environment, it is essential to consider the purpose of the environment before deciding on which argument to support. If the learning goal needs to be promoted or convinced, such as creating a marketing campaign, it is more appropriate to promote students building rhetorical arguments. When different opinions are needed for the learning purpose, a dialectical argument emphasizing the creation and improvement of counterarguments should be supported.

3.6

The Skills of Argumentation

How can different forms of argumentation be developed and evaluated? According to Blair and Johnson (1987), the three dialectical requirements for a good argument are as follows: 1. “acceptability’ (are the premises acceptable?), 2. “relevance” (premise relevant for the conclusion?) and 3. “sufficiency” (premise sufficient to support the conclusion?), These criteria are sufficient to assess most arguments’ efficiency. Kuhn (1991), who proposed a way of formulating and weighting the reasons for and against a course of action, a view, or a solution to a problem,” is the most comprehensive concept of argument skills. She identified five essential arguments skills: a. b. c. d.

the ability to generate cause theories to support claims (supportive theory), the ability to provide evidence to support (evidence), the ability to generate alternative theory (alternative theory), the ability to imagine conditions undermining their theories (counterarguments), and e. the skill to rebut alternative theories (rebuttal).

According to Kuhn, a strong argument can be considered if these components are included. Most scholars agree that proof supporting claims is an essential criterion

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for building arguments (Kuhn, 1991). Arguers often use insufficient evidence to support their arguments (Walton, 1996). Student abilities to generate and evaluate arguments remain unclear, even though they have been identified as skills in argumentation. Although some researchers demonstrate that an argument can be understood at the age of three (Stein & Bernas, 1999), most other researchers argue that even the majority of older students cannot argue. Kuhn (1991) reasoned that the argument skills between childhood and adolescence are increasing (6th–9th graders). Reznitskya et al. (2001), for instance, showed that most students do not understand arguments. They find it hard to write convincing essays, understand written arguments, differentiate between theory and evidence, and generate genuine evidence and alternative theories. In contrast to adults, adolescents and young adults cannot construct two-sided arguments or distinguish between evidence and explanation. The lack of counter argumentation is the most common weakness in argumentation. When people are asked to argue for themselves or against them, they typically have more reasons for supporting their position. However, very young children can generate positive and negative reasons for different actions or hold certain beliefs. Empirical research does not support the expectation that young students can comprehend and construct arguments. Why are students so unable to argue? Zeidler (2019) has identified some difficulties with students’ arguments, including selecting only evidence to support their claims. Students are more likely than to take evidence to confirm or disconfirm their arguments based on their own beliefs. Perkins et al. (1991) calls this tendency a “my-side bias,” a stronger belief than counter-evidence in personal beliefs, an over-generalization from one source of evidence, and statements without any evidence. Why do students argue with seeming blinders? Three main reasons appear to be the lack of teachers’ pedagogical skills to promote argumentation within the classroom and the absence of opportunities to practice argumentation; external pressures to cover materials without time to develop skills; and poor prior knowledge on behalf of teachers. It is believed that, as teachers, there is not enough knowledge of argumentative pedagogies, although no research confirms this.

3.7

Learning to Argue and Arguing to Learn

The relationship between discussion and learning is complex (Schwarz, 2009). In the first place, this complexity is determined by the many facets of the discussion. Rigotti and Greco (2009) described many of these facets in the chapter on “Argumentation as an Object of Interest and a Social and Cultural Resource.” They raised the term “reason” with an ambiguous meaning and the term “ratio” to characterize a way of thinking. On the other hand, arguments were also presented as a tool to reach objectives, discuss understanding, clarification of doubt, decision-making, conflict resolution, enhancement of knowledge, etc.

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There are at least two ways in which learning and argumentation are linked. It can consist of understanding, clarifying, or challenging. On the other hand, it can consist of learning to achieve a specific goal through argumentation. This distinction is made clear in their book “Arguing to Learn” by Andriessen et al. (2003): “Learning to Argue” includes acquiring general qualifications such as justifying, challenging, counter challenging, or conceding. In contrast, “Arguing to learn” often corresponds to a specific objective achieved by arguing, and the (implicit) goal is, in an educational context, to understand or construct specific knowledge. Do we want to concentrate on how people learn to argue or how people learn by argument? The two directions were presented as alternatives. However, are these two ways the only one? If a person challenges her peer in a debate, this step reveals the ability, counter challenges, and content invoked to justify a previously raised argument and thus reinforces the argument. Learning to argue and arguing to learn is not autonomous. Instead, when we observe classroom discussions, they are intertwined and often seem inseparable.

3.8

Preparing Classroom Environments for Collaborative Argumentation

Assuming that argumentation is an essential component of research in many fields, education professionals who have sought to promote research in schools have often tried to develop arguments on their classroom-based investigative interventions. However, it is very difficult to promote and sustain argumentation. Educators need to develop learning environments that motivate and scaffold students to discuss issues throughout a multi-week survey unit for a long time. This section will only touch on a few of the methods and approaches (Chinn & Clark, 2013) that can achieve these goals and the challenges they are designed to address. 1. Choice of Problems: A problem that supports the argument for an extended period of time is usually a complex problem on which students may have different views and cannot be responded to promptly. Real-world problems or issues relevant to students’ lives can also facilitate sustained discussion. 2. Building Domain Knowledge: Substantial domain knowledge is required for argumentation. This field knowledge could take little time for students to build on some topics. For example, consider a story about a farm girl who cares for an injured wild goose and must decide whether or not to release it back into the wilderness (Chinn et al., 2001). The story has much relevant information, and the students are familiar with their experiences with pets and other situations to support their arguments. Instead, elementary students who are asked to argue whether wolves are to be brought back to the wild will not know enough about the relevant evidence or arguments; in order to engage students, designers must incorporate activities into the training that provide a rich knowledge base that they can be used as

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evidence by group or class arguments. Productive collaborative argumentation is unlikely without sufficient domain knowledge. 3. Reducing differences in status: Collaborative groups, such as those that engage in argumentation, may have the potential to amplify differences in status. Students in a class can often agree on which students are of high and low status. High-level students are thought to participate in collaborative groups more than those whose status is low. In the context of complex problems that require the contribution of all students, Cohen (1994) has outlined interventions that can help counter this problem. Cohen recommended that students be taught that solving complex problems requires a wide range of abilities and that no one student in any group has all of the abilities required, but that every student in every group has at least some of the abilities required. Cohen also recommended techniques to enhance the status of students with low-level education, such as teachers working independently with students with lower-level status to promote the knowledge and skills required in the run-up to collaborative argumentation. 4. Cognitive roles: Many collaborative researchers recommended using cognitive roles in groups (e.g., (Cohen, 1994)). Herrenkohl and Guerra (1998) have developed three cognitive roles for students in groups and after each study when answering group presentations. The following were three roles: a. making a prediction and building a theory, b. Summarizing research findings and c. relating the results to the prediction and theory. In class discussions, the teacher worked together to develop questions related to each of those roles; According to Herrenkohl and Guerra (1998), students’ reasoning improved due to these interactions. Similarly, Weinberger et al. (2005) found that assigning students the roles of “case analyst” and “constructive critic” in computer-supported collaborative learning was beneficial. 5. Criteria and standards: Several scholars stressed how important it is to promote arguments to develop suitable class standards. Typical standards include understanding and treating others with respect rather than winning arguments. Educators are also working to advance standards specific to students’ disciplines. For example, students in mathematics, science, and history may adopt a norm that requires them to give deductive reasons for their mathematical claims, while students in other fields may adopt a norm that requires them to give substantial weight to empirical evidence in their arguments. The criteria used to assess ideas are one kind of disciplinary standard. Criteria for evaluating scientific models include how the model fits into a broad spectrum of evidence and how much it coincides with other acceptable models (Pluta et al., 2011). To develop useful scientific models, a group of researchers (Chinn et al., 2008) has focused on the use criteria. Teachers lead their classes in developing criteria for evaluating models, pictures, and diagrams; that the evidence supporting the model is true; that that model explains how or why, and that it

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provides the steps in the process; and that the project is completed timely and within budget. Each class improved its criteria throughout the year. When students discussed two or more models that would be better, they regularly referred to these criteria. The students considered whether their models had chosen to accept the various criteria as their class norms. The criteria provided a focus that could sustain argumentations.

3.9

Methods for Engaging and Supporting Argumentation in Learning Environments

How could teachers and educational designers help develop argumentative skills if students cannot understand or build cogent arguments? Many methods exist to develop argumentative discourse skills (Felton & Kuhn, 2001; Kuhn et al., 1997). These methods can enhance conceptual understanding and the resolution of problems in classroom instruction or open-end learning environments. The most common ways to engage and encourage argumentation by students (Jonassen & Kim, 2010) are described in this section. 1. Directions: The most obvious way to argue is to provide a range of instructions for arguments. As a teacher, it is your job to help students develop their critical thinking skills. The most common approaches are counterarguments because counterarguments are a defining attribute of good arguments. The rationale for counter argumentation is the assumption that reasoning is fundamentally dialogical. However, because of a focus on itself and a lack of knowledge, children can difficulty generate counterarguments to support opposing viewpoints. Two experiments were carried out by Nussbaum and Kartash (2005), in which three different kinds of student experiments were directed. In the first experiment, the directions they provided varied by three treatments. • Control Condition: Please respond to the following question in the form of an essay: “Does watch TV cause children become more violent?” • Reason Condition: Please respond to the following question in the form of an essay: “Does watching TV cause children to become more violent?” Put as many reasons for your position as you can, and try to provide proof that supports your reasons. • Counterargue/rebut Condition: Please respond to the following question in the form of an essay: “Does watching TV cause children to become more violent?” Then talk about two or three reasons why others may disagree with you and why they are wrong. Give as many reasons as possible for your position to justify and provide evidence supporting your reasons. The persuasion instructions, as expected, reduced the number of student counterarguments, according to Nussbaum and Kardash (2005). This finding was consistent with a study by Stein and Bernas (1999), which revealed that the arguments are more supportive than the opposing position because of their position versus the other, an excellent example of my-side tendencies. Nussbaum

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and Kardash’s study students believed identifying counterarguments would be less convincing. They focused on the purpose of the argument construction during the second experiment: to persuade or not. • Persuasion Condition: Congress is considering tougher laws on television violence due to a campaign. Send your congressional representative a letter in which you urge him or her to vote in favor of or against your position on the subject. “Does watching television cause children become more violent?” • No persuasion: Please respond to the following question in the form of an essay: “Does watch TV cause children to become more violent?” Research has shown that arguing affects students’ argumentative performance in various ways. In the second experiment, the instructions to persuade negatively impacted the overall quality of the essays and the number of reasons for their counterclaims. However, the contrasting text counteracted the negative effects of the persuasion instructions when it provided a text that outlines numerous arguments on either side of the issue. Students made more counterarguments. 2. Question prompts: Several researchers have investigated how students can squeeze their argumentation through questions. Kuhn (1991) asked students specific questions based on her argumentation skills. She concentrated on asking students about the controversy with questions like: i. What does school failure cause you to think? ii. How would you prove the cause? iii. What could be the cause of school failure for someone who disagrees with you? iv. What can you say to her to show that he/she is wrong? v. What could someone else say to demonstrate that your views are wrong on the cause of school failure? Using the refutation strategy, the synthesizing strategy, and the weighing strategy, Nussbaum and his colleagues looked at making better arguments (Nussbaum & Schraw, 2007). As part of the refutation strategy, which is an explicitly adversarial strategy, students learn how to recognize and rebut other arguments and alternative solutions. “Is there a compromise or creative solution?”’ is a common question students ask themselves when using the synthesizing strategy. Students need to learn the weighing strategy, which requires them to evaluate alternative arguments and support the strongest one based on the weight of evidence on that side of the issue (“Which side is stronger and why?”). In a study, Jonassen and others (Jonassen et al., 2009) used three versions of hypertext of cognitive flexibility to discuss engineering ethical dilemmas for students: assessment of treatments, construct therapy, and control (summary) treatment. Two alternative solutions were requested to be assessed while interacting with the case evidence by treatment participants. Each participant answered several questions: • What is the better solution, solution 1 or solution 2?

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Which view(s) support your choice? What is your selection theoretical approach(s)? What are your selection Ethical Codes? How could someone who supports the other solution disagree with the solution you want?

The participants were asked to build their solution to ethical cases in the constructed treatment. Please ask participants: • • • • •

As the engineer, what are you to do? How do you solve this ethical problem? Who(s) support this alternative perspective(s)? Who(s) supports your theoretical solution approach(s)? Which codes of ethics do you support? What else could somebody do? How can anyone recommend an alternative solution? • What are some reasons why this solution should be supported? Students were required to summarize the perspectives and theories in ethics cases during control treatment. Students who assessed alternative arguments supported their arguments on the immediate transfer task more effectively. They gave more detailed debates and justifications for their solutions to ethical problems. While evaluating treatment replaced argumentative abilities of students less successful, construct and control therapies have not improved the argumentative abilities of students. In keeping with other research studies, students in this study have not adequately considered and encouraged counterclaims to provide more elaborate support for their solutions. 3. Collaborative argumentation: While guidance and questions have been used to encourage individual construction of arguments, collaborative argumentation is more about enabling multiple participants to discuss, a form of collaborative thinking. Collaborative strategies include encouraging students to speak up, thinking aloud, posing contradictory ideas, praising students for good thinking, summarizing what others have said, and employing critical and reflective vocabulary. They found that collaborative group reasoning generated additional arguments, counterarguments, and refutations. Students were able to develop and transfer argument patterns into new contexts when collaborating with reasoning. They also helped students develop arguments that include a rhetorical structure, inferential reasoning rules, and other cognitive and social rules during their research. Instead of face-to-face discussion, collaborative argumentation is often done on online discussion boards. The common, threaded discussion board is the simplest type of online debate. Given guidance or questions that stimulate argumentation, students can build and convey arguments and counterarguments by answering or opening new topics in the conversation. Since students rarely build

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coherent arguments in open panels, some researchers have developed panels to support online arguments. An increasingly common type of scaffold includes pre-classifying messages which students can build. Note that starters are assigned to these different types of messages. Note that beginners are a menu of sentences, starting with the first sentence of the discussion in an online panel. For example, Nussbaum et al. (2004) used note starters and worked-out cases to support counter argumentation. The note at the beginning encouraged students to consider different viewpoints. The pre-classification of conversational needs provides a set of canonical relations that limit verbal interactions between conversations. These limitations form the links or relationships between the ideas that people produce. They provided predefined answers to specific messages that any participant in the discussion could provide. Students could only begin a thread with a solution proposal using an unstructured design problem. When responding to the suggestion, only one of the three warrants was available: a reason why the proposal could be supported, a reason why the proposal could be rejected, and a modification of the proposal. Students can only respond to any warrant by providing evidence (information or facts, personal opinion or belief, personal experience, or research findings). Research on the restrictive debate is relatively new. Oh and Jonassen (2007) found that the discussion group with note starters (e.g., I agree because...; my experience is....’) was a study of pre-service teachers solving diagnostic solutions to (classroom) problems Research shows...; I believe....... more evidence is generated and that people who trust simple knowledge and unstructured solutions are less likely to explore more solutions. While these environments promise enhanced thinking, further research is required to verify that these instruments are effective. Graphical argumentation aids: Another way to scaffold arguments is to help students visualize arguments to improve their design (Kirschner et al., 2003). The visualization of arguments allows the students and faculty to see the structure of the argument, thereby making the construction and communication more rigorous (Shum et al., 1997). It also helps students visualize “the important ideas in a debate as concrete objects that can be pointed to, linked to other objects, and discussed” (Suthers & Jones, 1997). A graphic organizer is the most basic form of graphic support. When preparing to write an argumentative essay, Nussbaum and Schraw (2007) developed a graphic organizer to organize arguments and counterarguments, bolster reasons, and a conclusion. Participants fill their arguments and supportive reasons in the circles. The purpose of this oval was to help students evaluate the relative strengths of arguments and counterarguments during the negotiations and the development of refutations. They found that counterarguments, one of the biggest weaknesses in the argument, were further disproved by using the graphic organizer. Belvedere is another popular graphical tool to help students build arguments. Belvedere provides diagrams of arguments representing the components

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(“hypothesis,” “data,” “principles,” and “unspecified”) and relationships (“for,” “against,” and “and”) (Suthers, 1998). These limitations form the connection or relationship between the ideas produced by the subjects. Three representatives of evidence models are integrated into Belvedere to support the students engaged in the critical discussion (graph, matrix, and Hierarchy representations). The representation of the graph is most useful in the collection of information according to the predefined relationships. The matrix display is best to check that all connections have been identified and scan for evidence patterns (Suthers & Hundhausen, 2003). Cho and Jonassen (2002) studied arguments for solving well-structured and ill-structured problems using Belvedere to scaffold economic studies. Students built diagrams of an argument that predicts inflation in the following year. The use of Belvedere improved their argumentation and their performance in solving problems both well-structured and, in particular, ill-structured substantially. Although several other visual argumentation tools, including ConvinceMe, DiaLab, and others, have been developed and tested, most were experimental products with inadequate empirical support. Involve your students in a collaborative argumentation in this way. Educators play a critical role in ensuring that everyone is on the same page and maintaining a learning environment. The best way to get students involved in group argumentation in the classroom is to pose questions and then organize them into small or large teams (Bhagi, 2020). After the activity, have a class discussion about the covered points. Consider the following illustration (Bhagi, 2020). Students should be given a task in math class such as “How many two-thirds are there in 2?” or “How many sixths are there in 4?” “How many thirds are there in 3?” Encourage students to give their answer and explain it, such as “There are only two halves to any number, or multiply this number into another to get the answer.” If they have a different answer or explanation, the other students present their case. Questions like “Please explain why you are saying this” can be asked by any student in order to get their point across. Allow the students to work together to come up with a final solution. Students should explain the solution and then write a summary of the discussion. Teachers can also use collaborative argumentation to enhance the learning of any subject, such as learning science or math, by argumentation.

3.10

Evaluating Argumentation

Researchers have developed several methods for analysis and assessment of argumentation (Chinn & Clark, 2013). 1. Analyzing functions of statements: The function or speech-act of the statements made in the discussion is one of the most common methods used to evaluate

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argumentation. For instance, during opposition dialogue episodes in the following categories, Osborne et al. (2004) categorized the argumentative operations of each conversational turn during episodes of oppositional dialogue into the following categories. i. Opposing a claim ii. elaborating a claim iii. reinforcement of a claim by additional data or warrants iv. advancing claims v. adding qualifications. In their approach, the structural quality of the dialogic argument was then categorized according to the types and the number of categories involved during the dialogue. There are 25 distinct discourse moves that Felton and Kuhn (2001) can break down into four broad categories: • Exposition (e.g., a follow-up to the previous statement or a way of describing the speech) • challenge (e.g., disagreements and counterarguments) • requests (questions) • other non-request discourse moves. In the framework of 29 more specific types of dialogue actions typified by students in their online collaboration and communication, Janssen et al. (2007) define the communication function of each utterance (e.g., arguable, responsive, informative, eliciting, and imperative). Analyses of statement functions highlight essential aspects of the quality of collaborative argumentation. They can also identify the sequence of speech acts that are more or less productive statistically. 2. Analyzing Collaborative Argument Structure: A second method is to map the argument structure for analyzing collaborative argumentation. Rather than simply “taking positions,” “giving reasons,” the analyst uses a chart structure to map the structural interrelationships between various positions and reasons. A simple example can be found in Chinn and Clark (2013). The plain arrows indicate that one statement supports another, and an X-arrow indicates that one statement is against another. Various letters point out various speakers. The argument in Chinn and Clark (2013) is structurally complex, although it only represents a few divulgations. Student A claims that this summer’s heatwave is proof that the earth is warming up. A hot summer is a sign that the earth is getting warmer, even though Student A does not say it explicitly. Student A appears committed to believing something like this: Student B argues in favor of Student A’s claim that “this summer has been particularly hot,” citing the fact that “there are many record highs in the United States in July.” As a result, Student C provides evidence to refute Student A’s reasoning, which is considered a rebuttal. When Student D says, “you cannot tell from just one summer,” he seems to be implying that longer-term trends and

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larger samples sizes than just one year are needed to conclude global warming. Chinn et al. (2000) demonstrate the correlation between collaborative argumentation with more complicated structure and more learning than simple structure argumentation. Rainbow (Baker et al., 2007) is another approach that emphasizes the epistemic nature of the contributions made by participants. Non-task-oriented activities are classified as either social relations or interaction management. Activities that are task-oriented include task management, opinion, argumentation, exploration, and deepening. In the ongoing debate, comments are coded according to their primary nature. 3. Assessing the Quality of Reasoning: An important limitation of the methods mentioned above is that the extent to which specific argument components are used is only very grossly measured (reasons, counter reasons, rebuttals, etc.). However, these methods do not differentiate between excellent reasoning (e.g., a student explains in detail why a few studies collectively indicate global warming) and terrible reasoning (e.g., a student living in Michigan says in February that global warming is not occurring because it snowed today). Similarly, these methods are not differentiated from common alternative conceptions among students who work with scientifically accepted ideas. Several researchers sought ways to analyze arguments that assess the quality of their arguments more accurately. As an example, Kuhn and Udell (2003) classified arguments made by participants in a discussion into three types: nonjustificatory arguments that have little or no argumentative force, nonfunctional arguments that focus on tangential aspects of the problem rather than core issues and functional arguments that deal with core aspects of the problem. Likewise, Sampson and Clark (2008) had developed analytical schemes to supply quality information. One was how student arguments used normative scientific ideas (i.e., the accepted scientific ideas about heat). The other issue was whether or not the students’ arguments were empirical proof and how thoroughly the proofs were linked. Walton (1996) discussed several arguments people use, such as classification arguments, analogic arguments, expert opinion arguments, and cause-to-effect arguments. There are critical questions associated with each, which could be asked in a particular situation concerning the appropriateness of that argument. Analogies, classification, expert opinion, and the cause-and-effect reasoning model are among the many argumentation schemas discussed by Walton (1996). Each has critical questions that can be asked about the appropriateness of the argument in a given situation. An argumentation research project could benefit from incorporating the work of Walton in rhetoric and philosophy, according to Nussbaum (2013). The argument from the sign, for example, asserts that a cause can be inferred because a sign is present (for example, bear tracks in the snow indicate that a bear has passed this way). The following questions are crucial to the development of this argument (Walton, 1996): i. What is the strength of the sign’s correlation to the event?

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ii. Is the sign more reliably accountable for any other events? Collaborative argumentation could be evaluated using Walton’s schema (Duschl, 2008; Nussbaum, 2011). For example, analysts can determine which specific argumentation schemes are in use and which arguments are responsive to the issues raised by the critical questions (for example, do students use sign argument and if so, they are aware that the connection between the sign and the cause must be considered as stranded?). If students look at critical matters and reach defensible answers, the argument quality would be higher (e.g., they consider other tracks that might look like bear tracks but conclude that all other candidates would be too small).

3.11

Factors Affecting Collaborative Argumentation

Education can be framed as a continuous discussion process from a rhetorical perspective on academic learning (Petraglia, 1998). This is the process of finding and generating reasoning based on scientific assumptions and knowledge bodies. In collaborative learning, students can outsource, articulate and negotiate various perspectives, leading to reflective behavior to discuss the arguments of each other. This co-constructive process of knowledge about specific learning objectives can be called the ‘knowledge transforming’ (Bereiter & Scardamalia, 1987). This section describes some key elements that influence collaborative argumentation (Veerman, 2000): • The student. Students typically argue about the strength of personal attitude when there is doubt or unbelief. In argumentation, the strength of the attitude can prejudice the evaluation of science: evidence supporting the attitude is seen as more convincing than the evidence which disagrees with this attitude. Many studies have revealed a tendency to favor a personal stance and attack the opposition, neglecting the possible plausibility or faults in personal positions. This type of partial behavior limits the room for negotiation and thus hampers the transformation of knowledge. Students are encouraged to understand and ask critical questions on multi-faceted arguments. • The peer. Interacting with fellow students can make learning realistic, relevant, and critical for a learner to take alternatives. On the other hand, politeness strategies can prevent students from having critical discussions, especially in authentic learning situations. One way to prevent students from becoming “too nice” to each other is to give them a competitive role, such as allowing them to defend (predefined) conflicting positions. Students may engage in argumentation if such an intervention is used more efficiently. • The tutor. The role of the tutor may be crucial in academic education. The tutor works as an expert to discuss the conceptual problems of the students. If a student has a question, a tutor can help clarify it by breaking down the task, making abstract situations concrete, or changing the context of the problem.

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• The task. It is common for students to learn through collaborative argumentation in specific educational contexts. Optimal tasks should be open-ended, and students can therefore share the different knowledge, experience, beliefs, and values of one another and learn from each other. Collaborative argumentation is a necessary activity to reach a common conclusion or solution. • Instruction. The kind of instruction necessary to encourage and support students in discussing various perspectives and elaboration plays a vital role. Options include asking questions, assigning students competitive roles, etc. • The Medium. Computer-mediated communication (CMC) is a slower mode of communication than oral discussion. It is easier for participants to re-read and reflect on information if there is a delay in time. Technology like text-based dialogue history or diagramming tools can structure and reflect on text-based interactions.

3.12

Conclusion

Argumentation pedagogy implementation can be complex. Most students and teachers use knowledgeable questions that allow students to demonstrate a mastery of a science idea or subject. On the other hand, argumentation allows students to gain knowledge by putting forward, critiquing, and defending arguments in a series of iterations. This can be a complex process. To learn the specialist argumentation used by scientists to develop a deeper understanding of the natural world, students need to be carefully guided by teachers. Teachers may need years of practice before they are proficient in leading classroom discussions that promote the development of scientific knowledge and the ability to argue logically. Developing rich, curriculum-based questions or topics for scientific argumentation can also be challenging. It is a good thing there are free online resources that can point teachers in the right direction. It is possible to use technology to support communication and keep track of student thinking and how it changes over time in well-designed science learning activities.

References Andriessen, J., Baker, M., & Suthers, D. (2003). Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments. Asterhan, C. S. C., & Schwarz, B. B. (2007). The effects of monological and dialogical argumentation on concept learning in evolutionary theory. Journal of Educational Psychology, 99(3), 626–639. https://doi.org/10.1037/0022-0663.99.3.626. Baker, M. J., Andriessen, J., Lund, K., van Amelsvoort, M., & Quignard, M. (2007). Rainbow: A framework for analysing computer-mediated pedagogical debates. International Journal of Computer-Supported Collaborative Learning, 2(2), 315–357. https://doi.org/10.1007/s11412007-9022-4. Barron, B. (2003). When smart groups fail. He Journal of the Learning Sciences, 12(3), 307–359.

References

55

Bell, P., Hoadley, C. M., & Linn, M. C. (2004). Design-based research in education. In Internet Environments for Science Education (pp. 73–85). Bereiter, C., & Scardamalia, M. (1987). The psychology of written composition. Bhagi, S. (2020). Innovative learning strategies. LinkedIn. https://www.linkedin.com/pulse/innova tive-learning-strategies-sukanya-bhagi/. Blair, J. A., & Johnson, R. H. (1987). Argumentation as dialectical. Argumentation, 1, 41–56. Cavagnetto, A., Hand, B. M., & Norton-meier, L. (2010). The nature of elementary student science discourse in the context of the science writing heuristic approach. International Journal of Science Education, 32(4), 427–449. https://doi.org/10.1080/09500690802627277. Chavez, J., & Romero, M. (2012). Group awareness, learning, and participation in computer supported collaborative learning ( CSCL ). Procedia—Social and Behavioral Sciences, 46, 3068–3073. https://doi.org/10.1016/j.sbspro.2012.06.012. Chinn, C. A. (2006). Learning to argue. In Collaborative learning, reasoning, and technology (pp. 355–383). Chinn, C., Anderson, R. C., & Waggoner, M. A. (2001). Patterns of discourse in two kinds of literature discussion. Reading Research Quarterly, 36(4), 378–411. https://doi.org/10.1598/RRQ. 36.4.3. Chinn, C., & Clark, D. B. (2013). Learning through collaborative argumentation. In International handbook of collaborative learning (pp. 314–332). Chinn, Clark A., Duschl, R. A., Duncan, R. G., Buckland, L. A., & Pluta, W. J. (2008). A microgenetic classroom study of learning to reason scientifically through modeling and argumentation. In Proceedings of the 8th International Conference for the Learning Sciences, ICLS ’08 (pp. 1–2). Chinn, C. A., Angela, M. O., & Theresa, S. (2000). The structure of discourse in collaborative learning. The Journal of Experimental Education, 69(1), 77–97. https://doi.org/10.1080/002 20970009600650. Cho, K., & Jonassen, D. H. (2002). The effects of argumentation scaffolds on argumentation and problem solving. Educational Technology Research and Development, 50(3), 5–22. Clark, D. B., & Sampson, V. D. (2007). Personally-seeded discussions to scaffold online argumentation. International Journal of Science Education, 29(3), 253–277. https://doi.org/10.1080/095 00690600560944. Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64(1), 1–35. Duschl, R. (2008). Science education in three-part harmony: Balancing conceptual, epistemic, and social learning goals. Review of Research in Education, 32(1), 268–291. https://doi.org/10.3102/ 0091732X07309371. Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38(1), 39–72. https://doi.org/10.1080/030572602085 60187. Evagorou, M., & Osborne, J. (2013). Exploring young students’ collaborative argumentation within a socioscientific issue. Journal of Research in Science Teaching, 50(2), 209–237. https:// doi.org/10.1002/tea.21076. Felton, M., & Kuhn, D. (2001). The development of argumentive discourse skill. Discourse Processes, 32(2&3), 135–153. Fransen, J., Kirschner, P. A., & Erkens, G. (2011). Mediating team effectiveness in the context of collaborative learning: The importance of team and task awareness. Computers in Human Behavior, 27(3), 1103–1113. https://doi.org/10.1016/j.chb.2010.05.017. Herrenkohl, L. R., & Guerra, M. R. (1998). Participant structures, scientific discourse, and student engagement in fourth grade. Cognition and Instruction, 16(4), 431–473. https://doi.org/ 10.1207/s1532690xci1604. Janssen, J., & Bodemer, D. (2013). Coordinated computer-supported collaborative learning: Awareness and awareness tools. Educational Psychologist, 48(1), 40–55. https://doi.org/10. 1080/00461520.2012.749153.

56

3

Learning Through Collaborative Argumentation

Janssen, J., Erkens, G., & Kanselaar, G. (2007). Visualization of agreement and discussion processes during computer-supported collaborative learning. Computers in Human Behavior Computers, 23(3), 1105–1125. https://doi.org/10.1016/j.chb.2006.10.005. Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition Learning, 7. https://doi.org/10.1007/ s11409-010-9061-5. Johnson, D. W., & Johnson, R. T. (1996). Cooperation and the use of technology. In Handbook of research for educational communications and technology (pp. 785–812). Johnson, D. W., & Johnson, R. T. (1999). Making cooperative learning work. Theory into Practice, 38(2), 67–73. https://doi.org/10.1080/00405849909543834. Jonassen, D. H., Cho, Y. H., Kwon, K., Henry, H. R., Shen, D., Easter, M., & Lo, J. (2009). Evaluating versus constructing arguments. Journal of Engineering Education, 98(3), 235–254. Jonassen, D. H., & Kim, B. (2010). Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology Research and Development, 58(4), 439–457. https:// doi.org/10.1007/s11423-009-9143-8. Kirschner, P. A., Kreijns, K., Phielix, C., & Fransen, J. (2015). Awareness of cognitive and social behaviour in a CSCL environment. Journal of Computer Assisted Learning, 31, 59–77. https:// doi.org/10.1111/jcal.12084. Kirschner, P. A. (2002). Three worlds of CSCL: Can we support CSCL?. Kirschner, P. A., Shum, S. J. B., & Carr, C. S. (2003). Visualizing argumentation. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353. Kuhn, D. (1991). The skills of argument. Kuhn, D., Shaw, V., & Felton, M. (1997). Effects of dyadic interaction on argumentive reasoning. Cognition and Instruction, 15(3), 287–315. https://doi.org/10.1207/s1532690xci1503. Kuhn, D., & Udell, W. (2003). The development of argument skills. Child Development, 74(5), 1245–1260. https://doi.org/10.1111/1467-8624.00605. Lakatos, I. (1976). Proofs and refutations. Cambridge University Press. Leinonen, P., Järvelä, S., & Häkkinen, P. (2005). Conceptualizing the awareness of collaboration: A qualitative study of a global virtual team. Computer Supported Cooperative Work, 14(4), 301–322. https://doi.org/10.1007/s10606-005-9002-z. Macarthur, C. A., Ferretti, R. P., & Okolo, C. M. (2002). On defending controversial viewpoints: Debates of sixth graders about the desirability of early 20th-century American immigration. Learning Disabilities Research & Practice, 17(3), 160–172. Mercer, N., Dawes, L., & Staarman, J. K. (2009). Dialogic teaching in the primary science classroom. Language and Education, 23(4), 353–369. https://doi.org/10.1080/09500780902954273. Miller, M., & Hadwin, A. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 1–16. https://doi.org/10.1016/j.chb.2015.01.050. Nelson, K. M., & Cooprider, J. G. (1996). The contribution of shared knowledge to IS group performance. MIS Quarterly, 20(4), 409–432. Nussbaum, E. M. (2011). Argumentation, dialogue theory, and probability modeling: Alternative frameworks for argumentation research in education. Educational Psychologist, 46(2), 84–106. https://doi.org/10.1080/00461520.2011.558816. Nussbaum, E. M., & Kardash, C. M. (2005). The effects of goal instructions and text on the generation of counterarguments during writing. Journal of Educational Psychology, 97(2), 157–169. Nussbaum, E. M., & Schraw, G. (2007). Promoting argument/counterargument integration in students’ writing. The Journal of Experimental Education, 76(1), 59–92. Nussbaum, E. M., & Sinatra, G. M. (2003). Argument and conceptual engagement. Contemporary Educational Psychology, 28, 384–395. https://doi.org/10.1016/S0361-476X(02)00038-3.

References

57

Nussbaum, M., Hartley, K., Sinatra, G. M., Reynolds, R. E., & Bendixen, L. (2004). Personality interactions and scaffolding in on-line discussions. Journal of Educational Computing Research, 30(1–2), 113–136. https://doi.org/10.2190/H8P4-QJUF-JXME-6JD8. Oh, S., & Jonassen, D. H. (2007). Scaffolding online argumentation during problem solving. Journal of Computer Assisted Learning, 23(2), 95–110. https://doi.org/10.1111/j.1365-2729.2006. 00206.x. Osborne, J., Erduran, S., & Simon, S. (2004). Enhancing the quality of argument in school science. Journal of Research in Science Teaching, 41(10), 994–1020. https://doi.org/10.1002/tea.20035. Perkins, D. N., Farady, M., & Bushey, B. (1991). Everyday reasoning and the roots of intelligence. In Informal reasoning and education (pp. 81–104). Petraglia, J. (1998). Reality by design. Popper, K. (1999). All life is problem solving. Routledge. Reznitskaya, A., Anderson, R. C., McNurlen, B., Jahiel, K. N., Archodidou, A., & Kim, S. (2001). Influence of oral discussion on written argument. Discourse Processes, 32(2–3), 155–175. https://doi.org/10.1080/0163853X.2001.9651596. Rigotti, E., & Morasso, S. G. (2009). Argumentation as an object of interest and as a social and cultural resource. In Argumentation and education (pp. 9–66). Springer. Ryu, S., & Sandoval, W. A. (2015). The influence of group dynamics on collaborative scientific argumentation. Eurasia Journal of Mathematics, Science & Technology Education, 11(2), 335– 351. https://doi.org/10.12973/eurasia.2015.1338a. Salas, E., Sims, D. E., & Burke, S. (2005). Is there a “big five” in teamwork? Small Group Research, 36, 555–599. https://doi.org/10.1177/1046496405277134. Sampson, V., & Clark, D. (2009). The impact of collaboration on the outcomes of scientific argumentation. Science Education, 93(3), 448–484. https://doi.org/10.1002/sce.20306. Sampson, V., & Clark, D. B. (2008). Assessment of the ways students generate arguments in science education: Current perspectives and recommendations for future. Science Education, 92(3), 447–472. https://doi.org/10.1002/sce.20276. Sandoval, W. A., & Millwood, K. A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23–55. https://doi.org/10.1207/s15326 90xci2301. Schwarz, B. B. (2009). Argumentation and learning. In Argumentation and education (pp. 91–126). Springer. https://doi.org/10.1007/978-0-387-98125-3. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Shum, S. J. B., MacLean, A., Bellotti, V. M. E., & Hammond, N. V. (1997). Graphical argumentation and design cognition. Human-Computer Interaction, 12(3), 267–300. https://doi.org/10. 1207/s15327051hci1203. Slof, B., Erkens, G., Kirschner, P. A., Jaspers, J. G. M., & Janssen, J. (2010). Guiding students’ online complex learning-task behavior through representational scripting. Computers in Human Behavior, 26(5), 927–939. https://doi.org/10.1016/j.chb.2010.02.007. Smith, K., Johnson, D. W., & Johnson, R. T. (1981). Can conflict be constructive? Controversy versus concurrence seeking in learning groups. Journal of Educational Psychology, 73(5), 651– 663. Stegmann, K., Wecker, C., Weinberger, A., & Fischer, F. (2012). Collaborative argumentation and cognitive elaboration in a computer-supported collaborative learning environment. Instructional Science, 40, 297–323. https://doi.org/10.1007/s11251-011-9174-5. Stein, N. L., & Bernas, R. (1999). The early emergence of argumentative knowledge and skill. In Foundations of argumentative text processing (No. 97–116). Amsterdam University Press. Suthers, D., & Hundhausen, C. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12(2), 183–218. https://doi.org/10.1207/S15327809JLS1202. Suthers, D., & Jones, D. (1997). An architecture for intelligent collaborative educational systems. In 8th World Conference on Artificial Intelligence in Education (AI&ED ’97) (pp. 55–62).

58

3

Learning Through Collaborative Argumentation

Toulmin, S. E. (2003). The uses of argument. Cambridge University Press. van Eemeren, F. H., & Grootendorst, R. (1992). Argumentation, communication, and fallacies. van Eemeren, F. H., & Grootendorst, R. (2004). A systematic theory of argumentation. Cambridge University Press. van Eemeren, F. H., Grootendorst, R., Johnson, R. H., Plantin, C., & Willard, C. A. (1996). Fundamentals of argumentation theory. Veerman, A., Andriessen, J., & Kanselaar, G. (2002). Collaborative argumentation in academic education. Instructional Science, 30, 155–186. Veerman, A. L. (2000). Computer-supported collaborative learning through argumentation. von Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: Case studies of how students’ argumentation relates to their scientific knowledge. Journal of Research in Science Teaching, 45(1), 101–131. https://doi.org/10.1002/tea.20213. Vygotsky, L. S. (1981). The genesis of higher mental functions. In The concept of activity in soviet psychology (pp. 144–188). Walton, D. (1996). Argumentation schemes for presumptive reasoning. Walton, D. N. (1989). Dialogue theory for critical thinking. Argumentation, 3(2), 169–184. https:// doi.org/10.1007/BF00128147. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computersupported collaborative learning. Instructional Science, 33(1), 1–3. https://doi.org/10.1007/s11 251-004-2322-4. Wineburg, S. S. (2001). Historical thinking and other unnatural acts: Charting the future of teaching the past. Zeidler, D. L. (2019). The central role of fallacious thinking in science education. Science Education, 81(4), 483–496. https://doi.org/10.1002/(SICI)1098-237X(199707)81. Zohar, A., & Nemet, F. (2002). Fostering students’ knowledge and argumentation skills through dilemmas in human genetics. Journal of Research in Science Teaching, 39(1), 35–62. https:// doi.org/10.1002/tea.10008.

4

Incidental Learning

Abstract

Incidental learning is unintended learning, where learners can infer an action about the topic unrelated to what they learned. The prior research on this topic will analyze how people learn incidentally in their workplace routines. For many people, mobile devices have become an essential object in their everyday lives, allowing them to facilitate themselves with technology. This could trigger selfreflection and prove more consistent for long-term learning that encourages the learners to re-conceive isolated learning fragments that otherwise might have been isolated. This chapter introduces the concepts of Incidental learning and its impact on the learners and how to engage and implement it in the classroom. Further, this chapter attempts to differences and similarities between Incidental Learning, Intentional Learning, and Informal Learning. Finally, it presents a case study. Keywords

Incidental learning • Incidental teaching • Incidental learning in the classroom • Informal learning • Intentional learning • Intentional teaching • Case studies

4.1

Introduction

Incidental learning (IL) refers to any unplanned/unintentional/indirect/additional learning resulting from other activities. For example, when playing video games at home, a child’s eye-hand coordination improves (EduTech Wiki, 2021). It is often done in the workplace while using computers and completing tasks. There are many ways in which accidental learning can take place: through observation, repetition, social interaction, and problem-solving; implicit meanings in classroom or workplace policies or expectations; by watching or talking to colleagues about

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_4

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tasks; and by being forced to accept situations (Kerka & Sandra, 2000). Incidental learning can also occur as a result of any of these methods. IL occurs in situations about context and social settings. Therefore, it is characterized by those features that in formal learning situations are considered most effective. Learning skills, changing attitudes, and growing personal skills, confidence, and self-awareness can be improved (Kerka & Sandra, 2000). It develops during a task or activity and may emerge as a by-product of the planned study. IL may imply that learning is unconscious in nature, although there is no expectation that this type of knowledge will remain largely unavailable for consciousness in contrast to implicit learning (Kelly, 2012). Nevertheless, some articles may refer to implicit learning tasks as incidental without distinguishing them. IL involves conscious reflection on materials deliberately noted in the study but not considered pertinent or useful, mainly from an educational perspective (Kelly, 2012). However, not everything unplanned is effective. As by-laws are often not recognized or identified as learning by students or others, it is difficult to measure and use them. Adult learners often do not distinguish formal from acquired education or prefer IL possibilities to formal learning opportunities. Several researchers have studied how educators can help students explicitly achieve accessible learning results (Kerka & Sandra, 2000). IL mainly features education/employment and cognitive psychology literature. In the first, IL is considered an un-structured and learned subset of “informal learning,” which is the difference that IL is almost always present in all situations. As the definition includes a wide variety of circumstances and knowledge, incidental/informal learning models cited factors, such as a learner’s life context, learning strategies, problem framing, and the need for some internal or external indicators of dissatisfaction with current solutions (e.g., (Marsick & Watkins, 2001)). In cognitive psychology, the definition of IL involves more unconscious information acquisition than the contrast between accidental and deliberate stimulation (Kelly, 2012). Early research found that differences between intentional and incidental learning are experimentally manipulated with variables such as interference in the list, distraction, and incentive (e.g., (Winnick & Lerner, 1963)). An example of a common paradigm would be to learn geometric forms with different colors; the form would be deliberately learned because this was included in the guidance for the study, but if colors were correctly remembered, it could be taken as evidence of the IL of color. While laboratory-based studies showed learning effects incidentally, there was little evidence that such learning was common in real life. The correspondence of the letters and numbers on telephone dials was examined in (MORTON, 1967). Even experienced telephone operators were unable to recognize and remember this material. Similarly, a lack of IL of these materials was also found in other studies that examined knowledge of similar correspondences and invariant information in the everyday environment (e.g., a saturation of the changing wavelengths of local radio stations from the moon and the old moon, features on coins). While it would be concluded from early

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research that IL is maintained by separate neural mechanisms or separate cognitive processes from intentional learning in the same way as implicit learning, the absence of learning from those particular boosting dimensions in real life suggests that intentional learning may simply be part of a lab situation.

4.2

Incidental Learning

Incidental learning is unplanned and unintended learning. It can be everywhere: at home, during work, or on the move at any given moment (Sharples et al., 2015). It often occurs within activities that students choose to do and are part of daily work and recreation. A tutor does not conduct incidental learning, nor is it a structured curriculum or a formal accreditation. How researchers have explored incidental learning. Young children learn by chance to speak, play with toys and interact with their families and friends. Through unstructured play, they can learn to solve problems and use language, social, physical, and self-regulation skills. A study of incidental learning of arithmetic (Sharples et al., 2015) examines children’s ability to make sums requiring approximation (more or less). The study (Sharples et al., 2015) found that 65% of children from 5 to 6 years could respond to problems like: “If you had 24 stickers and I gave you 27 more, would you have more or less than 35 stickers?” It appears that many young children can use incidental learning of valuable skills to do approximate arithmetic without being taught. These powerful and general techniques are not further developed when school starts and 2 + 2 = 4 begins. Schools realize that through playing and discovery, children can learn, making time for unstructured exploration. However, little awareness remains that young school kids have already gained estimating skills, creatively problem-solving, wordplay, and game design, which might form the basis of a new early years curriculum. Incidental learning is not valued by examiners or employers but continues into adulthood (Sharples et al., 2015). Early investigation in incidental learning investigated how people learned through observation and conversation, using work tools, and troubleshooting as part of their everyday routines. Researchers have identified factors that complete incidental learning (Sharples et al., 2015). This includes the objectives of students, the people around them for discussion and interaction, the tools available to them, their location, and the time available. Awareness of these factors improves the comprehension of incidents and allows the creation of environments that promote incidental learning. Persistence and trust contribute to the success of incidental learning (Sharples et al., 2015). The social environment is also essential because the incidental learning process often puts students at risk because they need help. Incidental learners can follow a path similar to the approach to inquiry learning: questions are framed and clarified, a research path is planned, and resources can be found

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that help. Alternatively, they can look for learning situations like public seminars or book-reading clubs. Assisting incidental learning. Computer game designers incorporate incidental learning opportunities through the use of challenges and rewards and the exploration of environments, the inference of rules, and the interpretation of the motivations and actions of the game characters (Sharples et al., 2015). Serious games employ similar forms of incidental learning by immersing people in a foreign environment to teach the language and cultural knowledge. This builds on the protracted approach of taking young adults abroad to learn the language and culture of a foreign country (from Europe’s ‘great tours’ of the 18th century). Migrants and travelers often pick up languages much more quickly than students can. The benefits of the contextual richness and the social learning experienced by incidental learners immersed in a natural foreign language environment have long been recognized by second language educators (Sharples et al., 2015). Techniques for emulating immersive incidental learning include encouraging learners to talk in the language they learn every day, teaching only in the language, and bringing examples of use back into the classroom for reflection and debate (e.g., watching a film that includes foreign films with subtitles). Many educators are also looking into designing educational experiences that allow students to pick up other learning along the way. Approaches include helping students reflect on how they learn and linking incidental and deliberate learning throughout their lives (Sharples et al., 2015).

4.3

The Premise of Incidental Learning

The key premise behind incidental learning is that if a student does something fun, he can learn a lot without noticing. A student’s throat does not need to be jammed down to learning. Instead, students should be allowed to pick up the desired information “in passing” and obtain materials. Designers must build situations in which factual knowledge can be acquired naturally. This is the basis of the incidental learning architecture (Engines For Education, 2020a). First, find fun things to make on a computer using the incidental learning architecture. For instance, this might be any good video game. The second trick is more complicated. It should be worth learning what the student wants to learn in the video game. One needs to know real information to achieve one’s computer objective by changing skills to be learned from hand-eye coordination to contentbased tasks. This works well if the content-based tasks naturally correlate with what is inherently fun. Since it stresses the need to engage students in exciting tasks, incidental learning architecture can be seen as an architecture for Simulation-Based LearningBy-Doing Architecture (Engines For Education, 2020a). However, the emphasis of the task in the architecture of incidental learning is different. We usually want the student to learn the skills involved in the job by doing exercises. However,

4.6 Using Incidental Learning

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the emphasis is on the facts involved in the incidental learning architecture. The student’s task is designed to make him naturally aware of the facts.

4.4

The Opportunity of Incidental Learning

The incidental learning architecture is particularly potent for young children because they have been using it naturally as a learning method (Engines For Education, 2020b). It is almost a pity for kids that school starts at five or six. At that point, incidental learning is cast off the window and substituted by architecture that “sits in your chair and do what I say.” Every program we create needs to ensure that the student wants to learn further. We want to allow children to keep learning without “study”—without forcing the process artificially. To achieve this, instead of pursuing a rigid teacher’s agenda, it is necessary to provide situations where exploration is encouraged. The task of the course designer is to find situations that allow exploration and enable incidental learning to occur (Engines For Education, 2020b).

4.5

How to Engage with Incidental Learning

Incidental learning occurs when we expect less—watching the television, reading a book, talking to a friend, playing video games, or going to a country surrounded by a new language as a whole, as many language students do (Flynn, 2021). Sounds fun. Sounds fun. Can it be checked, however? Well, incidental learning always occurs in the context of another event or experience (Flynn, 2021). The key is that the experience must be attributable to itself. Similar to playing a video game in a different language. Same goals, the same structure of playing, same commitment. All these fun elements make the thing you learn contextually. The main activity is to play; the new vocabulary is a good thing.

4.6

Using Incidental Learning

The phenomenon of incidental learning can be used as an instructional strategy that is more effective than the traditional teaching methodology, which involves students memorizing facts about the world. A problem with memorization is that it is neither fun nor productive. An incidental learning approach allows students to retain facts by avoiding memorization’s shortcomings (Engines For Education, 2020c). Just because something is fun does not mean you should learn. Learning some information is dull. Including incidental learning in the course content allows students to remember better facts that will be beneficial in their future lives.

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Incidental Learning

Incidental Learning in the Classroom

How can teachers introduce incidental learning into a structured environment? Use a two-stage approach to structure a lesson. Create a basic goal based on pupil participation and several secondary goals the pupil does not know about (Flynn, 2021). Example: Create your own classroom game—a treasure hunt based on written information, guiding children everywhere. Make sure you spread the clues with challenging words that need context and further discussion. The children will focus on the game, and the teacher will promote learning and complete their mission. This is the easiest way to learn. Resources include a pen and paper, a child’s adventurous spirit, and a few great places to hide. We can go a step further by bringing technology into the mix. The most powerful way to teach pupils incidental learning is to play educational games on in-class devices (Flynn, 2021). The pace of learning can be controlled in a way that cannot be done with a subtitled cartoon or educational song. Children are planning to explore their horizons and expand them. This can be done in a safe environment managed by teachers.

4.8

Informal and Incidental Learning

Lessons learned outside of a structured, institutionally sponsored classroom environment can apply throughout your life. It can result from day-to-day work, family, and leisure activities. It is, therefore, an unavoidable element of everyday life; for this reason, it is also known as experiential learning (Fontes, 2020). It does not lead to certification in most cases because it is not structured (in terms of learning objectives, learning time, or learning support). Most of the time, informal learning is not deliberate or random, but it is still intentional. Informal learning is a more general term that encompasses any learning outside of a traditional classroom setting; incidental learning is a subset that is defined as a side effect of some other activity (Fontes, 2020). One may plan or leave informal learning to chance, but conscious awareness of learning is always involved. In other words, incidental learning occurs in people’s closely held beliefs without actively seeking it out (Watkins & Marsick, 1992). What Is Informal Learning? Informal learning is autonomous learning driven by the passion and motivation of the learner. Informal learning gives students control over selecting content from various sources based on their interests, preferences, and relevance (Pandey, 2017). This can be consumed at the rate of the learner. Structured content alone need not provide informal learning. It can happen any day, including interactions with colleagues, older adults, or coaches. As a logical extension, informal learning has no fixed, always formal learning methodology. You can see that informal learning enables us to fulfill our curiosity and helps us improve our knowledge base, develop a new technique, and develop our current skills. It is spontaneous and can occur whenever the learner sees something which

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picks his curiosity or adds value to his work. In this regard, it is worth noting that learners rely on the successful use of informal learning. A learner who invests in informal learning typically has the profile of an individual who is aimed at exploring, experimenting, and learning. In a nutshell, informal learning is driven by the learner. It can be best characterized as a combination of reflection, practice, and learning from others when the situation calls for it (Pandey, 2018). • Informal education provides the control and freedom that adult learners crave. • Tests, grades, and deadlines put students under much stress, which can be detrimental to their academic performance. These are not the characteristics of informal learning. A more rewarding and meaningful learning experience can be achieved instead. • Learners wish to be flexible in choosing and implementing the data points from various sources. Informal learning enables them to do so. • Since informal learning has no stipulated completion time, it gives students the flexibility to learn at the best pace. • Informal learning has the greatest advantage: it enables students to continue improving their skills, develop new skills, or gain mastery and remain ahead of the competition. What Are the Benefits of Informal Learning? Children are naturally suited to informal learning, of course. Informal learning keeps us fresh and mentally active during our formative years and often aids our learning as new perspectives on knowledge emerge. Working and living more productive lives are within reach if we constantly desire to learn and put in the time. Here are the main advantages of informal learning for the learner (Pandey, 2017, 2018): 1. Learning is more focused because the learner is in complete control of the process. 2. More comprehensive learning—We want autonomy and control as adult learners. We have complete control over what we explore and learn with informal learning. 3. It is more fun as a result of the self-interest of a learner (unlike formal learning that is typically pushed by the L&D team). 4. It can occur at the student’s pace and is more likely to be completed promptly because it is motivated and encouraged by the learner’s interest. 5. Informality is more rewarding as it helps the learner decide on the learning journey (including the content, the depth of study, the time spent, and so on). 6. Because it is unplanned and unstructured, it does not require the same level of rigor as a formal learning plan. As the students are controlled, the scope (from the basic to the advanced) expands without an external push. 7. If no pressure exists for quizzes, tests, or summative tests, it is usually easier for learners to maintain momentum.

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8. Even peers and mentors will find improvised sessions easier to conduct without the pressure to deliver a structured training program. The integration of learning is also effective (as Formal learning). Characteristics of Informal Learning. Before going to school or participating in a Mom-and-tot program, informal learning begins on the day you are born and lasts until your death. The characteristics of informal learning are as follows (Eaton, 2012): 1. Informal learning is never done in a planned manner: No formulas or guidelines are established. Examples of informal learning include activities like alphabet teaching or teeth brushing. No prescription study program is available for this purpose. 2. Informal students are often highly motivated: In contrast to the school’s formal learning environment, informal students are often eager and careful. An example of informal learning is a teenager who shows a friend how to find an “Easter egg” in a video game. The gamer wants to find out how his goal should be achieved, so he embarks on a journey to learn how. He becomes his teacher with his friend. 3. Informal learning is often spontaneous: Learning can occur at any time and place. When a learner has an immediate need to know how to perform a task or comprehend a subject, they are motivated to learn. Alternatively, an informal “teacher” sees an opportunity to pass on their knowledge or wisdom to another person. For instance, we were recently in line at the airport, waiting for security to go through. In front of us was a family. The dad, who was holding his young son’s hand, who was about seven or eight years old, was teaching the child new words with his posters on the wall of the safety area. The boy heard the words and spoke of the poster content. This was a great example of spontaneous informal learning, not only to wait for a long time. 4. No formal curriculum: No study program or prescriptive methods are available. Whichever methods the individual is taught is often based on his own experience. 5. The “teacher” cares—and has experienced greater than that of the student: Even the word ‘teacher’ here is something of a misnomer, as all professional teachers have certificates or a license for teaching. In the informal learning context, informal learners are likely to be closer to the learner emotionally, such as a mother, dad, grandfather, or other careers. An adult child is an example of using new technologies to teach an older parent. 6. There is no better place to learn than the world: Learning in a school or a classroom is a myth. You learn as much from the places around you as you do from school. No classroom is available with informal learning. 7. Informal learning is difficult to quantify: There are no tests, and it is challenging to quantify informal learning. 8. Rejected frequently as worthless by academics and skeptics: Informal learning is often overlooked and regarded as less valuable than formal learning

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methods. Some researchers and academicians (but not all of us!) believe that informal learning is less valuable than formal education (some because it is difficult to quantify, and they believe it does not matter if it cannot be quantified). 9. Very important for early childhood development: Learning your mother’s language is an excellent example of informal learning. Everything a young child learns in the home, from brushing his teeth and saying the alphabet finely, is informal learning. Imagine if, for the first five years, a child has not been exposed to any language. How hard would the development of that child become? It would be too risky and unethical. We would never deal with a formal learning environment without informal learning. 10. Essential to lifelong learning of an adult: Informal learning is a process that takes a lifetime. It does not end when a child comes to school and “takes over” the formal system. Instead, children are still learning in their homes. We learn from our friends as we grow older. We learn from our employees when we enter the workforce. We still learn from friends, as well as from younger ones, when we retire. One example is an adult learning from a volunteer literacy tutor. Another example is a retired office worker who learns how to use an iPad from her grandson. Informal learning keeps us vibrant, mentally active, and interested in our world and growth. It does not mean that informal learning cannot be easily measured, that it does not make sense—or even that it is essential to our growth and development as human beings. Model for Enhancing Informal and Incidental Learning. Learner-centered adult education relies heavily on informal and incidental learning, gleaned from one’s own experiences. People learn wherever they have the opportunity, the need, and the motivation to do so. Marsick and Watkins’s (2001) proposed a model for enhancing informal and incidental learning, first developed in 1990 and refined. The circle at the center of the model shows that learning comes from daily meetings while working and living in a given context. A new experience in life could present a challenge, a problem, or a vision of the future. The outer circle in the model represents the context in which experience occurs, the personal, social, corporate, and cultural context of learning that is key to the way people interpret their situation, choices, actions, and learning. The model has a flow of thought, more ebb and flow than an evermoving cascade. They may need to go back and challenge earlier understandings with each new insight. The model is arranged in a circle, but the steps do not necessarily or linearly follow. This latest model version integrates the accessory learning process because it is evident that it always occurs with or without the knowledge of our consciousness. For example, we have observed that learning begins with a trigger, i.e., an inner or external stimulus that signals unhappiness in thinking or being. This trigger or experience is often a surprise, like a leader’s sudden departure. However, in the

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model, our worldview preceding this is our way of seeing things that frame how we see the new trigger. This framework is a pivotal point of the model, as the lessons learned at the end of the study cycle can also influence it. This model shows that people are diagnosing or shaping a new experience. They evaluate what is challenging. The new situation is compared with previous experience, similarities or discrepancies are identified, and the new challenge is seen in their interpretation. Through context interpretation, people refine their diagnosis. In the context that influences its interpretation, they address the different factors. The context could include simply someone else and a relatively routine interaction, say a family member or co-worker. Alternatively, it might be highly complex with multiple players and many political, social and cultural norms never previously addressed. Interpreting the context is a bigger challenge if social norms and expectations flow or if the person learns something new by himself. People are also unaware of contextual factors influencing interpretations and are subject to blind spots that can be strengthened when emotional factors occur. They have the same level of knowledge. Interpretation of the context leads to alternatives. These choices are guided by searching for past solutions and other potential action models. Implementation success depends on the capacity building which is appropriate for the task. When new skills are required in the solution, the individual needs to acquire them. Many contextual factors influence how well the desired solution can be implemented successfully. These include the availability of adequate resources, time, cash, people from whom to learn, available insight into an unknown or ambiguous phenomenon, readiness and motivation for learning, and the emotional ability to develop new capabilities in the midst of a stressful challenge. After an action is taken (a solution has been created), a person can evaluate the results and decide whether their aims match or not. When a person takes the time to clarify his or her objectives, it is relatively easy to assess the intended consequences. This assessment step allows an individual to learn lessons and to use these lessons in planning future actions. The final thoughts are the new understandings or frames that a person would bring when confronted with a new situation, which brings us back to the beginning of the cycle in a full circle. Implications for Practice. Informal and incidental learning is usually done without much external facilitation or structure (Marsick & Watkins, 2001). In their work Marsick and Watkins (2001) emphasized three conditions to improve this kind of learning: critically reflecting on one’s own experiences in order to uncover hidden assumptions and biases, motivating students to become more proactive in seeking out and implementing solutions, and encouraging students to use their imaginations to consider new possibilities. Adult educators might build on learning opportunities and gain insight into themselves as learners. Those who want to improve this learning can increase their awareness and insight into their learning preferences about the learning opportunities offered by life experiences. For example, guidance on self-analysis of the learning styles and action plans to improve capacity is provided in Mumford

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and Honey (1989). Many organizations use learning tools in professional planning workshops to encourage employees to be more autonomous in planning and developing their careers. People learn how they learn, look at various ways of learning and find ways to plan their future learning more effectively to fulfill their preferences. Adult educators can also help students identify socio-cultural conditions that enhance their learning or hinder learning. Upon identifying these factors, teachers can help or deal with learners. The role of society in creating the conditions which cause poverty or discrimination that people may experience as a result of their sex, race, or class may not be questionable by programs that will prepare recipients of welfare for work. For example, several governments no longer support poor people indefinitely and demand them to work instead. Poor women in welfare may learn by the way that many of their problems are to blame. These women can find socially sanctioned work channels that reinforce the cycle of poverty when learned informally about employment opportunities. Adult education providers in these programs could help women examine and thus help them to become more proactive about the validity of socially constructed positions. Due to the unstructured informal and incidental learning, blind spots are easy to trap around one’s own needs, assumptions, and values, which influence people’s approach to a situation and misunderstandings about one’s responsibility when mistakes occur (Marsick & Watkins, 2001). In families, groups, workplaces, or other social settings, people are strongly affected by other people’s social and cultural norms in their interpretation of a situation and consequent actions. However, people often do not profoundly question the views of themselves or others. The way they understand events can be distorted by power dynamics. Those problems require us to teach adult learner strategies to increase the visibility and rigor of this type of learning.

4.9

Intentional and Incidental Learning

The term “incidental” refers to unconscious phenomena in cognitive psychology and figures instead of “intentional,” which refers to conscious processes (Camacho, 2018). In the context of learning, we see incidental learning as knowledge acquisition without awareness of having learned it. However, most attempts to define this phenomenon coincide in conceiving it more broadly, stressing the unconsciousness of learning and the lack of intention to learn. In contrast, when we understand intentional learning, the subject uses conscious strategies to make knowledge available to consciousness (Camacho, 2018). When it comes to making decisions, we often rely on our intuition. At times, we can articulate the process by which we learn and think, which influences our decisions, so that our choices come with a substantial body of knowledge that can be explained verbally. The knowledge gained from this type of learning is considered incidental and non-verbalizable because we cannot explain why we made a particular decision (Camacho, 2018).

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Therefore, we say learning is by chance if two conditions are met: unintentionally learning and doing so without being aware of learning. On the contrary, when the learning is deliberately done and also verbalized, learning is explicit. Thus, we shall define a learning process to acquire knowledge of structural relations between subjects or events unintentionally and automatically and to learn deliberately when using the guidance that recognizes relations in the task or goals of the training process (Camacho, 2018). Each process leads to behavioral improvements. Intentional Learning. As defined by the American Accounting Association, intentional learning is the “persistent, continuous process to acquire, understand, and use a variety of strategies to improve one’s ability to acquire and apply knowledge” (eLearning snippets, 1995). In contrast to incidental learning, intentional learning is defined as learning that has a specific goal in mind and is based on those goals (Camacho, 2018). After Bereiter and Scardamalia published their seminal work (Bereiter & Scardamalia, 1989) on computer-supported intentional learning environments, intentional learning received much attention. According to Bereiter & Scardamella, Intentional learning is defined as “cognitive processes that have to learn as a goal rather than an incidental outcome.” The intentional learner is someone who (eLearning snippets, 1995): • is encouraged to learn • take charge of learning • engage actively in learning-enhancing strategies. The learner must reflect on, control, and maintain learning strategies to engage in intentional learning. Intentional learning can also be understood as management learning strategies and involves a conscious awareness of metacognitive learning strategies (Blumschein, 2012). While the intentional learner is self-directed, teachers can help create environments better suited to intentional learning. A voluntary learning environment promotes activity and feedback in which the learner can use tools (mental or technological) to improve this learning process, i.e., a culture that fosters transformation methods (eLearning snippets, 1995). According to intentional learning theory, it is less important for the students to pass tests than to organize and apply knowledge (Bereiter & Scardamalia, 1989). Importance of Intentional Learning. The Fourth Industrial Revolution could create millions of new jobs in the coming years (Fleming, 2020). As a result, we are in a global emergency of rehabilitation. In order to thrive in this new digital age, acquiring an “intentional learning” skill is critical, according to a recent McKinsey report (Christensen et al., 1977). Every experience is about being treated like a chance to learn at his heart. In our new digital age, learning is the most important skill. In his report, McKinsey describes “intense learning” as the primary skill to be developed for students and professionals in the next few decades (Christensen et al., 1977).

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Intentional learning is when all experiences are treated as an opportunity to learn something. The desire to learn should be an instinctive approach to everyday situations rather than something that occurs separately. “Although intentional learners are experiencing all the same daily moments anyone else might, they get more out of those opportunities because everything—every experience, conversation, meeting, and deliverable—carries with it an opportunity to develop and grow,” the McKinsey report argues (Fleming, 2020). Two things distinguish intentional learners from others (Fleming, 2020): attitudes towards growth and curiosity. While these are characteristics that people naturally have in different degrees, these views can also be adopted. People with a growth atmosphere feel that they can nurture, expand and change their capacity and intelligence over time. “A growth mindset releases you from the expectation of being perfect,” the report says (Fleming, 2020). Failures and errors do not indicate your intellect’s limitations but are tools that inform your development. Of course, some people are more curious than others. Moreover, curiosity starts all learning, right up to childhood. Cultivating your curiosity can overcome the fear of asking questions or trying new things. This may mean new challenges, such as learning a language or using a musical instrument, which does not relate to your day-to-day job. Intentional Teaching. Intentional teaching is an active process connected with children that embraces their strengths, interests, ideas, and needs and builds on them (QCAA, 2020). Intentional teaching extends children’s thinking, builds profound understanding, and takes place in emerging and planned experiences. Teachers need their decisions and actions to be purposeful. Intentional teaching practices. Teachers use various methods (see Fig. 4.1) to ensure that each child’s learning needs are met (QCAA, 2020). • Challenge: Offer children safe relationships to expand their knowledge and skills. Teachers judge how challenges and opportunities are offered through provocation and reflection that enhance children’s thinking and learning. • Cooperation: Enable children to take the lead in their learning and not to dominate the direction of their experiences while working with them. This may include the involvement of others (e.g., family members and community members) with special knowledge or expertise that can inform and support learning. • Encouraging: Comments which encourage children to persist, motivate and encourage. • Explanation: Make children’s ideas and demands clear. This is useful if children want or need to get a concept or idea. • Identification: Draw the attention of children to new ideas and topics. Specifications of interest can generate exploration and investigation.

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Fig. 4.1 Intentional teaching practices

• Imagination: Create an environment where children are encouraged to research, hypothesize and express themselves through imagination and creativity. Teachers plan opportunities for children to experience without expectations about results and explore their opportunities. • Instruction: Use explicit teaching strategies if other strategies are not safe or appropriate. • Listening: Encourage children to talk. Teachers create opportunities for shared, continuous conversations by listening carefully and actively to what children are saying. • Making connections: Learning to see the connections and inconsistencies between things. Teachers help and expand the thinking of children by comparing experience and ideas. • Modeling: Demonstrating a routine or skill. Teachers relieve responsibility gradually to allow children to practice and master skills or routines. • Negotiation: Working with children to look at their perspectives and others and develop problem-solving strategies and solutions that meet the various perspectives. • Providing choices and learning opportunities: Recognizing children’s agencies by giving children opportunities to make safe choices and experience the implications of their actions. Support for children to make choices promotes independence and autonomy. Elements of choice must be addressed within the context of interpersonal relationships and never put children in danger or peril. • Questioning: While open-ended questioning can help children think and problem-solving, it should be used cautiously not to stall their thinking. When gathering information from questions, teachers stress the importance of reasoning and the willingness to change one’s thinking.

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• Research: To help children collect information to solve problems. Research requires questions and the use of a variety of sources. • Reflection: Children should be encouraged to reflect on their day and educational experiences and engage in critical thinking that builds on their prior knowledge. Engaging in high-quality verbal interactions about current learning and what comes next for each child helps strengthen the reflective process. • Scaffolding: Providing children with the tools they need to progress to the next level of thinking. By analyzing each student’s strengths, interests, concepts, and routines, teachers can provide individualized instruction. Case Study: Intentional Teaching Examples In QKLG. The QKLG (Queensland kindergarten learning guideline) suggests advice for planning, documenting, and assessing children’s learning and development and communicating information with parents and caregivers. Additionally, it enables parents and caregivers to provide consent to have their children enroll in schools (Lorina, 2017). Examples of intentional teaching are provided for each key learning focus and essential element (Lorina, 2017). Intentional teaching occurs when educators help students acquire specific knowledge and skills to master the curriculum (Lorina, 2017). 1. Identity 1.1 Create a sense of security and trust. Teachers, for instance: • working together with children and their families to give them a sense of welcome • modeling how children can find help and comfort • offering children a learning opportunity to explore and participate in new experiences • providing them with options on environments and children’s spaces to feel calm and comfortable 1.2 Acting with independence and perseverance Teachers, for instance: • supporting the daily routines and personal belongings of children • use of open-ended questions to encourage children’s participation and decision making • verbally and non-verbally encouraging children to continue with what they are doing • teaching children about their progress in the classroom • scaffolding or modifying learning experiences so that children can make attempts and practice their skills 1.3 Developing a confident self-identity Teachers, for instance: • providing opportunities for learning to sensitize children to their own culture • encouraging children and families to share their culture, images, objects, and resources

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• reflect on children’s learning progress • link them to learning with images or artifacts that reflect their identities. Connectedness Positive relationship building Teachers, for instance: • to model and explain cooperation skills, offer children the opportunity for learning and targeted interactions • to develop a problem-solving approach that enables children to work together and resolve conflicts • identification of the rights and responsibilities of children in everyday situations • negotiation with children in situations surrounding others’ rights • establishing connections with different views, ideas, and opinions • an explanation for empathy through history and pictorial books to help children learn different ways of learning, skills, and perspectives. Show respect for diversity Teachers, for instance: • encouraging children to listen and respect differing ideas or beliefs • to challenge the stereotypical representation of people in text and everyday situations • to connect through stereotyping or prejudicial identification, using drama, books, photos, and age-appropriate multimedia • working with members of family and community to discuss the importance of their culture/s • connecting Elders and community members to advise and respectfully incorporate cultural resources into children’s education • researching and promoting cultural comprehension of Aboriginal peoples and Torre Strait Islanders. Recognizing environmental influences Teachers, for instance: • Collaborate in investigating relationships between people, soil, plants, and animals • encourage world curiosity and the impact of people on environments • research to talk about positive measures about current environmental issues in response to children’s concerns • reflect on learning; • explain how and why everyone cares for the kindergarten environment. Wellness Building a sense of autonomy Teachers, for instance: • Identifying and recognizing children’s emotions • connecting actions and emotions of children • modeling ways of recognizing and expressing emotions • encouraging children to use modeling approaches to regulate their emotions

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• explaining and developing a problem-solving strategy to encourage children to manage challenging interactions; • encouraging children to continue to try and deal with difficult experiences; • contemplating the use of relaxation techniques to control one’s emotional state Explore ways to be safe and healthy Teachers, for instance: • explaining healthy choices and routines • giving children opportunities to remember and practice health routines • cooperating with families and other professional partners to strengthen health-based routines • explaining the purposes of safety regulations and instructing children in the safe use of kindergarten equipment • Negotiating new or potential safety rules with children • making it possible for children to demonstrate their ability to follow safety rules through practice • supporting children in making informed decisions about their safety and well-being Explore ways of fostering physical health Teachers, for instance: • Promoting the involvement of children in gross motor learning to build strength to confidential handling of objects and equipment • providing children with choices and materials to exercise high motive control • clarifying the importance of physical activity for the body and mind of children • challenge children with various sensory characteristics to explore materials. Active learning Create positive learning provisions Teachers, for instance: • promote curiosity, investigate and solve problems in everyday situations • challenge children to connect ideas to experiences • to identify and imagine ways in which people may use their voices, language, gestures, costumes, and/or props related to roles in dramatic play; • explaining how to use a new word or phrase in various situations Confidence and participation in the learning process Teachers, for instance: • working together to share ideas, knowledge, and discoveries of children • encourage children into the discernment, hypothesis, experimental and reporting, and sharing of findings. • identification of new vocabulary in various contexts, • explaining why experiments worked or did not work. • making connections to other aspects of problem-solving that children may need to consider Learning and communicating with the help of technology

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Teachers, for instance: • model technology use • working together to investigate or resolve a problem with the use of technologies to learn • identifying ways to use learning technologies • providing child-friendly everyday technologies Communicating Language exploration and development Teachers, for instance: • instilling a love of language in children • spotting new vocabulary and language patterns in rhymes, songs, and stories • understanding how language is used for different purposes, such as describing and imagining things. • understanding the importance of active listening in conversations, as well as how to respond to others and take turns Personally meaningful exploration of literacy Teachers, for instance: • explanation of the purposes and use of various texts • making connections among sounds and lyrics, beginning with initial sounds of child names • listening to oral sounds and lyrics, • explaining ed how kids use sounds, lyrics, mots, and phrases • identifying the sounds, symbols, and visual images that children pay attention to when they read • assisting children in their attempts to express themselves through writing. Engaging in personal numeracy explorations • make connections in everyday contexts to mathematical concepts • explaining numbers, counting, ordering and comparing • encouraging children to use mathematical language • identifying similar attributes for object setting and compare object numbers in small collections • connecting to everyday patterns, paintings, constructions, dances, and/or architecture.

Incidental Learning Versus Intentional Learning. We gain knowledge and skills through various means, including direct experience, formal education, and selfinitiated study. Incidental learning refers to when a person picks up on one aspect of a stimulus while concentrating on another (Ahmed, 2017). It involves learning formal aspects by focusing on semantic aspects, which are more specific. In many ways, incidental learning may occur, including observations, communication with colleagues regarding tasks or projects, mistakes, and assumptions (Ahmed, 2017). A reactive component of incidental learning occurs when it takes little time to think about a task completion action. Incidental learning has also been

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implicitly described if knowledge is acquired regardless of conscious effort (Eraut, 2004). The more pessimistic interpretation of incidental learning is that it refers to learning that occurs without any conscious effort on the part of the learner. Intentional learning is the intention to learn and remember the material. It explains the study conditions in which participants have been warned that the material to which they are exposed will be tested. Intentional learners are told beforehand that they will be tested after the learning phase; they will try to store the word information to be learned in a form that can be taken to the test situation. Moreover, processing instructions in an incidental learning situation during the learning stage may be or may not help to transfer to the test situation successfully (Doughty & Long, 2003)—intentional learning results in better recalling and recognition than incidental learning. While learning is incidental and intentional in daily life, the difference lies in its desire. For example, learning to memorize a list of irregular verbs is intentional learning (Flynn, 2021). This is how you usually learn in schools and universities. A whole education system has been built around it. Incidental learning occurs if a specific goal is not considered (Flynn, 2021). For instance, spending time in a country where people speak a language other than theirs means learning random vocabulary from the various situations you live in. In order to learn five new words, you will not go out. The objective is to explore, connect and excel as you want in your home. The three features which distinguish incidental learning from intentional learning are (Flynn, 2021): • Encourage enjoyment and participation. • Encourage curiosity and understanding in a more enjoyable and less restricted environment. • Encourage an interest in learning new things and expanding one’s knowledge base.

4.10

Incidental Teaching

Incidental teaching is called because “incidents” naturally teach essential skills. Incidental learning is a type of teaching used in ABA (Applied Behavior Analysis) therapy, which follows the same principles for learning as Discrete Trial Training, but takes place in a natural environment and the child’s interest in an object or activity is a starting point for learning (McGee et al., 1994). Using incidental teaching to improve language skills and achieve social benefits was first theorized by Hart and Risley in 1978 and then implemented in classrooms (McGee et al., 1994). It was mainly used among children of pre-school age. Since his discovery, however, adjustment to a variety of abilities has been found for almost every age group.

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Who is incidental teaching for? Incidental teaching is usually used with children between two and nine years old, but it is suitable for autistic people or those with developmental delays at all ages (raisingchildren, 2021). What is incidental teaching used for? It aims to teach children how to initiate conversations (raisingchildren, 2021). Autistic children can improve their language and communication skills through incidental teaching. Another aspect is to equip children with new skills they can apply in different scenarios. Where does incidental teaching come from? Incidental teaching was an integral part of the 1970s Applied Behavior Analysis (ABA) approach. It was the first teaching technique of naturalism. It offered an alternative to traditional techniques like (Discrete Trial Training) DDT, which teaches skills in highly controlled environments (raisingchildren, 2021). What is the idea behind incidental teaching? All naturalistic teaching techniques assume that the child will more efficiently use skills in many situations when learning them, rather than in highly structured settings like a clinic, in natural environments like playtime. For incidental teaching, it is assumed that when rewards are given, kids will practice more. Incidental teaching also depends on the child’s natural interests as a basis for learning, following the child’s lead by the teacher, therapist, or parent (raisingchildren, 2021). Four Basic Stages of Incidental Teaching According to Nichols (2015), the following are the four basic stages of incidental teaching: 1. create an environment that will help a child find things of interest 2. wait for the child to make the first request about something they are interested in 3. request more detailed language or speech approaches 4. provide the object initiated by the child. How should the environment be arranged? The thing or activity that motivates the children should be located where they can see it, but they must actively attempt to obtain it (Nichols, 2015). What Makes Incidental Teaching Effective? The following makes the incidental teaching effective (McGee et al., 1994): • Incidental teaching capitalizes on children’s best interests so that children are highly motivated. • Incidental teaching usually takes place in a natural environment. It contributes to generalizing know-how beyond ABA and prepares children when teaching in the natural environment occurs later during the program’s maintenance. • This is a practice based on evidence.

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Fig. 4.2 Five steps of incidental teaching

Five Steps of Incidental Teaching Refer to these steps (shown in Fig. 4.2) when you are looking for opportunities to teach using the incidental teaching method and putting the incidental teaching method into practice (McGee et al., 1994). 1. 2. 3. 4. 5.

Wait for the child by indicating interest to begin teaching. Ask children generic questions like “What do you need to do?” Express what your child expects. Prompt when necessary. Reward with exposure and encouragement.

When and How Can Incidental Teaching Be Used to Teach? The teacher organizes the learning environment around a specific learning objective but considers the student’s individual preferences. When the student shows an interest in an item or an activity, the teacher encourages the student to question or encourage that interest. The interesting about incidental learning is that the learner is in charge of directing it. Various examples of when and how to use incidental teaching (McGee et al., 1994) can be found below. During Mealtime/Snacks • Before offering a desired food or beverage, ask the child, “What do you want?” or “What is this?” To get a response from the child, say something like, “You can say, ‘Cookie.’” The word “Juice” can be used to describe the beverage. In some cases, the word can be expanded to a phrase instead: “You can say, ‘I want juice.’” Upon receiving a response, provide the requested food or beverage (prompted or unprompted)—[Language skills, Imitation skills] • When a child learns to use utensils and frequently needs assistance, this is an excellent time to foster independence. Refrain from assisting at first, encourage

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the child to try things independently, and assist as needed. [Adaptive and selfhelp skills, Language skills] • After eating, hand the child cloth or napkin and ask the child to purify the face/hands. As necessary, prompt only. [Adaptive and self-help skills] • Place several food and drink options in front of the child with the instructions, “Tell me what you want” or “Pick one.” You can use “You can say ‘I want this.’” to prompt the child to say “You can do…” and then point or use PECS/signs. [Language skills] • Involve your kids in the kitchen and baking process. [Adaptive and self-help skills]. Ask children to measure out ingredients, place them where they need to be, mix them with utensils, and put the batter on cookie sheets. [Fine and gross motor skills] Set timers and practice figuring out cooking and baking times. [Adaptive and self-help skills, Math skills]. At School • Interact with your peers at the right times. If you want to get the conversation started, say, “Hey!” Please encourage your child to enlarge on the topics they are discussing. Asking, “Can I play?” is fine.” • Encourage students to collaborate and practice turn-taking. [Play/Social skills] • Make a habit of cleaning up after yourself and storing your belongings in a cubby, locker, or bin. [Self-help and Adaptive skills] • If children require help in their tasks, allow the child to request help with words, signs, PECS, or communicative devices before helping. As needed, prompt and help when requested. [Language skills] • Suppose the child needs teacher assistance prompt strategies for attention. This might lead children to raise their hands, say “you can ask help” to the child, and encourage using the “inside voice.” • Constantly reward the child for good classroom behavior such as waiting his/her turn, sitting quietly and politely, raising his/her hand, asking for assistance, and so on. Make a habit of cleaning up after yourself and storing your belongings in a cubby, locker, or bin. During Play • Set out a selection of the child’s favorite toys. Look at the toys a kid gravitates toward. Make sure the toy is at a safe distance from the child so that you can supervise it. Prompt the child to use words, PECS, or signs to request a toy by gesturing toward the PECS book, showing the appropriate sign, or saying, “You can say Teddy bear” or “You can say car.” Involve children after using communication by giving them the toy. [Language skills] • Use puzzles, string beads, or other start-to-finish tasks when playing with your child. [Spatial and visual skills] Remove a couple of puzzle pieces or beads and prompt children to use words, PECS, or signs when needed. [Language skills]

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• Encourage reading with children if they enjoy books. Ask, “What is the word?” or “What is the letter?” [Language skills, Academic skills] Or ask the children, while reading, to point to a few images in a book [Fine motor skills] or to some colors or forms in the book [Fine motor skills, Visual/Spatial skills] • When reading a story, ask social questions about the story, “How does this character feel?” How big of a problem is this?” What should the character do next?” “What is the character’s state of mind?” [Language Skills, Play/Social Skills] • Find songs children like to sing, especially those with accompanying motions. Leave out some words after chanting the song a few times and encourage the child to complete missing words. [Language skills] Invite the child to imitate the motions. [Imitation skills, Gross and fine motor skills] • Encourage and engage in role-playing games. [Play/Social skills] • Engage in turn-taking games or activities. [Social/Play skills] • Encourage your child to play with toys appropriate for their age and skill level and prompt the child to imitate. [Imitation skills, Play/Social skills] • Request the children to match the identical or similar toys while playing [Visual/Spatial skills] or label the colors and shapes of items [Language skills] • Play Simon Says or a similar game and perform tasks that require balance and coordination, such as touching the nose or head, standing on one foot, jumping, waving arms, etc. [Imitation skills, Gross and fine motor skills] • Using pretense play to practice event sequences (with or without objects/toys). Play social scenes and ask, “What is next?” [Visual/Spatial skills, Play/Social skills] • Please put them in a particular formation and copy the training to play with blocks or legos. [Spatial/Visual skills] • Coloring should be encouraged. [Academic skills, Fine motor skills] Ask your child, “What color is this?” while they are coloring. “Which blue crayon do you have?” [Visual/Spatial skills, Language skills] • Practice writing letters, numbers, and names: chalkboard, fat markers or crayons, or finger paints. These utensils are fun and a good starting point for children who are still working on fine motor control. These utensils should be used until the child has mastered them and improved fine motor control. [Fine Motor Skills, Language Skills, Academic Skills]. Community Outings 1. At the Store • Please ask the child to label items in stock or ask for items you want. [Language skills] • You can use grocery store outings for older children as an opportunity to teach them how to shop for themselves, how to plan a budget, and how to count and use money. [Adaptive and Self-Help Skills, Vocational Skills]

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• Please consult with the child about his or her dinner preferences. Give a variety of options. Try something new to elicit a discussion that results in a compromise. [Behavior and Emotional Regulation, Play/Social Skills]. 2. In The Park • Use swings, slides, and monkey bars to improve your child’s gross motor skills. This exercise can help build muscle mass and improve gross motor skills simultaneously. Running, walking, chasing, and playing tag can also be used to work on these skills. [Gross motor skills] • Strengthen children in order to encourage safe behaviors consistently and positively. [Emotional and Behavior Regulation]. 3. Out To Eat • Encourage and reward proper dinner sitting. [Attending skills] • Request the child to read menu items. [Language skills, Reading skills] • Give the child the chance to choose what to eat [Language skills, reading skills] and point to item what the child wants. [Fine motor skills] • Allow your child to use utensils independently if they do not usually. Prompt when it is needed. [Adaptive and self-help skills] • An excellent opportunity to practice one’s ability to control one’s behavior and emotions is in public. Perhaps the child is apprehensive about traveling in a car, shopping in a crowded mall, or sitting still for meals. Assist your child in using his or her coping mechanisms when he or she is upset, wants to flee, or engages in other harmful behaviors. For example, “You can use your words and tell yourself, ‘I am angry,’” or “You can say that you want to play, and then calm yourself down with a verbal solution.” It may be instructing the child to request the proper things or actions. There are many ways to keep your child on track, such as using visual reminders like “First store, then park” or “First sit nicely at dinner, then we can go home and watch movies.”. [Behavioral and Emotional Regulation]. Around the Home • Encourage your child to take responsibility for their hygiene. Play a game or sing a song while you clean to make it more enjoyable and help you stay motivated. [Adaptive and Self-help skills] Practice using oral guidelines like ‘picking up,’ ‘picking in,’ ‘clean up’ etc. [Language skills] • Include children in advanced cleaning (typical older children) tasks: cleansing, aspiration, washing, dusting. Strengthen independence in such tasks positively. • Involve children in cooking and baking [Gross and fine motor skills, Adaptive and Self-Help skills] • Reinforce the child positively when he/she leaves the room by turning off a light or a TV. [Fitness] [Fitness] • Ask children to label products receptively or explicitly: “Where is the fridge?” “Where are the snacks that we keep?” “Where is the sofa?” “That is called what?” [Competencies in Language].

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Waking Up and Bedtime Routines • Practice independently to wash and care for yourself in the bath. • Whenever possible, encourage independent dressing. • For Parents: Whenever you are washing your face or brushing your teeth, give your child a cloth or a toothbrush and encourage him or her to do the same thing. [Adaptive and self-help skills, imitation skills]. • If your child has trouble going to sleep at night without the lights on, gradually introduce him or her to the concept of sleeping in the dark by leaving the room with the lights off for longer and longer periods before bedtime. • As you are getting ready for the day’s events (like school, work, or whatever else you have got planned), walk your child through his or her morning routine. “Now that we have finished our breakfast, what should we do?” You read that correctly: “We have just finished brushing our teeth!” “Now that we have showered, what should we do?” “We get dressed!” is an acceptable response. Get ready for bed, in the same way, every night. [Language Skills, Adaptive/Self-Help Skills].

4.11

Case Studies on Incidental Teaching

• Betty Hart and Todd R. Risley at the University of Kentucky used incidental teaching strategies to help children with autism use compound sentences. Compound sentences directed at teachers increased in frequency following incidental teaching strategies for using compound sentences. Subjects’ use of compound sentences toward other children doubled when the same strategy was used in the same context (McGee et al., 1994). • Dennis J. Delprato: Comparisons of Discrete-Trial Normalized Behavioral Language Intervention for Children with Autism. Studies comparing the results of traditional operant behavior procedures (involving highly structured, direct teaching sessions) to more recent normalized language-teaching interventions were reviewed (involving aspects of incidental teaching such as natural environments and child initiation). According to the findings of this review, incidental teaching methods outperformed traditional, more structured ones in the eight studies that used language criteria (McGee et al., 1994). • H. Goldstein-Communication Intervention for Children with Autism: Of about 60 studies evaluating various treatment approaches for teaching language to children with ASD, around half evaluated several interventions. Incidental teaching methods and similar techniques such as exposure to natural environments were equally effective as DTT in language education (McGee et al., 1994). Other strategies in nature (incidental teaching, modeling, script-writing, parent training) were reviewed. • McGee and Daly—Incidental teaching of children with autism in ageappropriate social phrases: In this study, three young boys with autism were

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taught to speak in a socially acceptable manner by using incidental teaching methods. Teaching socially acceptable phrases, such as “All right.” and “You know what?” inadvertently led to students’ immediate use of target phrases. Later, subjects taught using incidental strategies used more target phrases (McGee et al., 1994).

4.12

Conclusion

Incidental learning can enhance formal learning, but it is challenging for teachers and learners throughout their lives. A teacher may find it difficult to tell when incidental learning occurred as it is not schedulable and is not often recorded. The challenge is to provide students with timely opportunities for reflection and help them reconstruct isolated learning fragments as part of more consistent, long-term learning journeys. Equally, it is difficult for students to appreciate their learning trips, find time and effort to develop and validate their personal learning, and resist any attempt by others—parents, teachers, managers. We know very little how young children develop their language, arithmetic, science, and social interaction skills, especially creativity, art appreciation, psychology, and philosophy. As researchers uncover these incidental learning processes, new pedagogies can emerge, building on children’s existing skills and turning them into adulthood.

References Ahmed, S. (2017). Intentional learning versus incidental learning. Journal of Psychology and Clinical Psychiatry, 7(2), 1–3. https://doi.org/10.15406/jpcpy.2017.07.00426. Bereiter, C., & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In Knowing, Learning, and instruction-essays in honor of robert glaser (pp. 361–392). Blumschein, P. (2012). Intentional learning. In Encyclopedia of the sciences of learning (pp. 1600– 1601). Camacho, P. (2018). Incidental or intentional learning? Two compatible processes. Journal of Physical Fitness, Medicine & Treatment in Sports, 2(3), 1–5. https://doi.org/10.19080/JPFMTS.2018. 02.555587. Christensen, L., Gittleson, J., & Smith, M. (1977). The most fundamental skill: Intentional learning and the career advantage. Psychological Review, 84(2), 191–215. Doughty, C. J., & Long, M. H. (2003). The handbook of second language acquisition. Eaton, S. E. (2012). 10 Characteristics of informal learning. Drsaraheaton.Wordpress.Com. EduTech Wiki. (2021). Incidental learning. EduTech Wiki. http://edutechwiki.unige.ch/en/Incide ntal_learning. eLearning snippets. (1995). Intentional learning. Sites.Google.Com/Site/Elearningsnippets. https://sites.google.com/site/elearningsnippets/a-wiki-page/intentional-learning. Engines For Education. (2020a). Premise of incidental learning. Engines4ed.Org. https://www.eng ines4ed.org/hyperbook/nodes/NODE-151-pg.html. Engines For Education. (2020b). The opportunity of incidental learning. Engines4ed.Org. https:// www.engines4ed.org/hyperbook/nodes/NODE-331-pg.html. Engines For Education. (2020c). Using incidental learning. Engines4ed.Org. https://www.engine s4ed.org/hyperbook/nodes/NODE-147-pg.html.

References

85

Eraut, M. (2004). The practice of reflection. Learning in Health and Social Care, 3(2), 47–52. Fleming, S. (2020). Why ‘intentional learning’ is the most important skill to learn right now. World Economic Forum. https://www.weforum.org/agenda/2020/10/intentional-learning-skills-jobsreset-fourth-industrial-revolution/. Flynn, T. (2021). Incidental learning in the classroom. Wibbu.Com. https://wibbu.com/incidentallearning-classroom/. Fontes, J. (2020). Formal, non-formal , informal and incidental education. Coletividad.Org. https:// www.coletividad.org/formal-non-formal-informal-and-incidental-education/. Kelly, S. W. (2012). Incidental learning. In Encyclopedia of the sciences of learning (pp. 1517– 1518). https://doi.org/10.1007/978-1-4419-1428-6. Kerka, & Sandra. (2000). Incidental learning. Trends and issues alert No. 18. (pp. 1–4). https:// files.eric.ed.gov/fulltext/ED446234.pdf. Lorina. (2017). Intentional teaching examples in QKLG. Aussie Childcare Network. https://aussie childcarenetwork.com.au/articles/childcare-programming/intentional-teaching-examples-inqklg. Marsick, V. J., & Watkins, K. E. (2001). Informal and incidental learning. The New Update on Adult Learning Theory, 89, 25–34. McGee et al. (1994). Incidental teaching guide. http://knappcenter.org/wp-content/uploads/2017/ 05/Topic-3_Incidental-Teaching-Guide.pdf. Morton, J. (1967). A singular lack of incidental learning. Nature, 215, 203–204. Mumford, A., & Honey, P. (1989). Capitalizing on your learning style (Issue March). Nichols, E. (2015). Incidental teaching procedures. Quizlet. Pandey, A. (2017). 8 Benefits of informal learning in the workplace. ELearning Industry. https:// elearningindustry.com/informal-learning-in-the-workplace-8-benefits. Pandey, A. (2018). 6 Examples on how you can promote informal learning in the workplace. QCAA. (2020). QKLG: Intentional teaching practices QKLG search. Qcaa.Qld.Edu.Au. https:// www.qcaa.qld.edu.au/kindergarten/qklg/practice/intentional-teaching-practices. raisingchildren. (2021). Incidental teaching. https://Raisingchildren.Net.Au/. https://raisingchild ren.net.au/autism/therapies-guide/incidental-teaching. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Watkins, K. E., & Marsick, V. J. (1992). Towards a theory of informal and incidental learning in organizations. International Journal of Lifelong Education, 11(4), 287–300. https://doi.org/10. 1080/0260137920110403. Winnick, W. A., & Lerner, R. A. (1963). Intentional and incidental learning under distraction. The American Journal of Psychology.

5

Context-Based Learning

Abstract

Students who use the Context-Based Learning approach can better understand the material by applying what they have learned to real-world situations and then comparing that information and what they already know. The background is limited to setting space and time in a classroom or lecture theatre. Learning can come from an enhanced environment outside the classroom, as a visit to a heritage site, a museum, or a good book. In this, learners will produce meaning through experiences with our environments, conversations, notes, and adjustments to nearby objects by witnessing the environment around us, assisting with the guides, and measuring the instruments, which will help us understand the meaning. This chapter aims to introduce Context-based Learning, its impact on students’ learning, and later on, creating Context-based learning environments. Later this chapter discusses how the assessment will be done in Context-based learning, along with case studies. Finally, this chapter throws a lite-on Contextual Teaching and Learning. Keywords

Context • Context in education • Context-based learning • Context-based learning environments • Context-sensitive technologies for learning • Context building • Assessment for learning • Contextual teaching and learning

5.1

Introduction

Context is how we make sense by distinguishing between what is significant and irrelevant (Sharples et al., 2015). For instance, the meaning of every word and sentence when reading a book is transmitted through its characteristics and position in

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_5

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other words or illustrations. Until recently, education was designed to reduce contextual impacts on learning so that children could acquire universal knowledge and take the appropriate tests wherever they are, during the day, or in the environment. However, many professions like medicine, art, or engineering require applying general professional skills in particular contexts. You also have to learn practical skills to work in many different situations. Therefore, it is essential to understand how the context relates to learning. Learning in context and learning by creating context. Consider a group of friends in an art gallery in front of a painting. These are the paintings, galleries, friends, and other people in a specific context. They also create a context by participating in common action and conversation, approaching the work, discussing the artist, and comparing it with others. Therefore, context is immersed in which we are immersed and create (Sharples et al., 2015). The same goes for a book: at the same time, we are on a particular page with a particular word, and we create a contextual meaning through our language and literature knowledge. As something around us and something we create through our activities, this dual nature of context create problems for teachers at all levels. A young teacher should offer them opportunities for exploratory playing to create contexts while preserving them so that they are not left outdoors and online in dangerous situations. A central question at the university level in subjects such as geology, archaeology, and environmental sciences is whether students should make an ongoing trip into an authentic context, with all their risks and uncertainties, or provide an experience similar to one of the field scientists by producing/simulating specific information. Augmented reality, virtual reality, and environmental modeling can enable students to view and sample actual data by viewing, for example, a virtual microscope showing pre-prepared rock images that can be zoomed into, scanned, and viewed under various lighting conditions (Sharples et al., 2015). These advanced techniques make the situation easier for teachers to access and to control, but these benefits must be weighed off against the value of science in real, potentially complex, and expensive places to access. Context-sensitive technologies for learning. New context-sensitive technologies provide opportunities to develop enriched learning contexts (Sharples et al., 2015). Mobile phone users with geolocation-aware applications and augmented reality apps can utilize audio, text, and imagery to guide them and provide information about the exhibit or museum object they are looking at. The aim is to provide the visitor with general information concerning the particular location or exhibit. Aris offers several tools for building and delivering locational games, including scavenger hunts and re-enacting historical events (Sharples et al., 2015). In the 1960s, on the University of Wisconsin Madison Campus, one Aris game recreated a student protest. A university visitor can act as a reporter, travel around the campus, observe past events via images and video at the campus venues where the events were held, and conduct simulated interviews with participants.

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Aris application users can also create contexts by photographing and adding notes to a map, so others can view them when they arrive at the appropriate places (Sharples et al., 2015). For example, in citizen journalism, when people report daily events and visit each location, they can use these and similar context-based systems to include labels in a foreign language in places and objects pop up in other people’s devices. When looking at migration and climate change data over time and place, it is possible to generalize knowledge. Data about local weather, wildlife, rocks, and fossils are currently being shared and compared by amateur scientists learning across contexts. These activities are made possible by Journey North, a site that engages students and citizen scientists in research on wildlife migration and seasonal change and allows students to track hummingbird migration, record sightings of butterflies, and keep tabs on when tulips bloom in the spring (Sharples et al., 2015). The context in education. The learning of young children is tied to contexts of time, place, people, objects. Applying general knowledge to a situation will create more contexts as children mature. This process can be supported by tools to access knowledge in context and abstract general knowledge across multiple contexts for children and adults alike (Sharples et al., 2015). A context-based learning method aims to assist students in learning from the world around them and see how general scientific and societal principles apply to their daily lives (Sharples et al., 2015). The pedagogical techniques and strategies, such as geo-learning, seamless learning, event-based learning, crowd learning, and citizen inquiry, are interrelated through contextual learning. The common theme is learning from situating and understanding a setting, reporting and comparing events in multiple contexts. It is often difficult to take and apply what is learned in one setting. Depending on their context, words and ideas vary. Words like ‘set’ in English have very different meanings in a kitchen, a tennis court, and a mathematical classroom. Similarly, thoughts and activities may need to be re-interpreted, and data items collected at specific sites may need to be checked or altered before being used in general. Therefore, context-based learning represents a powerful means of understanding locations and events and connecting general knowledge to everyday life (Sharples et al., 2015).

5.2

What is Context-Based Learning?

Learning is a joint activity focusing on interacting with people with significant interests, and standard structures in the classroom that do not react to it may impede learning success (Gallistel, 2012). According to Context-Based Learning (CBL), knowledge acquisition and processing can only occur when students are immersed in the social context of their learning environment and have access to real-world knowledge contexts. The approach is based on the firm conviction

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que because of inherent error representing how the mind acquires, processes, and produces knowledge; learning is a society that is poorly serving most classroom situations. In its broadest meaning, CBL describes how students, teachers, and institutions operate in the cultural and social environment (Overton, 2007). The media influence this context to provide a common culture for the academic community. Hansman (2001) states that adult learning occurs only when the context is established, and learning tools or methods are combined to foster student interaction. Using applications to illustrate and illuminate the curriculum is another aspect of CBL. This usually means offering science students opportunities for real-world testing of theories. It was shown that learners were encouraged and enthusiastic in using meaningful and appropriate contexts (Hennessy, 1993; Mandl et al., 1993). However, it may not be best to introduce these good examples after the theory has been covered. Science concepts are suggested into three forms which can be considered corners of a triangle and complement each other (Johnstone, 1991). These forms are • the macro: what can be seen, touched and smelled. • the sub macro: atomic, molecular, structural, chemical, etc. • the representational: symbolic, mathematical, and other such representations. Johnstone (1991) believes that we come across life on a macro level. Macro-level science involves activities like laboratory work and real-life experiences. Nevertheless, science must transition to the submicro realm to truly be understood, where substances and physical phenomena are interpreted from invisible interactions and recorded in some representation and model. The traditional approach to teaching science has always emphasized submicro and representational aspects, while the macro or real-world components were ignored or tacked on. Teaching using a real-life context is shown to engage students in their learning more intensely (Reid, 2000; Belt et al., 2002; Rayner, 2004). Context. The fundamental premise of CBL is that learning cannot occur in a vacuum but in some way be linked with real-world attributes so that learners can make sense (Westera, 2011). Such a practical context enables students to relate to their real-world references symbolic content, including concepts and principles. The interactions between an individual and the outside world give rise to the context (Westera, 2011). Figure 5.1 shows the overall layout of the different world compartments which contribute to the learning context. Different parts of the world surround the individual student in the center of the figure; the learner’s interactions with these parts create a learning context. The concrete operational setting in which the individual student acts is the most tangible compartment. The world as we directly perceive and act upon is this operational setting. This reflects our being’s ‘here’ and ‘now’ and may refer to a particular place, building, room, objects, and people nearby (Westera, 2011).

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Fig. 5.1 Different world compartments affect the individual’s learning context (Westera, 2011)

The domain knowledge compartment refers to students’ topics (Westera, 2011). It is a sub-set of human understanding, for example, language, mathematics, history, engineering, or food. Each field uses its vocabulary, approaches, and instruments, thereby creating its context. Pedagogy’s compartment addresses the various methods for teaching and learning, which define the distinct responsibilities of students and teachers, the activities in which they engage, and the methods used to assign assessment, feedback, and assistance (Westera, 2011). The outer shell in the diagram represents the larger world, especially human culture (Westera, 2011). It is the overall complex and interconnected body of knowledge, beliefs, arts, laws, morals, and skills that have been expanded and built upon from generation to generation and community to community. Virtual space refers to the world’s digital extensions made available in operational environments via digital devices (Westera, 2011). Virtual space provides digital tools and resources and enables outside world communication. It virtually stretches the human interaction horizon beyond the operational physical limits. Arrows in Fig. 5.1 represent the increase in virtualization and compartmentalization of the world, resulting from the compartment assimilating content from the other compartments and acting as a channel on its own. The context consists mainly of the interactions between people and entities in the various compartments of the world. Table 5.1 provides some practical examples of an overview of various components of learning context (Westera, 2011). Table 5.1 columns distinguish between the different world compartments; rows identify the world’s entities subdivided into objects, people, processes, and ideas. • Context induced by human culture: Human culture is a collection of ideas (Westera, 2011), such as social structures, love, economic systems, and moral values. These are abstract in nature (Cassirer, 2006; Mises, 1957). However, concrete artifacts such as books, buildings, works of art, products, or processes are created by expressive ideas. Culture is a characteristic of humanity so immanent and manifest: every human activity has cultural bias. Cultural differences

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Table 5.1 Exemplary context constituents from different world compartments (Westera, 2011) World entities

Human culture

Knowledge domain

Pedagogy

Objects

Paintings building products

Tools resources

Tools Any virtual Machines instructional object tools content products

People

Groups

Experts Teachers professionals fellow researchers learners

Any virtual Colleagues character customers suppliers

Working practices

Learning activities guidance feedback testing

Any virtual Working Behaviors process practice performances rain shower

Theories goals scenarios

Virtual Location culture and time procedures behavioral codes

Processes Economy press

Ideas

Values Vocabulary languages theories politics concepts

Virtual space

Operational Individual setting Personal objects Self

Personal profile internal reference

involve different contexts, behaviors, and meanings. Either wisely or unwittingly, any learner will conform to and act according to existing socio-cultural frameworks (Vygotsky, 1978). The social and cultural frameworks contribute significantly to the learning environment. • Context induced by the knowledge domain: Every learning refers to a (sub) human knowledge domain (Westera, 2011). The domain itself cannot prevent the context from contributing. In addition to the content of the domain, it mainly conveys the epistemic framework of the domain (Shaffer, 2006), including the vocabulary of the domain, its methods, its instruments, its main actors, its social framework, its challenges, its attainments, its work practice, etc. For example, a health care worker’s tool kit would include infusion systems, hypodermic needles, and blood pressure measurement instruments. The epistemic framework of the health domain refers to established social, cultural, and professional traditions and expectations, such as power dynamics, professional attitudes, and role perceptions. Epistemic frames may differ from domain to domain, such as medical ethics, compared to ballroom dancing. • Context induced by pedagogy: Pedagogy itself is a significant contextual factor in the next to the knowledge domain, and the student learning strategies are strongly influenced by pedagogical context (Westera, 2011). For example, it appears that how testing and rating are arranged is most critical for students. In addition, enforced high workload promotes rote learning in a curriculum. Pedagogical approaches imply various principles and beliefs about what the learning environment should fulfill. Despite the diversity of pedagogical approaches available, they all share the fundamental pedagogical concern of addressing some learning requirements and objectives by providing appropriate learning tasks, learning scenarios, learning content and tools, student testing, learner

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guidance, and feedback. The educational and boundary approaches involved have a strong background effect (Elton & Laurillard, 1979). • Context induced by virtual spaces: Learning environments increasingly include digital media (virtual spaces) (Westera, 2011). These media contribute in two different ways to the learning context. Firstly, interaction is replaced by digital representations to access real-world objects, phenomena, ideas, and subjects. An example of such a replacement would be communication via an avatar and not face-to-face. Digital media provide people with new opportunities to include external entities and thus to expand the context. Second, because they distort and filter their potential for improvement and increase, the digital media actively contribute to the context themselves (Baudrillard, 1995). Therefore, the advancing virtualization of life modifies modes of interaction and generates a media context that offers new communication opportunities and creates selfinduced limitations (Borgmann, 1984; McLuhan, 1964). Salomon (1979) found that symbol systems in media play an essential role in cognitive processing: symbolic activities help to learn as they directly influence the structure of the mind. It would not be possible to compare different pedagogical approaches without taking media contexts into account (Westera, 2005). • Context induced by operational setting: Of course, the operational environment in which the learning is carried out (i.e., the learner’s location) contributes to the context. It covers relevant objects and possible constraints (room, furniture, computers). The operating setting is also associated with time, geo-location, and location derivatives (temperature, good conditions) (Westera, 2011). For example, “school,” “work,” or “home” have a broader meaning than the operational level. In many instances, operating conditions are directly connected to a social-cultural context. In addition to specific physical conditions (products and machinery), socio-cultural patterns such as the site’s functionality, the cohorts, clients, and the underlying points of view and behavior codes that these carries are included in workplace learning (e.g., learning in a factory). • Context induced by the individual: While learning is essentially the development of individual skills, the characteristics of individuals greatly influence the learning process and conditions (Westera, 2011). These include age, personal goals and ambitions, prior knowledge, school background, and physiological constraints (blindness, weight). Those characteristics include the mental and physical profile of a pupil (Allen, 1990). These data can be dynamic as well (mood changes, fatigue). In addition, the student’s intrinsic socio-cultural background and profile contribute to the context of learning (family conditions, beliefs, hobbies, nationality, religion). In addition to these fundamental profiles and background data, the dynamics of actual learning activities and performances will greatly influence individual contexts. This data not only determines the learning contexts but can also be forwarded to the student model, reflecting intelligent and productive personalization of the learning environment to reach adaptive learning environments (Brusilovsky, 1999).

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Context-Based Learning

The various parts of the world are the context in which we operate. This context is important to us mainly through our interactions, allowing contextual learning. Importance of Context in Your Learning If the content is king, then context is god!—Gary Vaynerchuk.

One of the old sayings about marketing—that it is all about content—has been rendered moot by newer, more focused approaches that target the audience and provide a tailored context. While most people have heard before, it may be hard to see why this statement is true. This expression comes from the world of marketing. Learning takes a turn in a different direction. It has been found that learning is more effective when done in context (Quinn, 2016). Some of you may have heard that studying in the testing room is a good idea. You will perform better if you learn in a context like your current one. If you want your people to try new things, you must put them in situations where they can practice the new thing and allow them to try it. Such an experience is what we can call a designed learning environment (Quinn, 2016). However, there is much activity going on in this neighborhood. • Do Abstracts Work? The application of abstract learning is irrelevant in different situations. You might think that abstract learning would provide you with the versatility to transfer your skills to other settings, but it does not. You can demonstrate your capabilities with abstract problems, but you do not know how to apply them in context. The trigger does not get pulled. Problems that lack concrete elements cannot be made to work well. (Please note: More and more things occur in school.) You have to learn in a range of applications if you need to transfer across contexts in a range of contexts. You do not need to learn anything in the everyday context but in the representative context, which involves space (Quinn, 2016). For example, we can practically teach negotiation in a compensation argument, a seller negotiation, and a project role. In this way, you are more likely to access and use learning in a suitable context. • How does the brain function in context? Our brains abstract across contexts to separate the things that can change from those that define this as a situation for the topic (Quinn, 2016). Because of that, the basics of negotiating—for example, figuring out what you need to achieve your goal and persuading another party to give it to you—remain unchanged, but the goals, the entities, and the constraints can vary. There are some indications that this is a situation in which this special skill set can be used, while others are in flux. The learner’s brain will learn when to trigger the ability and which attributes can be changed without affecting the execution requirement. It is further used to show how the skill can be altered to fit different scenarios that are still relevant. The selling of automobiles is unlike selling personal computers, but some components are shared, and others diverge. When it comes

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to standard sales abilities, you must train across product and service lines. To master car selling, just drive different kinds of cars, such as mini-vans, sports cars, and SUVs. • How do we choose a suitable learning context? Ideally, we can take advantage of current situations. Alternatively, the appropriate situations can be anticipated. This means that our learning contexts must be carefully chosen. Moreover, again, we must choose representative ones that promote the decoupling and robust transferability of the required components. Moreover, contextualized practice, not the ability to recite information, is what makes a difference to be done (Quinn, 2016). In learning, we create contexts in which to practice. We can run role plays, scenarios, or even severe simulations and games. The aim is to minimize the difference between the learning event and the performance environment. Moreover, the closer we need to go, the more performance risk. There are, for example, several simulations and mentored practices in medicine and aviation when life is on the line. The context must not be entirely rigorous in development. While simulations and virtual worlds may create real depth, the minimum contextualization necessary is often better to support abstractions and transfer them to other situations (Quinn, 2016). It also works to make it more affordable. We know that foreign content may interfere cognitively, so it is essential to work on the elements that communicate a context and the factors that trigger the action. • How can the context of IRL be exploited? While opportunities to build context are still present, we also see the opposite occur (Quinn, 2016). Because of workrelated learning, there are more opportunities to learn. We can locate and assist the learner. So, we can have a learning challenge in either a particular physical location (say, a library or an office) or a particular part of the software. There are several benefits to contextualized learning (Quinn, 2016). First, we reduce the transfer distance if this is the real context. In a context where it arises, we can imitate an actual situation. Therefore, we do not have to give as much content to transmit a specific situation. Moreover, of course, we do this in developing work and mentoring. However, we can take it further. Nowadays, our software can anticipate our circumstances. An application can track the progress of a learner. When combined with the knowledge a learner has, the learning can be customized to meet the needs of each individual. A task, precisely, could also be provided as a reminder. • Do not Confuse Performance Support and Learning. You should be aware that learning and performance support are different here. Our software has intelligence that can know what you are trying to do, context-sensitive support to provide hints and tips on what is needed in this situation, and context-sensitive assistance to help with your effort. We can find a tutorial video on how to perform this task just as easily. Learning is a side effect and not the primary aim in both instances. It is, however, not just learning. In order to learn about how and why this is the right solution, a different situation would be needed to layer on some additional information (Quinn, 2016).

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This leads us back to marketing: the correct content for the right person (and more: on the right devices at the right time and place). Content models need to be granularized and engineered to deliver them systematically. Moreover, these capabilities are available now, and it is time to start marketing and start treating content as a discipline. Mobile learning will be focusing on contextual learning and performance support in the future (Quinn, 2016). It is becoming more common for sensors that come with these devices to provide multiple types of assistance, such as pinpointing a user’s location (not just GPS) and providing customized assistance based on the situation (such as when and where the user is). Plus, comprehending your learning goals could lead to implementing contextualized learning. It is vital to learn content regardless of the context, but being aware of context, and using it, can provide opportunities to improve outcomes, like learning and performance (Quinn, 2016).

5.3

Why Context-Based Learning?

A CBL style proves students’ competence by testing their ability to remember facts and concepts. The text-based design model is outdated (Sabramowicz, 2016b). When everyone who has a cell phone has access to all the world’s knowledge, do not waste time re-inventing the wheel. It is essential to understand that you are not competing with information on the internet. So, what do you think the educator’s role will be in the future? The new role of the educator in this age of the internet is, therefore, to assist students in connecting concepts with their own experiences and with the natural world (Sabramowicz, 2016b). Enter context-based learning. So, you have to ask yourself as an educator (Sabramowicz, 2016a): • Where can I direct my students to look for information on this topic? • What do I have to contribute to making the information meaningful to them? • How can I incorporate and share experiences or stories that help explain what we are doing? • How can I ask questions to help learners come to their conclusions? • What else should I be inquiring about? The internet age does not require learners to have any more information. To distinguish what is credible, relevant, and helpful to cite, they need to be able to use the enormous amount of information they have at their fingertips. Context-Based Learning and Competence. Research has shown that students are not competent in a course that focuses on content (Sabramowicz, 2016b). It is important to note that “skilled” signifies much more than the basic knowledge or

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ability to recall essential facts. A learning process would help learners remember specific information and apply this information through creative, practical, or critical thinking in their lives. To learn and apply the acquired knowledge, a learner must take in and apply what they have learned (Sabramowicz, 2016b). A history course that only provided content would fail to do this if all the learners could do at the end was to recite many dates and names from memory. Being able to draw more significant meaning from the past events to understand better what is going on today is a prime example of competence here. All students benefit from this capability, regardless of subject matter. Context is missing—nothing to do with reality. Simply keeping the information in your head is not how people learn new skills -it is certainly not the most effective way to become informed about something- and the ability to recall facts is no guarantee of wisdom or skill. Why? Context is lacking in some places when teaching math (Sabramowicz, 2016b). An example of this is the mathematical illiteracy in the United States. This problem is especially pronounced in Brazil, where students are far behind their counterparts from many other nations. Why? Students in grades 1 through 12 are given no context about how math formulas relate to the real world, so they end up studying them for years without understanding their purpose (Sabramowicz, 2016b). They are central to programming, architecture, engineering, etc. The mathematical context, including when, where, and why to use these formulas, is finally understood by adults. This is like spending 12 years learning music theory, and your first piano lesson was tomorrow! Don’t you think it is not the most efficient way to learn? Everyone realizes that math is entirely useless. Though millions of people still do not understand math because of how it was taught. This was because it was never taught (Sabramowicz, 2016b). So, what can we do to avoid this pitfall in our training programs? Using content in real-world situations or at least those that simulate real-world scenarios, learners can be prompted to do so. Students will master the content if they understand its practical use. People’s natural curiosity will be stimulated by providing context. They will recognize the value of the material immediately. In order to accomplish this, they will make a more significant effort. They will be focused on one another (Sabramowicz, 2016b). To help clarify, you will need to create learning experiences that are more than just vehicles for information delivery. Learners who learn in an environment can explore and use the information to build connections between things. Imagine it as a busy little hive of curiosity! You have to imagine you are teaching someone how to fix a car. Would you prefer they spend their time memorizing a repair manual? Or having them do manual labor, fix a garage full of wrecked cars, and equip them with tools? The garage will be more effective. It is a learning space (Sabramowicz, 2016b). Although auto repair may be one of the more hands-on subjects, it still bears mentioning that every subject has ways to provide context,

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and courses should never leave this vital aspect out. However, the quote “Context should matter all the time” is valid. Case Study: Creating Story-Based Learning Content—The Layers of Context to Consider. Let us experiment with the concept of layering context in narrativebased learning content. Effective learning is best done in a real-world setting because it is most likely to be used there (Kiznyte, 2017). Learners’ needs change, so the content must be exciting and relevant to serve them. Facts do not fill an emotional void, whereas stories do. Using stories that can be animated, contain real actors, or be written to show real-world scenarios that make the topic matter to the audience is another method of integrating this approach into eLearning (Kiznyte, 2017). The learner can better understand abstract knowledge and data in this way. This encourages learners to get involved, stimulate conversations, and exchange information. Context layers for learning content based on stories. First, let us compare this situation to a movie. Movies deliver a message that the viewer can learn from. However, how do we get this message across? Many characters, settings, environments, and storylines connect. They help create connections to the key messages and promote a common understanding of the movie among all viewers. Different viewers have differing opinions on the movie, and this is because they have had different life experiences, etc. As a result, after seeing a movie, viewers often come away with various insights they can discuss. By viewing the movie together, they learn from the movie and each other. In order to develop a compelling story that teaches what is needed, a threelayered context breakdown (see Fig. 5.2) can be used (Kiznyte, 2017): Outer Layer: Storyline (Environment). Things to be taken into account: • Which storytelling technologies will be employed? (e.g., animation, actual video footage, etc.). • How much student engagement will there be? (e.g., with the instructor, other students, etc.). • Configuration of the story (what is the science area to be explained). Fig. 5.2 A simple three-layered division of the story context (Kiznyte, 2017)

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• Characters description (their role, character, and so on). We need to make sure that we are clear about the narrative form and interactivity because those factors will determine how much information we can present in the story, though we always try to keep our videos two to three minutes long. The most imaginative aspect is an author visualizing the story and its subject. In order to get learners to care about the topic, you need to pick an interesting character with whom learners can form emotional connections. Perhaps the primary setup for the topic would take place in the lab if the topic focuses on chemistry. Another option is if the subject is about novice learners, the character can be an inexperienced learner in their subject area. Middle Layer: Situation (Topic). Things to be taken into account: • • • •

What are the main points that we should focus on? What problem is there with the situation? How can we solve this? What are the most important findings?

It is essential to pass on the key messages during the ‘Situation’ layer of the plan. The easiest method for doing this is to invent a problem then propose a solution, after which the main character will experience some important realizations. To encourage the learner to examine the topic in more detail and consider the story more deeply, it is vital that the scenario conceal some insights. This is particularly relevant if the topic is some tool or management strategy. For example, you can take a common issue in the workplace and use the tool or method to solve it, i.e., the solution. Inner Layer: Perception of The Learner. Things to take into account: • The learner’s context (e.g., experience, knowledge, artifacts, and so on). • The option to explore the content in-depth. • Environment for sharing views with other students and teachers (e.g., blended learning, social learning, etc.). The majority of this layer depends on the learner, and the environment plays a smaller role. The target group of learners must be considered and their sharing of personal insights (or not share at all). People can step into the context of other learners (e.g., experience, knowledge, examples, stories from work) and learn from each other, which is the exciting facet of this layer. The same type of sensation one feels after watching a movie. This contextual learning helps students learn a topic and lay out a framework for their self-study later on (Kiznyte, 2017). Additionally, blended learning facilitates teachers in providing a shared context for all students, which allows for an in-depth explanation of the topic.

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You can approach the different contexts and create a story-based learning material (Kiznyte, 2017). Ultimately, the objective is not just to impart knowledge but also to get learners engaged in additional learning processes, whether by getting knowledge from others or studying independently.

5.4

Increasing Performance with Context-Based Learning

Content and curriculum are essential for learning (The Learning Guild, 2021). But just as significant? Context. In other words, the environment in which students gain new knowledge and use it. Contextual learning uses real-life and fictitious examples in teaching and training to contribute to learning instead of simply engaging with the concepts (The Learning Guild, 2021). CBL is cooperative, active, and engaging, helping connect and maintain the learner’s skills. More technologically, CBL is a physical environment, behavioral needs, and emotional indications that imitate or closely relate to the learner’s work (The Learning Guild, 2021). Tips for context building. It is not very complicated to create learning instances in a context, but it may require further consideration of building context. Try to incorporate more context into your learning solutions by trying the following tips (Bucklin et al., 2021): • Bring learning, if possible, to the environment of students themselves. If that is impossible, simulate the learning environment with exact tools, forms, and systems. • Watch students at the workplace and provide guided feedback. • Make it easy for learners to devise the scenario response/reaction (if it is not an easy job), ensuring that their behavior needs match their real job by using realistic role-play storyline scenarios. • To replicate the real world, use problem-solving and scenarios. • Use storytelling or simulation to improve the environment. • Create a pressure emotional context.

5.5

Context-Based Learning Environments

Students are the central focus of CBL environments. For students to comprehend an idea, they must place it in the context they are used to (Taconis et al., 2016). Contexts are created in the classroom with real-life or scientifically authentic situations and activities (Gilbert, 2006). Context-based education tackles some of the problems in education worldwide (Lyons, 2006). School curriculums tend to be overwhelmed by isolated facts, mainly based on teaching with little or no relation to the reality of students (Gilbert, 2006). Students often perceive their learning environment as having low relevance and great theoretical complexity.

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CBL environments rely on context to construct a curriculum and implement a classroom teaching plan to deal with these problems (Pilot & Bulte, 2006). Contexts connect and bring coherence by connecting economic life and society’s day-to-day realities and issues (Taconis et al., 2016). Doing this frequently makes it hard to complete individual tasks within a single lesson instead of more traditional lessons where such tasks are often separate. CBL environments also support students in the practice of science (Schwartz et al., 2004). CBL environments are characterized by realistic contexts that make the lesson-related ideas and concepts relevant and significant. CBL environments equip students with the knowledge and skills that help them grasp deeper insight and understanding about their world (Taconis et al., 2016). Some believe that a relevant and challenging context is an excellent place to start learning because it gives context and meaning to the content—this concerns practices and results. Contexts and context use must meet specific requirements in order to be effective. Adequate contexts should provide “a consistent structural significance for something new that is placed in a broader context” (Gilbert, 2006). It is crucial to be effective that contexts are recognizable, comprehensible, relevant, valuable, inspiring, and linked to the student’s background (Gilbert et al., 2011). In addition, CBL environments should include a handling and productive compartmental environment that enables or encourages discussions on understanding structures (Gilbert et al., 2011). CBL is a coherent package in which context use is the critical feature. There are specific accompanying characteristics for the context-based learning environment. The educational effectiveness of these features is critically important (Pe¸sman & Özdemir, 2012). First, a clear constructivist perspective is adopted in context-based education. In line with current education research, learning is understood rather than “copying” knowledge from other sources as a process in which students create their meanings through their experiences (Bennett, 2005; de Putter-Smits et al., 2013). Within context-based education, knowledge-building is generated in the context and context-related tasks as something you need to know (Pilot & Bulte, 2006). CBL environments are constructivists (Taconis et al., 2016). In the majority of cases, students collaborate for much of the time. Based on students’ existing knowledge, CBL environments should promote questions and recompense finding answers (Bennett & Holman, 2002; Bennett et al., 2007). Hence, concepts can only be learned within and derived from their context. In contrast, examples from other contexts and situations are often used to facilitate transfer to new contexts in CBL environments. All this can be best done in learning settings that include ‘collaborative learning,’ with ample possibility for ideas exchange and understanding sharing (Taconis et al., 2016).

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The second significant and critical secondary aspect of CBL environments is active learning (Gilbert, 2006; Parchmann et al., 2006). The focus on active learning reflects the constructivist view of contextual education (Gilbert, 2006). Active learning requires students to develop a feeling of ownership and some room for their learning responsibilities. Decisions should be made on what, when, and how to learn within pre-set limits (de Putter-Smits et al., 2013). CBL environments generally emphasize debate and collaboration, while specific CBL environments involve students in a learner community that reflects as authentic as possible professional communities (Taconis et al., 2016). Teachers also act as designers and implementers of teaching material in CBL learning environments (Duit et al., 2007), (Parchmann et al., 2006; Martin Anton Jozef Vos et al., 2011). Four Models. According to Gilbert (2006), four different models are based on context using: 1. 2. 3. 4.

context as the immediate application of concepts, interdependency between concepts and applications, context of personal mental action, context as involving the social circumstances.

Model 2 combines contexts and concepts. Contexts in Model 1 are only applied to the content learned previously. There is little relation between the context and the concepts learned. A different series of concepts can have meaning in various contexts, and concepts in different contexts can have different meanings (Taconis et al., 2016). In other words, the concepts apply meaningfully to the contexts, give them a better insight and help find specific contextually relevant answers. Model 2 helps students understand the context and make concepts significant and relevant. However, there is no reasoning or reason for learning in the context. In these models, “the notion of ‘context’ is largely decorative: it is certainly not central to the learning that takes place” (Gilbert et al., 2011). In Model 3, the context takes the form of a realistic situation with a challenging problem that can be (only) resolved by mastering the target knowledge. The situation/problem, therefore, provokes and directs learning, but the environment of the behavior is not a context (Taconis et al., 2016). Models 2 and 4 focus on context. Model 4 fully recognizes the social dimension of a context (Gilbert et al., 2011). The context also defines the behavioral environment, for example, a unique role that the learner must play in a specific social context. Challenges. The practical application of the school curriculum must overcome a host of difficulties in learning environments (Taconis et al., 2016). First, because the context-based approach is still a work in progress, context information may still need refinement. One particular matter is important to mention, namely the connection between the subject knowledge obtained and the context. This is traditionally viewed as an issue of transferring (formalized) scientific knowledge. However, the

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problem is defined differently from a socio-cultural or competency-based perspective. There is an ongoing debate about re-contextualizing scientific knowledge and whether or not de-contextualized knowledge is valuable (Oers, 1998; Abreu, 2002; Gilbert et al., 2011; King, 2012). A further challenge is getting an accurate measure of CBL outcomes (Taconis et al., 2016). Context-based education typically produces inferior learning outcomes compared to evaluative studies. Classical tests have difficulty assessing context-based education’s valuable yet context-sensitive and sometimes idiosyncratic learning outcomes. The necessity of appropriate testing is stressed by Pilot and Bulte (2006). While “de-contextualized” knowledge and particular rewarding competencies in a context-based education, such as “explaining phenomena scientifically,” should be part of testing, it should not be the only component (Fensham, 2009; Sadler & Zeidler, 2009). Thirdly, there is a problem with educational innovation itself. It is not always easy to implement new ideas in the classroom. The transition from traditional science instruction to context-based instruction for learning environments research presents a significant challenge. Convincing teachers who hold different beliefs about good teaching or the advantages of context-based learning environments is a challenge: organizing the availability of materials, support, and teacher professional development (Taconis et al., 2016). Research in the learning environment holds excellent potential for initiating and monitoring innovation, but relatively few studies link research methods and tools in the learning environment to actual educational innovations (Fisher & Khine, 2006; Fraser, 2015). Research in learning environments could contribute critically by describing teachers in the actual learning environment, analyzing how and why teachers are successful or failing in creating it, and monitoring the progress of innovation. As a teacher, you can use these to support your decision-making and develop ideas for future development. Teachers and Context-Based Learning Environments. Both providing schoolwide learning environments and constructing classrooms full of contextual learning opportunities rely on teachers (Taconis et al., 2016). Teachers tend to accept new curricula and teaching materials from new programs and syllabi. Teachers are rarely apprised of the ideas behind the innovation or receive additional training (Vos et al., 2010). A CBL environment can only be created if teachers are involved (Yerrick et al., 1997; Mansour, 2009. There are some context-based innovation projects (PLON and ChiK) in which only a select group of teachers appear to be directly involved in creating teaching materials based on context. Vos and colleagues (2010) investigated whether or not the beginning and experienced teachers could successfully create context-based learning environments when confronted with context-based teaching materials. Other than direct instruction in the use of materials, they say that teachers should comprehend the framework of the materials, the values of education, and the necessary abilities to establish a context-based learning environment (see (Vos et al.,

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2011)). De Putter-Smits and colleagues (2012) researched the skills needed to create CBL environments in the classroom, which they found teachers must-have. The following list contains a preliminary list of needed teaching competencies (Taconis et al., 2016): • • • •

to grasp the context of the situation to be good at handling education contexts be prepared to concentrate on more than formal scientific knowledge to support student self-determined learning and regulation of the learning process • adapt the learning environment to help facilitate various learning paths (redesign) • to create effective exams for a fair and complete assessment • to advocate and show how context-based approaches are used in their schools and colleagues. Context-based education implementation at the school level is more difficult without this last trait. Furthermore, teachers should also be prepared to fulfill more general demands of effective learning environments and constructivist learning environments, such as having quick and adequate feedback, getting to know the students, and having a learner-centered teaching approach.

5.6

Assessment in Context-Based Teaching and Learning

Assessment in a context-based teaching approach is critical (Tout, 2016). Determining the learner’s level of understanding is crucial to student engagement and enhanced learning (Tout, 2016). Teachers can increase their understanding of individual students’ strengths and weaknesses, their ability to set and meet goals, and where to direct their attention, resources, and expertise, as well as how to adapt their teaching practice in order to promote greater student success when good assessment practices are used. Planning the assessment(s). Students need to decide when and how to assess and prove their success in the planning phase. Much of the learning and achievement of results occur during the task set for students in an applied, investigational, contextbased approach (Tout, 2016). Consequently, it is essential to plan the evaluation and record and document the results. You need to be prepared to monitor, record, and document learners’ achievements as they happen. If a single summative evaluation event is held, these results may be overlooked or missed; therefore, all the learners’ knowledge, accomplishments, and skills may not be practiced (Tout, 2016). Not all tasks or research must be viewed as evaluable tasks—this depends partly on the curriculum you teach and report on.

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The task itself forms the content of the assessment. A holistic skill development and assessment approach is best achieved by learning the skills necessary to succeed in different environments through project-based activities. The primary content of the assessment tasks and activities will come from the assigned assignments or projects, and they will be based on contextual factors (Tout, 2016). Learners should have the freedom to set their own pace, and their performance should be tracked in a way that reflects their abilities. This should be decided upon before the student’s work is assessed. Students are typically assessed by sitting in a classroom, but assessment in the real world is preferable, such as making shopping lists, cooking and measuring ingredients, or following directions (Tout, 2016). However, it is not always possible to rely on just one method, and an array of strategies should be used to accomplish assignments and assessments. Assessment could occur while students participate in simulated projects, role plays, or classroom tasks. Assessment for learning. If you are working with students who are doing a task, you can prepare to assist them by intervening and offering help to people who need it, especially if they have identified gaps in skills they need to develop (Tout, 2016). Evidence collection and student results recording. The critical aspect of assessment is often recording and documenting performance and outcomes in an investigative, context-based approach. A series of evaluation approaches and methods are suitable for recording student results for a contextual approach to teaching. These might include (Tout, 2016): • Products or reports that students have produced, including digital photos and videos of them working on their assignments (thanks to more readily available technology); • Brochures, posters, and presentation software used to document the task and its results; • Folios containing the results of the work or the results of the research; • oral submissions; • Observation and reporting of practical tasks by teachers and peers; • Student planning, research, and work reports; • student self-assessment, reflection, diary and journal entries; • teacher observations and checklists; • teacher review and study. • Sheets of observations. A good strategy for undertaking project-based activities is for teachers to create a monitoring note sheet mapped in advance according to the corresponding curriculum descriptions so that a skillful student can easily indicate the results obtained. One suggestion is to create an editable Checklist in Microsoft Excel, where the names of each student are shown according to your detailed curricula or performance criteria or your rubric (Tout, 2016).

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Contextual Teaching and Learning (CTL)

Teaching and learning that places content in context help teachers connect the information to real-world scenarios, motivating students to apply their knowledge to their lives as family members, citizens, and workers. It also necessitates the dedication to hard work that learning requires (Berns & Erickson, 2001). Students’ understanding is enhanced by understanding the learning process. They use their experience and knowledge to accomplish learning goals. They acquire usable knowledge and skills by learning in integrated, multidisciplinary ways and appropriate contexts (Berns & Erickson, 2001). As a result, CTL enables students to relate what they are learning to real-world situations in which knowledge can be applied. CTL needs to build on solid educational principles, theories, and practices to be regarded as a legitimate pedagogy to be implemented by students. The CTL builds on the literary bodies that include Dewey, Piaget, Bruner, and other theories and writings (Berns & Erickson, 2001). It, therefore, extends past thought, theories, tests, and texts. More contemporary pieces included Resnick and Hall synthesis and Borko and Putnam identified themes (Berns & Erickson, 2001). The following are examples of CTL-related theories and themes (Berns & Erickson, 2001): • Knowledge-based constructivism: Direct education and constructional activity in achieving learning targets can be compatible and effective. • Effort-based learning/Incremental theory of intelligence: Increasing efforts leads to more ability. This theory contradicts the idea that you cannot change your ability. The pursuit of learning objectives motivates people to engage in learning-based activities. • Socialization: Children will learn about standards, values, and societies’ knowledge by raising questions and accepting challenges to find solutions that are not immediately evident. This social nature of learning also helps to determine learning objectives. Learning is a social process, which needs to be taken into account by social and cultural factors during education planning. • Situated learning: While information and education can be found in many different places, they are at their core tied to a specific physical and social context. The settings for instruction could include homes, communities, and workplaces, depending on the purpose of the learning and the goals to be achieved. • Distributed learning: The knowledge of the individual, other persons, and various artifacts may be viewed as distributed or spread over each person, not just as individuals’ property. Thus, people must share knowledge and tasks as an integral part of learning. The different theories used in the CTL framework are based on these principles. Characteristics of Contextual Teaching and Learning. We can get a better understanding of CTL by looking at its characteristics. It includes the interdisciplinary

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and contextual nature of the concept, various methods for implementation, ways to address the needs of students, and the role of teachers (Berns & Erickson, 2001). 1. Interdisciplinary learning, problem-based learning, and external learning contexts. Situation and problems in the real world rarely constitute one discipline. To be CTL-friendly, learning must be broadened across disciplines in order for students to gain a real perspective. They see the relationship between knowledge and skills now and in the future. Also, the students are expected to recognize problems, make sound decisions, and think creatively, enabling them to better cope with life situations, such as those at work. Therefore, if students participate in a research project in a classroom where they study townships to transform a natural preserve into housing near the school, they must learn the language, math, and scientific information and apply it while tackling the agricultural issues inherent in such a situation. Whether the agriculture teacher is alone in this or a group of teachers, academics, and career and technical education (CTE) subject area experts are collaborating; the learning goals will extend beyond the boundaries of one subject area. In CTL, students learn to connect with internal and external contexts through their experiences. After starting with their previous knowledge, prior experiences, and current classes and situations, they put on other environments, such as school, home, workplace, and the Internet. Through these experiences, students’ understanding deepens, increasing their ability to retain competencies and apply them in real-world scenarios. Because of this, the blending of academic and vocational education increases the comprehension of the academic subject matter and the technical subject matter of the career and technical study. All teachers can employ CTL to help students achieve these integration efforts in either career-technical or academic teams, leading to better schools, classes, and overall education. 2. CTL Implementation Approaches. CTL should be implemented using a variety of teaching methods. Five teaching approaches with context as a central part have developed over the years. The students are stimulated to do more than simply sit and listen. There are no different solutions to this problem. They can be used by themselves or in conjunction with others. As different definitions have been proposed in the literature, the following descriptions are intended to define the concepts and serve as a foundation for implementing CTL: • Problem-based learning—is a strategy that gets learners involved in complex, problem-solving investigations that blend various content areas. This method includes collecting data, synthesizing that data, and presenting findings to the general public. • Cooperative learning—an approach that uses small groups of learners to work together to meet the learning goals. • Project-based learning—an approach that focuses on core discipline concepts and principles, involves students in problem-solving research and other

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significant tasks, enables students to build their learning autonomously, and culminates in realistic product designs. • Service-learning—an approach that implements newly acquired knowledge and skills in the community in practical terms through projects and activities. • Work-based learning—an approach that integrates class material with workplace-like activities to benefit students and businesses. Cooperative education, work experience programs, internships (paid and unpaid), apprenticeships, in-school laboratories, simulations, and school-based enterprises are just some of the CTL approaches offered by various CTE (Career and Technical Education) instructional models. However, they do not use the CTL process as a default mode of operation. When developing their lesson plans, educators must consider the unique characteristics of CTL students. Career technical student organizations’ activities and projects may include various CTL attributes. In a nutshell, an FFA service project in a nursing home might, for example, be designed based on problem-based learning and might begin with the vague scenario, “A nursing home in town might have some needs.” The students would spend time in the nursing home, after which they would return to the classroom. They may notice that the residents’ rooms are not very colorful. After that, they split up into smaller groups and began working on the problem. They may write messages to each other, perform mathematical calculations for driving to and from the nursing home, communicate with school and nursing home administrators, and use whatever career-technical skills are designed to assist them in their daily lives (e.g., the preparation of floral arrangements, if a horticulture class). Afterward, the students would meet with the nursing home’s administrators to discuss the flowers’ placement. In this instance, different aspects of problem-based learning, project-based learning, and service-learning fuse into a student learning experience. CTL requires the application of these approaches more holistically than in the past. Therefore, the project would be designed to achieve specific learning objectives in mathematics, language, and horticulture, as set in the standards and curricula. Moreover, because this type of activity may already be a part of the CTE instructor or CTE student organization’s repertoire, the variation in how it is performed will determine the extent to which the project reflects CTL. 3. Factors addressing students’ individual needs. Teachers must address the following factors when implementing CTL approaches in their classrooms. These ideas provide a solid foundation for people’s knowledge based on cognitive research. Teachers have to: • Plan lessons that are suitable for students’ development. The relationship between curricula content and teaching methods must be based on the student’s specific social, emotional, and mental development levels. Therefore, it is necessary to consider the age of students, their different individual characteristics, and their social and cultural environment. For example, a high

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school senior’s readiness to learn and do may be quite different from that of a high school sophomore. • Include interdependent groups of students. Through small group learning, students learn from each other and develop skills in working collaboratively, including quality circles and other collaboration strategies. • Provide a self-regulated learning environment. Students must understand their strengths and weaknesses, set achievable objectives, and develop strategies to achieve their objectives. They become self-confident and competent as they learn these skills. They are now aware of the importance of thinking and thinking about options before plunging into the challenges of life. Teachers can also create an environment through self-regulated learning in which students reflect on how they can learn, approach work at school, deal with obstacles, and work harmoniously with each other. Students must also be able to contribute to the success of their group with CTL approaches that require group work. • Include student diversity consideration. A teacher’s job is to educate different kinds of students. A variety of factors is considered, including students’ racial and ethnic backgrounds, socioeconomic status, primary household language, and disabilities. Materials, for example, are evaluated for gender bias and stereotypes by teachers. They also plan and respond to language so that students can surmount language barriers in their studies. • Address the multiple intelligences of students. In order to use a CTL approach, specific students’ preferred learning styles must be considered. Gardner (1993) identified eight learning directions: hearing or seeing language, involvement, music, numbers, visualization, human movement, interaction with others, and leading. Teachers integrate the CTL approach into their instruction by including strategies that work for students of varying intelligence. • Include questioning techniques that increase student learning and improve problem-solving and other skills in higher-order thinking. Adequate kinds and levels of questions must be asked for CTL to achieve its objectives. Questions must be carefully planned so that students and participants in the CTL approach can achieve the intended level of thinking, answers, and actions. • Include genuine evaluation. The authentic evaluation assesses a student’s knowledge and complex thinking instead of a rote recall of factual evidence. The interdisciplinary nature of CTL requires an assessment of the measurement of knowledge and skills in several disciplines. 4. Role of the Teacher. Teachers must plan, implement, reflect, and revise their lessons for the CTL approaches to student success. These plans are built on the CTL’s principles and methodologies, which call for teachers to take on the following roles: facilitator, organizer of the teaching/learning/assertion process, role model, learning mentor, content specialist, and knowledge dispenser. Individual CTL implementations may be possible, but they are more likely to yield

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greater interdisciplinary learning if teachers work together. Teachers who want to succeed with CTL should understand its various aspects. Benefits of Contextual Teaching and Learning Method. Of course, there are many things to discuss in the learning process, so there is no point in discussing them forever. You can see there are many other things worth discussing and examining, such as the use of learning methods that match the material, the readiness of students to learn, the availability of books as learning guides, and the learning infrastructure. Teachers can be disappointed, particularly if they have a good understanding of student learning about instructional materials. Education stakeholders have tried for decades to improve the quality of their education. One of those efforts is achieved through modifications and improvements in the learning methods. Lectures and question-and-answer techniques that are traditional learning methods have remained essentially unchanged, but they should be used in conjunction with other learning methods so that learning is not dull. The role of teachers is also vital in determining the success of learning and changes and improvements in learning methods. The Contextual Teaching and Learning Method is an educational method that helps teachers to relate the materials they teach to real-world situations and can enable students to link the knowledge they learn through daily life. It is thought to increase the interest of students in learning. Different efforts were made to increase students’ interest in the subject, making students focus on learning. In order to implement contextual learning, seven aspects must be strictly observed (Berns & Erickson, 2001): 1. Constructivism: A Constructivist approach to knowledge is the backbone of the contextual knowledge method, which results in expanded knowledge built piece by piece, not all at once. Students’ actual knowledge is a product of their own making. The critical point is that the learning process is embedded in the process of “constructing,” which does not mean accepting your information or learning by being actively involved in the process. 2. Find (Inquiries) is a critical activity-based contextual core activity. Inquiries measures include formulating the problem, observation, information gathering, analysis, and conclusion of work in various forms such as writings, reports, tables, images, and communications with teachers, classmates, and others to share results. A student’s education does not simply consist of acquiring facts; rather, it arises from the experience of discovering oneself. The teachers’ activities should be based on activities found in any of the materials they use. 3. The learning community: The teacher always acts as a facilitator in CTL-based classroom activities, encouraging multi-directional communication in a knowledge learning community. For example, the following can be achieved: (1) small group formation, (2) large group formation, (3) engaging experts in the classroom (via letters, figures, and others), (4) balanced classrooms, and (5) working with the community.

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4. Question: Usually, the depth and breadth of one’s knowledge and abilities can be gauged by asking a question. Encourage, guide, and evaluate students’ abilities with these questions. Students can use these questions to dig deeper into the data and verify the accuracy of the results, all while being guided along the way by the teacher. 5. Modeling: The next component is modeling, which means that a model can be imitated in study abilities and knowledge. The model could be anything from operating something to throwing a ball in sports, for example, paper, how to memorize the language of the UK, and so on. 6. Authentic evaluation: True assessment collects data that describes how students are progressing in their education. Students also do assessments throughout the period and do them at the end. The assessment you want is available from various sources, including assignments, quizzes, papers, demonstrations, reports, and so on. The assessment of authentic truth does, in essence, illustrate the entire learning process from start to finish. 7. Reflection: A reflection is the last one in the contextual method. Have you disclosed what was done in the past? If you have ever reflected on it, you have done so. A core activity of reflection is looking back, reminiscing, and creating or constructing the experience. Reflection is a response to experience, the activity of living, and new knowledge that has been gained, all of which is reflected on. Students feel they are getting something meaningful from learning about new ideas. A reflection can be made for any single lesson, chapter, or theme on an hour-by-hour, chapter-by-chapter, or theme-by-theme basis. The following advantages and disadvantages of the Contextual Teaching and Learning processes methods that my auto-resume from several sources. Excess 1. Learning to be more significant and real. Developing a more profound and authentic existence. Students must comprehend the link between the school’s learning environment and everyday life. This is significant because, with appropriate material found in real life, not only will the students’ learning be functional, but the students will have permanently engraved the material in their memories. Is one that will not easily be forgotten; 2. With Constructivism (in which students were able to discover solutions themselves) embraced by CTL, students have been able to learn more while simultaneously being able to help reinforce ideas. Students are expected to learn through experience, not through memorization, under basic philosophical Constructivism. Weakness 1. The teacher is more intensive in the Guide. This is because CTL methods eliminate the traditional role of teachers as information centers. The role of the

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teacher is to work with students as a team, discovering new knowledge and skills. Because students are individuals, they are seen as such. A person’s study habits are bound to be influenced by their level of maturity and how much they have done before. As such, the role of the teacher is not to act as an instructor or dictator, but instead as a supervisor who aids students in the understanding of their progress and where they are at in their development. 2. Rather than dictating how students should approach learning, teachers encourage them to develop and test out their approaches to learning. In this context, teachers unquestionably require additional support and attention to help students achieve their learning goals in light of the new context.

5.8

Conclusion

Content and curriculum are crucial when it comes to education. However, what is just as crucial? Context. In other words, this is the setting in which students acquire and put their new knowledge to use. Real-world examples and fictitious ones can be used in teaching environments to help students learn by experiencing the subject rather than merely reading about it. CBL is a student-centered approach to teaching and learning that uses scenarios to mimic the social and political context of a student’s current or future workplace. Using CBL, students can better connect with and retain the skills they learn. Besides CBL, students can apply their knowledge in real-world contexts by utilizing contextual teaching and learning (CTL) methods.

References Abreu, G. (2002). Mathematics learning in out-of-school contexts: A cultural psychology perspective. In Handbook of international research in mathematics education (pp. 323–353). Allen, R. B. (1990). User models: Theory, method, and practice. International Journal of ManMachine Studies, 32(5), 511–543. Baudrillard, J. (1995). The Gulf War did not take place. Indiana University Press. Belt, S. T., Evans, E. H., McCreedy, T., Overton, T. L., & Summerfield, S. (2002). A problem based learning approach to analytical and applied chemistry. University Chemistry Education, 6(6), 65–72. Bennett, J. (2005). Teaching and learning science: A guide to recent research and its applications. Bennett, J., & Holman, J. (2002). Context-based approaches to the teaching of chemistry: What are they and what are their effects? In Chemical education: Towards research-based practice (pp. 165–184). Bennett, J., Lubben, F., & Hogarth, S. (2007). Bringing science to life: A synthesis of the research evidence on the effects of context-based and STS approaches to science teaching. Science Education, 91(3), 347–370. https://doi.org/10.1002/sce. Berns, R. G., & Erickson, P. M. (2001). Contextual teaching and learning: Preparing students for the new economy (No. 5, pp. 1–8). Borgmann, A. (1984). Technology and the character of contemporary life. The University of Chicago Press.

References

113

Brusilovsky, P. (1999). Adaptive and intelligent technologies for web-based education. Special Issue on Intelligent Systems and Teleteaching, Künstliche Intelligenz, 4, 19–25. Bucklin, B. R., Brown, J., & McCalpin, A. L. (2021). Increasing performance in the automotive industry through context-based learning. Cassirer, E. (2006). An essay on man: An introduction to a philosophy of human culture. de Putter-Smits, L. G. A., Taconis, R., & Jochems, W. M. G. (2013). Mapping context-based learning environments: The construction of an instrument. Learning Environments Research, 16, 437–462. https://doi.org/10.1007/s10984-013-9143-9. Duit, R., Mikelskis-Seifert, S., & Wodzinski, C. T. (2007). Physics in context—A program for improving physics instruction in Germany. In Contributions from science education research (pp. 119–130). Elton, L. R. B., & Laurillard, D. M. (1979). Trends in research on student learning. Studies in Higher Education, 4(1), 87–102. https://doi.org/10.1080/03075077912331377131. Fensham, P. J. (2009). Real world contexts in PISA science: Implications for context-based science education. Journal of Research in Science Teaching, 46(8), 884–896. https://doi.org/10.1002/ tea.20334. Fisher, D. L., & Khine, M. S. (2006). Contemporary approaches to research on learning environments. Fraser, B. (2015). Classroom learning environments. In Encyclopedia of science education (pp. 154–157). Gallistel, C. R. (2012). Context-based learning. In Encyclopedia of the sciences of learning (pp. 799–802). Gardner, H. (1993). Multiple intelligences: The theory in practice. Basic Books. Gilbert, J. K. (2006). Chemical education on the nature of “Context” in chemical education. International Journal of Science Education, 28(9), 957–976. https://doi.org/10.1080/095006906007 02470. Gilbert, J. K., Bulte, A. M. W., & Pilot, A. (2011). Concept development and transfer in contextbased science education. International Journal of Science Education, 33(6), 817–837. https:// doi.org/10.1080/09500693.2010.493185. Hansman, C. A. (2001). Context-based adult learning. New Directions for Adult and Continuing Education, 2001(89), 43–52. Hennessy, S. (1993). Situated cognition and cognitive apprenticeship: Implications for classroom learning. Studies in Science Education, 21, 1–41. https://doi.org/10.1080/03057269308560019. Johnstone, A. H. (1991). Why is science difficult to learn? Things are seldom what they seem. Journal of Computer Assisted Learning, 7(2), 75–83. King, D. (2012). New perspectives on context-based chemistry education: Using a dialectical socio-cultural approach to view teaching and learning. Studies in Science Education, 48(1), 51–87. https://doi.org/10.1080/03057267.2012.655037. Kiznyte, J. (2017). The layers of context to consider when creating story-based learning content. ELearning Industry. https://elearningindustry.com/layers-of-context-story-based-learningcontent. Lyons, T. (2006). Different countries, same science classes: Students’ experiences of school science in their own words. International Journal of Science Education, 28(6), 591–613. https:// doi.org/10.1080/09500690500339621. Mandl, H., Gruber, H., & Renkl, A. (1993). Misconceptions and knowledge compartmentalization. Advances in Psychology, 101, 161–176. Mansour, N. (2009). Science-technology-society (STS): A new paradigm in science education. Bulletin of Science Technology Society, 29(4), 287–297. https://doi.org/10.1177/027046760933 6307. McLuhan, M. (1964). Understanding media: The extensions of man. McGraw-Hill. Mises, L. Von. (1957). Theory and history. van Oers, B. (1998). From context to contextualizing. Learning and Instruction, 8(6), 473–488. Overton, T. (2007). Context and problem-based learning. New Directions in the Teaching of Physical Sciences, 3, 7–12.

114

5

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Parchmann, I., Gräsel, C., Baer, A., Demuth, R., & Ralle, B. (2006). “Chemie im Kontext”: A symbiotic implementation of a context-based teaching and learning approach. International Journal of Science Education, 28(9), 1–43. https://doi.org/10.1080/09500690600702512. Pe¸sman, H., & Özdemir, Ö. F. (2012). Approach-method interaction: The role of teaching method on the effect of context-based approach in physics instruction. International Journal of Science Education, 34(14), 2127–2145. https://doi.org/10.1080/09500693.2012.700530. Pilot, A., & Bulte, A. M. W. (2006). Why do you ‘need to know’: Context-based education. International Journal of Science Education, 28(9), 953–956. https://doi.org/10.1080/095006906007 02462. Putter-Smits, Lesley G. A. de, Taconis, R., Jochems, W., & Van Driel, J. (2012). An Analysis of teaching competence in science teachers involved in the design of context-based curriculum materials. International Journal of Science Education, 34(5), 701–721. https://doi.org/10.1080/ 09500693.2012.656291. Quinn, C. (2016). Understanding the importance of context in your learning solutions. Litmos.Com. https://www.litmos.com/blog/articles/understanding-the-importance-of-contextin-your-learning-solutions. Rayner, A. (2004). Reflections on context-based science teaching: a case study of physics for students of physiotherapy (pp. 169–172). Reid, N. (2000). The presentation of chemistry logically driven or applications led. Chemistry Education Research and Practice, 1(3), 381–392. https://doi.org/10.1039/B0RP90018D. Sabramowicz, A. (2016a). Content-centered courses: How google has destroyed traditional education. ELearning Industry. https://elearningindustry.com/content-centered-courses-google-des troyed-traditional-education-thats-good-thing. Sabramowicz, A. (2016b). Context-based learning: The secret ingredient to all successful courses. ELearning Industry. https://elearningindustry.com/context-based-learning-secret-ing redient-successful-courses. Sadler, T. D., & Zeidler, D. L. (2009). Scientific literacy, PISA, and socioscientific discourse: Assessment for progressive aims of science education. Journal of Research in Science Teaching, 46(8), 909–921. https://doi.org/10.1002/tea.20327. Salomon, G. (1979). Interaction of media, cognition, and learning. Jossey-Bass. Schwartz, R. S., Lederman, N. G., & Crawford, B. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science Education, 88(4), 610–645. https://doi.org/10.1002/sce.10128. Shaffer, D. W. (2006). How computer games help children learn. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Taconis, R., den Brok, P., & Pilot, A. (2016). Teachers creating context-based learning environments in science. The Learning Guild. (2021). How to increase performance with context-based learning. Learningguild.Com. https://www.learningguild.com/sponsored/index.cfm?id=1631. Tout, D. (2016). Assessment in context-based teaching and learning. Teachermagazine.Com. Vos, M. A. J., Taconis, R., Jochems, W. M. G., & Pilot, A. (2010). Teachers implementing contextbased teaching materials: A framework for case-analysis in chemistry. Chemistry Education Research and Practice, 11, 193–206. https://doi.org/10.1039/C005468M. Vos, M. A. J., Taconis, R., Jochems, W. M. G., & Pilot, A. (2011). Classroom implementation of context-based chemistry education by teachers: The relation between experiences of teachers and the design of materials. International Journal of Science Education, 33(10), 1407–1432. https://doi.org/10.1080/09500693.2010.511659. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. Westera, W. (2005). Beyond functionality and technocracy: Creating human involvement with educational technology. Educational Technology & Society, 8(1), 28–37.

References

115

Westera, W. (2011). On the changing nature of learning context: Anticipating the virtual extensions of the world. Educational Technology & Society, 14(2), 201–212. Yerrick, R., Parke, H., & Nugent, J. (1997). Struggling to promote deeply rooted change: The “Filtering Effect” of teachers’ beliefs on understanding transformational views of teaching science. Science Education, 81, 137–159.

6

Computational Thinking

Abstract

The computational thinking approach helps us think about the problem to solve effectively. This method divides the more significant problem into more minor problems (decomposition). It also provides information on how they solved the past problems (pattern recognition) and set aside unimportant information (abstraction). This method provides which steps are needed to achieve a solution (algorithms) and optimizes those steps (debugging). This method makes the learner understand the problem from its structure, which helps them solve problems correctly. Computational thinking must be a part of the subjects like math, science, and arts. The main objective is to encourage the learner to become a computer coder and master the art of thought, which helps them handle complex challenges during their lifetime. This chapter presents Computational thinking and its principles, learning strategies for developing computational thinking skills, and assessing computational thinking. Keywords

Thinking computationally • Computational thinking • Process • Skills Principles • Assessment • Computational thinking in the classroom

6.1

•

Introduction

Algorithmic thinking, or structured thinking, is used to produce appropriate output in response to an input, and the concept of it has been known as Computational Thinking (CT) since the 1950s (Denning, 2017). Recent attempts to rehabilitate the significance of CT are geared toward encouraging the general public to learn more about the nature of computational understanding, as it is considered a vital body of knowledge for dealing with the issues of the 21st Century (Angeli & Giannakos, 2019). In 2006 Wing revived the term and the concern in this field by defining CT

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_6

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as a process to solve problems, design systems, and understand human behavior by drawing on the basic concepts of computer science (Wing, 2006). The generality of this definition has made it popular, but it also inspired a more precise definition for use in CT education (ISTE & CSTA, 2011; Selby & Woollard, 2014). As a result of the last decade’s studies and practice, CT has become a significant interest in education. The last few years have seen growing curiosity about the role of CT education in children’s thinking skills and digital competencies and its use in K-12 schools (Angeli & Giannakos, 2019). Because of this requirement, CT and coding have become an essential part of school curricula in numerous countries in recent years. Coding has become a new literacy in countries like Estonia, Israel, Finland, and the United Kingdom, which have all incorporated coding into their educational systems (Hubwieser et al., 2015). According to Computer Science Teachers Association (CSTA), International Society for Technology in Education (ISTE), Cyber Innovation Center (https://cyberinnovationcenter.org/), and the National Math and Science Initiative, there are conceptual guidelines for CT Education that have been developed by these organizations, as well as others. Organizations such as “code.org” and “codeacademy.com” are also promoting coding by offering learning environments (Angeli & Giannakos, 2019). Though the literature generally agrees that CT entails considerable skills, such as problem decomposition (breaking down complex problems into simpler ones), developing algorithms (step-by-step solutions to problems), and abstraction, there is little information on the issues and challenges of designing appropriate learning experiences for CT competences. Despite current curricula presenting several issues, CT can address them (Shute et al., 2017). Students may be inspired to study computer science (Allan et al., 2010) and other STEM-related majors (Sneider et al., 2014) due to the presence of a practical CT skillset. Studies have also linked CT to creativity and innovation (Mishra et al., 2013; Repenning et al., 2015), and it is helpful in other STEM areas (Barr & Stephenson, 2011; Sengupta et al., 2013).

6.2

What is Computational Thinking?

CT is a mindset and skillset that anyone can benefit from learning and practicing, not just computer scientists. CT is based on the power and limitations of computing processes, whether a human or a computer carries them out. Computational methods and models give us the confidence to overcome problems and construct systems that we would not be able to take on our own (Wing, 2006). The riddle of machine intelligence is faced by computational thinking (Wing, 2006): • What are some things computers cannot do better than humans? And • What is one area where computers have an advantage over humans? The essence of it is in answering the question: What is computable? We know only some of the answers to those questions today. CT is crucial for computer

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scientists, but it is also essential for everyone. Every child should be equipped with CT and reading, writing, and arithmetic. The three critical aspects of CT are problem-solving, systems design, and understanding human behavior, and they draw from the fundamental concepts of computer science. The field of computer science is vast, and CT reflects that variety with a range of mental tools (Wing, 2006). In the event of having to resolve a particular issue, we might ask (Wing, 2006): • How hard is it to find the solution? and • What do you think is the best way to address this issue? Computer science is founded on a solid theoretical foundation, enabling precise answers to these questions. Claiming that a complex problem is an implicit admission of the machine’s power, the computer that will run the proposed solution. We need to consider these factors when considering the computer’s instructions, resource constraints, and working environment (Wing, 2006). When resolving a problem, we might ask if an approximate solution is acceptable, if we can use randomization to our benefit, and if we allow for false positives or false negatives. Algorithmic thinking is how we solve a complex problem by turning it into one we already know how to resolve, such as reduction, embedding, transformation, or simulation (Wing, 2006). Computing is the ability to think recursively. It is parallel processing. It is translating between code and data. It is a generalization of dimensional analysis and type checking. It understands the importance of both the expense and the efficacy of indirect addressing and procedure call. The recognition of the virtues and pitfalls of giving someone or something a different name is an attribute of this model. In addition to evaluating a program’s functionality and efficiency, it also evaluates its aesthetics and the system’s design for simplicity and elegance (Wing, 2006). CT is abstracting and decomposing problems and systems with complicated, large tasks. Concerns are compartmentalized. It selects a good representation for a problem or develops a model that represents the relevant aspects of a problem, making it solvable. The invariants it employs let it define the system’s behavior concisely and declaratively. It is the ability to confidently operate, change, and influence a complex system without fully understanding its intricacies. It is breaking up what needs to be done into manageable parts in anticipation of multiple users or pre-fetching and caching to handle future demands better (Wing, 2006). CT looks at worst-case scenarios by considering protection, recovery, and backup plans. It refers to gridlock and contract interfaces as contract deadlock. They are learning how to avoid race conditions when synchronizing meetings with one another (Wing, 2006). To discover a solution, heuristic reasoning is used in CT. It is the ability to learn, plan, and schedule when uncertainty is present. By processing data quickly, CT enables more efficient computations. It compromises processing power and storage capacity and between time and space (Wing, 2006). You end up with web pages, strategies for winning games, and counterexamples galore.

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By the time words like algorithm and precondition are part of everyone’s vocabulary, along with nondeterminism and garbage collection when trees are drawn upside down, and when algorithms have become a part of everyday life, CT will have become a way of life for everyone (Wing, 2006). CT has been found to affect other disciplines. The study of statistics has been fundamentally altered through the introduction of machine learning. Recent advances in data collection and analysis have led to statistical learning in many applications. The employment of computer scientists is on the rise in organizations of all types, including statistics departments. Colleges that have or are in the process of forming computer science departments are enthusiastic about incorporating statistics programs (Wing, 2006). CT will benefit biologists, so computer scientists are now paying attention to biology. The ability to search through vast amounts of sequence data to find patterns is only one of the ways that computer science aids biology. Computational models and methods that model the structure of proteins may help us understand how proteins work. Biology is being revolutionized by computational biology (Wing, 2006). Like the way game theory, nano computing, and quantum computing have revolutionized economists, chemists, and physicists’ views. These kinds of attitudes will be a part of everyone’s skill set, not just that of other scientists. CT is tomorrow’s equivalent to ubiquitous computing. CT is the future that has come to pass, thanks to ubiquitous computing (Wing, 2006). CT is a collection of methods that aid in problem-solving. While other problemsolving approaches yield procedures that people or computers can perform, this method yields an algorithm, a sequence of steps for accomplishing a task or solving a problem (McVeigh-Murphy, 2019b). The process of writing code and communicating with computers through algorithms underlies CT. So, what is CT in regards to coding? It is not quite that simple. Coding algorithms are logic-based steps that use digital tools to carry out different actions. Algorithms are used to code various digital tools, which are the tools of CT. The application of CT solutions is possible with the help of coding. However, CT gives rise to algorithms for both people and computers, applicable across a broader spectrum of scenarios. CT is about helping people with their large and small problems. CT helps us address complex problems by helping us identify the issues and come up with options. In turn, we can present the proposed solutions in a manner that can be understood by a computer, a human, or both. You can use computers to assist you in solving issues. Before addressing a problem, you need to first learn about the problem itself and the possible solutions. Using CT, we can accomplish this. The following characteristics are present in CT (Wing, 2006): • Conceptualizing, not programming: There is a difference between computer science and computer programming. It is more than being able to program a computer. It takes multiple levels of abstraction to do it right. • Fundamental, not rote skill: Basic life skills must be acquired to function in modern society. In education, Rote refers to a rigid, repetitive process. Not until

6.3 Thinking Computationally

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•

•

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computers are made to think like humans can human thinking be considered ordinary. Humans, not computers, think: CT is a human approach to problem-solving, not attempting to make humans think like computers. Humans are more creative and inventive than computers. It is human beings who bring life to computers. We can now take on challenges we would not have dared to take on before the advent of computing devices and create systems whose functionality is only limited by our imaginations. Complements and combines mathematical and engineering thinking: Building virtual worlds to create new systems beyond the physical world. Since the formal foundations of all sciences rest on mathematics, computer science is predisposed to incorporate mathematical thinking. Since computers interact with the real world, engineering is a fundamental computer science component. Computer scientists are forced to think computationally, not just mathematically, because of the limitations of the underlying computing device. Ideas, not artifacts: It is not just the physical artifacts we create in the form of software and hardware that will be in our environment and with us all the time; it is the computational concepts we employ to handle problems, manage our daily lives, and communicate and interact with other people. For everyone, everywhere: The term “computational thinking” will cease to exist as a distinct ideology once it becomes so embedded in everyday life that it goes unnoticed.

Adding CT to the list of core school subjects is a top priority for nations. The National Curriculum in England mandates that children receive a first-rate computing education that will enable them to understand and manipulate the world by teaching computational thinking and creativity. CT is a set of essential problemsolving skills and techniques that large companies like Google and Microsoft Research believe are valuable to software engineers not working in academia.

6.3

Thinking Computationally

Computation is different from programming (BBC, 2021). Computers do not, and cannot, think like people. When you look at it this way, programming essentially tells a computer what to do and how to do it. With CT, you can develop complete instructions for what you want the computer to do. When making a new plan before you leave your house, you use the previous experience to guide you; for example, if you promise to meet your friends at a place you have never been before, you probably plan your route ahead of time. First, you should think about which route offers the best options. Then, it is essential to pick the shortest, quickest route or one that passes your favorite store along the way. The next step is to follow the steps one by one to get to your destination. Planned work follows step-by-step instructions, just like the step-by-step instructions in programming. Breaking down a complex problem into simple and

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understandable is an important skill. You already have the skill, just in a different context. For example, you may have to decide what to do with your friends. You all have different preferences, and that means you will need to decide (BBC, 2021): • • • • • • •

whatever you might be able to do where you would want to go Who wants to take part in what? your previous successful projects The price of all of the different options. what the weather is predicted to do the amount of time you have.

Based on this new information, you and your friends will know where to go and what to do to keep most of your friends happy. Computer usage could also be used to aid in your efforts to gather and examine data, thus leading to the formulation of the most suitable solution, both now and in the future, if you want. Playing a videogame may present an example like this. In order to complete a level, you must have a good understanding of (BBC, 2021): • What objects are to be collected, how they may be collected, and how long they may be collected • where the exit is and where the best route to get it is • What kind of enemies are there, and what are their weaknesses? You can plan an efficient route to complete the level with this information. Programming your own computer game necessitates that you think about and answer questions like these. CT has been applied to solve a complex problem in both of these examples (BBC, 2021): • every complex problem has been divided into several small decisions and steps (for example, where to go, completion—decomposition) • only the appropriate details (e.g., weather, exit location—abstraction) have been addressed • previous similar problems have been addressed (pattern recognition) • to come up with a step-by-step strategy (algorithms).

6.4

The Computational Thinking Process

Computational thinking is a process that begins with curiosity and concludes with comprehension, which can then be replicated or automated for future problemsolving.

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Four fundamental concepts can be found in the CT process (Learning.com, 2019): • Decomposition: Break the problem down into smaller parts. • Pattern Recognition: identify the differences and interconnections among the data points. • Abstraction: Eliminate the excess details and find the essential information to solve the problem. • Algorithmic Thinking: Develop a system to solve the problem in a way that humans or computers can replicate. Beyond the problem-solving process, CT is also about the process itself. The process begins with data as input, and like a computer, we process the information through a series of steps to arrive at some sort of output to the problem. Through the computational approach, students are given a concrete answer to their question at the start of the lesson, and they can see how they arrived at that answer systematically. CT is integral to both the acquisition of knowledge and the development of higher-order thinking skills, regardless of whether it is in the form of learning that is ‘plugged in’ or ‘unplugged.’ Moreover, students have to be mindful and thoughtful throughout the problem-solving process, which helps them develop a more positive attitude toward learning and a more challenging attitude about themselves. CT must be taught throughout a student’s education because it is a skill that must be continually developed. To be more specific, we will be going into greater detail about each part of computational thinking and providing real-world examples of using them in any area of study. Decomposition. The computational power of thinking is deconstructing problems into more manageable pieces (Learning.com, 2019). Decomposition eases what would otherwise be a nearly impossible task. We encounter numerous smaller problems throughout our learning and everyday lives that we can deal with more effectively. Solving problems is easier by breaking them down (as shown in Fig. 6.1) because this allows us to consider every aspect, establish a firm foundation, and find a definitive solution. The decomposition of objects is something we do in our daily lives, even if we are unaware of it (Learning.com, 2019). Examples of decomposition in everyday life. When hosting a holiday dinner, you would use decomposition to select the menu, involve others in the kitchen, give out individual tasks, determine the cooking process, and schedule the event. Decomposition played a role in the grocery list you used to create for your holiday dinner, the route you chose to walk around the store, and the vehicle you drove to and from the store. To construct your strategic plan, you utilized decomposition, which helped you include your new program’s vision, strategy for gaining buy-in, annual goals, and all the other aspects that made up your plan.

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Fig. 6.1 The process of decomposition (Learning.com, 2019)

Examples of curriculum decomposition. Decomposition is an effective tool that helps us systematically approach projects and tasks. In addition, it is used in student education. Below are some ways (see Table 6.1) to emphasize these themes in a curriculum. Examples of Decomposition in Computer Science. Decomposition may become helpful when students are programming a new game to analyze and tackle code complexity (Learning.com, 2019). Students must consider everything from the characters and settings to the plot and various actions, deployment, and so on. We understand that you know how intricately our daily lives and how we handle problems are bound up with decomposition. Many students have already discovered this process, but they still need to understand how to spot it in action and Table 6.1 Examples of decomposition in curriculum Subject area

How to accentuate?

English language arts

In order to understand the primary ideas in a story, students must answer who the protagonist and antagonist are. Where does the setting take place? What is the problem? How can we solve this problem?

Mathematics

The students will split the shapes into smaller triangles to find the area

Science

Students learn about how the human body processes food by studying the various organs

Social studies

Students examine the various cultures, customs, and traditions that define their cultures through their studies

Languages

Learning the grammar of a foreign language is a process in which students break the sentence down into subject, verb, and object

Arts

After going over the scenes, students help build the set for a play by determining their required settings and props

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when to use it when they are overburdened with a task, project, or problem. Students learn to accept uncertainty and better deal with challenges by studying things that confuse them. Pattern Recognition. Pattern recognition refers to finding patterns. CT, meanwhile, allows people to recognize patterns as they work to solve broken problems (Learning.com, 2019). Patterns can be both similarities and differences that are shared. The idea behind this concept is critical to creating understanding in a complicated setting, and it goes beyond recognizing patterns in a series of numbers, characters, or symbols. In-depth analysis allows students to identify patterns and similarities among various facets (as shown in Fig. 6.2) of the larger problem. Examples of Pattern Recognition in Everyday Life. The foundation of our knowledge is pattern recognition. As babies, we learn how to understand the world around us using patterns and speaking and build our language skills. We also learned how to behave and make connections in this world. Additionally, scientists utilize pattern recognition to discover the origin of an outbreak by analyzing similarities between a series of cases. Fig. 6.2 Different pieces of a larger problem (Learning.com, 2019)

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Table 6.2 Examples of pattern recognition in curriculum Subject area

How to accentuate?

English language arts After observing different examples of sonnets, students begin to classify them by similarities Mathematics

Many students know how to solve slope and intercept problems by identifying the correct formulas

Science

Students can sort animals by their attributes and define characteristics common to each group

Social studies

Students gauge the extent of different economic trends’ impacts by examining the relevant data

Languages

To better comprehend the vocabulary, students group the different words of a foreign language by looking at their roots

Arts

Students break paintings down into groups based on the common aesthetics and key features found in each of them

Moreover, the technology (Artificial Intelligence and Machine Learning) that helps your Netflix recommendations and your chatbot friend on a website is pattern recognition-based. Examples of Pattern Recognition in Curriculum. Pattern recognition is useful in the classroom as well. Table 6.2 presents some examples of pattern recognition in the curriculum. Pattern Recognition in Computer Science. Pattern recognition also assists computer science students in recognizing similarities between decomposed problems. When working on a game, they may recognize objects, patterns, and actions. They save time and resources by reusing or slightly modifying the same string of code to apply to each of these (Learning.com, 2019). When students work to build comprehension in strange circumstances or the face of uncertainty, they learn to persevere by going through iterations and experimenting with alternatives, and they learn that it is OK to fail and struggle as part of the learning process. Abstraction. Pattern recognition is needed to break down complex, abstract figures. Also known as the generalization of patterns, abstraction helps equally find complexity, relevance, and clarity (Learning.com, 2019). Identifying and connecting the most significant aspects (as shown in Fig. 6.3) is accomplished by filtering out the extraneous and irrelevant. These activities then guide us to operate efficiently and accurately with various parts. The process of abstraction acts similarly to the selective filtering process our brains use, which allows us to focus on what is important to us while filtering out the excess information. Examples of Abstraction in Everyday Life. Abstract concepts can also be viewed as those prominent theories, such as Newton’s Laws of Motion, the Law of Supply and Demand, or the Pythagorean Theorem, that inform how we see the world

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Fig. 6.3 Different abstract views of a complex problem (Learning.com, 2019)

(Learning.com, 2019). These required everyone who came after them to consider broad, complex issues, to deconstruct the problem, to experiment, to find patterns amongst the experiments, and to eventually abstract the concrete knowledge to encapsulate it in these sterile statements, which protect us from the complexity and difficulty of the journey to these laws. Developing these laws required people to think about broad, complex concepts, break down problems, experiment with different approaches until they found a pattern, and abstract their findings into these sterile statements. Educators use abstraction when examining large data sets to focus on the most important statistics and trends. Additionally, teachers employ it when helping students complete assignments. Although there may be kids running around or making loud noises, the kids in need can tune that out until the situation reaches a peak of rambunctiousness and an intervention is required. Examples of Abstraction in Curriculum. Just like the other components of CT, abstraction occurs automatically and can be utilized throughout the curriculum to improve student learning (Learning.com, 2019). The following (see Table 6.3) are some of the thoughts.

Table 6.3 Examples of abstraction in curriculum Subject area

How to accentuate?

English language arts Students produce book reviews as a summary of the novels they read Mathematics

Students complete a survey and analyze the data, creating visualizations and reporting their findings

Science

By examining similar formulas and equations, students develop laws and theorems

Social studies

After studying articles about current events, students collect the most relevant facts and write a brief synopsis

Languages

After learning about the proper use of ‘to know’ verbs in French and the use of formal and informal ‘you’ in Spanish class, students make a personalized guide that instructs them when to use each of these grammatical constructs

Arts

The students compile a set of general rules by which they can describe common chord progressions

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Examples of Abstractions in Computer Science. A program can be simplified using abstraction, separating code into functions (Learning.com, 2019). Its simple design allows for the more streamlined implementation of algorithms and improved communication with digital devices. By encouraging abstraction, students can refocus on the problem that initiated the entire computational thinking journey and discover the essential details from the previous stages. Students can better understand problems when they comprehend abstraction, enabling them to persist, compute, iterate, and develop new ideas while facing complex situations. Algorithmic Thinking. Algorithms are sets of instructions for performing a wide range of tasks, from sorting data to automating tasks, and algorithmic thinking is the method for developing a new algorithm (Learning.com, 2019). Algorithmic thinking is a subset of computer science that creates programs and codes. Algorithmic thinking encourages students to create an algorithm to solve a problem to make it possible for humans or computers to perform the same task. This approach automates the problem-solving process through a range of systematic, logical steps (as shown in Fig. 6.4), using a defined set of inputs and producing a defined set of outputs. An algorithm is a solution that enables us to build a completely effective process that reaches a particular endpoint. It has already been established that this is a fundamental component of computational thinking. Teaching students to design algorithms will help them better communicate and comprehend the guidelines that will yield a consistent output. Examples of Algorithms in Everyday Life. Algorithms are a frequent occurrence in our lives, like CT and the other things we have already discussed. If you are a Fig. 6.4 A step-by-step process for solving a problem (Learning.com, 2019)

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Table 6.4 Examples of algorithmic thinking in curriculum Subject area

How to accentuate?

English language arts Students create a flowchart detailing how to tell if a colon or dash should be used in a sentence Mathematics

Students create a step-by-step approach to solving other problems when solving word problems

Science

Students identify the classifications of elements in the periodic table

Social studies

Students can describe a series of preceding events in history as leading up to a significant event

Languages

The students use new vocabulary and practice speaking skills to give directions to another student, whether they are getting coffee at a café or are finding their way around a classroom

Arts

After students draw a picture, they create instructions to help another student draw a similar image

novice chef or a connoisseur of frozen meals, you follow a recipe to cook food, which is an algorithm. The best part is that when you bust out in a dance routine, you are not only following a routine that mimics an algorithm, but you are also just plain awesome. Developing an algorithm and showing your inner computer scientist is a worthwhile endeavor, especially when you create a process for checking out books in a school library or instructing students on how to clean up at the end of the day. Examples of Algorithms in Curriculum. Though students do not need to practice coding or be given access to technology to develop their algorithmic prowess, these skills must be incorporated early in their academic careers. The following tips (see Table 6.4) help implement algorithmic thinking in various subject areas to kick things off. Examples of Algorithms in Computer Science. Of course, these are just simple examples; the algorithms used in coding are generally much more complex and sophisticated. Two examples (Learning.com, 2019) of such algorithms are provided below to contextualize computer science and programming. • Standardized testing and algorithms: Coding allows adaptive learning tools, which are frequently employed in classrooms today. One significant development has been the advent of adaptive assessment software that chooses questions based on a student’s ability to correctly and incorrectly answer questions. The next question for the student is slightly more difficult if they answer it correctly. However, if they give the wrong answer, the assessment will provide a simpler question. The procedure starts with a pool of questions and goes through an iterative algorithm. The pool is set back to normal after the answer. It continues to repeat. • The Omnipotent Google and Algorithms: The search results provided by Google are partly determined by the PageRank algorithm, which assigns importance to

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web pages based on the number of sites that link to them. Although there are roughly 1.5 billion websites with trillions of pages to count, Google can give us search results in under a second, thanks to their algorithmic intelligence. The ability to apply algorithms is what makes this possible. Algorithmic thinking encourages students to express themselves clearly and logically, regardless of how it is used in the classroom. The students gain the skill of persevering through various trials, challenges, and solutions. They use CT and metacognition to arrive at an algorithm (especially as algorithms get more complex). This process positively affects the students’ ability to think critically, express themselves eloquently, and investigate complex problems where there is no simple solution. Computational thinking in practice. A complex problem is one that we cannot solve readily at first glance. When used to solve complex problems, CT uses smaller, more manageable challenges to tackle the larger issue (decomposition). A set of simple steps or rules can be designed to address each of the smaller problems (algorithms). In addition, smaller difficulties can be dealt with individually, considering patterns of previous resolution and only focusing on the crucial data while avoiding irrelevancies (abstraction).

6.5

Key Skills for Computational Thinking

The development of skills in solving problems is linked to computing. Those who have looked into children’s engagement with the Scratch programming environment have identified both computational thinking skills and cognitive shifts (Sharples et al., 2015). • Experimenting and iterating—creating something, testing it, and making improvements as needed. • Testing and debugging—Detecting and correcting errors as they occur. • Reusing and remixing—Revamping older work and concepts. • Abstracting and modularizing—Analyzing the various ways the individual pieces fit together. • Expressing—Recognizing that this is a creative activity and expressing that. • Connecting—The power of making with and for others. • Questioning—Feeling empowered to question the world. These findings clarify that CT is much more than a set of procedures to be followed. It is a method of thinking, doing work, and solving problems.

6.7 Computational Thinking in the Classroom

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Principles of Computational Thinking

The science of computer thinking has been taught mainly at the college level, although it has entered the K-12 elementary school level in recent years in STEM’s focused education curricula. Computing thinking classes were first introduced as a broad introduction in computer science at Carnegie Mellon University in 2005. Although approaches to the study of CT differ, six main principles of computer thinking include (Stroud, 2021): 1. Computer connectivity: Understanding computer-human connectivity. 2. Developing Computational Artifacts: creating an algorithmic or computer model and the techniques necessary to create artifacts to solve problems. 3. Abstracting: Identifying and defining the use of information in a computational context and modeling these abstractions. 4. Analyze Problems and Artifacts: Evaluate the value and feasibility of potential problem solutions and identify and resolve possible problems. 5. Communicating: Communicating the objective and significance of the problem effectively and its possible computational solution (s). 6. Effective work for teams: Active cooperation and multi-stakeholder contributions to solving issues and developing and implementing computational solutions.

6.7

Computational Thinking in the Classroom

CT is easy to learn in the classroom. CT can be introduced in the classroom by incorporating many of the resources and activities described in (Victoria, 2018). Classroom-Ready Computational Thinking Resources for K-12. CT is being adopted to aid in solving the problems in our nation’s K-12 school system (Jones, 2018). For the future of work, students must be prepared. CT helps us tackle any problem, regardless of how complicated, through an analytical and systematic process. To put it simply, CT is the process of acting like a computer and understanding information like a computer would. It uses a step-by-step approach like an algorithm to guide students through open-ended problems (Jones, 2018). The ubiquitous computational system currently runs the world, but new instructional approaches around CT are still unfamiliar to many educators. Ten credible online resources for igniting your lesson plans are listed below. Some of these platforms start at a basic level and can be used by preschoolers, while others offer engaging programs suitable for kids in the 6th and 7th grades. Want to see how computational thinking can be used in your classroom? Have a look at these resources to learn more (Jones, 2018; Ignite My Future, 2020):

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• Computer Science Unplugged: Sorting Algorithm Activities: The website Computer Science Unplugged features various resources that make computer science concepts more accessible by allowing the knowledge to be used in real life. These resources will assist students in integrating computer science concepts with other subjects. The resources featured here are free, do not require an account to download, and may be printed. • Data.gov: Data.gov is a vast data repository filled with federal government information. Using this free website, students can investigate economic growth, viral spread, and many other subjects. The datasets provide excellent predictive modeling and time-series data for visualizing trends. Anyone can use Data.gov, but using a computer with spreadsheet software is highly recommended for full use. • Google for Education: Exploring Computational Thinking: Students and educators can benefit from Google’s well-designed instructional programs designed to help them improve their CT skills. Learning how to incorporate CT into different subject areas is the focus of the Computational Thinking for Educators course. The Exploring Computational Thinking curriculum includes several short videos that can be used to supplement classroom activities and lessons based on computational thinking concepts. In addition to being completely free, it allows participants to go at their speed. • Hopscotch: Make Games: K-12 students and people interested in visual programming may benefit from the Hopscotch visual programming language app. Through a program called “Design, Build, Play,” students have the opportunity to design, publish, and play their games in a safe and moderated environment. Students analyze their games independently and through outside observations and feedback before designing new elements to meet the desired solution. You can find Hopscotch for free in the iOS App Store, and it allows in-app purchases. • Poll Everywhere: The Poll Everywhere program allows students to create and analyze polls in real-time without requiring the aid of a computer. Data collection is critical to CT for students, identifying problems and proposing solutions. Its simple and easy-to-use interface is designed to work well with the vast majority of classroom computing equipment, and the application is compatible with smartphones to facilitate participation in polls. • Scratch: To give younger coders the necessary building blocks for video game creation, the Lifelong Kindergarten group at MIT’s Media Lab created Scratch, a coding platform. Without any previous computer programming knowledge, students can make games, simulations, and animations using drag-and-drop blocks. The free website features lesson plans and an online community for parents and teachers with advice, events, and resources. • Thingiverse is a free library of 3D printing designs for computer-aided design (CAD) and 3D printing software. This online platform allows students to finetune existing 3D models instead of creating entirely new ones. The platform itself is free, but in order to use it, users must create an account beforehand.

6.8 Why is Computational Thinking an Essential Tool …

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• TinkerCAD: TinkerCAD is a flexible 3D prototyping platform that can design interior and video game prototypes. TinkerCAD is widely compatible with 3D printers, making digital drafting easier for students of all ages. Students under the age of 18 can use the free version of TinkerCAD, but they must register by providing their family member’s email addresses. • Irvine Machine Learning Repository: The UCI (University of California) Machine Learning Repository is a massive database of nearly 400 machine learning datasets. Through databases such as these, students can learn how computers identify patterns and improve sorting abilities over time. Students will learn about the various uses of machine learning and, in the process, gain practical experience in abstraction by classifying the available information. You are free to use the UCI Machine Learning Repository. • The Wolfram Computational Knowledge Engine: The Wolfram Computational Knowledge Engine links computational thinking directly with all curriculum areas. This particular search engine demonstrates how CT can help us break down information to find the best solutions for problems. Some search proposals from the computer science engine include “Step-by-Step Solutions” and “Culture and Media.” Students will be able to decipher problems and form solutions with the help of these resources in no time. We are preparing our students for bright futures to combine creativity and CT for ultimate innovation and success by teaching them to solve problems using this technical mindset. As you can see, CT is a vital understanding skill for students. Students’ skills from thinking like a computer will help them succeed in the future.

6.8

Why is Computational Thinking an Essential Tool for Teachers and Students?

Many countries and regions have started to redesign their curricula in recent years, and they all agree that it is essential for the students and teachers of the digital age to have CT. While it is a bit of a mouthful, the concept of CT is quite simple. Teaching and learning CT is an easy task, and it is enjoyable to teach. Now let us explore why CT has become so widespread in classrooms worldwide and how you can get started with it yourself (Cummins, 2020): 1. Computational thinkers are Problem Solvers: CT is a proven method that allows them to identify problems regardless of the user’s age or computer proficiency. It comprises four components: decomposition, pattern recognition, abstraction, and algorithms.

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Teachers and students in English classes can employ this process to help with spelling by recognizing patterns and developing plans. Develop different writing styles through algorithms and hone research skills by making abstractions. Computational Thinkers are Innovators: Inventors create new things, but innovators improve or apply a great idea to a new purpose. In comparison to other common approaches, such as De Bono’s Six Thinking Hats, the process of abstraction is distinctive. Students who can identify and pull out relevant elements from a system or problem and then build on those pieces to create a solution must think about the elements in a new way. We can concentrate on the resources, tools, and skills available to us by doing this. A classroom is a good place for students to practice graphic design abstraction and write easy-to-follow instructions. Computational Thinking is research-based and tested: Although Seymour Papert initially introduced the concept and term of CT in the eighties, Jeanette Wing “innovated” it with her research paper identifying the many ways computer science, algorithmic design, and technology affect our society. Wing’s research has changed how leaders such as Barack Obama and educational philosophers like Ken Robinson view CT, which they now understand as a crucial skill that allows students to apply technology, data, resources, and people in a way that turns them from consumers of technology into creators. Due to the increasing importance of CT as a business advantage, firms like Google, Apple, and Microsoft spend considerable time and resources training their staff. Computational Thinkers make the leap from consumers to creators: Computers excel at tedious, repetitive tasks because they are far more efficient and accurate than humans. However, they can only carry out these tasks if given specific instructions on what to do and how to carry it out. Computational Thinking is the term we use to describe this method. It is all about algorithms, and an algorithm is just a set of instructions. We call a recipe when we use it in the kitchen. The term “equation” is used when it is used in mathematics. When used in basketball is known as a play, while coding is known as programming. Using programming languages like Scratch and Python, students can program computers and machines to perform previously impossible tasks through Algorithmic Design, a logical step in the computational thinking process. Computational Thinking is simple to teach and fun to learn: Even if multiple teachers did not implement them, many of the teaching methods mentioned in this section could be accredited to individual teachers. CT is a skill that students of any age and discipline can employ, from kindergarten to college and throughout their careers.

6.9 Integrating Computational Thinking into Your Classroom

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Integrating Computational Thinking into Your Classroom

It is now imperative that individuals have computer skills to succeed in society. CT must be integrated into the classroom for this reason. The terminology used to describe this way of thinking has caused some teachers to be hesitant, but that is changing as more teachers adopt the tools needed to implement it in their lessons (Lynch, 2018). Wing (2006) states that CT involves solving problems, designing systems, and understanding human behavior by drawing on fundamental computer science concepts. CT emphasizes problem-solving abilities and trial-and-error discovery. Computer science has CT with its vast array of mental tools at its core. The notion of CT is often confused with computer programming and coding. Coding may be defined as a computational skill, but according to advocates, computational thinking can be done without using a computer. As a result, you must comprehend what CT is and how it differs from other problem-solving methods. As the International Society for Technology in Education (ISTE) explains, CT is a study of computers and a tool for enhancing deeper thinking and discovery. Once educators recognize this, CT becomes a catalyst for exploration, a vehicle of curiosity (https://www.iste.org/explore/Lead-the-way/It?articleid=501). Use the CSTA Guidelines. The Computer Science Teachers Association (CSTA) standards are an excellent place to start if you are new to CT in the classroom. These guidelines are in place at all grade levels (starting in preschool) and are designed to progress each year. Furthermore, they offer an assortment of classroom exercises and demonstrations to help illustrate the basic tenets (Lynch, 2018). Is the Technology in Place? CT must be woven into your lessons, and the necessary technology is imperative. Depending on your ultimate aims, this technology will change. On the other hand, many schools make special STEM labs and Makerspaces to teach CT. All of this must be available in the classrooms, so students can have their work done effectively (Lynch, 2018). Integrate computer thought into lesson plans. Consider how your students can work on problem-solving with technology. It is now time to begin including CT in lesson planning. There is a wealth of information on the Google’s Exploring Computational Thinking website for those new to computational thinking, including incorporating various components of computational thinking into your daily activities and CT-specific ideas and lesson plans for the classroom (Lynch, 2018). In the long run, Computational thinking will help students face challenges in the future, using the computing skills students are already familiar with.

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Examples of Computational Thinking in the Classroom

Even though CT is often linked to computer science, its process guides problems across various contexts. This section will review four mathematics, English-speaking arts, science, and social studies projects to teach CT in the classroom (McVeigh-Murphy, 2019a). All can be easily modified to fit different levels of grade. 1. Data Analysis in Math Class: In a middle school math class, the students started a survey-led project where they curated, collected, and analyzed data algebraically. The quantitative variables in dispersion plots were mapped to identify trends and r-value representations then used to show their findings. During the entire project, the teacher emphasized that “data is a tool to make people hear you.” With concise analysis and fascinating visuals, students could create convincing projects on topics they loved. One student analyzed breast cancer cases with an online database to cure data of 1995. She wanted to study the mortality rates and measure the increase in diagnoses after being diagnosed by her mother and two family friends. She employed CT skills to find the most important information and patterns when viewing the data. As a bonus for CT, students have learned other digital skills, such as browsing databases, using online data-crushing tools, inputting data into tablets, drawings and charts. 2. Understanding Character Connections in English Language Arts: Language classes also offer opportunities to use CT in schools. In this instance, students used CT skills to carry out literary analysis in books such as Hamlet and Harry Potter. In order to abstract the connections between characters, students developed network diagrams and interaction graphs. This technique helped contextualize student literature to understand better the work, such as power dynamics or meaningful narrative relationships. This also helped students gain a complete understanding of how narratives flow from the Cat in the Hat to the Beowulf. This analysis enabled students to understand the questions that data can answer and which data analysis can be automated. Besides improving understanding of reading and CT skills, students also practiced visual mapping and multimedia resources to understand design. 3. Using Design Thinking to Build Models in Science: The students used CT, physics, and engineering design to build earthquake-resistant bridges in this science class. The unit first understood the function of bridges and their various types. Students then went on to study earthquakes and their strengths. Students used design thinking and CT to design an earthquake-resistant bridge. CT allowed students to analyze a range of bridge models to find structural designs and abstract the essential elements required in functional design. When the various prototypes were tested, CT allowed them to gather information and identify areas for improvement. An extensive, unplugged project that focuses on building functional models and enhancing engineering design can help students work together and exercise critical thinking skills.

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4. Social Studies Decoding Cryptography: As students learned about the German Enigma machine and the importance of cryptography for sending coded messages during World War II, they also learned how algorithms and other computational thinking could develop and crack secret codes. In this CT case, students designed their chip wheel to send coded messages and learn how algorithms form an integral part of the development of coding languages. Students can use this example to learn about World War II through collaborative, handson practice, but it can also teach students about Morse code, other coding languages, or any other type of communication. In addition to increasing their understanding of code during World War II, students also deepen their knowledge of the language and the ability to identify patterns around us. In essence, a language has several patterns from which different rules can be abstracted, making it an excellent method for students to use CT in real-world contexts.

6.11

Computational Thinking Through Classroom Assessment

The CT framework focuses on how problems can be assessed, approached, and solved computationally. They can be decomposed into smaller, more manageable parts when complicated problems. Identifying patterns in a problem is a great way to find new solutions (Brooks, 2019). CT helps students improve their problem-solving skills when incorporated into classroom activities. All CT assignments are stored for later use, forming a large cognitive toolkit. CT, for this reason, may be used in class assignments like assessments to offer students an opportunity to grow and develop (Brooks, 2019). Formative Versus Summative. Classroom assessment can generally be broken down into two categories: formative and summative (Brooks, 2019). First, there is a formative assessment, which is informal and looks at students’ progress. The application keeps track of the student’s education. The second, summative, cumulative measurement of students’ learning, called a final grade, is created. The whole of learning is summed up in this statement. Formative assessment can assess how well students grasp new concepts (Brooks, 2019). Formative assessments show you if students are struggling. Use this information to delay new information until students are ready for it. In contrast, summative assessment reflects mastery. It indicates that students have gained the knowledge and skills needed to reach a certain standard. An assessment strategy that works well with CT questions, such as those that encourage critical thinking, is formative assessment. Formative assessments can be as simple as a quick exit slip or as complex as a semester-long assessment (grouping students for a think-pair-share activity). They are non-graded and have a low level of risk. Additionally, it integrates evidence-based teaching methods, such as retrieval practice.

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Objective, fact-based learning goals are typical of summative assessments. These could include tests and quizzes. Summative assessments are often associated with measurements, performance goals, rubrics, or standards, so they are more likely to be distorted (Brooks, 2019). However, they can still involve CT thinking tasks like decomposition, abstraction, etc. Computational Thinking and Assessments. It is difficult to summarise the concept of computational thinking because it encompasses multiple practices aimed at helping students learn to solve problems. Students can grasp the entirety of an object by first decomposing it, just as they can understand key points by abstracting an idea. All assignments offer a variety of ways to look at a topic. Below are some basic but essential skills you may want to include on assessment tasks, whether formative or summative (Brooks, 2019). 1. Decomposition/Abstraction: The decomposition process consists of breaking apart problems, ideas, or objects into their constituent parts, while abstraction simplifies ideas and objects or their categorization into distinct groups. Creating a realistic flower painting could evolve into a patchwork of different colors representing the flower’s colors, or the flower itself could be represented by the petals, pistil, and stem. To encourage students to think about decomposition and abstraction, here are some examples of questions to ask: • Could you go into more detail about this? • What can be done to simplify this? • Which category does this fit into? • What is the overall purpose? • Which of these categories would you put these in? One of the ways students can employ abstraction is by changing the format of their data. For instance, data visualizations are a form of data access via visuals. Visual representations, such as graphs and charts, help reduce the difficulty of processing information in data-heavy cases where reading a table can be tedious. • Illustrate the concept with a diagram. • Construct a chart. • Create a timeline for the project. • Enter the location of this graphic/map. Several software programs, including Mathematica, allow students to develop customizable visualizations. An example of a student-made coding tool would be a slider that demonstrates how variables impact an object’s appearance. The format in which the information is presented may be paper-based, but when students do so, they must take a holistic approach to their data. 2. Metacognition: Thinking about thinking, known as metacognition, is used to evaluate learning effectiveness. As it is a personal undertaking, there is no such thing as a “correct” answer to a reflective question. However, reflective questions provide students with insights about how they tackle problems, what blind spots they have, and their strengths.

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The following questions about metacognition are relevant: • What new information did you pick up today? • What did you find easy to learn? • How did the X concept assist you in comprehending Y? • Are there any other inquiries you still have? Knowing your strengths and weaknesses is critical when quickly learning new skills is critical. Assessment strategies that incorporate self-reflection can be aided by questions that promote metacognition since you can help students use the assessments to analyze their thoughts. When they see the value of reflecting on their learning outside the classroom, they may consider how they learn. 3. Pattern Recognition/Pattern Matching: Pattern recognition, or pattern matching, is used to understand how various items in a series relate to one another. Membership is regular when a pattern emerges. Patterns are helpful when writing computer code to keep it simple. They can be used to make predictions in machine learning or folded into algorithms. When thinking about how to approach the idea of grouping things together and seeing them as patterns, ask students to consider the following questions: • Do you notice a pattern? • What comes next? • What are these things alike in? • Which of these does not belong? • Which of these categories would you put these things in? Figurative language is essential for English Language Arts (ELA) courses, incorporating pattern recognition. It is not full pattern recognition—it cannot think about what is next—but it still assists in establishing a foundation. While analogies, symbols, metaphors, and similes connect concepts, they are generally used to connect concepts through shared qualities. Students can gain insight outside of the English class by comparing and contrasting. 4. Algorithmic Thinking: Pattern recognition, which focuses on adding items to a group, is contrasted by algorithmic thinking, which aims to help students understand and apply rules across situations. Algorithmic thinking is required in recipes because the steps must be done in a specific order, and one step triggers the next. Also, technical writing, such as instructional manuals, follows the rules. Providing support for coding in your course gives you a simple way to incorporate algorithmic thinking into your assessments by asking students to write conditionals or “if-then” statements. It is worth thinking about additional ways to get students to understand algorithms. Consider these ideas: • Develop a laboratory report. • Write a step-by-step guide for a friend who wants to re-create what you just did. • Try composing a recipe. In the absence of direct writing practice, you can ask questions that hint at technical writing. You could ask, “What are your plans to…?” or even “How would you…?” to encourage students to think about cause and effect.

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Students do not have to struggle to learn CT in the classroom. Even though the term implies a need for modern technology, CT is not only about coding. This framework, suitable for heavy computation, aligns well with the computational-heavy society we reside in. Classroom assessments are just part of the program. Students are bound to view their learning differently by either reflecting, retrieving, or to reconstruct their understanding. Why not maximize the potential that your resources have to offer?

6.12

Integrating into Existing Routines and Curricula

Understanding the future’s technologies requires a firm grasp of CT. It is a way of thinking about a device or a language rather than a complete body of knowledge. When they hear CT, people think of computers and coding, but CT can be taught without a computer (Thorson, 2018). CT is the foundation for future academic and professional success. So, CT can be used in primary classrooms, including those in the early years. Moreover, it is increasingly becoming a necessary skill for all students. CT can be taught to children, and they can develop it when given time to explore and discover independently. This will allow them to comprehend their digital world. It can also be used in established procedures and lesson plans (Thorson, 2018). Strategies for Incorporating Computational Thinking in Early Learning Classrooms. Some ideas for getting computational thinking into early learning classrooms are listed below (Thorson, 2018): 1. Teaching Decomposition: Helping young learners break down problems allows them to tackle issues. Educators work together to guide the students through their dissection, starting with the broad problem. Young students, regardless of developmental age, are capable of exposure to the thought processes of adults. To accomplish this, students will understand the computational strategic thinking frameworks. Ideas to try: A scenario could be described that involves multiple steps, such as planning a birthday party. The workload can quickly become overwhelming without a to-do list that breaks down more sizable tasks into smaller, less daunting tasks. Student and teacher collaboration can break down a large task and provide a pictorial representation of student thinking to help solve problems later. 2. Teaching Pattern Recognition: The cornerstone of CT, pattern recognition, starts with the basic ABAB pattern-creation program taught in primary school and continues into more complex levels of thought. Finding similarities between

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things and events enables young students to see trends and use that information to make predictions. A pattern recognition exercise will engage students in studying comparable objects or events and identifying similarities. Ideas to try: Start examining trees to teach students to identify patterns. Are there any similarities among all trees? All of them have a trunk. They all have roots. All of them have branches. Trees differ in various ways, but all trees share specific characteristics. The next step is to collaborate with your students to create a collage of trees. Their trunks, roots, and branches are all of note. You can then mention how the trunks are different from one another. Some are thicker than others. A few are dark, but most are light. Compare the differences between the root and branch structure. For your students to have something to do, invite them to draw a tree picture, with each part labeled (trunk, roots, and branches). While the trees in your class might vary in appearance, they are identical in their core components. Patterns help because you can use what you already know to do tasks. Students’ awareness of the world outside their sphere of influence is increased by recognizing patterns. They use the patterns they have found to solve future problems and predict the world’s future state. 3. Teaching Abstraction: The goal of abstraction is to focus on relevant and vital information. It consists of separating critical information from unnecessary details. Ideas to try: Teaching the skill of abstraction: Primary school teachers can teach students how to see the main idea and key details when reading a book by highlighting these points for them. Another way to promote comprehension is to help students locate information, clues, or other treasures by giving them a target before getting to a book or an experience. As a group of kindergarteners listens to a dental hygiene presentation in school, they may be searching for details about brushing their teeth. Teaching students to abstract gives them the ability to locate specific information amid all the other data. It is a valuable skill, as the texts students read grow longer and information gets more complex. 4. Teaching Algorithms: Algorithmic thinking is the process of finding solutions to a problem. Sequential rules are created to be followed to resolve a problem. In the early grades, children can learn that the order in which something is done has an impact. Ideas to try: Students might imagine making a sandwich to illustrate this concept. What do we need to do first? Second? My sandwich might taste better if I put cheese and lettuce on it first. Algorithmic thinking is based on discussions of sequence and order. Invite students to design a path from their classroom to the gym by outlining a series of steps. This will help students develop an understanding of algorithms. Then, give it a whirl! As a follow-up, ask students to reflect on their daily routines. Each morning, how do they get ready for school? What effect would the sequence have on the result? When students think about how

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inputs affect the outcome, they become more reflective and adapt their plans to achieve the desired outcome. 5. Linking our Youngest Minds to the Thinking of the Future: Teaching computational thinking strategies to young students has many benefits, and students’ comfort level with computers is just one of them. It is far more substantial and intricate. We live in a world where smartphones and smart homes are becoming more common, and knowing how to use devices gives us a way to use technology as a partner to solve problems. Computational thinking allows students to engage technology as a tool rather than as a crutch. As the 21st-century workforce continues to grow, it is crucial to understand and question the technologies we surround ourselves with. In the long run, those who are successful and efficient will be better off in both their professional and personal lives. Young students should be the first to start this process. Computational Thinking Across K-12 Education. The impacts of computational thinking are expected to occur in different areas of learning (see Fig. 6.5) at the elementary, middle, and high school levels (Angevine, 2018). CT in the K-12 curriculum does not compete or replace efforts to expand computer science education; rather, it enhances them. Where CT is not yet offered, educators and students who want to better understand and participate in a computational world can integrate CT into existing disciplines. Computer science education has a place, but schools that already focus on teaching coding and computer science will benefit from learning CT to enhance and amplify their existing lessons (Angevine, 2018). Challenges on the road ahead. The integration of computational thinking for all has many unanswered questions. Researchers have identified several issues, including the need for an agreed definition of CT; whether coding, computer science, and computational thinking can legitimately be separated; the best and most inclusive Fig. 6.5 CT’s impact on K-12 education regarding age and discipline (Angevine, 2018)

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pedagogy to promote CT in children; how to assess CT, and whether it is suitable for everyone, among others. Additionally, how do we simultaneously meet the rising demand for computational skills and the rising supply of ready and able teachers to provide instruction in all academic disciplines (Digital Promise, 2020)? While overcoming the barriers mentioned in (Digital Promise, 2020) is necessary to achieving widespread CT in the K-12 educational system, it is an important starting point for schools to introduce it throughout all their subjects. For example, if the goal is to acquire job-ready skills, schools may be more likely to provide coursework in current popular programming languages. Relying on this goal means devoting attention to programming languages that might soon become obsolete, while paying attention to the subject of coding means the importance of other fundamental CT principles may be overlooked. The idea here is that if you want to get every student involved in the computational world, you could integrate CT across all disciplines, even if computer science classes are already in place. In this way, this initiative can bring about a fundamental cultural shift in which all educators appreciate, value, and employ CT practices in their teaching. The long-term benefits of the skills students develop could be significant (Digital Promise, 2020).

6.13

Differences Between CT and Other Types of Thinking Skills

Researchers also examine the differences and similarities between CT and other forms of thought (Shute et al., 2017). This section compares CT with mathematics, engineering, design, and systems thinking (Shute et al., 2017). • Maths involves using mathematical abilities to resolve mathematical problems such as equations and functions (Sneider et al., 2014). There are three parts to mathematical thinking: mathematical beliefs, problem resolution processes, and solution justification. Problem-solving is one of the most common characteristics shared by CT and mathematical thinking (Wing, 2008). Figure 6.6 depicts a wide range of shared computational and mathematical thinking concepts: problem-solving, modeling, analysis, and interpretation of data, statistics, and probabilities. • Engineering includes skills required to construct or transform things in the world to build better lives and “applied science and mathematics, problems and things” (Shute et al., 2017). CT-engineering overlap involves problem-solving and understanding how complex systems work in the real world (Wing, 2008). However, unlike engineering, CT helps people understand complex phenomena through simulation and modeling to transcend physical limitations. In summary, CT, mathematics, and engineering come from various disciplines. Their differences lie in their domain in specific applications. • Design thinking calls for problems to be solved by thinking like a designer. CT and design thinking focus both on problem-solving. Design thinking, like

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Fig. 6.6 Similarities and differences between CT and mathematical thinking (Sneider et al., 2014)

engineering, concentrates on product specifications and the demands placed on people and the environment (i.e., practical problems). Again, CT is not limited by physical constraints that allow people to solve theoretical and practical problems. • Systems thinking refers to the ability to understand different relationships between elements. The competence model developed by Shute et al. (2010) requires those with system-thinking skills to be in a position to (a) define problem/system boundaries; (b) model/simulate the conceptual way the system works; (c) represent and test system model using computational instruments, and (d) make model-based decisions. CT. So to sum it up, design thinking and engineering, systems thinking, and mathematical thinking are all included under the CT umbrella to address a wide range of issues. Despite the similarities between CT and system thinking, CT is more expansive than systems thinking, which focuses on the entire system’s functioning. CT aims to solve problems efficiently, including the design of algorithms, automation, and generalization to other systems/problems beyond modeling and understanding.

6.14

Conclusion

Computational thinking is a shift in the approach to solving problems. We can navigate complexity through a formulaic process and focus on the important without losing the solution between the whole noise. We can solve problems with mass data quantities and carry out unknown journeys across these data-packed

References

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landscapes. The ability to navigate complex information and think in a way that complements technological processes is vital for students’ readiness. More than just a tool for problem-solving, computational thinking fosters various necessary attitudes and dispositions, such as the ability to embrace ambiguity with a positive attitude, persevere through repeated attempts, work well in groups, and see oneself as a lifelong learner. Students learn to raise audacious questions and develop complex solutions that are yet to be imagined through this process. A growth mindset is fostered through CT because students learn how to deal with ambiguity and challenges in a collaborative setting, both with and without the aid of technology. They also learn to collect and analyze resources. Students who possess these skills are better able to direct their thoughts, their actions, and the relationships they form with greater intention and mindfulness. It is easy to bring CT to your classroom and only help your students achieve the learning goals you have identified. In lesson planning, keep in mind and use the language of these abilities and dispositions. Make your projects ambiguous, combine lessons with real-world examples and evidence, and dream great—your students might over time surprise you with the connections that they build and their confidence in new challenges. Moreover, Google offers a great online course (https://edu.google.com/resources/progra mms/exploration-computerthought/) to teachers who want to bring computational thinking to their classrooms.

References Allan, V. H., Barr, V., Brylow, D., & Hambrusch, S. E. (2010). Computational thinking in high school courses. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE 2010 (pp. 390–391). https://doi.org/10.1145/1734263.1734395. Angeli, C., & Giannakos, M. (2019). Computational thinking education: Issues and challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185. Angevine, C. (2018). Advancing computational thinking across K-12 education. Gettingsmart.Com. https://www.gettingsmart.com/2018/02/23/advancing-computational-thinking-acr oss-k-12-education/. Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 111–122. https://doi.org/10.1145/1929887.1929905. BBC. (2021). Introduction to computational thinking. Bbc.Co.Uk. https://www.bbc.co.uk/bitesize/ guides/zp92mp3/revision/2. Brooks, J. (2019). Computational thinking through classroom assessment. Medium.Com. https:// www.youtube.com/watch?v=Xr_F5mx9G28&list=RDGMEMPipJmhsMq3GHGrfqf4WIqA& index=11. Cummins, K. (2020). Five reasons why computational thinking is an essential tool for teachers and students. Innovativeteachingideas.Com. https://www.innovativeteachingideas.com/blog/ five-reasons-why-computational-thinking-is-an-essential-tool-for-teachers-and-students. Denning, P. J. (2017). Viewpoint remaining trouble spots with computational thinking. Communications of the ACM, 33–39. https://doi.org/10.1145/2998438. Digital Promise. (2020). Computational thinking for a computational world. Hubwieser, P., Giannakos, M., Berges, M., Brinda, T., Diethelm, I., Magenheim, J., Pal, Y., Jacková, J., & Jasute, E. (2015). A global snapshot of computer science education in K-12 schools.

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In Proceedings of the 2015 ITiCSE Working Group Reports (ITICSE-WGR ’15), 65–83. https:// doi.org/10.1145/2858796.2858799. Ignite My Future. (2020). K12 computational thinking resources. Ignitemyfutureinschool.Org. https://www.ignitemyfutureinschool.org/resources/k12-computational-thinking-resources. ISTE & CSTA. (2011). Operational definition of computational thinking. https://cdn.iste.org/wwwroot/Computational_Thinking_Operational_Definition_ISTE.pdf. Jones, D. (2018). 10 Classroom-ready computational thinking resources for K-12. Www.Gettingsmart.Com. https://www.gettingsmart.com/2018/05/13/10-classroom-readycomputational-thinking-resources-for-k-12/. Learning.com. (2019). The ultimate guide to computational thinking for educators. Lynch, M. (2018). How to integrate computational thinking into your classroom. Thetechedvocate.Org. https://www.thetechedvocate.org/integrate-computational-thinking-classroom/. McVeigh-Murphy, A. (2019a). Four examples of computational thinking in the classroom. Equip.Learning.Com. https://equip.learning.com/examples-of-computational-thinking. McVeigh-Murphy, A. (2019b). What Is computational thinking? And why is it important for students? Equip.Learning.Com. https://equip.learning.com/computational-thinking. Mishra, P., Yadav, A., Henriksen, D., Kereluik, K., Terry, L., Fahnoe, C., & Terry, C. (2013). Of art and algorithms: Rethinking technology & creativity in the 21st century. TechTrends, 57(3), 10–14. https://doi.org/10.1007/s11528-013-0655-z. Repenning, A., Grover, R., Gutierrez, K. D., Repenning, N., Webb, D., Koh, K. H., Nickerson, H., Miller, S. B., Brand, C., Horses, I. H. M., Basawapatna, A., & Gluck, F. (2015). Scalable game design: A strategy to bring systemic computer science education to schools through game design and simulation creation. ACM Transactions on Computing Education, 15(2), 1–31. Selby, C., & Woollard, J. (2014). Computational thinking: The developing definitions. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education, SIGCSE 2014. Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380. https://doi.org/10.1007/s10639-0129240-x. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Shute, V. J., Masduki, I., & Donmez, O. (2010). Conceptual framework for modeling, assessing and supporting competencies within game environments. Technology, Instruction, Cognition & Learning, 8(2). Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22(1), 1–17. https://doi.org/10.1016/j.edurev.2017.09.003. Sneider, C., Stephenson, C., Schafer, B., & Flick, L. (2014). Computational thinking in high school science classrooms. The Science Teacher, 81(5), 53. Stroud, F. (2021). Computational thinking. Webopedia.Com. https://www.webopedia.com/defini tions/computational-thinking/. Thorson, K. (2018). Early learning strategies for developing computational thinking skills. Gettingsmart.Com. https://www.gettingsmart.com/2018/03/18/early-learning-strategies-for-dev eloping-computational-thinking-skills/. Victoria, K. (2018). What is computational thinking? Why thinking like a computer builds skills for success? Teachyourkidscode.Com. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society, 366, 3717–3725. https://doi.org/10.1098/rsta.2008.0118.

7

Learning by Doing

Abstract

In this method, Science research skills can be acquired, conceptual understanding generated by scientific instruments and activities by providing the remote laboratory experiments or instruments. Remote access to special equipment is now expanding for trainee teachers and students; in earlier days, it was utilized by scientists and university students. Access to remote laboratories will provide better food for thought. Instructors and learners need to provide adequate resources by providing hands-on investigations and direct observation, supplementing the typical learning. Such experiences also may be brought into the school classroom by having access to remote laboratories. The high-quality, distant telescope can provide the students with a daytime school science class to make night sky observations as an example of this method. This chapter presents the Learning-by-doing method, its importance, and how to do it. Also, we present the most important challenges of applying the learning-by-doing method. Keywords

Learning by doing • Learn by doing • Approach • Benefits • Drawbacks • Challenges • Remote labs • Simulations • Experiential learning • Inquiry-based learning

7.1

Introduction

When people actively participate in making things or exploring the world, they are more likely to use the learning by doing method to make sense of their experiences. It is a concept that can be applied to a wide range of learning situations (some would even argue that it applies to all learning) and a pedagogical approach that encourages teachers to get their students involved in more hands-on, creative learning (Bruce & Bloch, 2012). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_7

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The idea of learning by doing is to get involved in an activity and learn about things like (Wikiversity, 2019): How the activity functions? • How do you feel about the activity? • What do you think about when you do the activity? • What can you do as a result of engaging in this activity? You may also be asked to consider the general nature of the activity—that is, how other people carry out this activity in various situations. When all of this learning comes together, you will have a deeper understanding of the activity because you will have had hands-on experience with it. As a result, it can be a fun and engaging way for people to learn; they may be having so much fun doing it that they learn without even realizing it! In projects where participation is critical, this may be desirable. However, it can also cause issues because the learning gained from a specific task is dispersed and unrelated to other aspects of the learner’s experience, worldview, and field-of-studies. That is to say, learning by doing is a process that the learner should ideally reflect on both during and after, but it can also be a highly natural way of learning (sometimes referred to as “incidental learning”) that anyone can engage in at any time, consciously or unconsciously, to maximize their learning (Wikiversity, 2019). Regarding the way behavioral scientists view education, almost all learning occurs when students take actions in the real world that have outcomes for them, whether good or bad. Almost all major learning theories include some form of learning by doing, dating back to the Sophists in the West, who emphasized mind-and-and-body learning. Novices participate in activities before they are fully competent or understand them, according to Vygotsky’s sociocultural theory of learning from the 1930s (Bruce & Bloch, 2012). Rather than the other way around, activity (or “doing”) comes before development. Put another way; we do not learn something that allows us to do something else; instead, we learn something that allows us to learn something new. Conditioned responses or knowledge incorporation result from the learner’s exposure to these consequences (Skinner, 2002). According to Dewey (1938), the inquiry is how uncertain situations are transformed into a unified whole. Dewey and other pragmatists, on the other hand, believe that simply doing something or getting some experience is not enough to solidify and articulate knowledge (Schön, 1991). Though Dewey’s theory places so much emphasis on doing that his pedagogy is frequently confused with the idea of learning by doing (Bruce & Bloch, 2012). In recent years, numerous approaches to education have emphasized doing as an essential part of learning, such as active learning, problem-based learning, experiential learning, and service-learning. Learning is organized around social communication in computer-supported collaborative education, utilizing learning by doing (Bruce & Bloch, 2012). Similar ideas are held by many thinkers in the East (Bruce & Bloch, 2012). In formal learning settings, one of the most compelling arguments for promoting

7.2 What is Learning by Doing?

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learning-by-doing approaches is that we want our students to be active participants in the world outside of the classroom. It seems highly improbable that the learner can participate successfully in the formal learning experience without having some opportunity to perform the activities they will be expected to perform in the real world (Bruce & Bloch, 2012). Observations of the workplace and other methods of engaging in a profession are commonly included in professional learning programs, such as internships. Successive learners have legitimate peripheral participation (LPP) opportunities, according to Lave and Wenger (Lave & Wenger, 1994). Even though students are not given complete control over the task they are learning, experts still allow students to do things their way and in a valued way and contribute to the overall activity. The more they see themselves as a part of a larger situation, the more they comprehend their role within it and the bigger picture. In this model, Learners begin by observing and then gradually assume more responsibility for the larger task before becoming fully autonomous in the activity they have been learning.

7.2

What is Learning by Doing?

As there are numerous ways to gain knowledge, teaching techniques also grow. As a result, numerous strategies use those specific abilities—one of these methods, known as learning by doing (Ho, 2021). Experiential learning is another name for this (Ho, 2021). Despite its existence around for a long time, this method is surprisingly effective due to the numerous benefits it offers. I will explain what it is and why it is such a powerful learning tool. According to the principle of learning by doing, learn more when you actually “do” the activity (Boser, 2020). For example, you want to learn how chords relate to a jazz musician. In the past, you may have repeatedly played the chords in the studio by yourself. Instead of passively practicing the chords, you would gain a basic understanding of them through experience and then jump on stage with other musicians to play them as part of an improvised piece. The more actively you participate, the deeper you learn and the more likely you are to make mistakes—like playing the wrong chords—and learn from them. Learning by doing was first popularised by American philosopher John Dewey (Ho, 2021). Dewey made a name for himself by making the well-known claim that we learn best when actively involved in the subject matter. This meant a strong focus on student involvement for Dewey. This method shattered the conventional belief that learning occurs through lectures and rote memorization. A practical curriculum relevant to students’ lives and experiences, in his opinion, was the best approach. Modern researchers have demonstrated the importance of learning by doing, making Dewey’s insight, which is over a century old, all the more relevant today (with some significant caveats.).

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Learning by Doing

Why Should We Learn by Doing?

Learning by doing is effective because it targets human memory processes at their core. Scripts and generalizations of scripts are the foundations of human memory. To do something, we must first learn how to do it correctly. If our rules do not apply in a particular situation, we must revise them. We learn when to generalize our rules and take exceptions into account. Our rules can be domain-bound or domain-independent, which we discover (Engines For Education, 2020g). We can only learn by having new experiences and storing the memories of those experiences in our existing memory structures, and this is the only way we can do it. This process of integration is based on newly acquired data. When new information is told to us, we have no idea where it should go in our memory because we do not know what to do with it. When we see the data for ourselves, we also simultaneously see, feel, and remember other sights, sensations, and thoughts. Meaning we have enough context to characterize what we have learned to have a place in our memory, and we can start generalizing from it (Engines For Education, 2020g). For this reason, learning through doing will yield scriptlets, strategies independent of the domain (such as process participation strategies), and cases that stand on their own as exceptions awaiting integration into the memory system. These memory-related issues are addressed by learning by doing (Engines For Education, 2020g).

7.4

Why Learning by Doing is Effective

There are three types of reasons why learning by doing is more effective than other methods, according to Reese (2011): 1. Self-Shaping: Possibly, the benefits of learning by doing are because this method involves self-shaping. A self-shaping approach would optimize the steps and their sequence because contingencies are better suited to the doer by the doer and thus better devise and sequence the steps in the learning process. It has been suggested by Peláez and Moreno (Taxonomfa et al., 1998) that someone who uses a rule that others have provided “may have no understanding of how to arrive at, or devise such a rule, because he or she may ‘know that’ but not ‘know-how or why’ the contingencies specified in such rules are related. Rules taught by others are often learned via imitation processes.” However, Peláez and Moreno failed to point out the inverse principle, which is that a self-generated rule is based on direct experience and thus leads to not only “knowing that,” but also to “know-how or why,” and thus to know whether or not to generalize the rule (Reese, 2011). To put it another way, learning by emulating is not as effective as learning directly from experiences, such as trial and error.

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However, a study conducted by Rendell with nine collaborators (2010) found that the best way to adapt to an unfamiliar and unstable environment is by mimicking the actions of others rather than learning through trial and error (Pennisi, 2010). Using a simulation game, Rendell and colleagues found that focusing on imitation was more effective than focusing on experimentation and that this strategy turned out to be the winner in a simulation game they devised. They demonstrated that the simulation study results might interest those who enjoy game theory but not those interested in solving real-world problems (Reese, 2011). 2. The Marxist View: A theoretical foundation for learning by doing was provided by Soviet psychologists (Reese, 2011): When we learn something, we reflect on the world around us, not on the consciousness of others. Learning is an act of reflection. As a result, learning occurs through hands-on experience in and with the real world. According to a fundamental Marxist principle, the relationship between learning and doing is mediated by social relations because other people provide conditions that encourage (mediate) learning by doing (Reese, 2011). A Montessori school uses nonverbal conditions, such as instructions and other verbal communications, while Balanchine’s ballet school uses verbal and nonverbal conditions (Reese, 2011). Group problem-solving or group learning is another name for social mediation. It has been found that students working in pairs, rather than working alone, could solve problems more quickly when exchanging verbal information. This result was also found in samples from Japan and the United States for French children. College work-study programs are an alternative designed to give students real-world experience in their chosen fields. However, it appears that in practice, this concept is frequently strayed from. Many work-study programs were implemented under Mao Zedong in China, “in line with Mao’s desire to merge the practical with the theoretical, link labor and learning, make everyone work-conscious for nation-building,” “reduce differences between intellectual and manual workers, and eliminate elitist superiority from those in responsible positions” (Parker, 1977). One program’s goal was to bridge the gap between theory and practice in a specialty area. A few examples include repairing motors to learn about electronics, growing small crops on school grounds to learn about agricultural practice and theory, and making commercial drugs in a small factory for pharmacy students to learn about commercial production. Students and faculty alike were required to participate in another program requiring work outside their primary field of study. Electrical engineering students, for example, worked in the fields for six months of the year. This program’s stated goal was to reduce the gap between intellectuals and other workers to remove any sense of superiority among the former. However, both types of work-study reflected Mao’s belief that “only through practice (learning by doing and participation in productive and political activities, etc.) can man gain true knowledge” (Kuo, 1976).

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3. A Language Explanation: Learning by doing may be effective in humans because it reflects at least in part the importance of concrete language. Methodological distinctions in cultural anthropology are important. Similar to the linguistic phonetic-phonemic distinction, anthropologists borrowed the italicized suffixes from linguistics to make this distinction. As implied by their origin, the difference between etic and emic is structure versus meaning. Observed behaviors and informants’ reports are used in both methods to understand a culture or a group. To use the etic method, you must learn by reading a book and applying what you have learned in the form of preconceived concepts, theories, or interpretations. There are no assumptions about the meaning of behaviors and symbols encountered in the emic method (or as few as possible); the descriptions generated are based on firsthand experience and, therefore, likely to be stated in concrete language. A kind of practice-theory-practice dialectic is used in the emic method to continually “work through” the descriptions to come up with possible interpretations. Still, the sequential interpretations are likely to remain closer in level to the initial descriptions. It is nominally agreed upon as commonplace that definitions should spring from concrete and specific cases rather than be invented in the empty air and imposed on particularities. The emic method’s most important feature is that it necessitates firsthand knowledge of the subject matter. An important implication for memorability is that etic descriptions are abstract, and emic descriptions are concrete. In a large body of research on verbal memory, abstract nouns are less recollected than concrete nouns, most likely because concrete nouns have higher image values. If this hypothesis is correct, then etic descriptions will be less memorable than emic descriptions, reducing the effectiveness of book learning and other forms of instruction compared to learning through direct experience and other methods of learning by doing.

7.5

When Does Learning by Doing Work?

You must first lay the groundwork for learning by doing. Recent studies show how effective learning by doing is if used correctly (Boser, 2020). In other words, what do you make of this? To begin, it is critical to remember that learning is an activity that takes time and effort. Overwhelm occurs when people are introduced to learning by doing too early in the process. They do not seem to be able to adapt. Let us return to the story of the jazz musician once more. Assume you show up on stage knowing nothing about the new chords you are supposed to be using. You could not play the guitar because your fingers could not find the right frets. Rather than enjoying your time improvising with the other musicians, you would leave the stage dejected and reeling from frustration if the chords did not ring out. In

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real-life situations, this makes intuitive sense. You must be able to play the chords in your head before going onstage. In the classroom, this is also true. Short-term memory is a contributing factor. Learning experiences can be made more effective if you have this. According to experts like John Sweller, learning often occurs in short-term memory (Boser, 2020). Our short-term memory must process chords to improvise with new ones successfully. The chords can only be stored in long-term memory after being processed in short-term memory. On the other hand, short-term memory has the drawback of being, well, shortterm (Boser, 2020). Consider the musician’s hands in jazz. In order to play a chord, you must move your fingers up and down the guitar’s neck while simultaneously placing them on the correct strings at the correct times. This requires a lot of memorization and dexterity. Overloading your short-term memory by learning too many chords at once will result in shaky hand coordination and poor tone. Each chord becomes distorted as your fingers get out of sync. The result is a dissonant mess instead of the intended melody. It takes time for your hands to learn the fundamental techniques, just like it does for your brain. After laying this fundamental foundation, the musician’s hands will naturally follow. Being aware of the “limitations” of short-term memory allows us to understand better why learning by doing cannot happen too soon. Learners must break knowledge and skills into manageable chunks and focus on specific areas of mastery in order to learn effectively. Put another way, you must first master a few chords in a single key signature before moving on to more complex chords. We will be unable to learn if learning by doing is introduced too early. Many educational institutions do not use hands-on learning because our short-term memory has not yet deconstructed the material into manageable chunks. For the same reason, it is essential to set goals and targets when practicing a skill. Too often, people attempt to improve their abilities without knowing precisely what they are working on and without having any sort of direction or objectives in mind.

7.6

Why Does Learning by Doing Work?

To learn, you must first become familiar with the subject matter. This method is effective because it encourages you to actively engage with the material and create your new knowledge, one chord at a time. The “generation effect” is a valuable technique for promoting learning by doing (Boser, 2020). A phenomenon is known as the “generation effect,” which can also be referred to as the “testing effect,” “retrieval practice,” or even “learning by doing,” suggests that students retain more of the information they learn when they are asked to create it rather than read it. Rereading a section of a textbook or sight-reading a musical piece are two examples of this. Many teachers think of themselves as “putting information into students’ minds” in the classroom. According to the science of learning, students must build their knowledge, and in many cases, learning is more accurately described as a

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process of “pulling information out of students’ minds.” When you read a new text, ask yourself these questions (Boser, 2020): • • • •

What is the topic of this text? What is the author trying to say here? What is the author’s point here? Anything a little off to you here?

These questions help you focus on the content and use the learning-by-doing approach. Our knowledge grows exponentially when we ask these questions at the end of every paragraph—or even every sentence. This means that while textbooks may include “reading comprehension” questions at the end of each chapter, asking yourself these kinds of questions frequently will help you retain more information.

7.7

How to Use Learning by Doing?

Giving students frequently low-stakes quizzes is one way to implement the “learning by doing” method in the classroom (Boser, 2020). These tests are not meant to evaluate performance; they are just for fun. Instead, they ask students to engage with the content actively and generate new ideas based on what they have already learned. The very act of retrieving this information, according to research, increases understanding, improves recall, and aids in the “transfer” of knowledge to new situations. In other words, it makes learning a hands-on and active process. As an unlikely champion of the learning-by-doing method, psychologist Rich Mayer has written extensively on the subject. Mayer is known for being a kind and gentle soul from the Midwest. Mayer prefers the phrase “somewhat short of being exemplary” in conversation to the cruder (but no less accurate) phrase “someone screwed up.” According to Mayer, people do not have bad intentions; we are just stuck with the results of our bad choices. What is Mayer’s best advice? Avoid sending out any kind of ominous vibes (Boser, 2020). However, Mayer has become a trailblazer for learning as an active effort. His research at the University of California, Santa Barbara has shown time and time again that we gain expertise by producing what we know. For him, learning is an activity that generates new knowledge. Mayer does an excellent job explaining what we must do to make the technique work. First, we choose what we want to learn about Soviet history or Buddhist philosophy. Afterward, we incorporate the new information into what we already know by drawing a mental link between what we already know and what we hope to learn in the future. A person learning about Soviet dictator Stalin must therefore connect what he or she already knows (that Stalin was a dictator) to what they wish to learn (about Stalin) (that Stalin grew up in Georgia, killed millions, centralized power in Russia, and helped win World War II). Even the simplest memory tasks show the value

7.8 What are the Benefits of Learning by Doing?

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of mentally doing something—adding value in a specialized field. Consider the French word “Maison,” which means “home.” When you read a word, strike out the letter “o” to make it easier to recall. A letter deletion to recall a word may seem counterintuitive, but it makes sense to consider how we process the word after that deletion. By including the final “o,” the word is complete. After all, you are finishing the thought, and finishing it means that you have already put in some effort to produce learning, making it more memorable and likely to stick in your mind. Even more challenging cognitive tasks, like reading, can benefit from pushing oneself to work harder to learn more efficiently. Imagining the text in our minds helps us remember a lot more of what we read when we force ourselves to create a mental image of it as we read. By making a mind “movie,” we strengthen cognitive ties and increase the tenacity of our learning.

7.8

What are the Benefits of Learning by Doing?

People have used experimental learning for a long time to learn new things. In Aristotle’s words, “for the things we have to learn before we can do them, we learn by doing them” (Ho, 2021). This way of thinking evolved and changed over time, but it was eventually lost when computers were introduced into classrooms. Schools have only recently reintroduced this teaching strategy. It is easy to see why teachers promote this because it provides five significant advantages. 1. More commitment and memorable: The first benefit is that it is more exciting and memorable (Ho, 2021). You will not weaken your performance because this calls for your participation. Traditionally, people learned from lectures, books, or articles—and they could easily read—or not read—the text and walk away with no knowledge at all. It is much easier to retain that information when forced to learn something. Every action offers unique learning opportunities where motivation is born. That drive is linked to what you have learned and experienced. It conveys the message that education has value and purpose. It also gives students a chance to go through the learning cycle, which includes hard work, mistakes, and self-reflection before revising their approaches to solving problems. 2. More personalized: Learning by doing is a personal experience (Ho, 2021). The cycle of hard work and learning through trial and error is only possible if you are motivated by your values and ideals tied to a particular topic. This connection is strong, and as a result, reading from a book or articles like this one provides a richer experience. Because it fosters exploration and curiosity in students, making a personal connection is more important. You have a stronger attachment to the food you have prepared because you know you will be eating it. You can learn how to make a cake or a dish from scratch if you have always wanted to. You could also buy everything you need and do it all yourself. No matter how many times you try something new and fail, you will learn from them.

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3. Is connected to the Community: Learning by doing involves interacting with the natural world rather than isolating yourself in a classroom or library (Ho, 2021). Since the entire city serves as your classroom, you have access to many resources. Assemble local assets and partners, then connect them to global themes. In this way, the emphasis is shifted from the technical to the personal. It is important to remember that you are a part of a community and that this method of learning allows you to connect more with it—not necessarily with the people who live there, but with the environment in which they live for sure. 4. More integrated into people’s lives: This learning method is also very much a part of our daily lives. Learners are more likely to retain what they have learned when they can apply it outside of the classroom to real-world problems (Ho, 2021). The question “what is in it for me?” persists despite abundant information. Even when it comes to learning, people are more engaged if they believe that what they are learning is critical to their daily lives in some way. It is pointless to connect what they have learned to their personal experiences. On the other hand, experiential learning simplifies the application of theoretical knowledge. 5. Constructs skills for success: Making mistakes is an integral part of learning, but doing things correctly is even more critical. You are more likely to take risks, discover new things, and give things a shot first (Ho, 2021). Even if you make a few mistakes, this method does not hold it against you. As a result, learning by doing can help you develop your curiosity about new things and your perseverance in pursuing professional growth and development. Team management and collaboration skills may improve as a result of this. These things are critical for our personal development as we look to the future. 6. Short feedback loop: When learning a new skill, you have no idea if you will like it or not. You have a hunch it might be interesting, sound, or even enjoyable, but you have no way of knowing for sure. The significant advantage of learning by doing is that you can decide whether or not you want to continue learning after a week or two (Adamiak, 2020). If you enjoy learning and see your progress, you are on the right track. In the long run, you will not want to do something that is a constant struggle. Decide if you will keep creating new things by building new ones. 7. Building a portfolio: Most of the time, you want to gain some advantage by learning something new. It could be a pay increase, a new position, or even the ability to entertain family and friends with music. Building a portfolio of skills or projects, whichever you prefer, will benefit you in the long run (Adamiak, 2020). Assume you are a design student and hope to work as a user interface (UI) designer one day. In other words, work on developing a portfolio of accomplishments that you are proud of. If you have a portfolio of 25 websites, that is far more impressive than claiming to have watched 25 h of video and read 100 articles. Having a good grasp of three songs is more important than knowing 20 chords when playing the guitar.

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8. Beating procrastination: Waiting until the last minute to complete a task has a sneaky way of making you late for important occasions. Your brain is constantly perplexing you as a result of its energy-saving efforts. Putting together a video while also making dinner and texting will make you believe you are improving your skills. If you cannot concentrate on the article, think about the latest video game release. Consider whether your thoughts are putting you at a mental disadvantage. Procrastination is a method of learning that is ineffective because it only concentrates on theory (Adamiak, 2020). Fortunately, getting things done can help you beat procrastination. It is easy to get distracted while watching videos on the newest programming language features. You must, however, remain focused on the current task: creating an application. Do not allow yourself to be held back by procrastination. 9. Satisfaction: Being active, significantly when you can demonstrate your progress to others, can be gratifying. Long-term learning requires satisfaction as well. As a result, you will have the drive to continue the fight. So, no matter how long it takes, you will have something you worked hard for. As a result, it will serve as a constant reminder that you have chosen to run the gauntlet (Adamiak, 2020).

7.9

How to Get Started?

Even though these advantages are advantageous to you, where will you begin? As a result, there are numerous ways to go about it. As an example, consider the following (Ho, 2021). 1. Low-Stakes Quizzes: Many low-stakes quizzes can be used in classroom settings to introduce this technique. These tests are not meant to evaluate a person’s abilities. Instead, the quizzes encourage students to interact with the material and generate new information based on what they have already learned. Better understanding and recalling material helps students apply what they have learned in other contexts. Studies have shown this method to be an effective one for learning. 2. Type of Mental Doing: Rich Mayer, a psychologist, devised another strategy. He sees education as a creative process. Expertise can only be gained through action, but only if the action is based on the knowledge that we already possess. As an illustration, suppose you are interested in learning more about Soviet dictator Joseph Stalin. Simply connect what you already know—for example, Stalin was a dictator—with what you hope to learn and remember. Stalin was born in Georgia, where he went on to kill millions of people, consolidate Russian power, and help Russia win World War 2. We can use this technique even for simple memory tasks because our brain is constantly learning and revising. 3. Additional Mental Activities: Taking the literal approach is the last strategy; it requires you to go out and get your hands dirty. However, how you go about

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doing that is entirely up to your discretion. When reading an article, you may want to immediately apply what you have learned, like you can with this one. If you are interested in animal behavior patterns, you can read about them, observe animals, and see if they exhibit the behaviors you have read about. You could also try solving puzzles or creating a game out of the activity you are doing to keep yourself entertained.

7.10

Developing Learning by Doing Approach (How to Do It?)

Designing educational opportunities for young people should be guided by the philosophy of “learning by doing” and content based on scientific facts (researchbased). “Learning by doing” involves students in a hands-on, active learning process. Students will build mental models that allow for ‘higher-order’ performance, such as applied problem solving and transferring knowledge and skills through this teaching approach. Developing lesson plans should, in general, emphasize “making, producing, practicing, and observing” exercises than teacherled lectures. How do educators come up with such a strategy? Here are a few quick ideas (Hedrick, 2013): 1. Enable Students to Work Together: Collaborative learning is a teaching and learning approach in which students work in small groups to investigate essential questions or develop meaningful projects. For instance, you could ask a small group of students to develop a list of the skills they need to be a successful leader, or you could ask the group to figure out how to raise money for a class project. Two things can happen when you are facilitating productive group sessions. Students can share their own experiences in collaborative environments, creating teachable moments for the rest of the class. These small groups allow students to go from being learners to becoming teachers all at the same time. When working in small groups, students learn how to use and benefit from each other’s strengths. Second, students begin to develop their ability to work in teams. The experience improves collaboration, group communication, compromise, and listening skills. 2. Self-Directed Group Exploration: With the internet and other multi-media tools at our fingertips, it is simple to quickly access a wealth of information. The days of using library card catalogs and making copies of encyclopedias and journal articles for school projects are long gone. With just a few keystrokes, an enormous amount of data is loaded onto the computer’s display. When students work with educators, the challenge becomes sorting through a sea of information to determine true and false. As a result, most students live in an authoritarian environment where they have little or no opportunity to practice decision-making independently. Students’ reliance on evidence instead of authority (text, teacher, parent) encourages self-directed investigation and research. Acquiring the skills to find information for a group project improves

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fact-finding abilities and self-reliance. Small groups of students could be given the task of researching the best rabbit for a cold climate or the best rocket design by educators. Students will learn how to conduct their research to answer their questions. 3. Sharing the findings and results of experience-based activities: To implement an effective “learning by doing” strategy, students must be given a chance to discuss their experiences as a group and evaluate their performance. The question “if you could do it over, what would you do differently?” or “what improvements would you make” is an excellent follow-up question after allowing students to sum up their experience or share the knowledge they gained from an activity. Reflective questions like these help students identify areas for growth and develop more creative solutions. A good question to ask students is, “How is working in this group similar to being on a sports team?” for example, “how did you communicate effectively with your group?” or “what are some effective methods you used when serving on student council?” Finally, the activity-based learning sharing period is critical because it conveys the learning experiences of the smaller groups to the larger ones. During this time for sharing, educators can help students connect what they have learned and other aspects of their lives.

7.11

Drawbacks to Learning by Doing

Students cannot always learn everything they need to know by doing it themselves. Many jobs are either too expensive or too dangerous to let beginners handle. We can let someone work as a nursing assistant in an emergency room, but not as an “amateur brain surgeon,” in the words of Groucho Marx (Engines For Education, 2020c). Simply putting students into realistic situations will not cut it these days, either. When students are put in these situations, they can “try things out,” but there are two types of trying things out to consider (Engines For Education, 2020c). Unlike learning through experimentation, the other involves having a mentor or colleague “look over your shoulder” while you step into a new role. This second method enables a student to benefit from the observations and experiences of others. Because of this, he is open to interruption and perspective from others, who can share their own life experiences with him. The student may never have to ask a question in this type of “mentored role play.”. His actions are going to prompt a response. In order to give the student the information he needs, his mentor will wait until the right time comes along. Real-life situations have the disadvantage of not having mentors, which means they neglect both teaching and historical context. Learning a new role can be a tedious and frustrating experience without a mentor to guide you. It can cause bad habits to form or actions to be performed out of sync. Teachers set goals, encourage students to take risks, correct mistakes, and provide background information. Both the expert and the apprentice are required in an apprenticeship (Engines For Education, 2020c).

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The Challenges of Learning by Doing

I hope you have already decided to give learning by doing it a go. However, as Szymon Adamiak points out, you will still come up with some excuses (Adamiak, 2020). 1. Where to start, I do not know. It does not usually matter whether programming language or design tool you use? It is because most languages and tools are interchangeable. The fundamentals of programming and design tools are similar; once you have learned them, the move will be seamless. This is also true for other abilities. You can simply convert to French cuisine if you study Italian cookery and decide that you like it after a month. The basic culinary techniques, flavor combinations, and cutting abilities do not change. As a result, start anywhere and swap later if necessary. 2. I need general knowledge. You will require general knowledge at some point. You will not master something without first understanding the theory, but you can become competent in a skill by doing it. In programming, for example, you can get a job without having a deep understanding of algorithms and data structures. However, without this information, you will not become a great developer. Start small, improve your practical skills, and wait until you can make something before learning theory. 3. I do not know the valuable learning stuff . Yes, not every learning resource is made equal. You may come across materials that are obsolete or even slightly deceptive. It makes no difference. Modern search engines do a fantastic job at delivering relevant information. It is unlikely that you will come upon anything heinous. Almost everything you come across is satisfactory, and we are looking for satisfaction right now. You can get started with even low-quality materials. You will learn to distinguish between good and terrible materials as your experience grows. You should also start accumulating valuable assets in your library. However, for the time being, anything is preferable to nothing. As a result, do not use poor materials as an excuse to avoid creating. 4. I might fail. That is a massive undertaking. You are safe if all you do is watch and read instructions. Nothing could go wrong; your self-esteem is safe. It is possible to fail at something. Errors can pile up in your program. It is possible that your food is not edible. Your musical interpretation may resemble the whale song more closely. It occurs to everyone, and it will come to you as well. However, when you make things, try to fix them, damage them, and rebuild them, you learn the most. Your setbacks will allow you to learn and improve. Accept failure and uncertainty, then get your hands dirty and go to work.

7.13 Learning by Doing Science with Remote Labs

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Learning by Doing Science with Remote Labs

Laboratories allow students to experiment with the natural world or with data derived from it by utilizing the tools, data collection equipment, models, and scientific theories. According to the National Academy of Sciences report (Sharples et al., 2015) in the United States, “Practical laboratory work is the essence of science and should be at the heart of science learning,” the British Council for Science and Technology stated. It has also been argued that school laboratory work is of dubious value because the experience of experimenting in a school lab can overemphasize practicalities at the expense of deeper learning. Providing remote access to real scientific experiments to students in schools and universities has been an important area of innovation in this area. Over the internet, a student can operate real materials and equipment in a remote laboratory, and computers can carry out the procedure flawlessly (Sharples et al., 2015). As a result, students can develop their intellectual capabilities and conceptual understanding. Less time spent setting up and managing materials and equipment means more time for teachers to focus on framing and supporting student learning. Data comparison, collection, and replication are easier for students when working with larger data sets. Few schools in Brazil have science labs, giving students a chance to work with scientific equipment and conduct electrical circuits, mechanics, physics, and computer science experiments. In Brazil, the Federal University of Santa Catarina has a Remote Experimentation Laboratory (RExLab) (Sharples et al., 2015). Students operate sophisticated scientific equipment and gather data from realworld phenomena in a remote laboratory, not computer simulations. Students now have access to scientific equipment and materials via a remote lab previously unavailable due to cost, danger, difficulty, or time constraints. With the Radioactivity iLab, students can measure radiation from a strontium90 sample. As part of this iLab, students in the US move an Australian Geiger counter up and down to measure radioactivity at various distances while simultaneously viewing the results in real-time via an online video feed. A point source’s radiation intensity decreases in direct proportion to the inverse square of the distance from the point source. Many disciplines have remote labs available, including astronomy, biological research, chemical research, computer networking, and earth science. In addition, platforms like iLab Central, Go-Lab, and the OpenScience Laboratory are emerging (Sharples et al., 2015). Since remote labs have numerous advantages and are becoming increasingly common, this is an excellent time to concentrate on the pedagogical innovations that will be required to exploit the advantages of both local and remote labs fully. To what extent can laboratory procedures be simplified and accelerated by using new technologies? Innovations in pedagogy for learning by doing science revolve around six questions (Sharples et al., 2015):

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1. What is the objective of learning? The sole purpose of traditional teaching labs was to safely and precisely handle scientific equipment. In a remote lab, students no longer have control over this work. As a result, educators working in remote labs increasingly focus on conceptual understanding and inquiry objectives aligned with current curricular aspirations. More time for intellectual pursuits means less time for lab setup (and clean-up). While hands-on experience with local materials may be preferable in some cases, it is not universal. As an illustration, students working in a remote lab will have fewer options for customizing how they control and measure phenomena. Additionally, they may appreciate that the data set is of higher quality and requires less fine-tuning. When it comes to innovative pedagogy, focusing on real science learning objectives (rather than just using real materials) is critical. 2. How do students need help before and after the lab session? Educational designs frequently include specific resources for assisting students during the lab session. They frequently require less consciously organized instruction both before and after the event. Students, for example, require assistance in planning relevant experiments and research. They require assistance in forming concepts and hypotheses using approaches such as concept mapping. They will also require assistance in deciphering the data that emerges, connecting it to their original research questions, and deciding what to do next. Educators can better enhance students’ capacities as self-regulating learners by paying attention to the entire cycle of planning, acting, and reflecting. 3. How can students receive timely feedback on how they learn? According to researchers, teachers in traditional physical laboratories often identify problems students have with the lab and intervene appropriately; nevertheless, they may require assistance on how to engage students conceptually during the session. Students in a remote lab must also be assessed and assisted during their interaction with the lab, but the teacher may not be there. While conducting lab work, students can use digital tools to verify their understanding and progress, providing feedback to guide learning. 4. How can social roles promote objectives of learning? Meanwhile, physical labs in schools and universities are spaces where students connect socially to enhance each other’s learning. A foundation for communication can be established in remote labs via chat or online calling. However, communication is insufficient. The importance of structuring students’ collaboration has been highlighted in research on computer-supported collaborative learning. This can include assigning specific roles to students, providing a shared workspace, orchestrating when students should communicate in their work with the lab, and assisting them in monitoring and improving their social engagement. 5. Can sensory and data collection places be flipped? Students are traditionally required to collect data during class time and then organize and explain their findings as homework. This system, however, requires pupils to undertake difficult intellectual work alone at home in a less supportive atmosphere. Students can conduct their experiments outside of class with remote labs, reversing time at home and school. This may allow teachers to spend more time with pupils

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deciding what to study and discussing the findings. Overall, new pedagogies can balance time and support for conducting lab work and help learn from it, ensuring that students are not left to struggle alone with the arduous task of making sense of it. 6. How can teachers prepare? Teachers may learn in new ways via remote laboratories. Trainee teachers can practice with a remote lab on their university campus before instructing students with the same lab during a practicum in a school. Teachers can also use sample data sets collected by students in remote labs to help arrange their sessions. Additionally, because teachers from various places can use the same remote lab, they can more easily share their pedagogical approaches to conducting the lab class. Experiments in the real world are no longer restricted to the classroom and the university laboratory. Experiments can be carried out remotely, allowing students to use equipment and materials that might otherwise be out of their reach because of cost, difficulty, or time constraints. As a result, students and teachers can focus on learning goals and science education rather than handling technical equipment.

7.14

Realistic Learning Situations Through Simulations (Learning by Doing Through Simulations)

Simulations can provide students with a realistic experience in situations where real-world experience is impossible due to cost or safety concerns (Engines For Education, 2020d). • Using Simulators to Teach: Students can learn by doing with the help of simulations, which are both entertaining and effective. We can significantly expand the number of things students can learn by doing by employing computer-based simulations. Simulation-Based Learning By Doing is the instructional architecture that results. This design offers the possibility of converting the acquisition of any ability into practice-based learning. When it is impossible to construct real-life circumstances in which learners can engage in the tasks they wish to learn while being coached, simulations must be created that effectively imitate such situations so that the student is prepared for them without being in them (Engines For Education, 2020f). • Diverse Simulators for various capabilities: The use of tools like the flight simulator allows for more natural and effective learning of physical abilities. Although the physical world is complex, we have a good enough understanding of it to develop elaborate physical simulations. Because we live in a social and physical environment, we need to create social simulators to teach social skills efficiently. Unfortunately, our social environment is not fully understood. This is unfortunate because one of the primary goals of education is to teach students

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how to function in the human social world. This is what history, literature, psychology, economics, foreign languages, and practically every other discipline except science is all about. Much science is concerned with this (Engines For Education, 2020b). • Simulators as Complete Teaching Systems: A simulator without an instructor is not a comprehensive teaching method (or, better yet, many instructors). The GuSS system demonstrates how effective a simulation can be when accompanied by knowledgeable teachers who can keep track of it (Engines For Education, 2020e). GuSS programs include some teaching modules that monitor the simulation and interrupt with stories, commentary, and other guidance during the student’s activity, similar to how a flight instructor might interrupt a simulated flight to remind the student of some vital principle or share a real-life experience. • Complaints about Simulations: One criticism of simulation-based techniques is that they can never match the richness of real-world experiences. Simulations are inherently abstract representations of reality; they cannot capture all its complexities. This is a legitimate critique of simulations. When a student transitions from a simulator to the real world, he must map his experience from the simulator to the real world. This mapping task becomes easier as the simulation improves. The mapping task in a good simulation is so straightforward that the simulation appears transparent. It appears that the simulation’s experience is directly applicable to real-life situations. Many simulations have been created over the years using various types of computers (Engines For Education, 2020a). Some were excellent models of various physical things, their uses, and consequences. Simulators of many sorts of social behavior are also available, typically based on statistical models of decision consequences. Simulated voting behavior, games, and other statistical events are frequently found. These simulations can be fun to work with on occasion, but they are ineffective in the classroom (Engines For Education, 2020a). This is because most of these simulators are intended to be used as research tools. The user of such simulations remains “outside” the simulation, configuring the controls and monitoring the outcomes. On the other hand, simulations are the means of study in learning by doing, not the target of study. Users who learn through simulations are immersed in the virtual environment. This simulated world must also respond to the user in ways that classic simulations do not. Lessons should be planned so that students fall into well-known traps and then reason their way out. If a simulation lacks this feature, it may be entertaining to play with but has little impact on the pupil (Engines For Education, 2020a). Students absorb knowledge through conducting simulations in the same situation they will use it. Many teaching approaches supply knowledge, such as expert recommendations in simulation learning. However, learning through simulations allows students to desire information and deliver knowledge on-demand

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as needed if the simulations are appropriately created. Students can discover where to improve by working through a simulation and experiencing failures. Students love utilizing simulations, which is another reason why they are helpful for learning. These simulations enable a return to the apprenticeship method’s advantages. They provide a framework for students to apply what they have learned. Students are never presented with decontextualized data that appear to have only a muddled relationship, if any, to the goals they have. Students believe they have accomplished something after the process. More than any grade, the satisfaction of doing something is a reward.

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Approaches to Learning by Doing/Experiential Learning Models

Learning by doing is different from learning by watching others perform, reading others’ instructions or descriptions, or listening to others’ instructions or lectures since it involves direct experiences arising from one’s activities (Reese, 2011). The phrase “experiential learning” can describe a wide range of methods for learning by doing (Bates, 2015). Learning by doing is not just about leaving students to go and hoping that they will figure things out on their own in a haphazard manner. The structure provided by the teacher within which learning occurs is an essential component of experiential learning (Geography Discipline Theorey, 2001). The teacher may carefully plan the nature of the activity, and the experience may need to be carefully evaluated and analyzed afterward for learning to occur. There are a variety of design models that attempt to incorporate learning in real-world contexts, including (Bates, 2015): • • • • • •

laboratory, workshop, or studio work; apprenticeship; problem-based learning; case-based learning; project-based learning; inquiry-based learning;

1. Laboratory, workshop, or studio work: Today, we take laboratory classes for granted as a necessary part of a scientific or engineering degree. When it comes to learning new skills or developing one’s artistic abilities, workshops or studios are essential. Laboratories, workshops, and studios provide several vital services or goals, including (Bates, 2015): • to provide students with hands-on experience in selecting and effectively using standard scientific, engineering, or trades equipment. • to improve motor abilities in the use of scientific, engineering, and industrial tools, as well as creative mediums;

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• to help students comprehend the benefits and drawbacks of laboratory experiments; • to allow students to observe scientific, engineering, or trade work in action; • to allow students to test ideas and discover how effectively concepts, theories, and methods operate in the lab; • to instruct students on how to plan and/or carry out experiments; • to allow students to design and build objects or equipment using a variety of physical media. Laboratory classes provide a valuable learning opportunity for students because they help them move from the concrete (observing physical phenomena) to the abstract (thinking abstractly) stages of understanding the principles or theories derived from such observations. Another benefit of the laboratory is that it teaches students an essential cultural feature of science and engineering: all ideas must be rigorously evaluated before being regarded as “true.” Traditional educational labs or workshops have been criticized for lacking the types of equipment and experiences that today’s scientists, engineers, and tradespeople require. As scientific, engineering and crafts equipment becomes more complex and expensive, it becomes more difficult to provide direct access to such equipment to students, particularly in secondary schools, colleges, and universities. Furthermore, traditional teaching labs or workshops require a significant amount of capital and labor, making them difficult to scale, which is a significant disadvantage in rapidly increasing educational options. Because laboratory work is now such a common aspect of science education, it is essential to note that teaching science through laboratory work is a relatively new development in historical terms. Empirical science was not taught at Oxford or Cambridge in the 1860s. As a result, at the Royal School of Mines (now Imperial College, University of London), Thomas Huxley devised a program for training science teachers, which included the creation of classroom laboratories for the conduct of hands-on science experiments to students. This method is still widely used today in both schools and universities. Simultaneously, scientific and engineering advancement during the nineteenth century has resulted in additional types of scientific testing and validation outside of the kind of “wet labs” seen in schools and universities. There may now be newer, more practical, more cost-effective, or more powerful ways to achieve these goals through modern technology, such as remote labs, simulations, and experiential learning. Nuclear fusion, nanotechnology, quantum mechanics, and space exploration are only a few examples. Remote or digital observation and recording are frequently the only options in such situations. Knowing your lab, workshop, and studio goals is also crucial. 2. Problem-based learning: Howard Barrows and colleagues at McMaster University’s School of Medicine in Canada established the first type of systematized problem-based learning (PBL) in 1969, and it has since expanded to many other institutions, colleges, and schools. This strategy is becoming more popular in topic domains where the knowledge base is continually expanding, and students

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cannot master all of the domain’s knowledge in a short period. Students determine what they already know, what they need to know, and how and where to get new information that could solve the problem while working in groups. The instructor’s role (typically referred to as a tutor in traditional PBL) is crucial in facilitating and guiding the learning process. In most cases, PBL employs a highly systematized approach to problemsolving, albeit the specific phases and order may vary depending on the topic domain. For example, consider the following Maastricht Seven-Jump Method for PBL tutorials, as shown in Fig. 7.1. It is common for the first five steps to be completed in a small group tutorial of 20–25 students, with the sixth step needing private study and the seventh step being completed in a whole group meeting. While some instructors have managed the entire process entirely online using web conferencing and asynchronous online discussion as part of a blended learning approach, this method lends itself particularly well to blended learning. A comprehensive problem-based learning curriculum is difficult to create because the problems must be carefully chosen, rising in difficulty and complexity throughout the study, and problems must be chosen to cover all Fig. 7.1 The maastricht seven-jump method for PBL tutorials (Bates, 2015)

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the curriculum’s required components. Students frequently find problem-based learning difficult, especially in the early stages, when their core knowledge base may not handle some problems. (This has been referred to as ‘cognitive overload.’) Others say that lectures are more efficient and condensed to cover the same material. Assessment must also be carefully constructed, particularly if a final test is weighted heavily in grading, to ensure that problem-solving abilities and material coverage are assessed. According to research (see, for example, (Strobel & Barneveld, 2009)), if you want students to remember what they have learned for the long term, you should use problem-based learning. While the ‘pure’ PBL approach is still popular, many variations use tasks after the primary subject has been covered in more traditional ways, such as lectures or prior reading. 3. Case-based learning: Case-based learning is sometimes regarded as a variant of PBL, while others regard it as a distinct design model. Case-based learning, like PBL, uses a guided inquiry method, but it usually requires students to have some prior knowledge that might help them analyze the case. Compared to PBL, the approach to case-based learning is usually more flexible. Casebased learning is common in business schools, law schools, and clinical medical practice, but it may be applied to many subjects. Herreid (2007) sets out 11 essential case-by-case learning guidelines. a. Tell a story. b. Focuses on a topic that will pique your curiosity. c. It has taken place within the last five years. d. Empathizes with the central characters. e. Direct quotes from the characters are included. f. The reader will find it interesting. g. It must be pedagogically valuable. h. Causing conflict. i. Forcing decision. j. Has generality. k. Is short. Irby (1994) offers five steps in case-based learning, based on examples from clinical medicine practice: • use a (well-chosen) case to anchor your lessons; • aggressively engage students in debating, analyzing, and recommending solutions to the case; • as an educator, demonstrate professional thought and action when discussing the case with students; • guide and provide comments to students during conversations; • Provide a collaborative learning atmosphere in which all points of view are valued.

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Fig. 7.2 Blended learning sequence involving online learning resources, Marcus et al. (2004)

Both hybrid and wholly online contexts can benefit from case-based learning. Case-based learning is beneficial when dealing with complicated, interdisciplinary themes or challenges when there are no obvious “right” or “wrong” answers or when learners must assess and choose between competing, alternative explanations. For a case-based blended learning project in veterinary science, Marcus et al. (2004) employed the design model shown in Fig. 7.2. Of course, depending on the subject’s requirements, other configurations are also available. 4. Project-based learning: Project-based learning is comparable to case-based learning, but it is longer and has a greater scope and more student autonomy/responsibility in choosing sub-topics, organizing their work, and deciding how to perform the project. Projects are frequently centered on real-world issues, which gives students a sense of ownership over their learning. There are several best practices or principles for successful project work to follow. Larmer and Mergendoller (2010), for example, propose that any good project should satisfy two criteria: • Students must see the work as personally significant, as a task that they desire to complete effectively; • An educational goal is met when a project is meaningful. The most considerable risk of project-based learning is that it can take on a life of its own, causing students and instructors to lose focus on the most critical learning objectives or that critical content area may go unnoticed. As a result, project-based learning necessitates careful planning and supervision by the instructor. 5. Inquiry-based learning: While inquiry-based learning (IBL) is comparable to project-based learning, the teacher/role instructor’s is slightly different. In

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Fig. 7.3 Levels of inquiry-based learning (Banchi & Bell, 2008)

inquiry-based learning, the learner investigates a concept, selects a study topic, develops a research plan, and draws findings with the assistance of an instructor as needed. The instructor determines the “driving question” in project-based learning and takes a more active part in guiding the students through the process. According to Banchi and Bell (2008), there are multiple degrees of inquiry as shown in Fig. 7.3, and students must start at the first level and progress through the stages to reach ‘true’ or ‘open’ inquiry. Although proponents of inquiry-based learning have argued for its value at all levels of education, the fourth level of inquiry can be seen to define the graduate thesis process. Experiential Learning Activities to Engage Students. Students can benefit from experiential learning activities (Raudys, 2018): • Keep focused—Students actively engaged in their studies are less prone to grow bored or disinterested. • Learn differently—Students are more emotionally engaged when active in the learning process, which allows them to experience learning freshly and dynamically. • Learning faster—In-depth problem-solving and critical thinking are required while learning firsthand. These techniques improve student interest, learning speed, and knowledge retention. Table 7.1 compares traditional and experiential learning practices (Raudys, 2018). A list of exciting and engaging experiential learning activities for children in 1st– 8th grades with thorough descriptions and research-backed explanations indicating why they are effective is provided below (Raudys, 2018): 1. Pro and Con Grid: Decide on an open-ended topic for the class to discuss after the students have completed their assignments or lesson plans. As soon

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Table 7.1 Compares and contrasts traditional and experiential learning practices Traditional learning activities

Experiential learning activities

Teacher-centered/focused

Student-centered/focused

A predefined rubric or grading system is used to assign learning outcomes

The learning outcomes are open and flexible

By sharing information, you should explain your knowledge and/or talents

Make an effort to learn and improve your abilities by gaining new experiences

High degree of facilitation, fixed structure

An adaptable framework with minimum assistance is required

as students have completed their assignments or lesson plans, identify an openended topic in the lesson. Students should compile a list of both advantages and disadvantages of the topic. As long as you encourage your students to look at a topic from many perspectives and allow them to express themselves, you are helping them approach a lesson or project in a fresh, innovative way. Other aspects of experiential learning are also addressed in this practice, which helps students: • Become emotionally, intellectually, and socially engaged. • Take the initiative, make decisions, and accept responsibility for your actions. • Bring observations and reflections together in a new way to allow for new ways of looking at the course material in the future. • Learn from the natural consequences of group discussion and debate by creating a safe space for students to voice their opinions and make errors in public. This activity will help teachers discover more about their students than other highly effective experiential learning activities. 2. Cross-Age Peer Tutoring: Select your student’s role: tutor or tutee. Decide on a topic or a lesson plan that both students can contribute to. After the specified time has passed, follow up with the students to discuss their concerns and understand how the process has gone thus far. Peer learning can benefit students in various ways, including boosting their literacy levels, increasing their level of comfort and openness, and improving their critical thinking. Assisting a student in the process of explaining a piece of academic content to a colleague can benefit both parties: • Ask each other about their opinions before coming to a mutual agreement • Improve your ability to organize and design learning activities. • Encourage students to provide and receive feedback on their progress in learning • Put into practice what they have learned through more traditional means of instruction. Cross-age peer tutoring appears to benefit both tutors and students academically; studies have proven that this type of interaction benefits both parties.

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3. Student-Generated Test Questions: As soon as the lesson plan is complete, have the students answer three to five exam questions based on the class topic. Explain to your students that they must also develop relevant replies and formulate questions. This activity helps students better understand a lesson through provocative questions and replies by getting them to engage in an investigative and reflective process, which is at the core of experiential learning. Moreover, it is not just the pupils who are benefiting from this. Teachers can gain additional insight into what students find essential or memorable in a lesson by using experiential learning activities like this one. The questions and responses that the students provide teachers with an insight into: • What are the major topics inside a lesson? • What are the reasonable and valuable exam questions for students? • Whether student expectations for a future test are inaccurate? 4. Fishbowl: Assign a medium-sized group of students to debate a given topic freely in front of the class so that everyone can hear. Repeat the activity with the remaining students once this section is finished. The fishbowl activity relies heavily on your students’ comfort level with failure and taking risks. Experiential learning is enhanced when activities are done well because they encourage and open a student’s mind to new ideas, risk-taking, experimenting, and curiosity. Additionally, this action promotes student ownership of the topic and personal growth. 5. Prodigy Game: Set up a free parent or teacher account and have your student activate their account. Your students will be conquering their arithmetic skills in exciting math battles with in-game characters in a matter of minutes if you use this game. Students can gain several advantages as a result of this: • It is engaging: Studies have demonstrated that students’ attention, concentration, and enjoyment of instructional content increase when they use video games as a learning tool. Learning through video games is a fun and practical approach to incorporating technology into the classroom. In addition, it is altering the way experiential education is used in the classroom in new and exciting ways. • It is empowering: With immediate feedback, students can stay on task and invest in their success, which increases engagement and enthusiasm. By keeping students interested in the information and providing them with an incentive to accomplish assignments, features like in-game awards aid to drive the learning process. • It is competitive: Few other learning techniques can match the intensely competitive nature of video game-based education. Students are more likely to try something new if they have healthy rivalry to beat their previous best scores. 6. Make a Mnemonic: Students should be divided into teams with writing supplies to create their mnemonics for the course topic. Encourage them to share their mnemonic with the rest of the class. This activity’s design allows students the opportunity to link academic knowledge to personally meaningful subjects and visuals, resulting in an internal reflection on what they have learned and a

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deeper understanding of the lesson—a crucial aspect of experiential learning. Simply repeating knowledge will not help kids recall it later. The mnemonic is a versatile and successful teaching strategy that can help students learn everything from language skills to mental math. 7. Field trips: Consider taking students on field trips to apply the experiential learning approach by connecting what they learn in the classroom to real-world situations they will face. This example highlights how a field trip aids pupils in more ways than simply internalizing information from a textbook: • Make use of the knowledge they have gained. • Use enjoyable, interactive activities to help students connect and reinforce ideas they have already learned. Most teachers come to grips with the notion that just imparting information to a student does not guarantee that the student will internalize it in any meaningful sense. Experiential learning as pedagogy is demonstrated by these seven instances of experiential learning activities. Learning by Doing: Benefits of Experiential Learning. Projects, maker activities, and community-based learning experiences are all examples of experiential learning. Games, simulations, work-based learning, and service-learning are possible inclusions. Actively involving students has five main advantages (Ark & Meyers, 2018; Iberdrola, 2021): 1. Experiential learning is engaging and sticks: To learn, you have to put in the work, learn from your mistakes, reflect, and keep refining your techniques. That is Digital Promise’s first scientific lesson tip. Personal learning experiences that are both motivating and engaging students are essential. It establishes a link between what students have learned and how they feel. It gives meaning and relevance to what students are learning. Effort, mistakes, and reflection are all part of the process. Students in Whittle Schools participate in a weekly Expeditionary Day, or “X-Day,” where they explore questions raised in the classroom or their creation outside of the classroom within the larger school community and engage with the people, places, politics, and peculiarities of their city. They can also work on their projects. Makerspaces are available in Whittle Studios during and after school hours, so students can work on their projects whenever they want. 2. Experiential learning is personal: Taking the time to get to know each student and then participating in the learning process builds their sense of agency. Metacognition is cultivated in students by posing reflective questions. Student growth and development are closely monitored by a robust advisory system at Whittle, which also encourages students to discover their talents and interests, which can be developed further through hands-on learning. These trust-building and motivation-sparking partnerships with knowledgeable advisors generate an ever-expanding range of experiences.

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3. Experiential learning is community-connected: The city serves as the classroom for students participating in place-based learning. Learning uses local resources and collaborators to connect local concerns with global ones. Whittle uses host cities as a springboard to help students better understand how communities work, integrate classroom learning with real-world experiences, and tackle global issues. 4. Experiential learning is integrated: Experiential learning is practical, relevant, and integrated when students have the opportunity to dig deep. For students to learn deeply, they must apply what they have learned in the classroom to realworld problems and challenges they may encounter. Transparency increases with exposure to a wide range of situations. As Whittle students get older, they tackle more complex subjects and start working on design, problem-solving, and community service in collaboration. 5. Experiential learning builds success skills: In addition to developing project management and cooperation abilities, long-term challenges also promote initiative and perseverance among participants. Young people deserve experiential learning to make a difference now and in the future. These challenges, tied to the community, create agency and collaboration, bridge disciplines, and lead to useful public products. Strengths and weaknesses of experiential learning models. Experiential learning designs are evaluated in part based on one’s epistemological viewpoint. On the contrary, those who hold a solid objectivist stance are generally highly suspicious about the efficacy of experiential learning models. It is important to note that project-based and problem-based learning are widely employed across various disciplines and educational levels. Experiential learning has been very engaging for students and leads to enhanced long-term memory when correctly designed. In addition, proponents argue, doing so fosters critical thinking, problem-solving, and communication skills that are especially important in today’s digital age. It allows students to manage better highly complicated circumstances that straddle disciplinary boundaries and topic domains where knowledge boundaries are difficult to manage (Bates, 2015). Experiential learning approaches unquestionably necessitate a significant overhaul of the way classes are taught, as well as much pre-planning. For the most part, this involves significant re-training for instructors and meticulous orienting and preparing of students. In addition, according to Kirschner et al., simply assigning students real-world activities without providing direction and support is unlikely to yield positive results in the classroom (Bates, 2015). On the other hand, instructors can and do play an essential role in many forms of experiential learning, so when comparing groups, it is essential to make sure that assessments measure the skills developed through experiential learning rather than just relying on the same assessments as traditional methods, which tend to favor memorization and comprehension (Bates, 2015).

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Overall, experiential learning will help students get the knowledge and skills they will need in the digital age, but the work must be done correctly and by the design models’ best practices.

7.16

Conclusion

“Learning by doing” refers to John Dewey’s educational philosophy. Students must engage with their surroundings to adapt and learn in this style of instruction, which is known as “hands-on.” ‘Learning by doing,’ as the name suggests, holds that we retain more information when we put it into practice. The adage “practice makes perfect” needs to be replaced with the more accurate “actively engaged practice makes learning more effective.” in order to retain information, the most effective methods of instruction include those that encourage active participation and those that require you to exert additional mental effort. However, you must be careful when putting these strategies into action. You cannot reap the benefits of learning by doing if you do not lay a solid foundation first. When it comes to becoming an expert in a particular field, you must first feed your short-term memory with a steady diet of relevant information. Experiential learning, cooperative learning, adventure learning, and apprenticeship are just a few of the many variations on this theme. The term “experiential learning” is commonly used as an umbrella term to describe this wide range of approaches to learning by doing.

References Adamiak, S. (2020). How to simplify learning—Get your hands dirty. Medium.Com. https://med ium.com/the-ascent/how-to-simplify-learning-get-your-hands-dirty-3d8e61e0755c. Ark, T. V., & Meyers, A. (2018). Learning by doing: 6 benefits of experiential learning. LinkedIn. https://www.linkedin.com/pulse/learning-doing-6-benefits-experiential-tom-vander-ark/. Banchi, H., & Bell, R. (2008). The many levels of inquiry. Science and Children, 46, 26–29. Bates, T. (2015). Teaching in a digital age. BC Campus. Boser, U. (2020). Learning by doing: What you need to know. The-Learning-Agency-Lab.Com. https://www.the-learning-agency-lab.com/the-learning-curve/learning-by-doing/. Bruce, B. C., & Bloch, N. (2012). Learning by doing. In Encyclopedia of the sciences of learning (pp. 1821–1824). Dewey, J. (1938). Logic: The theory of inquiry, 1938. Henry Holt. Engines For Education. (2020a). Complaints about simulations. Engines4ed.Org. https://www.eng ines4ed.org/hyperbook/nodes/NODE-143-pg.html. Engines For Education. (2020b). Different simulators for different skills. Engines4ed.Org. https:// www.engines4ed.org/hyperbook/nodes/NODE-124-pg.html. Engines For Education. (2020c). Drawbacks to learning by doing. Engines4ed.Org2. https://www. engines4ed.org/hyperbook/nodes/NODE-122-pg.html. Engines For Education. (2020d). Realistic learning situations. Engines4ed.Org. https://www.eng ines4ed.org/hyperbook/nodes/NODE-123-pg.html. Engines For Education. (2020e). Simulators as complete teaching systems. Engines4ed.Org. https:// www.engines4ed.org/hyperbook/nodes/NODE-136-pg.html. Engines For Education. (2020f). Using simulators to teach. Engines4ed.Org2. https://www.engine s4ed.org/hyperbook/nodes/NODE-125-pg.html.

176

7

Learning by Doing

Engines For Education. (2020g). Why should we learn by doing? Engines4ed.Org. https://www. engines4ed.org/hyperbook/nodes/NODE-224-pg.html. Geography Discipline Theorey. (2001). Experiential learning theory. Gdn.Glos.Ac.Uk. https://gdn. glos.ac.uk/gibbs/ch2.htm. Hedrick, J. A. (2013). Implementing “Learning by Doing” strategies. Ohioline.Osu.Edu. https:// ohioline.osu.edu/factsheet/4H-33. Herreid, C. F. (2007). Start with a story. NSTA Press. Ho, L. (2021). What is learning by doing and why is it effective? Lifehack.Org. https://www.lif ehack.org/898427/learning-by-doing. Iberdrola. (2021). “Learning by doing”, a methodology to boost in-co training. Iberdrola.Com. https://www.iberdrola.com/talent/learning-by-doing. Irby, D. M. (1994). Three exemplary models of case-based teaching. Academic Medicine, 69, 947– 953. Kuo, Z.-Y. (1976). The dynamics of behavior development: An epigenetic view. Random House. Larmer, J., & Mergendoller, J. (2010). Seven essentials for project-based learning. https://www. ascd.org/el/articles/seven-essentials-for-project-based-learning. Lave, J., & Wenger, E. (1994). Situated learning: Legitimate peripheral participation. American Ethnologist, 21(4), 918–919. https://doi.org/10.1525/ae.1994.21.4.02a00340. Marcus, G., Taylor, R., & Ellis, R. A. (2004). Implications for the design of online case based learning activities based on the student blended learning experience. In Proceedings of the ACSCILITE Conference (pp. 577–586). Parker, F. (1977). What can we learn from the schools of China? Phi Delta Kappa Educational Foundation. Pennisi, E. (2010). Conquering by copying. Science, 328(5975), 165–167. Raudys, J. (2018). 7 Experiential learning activities to engage students. Prodigy. https://www.pro digygame.com/in-en/blog/experiential-learning-activities/#download. Reese, H. W. (2011). The learning-by-doing principle. Behavioral Development, 17(1), 1–19. https://www.engines4ed.org/hyperbook/nodes/NODE-224-pg.html. Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K., Feldman, M. W., Fogarty, L., Ghirlanda, S., Lillicrap, T., & Laland, K. N. (2010). Why copy others? Insights from the social learning strategies tournament. Science, 328(5975), 208–213. https://doi.org/10.1126/science. 1184719. Schön, D. A. (1991). The reflective turn: Case studies in and on educational practice. Teachers College, Columbia University. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Skinner, B. F. (2002). Beyond freedom and dignity. Hackett. Strobel, J., & van Barneveld, A. (2009). When is PBL more effective? A meta-synthesis of metaanalyses comparing PBL to conventional classrooms. Interdisciplinary Journal of ProblemBased Learning, 3(1), 44–58. Taxonomfa, U. N. A., Su, D. E. R. Y., & Con, C. (1998). A taxonomy of rules and their correspondence to rule-governed behavior. Revista Mexicana De Biodiversidad, 24, 197–214. Wikiversity. (2019). Wikiversity: Learning by doing. Wikiversity.Org. https://en.wikiversity.org/ wiki/Wikiversity:Learning_by_doing.

8

Embodied Learning

Abstract

Embodied learning method provides interaction with a real or virtual world when we start a learning process by using this method. For example, physical movement plays a significant role when we learn a new sport. The purpose of embodied learning will make the mind and body work together to enhance the learning process with physical feedback and actions. This technology collects the physical, biological, personal data, visual systems that keep track of movement with wearable sensors, and mobile devices respond to behavior such as tilting and supporting the motion. This chapter introduces Embodied learning, its principles on bringing it to the classroom, and how it can help the students. Keywords

Embodiment • Embodied learning • Principles • Virtual reality • Role-playing

8.1

Introduction

As a contemporary theory of learning embodied learning stresses the role of the body in educational practice and student-teacher interaction both within and outside of the classroom, including in digital settings (Smyrnaiou et al., 2016). Dance theatre, kinesiology, athletics, and even Mathematics and Physics all use the body as a learning tool, confirming the body’s importance when representing and communicating concepts. Students’ participation, movement, and cognitive development are the unifying denominators in these cognitive artifacts. The body has traditionally not been used in the classroom. There has not been much body usage in schooling in the past. The educational practice, the process of learning, and the interaction between students had consistently excluded any involvement of the body. Teachers and students did not accept embodied learning as a concept because it was not well understood (Smyrnaiou et al., 2016).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_8

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Embodied education is a broad concept that encompasses embodied teaching as well as embodied learning (Lindgren & Johnson-glenberg, 2013). The body is not just a medium of knowledge or a mediator, but it also reflects the student’s relationship with the world; therefore, understanding this was a challenge (Smyrnaiou et al., 2016). Embodied learning is closely associated with constructivist models and modern educational ideas on the role of the teacher, the student, and learning itself in educational practice. Embodied learning and embodied teaching are used interchangeably to describe innovative scientific and pedagogical techniques (Wilcox, 2009). The body is used both inside and outside the classroom for experiential learning by constructivist principles. In the Embodied Learning approach, students are treated as a whole, with their bodies viewed as tools for knowledge construction and as carriers of information. This approach departs from the academic model of knowledge perception and treats students as a whole (Caine & Caine, 1997). Using collaborative digital games built by the researchers (Smyrnaiou & Kynigos, 2012) in creative and new teaching methodologies, researchers have explored language and full-body action as important ways for students to convey their thoughts and meanings. Students become active participants instead of disinterested, and emotional neutrality turns into cooperation when placed in the center of the instructional process. The contexts in which new knowledge is applied and the activities in which students are expected to participate impact embodied learning. Because of this, when planning an activity, the following factors should be considered (Smyrnaiou et al., 2016): • cognitive engagement with the subject matter, cognitive processes, and the representation of a scientific idea • Movements of the body • Expression of the feelings of the student • Clearness of instructions • Designing activities in a holistic manner • Collaboration amongst students • To use what they have learned in different contexts Embodied learning is clearly in line with current educational techniques because it uses personality and emphasizes how students learn rather than what they learn. What is Embodiment? Embodiment as a primary learning mode has gained prominence due to advances in neuroscience (Stolz, 2015). Embodiment is defined as the “identification of an abstract idea with a physical entity” by MacLachlan (2004). ‘The enactment of knowledge and concepts through the activity of our bodies’ is what Lindgren and Johnson-Glenberg (2013) describe as embodiment. The concept of “thinking, being, doing, and interacting” morphs into a monist bodymind when the physical, biological, phenomenological, and experiential aspects of embodiment are considered (Hocking et al., 2001). As Lawrence (2012) points

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out, embodied (or “somatic”) learning “relies on the body’s knowledge,” which is why he calls it “embodied learning.” For some, embodied experience is defined as “the deliberate and mindful simultaneous body-minded engagement of one’s self with both the inner and outer environments.” Cho (2021) defined “embodiment” as “body-based actions including the educator and children’s gestures and wholebody movements to internalize and represent their understanding in a learning context.” How does embodiment affect learning? The use of a pen, pencil or brush to write or draw has been shown to alter how we learn. When writing or sketching by hand, rather than typing on a keyboard, we can cross out, add comments, and make diagrams in addition to the text. Text, mathematical solutions, and drawings created by students or teachers can be shared. Students can get insight into their thinking processes, identify knowledge gaps, and suggest methods to improve the technique by putting notes or drawings in their workings and crossings out. Mindbody exercises that help students articulate what they think can be highly effective teaching and learning tools for teachers and students (Sharples et al., 2015). As kids cross out and add numbers to work out subtraction problems, poets leave written evidence of their creative processes. An animated representation of problem-solving can be created using input devices that use a stylus or a touch screen. Create ‘working example’ videos to show students how a mathematical problem is solved or a subject is explained through diagrams (Sharples et al., 2015). Innovative new touch-sensitive technologies enable artists to communicate their work more nuancedly. Paints and computer screens use light to generate color, but paints use reflected light while screens transmit and filter light. These, on the other hand, are digital rip-offs of the ink’s flow and pigment’s color (Sharples et al., 2015). Technology can provide value when communicating by gesture with interactive surfaces like tabletops (giant multi-touch computer screens placed horizontally at waist height). Students can use their fingers, feet, or other body movements to manipulate digital information when using these interactive technologies (Sharples et al., 2015). By expressing or moving actual or virtual objects with gestures, users can communicate, create patterns and shapes, and even create music and dance. Children may benefit from using gestures when learning about mathematics and science since they can use gestures to move objects into alignment or start them moving when they rotate shapes or organize things in categories. In completing these acts, children’s words, such as “push” and “pour,” provide a scientific language foundation (Sharples et al., 2015). As there is a direct link between cognitive intent and physical actions, tabletops can be better for facilitating learning than computers with mouse and cursors. Schoolchildren in Europe worked together on digital tabletops to explain “tricky concepts” in science and mathematics as part of the JuxtaLearn initiative. They could move and rearrange the enormous text boxes on the screen that reflected the structure and content of their explanations by simply using their hands. They

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investigated their grasp of scientific principles as they recounted their actions to others and engaged physically with the text (Sharples et al., 2015).

8.2

Embodied Learning

Embodied learning includes student collaboration and movement whenever possible during the learning process, contributing to cognitive development (edsys, 2018a, 2018b). Students and teachers interact both within and outside of the classroom in this learning style. The body is used extensively in both contexts to communicate and represent. It is also known as robust learning or optimal learning, and the most advanced form of this learning uses motion capture technology. Instead of solely using intellectual learning techniques, the entire body is involved in the process. Students can better understand learning by experiencing it, which helps them learn about various concepts. Seeing how motor functions and chemical reactions work instead of just hearing about them makes a huge impact (edsys, 2018a, 2018b). As an example of how embodied learning benefits students in many situations, consider the following (edsys, 2018a, 2018b): • Better learning outcomes are achieved when learning sciences and humancomputer interfaces are combined. • When engaged in activities such as Lego building, children’s inventiveness enhances their vision and patience. • Smart thinking is combined with physical movements in Kinect-based educational games. • Project-based learning promotes their creative side and their desire to learn. • Field visits give students first-hand knowledge of a subject, which aids comprehension and long-term memory. Two things can be used to categorize the human body. There is a biological/sensual/embodied approach and a sociocultural and relational/interactive approach to developing skills. In Embodied Learning, the term “body” encompasses the physical body and the senses, the mind, and the brain, which is the entirety of the student’s personality. The body serves as a natural source of meaning production because it helps students express themselves more naturally. Human bodily experience and its psychological consequences are defined as a body, while others argue that these experiences are the foundation for cognitive activity and linguistic expression (Núñez et al., 1999). The following features characterize embodied learning (Dixon & Senior, 2011): • sensorimotor activity • the relevance of the gestures about the subject matter being reproduced • emotional involvement

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It is clear from the preceding that embodied learning includes coordinated movements of body parts or the entire body to attain a learning goal and sensorimotor activity, and emotional involvement on behalf of students. Embodied earning involves the sensorimotor system and body movements, and the perceived inputs can be turned into more reliable memory and cognitive representations (Abrahamson et al., 2012). On the other hand, the relevance of gestures relates to the analog or structural association between symbols and their meanings. Embodied Learning involves a step-by-step process that builds in complexity over time (Smyrnaiou et al., 2016). The student cannot move on to the representation of concepts during the first stage. On the other hand, students are aware that they will encounter scientific concepts and are anxious about being represented. Students are instructed to think of ways to portray the recommended information during the third stage, where movements are made unconsciously or perhaps due to imitation. Students apply their newly acquired knowledge to new environments in the final stage, which is also essential. They do this through dramatization (image/interactive theatre) or role play, in which they represent scientific concepts not only verbally or physically but also mentally and emotionally to the extent of embodying this new knowledge. Through emotional involvement and verbal communication abilities, it becomes clear that embodied learning is a technique during which the student uses mental processes communicated through coordinated body movements linked to the depicted knowledge (Smyrnaiou et al., 2016). If you look closely, you can see how well-coordinated the student’s motions are at all times, even when they are unintentional and seemingly random. Thus, the comprehension and incorporation of new knowledge into the student’s cognitive repertoire can be validated. Since the body is constantly active and serves as both a sender and receiver of messages, everything that occurs has significance. Embodied learning is the result of building a network out of all of our instant actions (Smyrnaiou et al., 2016). Embodied learning also relies heavily on student-teacher collaboration, whether in a physical school or a virtual one. Cooperation in the classroom improves learning results, motivates students, and helps them develop their social skills even further (Smyrnaiou et al., 2016). As a result, a specific individual or group personality traits may influence the other students. Students learn how their personal experiences meet and compliment others, even from diverse geopolitical and cultural backgrounds, by picturing this interaction in crossing circles. It is important to note that Embodied Learning allows students to participate in the learning process even if they are not physically present. Personality, physical presence, mental development, sensorimotor ability, and past experiences influence students’ responses (Smyrnaiou et al., 2016).

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Principles of Embodied Learning

This section answers this question “Based on what principles do educators from different fields design embodied learning strategies?” There are nine guiding principles for embodied learning (Munro, 2018). The following principles (see Fig. 8.1) should serve as the foundation (Munro, 2018). 1. Holistic Integration: According to Damasio (2010), humans are holistically integrated, and mindfulness is a byproduct of this monist orientation, resulting in the body mind. ‘Total body connectivity,’ as proposed by Hackney (1999), entails a fusion of the entire body. It is a holistic approach that recognizes the interconnectedness of the multimodal or multiple presences of self. An objective and subjective bodied being can exist simultaneously, and both are present in and because of each other. Shusterman (2012) claims that German phenomenology considers the body-mind nexus. According to Merleau-Ponty’s theory, this suggests that the body and mind cannot be separated (Stolz, 2015). A embodied being performing or projecting herself as an event results from this complete integration and interweaving of the objects and subjects (Budgeon, 2003). Holistic integration is a fundamental principle of embodied learning, and it should be acknowledged and accepted by all pedagogies that aim to utilize it. 2. According to the Organic congruencies principle, organic congruencies: Humans share many characteristics (Munro, 2018). We are all made of the same basic stuff. This is true regardless of how different we may look due to “plasticities,” which is how our bodies are classified as “objects.” Therefore, to facilitate learning, information about the different systems at play in the physical being and behavior (including sociocultural effects) is required, as this means humans share commonalities on all levels—physically, physiologically, psychologically, etc. As a result of organic congruencies, human behavior and activity follow a logical route and are more empathetic. Organic congruencies may appear to favor an essentialist perspective, but they honor human creation in all its complexity. Because of their extensive knowledge of organic Fig. 8.1 Principles of embodied learning

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humanness (about object/subject interweaving), facilitators in embodied learning ensure developmental markers and processes to pursue easy and efficient behavior within context (which may contribute to wellbeing). Organic congruencies have a relational principle at their core (Munro, 2018). It is based on the idea of what it means to be bodied and gives a baseline and basic map from and through which the unique individual can be navigated. Pupils can attain different levels of success based on how well they understand this notion. As Bowman (2004) points out, “[B]odies are always both thoroughly natural and thoroughly social and cultural”—this leads to the following principle, which is personal uniqueness.” 3. Personal uniqueness: As per personal uniqueness (Hackney, 2001), phenomenological experiences give rise to a unique and non-replicable identity for each human being. Because the body is such a finely built item, personal uniqueness is always becoming part of the self. Neuroplasticity and body plasticity plays a crucial role in creating a person’s individuality, with the environment also playing a role. However, it is also essential to the body’s response to the external and interior multimodal processes. Personal uniqueness dictates that everyone has a unique learning experience and engages differently because everyone has various viewpoints, perceptions, and paradigms (Munro, 2018). The multimodal bodily being simultaneously possesses these unique personal characteristics, displaying sociocultural and phenomenological identity and emotional reaction and feeling. Personal uniqueness is thus the focus— as embodied, performed, presented, and projected (MacLachlan, 2004). It is based on the principle of personal uniqueness that each person experiences and engages in the learning process in a slightly different way. As proposed by Lindgren and Johnson-Glenberg (2013), multiple and mixed realities should be incorporated into the embodied learning process. Adaptability, acceptance, inclusion, and transactional engagements are all prompted; as a result, giving the learner a chance to examine or engage with her filters and processes in order to accept or reject the shifts. According to Bresler (2004), ‘the body is personal. That being said, it may also be extremely social.’ As a result, the agency of the physical body is brought to the fore. Acknowledging and valuing each person’s unique sense of self as it is embodied in their learning process helps them become more aware of their feelings and experiences and how their embodied selves interact with one another. 4. Sensory awareness: Sensory awareness believes that all human beings can sense and feel within their bodies on various levels of consciousness. Included in sensory awareness is the term ‘interoception,’ a term used by Blakeslee and Blakeslee (2007) in which they describe an awareness of sensations in internal tissues viscera, and organs. In addition, ‘proprioception’ provides information about the body’s position and surroundings. Having a sense of one’s own body’s physical properties is one of the many benefits of proprioception. Physical awareness is not all there is to sensory awareness. Sensitivity to one’s own internal state provides an individual with a sense of wholeness

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and oneness. It is possible to be aware of one’s own identity about the environment and one’s sense of being due to sensory awareness that comes from the mind. The ability to be aware of one’s senses opens the door to developing a body-awareness, multimodal performance awareness, and an awareness of one’s performance. “An embodied consciousness,” according to Shusterman (2012), is “directed toward and experienced by a living, sentient body.” Regarding how we learn and perform and how we perceive, Stinson (2004) says that ‘internal sensing has great significance not only for how one learns and performs but also how we perceive,’ linking to Blakeslee and Blakeslee’s idea that ‘perception is an active construct.’ The development of sensory awareness toward a body-centered consciousness is essential to embodied learning strategies. 5. Inner and outer: Perception and sensory awareness are the foundations of the inner and outer principles. Both the individual’s internal environment (the sentient self) and the complex outer environment are processed multimodally and frequently at the same time through a body-minded consciousness. To the extent that something is felt inside, it appears and expresses itself via the physical body. Due to this inner-outer interweaving, many multimodal sensations constantly interact and contribute to embodied consciousness. When I am not in my “lived body,” Stolz (2015) says that I cease to be mindful of the world. A dynamic, interwoven, and a fluid link exists between the inner and the outside when the inner-outer concept is applied. When we say this idea applies to the multimodal and intersectional sense of being, we do not just mean one part or experience of ourselves. As a result, this principle recognizes and encourages the ongoing connection between one’s inner/self and the outside world. This principle emphasizes the interactive nature of the bodied person during the process of knowledge and skill acquisition in embodied learning methodologies. Continuous change is made possible by this multimodal involvement with one’s inner self and outer world. 6. Continuous change: Knowing that things are constantly changing is synonymous with knowing that things are always moving. According to Hackney, this is what constitutes life’s “change” (Hackney, 1999). Object-level phenomena like breathing patterns, a constantly pumping heart, and changes in body and brain structure are ever-present. It is essential to remember that things constantly change when studying the subjective self. This makes it easier for the embodied person to emerge continually. A variety of factors are at play in this ever-evolving situation. According to Csordas (1993), this shift is influenced by social factors. Current developments in cultural neuroscience, a discipline unto itself, support the idea that cultural patterns and practices might serve as “environmental forces that alter genetic selection in the long run” (Kim & Sasaki, 2013). An ever-emerging bodily mindedness is created by the interaction between the internal and external worlds. As long as things keep changing, you will have to deal with shifting environments. A person’s ability to learn is determined by whether or not they accept or reject the changes they experience on a conscious level.

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7. Habitual patterns: Habitual patterns emerge from the ongoing yet recurrent interplay between the inner and the outer (Hackney, 1999; Woodruff, 1992) across time and result from the embodied being’s patternmaking requirements. Habitual patterns can be beneficial if carried out with awareness and a sincere desire to learn. When habitual patterning, on the other hand, is not consciously engaged, it becomes conditioned and/or haphazard, resulting in redundant multimodal patterns that were formerly vital for survival. Eventually, they create roadblocks that result in discomfort, sickness, and malfunction. Through the aforementioned embodied consciousness, awareness of these habitual patterns is essential for self-teaching, critical reflection, and personal growth and for expanding the feeling and emerging from oneself as a distinct entity. 8. Re-patterning: Neuropathic re-patterning is conceivable since the brain (as an organism) is constantly modifying neurological connections to survive (Blakeslee & Blakeslee, 2007). Because of the body’s plasticity, the re-patterning continues organically throughout the entire body. A person’s functional and expressive behavior can be changed through conscious and purposeful decision-making, but repetition and habit can also be formed. As Woodruff (1992) described, re-patterning intentionally is known as “neuromuscular re-education.” To be continual, purposeful, and effective, re-patterning starts with embodied consciousness. It is beneficial to consciously re-pattern habitual patterns to improve wellbeing and learn new things on your own. 9. Self-teaching: Self-teaching is encompassed in the above-mentioned conscious application of the principles. In particular, it uses self-reflection skills (Schon, 1986; Leigh & Bailey, 2013), as well as body-mind awareness and cultivation (in and on the action). Using self-reflection as a guide, Burns (2012) asserts that we can make more informed decisions about ‘what’ to respond to and ‘how.’ Embodied learning is made possible by the concept of choice, which lets people accept or reject processes of constant change. A person’s ability to participate in higher order tactics with their sense of self—to think about thinking and sense about sensing—makes it possible for them to engage in conscious self-teaching. This allows them to develop an ‘embodied awareness’ (Shusterman, 2012). It involves the phenomenological and the pragmatic and leads to learning and overall wellbeing through free will. “The teacher-within” places the individual “in charge of the process,” which is critical for re-patterning, according to Goldman (2004). Consciously rely on awareness to guarantee that the research and application of embodied knowledge are monist endeavors. As previously indicated, the conscious application of mindfulness feeds the concept of creative self-fashioning (Shusterman, 2008).

8.4

Pros and Cons of Embodied Learning

New insights can be gained from embodied learning by generating personally relevant and timely data. It could also provide learners with fresh approaches to learning that they find intriguing. This will cause accessibility issues for people

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who have physical disabilities or have difficulty with physical exertion or complex bodily movements if it is not executed carefully. As a result, some students may feel frustrated or embarrassed, leading to lower levels of motivation and engagement (Sharples et al., 2015). There have been other influences on motivation discovered. Some people may become addicted to their fitness trackers, making it difficult to exercise without them since they feel like it is a waste of time. Aside from feeling “managed,” users have said that they feel “happy,” “satisfied,” “proud,” and “more motivated” when they meet their daily goals (Sharples et al., 2015).

8.5

Embodied Learning in Classrooms

Learning is a pleasurable phase for everyone who has the opportunity to become familiar with and understand things that are unfamiliar to them. Embodied learning has recently grown in popularity due to the unique approach it takes in order to assist students. Many studies have shown that this is one of the most successful ways for getting people to move and interact with one another physically. Human-computer interface (HCI) and learning sciences (LS) are easily integrated into this new discipline, resulting in a challenging experience with excellent learning outcomes. The method is simple and calls for a few moves during your training sessions. Student curiosity is piqued, and they have engaged in a pleasurable way. The concept of embodied learning, on the other hand, is proving difficult for educators to implement in the classroom. It is possible to achieve a final result for this learning strategy by combining intelligent physical items with wearables, motion-tracking, sensor technologies, and interactive video. Consider the tactics, methods, and activities (see Fig. 8.2) to integrate embodied learning into a classroom environment (Edsys, 2018a). 1. Lego building: You will have to use a variety of mathematical abilities and creative thinking to finish the construction. Fine motor skills, problem-solving, cooperative play, and perseverance are all critical for a student’s development during this time. You may help students develop both eyesight and patience while also encouraging their creativity by doing this. It is a great way to teach counting and measures and symmetry and patterns. It is also great for telling stories and picturing various topics. 2. Educational games based on Kinect: Schools implement cutting-edge instructional gaming systems that use motion sensing to enhance learning for the first time. Subjects like mathematics used to be taught solely through homework assignments in the classroom. Like Microsoft’s Kinect motion-sensor, the most advanced technologies combine physical motions with cognitive thinking to solve puzzles and other challenges. There are several ways to master complicated puzzles through fun activities like pricking the correct balloons, finding the correct path for hedgehogs, and magical baskets. Motion capturing technology is used in advanced learning methodologies such as SMALLab Learning.

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Fig. 8.2 Tactics, methods, and activities to integrate embodied learning into a classroom environment

3. Act out worksheet tasks: Please encourage students to come out and perform the worksheet tasks rather than simply completing them as instructed in the textbooks with pencil and paper. When figuring out chemical reactions Taking part in such an activity would be a blast and help keep students from getting bored in the classroom. Students will gain a more profound knowledge of the issues as they work through the dilemmas together. Studentsectrons, neutrons, or other elements when figuring out chemical reactions. Biologists can imitate a wide variety of animal species when teaching. As a result of such actions, students’ potential energy is more effectively converted into kinetic energy in the classroom. 4. Hopscotch math: Mastering mathematics is a difficult task for students from all academic backgrounds. Students can only master higher-level concepts with chalk and stones if they have a fundamental understanding of arithmetic. Using the hopscotch math concept, children can engage in an engaging, hands-on approach to arithmetic learning. This game can be reworked in various ways based on the underlying educational premise. • You can draw the hopscotch pattern that looks like a calculator in your classroom. • Boxes should be made to be near so that students can leap into them without difficulty. • The student must jump through equations that provide the outcome of 1 when the stone hits “1”. E.g., 4, 3, −, = or 1, *, 1, = , etc. • When a student makes a mistake, they are eliminated, and the last person to finish the round takes home the prize money. 5. Hands-on projects: Embodied learning relies heavily on hands-on learning, where students learn by actually doing the things they are studying. Openended projects allow students to express their creativity. It is possible to teach history in a fun and engaging way using folk art techniques. Science subjects

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are best learned by experiencing them virtually, so virtual science laboratories are ideal. Students do not have to follow strict guidelines and develop creative solutions to problems with open-ended projects. There are many different interactive ways to approach a challenging subject, such as brainstorming or storytelling. History mimes: History is a large subject, and many students find it challenging to keep track of the many phases and eras. Over time, there have been many changes in rulers, traditions, and manner of life. As a result of the students’ direct involvement with the subject, the history mimes technique has shown to be quite effective. Before getting into the specifics, an overview of historical periods and traditions is presented. Students would willingly create mimes based on their clothing choices or materials to represent certain customs or rulers. After the mime presentation, the other students in the audience must identify the historical time. Map art: This is a fantastic method to include embodied learning in social studies lessons. This method combines art with history in the most effective way possible, giving you a multi-layered grasp of the subject matter. Adventure exercises let you learn more about the subject matter by putting what you have learned into practice. Many strategies are devised in conventional methods to learn the map from printed materials. This technique necessitates that students create their maps, bringing them closer to the map’s elements. Teachers are encouraged to provide just minimal direction to students who are learning through this method of instruction since it promotes their creativity. Field trips: Taking a break from the classroom’s four walls can help the children. Take kids on field trips now and then so they can connect with the natural world while learning about various topics and concepts at the same time. With first-hand experience, students quickly grasp and retain information without putting in more effort beyond “normal” classroom learning. As we have all heard, a picture is worth a thousand words. So, you can imagine how useful it would be to observe chemical events, motor functions, or the transformation or growth of a plant up close. Energy breaks: Students need to have some energy breaks and all of these other activities that help bring the subject matter closer to them. A class full of sleepy kids is challenging for teachers to manage, and the learning session would be less fruitful. Warm-up exercises can serve as icebreakers and boredom relievers while boosting blood flow. Jump shots, swimming mimics, crossovers, weightlifting movements, or basic arm rotations can all be used to get the job done quickly and effectively. Consider incorporating some easy dancing steps and music to make class time more enjoyable. Students will quickly become energized.

Embedded learning can be implemented in various methods, and these are just a few examples. Abstract thinking is built on a foundation of contextual and bodily learning. The student gains multidimensional participation by exploring learning subject circumstances during the process. Physical interaction is said to increase

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existential learning. Many studies have shown how beneficial movement can be in stimulating a student’s intellect, which points to the importance of physical activity in the classroom. Embodied learning is sure to change the course of traditional education because physical activities are strongly linked to intellectual progress.

8.6

Embodied Learning Through Virtual Reality

A great deal has changed recently in the way teachers use various learning strategies and how students perceive them. Compared to old-school education, where teachers were one-sided tyrants, the new teaching method has given students new hope for engaging in learning on their own. Teachers and students will play equal roles in carrying out the educational process for the most part. Embodied learning is getting much attention as a cutting-edge learning technique since it significantly impacts productive learning. Virtual Reality (VR) is essential in restoring embodied cognition, which is intriguing. Using this method, the student experiences the material while using body and mind. Embedded learning, to put it another way, encourages students to learn by doing rather than listening or watching. Role of Virtual Reality in Embodied Learning. Compared to traditional training methods, virtual reality stimulates embodied cognition in some way. In embodied learning, students spontaneously learn from the environment without putting in the extra effort required in ‘normal learning.’ In this type of multi-sensory learning, students are allowed to experience or simply do what is required in the current situation. Instead of being a classroom, this is more like a playground, where kids may engage in more in-depth study and exploration, all while having fun! Saying “I learned this” simply signifies that the speaker has a good enough memory for the material to be recalled at any moment. The memory can be further boosted by adding a visual or audible cue. A solid foundation of memory on a particular subject is not surprising when based on VR-generated presence with bodily motions, gestures, and hand control-induced sensory-motor impulses. Virtual reality as a stimulator of embodied learning. Let us look at how virtual reality may be employed as a good stimulator of embodied learning (Edsys, 2018b). 1. Virtual tour of Google Expedition: Students can take a virtual reality tour of virtually any location in the globe utilizing this platform. Students can take advantage of this virtual tour using only their standard mobile phone with the help of the low-cost Google Cardboard. This immersive and interactive experience allows students to learn more deeply without incurring long-distance travel costs. The rise of technology has allowed teachers to explain a challenging subject in the classroom using images or even video documentaries. Nevertheless,

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kids can study any challenging subject by working closely with this virtual reality toy, thanks to its fantastic experience. Those who cannot afford expensive virtual reality software can use this fold-out cardboard viewer as an alternative. 2. VR headset: Students can experience virtual reality learning with this standalone classroom solution without a mobile device. ClassVR Teacher Portal utilizes today’s cutting-edge VR technology to provide students with an incredible VR learning experience. Students of various ages can use it because it is electronically administered. Students can visualize complex topics, allowing them to remember them in their minds for longer periods. Thanks to the well-organized lesson plans and exciting curriculum, students will remember the lessons they study. 3. VR platform peer: Students will have a mixed reality educational experience with this concept, which combines digital and physical aspects in the most productive way to be engaged. A more visual and concrete learning experience helps students become comfortable with abstract concepts or complex forces. Students can learn about physics principles like the ‘Doppler effect’ by using a digital headset and internet-enabled sensor together with a digitally enhanced environment. Students use this learning method to gain a more holistic view of the subject matter than relying on textbook assignments. 4. Applications for virtual reality: Another technique to make learning more fascinating and engaging is to use virtual reality apps in your classroom. There are several applications available aimed at helping children of all grade levels learn more effectively in a variety of areas. There are features in the Elements 4D software that let you combine various elements with seeing how chemistry reacts in real-time. In order to provide pupils with an extraordinary reading experience, Blippar, an AR (Augmented Reality) development tool, is commonly employed. Students can use the Arloon Plants app to see how plants grow by interacting with the parts and structure of the plants. Benefits of Embodied Learning through Virtual Reality. In today’s learning business, virtual reality concepts have a remarkable impact. Virtual reality can be used in various ways in the classroom (Edsys, 2018b) that restore embodied learning somehow. • A long period of reading or writing in front of the class would quickly become tedious for the students. However, studying through augmented reality keeps students interested and engaged all the way through, making them want to learn more. • Teachers can use this additional teaching tool to help students understand more complex concepts and make learning more fun. • Students’ attention spans are shorter than average; therefore, the instant learning effect of a VR encounter is advantageous. • Their attention cannot be diverted by anything outside the process since they are immersed in the experience.

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• Students in the digital era would be fascinated by the notion that interactive technology uses. • This type of hands-on technique for learning assists students in boosting their retention capacity. • This VR approach helps students understand what the teacher is saying instead of just reading from a book. • Furthermore, virtual reality-based embodied learning aids students in becoming self-directed learners. • Teachers can easily keep students engaged without making them feel like they are “learning” because it is more enjoyable. • Another advantage is that there will be no language barrier. Using a multidimensional picture, teachers would be better equipped to deal with students of any language. • In order to understand something properly, you need first-hand experience with it, and VR applications can provide you with that. • The quality of education is rising dramatically as professors and students alike may take advantage of the technology’s capabilities to delve deeper into various disciplines. For the most part, educators favor advanced virtual reality-induced embodied learning in their classrooms. They have demonstrated how this method of instruction has shifted students’ perspectives on a subject. Students seem more enthused than ever before, and the classrooms themselves are livelier and more exciting. Students ask more questions about the subject, showing that they start to think outside what is taught in the classroom and the textbooks. Students take much longer while learning a new subject to build a mental model around it. Thanks to virtual reality, they can connect the dots and construct mental models of the subject much easier. A generation of students raised on textbooks learns the theory but fails to put it into practice when confronted with real-world situations. However, with the advent of virtual reality technologies, there is reason to believe that embodied learning will come back in today’s classrooms.

8.7

Embodied Learning and Technological Developments in Educational Contexts

Because of the advent of new emerging technologies that necessitate physical interactions and bodily gestures from the user, embodied learning has recently gained attention in various learning scenarios. Motion-based games, such as those based on the Kinect sensor, can be used in educational settings to enhance the delivery of lessons (Kosmas et al., 2018a, 2018b). These computer games are often a new growing kind of entertainment that provides physical and social benefits for players’ mental and emotional wellbeing (Kosmas et al., 2018a, 2018b). A growing body of study investigating the impact of these motion-based games on students’

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ability to physically engage and interact with learning materials in both regular and special education. According to a local study, children’s cognitive functioning and academic achievement have benefited from such interventions and technological interactions in the past (Kosmas et al., 2017). According to a recent study, playing educational games based on the Kinect sensor improved children’s cognitive ability and math and language test scores (Kourakli et al., 2016). According to Kosmas et al.’s (2017) empirical findings, the adoption of Kinectbased instructional games aided ten children with special needs in improving their motor skills. An additional benefit of active-play video games in education was mentioned by Lieberman et al. (2011) in their research. According to specialists in the sector, further research shows a strong connection between physical activity and learning. Thus, the human sensorimotor system, perception, and muscular control can find solutions in the physical environment and comprehend specific learning tasks (Mcclelland et al., 2014). Numerous studies have demonstrated the sound effects on cognitive ability, memory, and academic accomplishment of increased physical involvement during the learning process. Donnelly & Lambourne (2011) and Gao et al. (2013) also acknowledge this. Studies show that playing Kinect-based games in the classroom boosts children’s short-term memory by as much as 20% (Kosmas et al., 2017). Comparative studies have found that using the Kinect-based learning environment improved the memory performance of university students by 32% when compared to the control group (Kosmas et al., 2018a, 2018b). A similar study by Donnelly and Lambourne (2011) found that children’s overall academic performance on a standardized test of academic success improved when they were physically engaged in their lessons. According to pre-and post-test results from an empirical educational inquiry, students made significant progress in learning physics (Enyedy et al., 2012). Studies have also indicated that embodied learning exercises positively impact the acquisition of language and the ability to comprehend foreign languages (Kosmas et al., 2018a, 2018b). For example, Cassar and Jang (2010) found that their game-based strategy helped primary school children with reading impairments improve their literacy skills. Other researchers have found that incorporating embodied learning exercises into the classroom helps students better grasp and retain spoken material (Chang et al., 2013). It also influences students’ understanding and memory of information (Donnelly & Lambourne, 2011) and improves second-language comprehension (Lee et al., 2012). According to other research, kinesthetic techniques and embodied activities can help students get better grades in math and science (Abrahamson, 2014; Chen & Fang, 2014; Kellman & Massey, 2013). A critical factor in motivating students to participate in the learning process is interacting socially with their classmates (Lieberman, 2006). Psychologically, these motion-based games may promote social contact and, as a result, increase feelings of self-efficacy, contentment, and enthusiasm (Staiano & Calvert, 2011). The entertaining character of these games catches children’s attention more

8.8 Role-Playing and Embodied Learning

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efficiently, enhancing their self-efficacy (Staiano & Calvert, 2011) and overall emotional condition as pleasant feelings are conveyed during play (Kosmas et al., 2018a, 2018b). Embodied learning practices such as motion/Kinect-based games can engage students in classrooms by providing stimulating activities that complement the traditional teaching and learning methods. However, implementing embodied learning as a part of the classroom curriculum in authentic classroom environments has not been thoroughly researched (Kosmas & Zaphiris, 2018).

8.8

Role-Playing and Embodied Learning

For various reasons, roleplaying exercises in math and science classes may have been underutilized (Barker, 2005). To begin with, courses in math and science have generally been content-heavy, with little time set aside for “play.” This has changed recently, however. This leaves little area for ordinary roleplaying activities that could be considered a social activity. The third reason is that arithmetic gets quite abstract by high school, so figuring out how to effectively incorporate roleplaying into lessons requires ingenuity. As a final point, roleplaying has a limited history in mathematics and science classrooms, and teachers tend to stick with what has worked in the past. Role-playing has been used in many math classes recently. Roleplaying might not be used as frequently as in other subjects in math and science classes (Barker, 2005). Students can act out transformations of quadratic functions by hopping around and waving their arms to show which way the parabola opens and whether it is “skinny” or “wide,” students can act out motion graphs while their companions draw what they observe and then compare the graphs. Students used their bodies to express and construct their knowledge in these activities, which did not include using any technology. “This physical dimension of cognition is referred to as “embodiment,” says Winn (2003).” By putting our thoughts into action through roleplaying, we can encourage embodiment. Conversations in math classes can also involve some roleplaying, albeit reduced form. When children talk about arithmetic, numerous semiotic techniques are employed, according to (Radford, 2005). Sometimes, a different semiotic system is employed for different aspects of a problem. Words, gestures, or actions can discuss time, speed, and direction (Barker, 2005). To fully grasp the children’s learning process, we must look at them all together in context. Gestures and sound effects are frequently used by students when conversing about mathematics. By performing these movements, the students are acting out a mathematical function or activity (basically, they are roleplaying) to assist them in better understanding what they are thinking about. In their acts, their thoughts are manifested, and they “see the world through a multi-sensory experience,” as they put it (Radford, 2009). It is easier for students to engage in metacognition when expressing their ideas through words, symbols, or physical movements. Metacognition and embodied learning can both benefit from roleplaying.

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Technology is not neutral when it comes to learning; instead, its affordances frame how it occurs (Barker, 2005). While it might make things more complex for students, incorporating technology into roleplaying can also increase the difficulty for teachers. According to Dall’Alba & Barnacle (2005), the interaction between the user and the technology affects learning more than the technology itself does from the perspective of embodied knowledge. Spatially directed communication has increased using technology in roleplaying-type activities (Roschelle, 2003). This occurs when students have to move or physically interact with their surroundings (such as using a probe to take the stream’s temperature or moving around the classroom to “catch” a virus on their mobile device). This type of learning can only be achieved by selecting and actively utilizing technology to extend one’s personality and self. Role-playing can be an important aspect of a math or science classroom, whether students act out a situation or interact with an idea during a conversation. When placed inside, the student’s body becomes an integral part of the problem or notion instead of merely their mind. A person’s entire environment and body are taken into consideration while teaching through embodied learning. Even if you do not use technology, a roleplaying game is an excellent first step (Barker, 2005).

8.9

Embodied Learning in Varied Disciplines

Embodied learning takes on many shapes according to the discipline and subject area (Nguyen & Larson, 2015). Implementing curricula necessitates identifying small distinctions in learners’ ways of knowing and teaching methods. Higher education courses can be divided into three groups (Nguyen & Larson, 2015). One category includes courses where the subject matter has an inherent physicality, while the other two include courses where the subject matter aids social classroom performance, and the third includes courses where there are implied spatial dimensions in the subject matter. Kinesiology and other body-focused fields like artisan-based disciplines and mechanics that need students to master hands-on skills are examples of studies with inherent physicality. The second group includes topics commonly seen in the social sciences. These disciplines, such as mathematics and physics, are grounded in conceptually spatial subject matter and provide the greatest challenge. Semi-structured classroom performances, common in sociology and counseling courses, are facilitated by socially-based content. According to Freiler’s (2008) concept of embodied learning, students can generate knowledge using physical, sensing, or being techniques in the three embodied disciplines. Integrating aspects applicable to all three categories of subject matter in a holistic curriculum of embodied pedagogy is necessary to develop guidelines. Subjects with Inherent Physicality. Bowman (2004) expanded on the biological and psychological aspects of embodied learning by looking at it in the context of music. The author condemned the “inadequacy of Cartesian dualism” that leaves the mind “ghostly, disembodied, and attached to the physical body by only the

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thinnest of threads.” According to him, the learner’s sense, perception, action, and conception are “mutually instructive, and structurally linked” when they experience motion, timbre, and rhythm. Furthermore, in a study of Taiko drumming, Powell (2004) referred to the instrument’s body, form, and drum as intertwined. “I felt a sensory shift in the way I perceive the boundaries of my body in relation to space. I am aware of a different sense of my body, the way that it occupies positive space against negative space,” Powell said. Physical proclivities and dispositions that serve cultural goals and enact systems of values are not independent of the social context in which they are manifested; rather, such a convergence of the physical and mental occurs as part of a larger social context (Powell, 2004; Bowman, 2004). According to Prentice (2007), the operating room is a place where people’s bodies, physical surroundings, and even clothing work together to inculcate learning that goes beyond the technical abilities of surgery. Aesthetics in the built environment, manual performance, and awareness of one’s own body in conjunction with others contribute to the development of embodied practice and socialization among professionals. Bowman (2004) and Prentice (2007) stated that mastering a manual, technical skill requires understanding physique and place. When Sutherland (2013) depicted theatre as a location of knowledge production “in and through the body,” it helped emphasize Prentice’s (2007) idea of the operating room as an event space. Bodies and space express a common meaning in both venues (Nguyen & Larson, 2015). Subject matters requiring action and spatial engagement highlight the mutually instructive activities of the mind in ways that lead to new modes of perception, reflection, and knowledge construction while also taking into account the culture of the learners as well as their linguistics and learning styles as well as their literacy skills and readiness to learn (Swartz, 2012, p.17). As Dewey (1997) proposed, students might reconnect mind and body in physical contexts to overcome traditional pedagogy’s dissociation. Subjects with Socially-based Content. In contrast to manual skills acquisition, disciplines of a sociocultural nature present knowledge that is intrinsically placed in society, but they also require students to integrate physicality thoughtfully. Sociology, psychology, history, and education are just a few disciplines that study human behavior in society (Nguyen & Larson, 2015). To learn social skills, students can use roleplaying (Kumagai & Lypson, 2009; Sutherland, 2013). Sutherland (2013) examined a South African college’s interactive performance to discover “the potential theatre and dramatic processes have in generating participatory settings” for exploring racism, class, and gender concerns. Through its capacity to exchange ideas and experiences, the embodied side of theatre had a significant impact on both the performers and the audience. Students can experience social dynamics by physically engaging ideological and intellectual ideas in conjunction with others through classroom performances (Perry & Medina, 2011). For example, Warren (2003) cites Cartesian dualism as obfuscating our physical internalization of social reality in experiential learning by constructing a false

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distinction between the intellect and the body. A course on race and power that Warren studied ethnographically in 2003 proposed that students act out social circumstances to recognize a “constitutive body” of social enactment and learn “how meaning gets formed on/through physical bodies.” Using Freirian issue presenting social circumstances, Warren suggested that students develop performances to bring their bodies into the social light. Learning takes on new meaning when students are immersed in dramatic and intellectual performances that enact social forces. After “students created a tug-of-war in which a student’s body became the rope between two opposing sides of antiracist desires and the seduction of privilege,” a “powerful conversation of resistance and cultural possibility” resulted, as recounted by Warren. It reifies and internalizes things that would otherwise remain conceptually disconnected conversation topics, such roleplaying raises awareness of hitherto undiscovered physical sub-details. Courses taught in a social environment offer a unique embodied experience since they rely on students’ emotions and bodily sensations to recall past experiences. By bringing together academic interests with hands-on learning opportunities, one can engage in dialogues that would otherwise be excluded from the mainstream. Empowering students in this way often reveals new learning methods and previously unconsidered knowledge and encourages students to go beyond the usual bounds imposed by normative academic discourses. Subjects with Implied Spatial Qualities. It is essential to consider how embodied pedagogy can be used in fields that lack physicality or stylize social enactment. There are a few areas where enterprising educators have not found a method to include the subject’s implicit spatial features into their teaching. Long before Bowman (2004) and Warren (2003) launched their attacks on Cartesian dualism, however, Tall (2003) made the case that mathematics lends itself to more than just symbolic and axiomatic ways of representation (p. 3). Tall proposed using computer programs that allow visual representations to be changed by eye and hand-movement, creating an embodied relationship between a problem and its solution to integrate embodied viewpoints into mathematics. Students who were previously scared by complex mathematical topics were not only able to understand them after using the application, but they also spontaneously engaged in discussions about their complexities (Tall, 2003). As a result, Tall said that embodied pedagogy produces the cognitive results that traditional educators value. When acquiring new ideas, such as mathematical concepts, learners employ gestures before communicating the ideas themselves, as Alibali and Nathan (2012). They also argued that using gestures to express ideas demonstrated the connection between the intellect and the body. Student explanations often refer to individual numbers within a formula, according to a study from 2012. A student who points out how they arrived at their answer allows the instructor to step in and help if the student fails. Researchers found that mathematical concepts and principles can benefit from embodied learning methods to improve memory.

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Even in the highly conceptual discipline of mathematics, Núñez et al. (1999) found that this human, embodied, and social component was present. When it comes to human thoughts, Warren (2003) and Freiler (2008) all agree that Bembodiment gives us an in-depth grasp of what those ideas are and how they are organized in (mainly unconscious) conceptual frameworks based on physical, lived reality, as well. Embedded learning of this type can now take on new forms thanks to computer and Internet technology advancements. Visual and physical components in online roleplaying games might help motivate and interest students while learning in virtual worlds (Nguyen & Larson, 2015). To take advantage of these advancements, instructors can now offer students a new set of hands-on training sessions. To summarise, the various modalities of physicality seen across disciplines create dynamics and characteristics unique to each circumstance. More than that, we have given instances to show that even the most theoretically abstract subjects can use embodied instructional tactics. A set of universally applicable criteria for embodied learning has been proposed to bring these disparate elements together in a single holistic curriculum.

8.10

Conclusion

Embodied learning proposes that learning occurs in mind and our physical bodies’ existing and new behaviors. When learning new material, using bodily actions can lead to deeper, longer-lasting memory traces and, in certain situations, higher test scores and increased retention. Wearable and implanted technology, such as internal microchips and digital tattoos, will deliver much more personal data regarding bodily movements and physiology in the future. However, these potential extensions or upgrades to minds and bodies will not always be a comfort or a gain. Before pursuing these advancements in greater depth, we must weigh the potential adverse effects on learning, such as demotivation and intrusion.

References Abrahamson, D. (2014). Building educational activities for understanding: An elaboration on the embodied-design framework and its epistemic grounds. International Journal of ChildComputer Interaction, 2(1), 1–3. Abrahamson, D., Gutiérrez, J., Charoenying, T., Negrete, A., & Bumbacher, E. (2012). Fostering hooks and shifts: Tutorial tactics for guided mathematical discovery. Technology, Knowledge, and Learning, 17(1–2), 61–86. https://doi.org/10.1007/s10758-012-9192-7. Alibali, M. W., & Nathan, M. J. (2012). Embodiment in mathematics teaching and learning: evidence from learners’ and teachers’ gestures. Journal of the Learning Sciences, 21(2), 247–286. https://doi.org/10.1080/10508406.2011.611446. Barker, J. (2005). Role-playing & embodied learning. Blogs.Ubc.Ca. https://blogs.ubc.ca/etec53 3eportfolio/e-folio-assignments/role-playing-embodied-learning/. Blakeslee, S., & Blakeslee, M. (2007). The body has a mind of its own. Random House.

198

8

Embodied Learning

Bowman, W. (2004). Cognition and the body: Perspectives from music education. In Knowing bodies, moving minds (pp. 29–50). Bresler, L. (2004). Knowing bodies, moving minds. Kluwer Academic publishers. Budgeon, S. (2003). Identity as an embodied event. Body and Society, 9(1), 35–55. https://doi.org/ 10.1177/1357034X030091003. Burns, C. A. (2012). Embodiment and embedment: Integrating dance/movement therapy, body psychotherapy, and ecopsychology. Body, Movement and Dance in Psychotherapy: An International Journal for Theory, Research and Practice, 7(1), 39–54. https://doi.org/10.1080/174 32979.2011.618513. Caine, R. N., & Caine, G. (1997). Unleashing the power of perceptual change: The potential of brain-based teaching (pp. 121–122). Cassar, A. G., & Jang, E. E. (2010). Investigating the effects of a game-based approach in teaching word recognition and spelling to students with reading disabilities and attention. Australian Journal of Learning Difficulties, 15(2), 193–211. https://doi.org/10.1080/19404151003796516. Chang, C., Chien, Y., Chiang, C., Lin, M., & Lai, H. (2013). Leveraging on human movements to facilitate multimedia learning. British Journal of Educational Technology, 44(1), E5–E9. https:// doi.org/10.1111/j.1467-8535.2012.01311.x. Chen, N., & Fang, W. (2014). Gesture-based technologies for enhancing learning. In The new development of technology enhanced learning (pp. 95–112). https://doi.org/10.1007/978-3-64238291-8. Cho, K. (2021). How do young children learn science through narrative, embodiment, and play? (Issue May). Csordas, T. J. (1993). The somatic modes of attention. Cultural Anthropology, 8(2), 135–156. https://doi.org/10.1525/can.1993.8.2.02a00010. Dall’Alba, G., & Barnacle, R. (2005). Embodied knowing in online environments. Educational Philosophy and Theory, 37(5), 719–744. Damasio, A. (2010). Self comes to mind-constructing the conscious brain. Knopf Doubleday Publishing Group. Dewey, J. (1997). Experience and education. Dixon, M., & Senior, K. (2011). Appearing pedagogy: From embodied learning and teaching to embodied pedagogy. Pedagogy, Culture & Society, 19(3), 473–484. Donnelly, J. E., & Lambourne, K. (2011). Classroom-based physical activity, cognition, and academic achievement. Preventive Medicine, 52, S36–S42. https://doi.org/10.1016/j.ypmed.2011. 01.021. edsys. (2018). Understanding student mindset to foster better learning experience. Edsys.In. https:// www.edsys.in/understanding-student-mindset-to-foster-a-better-learning-experience/. Edsys. (2018a). How to bring in the concept of embodied learning in classrooms? Edsys.In. https:// www.edsys.in/how-to-bring-in-the-concept-of-embodied-learning-in-classrooms/. Edsys. (2018b). Virtual reality is the key to reinstate embodied learning. Edsys.In. https://www. edsys.in/virtual-reality-is-the-key-to-reinstate-embodied-learning/. Enyedy, N., Danish, J. A., Delacruz, G., & Kumar, M. (2012). Learning physics through play in an augmented reality environment. International Journal of Computer-Supported Collaborative Learning, 7, 347–378. https://doi.org/10.1007/s11412-012-9150-3. Freiler, T. J. (2008). Learning through the body. New Directions for Adult and Continuing Education, 2008(119), 37–47. https://doi.org/10.1002/ace. Gao, Z., Hannan, P., Xiang, P., Stodden, D. F., & Valdez, V. E. (2013). Video game-based exercise, Latino children’s physical health, and academic achievement. American Journal of Preventive Medicine, 44(3), S240–S246. https://doi.org/10.1016/j.amepre.2012.11.023. Goldman, E. (2004). As others see us: Body movement and the art of successful communication. Routledge. Hackney, P. (1999). Making connections-total body integration through bartenieff fundamentals. Hackney, P. (2001). Making connections-total body integration through bartenieff fundamentals. Hocking, B., Haskell, J., & Linds, W. (2001). Unfolding bodymind: Exploring possibility through education. Foundation for Educational Renewal.

References

199

Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and expertise. In Psychology of learning and motivation (Vol. 58, pp. 117–165). Elsevier. https://doi.org/10.1016/B9780-12-407237-4.00004-9. Kim, H. S., & Sasaki, J. Y. (2013). Cultural neuroscience: Biology of the mind in cultural contexts. Annual Review of Psychology, 65(1), 487–514. https://doi.org/10.1146/annurev-psych-010213115040. Kosmas, P., Ioannou, A., & Retalis, S. (2017). Using embodied learning technology to advance motor performance of children with special educational needs and motor impairments. In Data driven approaches in digital education (pp. 111–124). https://doi.org/10.1007/978-3-319-666 10-5. Kosmas, P., Ioannou, A., & Retalis, S. (2018a). Moving bodies to moving minds: A study of the use of motion-based games in special education. TechTrends, 62(6), 594–601. https://doi.org/ 10.1007/s11528-018-0294-5. Kosmas, P., Ioannou, A., & Zaphiris, P. (2018b). Implementing embodied learning in the classroom: Effects on children’s memory and language skills. Educational Media International, 00(00), 1–16. https://doi.org/10.1080/09523987.2018.1547948. Kosmas, P., & Zaphiris, P. (2018). Embodied cognition and its implications in education: An overview of recent literature. International Journal of Educational and Pedagogical Sciences, 12(7), 970–976. https://doi.org/10.1999/1307-6892/10009334. Kourakli, M., Altanis, I, Retalis, S., Boloudakis, M., Zbainos, D., & Antonopoulou, K. (2016). Towards the improvement of the cognitive, motoric and academic skills of students with special educational needs using Kinect learning games. International Journal of Child-Computer Interaction, 11(2), 1–13. https://doi.org/10.1016/j.ijcci.2016.10.009. Kumagai, A. K., & Lypson, M. L. (2009). Beyond cultural competence: Critical consciousness, social justice, and multicultural education. Academic Medicine: Journal of the Association of American Medical Colleges, 84(6), 782–787. Lawrence, R. L. (2012). Bodies of knowledge: Embodied learning in adult education (No. 134). Lee, W., Huang, C., Wu, C., Huang, S., & Chen, G. (2012). The effects of using embodied interactions to improve learning performance. In 12th IEEE International Conference on Advanced Learning Technologies (pp. 557–559). https://doi.org/10.1109/ICALT.2012.104. Leigh, J., & Bailey, R. (2013). Reflection, reflective practice and embodied reflective practice. Body, Movement and Dance in Psychotherapy, 8(3), 160–171. https://doi.org/10.1080/174 32979.2013.797498. Lieberman, D. A. (2006). What can we learn from playing interactive games? In Playing video games: Motives, responses, and consequences. Lawrence Erlbaum Associates Publishers. Lieberman, D. A., Chamberlin, B., Medina, E., Franklin, B., Sanner, B. M., & Vafiadis, D. K. (2011). The power of play: Innovations in getting active summit 2011: A science panel proceedings report from the American heart association. Circulation, 123(11), 2507–2516. https:// doi.org/10.1161/CIR.0b013e318219661d. Lindgren, R., & Johnson-glenberg, M. (2013). Emboldened by embodiment: Six precepts for research on embodied learning and mixed reality. Educational Researcher, 42(8), 445–452. https://doi.org/10.3102/0013189X13511661. MacLachlan, M. (2004). Embodiment: Clinical, critical and cultural perspectives on health and illness. Open University Press. Mcclelland, E., Pitt, A., & Stein, J. F. (2014). Enhanced academic performance using a novel classroom physical activity intervention to increase awareness, attention and self-control: Putting embodied cognition into practice. Improving Schools, 18(1), 83–100. https://doi.org/10.1177/ 1365480214562125. Munro, M. (2018). Principles for embodied learning approaches. South African Theatre Journal, 1–10. https://doi.org/10.1080/10137548.2017.1404435. Nguyen, D. J., & Larson, J. B. (2015). Don’t Forget about the body: Exploring the curricular possibilities of embodied pedagogy. Innovative Higher Education, 40(4), 1–14. https://doi.org/10. 1007/s10755-015-9319-6.

200

8

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Núñez, R. E., Edwards, L., & Matos, J. (1999). Embodied cognition as grounding for situatedness and context in mathematics education. Educational Studies in Mathematics, 39(1), 45–65. https://doi.org/10.1023/A. Perry, M., & Medina, C. (2011). Embodiment and performance in pedagogy research: Investigating the possibility of the body in curriculum experience. Journal of Curriculum Theorizing, 27, 62–75. Powell, K. (2004). The apprenticeship of embodied knowledge in a Taiko drumming ensemble. In Knowing bodies, moving minds (pp. 183–195). Prentice, R. (2007). Drilling surgeons: The social lessons of embodied surgical learning. Science, Technology & Human Values, 32(5), 534–553. Radford, L. (2009). Signs, gestures, meanings: Algebraic thinking from a cultural semiotic perspective. In Proceedings of the Sixth Conference of European Research in Mathematics Education (CERME 6) (pp. 1–23). Radford, L. (2005). Why do gestures matter? Gestures as semiotic means of objectification. In Proceedings of the 29th Conference of the International Group for the Psychology of Mathematics Education (pp. 1–4). Roschelle, J. (2003). Unlocking the learning value of wireless mobile devices. Journal of Computer Assisted Learning, 19(3), 260–272. Schon, D. A. (1986). The reflective practitioner: How professionals think in action. The Journal of Continuing Higher Education, 34(3), 29–30. https://doi.org/10.1080/07377366.1986.104 01080. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Shusterman, R. (2008). Body consciousness: A philosophy of mindfulness and somaesthetics. Cambridge University Press. https://doi.org/10.1093/mind/fzv065. Shusterman, R. (2012). Thinking through the body: Essays in somaesthetics. Smyrnaiou, Z. G., & Kynigos, C. (2012). Interactive movement and talk in generating meanings from science. Bulletin of the Technical Committee on Learning Technology, 14(4), 17–20. Smyrnaiou, Z., Sotiriou, M., Georgakopoulou, E., & Papadopoulou, O. (2016). Connecting embodied learning in educational practice to the realisation of science educational scenarios through performing arts. Inspiring Science Education, 31, 31–38. Staiano, A. E., & Calvert, S. L. (2011). Exergames for physical education courses: Physical, social, and cognitive benefits. Child Development Perspectives, 5(2), 93–98. Stinson, S. W. (2004). My body/myself: Lessons from dance education. In Knowing bodies, moving minds (pp. 153–167). Stolz, S. A. (2015). Embodied learning. Educational Philosophy and Theory, 47(5), 474–487. https://doi.org/10.1080/00131857.2013.879694. Sutherland, A. (2013). The role of theatre and embodied knowledge in addressing race in South African higher education. Studies in Higher Education, 38(5), 728–740. https://doi.org/10. 1080/03075079.2011.593620. Swartz, A. L. (2012). Embodied learning and patient education: From nurses’ self-awareness to patient self-caring. New Directions for Adult and Continuing Education, 134, 15–24. https:// doi.org/10.1002/ace. Tall, D. (2003). Using technology to support an embodied approach to learning concepts in mathematics. In História e Tecnologia no Ensino da Matemática (Vol. 1, pp. 1–28). Warren, J. T. (2003). Performing purity: Whiteness, pedagogy, and the reconstitution of power. Wilcox, H. N. (2009). Embodied ways of knowing, pedagogies, and social justice: Inclusive science and beyond. NWSA Journal, 21(2), 104–120. Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87–114. Woodruff, D. L. (1992). Bartenieff fundamentals(tm): A somatic approach to movement rehabilitation. https://www.proquest.com/openview/57533456a338eb9a8a5e3d5a74d7fa22/1? pq-origsite=gscholar&cbl=18750&diss=y.

9

Adaptive Teaching/Learning

Abstract

Every learner is different. The presentations and materials are the same for everyone, which creates a burden on the learner to identify a solution or interact with that content, which creates a learning issue. This implies that some learners will be uninterested, confused, and likely to find the pathways leading to optimal learning through the content. Adaptive teaching provides a solution to these kinds of problems. It utilizes the past data and presents the learning habit to create a customized route through educational content. This method provides a better solution for starting new content and where old content needs to be updated. They also have several methods for tracking each learner’s progression based on longstanding learning habits, such as reading the textbooks and adding guidance provided with computers’ help. This chapter introduces the Adaptive Teaching/Learning method, presents various development tools, and furnishes a discussion on how to adapt and/or apply the adaptive teaching/learning strategies to students in practice. Keywords

Teaching • Learning • Adaptive teaching • Adaptive learning • Learning habits • Teaching/learning strategies

9.1

Introduction

Classroom instruction that assumes “one size fits all” has a severe issue; no two people are the same (Manipal Digital, 2021). People’s style, content, and assessment preferences are as diverse as they are. Contrarily, we live in a globally networked society fueled by smart devices (such as smartphones), rich content, and lightning-fast communication. A new content development paradigm called Adaptive Learning has recently been introduced

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_9

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in education to address problems caused by traditional teaching (Manipal Digital, 2021). As a result of the use of new technologies, adaptive learning aims to provide students with multiple options for learning routes, hoping that at least one of those options will be ideal for them. Of course, the concept of individualized education has existed for some time, but it has not been a reality until recently. Teachers cannot possibly teach the same idea in 40 different ways to 40 different students in a class. However, this is something that technology can do. The student is “pre-assessed” to determine their current level of knowledge in a standard adaptive learning exercise. After that, the learner is given the stuff to respond to, and the results are recorded. After that, the system will show you the content and ask you to take a quiz. This may lead to the system moving on to something else or re-presenting the material in a different media like a video, which would then be tested again (Manipal Digital, 2021). This level of training is sufficient in many circumstances. Most L&D businesses that advertise that they offer adaptive learning just provide the services. Adaptive learning, on the other hand, goes beyond computer-controlled numerous learning routes or simply alternatives provided in the expectation that one of them will match the learner (Manipal Digital, 2021; eCubed Training, 2019). In truth, none of them are guaranteed to work. For example, an adaptive learning system may select one of three approaches to illustrate an important topic such as office communication: first, a printed list of bullet-pointed “do’s and don’ts”; second, a recorded video; and third, a simulation through a mock exercise. However, the student may not grasp the finer points of the central learning due to various factors, such as a language barrier. However, the learning is not “adaptive” because it did not “adjust” to the learner’s existing ESL (English as a Second Language) speaker deficit. If such is the case, what exactly is adaptive learning? Here, we will get an in-depth look at adaptive learning.

9.2

What is Adaptive Teaching/Learning?

Adaptive learning, also known as adaptive teaching, is a teaching style that uses computer algorithms to arrange interactions with students and deliver resources and learning activities that are specifically tailored to meet the needs of each student (Wikipedia, 2021a). Individuals may “test out” some training in professional learning situations to ensure they engage with new education when it is offered. Computers use students’ responses to questions, tasks, and experiences to determine how educational material is presented. Computer science, artificial intelligence (AI), psychometrics, education, psychology, and brain science are all incorporated into the technology. Traditional, non-adaptive approaches have failed to deliver tailored learning on a broad scale, which has led to the development of adaptive learning. Adaptive learning systems aim to turn the learner into a partner in the educational process,

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rather than just a passive receiver of information. The primary use of adaptive learning systems is in education, although business training is another common application. They have been developed as desktop and web programs and are now making their way into the curriculum as a whole (Brusilovsky & Peylo, 2003). Adaptive Learning Examples from Everyday Life. Adaptive learning does not have to be computer-based (Cleave, 2020b). Take a look. Think about asking your mom if she can show you how to make bread. To create bread, she has mastered the techniques of mixing ingredients, kneading dough, and rising it before baking. The fact that you prefer to throw ingredients together carelessly, enjoy getting your hands filthy to learn, and might easily make a mess of things (such as forgetting to add flour while you knead) is also known to her. On the other hand, she is your mother. She lets you make mistakes and figure things out on your own, but she always steps in to save the day just in case you are about to fling your alleged loaf across the room out of anger. Moreover, she can answer all of your queries, such as how much salt to add to the flour as you mix it and what “proofing the yeast” means, so you gain expertise as you go along with it. Said, your mother will personalize the learning environment such that you pick up the skill of baking a loaf of bread far more quickly than you would have if she had forced you to watch a YouTube video tutorial. She accomplishes this by relying on her expertise of both you and the subject. The more patient one, your older brother may have a different approach because he has more expertise in the kitchen. However, your training is specific to you and your current situation as a learner. Imagine that you are a parent and attend your child’s first day at school. Parents arrive at the auditorium with a wide range of goals and objectives. Some parents have sent many children through the school and would like to know what is new. It is their first time for some, so they are soaking up all the information they can get their hands on! While some parents want answers to specific questions regarding their kids and their care at school, others want an overview of school policies and procedures. They all take their seats, and the principal begins by presenting slides and discussing the classes, the professors, and the policies and procedures, giving identical information to everyone. A few people will lose interest soon due to their prior knowledge of the subject matter. Others will be perplexed if the data presented has no bearing on what they already know. Some focus solely on a few key points, while others drift off to sleep. What have we learned as a result of this approach to education? There is no way anyone could have learned as much if they had been taught on an individual basis instead. Online training resembles a school assembly more closely than a toddler learning to make bread in the kitchen. Moreover, a lot of it is required: students have to watch every video and participate in every activity to pass the course. In other words, no matter how bored or lost someone is, they must put in at least 30 or

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Fig. 9.1 Functional diagram for a course that provides adaptive learning (Cleave, 2020b)

40 min. Besides being ineffective, this could cause the learner to develop a negative attitude about training in the future, making him or her reluctant to participate in any further instruction unless required to do so. Instead of a one-size-fits-all strategy, envision an eLearning course that adapts to each learner’s understandings, talents, and interests as they develop (Cleave, 2020b). Students learn what they need to know and nothing more in such a course. If you need further explanation, background, or practice, you will get it; if you do not, you can skip it. Training lasts as long as it takes for each participant to learn the material, but nothing is given to anyone who does not require it. What do you think? Does that sound like an impossible dream, a training utopia? This can be done in several ways. Some are technological, while others include Instructional Design. In order to better understand this, let us look at an example from the other end of the technology spectrum than baking bread. A Digital Adaptive Learning Technique. eLearning modules typically have lesson plans and a final mastery exam to ensure students have learned the material and use it. Here is a diagram (see Fig. 9.1) showing how an adaptive learning course works. It is possible for students who start the course with confidence that they are skilled to avoid the final exam and instead take a “test-out” assessment that indicates whether or not they have mastered the subject matter. If other students are unsure, they can practice the lessons before the final assessment. Another option is to start the classes, assess their proficiency, and then take the quiz to decide whether or not to continue. In the end, different participants will take different routes through the course, but they will all arrive at their goals in the same time frame. Also, except for the question, no new content was required beyond what would have been provided if the course had been given in a basic linear sequence (Cleave, 2020b). Different adaptivity types and adaptivity factors. Various tools are available for analyzing learner behavior and performance to tailor courseware to each student’s needs. Adaptability factors (see Fig. 9.2) include student performance (what they are doing now and what they have done in the past), knowledge level (previous or gained), content preferences, misconceptions, demographics, or other data sources (Smart Sparrow, 2018). Education technology can customize learning for each student based on various parameters.

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Fig. 9.2 Types of adaptivity factors

Unique responses can be triggered by adaptivity factors when adaptivity types are used. Do you continue the student’s learning journey as intended, give suggestions, or completely reroute their learning pathway based on what has happened in their learning adventure? An adaptable learning environment can be created in numerous ways. As an illustration of adaptability factors and adaptability types, see Table 9.1. Keeping teaching flexible and adaptive. Flexibility in teaching is critical to student success, regardless of learning style, and this is especially true for all students. The idea of adaptable teaching is more complex than it appears, as we have to find a way to speak to each student’s unique learning style while still keeping the class on track and adhering to the syllabus. As difficult as it may be, here are some ideas to keep teaching flexible and adaptable to best serve the students in the classroom (Lane, 2017). Table 9.1 Examples of adaptivity factors and adaptivity types Adaptivity factors

Adaptables

Information about a learner’s profile, behaviors, and performance can all be used to adapt a learning experience, e.g. Performance: Has the answer been correct or incorrect? What is the learner’s overall performance after completing a set of activities or lessons? Behaviors: How much time did the student take to complete the job? Information: Personal data, content preferences, and confidence levels can influence the next step in the learning process

The manner of adapting the instruction is based on the student’s performance. For instance Real-time Feedback: Help students when they need it most by giving them hints based on common misunderstandings about whether a tutor is watching over their shoulder Differentiated Pathways: As an example, you might give extra aid to a struggling student before moving on to the next topic, or you might prioritize advanced students and let them choose what they want to learn next

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1. One size does not fit all: Learning styles, backgrounds, belief systems, and upbringings are all important considerations when working with diverse people. These cultural elements are critical in recognizing that no two students are the same when learning. Taking into account the histories of students is critical to ensuring that all students are reached in the most effective and least restrictive manner possible. “Educators must have some basic knowledge of their students’ cultures so that student behaviors can be interpreted in their right cultural context,” says the National Education Association. Instead of presuming that all students study in the same narrow cultural context, we may change our style to reach cultural barriers and leave a lasting impression on all students. 2. “Menu” teaching: Children like choices. However, they do not receive many, especially at schools where they usually have to be somewhere and learn whatever the teaching plan requires. It does not imply that we can let the students control everything that goes down in school, but that it may truly assist children to feel authority and ownership in the classroom by providing options for activities. The idea of having a “menu” is, as it sounds, like running a restaurant, by presenting several choices, you will be more successful than just relying on your preferences. For example, students can work individually or hybrid with a group when teaching a unit. Through this paradigm, you can offer tasks for every degree of thought, every style of learning, and every intellect. 3. Flipped classroom: The “flipped classroom” is a structure that reverses the classrooms by, for example, distributing notes and power points before class instead of offering notes and slides to classes. In this approach, the class can concentrate on discussion and activity while acquiring the information needed for the greater part of the session. If you are adaptable and prepared to tackle things rather than remain rigid in your teaching, all students can learn without mentioning changes, including drudgery. 4. “Declared” and “taught” curriculum: It is a fact of life that we will never be able to do everything. We frequently begin with the best intentions, but we are forced to adapt as the unexpected happens. This is where the “declared” vs. “taught” curriculum diverges. While it is critical to cover everything that has to be taught, it is okay to stray from the path when it is in the best interest of the students. For example, a teacher may change the curriculum if she observes that the students are not grasping a particular concept. Student morale suffers when teachers strictly follow lesson plans, leading to student interest and attention loss. 5. Web-based learning: Although some teachers are sluggish to adapt to new approaches, the reality is that we are not incorporating this into their learning experience because of the ubiquity of the internet. Neither does it have to be a technical innovation that spreads throughout the world; something as basic as providing online flashcards or materials creates a “parallel” learning experience that complements everything done in school.

9.3 Technology and Methodology

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Technology and Methodology

Historically, there have been several models of adaptive learning systems, each with its own set of features. Even though various model groups have been given, the majority of systems include any or all of the following models (Wikipedia, 2021a): – Expert model—The model containing the material to be taught. – Student model—a student model keeps track of and learns about a particular student – Instructional model—the model that transmits information – Instructional environment—The user interface for interacting with the system. • Expert model: The expert model is a database that contains information about the course material. This might be as simple as the answers to the questions in the test, but it can also include lectures and tutorials and, in more advanced systems, even expert methodology to explain how to approach the questions. Without an expert model, adaptive learning systems often include these features in the instructional model. • Student model: The method used in CAT (Computerized adaptive testing) is the simplest way to assess a student’s ability level. When taking the CAT exam, questions are provided to the subject based on how challenging they are about their assumed ability level. When a person takes a test, the computer analyses their answers and adjusts their score based on their answers, selecting questions from a narrower range of difficulty. Creating a CAT-style algorithm is a comparatively simple task. Many questions are collected and graded on their difficulty, either through expert analysis or experimentation. If you have already determined the subject’s maximum and minimum skill levels by using this method, you will be presented with a question that falls somewhere in between those two numbers. These levels are then adjusted to the difficulty of the question, reassigning the minimum if the subject answered correctly and the maximum if the subject answered incorrectly. A margin of error must be built to account for situations where the subject’s answer does not reflect their true skill level. Assuming that a person is competent at answering multiple questions from the same difficulty level greatly reduces the likelihood of a misjudged answer. Programming the student model to analyze incorrect answers can help thoroughly identify conceptual weaknesses. Multi-choice questions, in particular, can benefit from this. • Instructional model: The instructional model often aims to blend the greatest teaching tools offered by technology (such as multimedia presentations) and expert advice from teachers on presentation strategies. The sophistication of the educational model is highly dependent on the sophistication of the student model. The instructional model in a CAT-style student model will order courses by the ranks of the question pool. Once the student’s skill level is

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accurately assessed, the instructional model delivers the relevant lesson. The more advanced student models that assess based on concepts require an educational model that organizes its lessons according to concepts. The instructional model can be created to examine a group’s deficiencies and create a lesson plan accordingly. Several systems attempt to give students ‘hints’ in response to wrong responses when the student model evaluates the answers. The more mistakes the student makes, the more suggestions crop up, such as “pay attention to the number’s sign.” If the hints are explicitly questioned, they can be part of both the instructional and expert models so that the student, instructional and expert models all overlap.

9.4

Implementations

In this section, we provide various options on how adaptive learning can be implemented (Wikipedia, 2021a): • Learning Management System: Adaptive learning features are increasingly common in learning management systems. A learning management system (LMS) is software used to administer, document, track, report, and provide educational courses, training programs, or programs for learning and development. • Distance learning: Distance learning and group collaboration can benefit from adaptive learning systems, which can be deployed online. Adaptive learning is increasingly being used in the realm of distance education. Students who were asked questions from a pre-selected question bank could receive an automatic response from early systems without adaptive learning. On the other hand, those methods are devoid of the guidance teachers may offer students. Adaptive learning is becoming increasingly popular in distant education because it allows for the implementation of dynamic, intelligent behavior in the learning environment. Students are assessed on their abilities as they learn a new subject, and databases keep track of their progress based on one of the models. To keep up with today’s modern distant learning systems, a concept is known as ’cognitive scaffolding’ considers students’ answers and adjusts to their cognitive capacities. Using cognitive scaffolding, it is possible to design a cognitive assessment path from lowest to highest based on demonstrated cognitive abilities. It is possible to use adaptive learning to improve group collaboration in online forums or resource-sharing platforms. Automated grouping and customizing links to information sources based on user interests or browsing patterns are two instances of how adaptive learning can aid cooperation. • Educational game design: An educational researcher completed a long-term study of adaptive learning in educational game design in 2014. An adaptive

9.5 Development Tools

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learning model based on game design theories and practices, instructional tactics, and adaptable models was developed and verified as part of the research. This model is called ALGAE (Adaptive Learning GAme dEsign). A complicated model was developed by merging past studies in game design, instructional tactics, and adaptive learning. The adaptive educational gaming design model has been developed for game designers, instructional designers, and educators to boost learning outcomes due to the study; this survey validated ALGAE’s worth and provided particular insights into the model’s creation, use, and benefits well as the difficulties that arose. These findings have formed the basis of the present ALGAE model. The approach is presently used to create instructional video games for the PC. In addition to government and military agencies/units, the game industry and academia can benefit from the model’s applicability. In time, as more people use ALGAE as a model, the model’s true worth and the best implementation strategy will become apparent.

9.5

Development Tools

Several programs tout adaptive learning capabilities (Wikipedia, 2021a), yet their capabilities might vary considerably. Ent entry-level programs use simple parameters such as answering a multiplechoice question to determine the learner’s path (Wikipedia, 2021a). Path A may lead the student to the proper answer, whereas Path B may lead them to the incorrect answer. Aside from providing a simple branching manner, these tools frequently use a linear model to drive learners to a certain point along the line. Their adaptability is limited as a result of this. Various factors can influence how well a student does, including what they are doing now, previous decisions, behaving, and even interactive and external activities. Adaptations based on complex variables can be created using powerful techniques at the other end of the range (Wikipedia, 2021a). Because they rely on artificial intelligence technologies like an inference engine, these high-end solutions typically do not have any underlying navigation. A key difference between modern tools and traditional ones is the underlying design difference. Instead of answering a basic multiple-choice question, the student may be asked to participate in a simulation where various elements are taken into account to determine how the learner should adapt. Popular tools • Adobe Captivate • Qualtrics • Adobe Captivate: Adobe Captivate is an authoring application that creates Shockwave Flash and HTML5 learning content such as interactive software

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demonstrations, software simulations, branching scenarios, and randomly generated quizzes. In addition, it can convert Adobe Captivate-generated file formats (.swf) to MP4 digital formats (.mp4), which may be played back on media devices or uploaded to video-hosting services. Captivate’s software simulations can use left- and right-clicking, key-pressing, and rolling-over visuals. It may also be used to make screencasts and convert Microsoft PowerPoint presentations to .swf and HTML5 formats (Wikipedia, 2003). • Qualtrics: Qualtrics is a U.S.-based firm that specializes in customer experience management. It has offices in Seattle, Washington, and Provo, Utah. Scott M. Smith, Ryan Smith, Jared Smith, and Stuart Orgill founded the company in 2002. As of March 2017, Qualtrics provides a cloud-based subscription software platform for managing customer experiences. According to the Wall Street Journal, SAP acquired Qualtrics for $8.5 billion on November 11, 2018. On January 23, 2019, the deal was finalized. According to an SAP press release dated July 26, 2020, the company plans to go public with Qualtrics, and on January 28, 2021, Qualtrics began trading on the Nasdaq. Microsoft executive Brad Anderson announced his departure for Qualtrics in the trade press on January 11, 2021 (Wikipedia, 2021b).

9.6

Adaptive Learning is the Future of Online Education

Adaptive learning makes it possible to provide each individual their full and undivided attention. Like an auto-grading system, the concept utilizes AI and ML algorithms to adapt to the learner’s needs based on the student’s activities and responses. As a result, the learning gap is narrowed, and students can access information whenever they want it (Michelle, 2021). It should be no surprise that individualized instruction benefits a child’s growth. A learner’s learning gaps can be more easily identified with individualized training, regardless of the subject. Adaptive learning, without a doubt, is a paradigm that meets the requirements above. Today, adaptive learning is the most sought-after career path in the educational system (Michelle, 2020). How is technology making a difference? Adaptive learning has been there since the late 1950s, although it was not well known due to a lack of available technology at the time. Education technology is the only way to keep going for students, learners, and educators right now; furthermore, with machine learning and artificial intelligence, technology aids in determining each student’s specific learning requirements based on their replies to questions, assignments, and exams (Michelle, 2020). Benefits of Adaptive Learning for Online Education The following are the various benefits of adaptive learning (Michelle, 2020):

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1. One-on-one learning: In order to provide a person full attention, adaptive learning is utilized. Like an auto-grading system, the concept uses AI and ML algorithms to adapt to the demands of the student. Because of this, students can learn at their own pace, which reduces the learning gap. 2. Time-effective approach: Adaptive learning makes it possible to provide each individual their full and undivided attention. Like an auto-grading system, the concept uses AI and ML algorithms to adapt to the demands of the student. As a result, the learning gap is narrowed, and students are given access to information at their own pace. 3. Time-effective strategy: Finding the gap between what students know and what they do not understand is more important than memorizing ideas they already know. When it comes to education, adaptive learning aids students and teachers in spotting problems and emphasizing the areas that need improvement. This method saves time for both the teacher and the student, who may struggle to balance multiple subjects. 4. Enhance confidence in students: The adaptive learning method adapts to the specific needs of each student, enabling them to acquire a skill more quickly. In order to help them perform better, the subjects are tailored to their learning needs, which in turn boosts their self-confidence.

9.7

How to Apply Adaptive Learning in Practice?

Instead of a general approach, adaptive learning allows students in schools and colleges to have learning modules personalized to their unique requirements, learning styles, and learning challenges (Lopukhina, 2020). Adaptive Learning Is the Future of Education. It would take a remarkable shift in the educational paradigm for schools and colleges to integrate this learning method across the curriculum and in every subject (Lopukhina, 2020). Only a few specialized private schools and colleges have attempted something like this throughout education’s history. As a departure from traditional teaching methods, adaptive learning has some educators concerned that in the long run, it will replace them with courses tailored to the individual needs of each student. Because every student learns differently, even if they are grouped into broad categories (e.g., visual, spatial, logical, social, etc.), they will have different learning results. As a result, some students may get the information a teacher is trying to impart, while others will have difficulty. Teachers are in charge of the way a course is taught and the results they should expect as a consequence. However, part of these outcomes is a function of individual traits, IQ levels, and known learning challenges. A one-size-fits-all strategy does not work as effectively for today’s learners because of their diverse needs (Lopukhina, 2020).

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Students of all ages are now more immersed in digital learning and thinking than before. Adaptive learning came into being when personal computers were becoming commonplace. Artificial intelligence (AI) programs were envisioned as being able to customize courses based on the needs of individual pupils. Adaptive learning systems like Scholar, which debuted at that time, established the framework for today’s systems (Lopukhina, 2020). Educators should combine adaptive learning with predictive analytics to improve students’ learning ability and achieve better results. Students’ learning activities, such as the time spent on each activity, reaction latency, and assessment scores, can be collected and processed by AI-based learning systems. Predictive models can be built using the data to find patterns and better meet the needs of each student. Many instructors understand adaptive learning’s advantages, but finding a costeffective way to put it into practice remains a struggle. Data is analyzed by algorithms considerably more quickly than by humans. As a result, students receive content, prompts, and interventions tailored to their specific needs and skills in real-time. Applying Adaptive Learning in Practice. Fortunately, educational software has improved to the point that it can now be tweaked or personalized to suit better the needs of students, instructors, and content creators (Lopukhina, 2020). Educational material makers and providers can create customized learning packages rather than deliver a single learning package for a specific course. Any instructor or tutor should conduct a quick assessment to determine the types of learners in any given course or class. The information teachers have at their disposal allows them to use educational management software to apply various solutions for different learning styles in each class (Lopukhina, 2020). It is possible to provide students with various options, from traditional Instructor-Led education to video interactions, quizzes, activities, learning sessions, and computer software programs. Adaptive learning will not take over the main aspects of a course, and this is great news for teachers. While some may be concerned about the expense, putting together a set of learning pathways does not cost any more than putting together traditional materials and education methods with adequate resources. An alternative is to give adaptable aspects first, followed by a lesson where different students approach studying in various ways depending on their needs and how they learn (Lopukhina, 2020). Although adaptive learning can connect with many courses, disciplines, and topic areas, it should be highlighted that this is not always the case. The requirements and styles of the students they are anticipating in a new intake and the demands of the course and expected learning objectives should all be considered by educators when deciding on teaching methods and curriculum (Lopukhina, 2020).

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Adaptive learning will play an increasingly important role in education going forward (Lopukhina, 2020). A better way to learn will be available for all kids at some point, whether it is today or in the future. In order to compete with those who do not offer adaptive courses, schools and institutions should develop the tools to deliver them.

9.8

Adapting to Adaptive Learning

Adaptivity in learning is defined as the ability to change the way content is presented based on how well a learner performs. After years of “adaptive learning,” education institutions are now focusing more on the advantages of utilizing adaptive techniques in evaluations and instructional programs, thanks to the widespread adoption of technology in classrooms (Dyro, 2016). For purposes of computerdelivered training, adaptive learning is now a generic phrase that encompasses everything from the most basic functions to the most sophisticated. Building Effective Adaptive Learning Content. During the 1970s and 1980s, “integrated learning systems” were created, which used complex and secret algorithms to determine students’ progress through a particular collection of materials (Dyro, 2016). Programs like Knewton, which changes and personalizes its presentation of material depending on its system’s cumulative experience of student replies and errors, show complex adaptability. For example, research-based, specialized programs like DreamBox (Math) or Carnegie Math use this form of complicated adaptivity to improve students’ grasp of mathematical concepts. The challenge for instructional content developers today is to incorporate some degree of “adaptability” into their programs or courses to better “personalize” or tailor instruction to a student’s needs. Branching technology, in its most basic form, allows a student’s actions and responses to be calibrated to determine the scope and level of the next activity (Dyro, 2016). Content Structure. Instructional courses designed to teach students new concepts usually include a hierarchical structure that allows for adaptability at various points along the way. However, while the proposed levels may not be a perfect fit for every form of instructional material, course, or program, they should cover most of them (Dyro, 2016). Additionally, there could be a hierarchy that correlates to a collection of Courses. The hierarchy has many different learning objects, but the foundation is usually a single screen (occasionally with pop-ups and scrolling) with text, different multimedia materials, and interactive exercises. These learning objects are then organized into an Instructional Session for a particular user. A Sequence can be compared to a Lesson or a Chapter in your mind’s eye. The “Course,” a collection of Sequences organized according to a hierarchical table of contents, is the next level of organization (Dyro, 2016).

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Content or Learning Management System. All functions and navigation features should be performed entirely within the Learning Object/Sequence (Dyro, 2016). To put it another way, a Learning Management System (LMS) is not required for any of a Learning Object’s content features. This also means that all adaptive learning elements presented in the Learning Object or Sequence at this level will operate on any Learning Management System. An LMS typically manages Higher-level courses and Set of Course structures, so we must make this assumption. This also means that the LMS will be in charge of navigating learners between Sequences within a single Course and between different Courses and their Sequences. Our discussion will be significantly aided by the assumptions listed above. It is possible to provide adaptive learning features at the Learning Object and Sequence level with a simple authoring tool. These adaptive learning features will work on any Learning Management System. At the same time, all adaptations involving more than one Sequence must be linked to Learning Management System functionality because the LMS is in charge of navigating between Sequences and assigning students to Sequence or Course functionality. Since there are no industry standards for interoperability, developing adaptivity at the Course or Set of Courser’s level is more difficult (Dyro, 2016). As a result, content creators can more easily concentrate on the first two adaptability levels: the Learning Object and the Sequence. Initially, focusing on the Learning Object and Sequence level may be very restrictive, but this should be sufficient for many subjects and topics. Learning Material: How to Measure Students’ Performance. In the past, we used to evaluate and measure students’ knowledge and skills by using interactive activities and, on occasion, adaptive paths. While interactive activities are becoming increasingly popular in learning content, they are still underutilized (Dyro, 2016). As evidenced by research, students’ information may be retrieved, results improved, and the learning process made far more efficient and exciting by implementing interactive activities (Dyro, 2016). Simply digitizing a textbook model and displaying large chunks of information followed by questions is not the most effective technique to provide educational material. Interweaving interactive activities with learning material that provides metacognition—the critical component of retrieval practice that provides students with immediate feedback on what they know and does not—is a more nuanced method. Instructional content creators face an additional challenge today with adaptivity to personalize learning and interactivity to increase engagement. Before providing additional content, students must have access to feedback on their interactions. There are two ways for students (Dyro, 2016) to proceed with their learning: following a predetermined path or going over everything again until they have gotten it right. Simple authoring tools like those shown below let you create Learning Objects and Sequences that simultaneously accomplish both of these tasks. As a result of using this tool’s interactivities, students answer all of the questions (which can be presented in a variety of interactive formats such

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as pick/drag/drop/edit/complete a graph, etc.) before selecting the “Check” icon found in each Learning Object. The correct and incorrect responses are each given a corresponding mark. When you select the Check icon multiple times, the Learning Object keeps track of how many times you answered incorrectly. The tool will compile and reveal to the student and teacher the number of attempts, the wrong answers selected and build a detailed report on the student’s interactions without any additional programming of the Learning Objects. For today’s purposes, adaptivity is being discussed in terms of how a tool can use student responses to determine what Learning Object or Sequence of Learning Objects to offer next, based on the quantity and type of errors in past interactivities. Authors of content can create adaptivity logic at the Learning Object and Sequence levels using the suggested basic authoring tool. Because the types and numbers used to develop algorithms vary by instructional material and difficulty, the content creators must decide what types and numbers of errors to employ for each activity (Dyro, 2016). Adaptive Learning Examples This section will look at two different levels of Adaptive Learning content: Learning Objects and Sequences (Dyro, 2016). 1. Adaptive Learning at The Learning Object Level: This Learning Object (LO) illustrates the simplest adaptive learning model at the Learning Object-level. There is only one activity on the second page of this LO. Students can provide and check their responses simultaneously. The user’s correct and incorrect answers will be marked by selecting the Check icon. After that, the user can go back and revise their answers by clicking the Check icon a second time. It must be repeated until all the answers are correct in this case. Only when all of the questions have been answered correctly does the Check icon appear. It is possible to use other methods that do not require all answers to be correct. After more attempts, students may also see their answers and then proceed to the following question. The new activity would be more complicated if the user made many mistakes while solving the first task. The number of errors (wrong answers currently displayed in the activity), the number of times the Check icon has been used, and a percentage result are all shown next to the Check icon. The next activity’s difficulty level was determined using simple logic. The most challenging activity will be shown to the user who has made no mistakes. An easy task can be accomplished if only one or two mistakes. More than one attempt can help teachers better understand the student’s mistakes and thus the next activity that should be given to them. 2. Adaptive Learning at The Sequence Level: A Lesson is a collection of Learning Objects. An example of Adaptive Learning at the Sequence level. The first page of this Sequence and this Lesson’s header feature a detailed graph. User performance drives a dynamic path that tailors the material to suit the learner’s abilities. Using interactive activities, the user’s skills and knowledge

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are assessed in this example of a learning activity that begins with some instruction. Exactly like in the previous example, students use the same methods for coping with their materials. If a user does not achieve 100% success, he or she cannot move on to the next learning object in the Sequence. After answering all of the questions correctly, selecting the Check icon will bring up the Next Page button for the next page. The next Learning Object will be chosen based on how many mistakes the user has made in the current task (Mistakes). An easy, medium, or difficult activity is assigned based on the user’s number of mistakes. The course author for each activity determined the number of Mistakes in the navigation algorithm. 3. Additionally, the final report page is built dynamically based on the user’s path. Only visited pages are included in the report to get an overall picture of the Sequence. It is important to note that this sample can also be evaluated by clicking on the graph that can be found at the top of this page. While using this method is fine, it will not work correctly with reporting (the last page of the Sequence). The Tool. MAuthor’s standard features were used to create the examples above. Visit the mAuthor samples section to see more examples of instructional content. With the WYSIWIG nature of the tool, editors were able to create the content without the assistance of software developers. Non-specialist developers can use this tool to create complex Learning Objects and Sequences, including Adaptive Learning capabilities. You (Dyro, 2016) can use adaptive learning elements at different levels of the content hierarchy. Learning objects, sequences, courses, and sets of courses have all been offered in this topic. Because adaptive learning capabilities may be built on any Learning Management System platform that supports the first two levels, they are available on all of them. In order to achieve higher degrees of adaptability, the content creation tool and Learning Management System must have a close relationship (Dyro, 2016). Learning materials have been proposed to create adaptive learning algorithms by counting and analyzing Mistakes (the number of errors made by students), which is essential for retrieval practice and metacognition. It is also evident that creating adaptive learning content takes more time and effort than creating specific single-track content because more information needs to be created to cover all tracks, yet only a small fraction of it will be used by a single student (Dyro, 2016). In the end, there is no magic algorithm that can remove this constraint. Authors and editorial staff can create adaptive learning content if they use the correct writing tool, but if they cannot, the development process will have to be outsourced to software programmers.

9.10 Strengths and Weaknesses of Adaptive Learning

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Adapting Adaptive Teaching

Adaptive learning has made significant progress, but many challenges are still to be overcome (Sharples et al., 2015). The costs can be considerable when adaptive teaching systems are developed because diverse learners’ requirements and interests must be catered to. If you use adaptive learning, you will not just use a one-size-fits-all approach; you will use a series of interconnected content packets at various levels that provide multiple pedagogical approaches and confront common misconceptions. In mathematics and language training, systems have been developed to create examples and clues of varying degrees of difficulty, matched to the student’s actions. They can develop tasks, spot mistakes in the student’s work, and point out the underlying misconceptions. Even so, developing these tutoring systems can take years of studying students’ misconceptions. The development of adaptive teaching technology relies heavily on cost-benefit analyses and efficiencies around content analysis and generation.

9.10

Strengths and Weaknesses of Adaptive Learning

Each learner’s experiences, degree of knowledge, and skill set are unique to that particular learner. Adaptive learning paths may be created on nearly all modern learning platforms (Kok, 2020). However, what are the advantages and disadvantages of adaptive learning from the viewpoints of both learners and instructional designers? Adaptive learning is a technique for tailoring large-scale online or in-person classes to smaller groups or even individual students (Kok, 2020). Because of this, the flexibility is governed by the learners’ prior relevant and predetermined attributes. In most cases, this results in various learning paths within a program—whether tracks or tailored training—or an online learning platform. Relevant variables include learning targets, level of knowledge, expertise, or time available (Kok, 2020). Students in online courses can be checked on these traits, and particular aspects of the course can be disclosed or hidden depending on their performance. The advantages for students are immediately apparent: adaptive learning is employed to speed up the learning process. This is a time-saving method. There will be no more content offered unless the students specifically request it. Students who already know the material can skip it. Moreover, this gives them the impression that the time they spent learning was worthwhile and the course material was relevant to their circumstances. Figure 9.3 depicts a pictorial representation of the difference between linear and adaptive learning paths. Other Adaptivity opportunities and Strengths The following are the other opportunities and strengths of adaptivity (Kok, 2020):

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Fig. 9.3 Difference between linear and adaptive learning paths (Kok, 2020)

• It is possible to alter adaptive learning to deliver value for the learner while remaining flexible at the same time. • By using adaptivity in a non-linear fashion, learners’ progress can be seen. Teachers and organizations could use this as a performance indicator. It might even be able to forecast the performance of other students who have similar profiles. • Learners’ increased engagement and enthusiasm could be achieved by incorporating adaptability into the learning process to keep things fresh and exciting for students. • Adaptive learning paths can prevent a deluge of irrelevant content when testing and scoring on a learning goal. • Self-assessments can be facilitated and simplified with adaptive learning. • When implemented correctly, adaptive learning considers students’ steadily improving learning abilities (mastery learning). • As long as teachers provide guidance and support, adaptive learning technology can help students learn at their own pace and achieve the highest possible level of proficiency. The Bloom taxonomy can help you create learning journeys based on learning objectives and appropriate learning strategies (see Fig. 9.4). In particular useful and appropriate for reproductive learning objectives are online adaptive elements. When it comes to language and mathematics, adaptive learning paths are frequently used because the learning goals are primarily reproductive. In order to assess higher-level learning objectives (analyze, evaluate, and create), you will need to use something like offline adaptivity (Kok, 2020). Having a conversation with students can help the teacher or coach provide a more personalized learning experience by helping them understand or apply. Intakes can also be used to match individual coaches to individual learners based on, for example, learning goals or learning preferences and the coaches’ skillsets. You can also use peer-to-peer learning to your advantage by pairing

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Fig. 9.4 Learning objectives based on Bloom’s taxonomy (Kok, 2020)

people up based on things like shared interests, educational background, or even demographics like age or location. This is a time-consuming and challenging process that requires extensive knowledge of didactics and Instructional Design. Adaptivity formats require you to create many content options or be creative in the test options (quizzes) that link them to relevant information (Kok, 2020). Other Weaknesses of Adaptivity The following are the weaknesses of adaptivity (Kok, 2020): • Having excessive quizzes, tests, and evaluations may make students feel rushed and discouraged. • Online-only and adaptive-only platforms may make learners feel isolated. • The learner’s paths may cause them to miss out on important learning content and connections to problem-solving and individual needs. • Learners may not always understand what adaptive means when making connections between the learning material and their personal experiences, current events, or other relevant contexts. • Teachers can use adaptive paths and the resulting output data to determine whether students require individualized support. However, teachers must understand the concept of adaptive learning and be comfortable using new technology. What, for instance, is the cause of a dropout? Is it because you did not get what the question was asking? That is odd. • It is essential to design and develop adaptive learning paths with great care, as mistakes are easily made due to the complexity of adaptivity creation, tests, and content serving. • While designing for adaptivity, keep in mind that it can be difficult or timeconsuming to accommodate different learning styles (auditive, visual, etc.). • Endless or full personalization is difficult to achieve, while a limited degree of adaptivity may be insufficient or cause learners to feel insecure. To summarize, adaptive learning can be highly effective in supporting learners, but it necessitates considerable forethought and careful consideration of the costs versus benefits. Consider these suggestions if you decide to go ahead with your plan (Kok, 2020):

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• Do not rush the creation process; it must be done correctly and tested before publishing. • Take into account how adaptability would help students learn best before deciding on a delivery method. • Content items and support are available in various online and offline formats (video, text, discussions, assessments, experiments, coaching, interaction with peers, etc.). • In order to avoid boredom or guesswork, use a variety of assessment formats to keep the learner interested. • The tone of your writing should match the demographic of the people who will read it. • Describe how much time it will take and the results for the learner. Last but not least, analyze whether the students’ experiences and achievement, completion, dropout rates, and causes can be achieved.

9.11

When and How to Apply Adaptive Learning

Many people are intrigued by adaptive learning, but they have no idea when, where, or how to use it. Adaptive learning approaches are discussed in this section for various training needs. There are times when adaptive learning is the best option for learning. It can be helpful at times, but it can also hinder. Moreover, based on the circumstances, different approaches can be taken. The one-size-fits-all approach is used when someone is carrying something in the office and slips on a slippery spot on the floor: “Hey everyone, there is a slippery spot on the floor right here”. “Be on the lookout!” This is not adaptive learning; it is better—quicker and to the point, with no need to figure out what people know or tailor the message to individuals. However, admonishing people about a spill is not the same as teaching them to be better managers. An experienced manager’s learning requirements are likely distinct from a recent college graduate. Adaptive learning promises more in-depth and practical instruction than a traditional training method. Adaptive learning may or may not be a viable approach in the following situations (Cleave, 2020a): • Teaching to those who are aware of their ignorance. • Teaching to a diverse group of learners with varying levels of ability and interests. • Teaching to a diverse group of people, each with different needs (department, job function, etc.). • Teaching to people of various skill levels that can be measured. • Teaching those with skill gaps that are oblivious to their deficiencies. • Teaching something that needs to be dealt with quickly.

9.12 How is Adaptive Learning Changing Traditional Teaching Methods?

• Teaching not. • Teaching • Teaching students. • Teaching • Teaching

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something that has elements that some students need and others do people a skill they will need in order to finish a task. something that necessitates interaction with and commitment from about the products and services of a company. subjects that are required by law.

Table 9.2 provides various adaptive learning approaches for diverse learning needs (Cleave, 2020a). These are examples of how we could tailor eLearning courses to our target audience’s understanding, skills, and interests and achieve success. Adaptive learning is a lens through which you can see more in training. The result is better education and happier students.

9.12

How is Adaptive Learning Changing Traditional Teaching Methods?

Innovative teaching methods such as adaptive learning are only possible in the twenty-first century. Although this new teaching method may seem obvious in retrospect, the potential impact of this new approach is enormous. Teachers are introducing online testing, smart boards, and personal tablet computers to younger and younger students as part of their academic routine, causing a paradigm shift in the classroom. Here are a few examples of how adaptive learning technology can change the classroom and traditional teaching methods (Welker, 2017). 1. Specialized Lesson Plans. Before, it would take a lot of time and effort for a single teacher to develop a personalized teaching strategy for each student. However, there will always be a demand for tutoring because every student is unique in their abilities and life experiences. Adaptive learning technology is helping educators gradually phase out the one-size-fits-all teaching method in favor of one that caters to the individual needs of each student. In practice, a current example of this method can be found at Arizona State University (ASU). Adaptive software is used in some of ASU’s online math courses to assess students’ knowledge. To help students improve in their problem areas, the software includes lesson plans tailored to each student’s needs. Students are tested on their knowledge of math concepts using McGraw-ALEKS Hill’s technology, and they are given extra help if they need it. Adaptive Technology is another example from Surgent (https://www.surgen tcpareview.com/), a CPA (Certified Public Accountant) test prep company. Using their online platform will only be possible after answering a series of questions about your current knowledge and areas that require improvement. To

To make the most of this type of situation, use pull-style learning, which offers a variety of options from which learners can choose. A compelling story will grab their attention and may serve as an effective means of enticing them to dig deeper by asking them a question and then answering it unexpectedly Adding a pre-training self-assessment at the beginning of the course can help people identify knowledge gaps and provide training to fill those gaps. This assessment can be used for the learner’s benefit or as part of an algorithm for automatically assigning modules to that learner in the future Analyze the profiles of students to determine their job functions such as department, role, and responsibilities, and then tailor training to meet those needs Involve students in a pre-training assessment that measures their abilities and then dynamically create a curriculum to address any discovered deficiencies Because it takes time to learn enough about your audience to differentiate instruction effectively, this is probably not a good candidate for adaptive learning. An ILT (Instructor Led Training) or a more traditional eLearning course could be a better option. Learners who want to know more can access additional resources via links provided as an option Providing a burst of content that addresses the most important information for everyone and then showing optional links (pull detours) for those who want more is an easy way to accomplish this goal quickly and effectively. Motivate your audience to want more by making them want more during the required portions of the lesson plan

Those being trained are aware of their ignorance

Teaching a wide range of people with various skills and interests makes it difficult to know exactly what people want or need

Our students (from various departments, job functions, experience, and responsibilities) have different learning needs, so we have to cater to their needs individually

People’s skill levels vary, and we are confident we can develop an assessment that measures those differences in skill. We also know what to do with the results of that assessment

Due to time constraints, we need to get something out as soon as possible, but we are not familiar with our target audience

We believe that people must learn most of what we have to offer; however, certain aspects of what we offer are inappropriate for all audiences

9 (continued)

Adaptive learning approach

Learning need

Table 9.2 Various learning needs and adaptive learning approaches

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That is a tricky question. An easy-to-use search engine or chatbot or an index based on the task at hand can help solve this problem. Other options include creating an index that organizes information by category. All of them are viable options; the best option is the one that is the fastest and easiest to execute in the given situation Include a simulation for the audience to participate in, and when they make a mistake, offer tutoring to help them apply what they have learned

To accomplish a task, people require detailed instructions

While our audience’s gaps in knowledge vary by topic, the majority do not know what they do not know

If you want learners to find information quickly, you should train them. It could include a search function and an effective way to organize the content (such as a hierarchical menu structure or decision tree) Do you have to provide evidence that everyone in the training room saw everything? If this is the case, adaptive learning will be hampered. However, many adaptive learning approaches described above apply if requirements vary by job role or function or if flexibility is provided in what training is provided. Learners should be given the freedom to test out, choose (and perhaps be drawn in with stories), and so on

We need our audience to master a large amount of information about our products and services, but they cannot memorize it all

To fulfill our responsibilities, we must provide training that is required by law or by our legal or human resources departments (Compliance Training)

Coaching or collaboration are examples of subjects in which human touch is Set the stage with eLearning, then move on to ILT or vILT (Virtual required Instructor-Led Training) to incorporate the human element. Bring in community forum training using a social media site like Facebook or a collaboration app like Slack

Adaptive learning approach

Learning need

Table 9.2 (continued)

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avoid wasting time on content you have already mastered, the program tracks your progress and makes adjustments to the questions you get. When it comes to preparing for the CPA exam, “most clients [using Surgent] spend less than 100 h studying for each part of the exam,” according to Crush the CPA Exam (https://crushthecpaexam.com/surgent-cpa-review/). In the CPA world, this is a lot less time-consuming than some of the other models and companies that have been around longer. 2. Grading Papers. Grading papers used to be the bane of every teacher’s existence, taking up the better part of a weekend to complete. Many educators were forced to work long hours for no pay, checking hundreds of identical tests one at a time to determine grades. ScanTron (https://www.scantron.com) technology, used in standardized testing, is an example of a technology with a specific skillset ideal for processing large amounts of raw data. Essays and research papers do not work well with ScanTron because it is designed for multiple-choice tests. These are much more difficult because the metrics used to grade them are far more complex. Grammatical mistakes and plagiarism can be detected by computer technology, but this is only one small part of the grading process. Creating a well-written piece of writing is not an either-or proposition. It is more like a spectrum with many different factors determining how good it is. Adaptive learning technology, such as M-Write, can save teachers a significant amount of time when grading complex writing assignments. Enter M-Write (https://lsa.umich.edu/sweetland/m-write.html), the University of Michigan’s new writing assessment tool that uses machine learning to improve student writing. M-Write can evaluate a paper’s vocabulary and relevance to the original topic using ATA or automated text analysis. The software will send a list of revisions and a current grade to the student after she/he has completed the assessment. 3. Making Learning Fun. It is a difficult task to get a whole class excited about algebra. Even if a teacher is the best in the world, no amount of knowledge will get into the heads of students who lack the desire to learn. Teacher reward systems for good behavior, video integration into lesson plans, and songs and games help combat this problem. Regardless of the method, the end goal is to make learning more enjoyable for students. Adaptive learning technology, such as video games, can also help teachers with this aspect of education. Everyone loves video games, and they are great for teaching a lot quicker to people of all ages. Players are taught how to shoot bad guys and jump on platforms primarily using this incredible teaching technology. Many video games have educational content built-in, such as geometry, fractions, and algebra. Videogame developer Mangahigh (https://www.mangahigh. com/en-us/) is known for incorporating powerful educational tools into their colorful and entertaining games. As a result, students are more enthusiastic about learning, and teachers have access to comprehensive data that shows

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where students are improving and where they need additional support. Mangahigh has made it possible for students to have fun while learning in the classroom. There will be a growing role for adaptive learning in EdTech, especially as efforts to spread personalized learning and move towards competency-based education are made. We have only touched the surface of what adaptive technology is capable of, and based on the above examples, we can expect to see even more creative applications shortly.

9.13

Design Principles in Adaptive Learning

Put yourself in the position of a teacher who must modify his or her teaching methods to suit the needs of each student. A classroom teacher can adapt their teaching strategies to fit the knowledge levels of their students, but tailoring training to meet the needs and pace of individual students will be more difficult. Adaptive learning, on the other hand, is poised to disrupt the eLearning market. Adaptive learning can reduce training time in half by intelligently adapting content displayed to each learner based on algorithms and analytics. In order to tailor content to the needs of each learner, a baseline assessment of their current knowledge levels is carried out. Exciting, right? You will need to devote a significant amount of time to designing the adaptive learning course if you plan on implementing it. When it comes to designing adaptive learning, Dr. Howard Lewis, a performance technologist with 35 years of experience in instructional design and performance technology, shared his insights. Let us take a look at the principles of adaptive learning design. If you want to create adaptive learning, you must follow these design principles (Katambur, 2020): • • • • •

Clarity regarding learning objectives. Content curation from already-existing training materials Chunking content for prescriptive learning Creativity in instructional methods and activities Creating diverse learning opportunities for the same objective.

1. Clarity regarding learning objectives: You need your students to perform well in the classroom and maintain that performance over the long term. Because of this, designing adaptive learning can be difficult, especially when you do not have a clear idea of what you want students to accomplish through the training program. If you want your learners to succeed, first and foremost, be crystal clear about what you expect them to accomplish. Clarity on learning objectives is critical for any training program, but adaptive learning becomes even more so

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because each learning objective and the tasks learners must complete to achieve it must be meticulously mapped out. Ambiguity is out because the learning journey would be in disarray without clearly defined objectives. As a result, if your course’s learning objectives are not properly framed, you may end up with an instructional strategy that is very different from what you originally intended. In addition, your evaluation must assess the learning objective. Content curation from already-existing training materials: Content is at the core of every system for adaptive learning. Adaptive learning necessitates more content than a traditional eLearning, or blended learning program would typically require. Why? Because the system itself adapts in real-time based on the learners’ needs when using an adaptive learning program. This would necessitate the inclusion of both simple and complex content, depending on the current knowledge levels of each learner. Utilizing your institution’s existing training resources makes perfect sense. Existing eLearning courses and manuals and PPT decks, videos, infographics, and other learning resources can be incorporated into adaptive learning. However, the use of the content as-is will almost always be impractical due to the linear structure of the content. It is time to start chunking up your data! Do you have any ideas on how to make use of this content? Chunking content for prescriptive learning: Without chunked content, designing adaptive learning is difficult. For the most part, online and offline training resources follow a linear progression. One of these problems is that adaptive learning design and development have two potential stumbling blocks: • Adaptive learning requires modular material, which means you have to add to the project’s costs and rewrite content. • Adaptive learning is intended to allow learners to skip the contents they know and move directly to the knowledge they need. Thus, this must be divided into smaller, separate bits by linear content. It is easier to make learning prescriptive if you divide content into smaller portions. We mean here by prescribing learning that the adaptive learning program presents the contents that fit precisely what the learner needs to know. Creativity in instructional methods and activities: To provide personalized and differentiated instruction, adaptive learning is an excellent option. One of the most challenging aspects of creating adaptive learning programs involves addressing the issue of “creative redundancy,” which means coming up with new instructional methods to present the same content in different ways and including learning activities tailored to each piece of content. An adaptive learning system that mimics a teacher’s actions can accommodate learners’ needs. The same applies to learning activities, which must be developed with various levels of knowledge in mind. Think about what needs to be taught, how it should be taught, and then choose a format. Creating diverse learning opportunities for the same objective: Learning paths may vary, but the end goals are the same for everyone who participates in an adaptive learning program. The adaptive learning system aims to provide students with various learning opportunities while staying focused on the end

9.14 Adaptive Classrooms: How Accessible Furniture is Paving the Way ...

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goal. It creates a learner profile by keeping track of the students’ preferences. Learning styles vary widely, and some students may prefer videos, while others will opt for audiobooks, podcasts, or an interactive eBook. If you want a range of learning experiences, you will need to enable learning analytics to track your learners’ decisions.

9.14

Adaptive Classrooms: How Accessible Furniture is Paving the Way to Success for Students with Special Needs

Students with disabilities can use accessible furniture and adaptive classrooms; it helps them develop their fine motor skills, concentrate for more extended periods, and encourage them to socialize and play more. Children with special needs have an easier time using adaptive classrooms because of the specialized furniture available in these spaces. According to numerous researches, good classroom furniture helps students learn better by increasing their attention span and decreasing their fatigue (Wagner, 2021). In the same way, students with special needs can achieve their full learning potential when they are placed in a stable and comfortable position. There is much discussion in the educational community about what factors influence learning and how individual differences affect learning. It is easier for students to gain self-esteem and academic skills when differences are accepted as part of everyday life rather than feared. An adaptive classroom has been shown to improve student outcomes by providing instructional methods and classroom furniture tailored to each student’s specific learning preferences. It is not easy for students with disabilities to adjust to life in the classroom. Students with disabilities benefit from adaptive play furniture because it allows them to engage in eye-to-eye interaction, promoting social engagement. Adjustable desks and high-low seating options are essential when it comes to learning. Inappropriate desk/chair height can prevent students from participating in classroom activities when using traditional school chairs and desks. School furniture engineers worked with therapists to ensure that students were in the proper posture to identify the following factors (Wagner, 2021): trunk-head alignment, pelvic stability; leg-and-foot positioning; weight distribution; and comfort. Good posture, alignment, arm-to-hand coordination, reading, and writing are all enhanced. It also saves students energy because they can concentrate better. Adaptive classroom furniture includes the following items (Wagner, 2021): • • • • • •

Desks that are accessible to people in wheelchairs Accessible computer workstations for people in wheelchairs Chairs with a low backrest Chairs in the corners Chair bolsters Adjustable-height tables for science labs

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• Tilting work surfaces for tables (allowing students to be positioned at the optimal distance from their work) • Wells for the feet • Tables for drafting on the go • Workbenches that are easy to get to. Even if you have special seating requirements, how students sit and listen in class impacts how much they remember and retain information. As a result, adaptive classrooms are an excellent investment for schools, parents, and students alike.

9.15

Intelligent Adaptive Learning

Intelligent adaptive learning (IAL) is a next-generation technology innovation now available to schools that helps individualize and personalize learning for each student to some extent (Lemke, 2013). A new window of opportunity has just opened, and it has been long overdue. Because of the availability of low-cost technology and cutting-edge data and learning analytics, educators now have unprecedented access to teaching and learning resources (Lemke, 2013). Smart teachers are now providing their students with “personal tutors”—or IAL systems—technology-enabled. It is now possible to create turnkey digital learning systems that use real-time data to adapt to the user, thanks to the convergence of these three phenomena. IAL can be defined as digital learning where every decision a student makes is recorded and taken into account in the context of sound learning theory before being used to guide a student’s learning experiences, to adjust the student’s path and pace within and between lessons, as well as to provide the student’s instructor with formative and summative data (Lemke, 2013). As a result, instruction is customized to meet every student’s individual learning needs and interests while ensuring that all responses adhere to sound pedagogy. IAL keeps students in their optimal learning zone by creating a digital learning environment. Every student’s choice is recorded, and the student’s learning trajectory is adjusted both within and across lessons. Students will learn from this system because of its focus on “identifying the psychological causes of mistakes,” which includes giving them intelligent feedback and prompts for reflection and rethinking, rather than simply fixing their errors right away. This approach “lowers(s) the probability that such mistakes will occur again.” A personal tutor could use large datasets of a student’s actions and interactions to check for understanding in real-time constantly. They could then compare this data to a database of known misconceptions or errors made by other students studying the same topic as the student being tested. This tutor offers students, teachers, and parents various learning options and real-time, intelligent feedback, and access to progress reports. Students enjoy IAL systems because of the variety of features they have access to. Some examples are allowing students to play video

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games or giving them some control over their activities, as long as they stay within specific parameters based on their current expertise and desired outcomes. Five key factors are inherent in the design of IAL systems (Lemke, 2013): 1. lessons or activities the learner follows in a specific order to meet his or her needs 2. the methods of instruction used to instruct and guide the student 3. measurements of the student’s learning attitude 4. measurement and understanding mechanisms for what a student knows and does not know 5. the use of a feedback mechanism to ensure that the content, instruction, and motivation students encounter are all informed by the data collected about them. In order to begin using the IAL system, the student must first complete an adaptive assessment to determine where he or she should begin. Afterward, the curriculum, pace of learning, and feedback given to each student are tailored to meet those needs and experiences. The student has an intelligent tutor who adjusts the curriculum, pace, pedagogy, and presentation of lessons based on the student’s progress and learning preferences. All of these changes directly result from his behavior and the system’s reactions as a whole. It is common for educators to use IAL to help guide their students’ learning on specific topics. These teachers have access to comprehensive reports on their student’s performance about the learning standards they are responsible for. Computer-assisted instruction (CAI), computer-supported collaborative learning (CSCL), intelligent tutors, adaptive hypermedia, and individualized learning systems (ILS) of the past typically lacked the speed and sophistication of today’s IAL systems. Today’s IAL systems are far more advanced than previous generations’ equivalents. Due to faster processing speeds, advanced learner analytics, and the sheer volume of data collected and analyzed, the learner’s experience is optimized. Many modern-day IAL systems can quickly adapt instruction and content to meet the changing needs of students as they acquire new skills or advance in their understanding. The system’s cognitive modeling is its fulcrum. Artificial intelligence (AI) advancements inspired the cognitive modeling that underpins IAL. In order to mimic the behavior of experts, these AI systems used a set of knowledge-based expert rules. The use of symbolic representations by IAL systems helps them optimize the learning experience for their users by constantly monitoring and guiding their behavior. A pedagogical philosophy dictates how much scaffolding (intelligent feedback) and when to provide it in the cognitive model and the logic of presenting curriculum units or lessons to students. Additionally, the system determines the student’s optimal learning style and presents the content by that learning style when determining the level of mastery required of the student.

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The name of this resource—intelligent adaptive learning system—indicates that the system combines intelligence and adaptability. Indeed, both are critical components supported by solid scholarly research in education. Because of this, the user receives intelligent feedback from the system as it collects and analyses user data continuously and in real-time. In order to keep the user on a positive learning trajectory, this constant stream of intelligent feedback is provided to him. Also, IAL systems use the data stream to determine whether the user is ready to move onto new curricular units and which curriculum units would optimize the user’s learning trajectory based on his progress. These data are also used in the reports provided to the teacher, which is a third way of utilizing them. Even though teachers will use the system in various ways, it is the responsibility of each teacher-student pair to ensure that the student stays on a positive learning trajectory toward mastery of the specified standards. Teachers frequently view the IAL system as just one of many tools and resources available to students to help them achieve their learning objectives. IAL elements that contribute to the attribute of “intelligent” include (Lemke, 2013): • Intelligent evaluation of a student’s solutions: The system communicates with the student by examining the data gleaned from his problem-solving, conceptexploring, and decision-making activities. Even when something goes wrong, the system has enough intelligence to identify where the misunderstanding occurred and, therefore, what caused the problem. • Support for interactive problem solving: It is possible to provide detailed feedback to students through solution analysis, which prompts them to rethink their strategies and solutions, ultimately correcting their misunderstandings or mistakes. When a student gets stuck, this “intelligent help” provides hints to get them thinking about the problem and the context in which it occurs. Rather than simply telling a novice student what an expert’s strategy’s “next step” would be, the intelligent adaptive approach views these hints as a chance for critical thinking. In this way, the system mimics the actions of a competent tutor. Modularized curricula designed for and by the student are the foundation of IAL systems. The following components of IAL systems contribute to adaptive learning in those curriculum units and lessons (Lemke, 2013): • Sequencing of the curriculum: With the help of artificial intelligence, the system determines how far a student has progressed in terms of knowledge and understanding. That is done by delivering curriculum units in the best possible order based on the student’s readiness and customizing learning tasks with different teaching methods within the module based on new student data. • Various learning opportunities: Each concept can be taught using several pedagogical approaches provided by the IAL system. This covers a wide range of learning activities, settings, and experiences. According to deep learning research, multiple experiences are required to help students grasp the concept

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deeper rather than just on the surface. To encourage and enable deep learning, tasks must be meaningful, at an optimal level of difficulty for the student, and contextualized so that students can build schemas to understand the concept in the context of the world around them. • Individualized delivery and pacing: The system gathers data on the student, and that data is then used to create and present digital content to the pupil dynamically. Diagnostic and adaptive assessments are integrated into each lesson to measure mastery transparently and fluidly, which does not cause students anxiety. Learning activities are presented in various ways so that each learner has a unique opportunity to gain a deeper understanding of a concept. Filtering is common to ensure that educational materials are presented only to those who benefit most from them. However, the more time a student spends in a module, the more alternates he will encounter as he works to master the subject matter. Student progress is determined by how quickly they demonstrate mastery of a concept, so the pace differs between learners.

The Purpose of an IAL System The purpose of an IAL system is (Lemke, 2013): 1. 2. 3. 4. 5.

serve as a student’s personalized teacher, adjust the curriculum sequencing and associated learning experiences, personalize the speed of learning, regulate student cognitive burden and include students in game learning.

Every single one of these design aspects has been thoroughly researched. They provide educators with sufficient justification to give next-generation learning tools a close look when taken as a whole. 1. Serve the student by acting as a tutor: According to studies, tutoring from a highly qualified personal tutor is two times more effective than classroom instruction in many cases. Although this is common knowledge, private tutoring has been out of reach for most public schools until now. A new study shows that intelligent tutoring systems today are on par with human tutors in terms of effectiveness. A next-generation intelligent tutoring system, an IAL, is what it sounds like. With artificial intelligence built into the product’s design, not only is this a tutor, but the product also uses cognitive modeling to tailor the content, timing, and format of delivery to meet each learner’s needs. The system’s ability to tutor students effectively relies on this real-time feedback loop. Feedback, when used properly, has the potential to boost student performance by 27 percentile points on average (effect size of 0.79). Learning was enhanced by the frequency of feedback, the provision of formative feedback tailored to specific learning objectives, and questioning/learning prompts—all of which are

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included in the design of IAL systems. It has also been shown that students are more comfortable receiving feedback via computers than in person. 2. Adapt the curriculum’s sequencing and the associated learning experiences: When curriculum units and learning activities are appropriately sequenced, it significantly impacts how quickly students learn. Different students take different routes through an IAL system. Option selection is based on student readiness and the diversity of learning experiences required to ensure connections are made and deep understanding. A certain amount of autonomy is given to the student, but only within parameters meant to ensure a steady progression of knowledge acquisition. These learning systems place a high value on student agency. According to research, students who are given options are more engaged, which results in them spending more time on the task and achieving more. It is critical to base student experiences on prior knowledge when implementing an adaptive curriculum sequencing for each student. For one thing, this allows for better identification and correction of misconceptions a student may have, but it also allows for a more engaging presentation of learning activities based on their prior knowledge and interests. This adaptive curriculum sequencing also allows students to cycle through the entire course of study until they have mastered it. Studies on tutoring show that allowing students to work at their own pace to mastery while receiving appropriate feedback from the tutor is a successful learning strategy. Researchers have discovered that mastery learning, which keeps constantly learning while seat-time varies, results in significant increases in student achievement compared to the opposite (which keeps seat-time constant while learning varies) (effect size of 0.58 or a gain of 22 percentile points for the average student). 3. Adapt the learning pace to each student’s needs: In the United States, there is a growing preference for student-centered learning over seat-time or competencybased approaches. 4. Regulating the student’s cognitive load: Teachers must adapt learning activities to make steady academic progress toward specific learning standards. No matter where a student starts, the learning process should support his or her steady progress toward the learning standards regardless of where the student has been. A student’s “zone of optimized learning” is defined as the space between these two points. Because each student’s zone is unique, keeping everyone in their zone simultaneously during class is not easy. If a student is given tasks beyond his or her ability level, he or she may get frustrated. However, if the tasks are not difficult enough for him, he will get bored and stop working on them as much. Consequently, to maintain a healthy balance between task complexity and student skill level, the teacher must constantly monitor each student’s learning experience. While this is going on, the curriculum should also include learning activities that build on the student’s prior knowledge and personal interests. An expertly orchestrated balance between task complexity and skill level makes learning challenging while allowing students to move at their own pace.

9.15 Intelligent Adaptive Learning

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5. Encourage student participation in learning through gaming: An individual’s commitment to learning can be gauged by how much time and effort he or she puts forth to grasp difficult concepts and master complex skills. Curriculum sequencing is only one part of increasing student engagement in the classroom. Other components include student choice, intellectual safety (ensuring that intellectual risks are not ridiculed), affirmation of progress and work, and clarity in goals. A funny thing about students who have difficulty concentrating in class is how easily they can lose themselves in strategy games. When it comes to gaming and learning, the evidence is mixed. The five basic principles of IAL system design can also be found in serious games. In an exciting twist, the strategies listed here closely resemble gaming principles. (1) sequenced difficulties, (2) information “on request” and “on time,” (3) performance before the skills, (4) motivation and focus, (5) prompt and timely feedback. When working with students and teachers in the twenty-first century, it is critical to access the IAL as an essential teaching tool. For this reason, some IAL systems employ game-based assessment, learning strategies, and learning activities to help students improve their skills. Affordability studies on these new digital learning tools are not yet complete, but their design has a solid theoretical foundation that demands educators’ full attention. As a result, educators must thoroughly examine each IAL resource to ensure that it is pedagogically aligned and carefully plan how it will be integrated into the overall learning environment. Intelligent adaptive learning technology in the classroom. Intelligent adaptive learning technology is based on pedagogy and research, and it keeps each learner in their Zone of Proximal Development (ZPD) (Bainbridge, 2020). That means it provides the next lesson at the right level of difficulty at the right time. When the work is simple, students can complete it independently, without the need for additional guidance. It is right in their “sweet spot.” Learners who are constantly asked to do work outside of their comfort zone will never learn anything new, and they will eventually lose interest. A student becomes frustrated and gives up if the work is too difficult. True learning takes place in the zone between comfort and frustration, which is known as the optimal learning zone. When a student struggles to grasp a concept or complete a task, this is where tutoring or hard work will be required. This helps students become mathematical “doers” who think and strategize in ways they can apply both in school and real-life experiences. In other words, this is the best possible scenario for both teachers and students.

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Adaptive Learning Systems and Platforms

Adaptive learning systems, platforms, and evaluation criteria are discussed briefly in this section. Adaptive Learning Systems. Students’ abilities or skill attainment determines how adaptive a learning system is and how quickly a learner’s performance can be accelerated by automated and instructor interventions. For this reason, adaptive systems help educators deal with issues like differing student abilities, diverse student backgrounds, and limited resources. A machine learning system uses proficiency and the student’s knowledge to determine what the student knows to guide them along the prescribed learning path to mastery of the desired skills. As a result of these new features, first-generation digital learning systems will be transformed forever. It is possible to categorize adaptive learning systems into four groups broadly (Pugliese, 2016). • Adaptive Systems Based on Machine Learning: The most advanced scientific method relies on machine learning-based adaptive platforms to achieve true adaptation. An ML-based adaptive platform uses learning algorithms, also referred to as “learners,” to create other algorithms, creating adaptive sequences and predictive analytics that can continuously collect data and use it to move students through a guided learning path. Students’ ability to master objectivespecific content modules is assessed using ML techniques that continuously harvest data in real-time. After that, the information is put to work, automatically adjusting a student’s overall skill set or the type of content they encounter. Using learning algorithms, analytic models can be created that produce repeatable, dependable decisions, and the results reveal previously unknown insights into student mastery as historical relationships and trends in the data continue to be evaluated. These intelligent systems can detect how an individual learns and approaches a learning task, give students accurate and timely feedback, and help them perform better. This makes ML-based adaptive systems unique. • Advanced Algorithm Adaptive Systems: Computer-to-student interaction is provided by advanced algorithm (AA) adaptive systems that are scalable to the content taught (usually mathematics and sciences). There are predetermined content modules for specific learner profiles based on prior demonstrated mastery of knowledge and application of knowledge. Learners’ progress is tracked in real-time by comparing their data to that of other students who have been exposed to similar or identical content in AA systems. Learner profiles, clickstreams, intervals, assessment attempts, and other transactional behavioral data are all recorded and managed by AA adaptive systems. Real-time learning paths are determined by AA systems, which run in the background while providing immediate feedback in response to the data. Alternate learning paths are used when the predetermined learning paths fail to produce the desired results.

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• Rules-Based Adaptive Systems: Rules-based (RB) adaptive systems use scientific methods similar to machine learning (ML) but do not precisely adapt to each learner. Rather than using an algorithmic approach, RB relies on predetermined learning paths determined by rule sets that can be altered for individual learners. Once a learning unit is complete, feedback is provided. In the selfpaced learner use case, the student’s learning path is typically sequential and linear, with little to no personalized adaptation aside from a predetermined set of conditions. Students can take a differentiated path based on their prior knowledge and progress at their rate. RB systems do not utilize learning characteristics and learner profiles. Regardless of whether or not they progress along a predetermined learning path with predetermined assignment sequences, students can advance or regress based on ongoing diagnostic assessments. A constant flow of feedback is given, and an established set of regulations prescribes remediation. In RB systems, diagnostic and formative assessment rules sync with the upcoming content sequences. Students progress through a path, and the instructional method does not improve until the content sequence is manually altered. • Decision Tree Adaptive Systems: A decision tree (DT) is a simple classification based on a “tree” of content repositories, assessments, and answer banks that have been prescribed. DT systems typically have a small number of assessment item types that are either one or the other. Rules and associated learning materials in DT are more static than RB, so it is easier to compare them. When using DT systems, no consideration is given to a learner’s profile; instead, a series of static “if this, then that” (IFTT) branching sequences is used instead. DT systems can take the form of intelligent recommending systems depending on their design sophistication, but they are not knowledge-based in the sense that the system has captured no inherent collective and historical knowledge. While using case-based reasoning, these systems can infer an individual learning path, which interacts with an inventory of didactic learning content, and supports users in the learning process. A predetermined set of content modules and a predetermined set of assessments and answer banks are used by DT systems. Learner workflows are developed and individualized work streams assigned at a predetermined pace using data and feedback intervals. Adaptive learning platforms. Teachers and students can access various games, quizzes, and brain-training exercises on adaptive learning platforms such as websites and apps. This type of platform allows teachers to gather useful information about each student’s progress and create a highly effective personalized learning experience for them. Learning platforms that use data mining to create learning content specifically tailored to the needs of students are the best. Every time a student engages with any platform’s learning content, the platform gathers data. A platform like this can help identify which learning activities will best benefit a student’s learning ability, no matter how they are delivered or in what order they are presented.

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For instance, Prodigy Math teaches children basic math skills using adaptive learning platforms and technologies. Other examples include ClassK12 and Oli, which teach math, grammar, and geography to young students, and ALEKS, which teaches university students math, science, and business. East Asian languages are the focus of IKnow, a different learning experience platform. EdApp, on the other hand, is an excellent illustration of an adaptive learning platform because it allows for a learning continuum by providing users with ongoing learning support. Teachers can easily make changes to their lessons based on the current circumstances. Evaluating adaptive learning systems and platforms. Many adaptive learning platforms and systems deliver textbook content at varying speeds, but they cannot tailor learning and seamlessly provide an assessment. You should think about the following factors when evaluating various adaptive learning programs (Odeyinde, 2020): • A wide range of educational sequences: The sequencing of a curriculum can be changed so that a student’s learning experience is as practical as possible when teachers or guardians work with them one-on-one. Regardless of which adaptive learning system you choose, it must achieve the same result for your students. • Adapting to the students’ learning pace: Learning at their own optimal pace is an effective learning strategy in studies. Students should only move on to the next concept in the system once they have proven mastery of the one they are learning right now. • Consider prior knowledge: Make sure that your student’s starting point is based on their prior knowledge when selecting an adaptive learning program and that you help that student make steady academic progress towards desired learning objectives. In this way, students with difficulties will not become frustrated, and naturally gifted students will not become bored. • Strategies for increasing student engagement: Gaming is an essential tool for enticing students to learn in the digital age when they are so accustomed to using technology in every aspect of their lives. Students benefit from adaptive learning programs that mimic strategy games by seeing learning as looking forward to rather than dreading. • Interactive assistance with problem-solving: Instead of telling students what to do next, the system should emulate a live tutor, encouraging them to rethink strategies that are not working. • Customized presentation: The presentation of lessons should be customized to meet the needs of each student in adaptive learning systems. With the constant analysis of students’ responses and ways of thinking, new material is presented to ensure that each student understands it. • Student solutions analysis: Students and teachers will benefit from an online learning platform that pulls data from their answers at the end of the lesson.

9.17 Benefits of Adaptive Learning

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IALs use real-time data analysis to adjust their teaching strategies as students work through problems, discover new ideas, and make decisions.

9.17

Benefits of Adaptive Learning

Adaptive learning is reviving the classroom in an era of tight budgets and overburdened teachers by redefining how education is approached. When instructional content and methods are tailored to the needs of individual students, it makes a significant difference in how well they learn and master new concepts. New learning styles are data-driven and non-linear. It can keep track of each student’s progress when it teaches, adjusting its approach as necessary. It also provides teachers with valuable data to help them become better educators. The following are the various benefits of adaptive learning (Smart Sparrow, 2018): 1. Students can learn at their own pace: Adaptive learning will no longer feel like they fall behind if they take longer than expected to grasp certain concepts and teaching material. Students who need more time to absorb the material will benefit from an adaptive learning program that goes at a slower pace. When the student feels ready, they can rewind and fast forward the program. Students who already know the material will be able to move on to a more complex area of study without waiting for other students to catch up with them because of technological advancement. 2. Higher advancement for students: There may never be enough hours in the day for teachers to give all of their students the individualized attention they require when dealing with growing class sizes. Increased student numbers mean that teachers must teach an ever-widening spectrum of curricular content to meet all students’ needs, making it difficult for teachers and administrators to check in with all students and make sure that they understand the lesson. It is possible to tailor instruction to meet the needs of individual students or small groups with adaptive learning. 3. Enhanced Student Confidence: Students no longer have to worry about being left behind if they grasp certain concepts more slowly than the rest of the class. When a lesson progresses faster than the learner can keep up with, it can deflate a student’s self-confidence. Adaptive learning, on the other hand, only moves the student on to the next set of questions once they have mastered the previous set. They will only be moved to the next phase when an adaptive learning program determines that the learner has grasped the material. This can significantly impact people’s self-esteem. To add to this benefit, students can feel that they are receiving one-on-one attention because of the personalized nature of the learning process. Because of this, they may even feel as if someone is finally paying attention! 4. Increased engagement of students: Because of the associations with tests and exams, it is typical to think that asking students many questions will intimidate

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them. However, asking many questions in an open and non-threatening way can help students develop a stronger connection to the content and increase their interest in it overall. Another consideration is that today’s students use technology in their personal lives, so implementing it in the classroom will be more appealing than simply reading a textbook. Personalized learning generates higher levels of engagement because it is so much more relevant to the learner. When a person realizes that the learning process changes to fit their needs, it makes them feel more involved rather than like a spectator. Improves understanding: If a teacher is standing in front of a classroom, it is difficult to tell if the students “get it,” Adaptive learning tailors the method and pace of instruction to each student, increasing the likelihood that they will truly understand new academic concepts. Allows students to work at various paces: Recall how classrooms used to be set up in elementary school. The teacher would stand in the front and lecture on new material, while students of varying academic abilities would sit in rows of desks and try to absorb it all at once. All students are expected to learn simultaneously with this instructional design, even if they excel in different subjects. This makes it difficult for teachers to give extra time to students who need it while also challenging those who are doing well. As students respond to questions, the software analyses their answers in real-time and adapts its instruction. As a result, students can work at their own pace and achieve their full potential in the academic realm. Free up space for one-on-one instruction: Due to the nature of blended learning, teachers can devote more time to one-on-one instruction when adaptive learning programs are used in this manner to implement learning programs. There are various ways to implement a rotational blended learning model that allows students to receive one-on-one attention while others use computers. Learning management systems (LMS) monitor and analyze student responses in real-time, providing teachers with a wealth of data to personalize instruction. Teachers can use this information to determine whether or not their students have mastered new concepts and whether or not students in their classes would benefit from individualized instruction. Our emotions can taint our judgment: There are no preconceived notions about students regarding adaptive learning programs. Unfortunately, because we are fallible beings, we can form preconceived notions about students based on our experiences in the past. This can impact our perceptions of what our students are capable of and how we approach teaching them. Because adaptive learning technology is apolitical and does not have any preconceived notions about students, the teaching materials are presented in a uniform and unbiased fashion. Data Collection: Math students, for example, are typically instructed to “show your work” while attempting to solve complex equations. This aids teachers in deciphering their students’ thought processes and pinpointing exactly where they went wrong in their lesson plans. However, gathering and archiving all of this data is time-consuming and may not be feasible shortly. To help teachers

References

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better understand their students’ progress and areas of need, adaptive learning collects a wealth of data at a highly granular level. If the teacher can find commonalities among the areas of need in the classroom, the data can be used to assist her in reaching out to struggling students and facilitating their overall lesson plans. 10. Enhances teaching experience: Adaptive technology can help students struggling in class because class sizes are unlikely to shrink, and instructors are unlikely to guide all students who are having a difficult time through each problematic lesson. Instructors and learning designers can design a more inclusive approach to education that includes students with varying ability levels, ensuring that at-risk and advanced learners receive the individualized attention they require. It can share introductory materials, pre-work in the classroom, remedial lessons, case studies, and explorations of new and old concepts. This technology, when used diligently, allows instructors to improve learning outcomes for all of their students. 11. Expands learning opportunities: Formal and informal learning have combined to make education a lifelong pursuit. It includes various learning opportunities, such as classroom instruction, online coursework, career development, and personal interests. Students who do not have direct or immediate access to an instructor can continue their studies without an instructor by creating adaptive learning experiences that provide feedback and help them navigate complex material. As well as this, students have the freedom to take control of their learning process, discover new learning paths, and work at a pace that suits them.

9.18

Conclusion

The future of education is adaptive learning. Sooner or later, students will be able to select courses and modules that are more adapted to their preferences and needs to learn everywhere. Schools and educational institutions that offer adaptive courses—with software to offer them—will benefit from them rather than those that do not.

References Bainbridge, C. (2020). The zone of proximal development in child cognitive theory. Verywellfamily.Com. https://doi.org/10.1186/s12889-018-5617-0. Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13(2–4), 156–169. Cleave, J. (2020a). Instructional design techniques for adaptive learning. ELearning Industry. https://elearningindustry.com/instructional-design-techniques-adaptive-learning. Cleave, J. (2020b). What is adaptive learning and how does it benefit. ELearning Industry. https:// elearningindustry.com/adaptive-learning-benefits-corporate-training.

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Dyro, A. (2016). Adapting to adaptive learning. ELearning Industry. https://elearningindustry.com/ adapting-to-adaptive-learning. eCubed Training. (2019). Introduction to adaptive learning. Medium.Com. https://medium.com/ ecubed-training/introduction-to-adaptive-learning-dfd2d1429af8. Katambur, D. (2020). Design principles for adaptive learning: the 5 Cs you can’t skip. Commlabindia.Com. https://blog.commlabindia.com/elearning-design/adaptive-learningdesign-principles. Kok, M.-L. (2020). Strengths and weaknesses of adaptive learning: A case study. ELearning Industry. https://elearningindustry.com/strenghts-weaknesses-adaptive-learning-paths-case-study. Lane, P. (2017). What is adaptive teaching? IAchieve Learning. https://iachievelearning.com/2017/ 12/what-is-adaptive-teaching/. Lemke, C. (2013). Intelligent adaptive learning: An essential element of 21st century teaching and learning. Lopukhina, D. (2020). How to adapt to the reality of adaptive learning in schools and colleges. ELearning Industry. https://elearningindustry.com/adaptive-learning-for-schools-colleges. Manipal Digital. (2021). Introduction to adaptive learning. Manipaldigital.Info. https://manipaldi gital.info/blog/introduction-to-adaptive-learning/. Michelle. (2020). Adaptive learning is the future of online education. Learn how! Proctortrack.Com. https://www.proctortrack.com/blog/article/adaptive-learning-is-the-future-of-onl ine-education-learn-how/. Michelle. (2021). EdTech trends that will fuel E-Learning space in 2021. Proctortrack.Com. https:// www.proctortrack.com/blog/article/edtech-trends-that-will-fuel-e-learning-space-in-2021/. Odeyinde, T. (2020). e-Learning ecologies MOOC’s updates. Cgscholar.Com. https://cgscho lar.com/community/community_profiles/e-learning-ecologies-mooc/community_updates/ 112020. Pugliese, L. (2016). Adaptive learning systems: Surviving the storm. EDUCAUSE Review. https:// er.educause.edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Smart Sparrow. (2018). Let’s talk about adaptive learning. Smartsparrow.Com. https://www.sma rtsparrow.com/what-is-adaptive-learning/. Wagner, S. (2021). Adaptive Classrooms: How accessible furniture is paving the way to success for students with special needs. Hertzfurniture.Com. https://www.hertzfurniture.com/buyingguide/classroom-design/adaptive_classrooms.html. Welker, B. (2017). How adaptive learning is changing traditional teaching methods. Gettingsmart.Com. https://www.gettingsmart.com/2017/07/08/how-adaptive-learning-is-changing-tra ditional-teaching-methods/. Wikipedia. (2003). Adobe captivate. En.Wikipedia.Org. https://en.wikipedia.org/wiki/Adobe_Cap tivate. Wikipedia. (2021a). Adaptive learning. En.Wikipedia.Org. https://en.wikipedia.org/wiki/Ada ptive_learning. Wikipedia. (2021b). Qualtrics. En.Wikipedia.Org. https://en.wikipedia.org/wiki/Qualtrics.

Analytics of Emotions

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Abstract

Emotion analytics helps the instructor analyze the learner’s responses to their emotional and cognitive states with automated eye tracking and face recognition methods. It helps us identify students’ knowledge and how they answer the questions. If a student is frustrated, confused, or distracted includes noncognitive aspects. Here teachers who are experts in responding to the learners based on their emotions and dispositions with the computer-based cognitive tutoring systems in classroom teaching. This chapter discusses the ubiquity and importance of emotions in learning. Keywords

Emotions • Learning • Classroom teaching • Emotional states • Cognitive states • Analyze learner’s responses • Tutoring systems

10.1

Introduction

Learning is primarily influenced by three factors: emotions, attention, and engagement (Sharples et al., 2015). No matter what you decide to do, your attitude toward it will determine whether or not you will do it. If you do not like doing something, you are less likely to stick with it and more likely to give up. In marketing and advertising, one strategy is to detect emotions through cutting-edge artificial intelligence methods (Sharples et al., 2015). Incorporating eye-tracking movements (the way a viewer’s eyes focus on specific elements in a video or text), facial expressions (such as shock or shaking the head), and posture (such as leaning backward and forwards), several companies can already track, trace, and predict how people react to particular advertisements. A simple webcam

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_10

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on a tablet, smartphone, or laptop can be used by several companies to accurately measure how viewers feel, engage, and pay attention while watching an advertisement. To save money on expensive customer and expert focus groups, marketing firms have discovered that thousands of people are willing to sit in front of a computer and watch the featured advertisements. Using the emotional reactions of volunteers to reposition elements and scenes, these companies alter how large groups of people react to emotional inputs, maximizing the likelihood that people will remember a brand favorably. In the last ten years, techniques for tracking eye movements, emotions, and involvement have improved. Learning scientists are poised for a similar breakthrough in the field of study. Researchers, teachers, and students will learn more about learning by combining a well-established discipline with low-cost methods of determining where students watch and click on learning materials. While reading this article, listening to Spotify, keeping an eye on a chat stream, or overhearing a nearby conversation are all possible distractions. If the text you are reading is fascinating and engaging, you will be fully immersed in the learning process. Emotion analytics will allow educational institutions to see which learning materials students are using and whether they are focused on the task at hand or just winging it on quiz tests in the future (Sharples et al., 2015). People’s clicking and typing behavior can be combined with where they look when they look at a screen, opening new possibilities for individualized learning. If a student repeatedly refers back to earlier material, it is possible the text is not clear enough, and he is having trouble grasping certain concepts. A short video engagingly explaining key concepts may be suggested, or a quick help function may be offered if he is searching for key terms, scrolling back, and having a puzzled facial expression (or worse). A student skimming through text may already be familiar with key concepts and be bored; in this case, an engaging exercise or a chat opportunity to discuss an intricate problem with another student may be presented as an option. Analyzing emotions in conjunction with adaptive teaching can offer students a more tailored educational experience. Providing accurate, adaptive learning solutions based on actual learning emotions and needs will help them build trust with students and educators. An example of this benefit is the ability of the analytical tools to correctly identify the emotions of learners and then provide practical teaching and valuable feedback. However, while eye-tracking and emotional data can help researchers better understand how learners behave, students will only share information if they see a positive return on their time and effort (Sharples et al., 2015). Students’ emotional reactions will need to be monitored even if the technical issues are resolved. This raises complex ethical and privacy issues.

10.2 The Importance of Emotions in Learning

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The Importance of Emotions in Learning

Learning was viewed as a purely rational process in which emotions played a minor role. That view has since shifted dramatically. With this belief came an understanding of intelligence from the “famous” IQ tests meant to detect learning difficulties in children rather than identify general intellectual ability. Because of this new knowledge, we can use emotions as either a lever or a brake for cognition and decision-making. Learners and teachers alike should keep these tips in mind (Meilleur, 2020b). • A vital protection system. According to Jacques Lecomte’s Les 30 notions de Psychologie, an emotion is an “organism’s reaction to an external event that has physiological, cognitive and behavioral aspects.” People tend to dismiss our feelings as unimportant, even embarrassing, or burdensome. This is still true, even though our surroundings are different and our emotions are more important. Today, we know that they are critical to our personal and social wellbeing. The trend now is to listen to, tame, express, and learn to better manage them instead of silencing or hiding them. When our emotions fluctuate, they are doing their job as a sophisticated defense system, which they are designed to do. As a result of using this system, we were able to protect our species by identifying the elements in our environment that could either benefit or harm us and then advising us on how to react to maintain our integrity or well-being. As a bonus, the word “emotion” is derived from the Latin verb “remove,” which means “to set in motion.”. • Every emotion is proper! Positive or negative emotions indicate the presence of events or situations that may impact our integrity or well-being, either positively or negatively, strongly or weakly. Anger, fear, sadness, and other negative emotions all have corresponding actions or adjustments that must be made in order for us to regain or maintain our well-being. Even though some emotions are referred to as “positive” because they are pleasurable and others are “negative” because they are unpleasant, all emotions serve a purpose. Learning is influenced by four distinct emotional states, detailed in the “Four emotions of learning.” • Learning is destabilizing! Thinking critically is an essential part of learning, as is being open to new ideas and complexity and putting forth effort even when the results are uncertain. In other words, it is a dangerous step that, even though it is filled with positive feelings, will not prevent us from experiencing negative ones as well. The learners must be reminded of this fact. They must be encouraged to express what disrupts their learning process, and they must be provided with the resources they need to do so. To deny or repress one’s emotions does not make them go away; instead, it increases the likelihood of them becoming amplified. • Similarly different. Emotions experienced in a group vary from learner to learner because of subjectivity and impalpability. According to research, exam

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anxiety is more common in some parts of the world than others. Numerous factors specific to an individual group can explain this discrepancy: ethnic origin, culture, gender, and sense of belonging to the educational institution are just a few. Despite the potential influence of these factors, the differences between individuals are most important: our physiology and genetics, our life experiences, our values, and so forth as well as personality traits like self-confidence and interest in a particular subject, people’s emotional states change over time, making them even more unique. • Accelerator or brake on the learning process. At various stages of the learning process, emotions can impact the learner. According to research, they can improve or impair a person’s learning ability by influencing their attention, motivation, learning strategies, and self-regulation. There are a variety of negative emotions that can get in the way of learning at any time. – Anxiety – fear of failure – embarrassment – the inability to comprehend an exercise – discouragement – boredom In terms of positive emotions that aid this process, the following stand out (Meilleur, 2020b): – the enjoyment of learning in general – the enjoyment of learning about a specific subject – interest in the learning materials – hoping for success – pride due to achievements • Positive emotion is not beneficial, and negative emotion is not so bad. It is essential to understand that just because something feels good does not mean it will help you learn more. To be of use, it must be connected to learning or a specific task; otherwise, it may impair attention and affect performance. Parallel to this, just because emotion is negative does not necessarily mean that it is a stumbling block for the learner in every situation. Learners can be motivated to work harder by feelings of anxiety, embarrassment, or anger, as long as they want to succeed and have faith in their abilities. A negative emotion’s strength and frequency will also have an effect—if it is too strong or frequent, the learner runs the risk of being overwhelmed by a sense of helplessness. • Emotional contagion, yes, it exists. The teacher’s emotional state can significantly impact the student. A recent large-scale Canadian study found that teacher stress spreads to students, supporting the theory that it is contagious. Even though the study was conducted with elementary school students, the same phenomenon could also occur in classes for adult learners. Emotional contagion is a well-documented, partly genetic process that involves our “mirror neurons,” through which we unwittingly become infected with the emotions of others. The intensity of this phenomenon varies from person to person due

10.2 The Importance of Emotions in Learning

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to our psychophysiological differences and life experiences. Even though it is an automatic mechanism, just being aware of it and realizing how easily emotions, whether positive or negative, can spread throughout our surroundings can motivate a teacher to assess his or her state of being and, if necessary, make changes. Memory and emotions: a duet of explosion. Because of the importance of memory in learning, the two concepts are frequently misunderstood. In contrast to learning, memory refers to storing and retrieving information. Emotions have a significant impact on how well we remember things. This explains why we recall more intensely emotional events. If you were alive on September 11, 2001, it is safe to say you remember precisely where you were. It is an example of the so-called Flashbulb memory phenomenon, which is why it happened. Recent discoveries in neuroscience show that in order for learning to be “encoded,” the brain requires feedback on its predictions, precisely an error signal that makes the learner feel surprised (see Neuroscience: Learning in 4 steps). Include the fact that emotions mainly act on long-term consolidation as an additional point of clarification. On the other hand, we know that the closer something is to us, the more it will challenge us emotionally, especially if it touches on one of our basic needs (think Maslow’s pyramid). Empathic teaching. You can use this exercise to recall the feelings that have shaped your education and the situations and attitudes of teachers who have significantly impacted you. While emotional contagion is a subconscious phenomenon that spreads from one person to another, empathy is a conscious phenomenon that allows one to feel another’s emotions. An essential part of “emotional intelligence” is the ability to listen to one’s own emotions or “self-awareness.” One of the keys to effective communication is the ability to decipher the feelings of others, which implies being open and respectful toward others. Make use of the learning environment. A learning environment is generally an excellent place to develop emotional “skills” because it encourages stimulating exchanges and pushing oneself beyond one’s limits. This applies to young and adult learners because our emotional makeup changes over time. Therefore, everyone can improve their social skills and abilities such as collaboration, expressing one’s point of view, and taking the initiative by being in this environment and listening more to others. On the other hand, the learner must be eager to acquire these abilities. Teaching methods and activities that stimulate this dimension and the delivery of learning materials are available. The learner’s two great allies. Cultivating self-confidence and assigning value to your work is beneficial for learners in general. They are necessary to emerge all the positive emotions associated with learning and prevent or reduce negative ones. Because adults control their emotions, they should pause and assess their emotional well-being about these two aspects. The decision to make changes rests with them, so they must decide what steps to take or whom to turn to for assistance. Additionally, the teacher can influence these two learner allies by focusing on the learner’s strengths rather than his weaknesses, promoting his

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interest in the course-related tasks, and creating an environment where mistakes are viewed as opportunities to learn rather than failures to its teaching. When teaching adults, it is essential to explain how their knowledge will help them in their everyday lives. • Online training advantages. To help students develop positive emotions and foster interpersonal exchanges throughout the learning process, some e-learning tools have piqued their interest. Forums such as these and synchronous learning sessions and social networks are viable options. It is a great way to gauge how much the students are struggling, have a regular discussion about the most challenging problems they are having, or get them started on coming up with discussion topics of their own. Simulated exercises with relatable actors/characters are a powerful tool for online training because of the close connection between emotions and memory. To be successful, the scenarios must challenge the learner and keep him interested by distributing context and educational interventions equitable (questions and explanations). • Virtual reality for empathy. A word on virtual reality, the cutting-edge solution increasingly being incorporated into training programs where the immersive nature of the experience aids learning. For this reason, virtual reality is the tool that can make learners feel as though they are learning “on the field,” as if they are in a real-world situation. There is an advantage to this: they can experiment indefinitely without risking their safety or the safety of others. The results of a Stanford University experiment published recently suggest that virtual reality could be used to improve empathy. Scientists used a virtual reality headset to immerse participants in the world of a person who is slowly evicted from society until they are forced to become homeless as part of this experiment called Becoming Homeless. Text or scenario 2 D presented the same story to different participants. After that, everyone was asked to sign a petition supporting low-cost housing. Those immersed in the story using VR applications signed this petition at an 82% rate, compared to 67% for the rest of the study participants. The proportions were 85% in the second study and 63% in the third. Developing empathy is a skill that could benefit from this new technology. When we start paying attention to our own and others’ emotional states, we have a better chance of controlling our emotions and getting more satisfaction in the long run. You can also discover and improve your emotional skills in a learning environment.

10.3

Four Emotions of Learning

Educators and students can benefit from understanding the most frequently encountered emotions while learning new things. Before developing adapted approaches, however, it is essential to recognize these behaviors in students and intervene with tact.

10.4 How Children Use Their Emotions to Learn

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Fig. 10.1 Four emotions of learning (Meilleur, 2020a)

The following are the four most important types of emotions (see Fig. 10.1) that affect learning (Meilleur, 2020a): 1. Achievement Emotions: Goal-oriented activities and their success or failure are closely linked. Learning should be fun, but it should also be accompanied by a healthy dose of anxiety about succeeding. 2. Epistemic emotions: They are brought on as a result of cognitive problems. Examples: being surprised, motivated, or perplexed about a proposed exercise, satisfied after successfully solving a challenge, etc. 3. Topic Emotions: Whether positive or negative, these are connected to a single study area. As an illustration, consider someone who has an intense dislike of mathematics but gets giddy when discussing politics. 4. Social emotions: Whether positive or negative, the learner’s relationships with their teacher and peers shape these perceptions. Examples include admiration, expectations, social anxiety, envy, etc.

10.4

How Children Use Their Emotions to Learn

Emotions significantly impact how we live our daily lives (Aznar et al., 2016). Emotional competence, or the ability to express, regulate, and understand one’s own and others’ emotions, is associated with better social skills and academic performance. Emotionally competent individuals, whether children or adults, have better social skills. Compared to their less emotionally competent peers, emotionally competent students perform better in school and learn more effectively. Emotionally competent children have more friends, are more popular, and exhibit higher levels of pro-social behavior than less emotionally competent children. Even at a young age, differences in a child’s emotional competency can be observed. People with better emotional regulation can control their toddlers’ tantrums, but some toddlers who are not allowed an ice cream before lunch will. Children learn about emotions in many different contexts, but their families are most important. A child learns how to deal with his mother’s anger or his sibling’s rage when he breaks his favorite toy by interacting with his siblings and parents. The extended family, peers, teachers, and the media they consume impact children’s emotional competence as they grow. Mothers’ emotional words and phrases affect their children in frequency and quality. More emotional words like “sad,” “guilty,” or “happy” mentioned by mothers in conversation with their children have a better emotional understanding than

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children whose mothers do not do this. Emotion understanding is higher in children whose mothers explicitly explain the reasons behind and consequences of their feelings—“I am angry because you painted on the wall”—than it is in children whose mothers simply say “I am angry.” Academic boost. Emotionally competent children can better adjust to the change from nursery to school if they are taught this skill early (Aznar et al., 2016). Having less one-on-one support allows them better to handle the more challenging demands of school life. They continue to do better in school because they can better cope with the stress and anxiety of school. Emotionally competent students perform better academically for two main reasons (Aznar et al., 2016). To begin with, emotionally competent children have more friends and are more well-liked by their classmates. Children who are well-adapted to school life tend to do better in school. The converse is also true: children who have difficulty relating to their peers at school could suffer from attention deficit disorder (ADD). The likelihood of children with emotional management issues such as anti-social behavior or anxiety problems is higher. As a result, the child will have a more difficult time learning throughout their school career. As a second factor, emotionally competent students have better relationships with educators than less emotionally competent students. It has been observed that teachers expect more from students with whom they have a good working relationship, and as a result, these students work harder to please their instructors. Watching emotions at work. Emotions appear to be important in learning. Some researchers believe that learning is merely an emotional one (Aznar et al., 2016). These issues are beginning to be addressed outside of the confines of the traditional research laboratory. Computer scientists have devised many techniques for detecting emotional expression to make educated guesses about people’s feelings. Facial expressions, heart rates, and even the comments that students jot down can all be monitored. The Open University is currently investigating these methods, which can be applied to a broader spectrum of students (Aznar et al., 2016). When discussing the use of technology to gauge emotions, obvious ethical concerns arise. Parents, educators, and administrators may be concerned about tracking students’ emotions through technological means. Studies that employ these metrics must demonstrate how they improve student outcomes. It will not be long before we start seeing emotional measures alongside more traditional ones like attendance and grades to help students succeed.

10.5

How Emotions Affect Learning and Teaching (Emotions in Classrooms)

A classroom is a highly charged emotional environment. A student’s emotional state can affect his or her academic performance, school involvement, and future

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career options. However, education research all too frequently ignores or neutralizes feelings. Studies on how students can learn despite their emotional states should be prioritized for improving students’ academic performance while also reducing the workload on teachers (Trezise, 2017). We know that negative emotions can hinder students’ ability to focus and perform well on tests. Academic anxieties, such as a fear of mathematics, can have far-reaching consequences, affecting test performance, strategy use, and even subject selection. However, not every student is affected in the same way by anxiety. It has been found that students who can control their anxiety while solving math problems perform better than those who cannot (Trezise & Reeve, 2014, 2016). According to neuroscientific research, cognitive control and motivation-related brain activation appear to eliminate math deficits caused by anxiety (Lyons & Beilock, 2012). Understanding how some pupils can learn despite having negative emotions can help us better understand both learning and emotions. What strategies can we use to break down the emotional barrier in the classroom? One option is to go after the emotion directly, such as with interventions that go after the anxiety associated with math anxiety. Such strategies may have a limited effect and a limited reach. According to Luck and Lipp (2015), treatments for anxiety reduce physiological symptoms, but negative attitudes toward anxiety continue to be present in society. Relapse is more likely when people have a negative attitude toward their recovery. Therefore, even if anxiety disorders like math anxiety are treated, the negative effects on students’ educational outcomes are likely to persist. In other words, students with negative math attitudes will have lower math achievement and interest in higher-level math (Singh et al., 2002), and as they continue their math education, their math anxiety will return. A focus on problematic emotions alone is unlikely to produce long-term results, which means that students may still face difficulties in the classroom. In the classroom, students may also feel other emotions besides anxiety. A variety of emotions, including happiness, rage, hope, pride, and boredom, can impact students and their learning (Pekrun et al., 2002). These feelings can be influenced by factors in the classroom (such as curriculum content or the environment), student differences (such as genetics or general tendencies), and external factors (such as social interactions or the home environment) (Pekrun & Linnenbrink-Garcia, 2014). Teachers simply cannot cope with the sheer volume of students and the wide range of emotions they experience, let alone the underlying causes of those emotions. Adopting this strategy has several advantages (Trezise, 2017): • Students’ knowledge and skill development will be affected if emotions significantly impact test/assessment performance. Learning theories currently assume that students are emotionally neutral, which is simply not true. Understanding how learning can occur while in an emotional state may be more beneficial, given the difficulties of directly managing students’ emotions. Studies that look at how students feel about a subject or a test tend to focus on the results rather than the process of learning itself. These studies (e.g., (Alibali et al., 2007))

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can be used to examine how new concepts and procedures are learned using measure significantly impactful understanding. • Adjusting learning contexts may help students learn better and overcome negative emotions if classroom factors like content difficulty cause emotional states. • Child development can be jeopardized when social interactions or the separation of parents have an impact. Schools may not change students’ emotional reactions in such situations, but they can minimize the impact on education. For clarity, we are not advocating ignoring students’ feelings but rather providing resources to aid in their learning. • Many schools and teachers do an excellent job of supporting students’ emotions and learning, but research, teacher education, or “best practice” lack to guide policy and decision-making. In order to improve educational and emotional outcomes, what steps must be undertaken? Emotions, cognition, and learning are all intertwined, and more research is needed to understand how they work together and the connection between neural activity and emotional states during learning. Education, psychology, and neuroscience must be integrated to achieve these objectives. Educational neuroscience offers a unique perspective on emotions and learning in children, especially those with learning disabilities. This is especially true. Psychologists can characterize learners’ cognitive abilities and disabilities using neurophysiology methods, for example, and educational research can inform educators about the best practices to use when teaching emotionally and cognitively challenged students using those same methods. This study will help us better understand emotional and learning difficulties and develop an intervention strategy backed by scientific evidence. Effective interventions for emotional and learning difficulties, on the other hand, have the potential to improve student learning, alter career choices, and reduce teacher workload (Trezise, 2017).

10.6

Emotion-Aware E-learning Platform Architecture

It was proposed in (Muñoz et al., 2020) that students’ emotions and attention be recognized and used to improve their academic performance on an emotionaware e-learning platform architecture. There is a built-in system for creating and configuring semantic task automation rules, so users can customize the study environment as needed. The goal is to create a system that allows students’ emotions to guide the learning process. For these reasons, the authors have determined that the architecture must meet the following requirements (Muñoz et al., 2020) based on e-learning literature. • integration with the chosen e-learning platform • recognize and save the emotions of the students

10.6 Emotion-Aware E-learning Platform Architecture

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Fig. 10.2 Emotion-aware e-learning platform architecture (Muñoz et al., 2020)

• the ability to transform data into useful information for users • to enable automatic adaptation of the student environment. Figure 10.2 depicts the proposed architecture for meeting the requirements above. Two major components are the architecture of the platform (Muñoz et al., 2020): • emotion-aware e-learning module • emotion recognition module. The former is the platform’s core, enabling the system to collect, store, process, analyze, and visualize relevant data. A semantic task automation platform is also included. On the other hand, the emotion recognition module provides the system with emotional and engagement detection. A learning management system (LMS) has been used to integrate this module into it. 1. Emotion Recognition Component: Data is collected from the e-learning platform by the Emotion Recognition component. These web components must be integrated into the original e-learning platform because they consist of multiple views. We can choose Moodle as the e-learning platform because of its flexibility and ease of use. Moodle’s user-friendly web interface makes it simple to create online courses. Furthermore, it is widely used in academia, so teachers and students do not face any obstacles due to using it. Using Moodle, we can easily incorporate an emotion recognition component. Emotion widget and emotion recognition tools make up this component. The Emotion-aware e-learning component receives

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all captured data from the platform, while the latter collects data on students’ emotional and engagement levels. This component includes three tools for recognizing emotions: self-reporter, videogazer, and emotestcapturer. For each day of the course, the self-report tool provides a form to record the emotions they are experiencing. Choosing an emotion and its intensity allows students to complete the report every day. These data will enable us to monitor students’ moods throughout the course using accurate metrics derived directly from the subject matter. The correlations between these data and other submodule measurements and the academic results achieved in each course phase are also possible. Students’ attention can be tracked using the videogazer tool, based on the WebGazer library. Based on the position of the user’s gaze, this tool can tell whether or not he or she is paying attention to the lecture. As a result, it is possible to connect the various stages of the video lesson to the level of interest and attention they generate. Students’ emotions can be captured while taking a test using the emotestcapturer. Using the Clmtracker library, this tool can determine a user’s emotional state based on the coordinates used to draw their face. Emotionaware e-learning processes all data collected by these tools before sending it to the e-learning component. 2. Emotion-Aware E-Learning Component: Collection, storage, processing, analysis, and visualization of data obtained from the emotion recognition component are significant responsibilities of the Emotion Aware E-learning component. A task automation platform is integrated into the module, adapting to the smart environment based on automation rules. Figure 10.3 shows an overview of this module’s data interchange. The emotion server submodule receives the data.

Fig. 10.3 Emotion aware E-learning module flow diagram (Muñoz et al., 2020)

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The web service in this submodule receives and responds to requests from the emotion recognition module. Depending on its nature, it receives information about management or students and passes it to the appropriate system submodule for processing. Emotion event triggers and learning analytics receive this data stored in the storage module. System logic data and user-related data are stored separately in the storage submodule’s repositories. An academic database (courses, lessons, etc.) and a personal user information repository (age, sex, etc.) are housed in the same MongoDB database. Elasticsearch, a distributed analytics and search engine, is the second repository to store information about emotions and attention. Students’ moods, personal data, and grades are included in this data set. The Elasticsearch repository connects to the visualization module via the dashboard-based visualization tool Kibana to visualize the data. To ensure that the data is easily understood, it must be presented in a way that does not require the users to have any prior knowledge. Kibana was chosen as the implementation tool for these visualizations to make it possible. In order to implement our visualizations, Kibana gives us a web-based environment to work in. Different chart types have been used to create various visualizations. There are three dashboards here: two for teachers one for students. There are a variety of data points used to create these dashboards, including average self-reported emotions per time (area), the number of students enrolled (gauge), and maximum and minimum grades per topic (table). Other data used to create these dashboards include success rates (goal), grades (heatmap), attention levels (heatmap), and time spent watching videos (bar) (region). Learning analytics is a submodule designed to help users draw conclusions that are not possible with standard research and visualization methods. With the help of K-Means and finding correlations between different features, this submodule can detect anomalous cases and trends in student data. In addition to using visualization to analyze data, the system also provides a way to use machine learning techniques to analyze data. Finally, a semantic task automation platform was integrated to automatically adapt the students’ smart environment. EWE Tasker, a previously developed automation platform, serves as the foundation for the new system. On this platform, smart devices and services can be easily integrated and used to automate daily tasks in various environments. Smart lights, sensors, and smartphones all play a role in these integrations, as do services like Gmail and Twitter. The platform’s semantic core, based on the EWE ontology, allows users to create and configure their own automation rules and integrate new devices and services. The emotion event trigger submodule applies a semantic layer to the received data before importing it into the platform. These events are processed by the task automation platform, which takes the appropriate actions in the environment based on user-defined automation rules. Various services and devices are integrated into the platform, allowing teachers to adapt the learning environment based on their students’ responses or engagement. It also has a smartphone app that connects to various devices to

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provide new options. Empatica E4 wristbands can detect stress, and Estimote iBeacons can locate people inside buildings. Students’ mood and comfort can be automatically improved with the integration of the automation platform.

10.7

Emotion AI in Education

Since Covid-19’s inception, educators have realized that education models are out of date and need to be updated. Long-distance teaching can be more effective with emotional AI, a cutting-edge educational concept (Kumar, 2020). The Covid-19 pandemic has wreaked havoc on various industries worldwide, but education has been particularly hard hit. In a matter of hours, all educational institutions had to convert to a purely virtual mode of instruction. The challenge remains in recognizing and responding to students’ emotional nuances like faceto-face interaction, even as more EdTech firms offer online learning solutions for students (Kumar, 2020). Teachers and students alike struggle with online education because of communication breakdowns. Instead of providing a wealth of feedback like in-person learning did, online learning has replaced it with a sense of unpredictability. The ability of humans to recognize and extract meaningful information from facial and auditory cues has mainly been overlooked until now (Kumar, 2020). There are many ways emotion AI can help improve the educational system. The physical interaction between students and faculty can be replaced by collecting and analyzing student response data. Curriculum developers have much room to innovate and improve the student experience (Kumar, 2020). What is Emotion AI? It is not a new concept; artificial intelligence (AI) has long been around. Affective computing, also known as emotion AI, has been around for a while, but the goal has always been to understand emotions in context. What is being said has a different meaning depending on the context. For instance, the NLP engine can interpret the phrase “I am hungry” as a request. It may grasp the situation’s urgency if it is made aware of the feelings associated with the request. In order to understand what “I am hungry” means, we need to know whether “I want to eat now” or “I plan to eat in an hour” is the correct meaning (Speakia, 2020). Students’ emotional states can be monitored using sensors such as video cameras or microphones during lessons. According to emotion AI, students may be satisfied or frustrated with lessons if a task is too difficult or simple. As a result, educators can customize their class loads to meet student needs. Similar methods can be used to evaluate online learning software prototypes. Emotion AI can also assist autistic students in identifying the emotions of their peers at school (Dilmegani, 2021). Potential of emotion AI. Emotion AI in the education industry has enormous potential, especially in light of the rise of online education following Covid-19.

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An analysis by Gartner shows that 37% of businesses have implemented AI since the Covid19 outbreak, and now schools are also interested (Kumar, 2020). Understanding different components of emotion AI. In order to use Emotion AI, you must use both Emotion Recognition and Emotional Response capabilities. In the context of emotion recognition, a machine’s ability to build a repository of different emotional responses is referred to as this. Students’ responses in a virtual classroom scenario can be captured using facial and voice recognition software to identify concentration, hesitation, and other characteristics. On the other hand, the emotional response is the affective computation and application of the recognized emotional subfields (Kumar, 2020). Role of emotion AI in Education. When it comes to improving educators’ ability to detect, identify, measure, and respond to a wide range of student learning behaviors, emotion AI can make a significant difference (Kumar, 2020). It can help determine whether or not a student is paying attention and which part of the content is confusing to them. Teachers can use the gathered data to get helpful feedback and quickly make instructional changes. We can also identify stress signals in student voice data using voice recognition, which alerts teachers to reinforce and clarify concepts. Emotion AI can help improve the effectiveness of long-distance teaching by addressing some of the nuances that teachers are already familiar with in a traditional classroom setting. Educators can rewrite their lessons to serve students better by using eyetracking, facial coding, and UX testing techniques in emotion AI. To help educators better understand their students’ emotional intelligence and learning abilities, AI-driven emotion analytics and algorithms are available. With the help of technology, teachers can track and analyze the learning patterns of their students and then make adjustments to the curriculum as necessary. Educational models have been outdated for some time, and Covid-19 has been a catalyst for realizing this. Online teaching tools, remote learning platforms, and the creation of intelligent digital learning environments are becoming increasingly popular. In the post-Covid-19 world, thinkers, policymakers, and educators can make a real difference by incorporating Emotion AI into their platforms. Emotion AI in education is a new idea, and it is a powerful way to help students learn in a way that’s tailored to their individual needs.

10.8

Emotional Learning Analytics

The importance of emotion to learning was discussed by D’Mello (2014). Learning is not just a cerebral exercise; it is also filled with feelings. Emotions are not just for show; they are active. Emotion, on the other hand, is a multi-scale phenomenon. Despite significant advances in the fields of affective sciences and affective neuroscience, we know very little about emotions, and we know even less about how emotions influence learning. This does not imply that modeling emotion should be postponed until more theoretical clarity is achieved. The exact opposite

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is true. It simply means that when we say we are modeling emotion, we should be aware of what we are modeling. Emotion’s complexity and ambiguity must be embraced rather than minimized. Los Angeles and EDM, with their focus on collecting real-world data and discovery-oriented analytic methods, have the potential to advance both learning science and emotion science at the same time. Sidney K. D’Mello spoke about the most important and cutting-edge research areas being conducted at the nexus of these disciplines. It all begins with the consideration of feelings when conducting a learning analysis. Core Themes. D’Mello (2014) chose the following four themes to illustrate how LA/EDM methods can be used to study the effects of learning on one’s emotional state. 1. Affect Analysis from Click-Stream Data: The rich stream of data generated during interactions with learning technologies is one of the most common LA/EDM techniques for understanding learners’ cognitive processes. A new set of insights becomes available when the effect is factored in. 2. Affect Detection from Interaction Patterns: Affective states cannot be directly measured because they are conceptual entities (constructs). When people interact with their environment (context), they form relationships that influence their cognition and behavior. As a result, analyzing the unfolding context and learner actions should allow us to “infer” affect. About two decades ago, researchers began working on what they call “interaction-based,” “log-file-based,” or “sensor-free” affect detection 3. Affect Detection from Body Signals: To have an effect, one must be embodied in the sense that one must use bodily response systems to take action. The use of bodily signals to detect effect has a large body of research that discusses it in great depth. Traditionally, research has focused on interactions in lab settings, but in recent years, researchers have begun to apply their findings in more realistic settings, such as computer-equipped classrooms. Learning effect (a latent variable) can now be deduced from machine-readable bodily signals (observables). 4. Integrating Affect Models in Affect-Aware Learning Technologies: We have seen how tangible artifacts such as interaction and bodily-based affect detectors can be instrumented to provide real-time assessments of students’ feelings while interacting with a learning device. Close the loop by dynamically responding to sensed effect is possible with this approach. Technology responding to students’ feelings, thoughts, and actions is a crucial objective of affect-aware learning. Emerging Themes. When it comes to research on the intersection of emotions, learning, LA, and EDM, the focus has typically been on one-on-one tutoring with intelligent tutoring systems, educational games, or interfaces that support essential competencies such as reading and writing, text-diagram integration, and problemsolving (Mello, 2014). These basic research areas are very active, but recent work

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has focused on analyzing effect in more expansive interaction contexts that capture the broader socio-cultural context around learning more closely than previous work. Sidney K. D’Mello briefly described some current developments (Mello, 2014). 1. Affect-Based Predictors of Attrition and Dropout: Some of the ‘killer apps’ for LA and EDM are early risk indicators and early intervention systems. Data on academic performance, demographics, and availability of financial assistance are all important, but most fielded systems only look at these. These aspects are unquestionably critical, but there are likely to be additional factors at work. 2. Sentiment Analysis of Discussion Forums: Language means expressing one’s emotions. This means that using techniques such as sentiment analysis and opinion mining, it is possible to study how students’ thoughts about a learning experience (expressed in written language) predict appropriate behaviors (most notably, attrition). 3. Classroom Learning Analytics: It is now possible to automatically model aspects of students’ classroom experiences that were previously only obtained through self-reports and time-consuming human observations, thanks to recent advances in sensing and signal processing technologies. 4. Teacher Analytics: It is important to include teachers in the conversation regarding student engagement and motivation. As a result, quantifying teacher practices is difficult due to the reliance on on-the-spot classroom observations. So, to deal with this problem, scientists have started working on software that can automatically analyze teachers’ instructional practices. Other Themes. Additionally, Sidney K. D’Mello emphasized various other areas of research (Mello, 2014). 1. Students’ emotional experiences in traditional courses, flipped courses, or MOOCs can be studied in depth using methods such as comparative emotional analysis. 2. Emotion regulation during learning is the subject of the second study, which focuses on how LA/EDM methods can identify different regulatory strategies and encourage the most beneficial ones. 3. A third would look at how emotions and attentional states like mindfulness and mind-wandering blend to produce “flow experiences” in the body and behavior. 4. Fourth, the study looks at how so-called “non-cognitive” characteristics like grit, self-control, and diligence affect learner emotions and their attempts to control them. 5. Since collaboration is a critical 21st-century skill, a fifth would keep track of how students feel while working together on a collaborative project or solving a problem as a team.

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Analytics of Emotions

Examples of Emotion Analytics in the Real World

Examples of emotion analytics in the real world outside the education sector are discussed here. It is critical to understand how your customers feel about your products and services when running a business. You can get a general idea of customer views from sales figures, surveys, social media posts, and ratings. However, these methods do not provide the finer, granular insights into what customers are not saying out loud. Emotion analysis comes in handy in this situation, as you will see. The emotional state of an individual or group can be deduced from facial expressions and voice modulation. Beyond facial recognition and sentiment analysis, this technology provides a deeper understanding of how customers feel at a given time. There are numerous examples of how emotion analysis helps a business improve existing processes, seize new opportunities, and cut costs. The following are some practical applications of emotion analysis in a variety of industries (Bundela, 2019): 1. Removing bias from the interview process: Unilever, a company that makes consumer goods, is looking at the emotional expressions of job applicants. Once they have applied for the position, job seekers will be required to participate in a video interview, including a standard set of questions. An AI algorithm monitors the candidate’s face, emotions, and other personality traits during the interview to see if they match the questions. The recruiter receives a detailed report on the candidate’s emotional response to each question. AI also assists in narrowing down the field of applicants by using emotional analysis to determine who is the best fit for the position. It saves Unilever a significant amount of time by using emotion recognition technology to screen candidates. In contrast to human recruiters prone to bias, the algorithm makes its decisions solely based on the emotions they detect. As a result, the company has reaped social and economic benefits from incorporating emotional analytics into its hiring process. Furthermore, it has helped the company increase its ethnic diversity by hiring a greater number of people who are not white. 2. Ensure every scene has an adequate response: Disney, the world’s largest entertainment company, employs emotion analysis to gauge how audiences react to their films. Infrared cameras placed at various angles in the theatre capture the audience’s facial expressions while the film is playing. An artificial intelligence algorithm then feeds these data points, which provide the company with a plethora of information. It only takes a little bit of practice before the model can recognize various facial expressions. The emotion recognition algorithm can also anticipate the audience’s future emotions, such as when they will laugh or be sad during a particular scene in the movie. This cutting-edge technology aids Disney in determining whether or not the movie’s audience enjoys it. Emotion analytics services provide more reliable and up-to-date data when compared to reviews and surveys. This aids the company in gaining insights

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into the various factors that elicit different types of emotions, which can then be used to better connect with their target audiences in the future. 3. Promoting digital advertising with high levels of engagement: For branding and advertising, Kellogg’s, a multinational food manufacturer, uses emotion recognition technology. Facial recognition software captures viewers’ emotions after multiple versions of an advertisement are shown to them. As it is run through the model’s analysis, it identifies versions that had high engagement rates during the first watch but dropped off after that and those with a consistent engagement rate across multiple views. The company then finalizes the ad based on the findings to achieve the desired level of engagement. In order to create and distribute more emotionally targeted videos, Kellogg’s uses the technology to understand better how emotions influence purchasing decisions. A company can better target its advertising and persuade customers to buy a product based on the information provided by the software. Furthermore, it gives the ad team a chance to make the necessary adjustments to improve its performance before it hits the market. 4. Improved customer experience: Cogito Corporation, a leader in emotional intelligence solutions, has worked with Humana’s (a health insurance company) call centers to improve the emotional experience for customers. They use real-time voice emotion analytics to determine how the customer is feeling at any given time during the call. Rather than what the customer says, the emphasis is placed on how the customer says it in this instance. These findings are communicated in real-time to the agent to serve the client better. This tool also gives the agent suggestions on improving the customer experience by changing their tone of voice, speaking speed, and displaying empathy when necessary. Humana’s customer service representatives use Cogito’s Emotion Analytics services to help solve customer problems on their first call. Because of this, the first-call resolution rate and Net Promoter Score for the company both increased significantly (NPS). Aside from that, it helps agents by streamlining their tasks and revealing information they otherwise would not have known about. 5. Personalized gaming takes emotional intelligence into account: Emotion recognition technology is being used by the developers of Nevermind, a thrilleradventure game. Facial expressions are recorded via a webcam as players engage in the game. An app that recognizes emotions such as fear and anxiety in real-time uses the collected data and infers it. The game’s gameplay is tailored to the player’s emotional state, resulting in a unique experience. It helps Nevermind find accurate information about player frustration levels, game difficulty, and reasons for leaving the game by analyzing their emotions. As a result of this data, the game can offer players better dynamic changes, which improves the overall experience while also increasing gaming time and player loyalty. We have only seen the tip of the iceberg for emotion analytics. Companies are using emotional intelligence worldwide to improve their business processes.

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10.10 Conclusion A student’s approach to learning is greatly influenced by many factors, including how they perceive their brains to be fixed or malleable, reflect on their learning, seek out assistance, and devise a strategy for learning. A student’s ability to explain their understanding of a concept and how they arrived at that conclusion are two common cognitive aspects of learning. It is essential to consider noncognitive factors such as a student’s level of frustration, confusion, or distraction. It is possible to use eye-tracking and facial recognition software to monitor students’ emotional and cognitive states and tailor the teaching experience to their needs. In combination with human teachers’ expertise in responding to students’ emotions and dispositions, computer-based cognitive tutoring systems offer a promising approach to classroom instruction.

References Alibali, M. W., Knuth, E. J., Hattikudur, S., McNeil, N. M., & Stephens, A. C. (2007). A longitudinal examination of middle school students’ understanding of the equal sign and equivalent equations. Mathematical Thinking and Learning, 9(3), 221–247. Aznar, A., Rienties, B. C., & Hillaire, G. (2016). How children use their emotions to learn. Theconversation.Com. https://theconversation.com/how-children-use-their-emotions-to-learn-57938. Bundela, V. (2019). 5 best examples of emotion analytics in the real world. Softwebsolutions.Com. https://www.softwebsolutions.com/resources/5-examples-of-emotion-analytics.html. Dilmegani, C. (2021). 24 affective computing (emotion AI) applications/use cases. research. Aimultiple.Com. https://research.aimultiple.com/affective-computing-applications/. Kumar, R. (2020). Future of emotion AI in the education sector. Indiatoday.In. https://www.indiat oday.in/education-today/featurephilia/story/future-of-emotion-ai-in-the-education-sector-173 9350-2020-11-09. Luck, C. C., & Lipp, O. V. (2015). A potential pathway to the relapse of fear? Conditioned negative stimulus evaluation (but not physiological responding) resists instructed extinction. Behaviour Research and Therapy, 66, 18–31. https://doi.org/10.1016/j.brat.2015.01.001. Lyons, I. M., & Beilock, S. L. (2012). When math hurts: Math anxiety predicts pain network activation in anticipation of doing math. PLoS ONE, 7(10), 1–6. https://doi.org/10.1371/journal. pone.0048076. Meilleur, C. (2020a). 4 emotions of learning. Knowledgeone.Ca. https://knowledgeone.ca/4-emo tions-of-learning/. Meilleur, C. (2020b). The importance of emotions in learning. Knowledgeone.Ca. https://knowle dgeone.ca/in-depth-analysis-the-importance-of-emotions-in-learning/. Mello, S. K. D. (2014). Emotional learning analytics. In Handbook of learning analytics (pp. 115– 127). https://doi.org/10.18608/hla17.010. Muñoz, S., Sánchez, E., & Iglesias, C. A. (2020). An emotion-aware learning analytics system based on semantic task automation. Electronics, 9(8), 1–24. https://doi.org/10.3390/electroni cs9081194. Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ selfregulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702. Pekrun, R., & Linnenbrink-Garcia, L. (2014). Introduction to emotions in education. In International handbook of emotions in education (pp. 1–10).

References

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Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Singh, K., Granville, M., & Dika, S. (2002). Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95(6), 323–332. Speakia. (2020). Emotion AI in education. Speakia.App. https://speakia.app/blogs/emotion-ai-ineducation?lang=cn. Trezise, K. (2017). Emotions in classrooms: The need to understand how emotions affect learning and education. Npjscilearncommunity.Nature.Com. https://npjscilearncommunity.nature.com/ posts/18507-emotions-in-classrooms-the-need-to-understand-how-emotions-affect-learningand-education. Trezise, K., & Reeve, R. A. (2014). Cognition-emotion interactions: Patterns of change and implications for math problem solving. Frontiers in Psychology, 5, 1–15. https://doi.org/10.3389/ fpsyg.2014.00840. Trezise, K., & Reeve, R. A. (2016). Worry and working memory influence each other iteratively over time. Cognition and Emotion, 30(2), 353–368. https://doi.org/10.1080/02699931.2014. 1002755.

Stealth Assessment

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Abstract

This method automatically collects the information from the learner when working with rich digital environments. This will help us to assess their learning processes unobtrusively. This technique collects the information of learners like online gaming, in which the framework continuously gathers information about the actions of players, drawing interpretations about their goals and tactics to present new relevant obstacles. The concept of a virtual learning environment is introduced to the schools for subjects like science and history. The idea behind this method is accessing the facts of learning, which are difficult to measure, such as determination, imagination, and tactical thinking. It can also collect learners’ details, learning methods, and processes to stop the assessment. This method provides the instructor with continuous data on how each learner progresses. This chapter describes the problems with the current assessment techniques and how the stealth assessment can be effective. Keywords

Digital environments • Assessments • E-learning • Online learning • Stealth assessment

11.1

Assessment: Overview

Students in the classroom are frequently evaluated through exams, tests, projects, and papers. It is not uncommon for students to take these exams in the middle of a semester or year-end (ResourceEd, 2020). Students’ motivation and engagement can be gleaned from class observations, but they do not always indicate how much knowledge they retain. Therefore, it is critical to conduct informal assessments regularly to monitor students’ progress on a particular subject. This type of assessment is critical for students’ continued

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_11

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education, but it can leave educators lacking information about how to help them improve (ResourceEd, 2020). This means that various approaches and mediums should be used to gather the most comprehensive information about students (ResourceEd, 2020). To help students achieve their learning goals, teachers can use this data to predict how far they will progress in their studies and perform academically in the future. What is Assessment? Many assessment definitions exist, but assessment is generally the systematic collection, review, and use of information about educational programs and services for quality improvement, planning, and decision-making (Fredonia, 2020). The following are just a few of the advantages of using assessments in higher education (Fredonia, 2020): • • • • •

Increased educational opportunities for students Programs and offerings that are self-evaluated and fine-tuned Ability to improve based on accurate assessments of requirements Enhanced inter-office and inter-department communication and cooperation Stakeholders are now held more accountable.

Purposes of Assessment. “Assessment for improvement” and “Assessment for accountability” are two of the most frequently used terms in the field of assessment today (Fredonia, 2020). There are many advantages to focusing on assessment for bettering the quality of instruction, learning, programs, or services and the process of making plans and making decisions. Students, parents, taxpayers, and employers are all potential stakeholders in an institution’s programs and services, so it is essential to demonstrate their value to these groups through an assessment process. An effective assessment cycle will also provide the necessary evidence for accountability if it addresses assessment for improvement. Why Is Assessment Important? In order to determine whether or not the lesson’s educational objectives and standards are being met, students must be asked to demonstrate their knowledge of the subject at hand (Edutopia, 2008). If the educational objectives are not being met, assessment is essential for instruction. Decisions based on assessment results impact all things (Edutopia, 2008). The question is, “Is there a better way to teach the subject?” how can we be sure we are teaching what we are supposed to be teaching? Do students understand what they are expected to learn? Assessment and instruction must be re-examined in light of the changing needs of our student’s skill sets and knowledge (Edutopia, 2008). It is no longer enough for students to be literate and numerate; they must adapt to a constantly changing world. They must be able to analyze, evaluate, and draw conclusions. It is up to teachers to decide on the purpose of an assessment and the content that will be tested.

11.2 Problems with Current Assessments

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Problems with Current Assessments

This section presents an overview of specific problems surrounding current assessment practices (Shute & Ventura, 2013). Traditional Classroom Assessments Are Detached Events. The way assessments are done today is usually disconnected from how students learn. In other words, the typical educational cycle is as follows: teach; stop; administer test; loop back around to start again (with new content). The metaphor proposed by Pellegrino et al. (2001) (from small businesses to supermarkets to department stores) represents an important shift in the retail industry. These companies no longer have to shut down every year or two to conduct an inventory of their stock. It is more likely that thanks to innovations like automated checkout and bar codes on everything, these businesses can keep tabs on inventory and the flow of goods in real-time. While a business can continue, the information obtained is far richer than before, allowing stores to keep track of trends and aggregate data into various types of summaries, all while supporting real-time and just-in-time inventory management. This also means that schools will no longer have to interrupt their regular instructional schedules at various points throughout the year to administer external tests. Instead, evaluation should be ongoing and imperceptible to students, supporting on-demand, just-in-time learning (for more, see (Shute et al., 2009)). Traditional Classroom Assessments Rarely Influence Learning. Many classroom assessments used today do not support the acquisition of complex competencies or deep learning. Students (or a group of students) are currently assessed (referred to as “assessments of learning”) in the classroom at a single point in time, without providing diagnostic support or diagnostic information to students or teachers. Alternately, assessments can be used to help students and teachers learn by providing formative and summative information (e.g., useful feedback during the learning process rather than a single judgment at completion); interpreting information about understanding and/or performance about educational goals (local to the curriculum and broader to the state or common core standards), and be responsible. An assessment for a learning system called ACED (for “adaptive content with the evidence-based diagnosis”) was evaluated by Shute et al. (2008) in order to demonstrate how classroom assessments can be used to support learning. They used an evidence-centered design approach to develop an adaptive diagnostic assessment system with instructional support and elaborated feedback (Mislevy et al., 2003). The key question was whether adding feedback to the system degrades the assessment’s validity, reliability, and efficiency or improves student learning. According to the results of a rigorous study involving 268 high school students, providing feedback did not affect the accuracy of the results. As a result, compared to a control group (students who used the system but did not receive detailed feedback—only correct/incorrect feedback), students who used the ACED system learned significantly more about the content (geometric sequences). According to these findings, other assessments (such as state-mandated tests) could

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benefit from instructional feedback to support student learning. This would not compromise the assessment’s primary goal, however. Traditional Assessment and Validity Issues. Reliability and validity are the two main criteria used to evaluate assessments. For an assessment to be reliable, it must consistently measure a given attribute under similar circumstances (Shute & Ventura, 2013). As an example of reliability in assessment, consider someone who performs exceptionally well on an algebra test one day and performs similarly the next. Assessment tasks are reduced to independent pieces of evidence that can be modeled using existing measurement models to achieve high reliability. One interesting question is how far this simplification process can be taken without affecting the test’s validity. Due to the importance of high reliability in supporting high-stakes decision-making, other aspects of the test may be compromised (e.g., engagement and some types of validity). If construct-irrelevant variance and dependencies are removed from tasks, they can appear as isolated pieces of evidence. Additionally, standardized assessments stress the importance of coping with operational constraints. Consider the challenge of gathering and evaluating sufficient evidence while working with a constrained administration timeline and financial budget. As a result of this problem and the current state of specific measurement models, many of the above-described simplifications could be explained. In general, validity refers to how well the assessment measures what it is supposed to measure. This section addresses traditional assessment validity issues in more depth. Traditional assessment has several problems with its validity, including the following ones (Shute & Ventura, 2013). • Face validity: According to the concept of “face validity,” an assessment should “appear” to measure what it is supposed to measure. For example, answering multiple-choice questions about an uninteresting topic after reading a few excerpted paragraphs may not be the best way to assess reading comprehension (i.e., it lacks good face validity). Instead of filling out bubbles on a prepared form in response to decontextualized questions, as previously suggested, students should be assessed in meaningful settings. Digital games, which can provide such meaningful environments for students, can provide scenarios that require the application of various competencies, such as reading comprehension and problem-solving skills. • Predictive validity: Predictive validity refers to a test’s ability to predict future behavior accurately. Studies have shown that noncognitive variables (such as psychosocial factors) have a higher predictive value for college success than traditional academic indicators like the SAT (Scholastic Assessment Test) and grade point average (GPA) (see, e.g., (Robbins et al., 2004)). These are generally deficient in today’s large-scale, standardized evaluations. For instance, according to a recent College Board study, the SAT only marginally predicted college success beyond high school GPA at r = 0.10 (Kobrin et al., 2008). As

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a result, after accounting for GPA, the SAT scores only account for about 1% of the unique prediction of college success. • Consequential Validity: The validity of a particular assessment in terms of societal and policy decisions is known as consequential validity. Teachers who “teach to the test” result from the No Child Left Behind (2002) initiative’s emphasis on accountability. Teachers who only teach material relevant to answering test questions rather than helping students solve real-world problems risk having their students become disengaged in school, which can lead to higher dropout rates (Bridgeland et al., 2006). Furthermore, because of the low predictive validity of current assessments, students may be denied admission to their first choice of university. However, the SAT and other similar tests are still used as the primary basis for making college admissions decisions, resulting in some students, particularly those from disadvantaged backgrounds, missing out on fulfilling careers and lives. Conscientiousness is an excellent example of how traditional and new performance-based assessments compare (Shute & Ventura, 2013). In general, conscientiousness can be defined as the desire to work hard despite difficult circumstances—a characteristic linked to higher academic achievement throughout the lifespan, from preschool through high school and into adulthood. Because conscientiousness measurements are primarily self-report (for example, “I work hard no matter how difficult the task is”; “I complete my work on time”), they have several flaws as an assessment strategy. Because self-report measures are subject to “social desirability effects,” they may yield inaccurate information about a person’s character traits, attitudes, and beliefs (see (Paulhus, 1991)). Second, test takers may interpret certain self-report items differently (e.g., what it means to “work hard”), resulting in unreliability and lower validity (Goodstein & Lanyon, 1999). For the third reason, it is not always possible for people to be honest about their dispositions when answering self-report questions. With good games and assessments, conscientiousness and other essential competencies can be dynamically measured more accurately than traditional methods (e.g., (Shute et al., 2010)). Multi-behavior and measurable artifacts in the game can be assessed with evidence-based tests that record and score multiple behaviors. The amount of time a person spends on a complex problem (where more time equals more persistence), the number of failures and retries before success, the number of times a player returns to a complex problem after skipping it, and so on can all inform conscientiousness. These “conscientiousness indicators” would update the student model of this variable, making it current and available at all times. It is believed that good games can foster conscientiousness in players because so many problems necessitate perseverance in the face of setbacks and frustrations. Meaning that good games can be highly challenging, and this is a great way to increase one’s persistence because it comes with the added satisfaction of completing a complicated problem (see, e.g., (Eisenberger, 1992; Eisenberger & Leonard, 1980)). Students who are uncomfortable with or do not have access to games may not participate. These students should have access to different methods of instruction.

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What is Stealth Assessment?

Stealth Assessment—Background. Stealth assessment (SA) combines a principled conceptual framework for serious game designers to design data-driven assessments. It makes use of both the ECD (Evidence-Centered Design) framework and machine learning algorithms (Georgiadis et al., 2019). • Evidence-Centered Design (ECD): The ECD (Mislevy, 2011) is a framework for developing assessments in serious games based on various generic conceptual models. ECD describes the assessment design as a part of three different model types. This includes the competency model, which describes the assessed competence and its underlying facets (i.e., sub-skills), the task model, which describes in-game tasks that can elicit data about the assessed competency constructs, and the evidence model, which describes the relationships between the elicited data (i.e., game variables/observables) and the tasks in the game as well as the competency constructs. As a result of this relationship, SA can be applied to games in various ways, including ECD assessment was the sole focus of GSAT’s design (such as the statistical and competency models). • Machine learning: Because it can provide probabilistic solutions to non-binary and non-deterministic problems, machine learning (ML) is becoming a data science subfield due to its close association with artificial intelligence (AI). Most ML algorithms can be classified as supervised, unsupervised, or even semisupervised. Algorithms that fall into these categories of ML can be further divided based on the different types of data they can process (e.g., numerical or categorical, or ordinal) (Georgiadis et al., 2019). Data labeled for training is used as a reference point by supervised algorithms to make inferences (e.g., a pre-annotated dataset with classifications by experts). Unsupervised ML algorithms can classify the data using clustering techniques on unlabeled datasets. Supervised and unsupervised data are both used in semi-supervised ML algorithms to provide classifications, which are in between the last two categories. A traditional classroom teacher cannot collect the vast amounts of data collected through serious games. After structuring this data into meaningful statistical models (e.g., through ECD), ML algorithms can assess (i.e., classify) the learners’ performance. It has been found that various ML algorithms (such as Bayesian networks) have been used for SA (Sabourin, 2013; Min et al., 2015). Bayesian Networks have been the most widely used algorithm in existing SA studies (Shute et al., 2013a, 2013b, 2016; Ventura et al., 2014) due to their generative nature, but other algorithms have been considered. Stealth Assessment. The two primary assessment types are formative and summative (UKDiss, 2019). Summative occurs after learning has taken place, while formative occurs during learning. Summative tests are high stakes and provide feedback to students, whereas formative tests evaluate the student’s strengths and weaknesses and help educators tailor their practice accordingly. In order to deliver

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high-quality instruction, teachers must use data collected during formative assessments. Using them is a powerful but resource-intensive teaching tool (UKDiss, 2019). Instead of using the copious amounts of data generated while playing, a stealth assessment uses a ubiquitous and unobtrusive technique (Zhao et al., 2015). Teachers can use it to address students’ concerns ahead of time (Arnab et al., 2015) and reduce test anxiety for students while still ensuring validity and reliability (Shute & Zapata-Rivera, 2010). Students’ achievement and competencies are monitored thanks to an evidence-centered design approach, a key component of stealth assessment (Miles & Crisp, 2014) effectively. With ECD, diagnostic, summative, and formative assessments can be integrated holistically into the student’s journey throughout the game. The main disadvantage is the high cost of implementing a full-scale model that goes hand in hand with ECD. ECD’s design framework, which includes the student, task, and evidence models, is better than full implementation. In contrast, to survey questionnaires and other assessment modes, this assessment does not interrupt the participant’s flow during the activity and does not rely on the learner’s perception or memory of the learning task as a key feature (Snow et al., 2014). While ensuring engagement, stealth assessment saves learners time by integrating into the game and not requiring additional time allocation (Baron et al., 2016). Traditional teaching involves interrupting the flow of instruction to conduct assessments, followed by delayed feedback that is of little use because new learning has already begun when the feedback is delivered (Miles & Crisp, 2014). That is why stealth assessment exists: blur the line between learning and assessment as much as possible (Moreno-ger et al., 2014). In addition, traditional tests have a unique answer for each question, whereas, in a game setting, the sequence of events is highly dependent on each other. Furthermore, traditional exams use questions that measure mastery of a single fact. On the other hand, an in-game assessment allows you to look at a series of actions and determine whether or not the students have the necessary knowledge at any given time (Shute et al., 2013a, 2013b). The log data from gameplay can be used to create a stealth assessment. During gameplay, the learner’s interactions should generate the assessment for a formative evaluation. A summative rather than formative assessment would be used to overlook significant gameplay changes (Eseryel et al., 2011). Do you think the learner was confused by the assignment? Was the job too difficult for you to complete? Was he/she overly enthused? Do you think it had anything to do with my motivation? Shute and colleagues (2013a, 2013b) created a game based on Newton’s three laws of motion called Newton’s Playground that incorporates stealth assessment. To move on to the next level, the challenges in the game required the player to use multiple agents of force and motion. This game tested the players’ understanding of physics and staying focused. Torque, linear momentum, angular momentum, and levers are all examples of physical principles that a successful player would demonstrate. A player’s perseverance was assessed by how long they tried and failed to solve a complex problem. Other games created by Shute and colleagues

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have an embedded stealth assessment measuring various types of constructs like creativity, which is what this one does (Kim & Shute, 2015)). If stealth assessments become more widely accepted, some ethical issues may arise, regardless of how fair they are. Students’ behavior may be influenced if they are aware they are being evaluated. Assessing them without their knowledge could lead us astray (Walkera & Jr, 2014). Why is Stealth Assessment Needed? Just to sum it up, the world is getting smaller while at the same time becoming increasingly intricate. Problems of enormous complexity and global reach face us (e.g., nuclear proliferation, global warming, antibiotic-resistant microbes, and destruction of the rain forests). Thinking critically and creatively while also working collaboratively and systemically is crucial when confronted with complex problems (Shute, 2011). Multiple-choice responses on a trivial knowledge test cannot easily measure learning and success in a dynamic and complex world. Instead, solutions should start with a rethinking of assessment, identifying new skills and standards relevant for the twenty-first century, and then figuring out how to best assess students’ acquisition of the key competencies (Shute, 2011). Identifying key competencies and using games as instructional learning vehicles motivates you. Today, there is a significant disconnect between what children do outside of school and what they must do in school. While school teaches students what is deemed “important,” many students are unimpressed. On the other hand, this group of children is highly motivated by what they do for fun (e.g., play games, participate in social networking sites). This discrepancy between school-mandated and extracurricular activities raises questions about the school’s motivational impact (or lack thereof), but it does not have to be that way. Consider combining these two worlds. School material embedded in game-like environments has a great potential to increase learning, especially for disengaged students. According to stealth assessment research, the following assumptions are valid (Shute, 2011): • Learning processes and outcomes are improved when students learn by doing. • Gameplay can verify and measure a variety of learning and learner attributes. • The learner’s strengths and weaknesses can be exploited and bolstered to improve learning. • The use of formative feedback can help students learn more effectively.

Why use stealth assessment? Educators are increasingly using stealth assessment. Because dynamic evidence of learning in real-time can be collected in a more interactive manner than other assessments and in various grain sizes, this is taking place. Game-based assessments have several advantages, including the following (Owiwi, 2019):

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• Engagement of students: Student attendance can be boosted by enticing them with engaging games. Students are more likely to learn about a subject if they want to be the best “player” and receive the highest reward. • Instant Feedback: Students can see their results immediately after completing a task in most games. It allows them to compare themselves to their peers. As a result of this feedback, they may decide to try harder in the future. • Unique experience: Provides a one-of-a-kind experience to each user, which increases student engagement during the assessment. Students who have previously been assessed solely through multiple-choice questions may prefer this new evaluation method. • Reduction of anxiety among students: Every student gets nervous when it is time for the test. Because it is a game they have played before, students’ anxiety goes down before it starts. • Productivity of students: Making learning more fun and engaging for students is possible using gamified systems. There is progress even in subjects where most students struggle. • Social connection: Students in gamified classrooms must work together with their classmates to succeed. Students who struggle with social interaction might find this an excellent opportunity. There is one factor that could have a negative impact on a stealth assessment. Teachers need to know exactly what they want to measure from each game before beginning. There are times when it is not clear how the outcome of a game relates to your final grade in the class. As a result, they have to search for the perfect match to achieve their desired outcomes. This necessitates thorough game research. Examples of stealth assessment. The name of the computer game is Physics Playground, and it features two-dimensional physics simulations of gravity, mass, potential and kinetic energy, momentum transfer, and other concepts. All 75 levels of the game are focused on getting a green ball to land on a red balloon. In the game, everything is governed by the fundamental laws of physics. Players ‘bring to life’ colored objects on the screen by tracing them with the mouse. Simple machines like levers, ramps, pendulums, and springboards are used with Newtonian mechanics to inflate the ball (Shute, 2015). There are three hidden assessments in the game: one for creativity, one for diligence, and one for qualitative understanding of physics. Each of the constructs had a competency and evidence model created for it. All of the game’s levels were built using task models. It got more difficult as you progressed through the seven different playgrounds, and each one demanded that you gather evidence about different aspects of Newton’s laws of motion (Shute, 2015). Conscientiousness, for example, had four main facets: persistence, perfectionism, organization, and carefulness. Observables were defined for the persistence facet: time spent on unsolved levels, several restarts, and several revisits to unsolved levels. These observables provided relevant evidence for the persistence

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facet. The game automatically tallied this information and then analyzed by the stealth assessment system (Shute, 2015). There is a big difference between saying “I always try my hardest” and putting in the effort to solve a complex problem when you are playing a game. Moreover, that is precisely what they do. The TAALES tool provides yet another example of stealth assessment. To help students improve their essay writing skills, TAALES is used to assess student essays in secret (Sharples et al., 2015). Students’ vocabulary knowledge is assessed by looking at the lexical properties of their essays (such as word frequency and academic language use.

11.4

Is Stealth Assessment Practical?

During playing computer games, the computer software tracks the players’ progress. New challenges are constantly generated based on players’ actions and inferences about their goals and strategies. ‘Stealth assessment’ refers to the practice of constantly monitoring a user’s progress while providing immediate automated responses, and it is being used in educational games and simulations (Sharples, 2019). Using stealth assessment, it is claimed that learners’ ability to persevere, be creative, and think strategically can be tested. Without asking students to stop and take a test, it can gather data on their learning states and processes. As a result, teachers would have access to more up-to-the-minute information about their students’ progress. Inquiry learning. In 2005, Valerie Shute coined the term ‘stealth assessment’ to describe the Smithtown microeconomics teaching system (for example, the laws of supply and demand) (Sharples, 2019). Students were able to experiment with variables in the Smithtown simulated world, such as the cost of coffee and residents’ incomes. They used inquiry learning to generate hypotheses and conduct experiments to verify them. Artificial intelligence methods were used in the software to keep track of and analyze the students’ behavior, providing them with feedback to help them improve their inquiry skills without interfering with the game. Instead of selecting a path or exercise based on the learner’s knowledge and misconceptions, stealth assessment extends adaptive teaching by continuously adjusting a simulated environment (Sharples, 2019). Refinements consider what evidence a player has collected before making a prediction and which game characters they have asked for help from in the simulated world. Students may not realize that they are being monitored and responded to in real-time as they play the game because the assessment is integrated into the game flow. Although computer implementations of stealth assessment can be difficult, an excellent human sports coach would use the same principles when teaching tennis or soccer. When students are working on a game, the coach keeps an eye on them and assigns new challenges based on how well they are doing. These are

11.4 Is Stealth Assessment Practical?

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not stand-alone tests; they are part of the game (like a serve in tennis or a penalty kick in soccer). The coach is continually assessing each student’s strengths and weaknesses. Assessment design. Competency learning is the pedagogy that underpins stealth assessment (Sharples, 2019). A student’s performance on a specific problem must be diagnosed, and then an inference of the student’s competency across a network of skills to achieve this goal. The teacher (in the case of stealth learning, the computer) continually provides tasks and assessments that are matched to the student’s competence. Students will be tested on problem-solving skills such as knowledge, comprehension, application, and higher-level creativity and critical thinking abilities. As a starting point, educational game designers must identify which concepts will be assessed and then incorporate those concepts into the game’s design. ‘Evidence-centred design’ is a proven method for creating stealth assessment games (Sharples, 2019). Because the game has no way of knowing what a student is thinking and the stealth approach does not set explicit knowledge tests, the designer must figure out which behaviors and interactions will demonstrate a player’s knowledge, abilities, and skills. Designers can use this information to determine how the game should be played and then choose appropriate actions for the player’s skill level. As the learner completes a mission or solves a game problem, the designer incorporates measures of success and failure into the game. With these measures linked together, it becomes more likely that the learner has acquired the desired skill or reached a required level of competency. Opportunities and challenges. For a stealth assessment to be most effective, it must be developed through an evidence-based design that applies to both the assessment and gameplay so that the game elements can be included to stimulate engagement and learning (Sharples, 2019). An approach that has had less success is adding dynamic assessment to a game or simulation that has already been developed from scratch. When used in conjunction with stealth assessment methods, learners can receive immediate feedback on their actions, while teachers receive information on how each learner is acquiring skills in inquiry, critical thinking, decision-making, and creativity. Currently, the research on stealth assessment is in its infancy, so it is not clear if specific methods must be developed at this time for every game and topic. Assessment in practice. Portal 2, created by Valve Corporation, is a computer game that uses stealth assessment. The player assumes the role of Chell, who must use various tools to navigate a complex mechanized maze in an advanced science laboratory to find an exit door. Educational objectives include teaching the user physics concepts, improving their visual-spatial skills, and sharpening their critical thinking (Sharples, 2019).

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TAALES is an entirely different case in point. To help students improve their essay writing skills, TAALES is used to assess student essays in secret. Students’ vocabulary knowledge is assessed by examining their essays’ lexical properties (such as word frequency and academic language) (Sharples et al., 2015). Shute and colleagues used the educational game Use Your Brainz to teach middle school students problem-solving skills while conducting stealth assessments. It was found that the computer’s stealth assessment matched standard measures of problem-solving ability in a study with 55 school students over three days (an hour a day) (Sharples, 2019).

11.5

Stealth Assessment in the Classroom

“Stealth” assessment is one method that is slowly gaining traction. The unobtrusiveness essential for a stealth assessment has its origins in video games. According to this theory, the decisions and strategies a player employ constantly inform him or her progress and success (Lynch, 2016). Stealth assessment in education can be a powerful tool to minimize and eventually close the loop between teaching and learning. The speed with which you receive feedback is crucial. We have spent far too much time looking at learning’s “trailing” indicators. With the advancement of technology, we can now concentrate on the most important teaching and learning indicators. In fact, rather than focusing on remediating failed learning, we can now disrupt or even stop it in its tracks. When you disrupt someone’s inability to learn, it does not mean you are also disrupting their process. Project-based learning is becoming increasingly popular among teachers (Lynch, 2016). As a result, they must have the tools to observe how students work together to create meaning. Teacher monitoring and assessment from anywhere in the room is a massive step towards personalized teaching and learning (Lynch, 2016). However, it has never been more difficult for educators to be everywhere at once. Technology can assist in overcoming this obstacle and enable stealth assessment. For example, the Flexcat system from Lightspeed Learning (https://www.lightspeed-tek.com/pro ducts/flexcat/) is a powerful tool for implementing “stealth” assessment. Flexcat allows teachers to listen in on any small group without the students knowing about it. Because of the stealth assessment concept, current and future technologies should cause a fundamental shift in education, not just a small adjustment. As a result, both the teacher and the student can keep track of their progress while receiving immediate feedback. In the “student as worker, teacher as guide” mindset, learners are key architects of their learning, and participatory learning and assessment are essential components (Lynch, 2016). Each of them contributes to creating new ideas and the development of existing ones. It is also crucial in the 21st Century to critically examine and assess ideas, concepts, and constructs.

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Using technology, students can create, collaborate, demonstrate, and assess their learning in various ways. Educators must keep up their education to ensure that they can adapt their teaching methods based on technology so that each student has the best chance of succeeding.

11.6

Principles and Theories of Stealth Assessment

Stealth assessment is based on the following principles (Sharples, 2019; Sharples et al., 2015): • The software examines how students interact with a computer game or simulation. • The system continuously adjusts the game’s structure to support learning by offering new challenges based on how well students perform. • The system keeps the game moving to integrate teaching and assessment into the game rather than separate tests or exams. • Learners’ abilities and competencies are displayed in real-time via a dynamic model created by the system. • The line between assessment and learning is blurred to reduce learners’ test anxiety to ensure an accurate diagnosis. The competency-based pedagogy underpins stealth assessment. A student’s knowledge and abilities are continually assessed by the teacher (or, in the case of stealth learning, by a computer), which results in work that is appropriately challenging for the student (Sharples et al., 2015). Teaching systems and teachers must diagnose how students perform on specific problems and infer competency levels across a network of skills to accomplish this. As a result, it is essential to identify students’ problem-solving abilities by testing their understanding, application, and higher-level abilities of creativity and critical thinking skills. Stealth assessment is based on a pedagogy known as competency-based education. Assessment data can be collected automatically by matching competency learning pedagogy with computer game design methods, such as setting goals and managing conflict or challenge, providing continuous responses, and creating an engaging game story (Sharples et al., 2015). These methods also design a realistic simulation environment and provide meaningful interactions within the simulation.

11.7

Stealth Assessment and Evidence-Centred Design

Evidence-centred Design is a proven method for creating stealth assessment games. In the late 1990s, Robert Mislevy, Linda Steinberg, and Russell Almond developed a framework (see Fig. 11.1) for assessment design called “evidencecentered design” and used it to formalize stealth assessment (Shute, 2015). The

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Fig. 11.1 The three main models of ECD (Mislevy et al., 2003)

evidence-based design identifies a framework of several conceptual and computational models that work together. For the most part, any assessment aims to gather data that will allow the assessor to draw valid conclusions about what people know, belief, and do, and to what degree. Teaching decisions that support learning are based on accurate inferences about competency states. According to the framework, an assessor must (Shute, 2015): • define the claims you want to make about learners’ competencies • determine what evidence you need to support those claims, and • decide what tasks or circumstances will provide you with that evidence. We have now gone over each of these models in detail (Shute, 2015). • Competency Model: When conducting an assessment, the first question to ask is, “What should be measured?” The term “student” (or “learner”) model refers to an instantiated version of the competency model, similar to a profile or report card but with finer grain size. Assumptions about the learner’s competency model are expressed in the learner model by the assessment’s values. The set of personal attributes is used to conclude the competency model variables. • Evidence model: Model two is based on evidence. It looks for behaviors or performances that demonstrate the competency-based constructs identified and structured in Model 1. An evidence model explains how a student’s interactions with and responses to a problem serve as evidence for further examination of competency model variables. The evidence model seeks to provide answers to the following two inquiries: – What behavior or performance is targeted; and – What is the statistical relationship between these behavior(s) with the variable(s) of the competency model? For the most part, an evidence model explains why and how a specific task situation (i.e., student performance data) provides evidence about competency model variables. • Task model: In the third model, tasks and situations are discussed to elicit evidence-gathering behaviors. Using a task model, you can build situations in which learners will interact to provide evidence of specific knowledge or skills related to competencies. Students generate a steady stream of information that the evidence model analyses in solving tasks or problems. Afterward, the probabilistic estimates of competency state are passed on to the competency model, which updates the claims about relevant competencies due to the analysis’ data

11.9 Stealth Assessments: Success Stories

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(such as scores). As a result of evidence-centered design, assessment tasks can be linked to claims about personal competencies via an evidentiary chain (for example, valid arguments that link task performance to competency estimates) and are thus valid for their intended purposes.

11.8

How to Design and Develop Good Stealth Assessment

There are several obstacles to be overcome before an assessment can be implemented in games (Shute, 2011). Mislevy (2013) addresses many of these same issues when incorporating assessment into interactive simulations. Students/players naturally produce complex action sequences while playing games, drawing on the skills or competencies we want to assess. A player’s interactions with the game provide the evidence needed to assess their skills (i.e., the processes of play), which can be compared to the output of an activity, which is the norm in educational and training environments. These new evidence streams present problems for traditional assessment measurement models because they cannot account for new learner characteristics such as beliefs, feelings, and other states and traits. To begin, traditional tests treat each answer as a separate piece of data. As opposed to this, individual actions within a simulation or game are frequently highly dependent on one another (e.g., (Brown et al., 1982)). For example, what you do now in a combat game affects what you do later. Second, questions on traditional tests are frequently crafted to elicit a single piece of information or skill. Correctly answering a question demonstrates knowledge of a single fact. In the meantime, instructional environments can infer what learners know and do not know by looking at the responses to all of the questions or a sequence of actions (where each response or action provides incremental evidence about the current mastery of a particular fact, concept, or skill). Methods for analyzing the sequence of behaviors to infer abilities are less evident because we typically want to assess an entire cluster of skills and abilities from evidence obtained through learners’ interactions within a game or simulation. Bayesian networks are an effective method for achieving these objectives (Shute, 2011). ECD is a technique that addresses these issues and makes it possible to create simulation- or game-based learning systems that are reliable and valid.

11.9

Stealth Assessments: Success Stories

Stealth assessments constitute a significant step forward in assessment technology. Formative assessments are incorporated into games using stealth assessment techniques. Professor Valerie Shute and her colleagues developed the first stealth assessment at Florida State University to gather data rather than mislead students (West & Bleiberg, 2013). There are numerous advantages to conducting

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stealth assessments, Shute has discovered. Teachers can then use this information to improve and tailor their lessons based on the data they collect. Traditional education resource issues can be addressed with stealth assessment (West & Bleiberg, 2013). When viewed from a psychometric standpoint, the lowstakes setting is beneficial. Understanding how children learn through play is a long-held belief in psychology. Students use different strategies to solve problems than on the SAT in a sandbox because of the different stakes. Unlike high stakes assessments, stealth assessments collect different data because students’ behavior changes when playing a game rather than focusing on an assessment (West & Bleiberg, 2013). Another advantage of games is that they help students stay motivated. The player is drawn into the story in games and becomes an active participant. Students are encouraged to push themselves further by participating in games despite the difficulties. On the other hand, paper and pencil tests do not compel students to work hard. Teachers could benefit significantly from students’ willingness to play stealth assessment games outside of the classroom, which would provide data without consuming class time. As a proof-of-concept, Shute has developed a method for gathering data from widely available video games like “The Elder Scrolls IV: Oblivion.” In the tradition of “Dungeons & Dragons,” “Oblivion” is an open-world role-playing game. Shute and her colleague built models that can examine players’ actions in a gaming environment for evidence of various abilities. Students’ ability to solve problems was assessed using this model. Players in “Oblivion” had to cross a river filled with ferocious fish. The options available were using magic, building a bridge, or simply swimming across. In order to evaluate how players’ responses were novel and efficient, they used game data to build the model. Educators can gain insight into students’ abilities by analyzing such data. “Newton’s Playground” was created by Shute and her team to test high school students’ knowledge of physics (West & Bleiberg, 2013). Save Patch, developed by the National Center for Research on Evaluation, Standards, and Student Testing (CRESST), is another example of a “stealth assessment.” Students in grades 4 through 6 will benefit from playing this game because it teaches fractions. As a first step, the game provided them with a wealth of information about students’ ability to solve fractional issues. Although it was not entirely successful, they made progress. Students performed better on a posttest than the control group in math, but the differences were only statistically significant enough to warrant further study. Unexpectedly, the “Save Patch” experiment also yielded two surprising results. Students with low self-efficacy in math scored significantly better after playing the game (West & Bleiberg, 2013). In “Save Patch,” we see how a game can generate data that can be used to target interventions for students. Large data sets on student learning have been the primary focus of current education reform efforts. The next phase of education reform will use this data to improve instruction. There is great potential in these new data systems to revolutionize education. An essential part of making these new systems work better is incorporating data from stealth assessments. Low-stakes assessments can provide a

References

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comprehensive view of student competencies without requiring significant school resources.

11.10 Conclusion “Stealth assessment” has provoked debate. Creating a computer system that monitors students’ actions and assesses their problem-solving and creativity skills while also claiming to be fun is questionable. How about letting the students know they are under constant observation and evaluation, just like an excellent human coach would? These systems can and should be developed for research projects under strict ethical guidelines that include informing students how they are being monitored, using the information, and obtaining informed and willing consent. On the other hand, commercial games use stealth assessment to evaluate insurance risks without players’ knowledge. A promising combination of simulation games and dynamic assessment is effective early. Stealth assessment engages creativity, problem-solving, persistence, and collaboration by incorporating dynamic assessment and feedback into computer games.

References Arnab, S., Lim, T., Carvalho, M. B., Bellotti, F., de Freitas, S., Louchart, S., Suttie, N., Berta, R., & Gloria, A. D. (2015). Mapping learning and game mechanics for serious games analysis. British Journal of Educational Technology, 46(2), 391–411. Baron, T., Heath, C., & Amresh, A. (2016). Towards a context agnostic platform for design and assessment of educational games. In 10th European Conference on Game Based Learning (pp. 34–40). Bridgeland, J. M., John J. DiIulio, J., & Morison, K. B. (2006). The silent epidemic: Perspectives of high school dropouts. Brown, J. S., Burton, R. R., & de Kleer, J. (1982). Pedagogical, natural language, and knowledge engineering techniques in SOPHIE-I, II and III. In Intelligent tutoring systems (pp. 227–282). Edutopia. (2008). Why is assessment important? Edutopia.Org. https://www.edutopia.org/assess ment-guide-importance. Eisenberger, R. (1992). Learned industriousness. Psychological Review, 99(2), 248–267. Eisenberger, R., & Leonard, J. M. (1980). Effects of conceptual task difficulty on generalized persistence. American Journal of Psychology, 93(2), 285–298. Eseryel, D., Ifenthaler, D., & Ge, X. (2011). Alternative assessment strategies for complex problem solving in game-based learning environments. In Multiple perspectives on problem solving and learning in the digital age (pp. 159–178). https://doi.org/10.1007/978-1-4419-7612-3. Fredonia. (2020). Assessment: Definition and overview. https://www.fredonia.edu/about/campusassessment/assmtinforesources/overview. Georgiadis, K., Faber, T., & Westera, W. (2019). Bolstering stealth assessment in serious games. In Games and Learning Alliance, 8th International Conference, GALA 2019 (pp. 211–220). Goodstein, L. D., & Lanyon, R. I. (1999). Applications of personality assessment to the workplace: A review. Journal of Business and Psychology, 13(3), 291–322. Kim, Y. J., & Shute, V. J. (2015). The interplay of game elements with psychometric qualities, learning, and enjoyment in game-based assessment. Computers & Education, 87, 340–356.

280

11

Stealth Assessment

Kobrin, J. L., Patterson, B. F., Shaw, E. J., Mattern, K. D., & Barbuti, S. M. (2008). Validity of the SAT for predicting first-year college grade point average. Lynch, M. (2016). Stealth assessment: Reimagining learning and testing for the 21st century. Theedadvocate.Org. https://www.theedadvocate.org/stealth-assessment-reimagining-teachingand-learning-for-the-21st-century/. Miles, E., & Crisp, R. J. (2014). A meta-analytic test of the imagined contact hypothesis. Group Processes & Intergroup Relations, 17(1), 3–26. https://doi.org/10.1177/1368430213510573. Min, W., Frankosky, M. H., Mott, B. W., Rowe, J. P., Wiebe, E., Boyer, K. E., & Lester, J. C. (2015). DeepStealth: Leveraging deep learning models for stealth assessment in game-based learning environments. In International Conference on Artificial Intelligence in Education (pp. 277–286). https://doi.org/10.1007/978-3-319-19773-9. Mislevy, R. J. (2011). Evidence-centered design for simulation-based assessment. CRESST Report 800. Mislevy, R. J. (2013). Evidence-centered design for simulation-based assessment. Military Medicine, 178(10), 107–114. Mislevy, R. J., Steinberg, L. S., & Almond, R. G. (2003). On the structure of educational assessments. Measurement Interdisciplinary Research and Perspectives, 1(1), 3–62. https://doi.org/ 10.1207/S15366359MEA0101. Moreno-ger, P., Martinez-ortiz, I., Freire, M., Manero, B., & Fernandez-manjon, B. (2014). Serious games: A journey from research to application. In 2014 IEEE Frontiers in Education Conference (pp. 391–394). https://doi.org/10.13140/2.1.3845.3446. Owiwi. (2019). Stealth assessment as a recruitment tool. Owiwi.Co.Uk. https://owiwi.co.uk/blogpage/stealth-assessment-as-a-recruitment-tool/. Paulhus, D. (1991). Measurement and control of response bias. In Measures of social psychological attitudes (pp. 17–59). https://doi.org/10.1016/B978-0-12-590241-0.50006-X. Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. National Academy Press. ResourceEd. (2020). A complete look at assessment in education. In resourced.prometheanworld.com. https://resourced.prometheanworld.com/assessment-ineducation/#chapter-1. Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261–288. https://doi.org/10.1037/0033-2909.130.2.261. Sabourin, J. L. (2013). Stealth assessment of self-regulated learning in game-based learning environments. Sharples, M. (2019). Is stealth assessment practical. Theheadteacher.Com. https://www.theheadte acher.com/attainment-and-assessment/is-stealth-assessment-practical. Sharples, M., Adams, A., Alozie, N., Ferguson, R., Fitzgerald, E., Gaved, M., Mcandrew, P., Means, B., Remold, J., Rienties, B., Roschelle, J., Vogt, K., Whitelock, D., & Yarnall, L. (2015). Innovating pedagogy 2015. Shute, V. J., & Zapata-Rivera, D. (2010). Intelligent systems. In International encyclopedia of education (Vol. 4, pp. 75–80). Shute, V. (2015). Stealth assessment in video games. Research Conference, 2015, 61–64. Shute, Valerie J., Levy, R., Baker, R., Beck, J., & Zapata-Rivera, D. (2009). Assessment and learning in intelligent educational systems: A peek into the future. In Proceedings of the Artificial Intelligence and Education (AIED ’09) Workshop on Intelligent Educational Games (pp. 1–10). Shute, V. J. (2011). Stealth assessment in computer-based games to support learning. In Computer games and instruction (pp. 503–523). Shute, V. J., Hansen, E. G., & Almond, R. (2008). You can’t fatten a hog by weighing It—Or can you? Evaluating an assessment for learning system called ACED. International Journal of Artificial Intelligence in Education, 18(4), 289–316. Shute, V. J., Masduki, I., & Donmez, O. (2010). Conceptual framework for modeling, assessing and supporting competencies within game environments. Technology, Institution Cognition and Learning, 8, 137–161.

References

281

Shute, V. J., Ventura, M., & Kim, Y. J. (2013a). Assessment and learning of qualitative physics in newton’s playground. The Journal OfEducational Research, 106, 423–430. https://doi.org/10. 1080/00220671.2013.832970. Shute, V. J., Wang, L., Greiff, S., Zhao, W., & Moore, G. (2016). Measuring problem solving skills via stealth assessment in an engaging video game. Computers in Human Behavior, 63, 106–117. https://doi.org/10.1016/j.chb.2016.05.047. Shute, V., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games (p. 102). The Mit Press. Shute, V., Ventura, M., Small, M., & Goldberg, B. (2013b). Modeling student competencies in video games using stealth assessment. In Design recommendations for intelligent tutoring systems (pp. 141–152). Snow, E., Jacovina, M., Varner, L., Dai, J., & McNamara, D. (2014). Entropy: A stealth measure of agency in learning environments. In Educational data mining 2014 (pp. 241–244). UKDiss. (2019). Stealth assessment in educational games: An in-depth literature review. Ukdiss.Com. https://ukdiss.com/litreview/stealth-assessment-educational-games.php?vref=1. Ventura, M., Shute, V., & Small, M. (2014). Assessing persistence in educational games. In Design recommendations for intelligent tutoring systems—Volume 2: Adaptive instructional management (Vol. 2, pp. 93–101). Walkera, A. A., & Engelhard, G., Jr. (2014). Game-based assessments: A promising way to create idiographic perspectives. Measurement: Interdisciplinary Research and Perspectives, 12(1–2), 57–61. https://doi.org/10.1080/15366367.2014.921037. West, D. M., & Bleiberg, J. (2013). Education technology success stories. Zhao, W., Shute, V., & Wang, L. (2015). Stealth Assessment of problem-solving skills from gameplay. In Proceedings of the Interservice/Industry Training, Simulation, and Education Conference (pp. 2226–2236).

Pedagogy for E-learning

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Abstract

Pedagogy can be described as the art of teaching. It refers to the techniques, approaches, and types of instruction. The adoption of technology adds another factor in course design to consider. To produce successful e-learning and teaching require a comprehension of how students learn and communicate with technology. Before new courses are developed, teachers understand the pedagogy that will underpin their e-learning setting. This chapter aims to provide the basis by which teachers can comprehend the strategies for developing effective online courses. Keywords

Pedagogy • E-learning • Online learning • Strategies • Models • Types

12.1

What is E-learning?

E-learning, also known as online learning or electronic learning, is gaining knowledge through electronic media and technologies. A simple definition of e-learning is “learning made possible through the use of electronic devices” (Tamm, 2020). E-learning is usually done on the Internet, so students can access their course materials from anywhere and at any time using a computer and an Internet connection. Online courses, online degrees, and online programs are the most common forms of e-learning in the world. You can find numerous e-learning examples on the Internet. What is the Value of E-Learning? When compared to other forms of education, online learning offers numerous advantages. Self-paced learning and the option to pick one’s learning environment are two examples of these. E-learning is costeffective and cost-efficient because it eliminates the geographical barriers often associated with traditional education.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_12

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With all of these advantages in mind, it is easy to see why e-learning is experiencing such rapid growth right now. To put this in perspective, the global e-learning market is expected to generate $325 billion in revenue by 2025, a threefold increase from the $107 billion it generated in 2015 (Tamm, 2020). E-learning is not without its flaws. Using the Internet to deliver any type of e-learning involves making compromises of some sort. Some of the issues with e-learning that needs to be addressed include increased cheating risk during assessments, social isolation, and a lack of communicational skill development among online students. What is the Definition of E-Learning? E-learning has as many definitions as educational scientists working on it today. In her 2005 research paper (Guri-rosenblit, 2005), Sarah Guri-Rosenbilt from the Open University of Israel examined the precise definition of e-learning in great detail. Concerning her definition, she said that electronic media are used for various learning purposes, such as additional functions in the conventional classroom or replacing face-to-face interactions with online meetings. Examine a few examples from various academic institutions and educational researchers to better understand how different definitions of electronic learning are developed. In their 2016 research paper, Clark and Mayer defined e-learning as instructions delivered via digital devices to assist learning (Clark & Mayer, 2016). When Arkorful and Abaidoo published their study in 2015, they defined e-learning as information and communication technologies to enable students and teachers to access online resources (Arkorful & Abaidoo, 2014). In their 2006 research paper, Ruiz, Mintzer, and Leipzig defined e-learning as Internet technologies to improve performance and knowledge (Ruiz et al., 2006). ELearningNC.gov defines e-learning as learning outside traditional classrooms using electronic technologies for online resources (Tamm, 2020). As it turns out, the answer to what exactly constitutes e-learning is more complicated than it appears. E-learning and distance learning have subtle but significant differences, and it is critical to understand the differences. What is Online Learning Going to Look Like in the Future? Online education’s growth will continue to be exponential in the future (Tamm, 2020). Because of the increasing number of educational institutions, corporations, and online learners worldwide that understand the value of online learning, its influence on education will only grow in the future. Online learning already has numerous educational applications and will continue to play an increasingly important role in the future. Some of the world’s most successful educational institutions already recognize that online learning can transform people’s knowledge, skills, and performance. On the other hand, we should not get too excited yet. Many students prefer the traditional live, in-person teaching methods, despite online education being an exciting new frontier. Every student has a different learning style, so there will never be a perfect online learning solution.

12.2 Types of E-learning

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The fact is that we are at the beginning of a new educational era. With that in mind, future developments in online learning will dwarf the current state of e-learning.

12.2

Types of E-learning

Educational scientists have classified e-learning into different categories (as shown in Fig. 12.1) based on the tools used to learn, while others have chosen to focus on different metrics like synchronicity and the content of what students are learning. These findings will be distilled into ten distinct e-learning categories in this article. E-learning can be classified as follows (Tamm, 2021b): 1. 2. 3. 4. 5. 6. 7. 8. 9.

Computer Managed Learning (CML) Computer Assisted Instruction (CAI) Synchronous Online Learning Asynchronous Online Learning Fixed E-Learning Adaptive E-Learning Linear E-Learning Interactive Online Learning Individual Online Learning

Fig. 12.1 Types of E-learning

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10. Collaborative Online Learning. E-learning can be divided into two categories: computer-based and internet-based. This classification method may be more accurate because it distinguishes between e-learning and online learning, frequently used interchangeably. For example, CML and CAL are not conducted online to qualify as e-learning activities. On the other hand, some educational scientists have opted for a simpler classification of e-learning types. 1. Computer Managed Learning (CML): Computer-managed learning (CML), also known as Computer Managed Instruction (CMI), makes use of computers to oversee and evaluate the progress of students’ learning. Computer-assisted learning methods make use of massive information databases to accomplish their goals. In addition to the information the student must learn, these databases include ranking parameters that allow the system to be customized based on the preferences of each student. When the student and computer are in constant communication, it is possible to tell if they have met their learning objectives. Repeat the processes to determine if the student has achieved their learning objectives. Additionally, educational institutions use computer-managed learning systems to store and retrieve information that aids in educational management. This could be anything from lecture notes to training materials to grades to curriculum details to enrolment details. 2. Computer-Assisted Instruction (CAI): Traditional teaching is combined with computers in Computer Assisted Instruction (CAI), also known as computerassisted learning (CAL). If we are talking about interactive software, we are talking about what Patrick Suppes at Stanford University used in 1966. Computer-assisted training methods combine text, graphics, sound, and video to make learning more exciting and more effective. When it comes to interactivity, CAI excels because it empowers learners to become active participants in their education rather than passive recipients of the information. There are numerous computer-assisted learning programs available in most schools today, whether online or traditional. 3. Synchronous online learning: When students participate in a learning activity simultaneously from different locations worldwide, it is called synchronous online learning. Because they allow participants to communicate with each other while also asking and answering questions instantly, online chats and videoconferencing are standard tools in real-time synchronous online learning. Technology advances have made it possible for online learning to become more socially focused. Before the invention of computer networks in the 1960s, it was virtually impossible to implement synchronous e-learning. It is widely accepted that having students and teachers interact in real-time while learning is a huge advantage over traditional distance learning methods. At present, synchronous e-learning is one of the most popular and fastest-growing e-learning methods.

12.2 Types of E-learning

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4. Asynchronous online learning: Asynchronous online learning involves groups of students studying separately at different times and locations without any real-time communication. This is known as asynchronous learning. Online courses delivered in this manner are frequently considered more studentcentered than those delivered synchronously because they allow students more freedom and flexibility. As a result, students who are pressed for time prefer asynchronous e-learning because it gives them the flexibility to learn at their own pace. Because they set their schedules, they are not obligated to learn at specific intervals when other students are. E-learning had always been considered asynchronous before the PLATO computer system was created because there were no methods for connecting computers back then. Choosing between synchronous and asynchronous e-learning has never been easier than today, thanks to the widespread use of computers and the World Wide Web. Both have advantages and disadvantages. 5. Fixed e-learning: Fixed e-learning is just another name for something you are already familiar with. According to this definition, “fixed” refers to a learning process in which all students receive the same information regardless of whether they are actively participating. The teachers predetermined the materials and did not accommodate the students’ preferences. While this learning method has been used in traditional classrooms for millennia, it does not work well in e-learning environments due to fixed e-learning not using valuable real-time data from student inputs. The learning outcomes for all students are improved when each student’s data is analyzed, and this data change the materials. 6. Adaptive e-Learning: Learning materials can be customized for each learner using adaptive e-learning, a brand-new and innovative approach to e-learning. As difficult as it is to plan and implement, this type of e-learning can be more effective and valuable in the long run than more traditional teaching methods. There are many ways to consider students’ performance, goals, abilities, skill sets, and characteristics when using adaptive e-learning tools. Our time has come when it is possible to use laboratory-based adaptive instructional techniques to sequence student data mathematically. This could usher in an entirely new era in educational science in the right hands. 7. Linear e-learning: Human-computer interaction is referred to as linear communication when information is sent from one party to the other without interruption. E-learning delivered to students linearly, such as through broadcast television or radio, is common. The lack of two-way communication between teachers and students is a major drawback of e-learning, and this is a significant limiting factor. However, as time goes on, this type of e-learning will become less relevant. 8. Interactive online learning: Senders can become receivers, and vice versa, in interactive e-learning, creating a two-way communication channel. Various teaching and learning strategies can be derived from the messages teachers and students exchange. Teachers and students can interact more freely with interactive e-learning than with linear e-learning, making it more widespread.

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9. Individual Online Learning: When we talk about individual learning, we talk about how many students are involved in achieving the learning objectives rather than how student-centered the content is. There have been thousands of years of this learning in traditional classrooms. Individual learning involves students studying the learning materials on their own (separately) and meeting their learning objectives independently. Instead of developing communication and teamwork skills, this learning style emphasizes learning independently rather than communicating with others. Therefore, a more up-to-date approach is required to replace communicational skills and abilities with something new. 10. Collaborative Online Learning: In collaborative e-learning, students work together to learn and achieve their learning goals using a modern learning method. Student collaboration and teamwork are essential to meet their learning objectives. To do this, students must form influential groups and consider the strengths and weaknesses of their fellow students. This improves the students’ ability to communicate and work in a team. It is best developed in a group where people can interact and learn from one another when it comes to knowledge. This learning method is more common in traditional classrooms than in online courses, but it can be highly effective if used correctly. It is just less common in online courses.

12.3

Advantages of E-learning

E-learning has numerous advantages; this section focuses on a few key advantages of e-Learning. These are the most significant advantages of e-learning over traditional classroom settings. Figure 12.2 shows a glimpse of the advantages of e-learning. The following are the top ten benefits of using e-learning (Tamm, 2021a): • Online learning is self-paced: Students who take classes online have more freedom in setting their schedules and do not have to make personal sacrifices to meet the attendance requirements of professors and traditional universities. Students who learn at their own pace report greater satisfaction and less stress, which leads to better learning outcomes for everyone involved. Self-paced learning has several advantages, including effectiveness, efficiency, convenience, scalability, and reusability. • E-Learning is student-centered: Modern learning methods such as studentcentered learning (SCL) and learner-centered education (LCE) put the students in the spotlight rather than the teachers. Because student-centered learning goes hand in hand with online education, it will be an advantage of online education due to how easily student discussion boards and peer grading systems can be implemented. Student-centeredness is a central theme in many of the ten types of e-learning described here. Interaction between students is fostered by both

12.3 Advantages of E-learning

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Fig. 12.2 Advantages of E-Learning

synchronous and asynchronous online learning. One of the e-Learning’s greatest advantages is its ability to plan and design online learning environments that are entirely student-centered. The importance of putting students first cannot be overstated. • E-Learning is cost-effective: Simplified logistics and reduced travel expenses, among other things, allow educational institutions that use e-Learning to save 50–70% on overall training costs. That is a good question. Let us look at some real-world e-learning examples. The University of North Carolina at Charlotte (UNC) is an excellent illustration of saving money while still getting a quality education. Using e-learning instead of traditional classroom-based learning, UNC claims to have saved $5,000,000 in 2010. As a result, the total number of students enrolled in their faculties increased. They saved money because they did not have to invest in business premises to facilitate learning. The use of physical lecture halls, which can be very expensive, is unnecessary in virtual classrooms. The University of Wisconsin-Madison saved US$172,000 solely by saving professors’ time through e-learning, which is another excellent example of costeffectiveness. With e-Learning, professors could spend less time on learning sessions, which resulted in lower university costs. The cost-effectiveness of e-Learning for educational institutions can also be advantageous when used in conjunction with classroom-based learning. A good

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alternative for organizations not yet ready to make the complete transition to elearning is blended learning, which combines traditional classroom instruction with online components. E-learning cost-effectiveness does not just apply to educational institutions; it also impacts students. E-Learning, for example, eliminates the expenses of commuting, textbooks, and child care. Individual styles of learning: Students can practice their learning styles through e-learning, which considers the differences between individual learners. Instead of taking every course in the curriculum, students can choose which ones to take. There will never be a solution that fits all students at once because they all have different learning styles. For this reason, e-learning’s individualistic methods are so valuable. Individualistic learning methods like adaptive e-Learning can become the most effective teaching tools ever created if they are implemented correctly. Just to be clear, adaptive e-learning materials are those that change and adapt automatically based on what the student already knows, can do, and needs. • Personalized learning environments: Workplaces devoid of “distractors” such as artwork and photographs were found to have productivity losses of 15% by the researchers’ estimates. Student learning performance and mental health will suffer when they are in an unsatisfactory educational setting, which also applies to the educational setting in general. This is where the problem lies—educational institutions’ learning environments are predetermined based on their preferences in traditional classrooms. As a result, students seldom have any influence over the design of their educational surroundings. Using e-learning instead of traditional classroom teaching methods can give students complete control over their learning environment. E-Learning environments can be tailored to meet the needs of students who prefer a green environment. Such an e-Learning environment is ideal for students who learn better in a distract-free environment. • E-Learning fully utilizes analytics: Data is the new oil, and e-Learning utilizes student data far more efficiently than any other method of teaching or learning in history. E-Learning Analytics is to blame for this development. E-Learning analytics is the process of extracting useful information from online learning management systems, and it is yet another great advantage of e-Learning. In education, the value of data is immeasurable, and we have only just begun to tap into it. Students’ e-Learning analytics data can be used in various ways to improve training materials and increase student learning outcomes. We can, for example, identify and remove potential pitfalls in our course materials if we have data on student dropout rates. It will take some time to see whether or not our e-Learning goals have been met after implementing the change. • Online education could solve the shortage of teachers: The shortage of experienced teachers is “real, large, growing, and worse than we thought,” according to EPI (Economic Policy Institute) educational economists in 2019. According

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to the LPI (Learning Policy Institute), teachers are also a scarce resource, which has called it “one of the most pressing issues facing policymakers.” It will be challenging to resolve the issue of teacher scarcity, but the advantages of e-learning in combating it are likely to be vastly undervalued. To make things more transparent, let us draw a comparison between the two. A typical university has 16.5 students per faculty member, whereas e-Learning courses created by one or two experienced teachers can be available to tens of thousands of people. E-learning can reduce or eliminate the current 3-million-strong teaching force currently employed in the United States. As opposed to finding more teachers, perhaps we should work to make it possible for our best teachers to deliver their materials to a broader range of students via e-Learning. • E-Learning is environmentally friendly: Compared to traditional university courses, distance learning courses use 90% less energy and emit 85% less CO2 . There are no environmental issues associated with paper production because ELearning is a paperless learning method. Paper-cellulose production factories, for example, are known to pollute the environment because they cut down trees for paper. • No textbook requirement: Between $7 and $10 billion is spent annually by college students on textbooks, and that figure rises as high as $1,200. Graduates who have debt from their time in school are at greater risk of mental health problems, such as anxiety and depression. Students do not have to worry about lugging around heavy textbooks with E-Learning. There are no limitations to what you can access for online learning materials. For example, online learning materials, unlike textbooks, can be updated and retaken an infinite number of times. This saves students money over the long term. • Online learning is time-efficient: Compared to traditional classroom learning, online learning reduces learning time by 25–60%. When combined with the benefits of self-paced e-learning, this results in a highly time-efficient learning solution for students and teachers alike. It is a long and challenging process in traditional educational institutions to change school curricula. With E-Learning, lessons can be delivered and updated much faster—even within a few days. With data-driven E-Learning Analytics, these changes to learning materials will be supported by concrete evidence and theory.

12.4

Disadvantages of E-learning

E-Learning has several significant drawbacks that are frequently overlooked in online discussions. Nobody wants to slow down educational progress, after all. E- Learning’s statistics for 2020 show massive growth, which shows how enthusiastic the industry is about e-learning as a whole. On the other hand, educators worldwide are well aware of the industry’s problems. Let us dig a little deeper into these issues in Table 12.1.

E-learning may make people feel isolated from their peers

2

Because of the current e-learning methods used in education, students are forced to sit in silence, feel distant, and have little chance to interact. As a result, many students and teachers who spend much time online may begin to show signs of social isolation due to the lack of human contact in their lives. Being socially isolated and communicating with others can lead to mental health issues, such as increased stress, anxiety, and negative thoughts

Description Teachers in traditional classrooms can provide students with immediate feedback by speaking to them directly. Students with difficulties with the curriculum can get immediate help during the lecture or the designated office hours by asking their professors directly. A positive effect of personalized feedback on students is that it makes learning processes easier, richer, and more significant, raising students’ motivation levels As opposed to classroom instruction, online learning has yet to master the art of soliciting student input. Students who take regular assessments and do not receive personalized feedback are dissatisfied. Providing feedback traditionally does not always work in an e-Learning environment, so educators are forced to find new ways of doing it in an e-Learning setting. It may take some time before any specific strategies are thoroughly researched and proven effective when providing student feedback online

Disadvantage

There is a limited amount of student feedback available online

S.no

1

Table 12.1 Disadvantages of E-learning Solution

12 (continued)

In online learning, there are a variety of ways to combat loneliness and social isolation • Encouraging better communication among online students • Making use of a blended learning environment • Keeping an eye out for signs of social isolation among the students

Peer feedback systems in online learning are effective by researchers, and this could be a solution to the problem of limited student feedback in e-learning. Video chats with professors, which function like office hours during on-campus training, can also help solve this e-learning drawback

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Lack of development of communication skills in online students

4 Students’ academic knowledge has improved dramatically using e-Learning methods. However, online lessons frequently overlook the importance of helping students’ communication skills. Students may find it difficult to collaborate effectively with their peers, teachers, and professors when working in an online environment because of the lack of face-to-face communication. Ignoring student communication skills will produce many graduates who excel in theoretical knowledge but cannot transfer that knowledge to others effectively and successfully

Description Students’ inability to motivate themselves to finish online courses remains a significant problem. Students’ learning goals are constantly pushed in traditional classrooms by numerous factors. For the most part, direct contact with professors and group projects and time constraints keep students on track However, fewer external factors encourage students to do well on their assessments in an online learning environment. Students are frequently left on their own when it comes to learning activities, with no one to push them forward towards their learning objectives constantly. Learning complex material at home without the added stress generally associated with traditional colleges is advantageous for students taking e-learning courses. Online studies can be particularly challenging if a student lacks sufficient self-motivation and time management skills

Disadvantage

Strong motivation and the ability to manage time are required for e-learning

S.no

3

Table 12.1 (continued) Solution

(continued)

Even in an online learning environment, peer-to-peer group activities and online lectures that necessitate communication should be used. E-Learning will adequately prepare students for success in real-world workplaces by teaching them communication skills if we follow this strategy

To be successful in an online learning environment, students must develop strong self-motivation and discipline skills. In addition, online communication can replace face-to-face communication with professors, and peer-to-peer activities should be promoted among online students in the same way they would be in traditional classrooms

12.4 Disadvantages of E-learning 293

Disadvantage

Preventing cheating on online tests is a difficult task

Online instructors frequently emphasize theory over application

S.no

5

6

Table 12.1 (continued) Description

Some of the more innovative online learning platforms are beginning to address and fix this drawback of e-learning, but it has not yet entirely disappeared. Rather than helping students develop practical skills, many e-learning training providers choose to place a heavy emphasis on theoretical knowledge development. For obvious reasons, practical lectures are more difficult to implement in an online learning environment. Due to the lack of face-to-face communication and physical classrooms that can be used as a workshop, implementing practical projects in an online course necessitates more advanced planning than purely theoretical instruction

Unfortunately, cheating through various methods is still one of the most significant drawbacks of e-learning. Online students have an advantage over their on-campus counterparts in cheating on exams because they take them in their environment and personal computers. Teachers cannot watch their students during tests with no video feed, making it more difficult to detect cheating when using online assessments. Aside from that, students who take online assessments may allow a third party to take the test instead of them, resulting in a fictitious test result without proper identity verification

Solution

12 (continued)

Hands-on student projects and 1:1 mentorship are two of the most effective ways to help online students develop practical skills. Practice-based online courses such as Udacity’s and Springboard have been a massive success in the past

All online education institutions must implement anti-cheating measures to safeguard the quality of their courses. Online proctoring systems like examity, which use anti-cheating measures like automated ID verification and machine learning to detect fraudulent test-takers, are currently the most popular anti-cheating tools in e-learning

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Disadvantage

Face-to-face communication is missing from e-learning

E-learning can only be used in specific disciplines

S.no

7

8

Table 12.1 (continued) Description

E-learning is not a good fit for every field of study. At the very least, for the time being. E-learning is better suited for social science and humanities fields than medical science and engineering, requiring more hands-on experience. No online lectures can replace an autopsy for medical students or real-life industrial training for a future engineer. While this may change in the future, we are not yet where all professions can be taught solely through online courses

Many disadvantages of online learning are linked because of the absence of face-to-face communication. Student feedback is inhibited when there is no face-to-face communication between them and the instructor. This results in social isolation and could lead to a lack of motivation. Students are more likely to drop out of school when they feel no pressure, which is a disadvantage. Many students dislike being nudged by professors, but it is an effective way to keep students interested in the material they are learning in class

Solution

(continued)

Medical education, for example, may benefit significantly from blended learning as an alternative to 100% online instruction. According to research, blended learning courses for nursing students produced similar post-test results to traditional course formats while significantly increasing the satisfaction ratings of participating students

Face-to-face communication must be replaced in e-learning with a different communication method. Video chats, discussion boards, and chatrooms, for example, could help mitigate the adverse effects of online learning being devoid of face-to-face communication

12.4 Disadvantages of E-learning 295

Online education is plagued by a lack of accreditation and quality assurance

10

If e-learning is to be taken seriously, all online schools must be qualified and accredited before being considered authentic. Unfortunately, there are still many unaccredited online learning platforms where no one but the instructors themselves checks the quality of all the materials. Low standards for online learning and a scarcity of accredited organizations further undermine the value of distance learning

Description In the United States, 96% of people own a mobile device, and 73% own a personal computer. As a result, it is simple to say that online education is readily available to the majority of the population. These figures, however, do not represent the whole picture. Computer literacy is still far from perfect despite rapid growth in technological capabilities. According to the Organization for Economic Cooperation and Development (OECD), a quarter of the population is computer illiterate. These are developed countries with a high-income economy, as defined by the OECD. As a result, they will have difficulty using e-Learning as a teaching tool Online education will not reach all citizens as long as social gaps exist. Around the world, things are often much worse. Despite being a significant player in e-learning, India, for example, still has a significant gap in computer literacy among the population. Online learning should not be seen as a replacement for traditional education; instead, it should complement it

Disadvantage

The computer-illiterate population cannot participate in online learning

S.no

9

Table 12.1 (continued) Solution

E-learning quality assurance involves many considerations that must be followed to ensure the integrity of e-Learning. Accreditation management systems like Creatrix provide a centralized solution for the accreditation process

Making computer literacy more accessible is not an easy task. However, there is some hope that initiatives like Digital India can help improve computer literacy rates among the general population

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12.5 Contributions of E-learning to Education

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The following are some drawbacks to using e-learning (Tamm, 2022).

12.5

Contributions of E-learning to Education

Studies show that e-learning and online learning have a variety of benefits for students (Ananga, 2020). Furthermore, e-learning can be advantageous for both the teacher and the student (Maikish, 2006). Naturally, e-learning can help students in higher education institutions complete their degrees while balancing other obligations like family and work-life (Borstorff & Lowe, 2007). Gains for both students and institutions have led to a dramatic increase in online course offerings (Kartha, 2006). e-learning tools, such as Learning Management Systems (LMSs), allow for greater flexibility in physical location and time. They are online hubs that link educators and students, and they make it possible for course materials and activities to be easily shared (Adzharuddin & Ling, 2013). They accomplish this by enabling more advanced interactions between instructors and students and making educational resources more readily available. It has been suggested that Learning Management Systems (LMS) serve as a single point of entry for all interactions between students, teachers, and administrators (Ananga, 2020). It has been proposed by Hsu (Lee et al., 2011) that students and instructors can communicate better when face-to-face learning is combined with e-learning. When used in conjunction with face-to-face sessions, LMSs can improve teaching and learning significantly. It is interesting to see how students heavily emphasize information and communication technology courses. According to (Alkhanak & Abdul, 2011), students prefer to enroll in courses that incorporate the use of Information Technology (IT). Compared to traditional classroom activities, e-learning systems offer more useful and valuable. Evidence suggests that LMSs are widely used in higher education, particularly students’ perspectives. Students at the University of Minho completed a survey about their experiences with MOODLE and Blackboard, and the results revealed how they engage with courses, their preferences, and their level of satisfaction, as well as how they rate various LMS features and functionalities (Carvalho et al., 2011). According to the findings, students valued the role LMSs played in their education and saw them as a supplement rather than a replacement for traditional classroom instruction (Ananga, 2020). Using MOODLE as a learning tool improved students’ attitudes and approaches, according to a previous study by Hölbl and Welzer (2010). The students, according to them, liked the approach because it was relevant to modern teaching and learning. In the same way, students believed that despite e-learning’s many advantages, face-to-face learning could not be replaced entirely (Ananga, 2020). Daoud (2007) found that MOODLE is mainly used for sharing and distributing learning materials in his study on the LMS use of MOODLE. Most users said it was simple to use, and they were pleased with that. Even the educators were not forgotten. They thought students could communicate with one another outside of the classroom.

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They also liked how easy it was to manage digital resources and get them to students. When it comes to learning modes, it is essential to keep in mind that e-learning has several advantages (Algahtani 2011; Hameed et al., 2008; Klein & Ware, 2003; Hanson, 2003; Nichols, 2003; Wentling et al., 2000). As a result, it is a strong contender as the best option in our situation, mainly because homeschooling is now required under COVID-19. Due to the nature of 21st-century students’ learning requirements, flexibility and convenience must be taken into account without sacrificing quality. An excellent way to make learning more flexible is to use online tools that cater to the various learning requirements of the student (Ananga, 2020). As a result of the freedom and convenience that online and e-learning provide in terms of both space and time, they have risen to prominence as the preferred mode of instruction. As a result of the online and e-learning modes’ flexibility, students now have more say in educational decisions and the learning environment (Yelland et al., 2008). Students and teachers can benefit from online teaching and learning environments, according to (Maikish, 2006). These environments are suitable for curriculum goals in education. Learning Management Systems (LMS) can ensure and facilitate this flexibility and convenience for students (LMSs). By utilizing Learning Management Systems (LMSs), instructors can better deliver instruction to students and teachers alike. Students and lecturers can interact online with the help of LMSs, which are software platforms (Ananga, 2020). LMSs, according to Gallagher-Lepak et al. (2009), serve as a single point of contact for students, teachers, and administrators at all levels of the educational continuum. To better manage their teaching-learning resources, several institutions are turning to LMSs. As a whole class or in small groups, students can have discussions. Online learning has been shown in studies like Rovia (2004) to be beneficial in both large and small groups. The presence of both a tyro and an expert learner in class can contribute to a component of interdependence among students as they construct meaning together, according to Stepich and Ertmer (2003). Interaction between a teacher and a student is critical in an online learning environment. Both instructors and students highly value synchronous discussions because they allow them to communicate in real-time. A study of distance courses found that both students and instructors preferred two-way synchronous discussion for questioning each other and providing feedback (Rogers et al., 2003). The informal bonds that help facilitate learning are strengthened by all forms of communication on LMSs aside from the formal academic work. Students form bonds of friendship and camaraderie during informal chats, confirmed by Gallagher-Lepak et al. (2009). This is critical to their understanding, according to them. Furthermore, e-learning has long been regarded as one of the most effective teaching strategies (Algahtani, 2011; Hameed et al., 2008; Klein & Ware, 2003; Rosenberg, 2002; Nichols, 2003; Wentling et al., 2000). Rosenberg (2002) posited that one of the advantages of e-learning in education is that it focuses on the needs of individual learners as he reviewed strategies for delivering knowledge in the digital age. This, in his opinion, is a crucial part of the educational process because it puts the learner first rather than the teacher

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or the institution. Similarly, Holmes and Gardner (2006) believe that one of the benefits of e-learning is assessing students as they learn and deepen their education knowledge. According to them, this can be accomplished through appropriate interactivity for society’s education, cultural diversity and globalization, and the removal of spatial and temporal boundaries (Holmes & Gardner, 2006). According to them, e- learning’s greatest strength is its ability to focus on students. Because of the interactive video facility built into the e-learning platform, students can watch everything in the classroom and listen to instructors whenever they want (Zhang et al., 2006). This summary of the benefits shows that the learner is always the primary concern. It is important to remember that no matter what happens in the teaching and learning process, the learner will always be at the center of it all. Educators and stakeholders must take all necessary steps to ensure that the learner is at the center of the instructional process. Although e-learning has many advantages, it is essential to point out that some drawbacks need to be addressed. For example, e-learning’s most glaring drawback is the absence of meaningful personal interaction between students and instructors (Young, 2013). These are the disadvantages of online learning, according to other research (Ananga, 2020): 1. One of the disadvantages of e-learning is that it forces students to engage in contemplation, remoteness, and a lack of interaction or relationship. Because of this, a powerful source of inspiration is required. 2. The e-learning method may be less effective than the traditional learning method for clarifications. 3. In terms of improving learners’ communication skills, e-learning may be detrimental. 4. The use of proxies in e-learning assessments makes it difficult, if not impossible, to monitor or regulate unethical behavior such as cheating. 5. Inadequate selection skills and the ease of copying and pasting can predispose e-learning to piracy and plagiarism. 6. E-learning can weaken institutions, socialization, and instructors in the educational process. 7. There are some fields or disciplines where e-learning cannot be used effectively. For example, e-learning is inadequate for studying fields such as pure science that include practical applications. 8. E-learning can also cause website congestion or heavy use. Thus, there may be unanticipated costs in terms of time and money for the project in the long run. Although e-learning has some disadvantages, its education benefits outweigh those drawbacks. Another critical aspect of e-learning to consider is the acceptability of issues relating to instructors, who are unquestionably meant to guide learning (Ananga, 2020). Because of this, the success of learning management systems (LMSs) in academic settings is primarily determined by how well instructors accept the tools. To put it another way, LMSs directly impact how students use them, so any signs of resistance or disinterest on the part of students have severe

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ramifications for student use. Concerns from most faculty about work overload when using e-learning platforms in conjunction with other time-consuming duties, such as teaching and learning in person supervising students’ project work/theses, are another significant source of concern. As a result, researchers are looking into ways to reduce the risk.

12.6

Modern E-learning Pedagogy

Educators have searched for ways to integrate new electronic communication technologies into their teaching and students’ learning, both inside and outside the classroom. The modern pedagogy of e-learning has evolved from these nearcentury-old experiences. There are three stages in the evolution of this idea. One-way broadcasting was the norm for the first half of the twentieth century. Teleconferencing technologies evolved from audio to video and computer-based in the 1970s through the 1990s. These allowed instructors and students to interact with each other. The Internet, browser, and the World Wide Web eventually supplanted this (Moore, 2015). When we first tried to use technology in education, we used computers to ‘rinse’ traditional learning models. Because no one knew for sure, no one could define the value. Our thinking was that the more machines we owned, the better off we would be. By sharing what they were doing, further education and training providers could learn from each other. Once we realized the value of replication, we shifted our focus to using information learning technology in standard systems and processes (Rebbeck, 2021). The personalization of technology sparked the revolution. Thus, students now bring their content and technology to class, and the emphasis has shifted from learning about processes to learning about experiences. Students are no longer required to face the same way for learning purposes. It is no longer news that e-learning has taken center stage in higher education administration, thanks to the 2014 report from the FELTAG (Further Education Learning Technology Action Group). Students can use it to capture, record, and marshal their learning experiences before publishing and presenting them as unique learning journeys (Rebbeck, 2021). The following are features of this modern e-learning pedagogy (Rebbeck, 2021): 1. Incorporating more project-based and inquiry-based learning to foster critical thinking among students outside of the classroom 2. Acquiring critical filtering skills such as validity and reliability 3. Promoting reflective learning and presenting as a way to demonstrate diverse competencies, achievements, and qualities (the rounded self) in unique learning journeys 4. The power of ideas can be harnessed by interacting with like-minded individuals through online networks.

12.7 Models of E-learning and Teaching

12.7

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Models of E-learning and Teaching

Mayes’ Conceptualization Cycle, Laurillard’s Conversational Model, and Salmon’s E-tivities are three learning pedagogy models (shown in Fig. 12.3) explicitly developed for eLearning (Manchester University, 2020). They are all significant in conceptualizing eLearning levels and online learning and teaching activities. 1. Mayes: The Conceptualisation Cycle. According to Mayes’ theory, using technology to teach involves a cycle of conceptualization, construction, and dialogue. The Conceptualisation Cycle is a three-stage process. Mayes examines students’ understanding of new concepts and the revision of incorrect concepts in an article written by Mayes & Fowler. – Level One e-Learning (students given information). Students contact other people’s work as part of the conceptualization process. Observing images or videos on the Internet, for example, is an example of this. – Level Two e-Learning (students perform a task). Students put these new concepts to use by performing valuable tasks during the construction phase. Students may be asked to take a quiz or write a journal entry to complete an assignment like that. – Level Three e-Learning (students given feedback). However, learning only occurs during dialogue, when students perform tasks that put these new concepts to the test in conversation with tutors and peers. Students’ misunderstandings are cleared up thanks to the feedback they receive. Automatic feedback, quiz answers, or feedback from tutors on discussion boards are just a few examples. Fig. 12.3 Models of E-learning

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There are three levels of learning activity, according to Mayes, and each of these levels can be supported by three different classifications of courseware or online material designed to help students learn (Manchester University, 2020): – Level one. Primary courseware supports things like online lecture notes and reading lists. This is a great way to give students the information. – Level Two. It is the responsibility of secondary courseware to assist students in completing a task. As an illustration, consider computer-assisted assessments, in which students are required to provide answers to questions. Some examples of this are computer-assisted evaluations and online examinations. – Level three. In Tertiary Courseware, learning is only possible when there is a two-way dialogue. Online discussions, video conferencing, shared workspaces with external feedback, and online simulations are examples of these types of communication. It is good to get your students involved from the beginning when creating online resources. However, according to Mayes, the focus on primary courseware will not provide enough learning support. As a result, the course design must include a variety of teaching methods in order to support students at all three stages of the conceptualization cycle. In order to achieve high levels of learning, there must be two-way communication (either between the tutor and students or between students themselves) or an internal dialogue that may take place in the student’s mind. Higher education can only use courseware or face-to-face learning methods integrated with technology-enhanced teaching to accomplish this. 2. The conversational model of Laurillard. Using Vygotsky’s earlier theories, Professor Diana Laurillard developed a conversational model that emphasizes the two-way exchange of ideas between tutor and student. Laurillard emphasizes that there must be a dialogue between theorists and practitioners to learn at a higher level. Not only does this help students make the connection between theory and practice (which can be difficult in many subjects), but it also lets the tutor know if they have given the student appropriate homework. In this model, students and tutors have a close relationship, one of its most distinctive features. The spontaneous and intuitive nature of face-to-face interactions makes it easy to overlook them when developing a technologysupported learning environment. This interaction was made explicit by Laurillard. These interactions can be aided by technology in the following ways (Manchester University, 2020): – Narrative—In this scenario, knowledge is being told or imparted to the learner. – Communicative/discursive—The tutor facilitates processes where students talk about and reflect on what they have been taught. – Interactive—This is a result of what you learned. Students receive feedback from their tutor based on the results of tasks they complete, which helps to reinforce their learning and boost their performance. The tutor also uses

12.7 Models of E-learning and Teaching

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this information for adaptive purposes, such as revising the learning that has already occurred and changing the dialogue’s focus as necessary. 3. Gilly Salmon: 5-stage model and e-Moderating. Salmon has proposed a five-stage model for computer-mediated communication (CMC) based on her research. The primary goals of the first two stages of Salmon’s model are acclimatizing the learner to the online environment and cultivating a supportive social environment. Learners interact with course materials and activities online and share additional resources during the ‘information exchange’ stage, the third stage in the process. As learners move into the fourth stage, dubbed “knowledge construction,” they begin to work together to share ideas, pose problems, and challenge one another in an exploratory spirit. Taking ownership and reflecting on one’s learning is the goal at the end of the process. An essential part of any design or implementation involves the tutor, also known as the moderator, who aids and encourages students to achieve their goals. – Step 1—Access and Motivation: Tutors must make sure that learners have easy access to a virtual learning environment (VLE) for this first stage. Usually, this is done to ensure no technical issues, like a password issue. – Stage 2—Online Socialisation: At this point, students should feel at ease in the online setting and interact with one another. With the help of Salmon’s book e-tivities, new learners to the online environment can become more comfortable and ready to communicate and collaborate with others online. It is critical to foster an atmosphere where students are treated with respect and reciprocate that respect. A learner has entered the “self-disclosure stage” when posting personal information about themselves on the Internet. – Stage 3—Information Exchange: Most conferences end with a frenetic exchange of messages during this phase. Learning resources such as weblinks, databases, case studies, and other students will interact with the learner in the VLE. Information overload is a concern at this stage, and some attendees lament the conference’s haphazardness. The tutor’s job is to provide structure and organization. When the tutor does not respond to every message, the online discussions must be summarised and focused. – Stage 4—Knowledge Construction: The main objective is to create an online learning community. The tutor will summarise the material while introducing the class to new subjects and ideas as needed. The instructor will link messages to theories and concepts, and he or she will also encourage students to respond in kind. The tutor and students may be both sharing leadership responsibilities at this point. – Stage 5—Development: The connection between Salmon and constructivism becomes apparent at this point. High-level learning is the focus, with the tutor encouraging students to delve deeper into concepts and ideas. As a result, online students are increasingly self-motivated, self-assured, and capable of critical thinking.

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Case Study

Using the Community of Inquiry model, the Conversational Framework, and Computer-Mediated Communication models, this section examines how e-learning in a hybrid course on the Western humanities can be improved. Each of the three models will be described in detail before the discussion begins. E-learning models are theoretical constructions that aid educators in creating engaging online learning environments for their students (Theriault, 2015). Elearning models are distinct from learning theories in that they are focused on the pedagogical principles that underpin instructional practices or the effective implementation of such instructional practices. When it comes to the successful implementation of e-learning, three models stand out among the many presented in Pachler and Daly’s Key Issues in E-Learning (Pachler & Daly, 2011), most of which are built on socio-constructivist learning theories: Community of Inquiry model, Conversational Framework, and Computer-Mediated Communication (CMC). • The Community of Inquiry Model: Individuals and groups are seen as equal participants in the Community of Inquiry model. Through social interaction with their peers, students construct knowledge from a personal point of view, then compare and confirm it with societal norms, values, and general knowledge (Pachler & Daly, 2011). As opposed to a formal group with predefined and preplanned objectives, membership, and communication methods, a community in this context is less structured but more structured than a group of friends, where there are no objectives and membership is voluntary. If the Community of Inquiry model is to be implemented successfully, it necessitates forming a community where members can work toward a common goal while also pursuing their own goals (Jezegou, 2010). When forming an online learning community, one must consider three types of presence that emerge: social presence, cognitive presence, and teaching presence. Each of these three types of presence must be considered (Liu & Yang, 2014). There are three types of presence: social, cognitive, and teaching. Social presence refers to how learners and instructors present their identities in an elearning environment; cognitive presence refers to how learners communicate with their peers to build knowledge, and teaching presence refers to the actions and tools used by instructors to help learners develop social and cognitive processes in pursuit of specific learning goals (Liu & Yang, 2014). A teaching presence gives a community the structures it needs to form, a social presence helps students and instructors connect, and a cognitive presence ensures that the community remains helpful in its members in the long term. All three types of presence are essential to forming and maintaining a community of inquiry. Integrating personal experiences and course content into online discussions can help students get the most out of their community of inquiry and activities. Liu and Yang’s study of asynchronous online discussions (Liu & Yang, 2014) examines how social interaction enhances learning, using the Community of

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Inquiry model as a theoretical foundation and content analysis to examine messages. According to the findings, students showed the most cognitive presence when discussing case studies that involved case analysis and the least cognitive presence when discussing theoretical concepts that involved exploration. On the other hand, in their course evaluations, students stated that they preferred the discussions of theoretical concepts to those of case studies. When it came to low-level cognitive presence, students were asked to connect course content with their personal experiences, which produced the most high-level cognitive presence. Researchers recommended, based on their findings, choosing case studies with a vital life experience component to develop discussion topics that would take into account both student interests and student learning. • The Conversational Framework: As described by Laurillard (2009), the Conversational Framework integrates four pedagogical approaches: instructionism, constructionism, sociocultural learning, and collaborative learning. Learning is seen as a “continual iteration between teachers and learners and between the levels of theory and practice,” which can be used in face-to-face and e-learning settings alike because of the emphasis on idea exchange. In this model, the instructor has a significant advantage because he or she is in charge of defining the concepts to be taught and creating the assignments that students must complete. In order to achieve learning objectives, the Conversational Framework makes proper use of technology a priority. This framework challenges technology to provide a better learning experience by supplementing rather than replacing pedagogy (Laurillard, 2009). Using a museum field trip as an example, Laurillard (2009) examines the differences between a traditional learning design and one that incorporates technology. Students have numerous opportunities to engage in an "iterative exchange of ideas" in the technology-enhanced museum field trip. For example, students can use mobile devices to answer questions about the connections between paintings, create quizzes for their peers, and answer quizzes set by their peers. They can also share observations and photos on a collaborative website and take notes. When the students return to class, the instructor can use their notes and photos to create a digital catalog of the event, allowing them to take ownership of their learning. However, even though the Conversational Framework is not explicitly designed for e-learning, it can and should be used to design meaningful learning experiences in which technology plays an important role. The model supports socio-constructivist learning principles and can easily be combined with the Community of Inquiry model to provide students with a rich and satisfying learning experience (Theriault, 2015). • Computer-Mediated Communication: In the Computer-Mediated Communication model of e-learning, students communicate asynchronously using discussion forums and e-mail, with text as the primary medium (Theriault, 2015). When using this e-learning model, students do not have to worry about the “constraints of time, space, and physical context” (Pachler & Daly, 2011) that come with traditional classroom discussion. Instead, they can respond at their

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own pace to what their instructor or fellow students have to say about an issue. Thus, students enter an asynchronous “conversation” with enhanced opportunities for knowledge construction and begin the process of sorting through meanings provided by their peers before resolving diverse ideas. This means that Computer-Mediated Communication encourages the development of academic discourse through the exchange, organization, and re-organization of ideas, which are essential in supporting the active and collaborative learning at the core of socio-constructivist theories (Keengwe et al., 2014). The Conversational Framework and the Community of Inquiry model benefit from computer-mediated communication because they emphasize collaborative, constructive transactions between students (Pachler & Daly, 2011). Cognitive presence in the Community of Inquiry model and iterative exchange in the Conversational Framework have counterparts in these transactions. Incorporating and using these three models in an e-learning setting would give instructors various tools to help students develop their knowledge creation and dissemination capacity (Theriault, 2015). Implementation of E-Learning: Current Practices. Dr. Normand Theriault taught a hybrid course on Western humanities from prehistory to the Gothic period as an adjunct professor at Houston Community College in Texas. Due to time constraints, he did not include learning tasks that would promote active learning and collaboration among students in his course prior to the start of the semester. He regrets this. Using the Desire2Learn learning management system, students must complete quizzes covering textbook material and short writing assignments responding to a topic they choose from a list of options. Discussion forums, downloadable presentations from lectures, and embedded videos related to the week’s lessons are optional features of the course. Students can also ask questions or comment on the week’s subject matter in the discussion forums. Most students appear to be content with submitting their tests and short writing assignments without ever interacting with their classmates, whether online or in person. Few students have taken advantage of the discussion forums and supplemental videos available. The models of e-learning described above were used in this course to foster social interaction and, as a result, knowledge construction for the students. Implementation of E-Learning: Improved Practices. The first thing Dr. Normand Theriault did was make it a requirement for students to post to the discussion forums on a topic related to their life experiences in the hybrid course, which improved practices. Students would be expected to participate in the discussion forums by posting comments, questions, and criticism on a variety of carefully chosen topics. Students are tasked with creating a self-portrait from text, images, or sounds that they can share with their peers to encourage the development of a community of inquiry. His moderating skills also extended to gathering and summarising students’ points of view before asking for more posts in response to the questions posed by the overview. Consequently, he incorporated social presence

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into the course, facilitated iterative exchange through repeated interaction on discussion forums, and encouraged collaboration among students by requiring them to respond directly to the ideas of their fellows. This simple shift in his teaching methods reflects all three e-learning models. Further changes to practice could be made, but they would likely necessitate more forethought before they could be fully implemented. Wikis can create collaborative knowledge and exchange ideas, social media can build a community of inquiry, and students’ critical thinking skills can be developed by using peer assessment on short writing assignments. These are just a few ideas for making changes in education. A complimentary trio of online learning models can help instructors design learning tasks that improve student learning outcomes by fostering collaborative interaction with peers, as shown above in the discussion of the models. They are based on socio-constructivist learning theories and provide the dedicated instructor with effective tools for bringing theory and practice together. Because the distance between instructors and students, and between students and their peers, is imposed by e-learning, a course’s design must include features that help all participants overcome the sense of isolation that can result. That monumental task is wellsuited to the models presented here.

12.9

Conclusion

In terms of instructional strategies, a sea change is underway. Cutting-edge technology teaching and learning applications are being developed, researched, and tested worldwide. E-learning is not a substitute for classroom instruction. Internet or Blackboard as a delivery tool should not define a teacher’s approach to their craft of instruction. The technology should allow the instructor to implement the best pedagogical practices for an online course or topic. Students expect their instructors to incorporate cutting-edge technology into their lessons. The online teacher can provide a high-quality educational learning experience by using the positive aspects of technology. An effective e-learning pedagogy emphasizes student-centered learning and employs active learning activities. Students, faculty, and other participants all need to be involved for an online course to be successful.

References Adzharuddin, N. A., & Ling, L. H. (2013). Learning management system (LMS ) among university students: Does it work? International Journal of E-Education, e-Business, e-Management and e-Learning, 3(3), 248–252. https://doi.org/10.7763/IJEEEE.2013.V3.233. Algahtani, A. F. (2011). Evaluating the E ectiveness of the E-learning experience in some universities in Saudi Arabia from male students’ perceptions. Alkhanak, S. A. K., & Abdul, I. (2011). Information technology usage and attitudes towards online resources-Students perspective. African Journal of Business Management, 5(7), 2582–2589. https://doi.org/10.5897/AJBM10.663.

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12

Pedagogy for E-learning

Ananga, P. (2020). Pedagogical considerations of E-learning in education for development in the face of COVID-19. International Journal of Technology in Education and Science, 4(4), 310– 321. Arkorful, V., & Abaidoo, N. (2014). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Education and Research, 2(12), 397–410. Borstorff, P. C., & Lowe, S. K. (2007). Student perceptions and opinions toward e-learning in the college environment. Academy of Educational Leadership Journal, 11, 13–30. Carvalho, A., Areal, N., & Silva, J. (2011). Students’ perceptions of blackboard and Moodle in a Portuguese University. British Journal of Educational Technology, 42(5), 824–841. https://doi. org/10.1111/j.1467-8535.2010.01097.x. Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. Daoud, A. M. (2007). e-Saf MOODLE LMS in Saudi Higher Education: Implementation and experiences. In Proceedings of the 2007 MIT LINC Conference (pp. 206–210). Gallagher-Lepak, S., Reilly, J., & Killion, C. M. (2009). Nursing student perceptions of community in online learning. Contemporary Nurse, 32(1), 133–146. Guri-rosenblit, S. (2005). ‘Distance education’ and ‘e-learning’: Not the same thing. Higher Education, 49(4), 467–493. https://doi.org/10.1007/s10734-004-0040-0. Hameed, S., Badii, A., & Cullen, A. J. (2008). Effective e-learning integration with traditional learning in a blended learning environment. In European and Mediterranean Conference on Information Systems (pp. 1–16). Hanson, D. J. (2003). Book review: E-learning: Strategies for delivering knowledge in the digital age. Educational Technology & Society, 6(3), 80–81. Hölbl, M., & Welzer, T. (2010). Students’ feedback and communication habits using Moodle. Electronics and Electrical Engineering, 6(102), 63–66. Holmes, B., & Gardner, J. (2006). e-Learning: Concepts and practice. Jezegou, A. (2010). Community of inquiry in e-learning: A critical analysis of the Garrison and Anderson model. Journal of Distance Education, 24(3). Kartha, C. P. (2006). Learning business statistics versus traditional. Business Review, 5, 27–33. https://doi.org/10.4236/ti.2011.22014. Keengwe, J., Onchwari, G., & Agamba, J. (2014). Promoting effective e-learning practices through the constructivist pedagogy. Education and Information Technologies, 19(4), 1–12. https://doi. org/10.1007/s10639-013-9260-1. Klein, D., & Ware, M. (2003). E-learning new opportunities in continuing professional development. Learned Publishing, 16(1), 34–46. Laurillard, D. (2009). The pedagogical challenges to collaborative technologies. ComputerSupported Collaborative Learning, 4, 5–20. https://doi.org/10.1007/s11412-008-9056-2. Lee, A. Y., Hsieh, Y., & Hsu, C. (2011). International forum of educational technology & society adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Educational Technology & Society, 14(4), 124–137. Liu, C.-J., & Yang, S. C. (2014). Using the community of inquiry model to investigate students’ knowledge construction in asynchronous online discussions. Journal of Educational Computing Research, 51(3), 327–354. Maikish, A. (2006). MOODLE: A free, easy, and constructivist online learning tool. Multimedia & Internet @ Schools, 13(3), 26–28. Manchester University. (2020). Introduction to eLearning pedagogy. Moore, M. (2015). Historical perspectives on E-learning. In International handbook of e-learning (pp. 41–49). https://doi.org/10.4324/9781315760933.ch2. Nichols, M. (2003). A theory for e-learning. Journal of Educational Technology and Society, 6(2), 1–10. Pachler, N., & Daly, C. (2011). Key issues in e-learning: Research and practice. Continuum International Publishing Group. https://doi.org/10.4236/ce.2014.53023. Rebbeck, G. (2021). A pedagogy of e-learning. Set.et-Foundation.Co.Uk. https://set.et-foundation. co.uk/publications/intuition/a-pedagogy-of-e-learning/#.

References

309

Rogers, P. C., Graham, C. R., Rasmussen, R., Campbell, J. O., & Ure, D. M. (2003). Case 2: Blending face-to-face and distance learners in a synchronous class: Instructor and learner experiences. Quarterly Review of Distance Education, 4(3). Rosenberg, M. J. (2002). Book review: E-learning strategies for delivering knowledge in the digital age. Internet and Higher Education, 5, 185–188. Rovai, A. P. (2004). A constructivist approach to online college learning. Internet and Higher Education, 7, 7(2), 79–93. https://doi.org/10.1016/j.iheduc.2003.10.002. Ruiz, J., Mintzer, M. J., & States, U. (2006). The impact of e-learning in medical education. Academic Medicine, 81(3), 207–212. https://doi.org/10.1097/00001888-200603000-00002. Stepich, D. A., & Ertmer, P. A. (2003). Building community as a critical element of online course design. Educational Technology, 43(5), 33–43. Tamm, S. (2020). What is the definition of e-learning? E-Student.Org. https://e-student.org/whatis-e-learning/. Tamm, S. (2021a). 10 major advantages of e-learning. E-Student.Org. https://e-student.org/advant ages-of-e-learning/. Tamm, S. (2021b). All 10 types of e-learning explained. E-Student.Org. https://e-student.org/typesof-e-learning/. Tamm, S. (2022). 10 biggest disadvantages of e-learning. E-Student.Org. https://e-student.org/dis advantages-of-e-learning/. Theriault, N. (2015). Three models of e-learning to improve pedagogy. LinkedIn. https://www.lin kedin.com/pulse/three-models-e-learning-improve-pedagogy-normand-theriault/. Wentling, T. L., Waight, C., Gallaher, J., Fleur, J. L., Wang, X. C., & Kanfer, A. (2000). E-learning: A review of literature. Journal of Software Engineering and Applications, 12(2), 1–73. Yelland, N., Tsembas, S., & Hall, L. (2008). E learning: Issues of pedagogy and practice for the information age. In Learning and the learner: exploring learning for new times (pp. 1–236). Young, G. (2013). Adult development, therapy, and culture: A postmodern synthesis. Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43, 15–27. https://doi.org/10.1016/j.im.2005.01.004.

Harnessing the Power of AI to Education

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Abstract

As a result of artificial intelligence (AI), the world is changing rapidly. Societies, organizations, work, and education are all affected, becoming a significant part of daily life. As a result, new opportunities for industry arise, and the status quo is upended in profound ways. AI will also significantly impact the concept of expertise and the businesses that rely on it. AI will change everything. As a result, there are tremendous opportunities to expand educational settings for learning both inside and outside of the traditional classroom, but doing so will necessitate a significant financial investment at all educational levels. AI began as computer and computer-related technologies, then moved to web-based and online intelligent education systems. Finally, embedded computer systems were used in conjunction with other technologies, such as humanoid robots and webbased chatbots, to carry out the duties and functions of instructors on their own or with the assistance of instructors. AI is still evolving. Students’ assignments have been reviewed and graded more effectively and efficiently, and instructors’ teaching activities have improved using these platforms. As a result of machine learning and adaptability, curriculum and content have been customized and personalized to meet students’ needs, promote uptake and retention, and ultimately improve students’ learning experience and the overall quality of education. As a result, this chapter examines the potential impact of utilizing AI in education. Keywords

Artificial intelligence • Education • Learner • Classroom • Intelligent education systems

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 K. G. Srinivasa et al., Learning, Teaching, and Assessment Methods for Contemporary Learners, Springer Texts in Education, https://doi.org/10.1007/978-981-19-6734-4_13

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Introduction

In recent years, artificial intelligence (AI) has significantly impacted many industries, including education. Machine-based intelligence is becoming an increasingly valuable tool for educational institutions today and in the future. The education sector is attempting to keep up with the rapid advancements in AI. A growing number of educational establishments, such as elementary schools, colleges, and universities, are embracing AI. In order to better serve students, this transformation is being undertaken in the first place. Educators can now use AIpowered learning methods to assess their students’ comprehension abilities. What works for one person may not work for another. Students grasp the concept at varying rates. Each student has a unique set of learning and thinking capabilities. AI is the most effective solution for overcoming the difficulties of learning new information. In education, AI technology accelerates your learning (USM, 2021). As a result, educators and mentors can provide students with more informed and personalized assistance. Because AI allows for more personalized learning environments with custom content, rather than the old “one size fits all” style associated with computerbased instruction in the past, students now have an opportunity to learn in an environment that is tailored to their specific needs. Schools that implement this kind of technology create an environment where students can learn and retain new information better than in a traditional classroom, giving them an advantage in mastering the subject matter. Lessons are more meaningful, and students have a more enjoyable time in school due to this improvement (USM, 2021). Data collection and analysis are at the heart of AI, as are many contemporary business and industrial tools. No matter what industry you work in, you are in today; you are in the data industry. Data collection and use allow you to identify trends, improve systems, personalize content and services, and allocate resources accordingly. So that is a benefit of AI in education: it gives teachers and educators the information they need to make lesson plans and instruction more effective for their students, which leads to greater success and a higher return on investment (USM, 2021). This chapter explores all of the potential benefits of AI for the student fraternity in the future. This chapter aims to determine the effects AI use in education has had on various aspects of education. An investigation into AI’s underlying technologies and current educational applications is conducted in this study. Finally, we tally up the top educational technology firms and AI-driven solutions. The findings of this study will be helpful to a wide range of people, including educators, researchers, and policymakers.

13.3 The Role of AI in Education

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The Need for AI in Education

AI is widely regarded as having great potential for personal and societal benefit, but it is currently underutilized (Chaudhary, 2018). This is partly due to our resistance to change and our inability to accept AI in our daily lives. Education is one of the many areas where AI can make a significant impact (Chaudhary, 2018). Everyone is generally against the idea that AI can help us improve our educational systems by replacing human teachers. However, this is where they are mistaken. It is important to remember that AI’s goal is not to replace people but rather to assist and empower them. We have to realize that AI is not a science fiction concept. Although AI will never replace human teachers completely, it can provide support and help them be more effective. With the rapid advancement of this technology, we should take advantage of all its benefits. Automating administrative tasks such as grading tests and measuring student responses can free up teachers’ time to do more productive work (Chaudhary, 2018). Currently, machines are fully capable of completing this task with pinpoint accuracy, and as time goes on, machines will be able to perform increasingly detailed and personalized tasks, such as grading essays. Students who are struggling in various subjects may find that AI can assist them in creating study plans that are tailored to each individual’s needs, which saves teachers a significant amount of time. In order to assist teachers in understanding how a specific student responds to a teaching method, machine learning algorithms can be implemented. Because not all students can connect with a teacher simultaneously, this has the potential to revolutionize teacher-student relationships and bridge the gap between them ultimately. Students’ progress can be improved by using this method, which can help create personalized content for all students. For students who are afraid of receiving negative feedback from their peers, AI can help create personal mentors for them and bring their problems to their teachers’ attention (Chaudhary, 2018). This is particularly useful for shy students. This helps close the communication gap in classrooms worldwide between students and teachers. Education professionals worldwide are wary of making significant systemic changes, and these concerns are not always unfounded. It is clear that AI has enormous potential to improve student learning, and we cannot completely ignore it. This new technology faces less resistance because it can help teachers be more productive while also making them more responsive in meeting the needs of their students in the future.

13.3

The Role of AI in Education

As a result of its immense power, AI can drastically alter many aspects, including education. According to (Chen et al., 2020), AI has been used in education in various ways, including automating administrative processes and tasks, developing curriculum and content, teaching, and students’ learning processes. AI has

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improved administrative efficiency by automating tasks such as reviewing student work, grading, and providing feedback on assignments. Another area in which AI has been used in education is curriculum and content development and instruction using virtual reality, web-based platforms, and robotics. These have all helped students learn more effectively. With better teachers and better educational technology comes a better learning or educational experience for the students. In scenarios of instruction, learning, and administration, the functions of AI are summarized as follows. When it comes to administration, AI can help with the following tasks (Chen et al., 2020): • Grading exams and providing feedback are administrative tasks that take up the instructor’s time. • Identify their students’ learning styles and preferences to create individualized learning plans for each of them. • Assist instructors with data-driven and decision-support work. • Offer timely and direct feedback and interaction with students. The following are some examples of what AI can accomplish when used in instruction (Chen et al., 2020): • Examine student performance on projects and exercises and the likelihood of the student failing. • Analyze the syllabus and course materials and develop new ideas for assignments and learning activities. • Allow education to extend beyond the classroom and support collaboration at a higher level of education. • Customize the teaching approach for each student based on the individual’s data • Assist teachers in creating student-specific learning plans. When it comes to learning, AI can do the following (Chen et al., 2020): • Uncover student learning shortcomings and intervene early on in the educational process. • Allow students to select from a broader range of university courses. • Predict the future career path of each student based on the data collected during their studies. • Identify students’ current learning status and provide intelligent, adaptive interventions.

13.4

The Impact of AI on Education

Education may be the most affected by AI. AI’s use in the classroom provides a significant opportunity for educational reform. For two reasons: first, learning is

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essential, and second, current educational offerings frequently fall short of expectations (Chace, 2020). AI’s effects on administration, instruction, and learning were explored in greater depth in (Chen et al., 2020): 1. Education Administration. When it comes to administrative and management functions in education, AI has had a significant impact. Administrators and teachers have been more effective since it has improved administrative functions, such as grading and feedback. It is now easier for instructors to grade students’ work and give feedback thanks to adaptive and intelligent web-based educational systems (AIWBES) programs. It is possible to find programs like Knewton, which provide instructors with built-in functionalities for grading and providing feedback to students to help them improve their performance. Administrative tasks have been streamlined thanks to AI, which also helps educators be more effective in guiding students. Intelligent tutoring systems give instructors a wide range of administrative capabilities, including grading and providing feedback. Many other AI-enabled programs, such as Grammarly, PaperRater, Ecree, and TurnItIn, provide professors with administrative functions like plagiarism detection, rating and grading, and student feedback on areas for improvement. By automating administrative tasks, AI has freed instructors to focus on their core responsibilities, such as instruction and disseminating curriculumaligned content and materials at their institutions or nationally. Even though many articles did not focus on this aspect of education, evidence was found that administrative processes and tasks were improved, as were instructors’ and educators’ efficacy and efficiency in carrying out various administrative tasks. 2. In Instruction. Using AI in instruction or by instructors is another aspect of education. AI in education has had a significant impact on teaching or pedagogical tools. As a result of AI, instructors are now more effective, efficient, and capable of producing higher-quality work. The delivery of relevant content according to the curriculum and the learner’s specific needs and capabilities is a measure of efficiency and quality in this context. Efficacy is measured by how well students or learners take in and retain the information they have learned. AI has aided educators in realizing the value of high-quality instruction while also reducing the time and effort it takes to deliver it. AI has improved the effectiveness of instruction by leveraging evidence-based or empirically supported practices, such as extensive use of cognition and learning models, and has ensured optimal uptake and retention of materials or optimized learning among students. Learner-centered programs like DeepTutor and AutoTutor encourage customization and personalized content based on a learner’s abilities and needs, thereby improving the learning experience and promoting the accomplishment of predetermined learning objectives. Because the materials or content presented are based on the learners’ requirements, AI has also improved instructional quality and effectiveness.

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Improvements in instruction have been made possible by AI development and use, especially in online and web-based learning platforms. AI ensures better dissemination of course content from curriculum development to delivery when it comes to online and web-based learning platforms. AI also promoted academic integrity by using plagiarism checkers, proctoring, and online supervision of students’ activities on Grammarly, White Smoke, and TurnItIn. With the integration of virtual reality and 3-D technologies, gamification enhances the quality of instruction by leveraging AI. Expressional humanoid robots with the ability to converse improved the quality of instruction by fostering student engagement due to their enhanced capabilities and human-like looks. 3. In Learning. Adaptive content and intelligent learning systems have been made possible by AI, such as intelligent virtual reality and its use in simulation teaching and learning, which has been shown to affect student learning positively. Students’ learning experiences have also been significantly impacted by AI’s implementation and use in education. When using conversational agents, students will be probed and prodded until they can adequately explain their positions, including their reasoning, thus improving their uptake and retention of information. This is how ITS (intelligent tutoring services) fosters deep learning. It is possible to track students’ learning progress using AI, which uses the findings to improve the system’s ability to tailor content to meet the needs and capabilities of each student. This helps students stay motivated and uses their capabilities to increase uptake and retention. Simulation and related technologies allow students to gain practical experience and learn. Learning with VR and 3-D technology piqued students’ curiosity and stoked their enthusiasm and motivation, all at the same time. Using web-based platforms, other advantages of AI and its impact on learning quality are available. AIWBES encouraged student collaboration, interaction, and learning by customizing instructions and content based on learner behaviors. StudentTracker middleware will work with online learner information, including completed activities, learning tracking, time, and other components to adapt pedagogical approaches to the AIWBES. Other advantages of webbased platforms and proven benefits to learning include promoting global access and affordability of education. In general, these tools have made learning more enjoyable for users. Due to the ease with which paper mills and paper churning platforms are now accessible to students, artificial intelligence (AI) poses a threat to academic integrity. However, the advantages of artificial intelligence for learning far outweigh the disadvantages. Using tools like TurnItIn and Pearson’s Writeto-Learn, AI has promoted academic integrity, better studies, and better learning through revision and writing assistance. 4. Performance of Instructor and Student. A fascinating aspect of AI as an intelligent system is how it impacts the performance of teachers and students. To help instructors deal with an increasing number of students at schools, AI systems will become increasingly helpful. AI systems assist instructors in analyzing syllabuses and course materials to develop individualized lesson plans relevant to

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their students. Additionally, these systems can generate and grade exams after analyzing. This would free up instructors to work on more pressing issues like improving student achievement in the long run, for instance. Personal learning plans can be created for each student using AI solutions in individualized teaching and self-directed learning. AI in education is also encountering new challenges due to human bias. In order to eliminate bias, an AI solution can grade papers and exams according to pre-set rubrics and benchmarks. Computer vision-based AI systems that read and detect images of handwritten papers can be used to do this. As well as eliminating bias, these systems guard against academic dishonesty like cheating and plagiarism. AI systems have identified students’ learning deficiencies early on by analyzing student data. According to traditional educational practices, most students are treated the same way. A teaching method applied to all students will not yield optimal results. AI could assist in determining the best teaching method for each student based on their unique personality traits, strengths, and weaknesses. As a result, all students will benefit from and enjoy their schoolwork. While increasing students’ knowledge, it also aids in the development of a knowledge system that includes better learning capabilities, habits, and creativity on the part of the students. Additionally, AI systems can forecast each student’s future career path based on their academic performance, and this data is used to personalize the university course selection for that student. Students can improve their grades and acquire valuable skills in the real world by taking their ability and career path into account. For institutions and instructors, automation and expediency of administrative tasks can be highly beneficial due to the discussion above. When it comes to graded homework and essay evaluation, AI can already automate it, allowing instructors to devote more of their time to students. AI developers are developing new methods of grading written assignments and exams. AI creates customizable digital learning interfaces for students of all ages and grade levels regarding educational materials. Furthermore, AI in the learning process enables instructors to gain student insight based on the entire ecosystem of learning tools. AI systems use the difficulty a student has with the course material to tutor them. For a long time, students had to wait until office hours or send an email to reach out to their professors if they needed help. For students who want personalized feedback, smart tutoring systems like Carnegie Learning use their data to tailor their tutoring. To help instructors and students, AI will soon work as a full-fledged assistant that can accommodate a wide range of learning styles. It helps instructors and students with their educational requirements in virtually any field.

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Technologies for AI in Education

Using various computing technologies, including machine learning-related ones like deep learning (Kahraman et al., 2010), aims to improve students’ quality and value of learning. With AI-assisted learning, students can learn intelligently, innovatively, and virtually. Also, as learning requirements increase, AI-enabled education becomes more important (Rus et al., 2013). Teachers and students benefit from personalized and timely feedback provided by intelligent educational systems (IES) (Chen et al., 2020). Various educational AI scenarios are outlined in Table 13.1, along with the key technologies that will support them (Chen et al., 2020). Data mining, learning analytics, and machine learning are all aspects of educational technology (Chen et al., 2020). Figure 13.1 shows the three major technologies for AI in education. Table 13.1 Techniques for scenarios of AI education Scenarios of AI education

AI-related techniques

Student and school evaluations

Academic analytics using an adaptive learning approach and a personalized learning plan

Paper and exam evaluation and grading

Computer vision, image recognition, and prediction system

Intelligent, personalized instruction

Intelligent teaching systems, learning analytics, and data mining or Bayesian knowledge inference

Smart school

Virtual labs, A/R, V/R, hearing and sensing technologies, face recognition, speech recognition

Online and mobile-based distance learning Real-time analysis, virtual personal assistants, and edge computing

Fig. 13.1 Technologies for AI in education

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1. Machine Learning. Using machine learning, the process of parsing a sampling data set known as “training data” generates meaningful patterns and structured knowledge. This is the heart of machine learning. When it comes to helping students choose classes and even universities, machine learning can make recommendations. It uses student achievement data, aspirations, and preferences to “match-make” them with educational institutions where they can flourish. Besides that, this technology can assist instructors in figuring out how their students are absorbing each concept. This allows instructors to make adjustments to their teaching methods based on the cumulative records of their students, which may help them better understand the course material. Image recognition and machine learning can grade student assignments and exams with faster and more reliable results than a human. Keep in mind that the subfield of machine learning, known as deep learning, has recently received much attention. Decision tree learning, logic programming, clustering, reinforcement learning, and Bayesian networks are widely used. These techniques are all widely used. To put it another way, deep learning emphasizes the acquisition of ever-moremeaningful representations as the learning process progresses. Neural networks are models that extract the layer features by stacking them on top of each other like a stack of books. 2. Learning Analytics. The learner and knowledge field models focus on learning analytics, using data from student characteristics and knowledge objects. With learning analytics, you can apply machine learning to the non-technical world of education, introducing new technology. It uses machine learning, data visualization, learning sciences, and semantics techniques. Students at risk can be helped by intervening or providing feedback and instructional content tailored to their needs and abilities (Tsai & Gasevic, 2017). By using AI-based competency learning to collect and analyze student data, educational institutions can better understand their students and identify the critical competencies they need to develop. In addition to competency-based instruction, learning analytics use the AI’s overall capacity for learning. AI can help identify students who are more likely to leave school early, providing institutions with early warning systems and actionable data. After that, learning analytics must expand beyond its current comfort zone to include a broader range of competencies or learning outcomes that are more difficult to measure and assess, such as interpersonal skills, arts, or literary works. Using learning analytics in specific learning contexts is difficult because they must be general enough to be used in various courses and institutions. Students, instructors, administrators, and institutions will benefit from the increased use of learning analytics, incorporating cutting-edge techniques. 3. Data Mining. More and more educational settings and students are being studied using data mining, an effective tool for improving learning and knowledge acquisition. Educational data mining aims to provide learners with systematic and automated responses. In order to develop inherent association rules and provide knowledge objects to students, AI-based educational data mining is being used. For example, a small number of written assignments can analyze student demographic and grading data (Deloitte, 2021). A machine learning regression method can be used to achieve this and forecast a student’s future

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academic performance. Data mining can be viewed as pattern discovery and predictive modeling used to extract hidden knowledge, allowing instructors to adjust curriculum development in the educational system. Personalized learning from knowledge field data can be achieved using data mining-based AI, where students learn at their own pace and choose their learning method with the help of AI. By personalizing their education, students can choose what they are most interested in learning about, and teachers can then modify their lesson plans accordingly (Kim et al., 2018). AI can (for example, using machine learning) be built with greater accuracy and with a more reliable outcome with data mining.

13.6

Best Practices for Incorporating AI in Education

There have been some attempts to introduce AI into educational institutions, but these are merely the tip of the iceberg. Educators must rethink how they teach students about a technology that will only grow in importance. The following are the three best ways to use AI in the classroom (Freeze, 2020). 1. Educate Educators. Teachers cannot pass on the knowledge they do not possess, and as we have already established, many people have had no formal education in AI. As a first step, teachers should be provided with the resources and knowledge to discuss AI. If you are feeling overwhelmed, do not be. Working knowledge of technology and understanding its impact on our daily lives is required for the subject matter, especially in the lower grades. Google’s Teachable Machine and TensorFlow Playground are just two examples of online resources that can help teachers better understand the fundamentals of machine learning. Using AI in the classroom can help teachers become more familiar with the technology, which is still a relatively new application. However, teachers will be better prepared to teach students about AI concepts and applications if they are already familiar with some fundamentals. 2. Start Early, Incorporate Often. While many curriculums lack the room for a dedicated AI class, there are still ways to incorporate AI learning into other curriculum areas. Students in economics and history classes can examine the economic impact of new technologies like artificial intelligence (AI). In contrast, current events classes can look at recent national news stories on hot topics like facial recognition, artificial intelligence (AI), and the smart home. AI does not have to take over to be an essential part of the curriculum. There are numerous opportunities to look at how AI intersects with other fields and to give students a more comprehensive understanding of the subject matter in general. 3. Encourage Students to Question Everything. We should not blindly accept all AI applications because it is good for business and society. Considerations like these should be weighed collectively, starting with institutions. As educators

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introduce new concepts and cutting-edge technologies, it is critical to consider these innovations’ advantages, disadvantages, and potential ramifications. It is essential to think about a wide range of issues regarding AI, such as where it fits in the workplace and the government and cities. For example, ask yourself, “Who will benefit from this technology?” and “Who will be harmed by this technology?” if you just tell people about the technology, you are not doing anything. Learning about AI may at first seem difficult or even impossible to the average person, including educators and students. It is time to stop treating AI as a side issue in our classrooms because it is not. Students should not need a master’s degree to understand the technology that surrounds us every day and how it affects our lives, from our jobs to our Amazon recommendations to the Google autofill that suggests the end of this sentence. Now that we re-evaluate what makes for a good education, we should start preparing students for a world driven by AI by teaching them the knowledge and critical thinking skills to succeed.

13.7

Applications of AI in Education

As a result of educational AI applications, various organizations are engaged in the field. AI applications are being tested in education by start-ups and major tech companies alike, including Google and Facebook, on the one hand, and the other. In the field of AI, academic research is not always foundational. AI applications in education can be used to gain real-world experience and knowledge. For the most part, AI in education can help students learn new skills and knowledge and assess their skills and knowledge. Below you will find an overview of how AI is used in education (Sahu, 2021; Schmelzer, 2019). Figure 13.2 shows a list of AI applications in education. Fig. 13.2 Applications of AI in education

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1. Intelligent Tutoring Systems. AI applications in education include so-called intelligent tutoring services (ITS). In general, ITS offers step-by-step tutorials tailored to the needs of each student on topics in well-defined, structured subjects like mathematics or physics. Students progress through a system that adjusts the difficulty and offers hints or guidance to help them learn the material. The system determines an optimal step-by-step pathway through the learning materials and activities based on expert knowledge and pedagogy and individual students’ misconceptions and successes. 2. Grading software. To create calculating systems, AI-powered grading software integrates Machine Learning after it collects crucial information about the metrics for grading assignments from papers that have been graded by teachers/professors. The tools are intended to understand better and replicate the human grading process used by teachers during the earlier stages of the course. They come in handy when there are many papers to grade so that teachers can spend their time on more important tasks rather than grading. Their virtual environments or cloud-based platforms can easily be integrated with them. Essays, papers, and tests can be graded in seconds using teachers’ input and AI technology, even if written in a different language. 3. Analytical dashboards. More and more educational institutions rely on digital learning resources, generating many data. It is possible to use this information to provide teachers with insight into their students’ progress. Dashboards, also known as analytical displays, frequently employ this data visualization method. Student progress and performance are displayed in dashboards like pie charts that show how many students master a particular skill or line graphs that show how well students perform over time. Even though descriptive statistics are not exceptionally ‘intelligent’ in and of themselves, predictive modeling is becoming increasingly popular. One example is linking data from a pupil tracking system with personality characteristics to create a predictive model of pupil performance both inside and outside the classroom. 4. Personalized learning. With the help of AI tools, students can be assisted in their learning by creating personalized study schedules and learning materials tailored to their individual needs. They look for knowledge gaps and develop instruction, testing, and feedback systems for students from pre-school through college. Students can learn at their own pace, time, and requirement for repeated practice with AI-powered software, games, and tools. Teacher customization of individual lesson plans based on student needs can be made easier with this machine-assisted classroom environment, which can go a long way toward building differentiated and adaptive learning for all types of learners. 5. Smart content. Personalization in education is the future global trend that can be achieved by identifying the areas where AI tools can play a role. From digital textbooks and guides to instructional videos and AI tools, smart content can create customized learning environments for educational organizations based on strategies and goals. Smart content can also be instructional snippets and videos. When it comes to AR/VR-based learning environments, schools can

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develop them in tandem with web-based lessons. It is possible to streamline content for various learning styles and learning curves using AI Monitoring and Evaluation tools. AI and ML-powered algorithms can identify areas in the curriculum that need improvement when many students give incorrect answers. This helps teachers by filling in the gaps of defective or unhelpful materials. Voice assistants. Voice assistants are a fun and convenient way to bring learning into the home. They can also help students plan their study schedules, listen to coaching instructions on the go, and get quick answers to basic questions in class. One of the advantages of using voice assistants in education is that it saves time for both students and teachers while also providing opportunities for community learning. Even if you do not have a smart speaker, you can still use these AI-powered voice assistants in smartphone apps. Classroom monitoring. Machine learning technologies in the classroom monitor students with cameras and provide instant feedback to the teacher. Students are monitored by an AI using cameras to track who is paying attention, and AI is used for both learning and testing purposes. Using this information, a teacher can directly address a student or find another way to get the student’s attention. Virtual learning environment. Students can interact with content on their mobile device or laptop using VR technologies. In a virtual learning environment, students can participate in educational activities in groups, access counseling services, and learn in an immersive way. Interactive virtual simulations allow students to assist each other in soft skill coaching, life skills, and self-development. Students with ADD/ADHD (Attention-deficit disorder/Attention deficit hyperactivity disorder) may benefit from virtual reality headsets because they block out distractions and lengthen attention spans. Teachable robots. The use of robots to develop new pedagogical methods is a novel application of AI. Students may be required to teach AI-powered robots new information or skills in the future. A robot that can be taught can be given ‘infinite’ explanations, whereas a person might pretend to understand explanations to keep their reputation intact. As a result of the protégé effect, students understand the lesson material by explaining it to others. According to the protégé effect, students learn faster when explaining what they have learned to a different classmate. Even outside of the classroom, students with younger siblings often have higher IQs than their non-sibling counterparts, demonstrating the protégé effect. Augmented reality, virtual reality & simulations. More and more people worldwide are working to combine AI with other digital innovations. AI can be combined with augmented reality (AR) via smart glasses, allowing students to see instructions right in front of their faces. Intelligent tutoring systems and augmented reality are among the many topics researched by researchers. Additionally, scientists are investigating how simulations and virtual reality can be used with AI to enhance learning. Students receive practical instructions from

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an AI in these simulation environments and immediately start working on a problem. 11. Universal access to education. Maintaining uniform educational standards across ethnicities and geographical regions is a constant challenge. Though AI can reduce educational divides significantly, it can also help build bridges between the worlds of education and the workforce. Intelligent data collection, customized schedules, custom tasks, and constant access to education are all made possible by AI tools. They can also produce subtitles, language translations, and different plug-and-play software distributed across regions to boost global and universal learning, breaking down the barriers between outdated or insufficient traditional educational approaches. As a result, any global institution with branches can implement quick admin, testing, and teaching tasks uniformly across centers using AI-powered tools and cloud integration. This allows for real-time data analysis. 12. Admin Tasks. Education administrators benefit from AI with administrative tasks by using intelligent assistants to help with various administrative needs, such as budgeting, student applications, enrollment, course management, educator HR-related issues, purchasing and procurement activities, expense management, and facility management. Many educational institutions can improve their efficiency, lower operating costs, gain greater visibility into income and expenses, and improve responsiveness by implementing intelligent AI-powered systems. College admissions officials use AI systems to make the admissions process fairer and more effective. AI systems that have been trained to remove most human bias are now being used to provide a credible and fair comparison of admissions criteria to humans. Due to the recent college admissions scandals, admissions processes are now subject to greater scrutiny and regulation, and machine learning systems to provide a more systematic method of handling admissions are showing promising results. In the not-too-distant future, all educational experiences will include AI and machine learning. Learning outcomes are expected to improve significantly as AI demonstrates its benefits and applications across a wide range of educational needs.

13.8

Pros and Cons of Using AI in Education

Education and technology experts have debated the pros and cons of AI as technology has become more commonplace and classrooms have gone digital. Nearly every industry has incorporated technology, and education is one of many that will significantly benefit from AI. For this reason, educators may soon find themselves in charge of a digital classroom where AI plays a central role. Computers are becoming increasingly intelligent, and AI is no longer science fiction but fact. In this section, let us take a closer look at the pros and cons of incorporating AI into education (LiveTiles, 2021; Knowledgereview, 2021). Figure 13.3 shows a summary of the pros and cons.

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Fig. 13.3 Pros and cons of using AI in education

Pros of AI • Reducing teachers’ workload: AI in education has a significant role in helping teachers deal with workload-related issues. Recently, teachers have frequently expressed their displeasure with the excessive workload. This increased workload is partly because teachers have been given additional administrative tasks on top of their already heavy workloads, and fewer new teachers are entering the workforce. By automating (administrative) tasks, AI can lighten the load on teachers. AI soon includes tasks like proofreading (such as highlighting an essay’s strong and weak points and then the instructor primarily evaluating these points) and course material composition (with the help of automatic classification of content). • Personalized learning: We can see that AI in personalized education is seen as a huge opportunity. Teachers cannot teach every student individually due to time and attention constraints. This is a limitation that does not apply to AI. As a result, an AI can better match educational objectives to student preferences. When teachers have more time to focus on “problem students,” students can

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progress at their own pace and level. The process of automating tasks will be gradual. Small tasks like selecting and practicing course material should be the starting point. Deep learning algorithms implemented in adaptive learning systems could help with these tasks. Supporting the teacher with data-driven insights (learning analytics): AI can assist teachers by combining and interpreting data. Using these learning analytics, the teacher can gain holistic and well-founded insights into their students. AI can correct any teacher’s prejudices (even unconscious). AI can reveal cognitive biases, making education more equitable for all students, regardless of race or gender. So, an AI that does not know about racial or gender identity cannot factor that into school recommendations. Improved assessment: When it comes to assessment, AI can help us move from periodic assessment to continuous assessment. A student’s knowledge level can be continuously monitored with AI without periodic tests. Standardized tests have received much flak for unreliable student knowledge indicators. Intelligent Tutoring Systems: However, AI has helped smooth out the rough edges of the ITS concept, which is not new. Teachers and tutors benefit from intelligent moderation because it allows them to analyze information more thoroughly. It will improve the grading systems, allowing teachers to be more productive in the classroom. Systems using AI can also spot patterns, alerting educators to them. To better serve their students, teachers can better understand their habits and develop a study schedule tailored to meet their specific needs in this way. Adaptive Group Formation: Artificial Intelligence (AI) can create groups tailored to a specific task or balance one learner’s weaknesses and strengths. Facilitation by Example It is possible to use collaboration models to assist students in identifying effective approaches to problem-solving. Intelligent Moderation: Human tutors, moderators, and teachers can now use AI techniques like machine learning to analyze large groups’ data using Intelligent Moderation. In the end, teachers will be more effective in the classroom. Virtual Reality Learning: A virtual reality-assisted learning approach borrows from aviation education, allowing students to learn in real-world settings while expanding their horizons outside of the classroom. When learners are immersed in realistic virtual environments, they gain a deeper understanding of the subject matter. Artificial intelligence (AI) will play an increasingly important role in various human endeavors, from space and ocean exploration to fraud detection, knowledge management, and even precision surgery. Improving Course Quality: Students who repeatedly answer questions incorrectly can be identified by AI. AI can help teachers be more effective by alerting them to these patterns. Dynamic Scheduling and Predictive Analysis: To better serve students, predictive computing can track their habits and recommend spending their time studying. There is no need for a customer service agent to get tired or bored because if the machine has a question beyond its programming, the human will be alerted to assist.

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• Custom Textbooks: Educators can import a syllabus, and AI will generate a textbook with all necessary content. So much can be customized between classes and grades and even within the school year. • Virtual Humans: Virtual people are already a reality, despite their science fiction appearance. When it comes to repetitive and time-consuming tasks, virtual humans like avatars, digital assistants, or Chatbots are the most cost-effective and time-efficient options. Consider the Boston Museum of Science’s “the twins” interactive interface as an example of a social dynamic for AI. • Intelligent Game-Based Learning Environments: Gamification can help increase retention while also making learning more enjoyable for students. • Machine Translation: Even though machine translation is not as accurate as human translation, it can still be more convenient and time-saving than the alternative. For many second-language learners, machine translation holds the promise of bridging the gap between the two languages. • Empowering the Disabled (Differently-Abled): Tech giants like Facebook and Google are already working on AI-enhanced learning programs for people with disabilities. Advances like these can give students with disabilities a greater sense of agency. • Synergy with other digital learning applications: Lastly, AI can improve the efficiency of current digital learning resources by replacing manually programmed rules with rules discovered by an AI. Students may be more likely to ignore less formal, non-technical knowledge they acquire at school (such as social skills) because it is easier to generalize what they learn in the virtual world. New technologies like VR, AR, and serious games can benefit from the synergistic use of AI. A virtual space can be created using these technologies, and it is entirely customizable, allowing for the best possible learning outcomes. Cons of AI • AI can jeopardize educational objectives if the emphasis is on technology: AI applications may no longer be aligned with educational goals if AI development shifts from focused data/technology to broad application domains. Other elements such as motivation may not be incorporated into an AI optimized for knowledge transfer, such as curiosity. International education players could also disrupt the Dutch education market. It is unlikely that these actors all have the same educational aspirations. As a result, education may slip from our grasp. One may wonder if education will not become inhumane if it is fully automated and students go through the educational process without the involvement of people. When simulations are overused as a teaching tool, the danger increases. Simulations cannot always be relied upon to deliver the results that are expected in reality. • Bias in humans translated to data and incorporated by AI: Data availability for training models poses a significant risk when using AI. Apart from being readily available, there is also a chance it will contain human biases. Data from the past may have ethnic and gender biases. There is a chance that AI will pick

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up on and even reinforce these prejudices. If this happens, it could set off a feedback loop that disproportionately affects people of color. The labor market for teachers: Teachers could find themselves needing new skills due to using AI responsibly. Currently, teacher training does not include data-driven education, while AI assumes users have at least rudimentary digital skills. There is a possibility that teachers in the future will be data scientists with less formal education if this trend continues. However, the likelihood of ‘the teacher’ being replaced by technology is extremely low. An AGI-based AI that can perform all of a teacher’s duties would require an AGI (Artificial General Intelligence) that we currently believe is unattainable shortly. Dependency on black-box models versus the responsibility of teachers: We run the risk of becoming overly reliant on AI systems in education. Teachers and AI both need to adapt if this danger is to be avoided. To avoid this problem, machine learning models’ explainability and teachers’ knowledge of AI must be improved. A teacher cannot be held accountable for systems that are not understandable to students. Applying AI without the necessary prerequisites: Several conditions must be fulfilled before successfully implementing AI in education. Before applying AI, for example, you will need to put together a technical infrastructure and gather data. It is possible that AI will not live up to expectations if implemented too quickly. So, AI can be a disappointment, just like other cutting-edge learning tools. This may eventually cause a reluctance to use AI in education, which could impede the development of other (digital) innovations in the field. Additionally, several legal roadblocks (many valid) prevent AI from being widely implemented overnight. A power shift: In some quarters, the introduction of AI has sparked fears of a single entity gaining disproportionate control. A tech giant or an existing educational resource publisher can be this entity. On the other hand, recent events suggest that this threat can be contained. According to a recent court decision, schools own the data generated by digital educational resources, not the creators of those resources. Policymakers, on the other hand, are still concerned about it. Cost: Installing, maintaining, and repairing AI systems adds up to a significant financial commitment. AI will only be beneficial to the wealthiest educational institutions. Addiction: We risk becoming addicted to technology if we rely on it to make our daily lives easier. Lack of Personal Connections: Smart machines can enhance the educational process, but they should not replace human contact. If we put too much trust in these machines to grade or tutor, we risk making more harmful mistakes than helpful to students. Unemployment: Making education more efficient may lead to a decrease in the need for teachers. A decrease in teaching assistants and assistantships may result from the implementation of AI, as class size is no longer as important in determining quality education as it once was.

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• Efficient Decision Making: Every day, computers become more intelligent. They are showing off their learning prowess and teaching other computers. A vital issue with implementing intuition-based decision-making in the classroom is that new situations frequently arise. How much will data be lost if an AI malfunctions and needs to be repaired? • Misuse of information: Nowadays, the only option for storing information is to digitize it. However, the students’ data could be misused just like any other piece of technology. Schools always run the risk of personal information being misused if it falls into the wrong hands. • Vulnerable to Cyber Attacks: Cyber-attacks on AI systems are extremely likely. As a result, hackers are constantly coming up with new ways to attack it. Assume that an entire database with the personal information of students, teachers, parents, and administrators has been accessed by hackers. With their personal information out there, cyber-attack victims could suffer much harm. The only thing a college or university can do is install data security protection software to keep student information safe. Even so, hackers have been known to breach school systems. When it comes to AI benefits and drawbacks, there is always the possibility that the benefits outweigh the drawbacks. However, to fully benefit from AI, a balance must be struck between machines that optimize tasks and people who use the machines. Classroom AI should not be used to replace teachers but rather to supplement them with AI. It ought to make their work a lot simpler for them.

13.9

Companies Using AI in Education to Enhance the Classroom

Learning has always been and will continue to be an essential part of life for everyone. No matter how old we get, we are always learning new things. As our collective intelligence improves, we create machines with human-like learning and strategic decision-making abilities. Deep learning systems are being used by companies like the ones listed below to reimagine how people are educated. Figure 13.4 shows the list of companies using AI in education (Schroer, 2021) to enhance the classroom. • Nuance: Many students and professors use Nuance’s speech recognition tools. Students who have difficulty writing or use a wheelchair will appreciate the speed at which the technology can transcribe up to 160 words per minute. Additionally, the software improves spelling and word recognition. Instructors can automate time-consuming tasks like creating documents and emails by dictating lectures into the software and then using it when they need them.

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Fig. 13.4 Applications of AI in Education

• Knewton: In higher education, Knewton develops adaptive learning technology. Its program, Alta, assists students in identifying knowledge gaps, provides relevant coursework, and gets them back on track for college-level courses. Currently, Alta is used in math, chemistry, statistics, and economics, with instructors using it at various educational levels. • Cognii: Cognii is a company that develops AI-based learning solutions for K-12 and higher education institutions and corporations. Its virtual learning assistant uses conversational technology to help students improve their critical-thinking skills. In addition, the assistant provides one-on-one tutoring and is tailored to meet the specific needs of each student. • Querium: Querium employs AI to provide high school and college students with individualized STEM (science, technology, engineering, and mathematics) tutoring. Querium’s AI provides teachers with insights into their students’ learning habits and identifies areas for improvement based on answers and the length of time it took to complete tutoring sessions in STEM. • Century Tech: Cognitive neuroscience and data analytics are used by Century Tech’s platform to create personalized learning plans and reduce instructor workloads. Student progress is tracked using AI, identified knowledge gaps, and provided study recommendations and feedback. Teaching is easier with Century’s resources and time-saving features for teachers and students. • KidSense: Children are the target audience for KidSense’s AI educational tools. Speech-to-text software developed by the company uses AI to convert a child’s speech into text for note-taking, vocabulary practice, and even test preparation. Kids’ speech is more challenging to translate, so the KidSense tool’s AI uses specialized algorithms to do so accurately and privately. • Carnegie Learning: To help students better understand math concepts, Carnegie Learning uses AI and machine learning. Students in high school and college can use the company’s math learning platforms, which employ responsive AI that

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learns a student’s habits and tailors the learning experience to help them fully grasp mathematical concepts. Kidaptive: An adaptive learning platform, such as the one developed by the company Kidaptive, uses AI algorithms to assist educational institutions in gathering data and increasing student engagement. Through AI, the ALP (Adaptive Learning Platform) can better introduce and challenge students based on their strengths and weaknesses. Using underlying patterns and relationships, the platform can forecast future academic performance. Blippar: In the classroom, Blippar’s products use computer vision and augmented reality to help students learn more quickly and effectively. Instead of reading about a volcanic eruption, students can view a virtual 3-D model of it instead. Geographical, biological, and physical concepts come to life through interactive materials. Volley: It is not necessary to implement educational technology in the classroom. Volley’s AI-based “Knowledge Engine” continuously synthesizes course and quiz results and briefings to find knowledge gaps in enterprises among employees. With Volley’s AI, companies worldwide can quickly and efficiently fill in knowledge gaps that could be harmful (such as a lack of general company knowledge, compliance methods, or even technical skills). Quizlet: Quizlet is a web-based resource for study and education. Quizlet Learn, a new study resource from the company, offers adaptive study plans and removes the guesswork from what to study. Millions of study sessions and machine learning are combined to provide students with the most relevant study material.

13.10 AI-Driven Solutions in Education/AI Apps and Tools for Education Learning and student development and educators’ performance are all benefited by AI. To help students of all ages—from elementary school through college—learn more effectively, here are some examples of AI-driven solutions that harness the power of AI. Figure 13.5 shows the list of AI Apps and Tools for Education. • ARTIE: A child’s emotional state impacts his or her ability to concentrate, engage, and remain motivated while learning. To increase the interest and motivation of the students towards a specific learning goal, you can use encouraging words, gestures, or other methods. As a result, a team led by Dr. Imbernon Cuadrado at the Madrid Department of Artificial Intelligence developed ARTIE, a robot (Affective Robot Tutor Integrated Environment). ARTIE detects a student’s emotional state via keyboard and mouse action; it uses an algorithm to determine the most appropriate intervention to provide individualized educational support (Getsmarter, 2019). The ARTIE design team focused on three cognitive states: concentrating, distracted and inactive. It was discovered that

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Fig. 13.5 AI apps and tools for education

these robot tutors positively impacted students’ ability to learn by providing social support behavior. • Nao: Nao, a humanoid robot, can teach children as young as seven how to read and write, as well as how to program a computer. Nao makes it fun for kids by offering a coding lab they can enjoy when it comes to learning STEM subjects. Basic coding instruction allows students to program the robot to do various things, such as making hand gestures, moving emotionally, and even dancing. Students will learn how to tell a robot (or program) what to do, which will better prepare them for using and training an AI in the future (Getsmarter, 2019). • Jill Watson: To keep students from dropping out of school, the Georgia Institute of Technology introduced Jill Watson, a virtual teaching assistant. It is an AI program that can answer many questions, such as formatting a paper. This virtual assistant has been trained using a comprehensive database of questions and answers from students and educators about the course and emails sent to new students. Watson connects a request to a relevant question-response pair in the teaching database and returns a corresponding answer (Gupta, 2020). A human expert evaluated Watson’s answers before they went live to determine whether or not they were accurate. Once Watson recognized the scholars’ introductions and recurring questions, he stopped responding manually. • Duolingo: Using artificial intelligence creates a language-learning tool that administers a placement test. As you answer the questions, you will see how they change. This is because the test is adaptive. The question will be more straightforward if you do not answer correctly and more difficult otherwise. You may also be given a more difficult test based on the difficulty of the phrases and grammar used. Students often make the same mistakes repeatedly when they practice using new words. Duolingo also uses AI to enhance and tailor the learning experience for each user. The program can tell how often you forget a particular term using this data. This feature takes phrase complexity into account as well. All of this information aids in figuring out when is the

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best time to work on your language skills. Eventually, AI-driven chatbots were incorporated into this app and could converse with users. They also provided appropriate responses based on each correct answer the language learner gave by the chatbots themselves. Notably, these features included assistance functionality to respond when it is difficult to figure out the right words or grammar (Gupta, 2020). Brainly: Brainly is a question-and-answer platform for the classroom. Using Machine Learning algorithms, Brainly allows students to ask homework questions and receive verified answers from their peers without worrying about the questions being marked as spam. It even aids students in teaming up to find solutions on their own. Having a student in charge of the classroom can help him or her become a Brainly community moderator by answering questions. Brainly’s experts cover a wide range of school subjects and strive to create a community that feels like a classroom for each user (Gupta, 2020). Thinkster Math: Thinkster Math is a tutoring app that combines a traditional math curriculum with a student’s unique learning style. Each student is assigned a personal tutor who watches their thought process unfold on an iPad in realtime. To begin, Thinkster Math gives the user a variety of problems to choose from based on their current level of expertise. It analyses students’ written work to describe their problem-solving process to see where they went wrong or misunderstood a critical step. By providing video assistance and immediate, personalized feedback for students who get stuck, Thinkster Math improves the logical process of each student (Gupta, 2020). MATHiaU: Carnegie Learning’s MATHiaU offers AI-based tutoring tools for college students who feel lost in lecturer-sized classrooms, similar to Thinkster Math. Each student’s learning process is guided by the app, which keeps students up to date on their daily progress and assists teachers in customizing lessons to meet the needs of individual students (Getsmarter, 2019). Mika: Carnegie Learning’s Mika offers AI-based tutoring tools for students too busy for after-school tutors and too lost in a sea of other students for personalized attention, similar to Thinkster Math. Also, if you believe that individualized attention should be restricted to students struggling with long division in elementary school, think again. Mika specializes in higher education tutoring to fill the voids left by lecturer-sized class sizes in colleges. Since each student has their learning style and approach to problem-solving and learning, the app tailors lessons to meet that individual’s needs (Lynch, 2017a). Netex Learning: A variety of digital platforms and devices are supported by Netex Learning, which allows teachers to create curriculum. For even the least tech-savvy teachers, this website allows them to add multimedia interactives like videos or audio to their lesson plans, all within a personalized cloud platform for learning. Teaching with Netex allows for creating personalized student materials that can be shared across digital platforms and includes tools such as digital discussions and personalized assignments along with learning analytics that show how each student is progressing (Lynch, 2017a).

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• Third Space Learning: Numerous students benefit from private math lessons on this online learning platform every week. It excels at increasing student involvement and figuring out the best teaching methods. This solution uses AI to analyze recorded sessions to find patterns in the teacher’s and learner’s behavior. AI can tell if a student loses interest in the subject matter based on their behavior. Additionally, this tool aims to provide educators with real-time AI-driven feedback throughout each session in the future. As a result, educators who speak too quickly will be told to slow down and vice versa (Gupta, 2020). • iTalk2Learn: This open-source math tutoring app features a revolutionary adaptive sequencer that recommends lessons based on the student’s ability and voice recognition that learns from the student’s behavior (Lynch, 2017b). • Front Row: The app generates math, English language arts, social studies, and science lessons for each student. Students can work at their own pace, and the app keeps track of their progress and generates reports on it (Lynch, 2017b). • SmartEd: Apps like this one let teachers and students collaborate in real-time on textbook content and other educational materials, regardless of the students’ learning styles or needs. Gamification features make it simple to present your content entertainingly and engagingly (Lynch, 2017b). • Altitude Learning: Learner-centered education is powered by Altitude Learning (previously known as AltSchool), a software platform for professional learning. Students can take control of their education with this cutting-edge platform. To focus on each learner’s specific needs, educators can use this tool to plan, engage and assess students at every stage of the learning process. Each student has a customized learning path that they and their teacher design together (RoboticsBiz, 2020). Educators can distribute assignments across small groups or entire classrooms by using the platform, putting students in control of their education. • Gradescope: Gradescope is a grading platform that automates the timeconsuming tasks of providing feedback and assessing student work. It utilizes the latest machine learning AI to drastically cut down on time and effort typically associated with grading assignments by hand. The review and grading of assignments can be outsourced, which frees up time for the instructors to spend teaching. A single platform allows instructors to grade paper-based exams and online homework and programming projects. It is faster and more unbiased to grade with Gradescope, and it provides valuable statistics to help identify trends in the classroom and student needs (RoboticsBiz, 2020). • Hugh Library Assistant: Hugh is a voice-activated, artificially intelligent robot that assists library patrons in finding any book in the library in a matter of seconds. To get to any book in the library, Hugh just moves through it (RoboticsBiz, 2020). • Ivy.ai: Ivy is a self-service chatbot for colleges and universities that AI powers. What is the deal with that? You can ask questions about application forms, program specifics, tuition costs, and deadlines. The chatbot can also collect data to help with recruitment efforts. As a result, it is an excellent resource for students with questions about financial aid and other campus-specific procedures such

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as scholarships, work-study, grants, and loans. Using the IT services chatbot, customers will access their email, retrieve forgotten passwords and connect to WiFi. They will also be able to install software and set up printers. Creating chatbots to answer the most frequently asked and relevant questions on campus is possible for any department (RoboticsBiz, 2020). • Knowji: Knowji is a research-based audiovisual vocabulary app popular with language learners. Utilizing a combination of scientifically validated learning methods and engaging content aids users in learning faster and remembering more. For each word in the dictionary, Knowji keeps track of how far the user has learned it and brings up words that the user has trouble with more often than others. Another feature of this app is that it anticipates when a user is about to forget a word and prompts him to drill it again before he does so. In this way, long-term memory is ensured (RoboticsBiz, 2020). • Quikik: The Pearson learning company developed this app specifically for students between 10 and 15. Science and math are the primary subjects covered, and students receive individualized instruction to help them improve in areas where they are weak. The app helps the learner’s skills by using clear illustrations and various problem-solving techniques (Moore, 2020). • ELSA: Non-native students may face difficulties if their institutions require them to communicate in English orally and in writing. On the other hand, students can improve their fluency and performance by using AI apps like ELSA (English Language Speech Assistant) to practice and learn new words (Moore, 2020). Some examples of AI-driven educational solutions include those listed above. However, even when used independently, they are most effective under the guidance of human instructors.

13.11 Is AI Replacing Human Teachers, or Does It Assist Teachers? Sustainable Development 2030 Agenda was adopted in 2015 and included equal access to quality education for all people worldwide (https://www.un.org/sus tainabledevelopment/development-agenda/). However, a teacher’s job changes in response to rapid technological advancement. Many people are worried that AIpowered robots will eventually replace all of the school’s human teachers. If so, what would be the basis for that claim? Teacher’s role in an AI golden age. Machines taking over from humans in a large percentage of tasks may be alarming without getting to the heart of the matter. Amazon CEO Jeff Bezos recently stated that AI is experiencing a renaissance (Spirina, 2018). As a result of AI, the demand for workers doing routine tasks has decreased, leaving more time for people to focus on more important things like healthcare, logistics, and security. Moreover, it is all for the better

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now. Artificial intelligence (AI) is not a dangerous Pandora’s Box to open without warning. On the contrary, it is expected to create more jobs rather than eliminate them (https://www.itpro.co.uk/automation/30463/gartner-by-2020-ai-will-cre ate-more-jobs-than-it-eliminates). This means that the most sought-after specialists will be educated to communicate ideas between people and machines. Teaching is one such profession that necessitates using a human mind rather than a machine. When a teacher needs an AI assistant? The amount of work thrown at a teacher often outpaces what is considered reasonable. To be a good teacher, you must deal with a classroom of 30 students while also motivating and inspiring them to learn. You must also stop them from giggling and fiddling with technology while in the classroom. These difficulties are enormous, even without factoring in teacher compensation. The teaching profession is dwindling in popularity due to its long hours and low pay, resulting in a global shortage of teachers. On the other hand, the tech revolution is poised to alter the way we currently teach and learn fundamentally. AI is on a mission to assist in the learning process by planning, personalizing, visualizing, and facilitating it. Explore the following AI-powered innovations to help teachers make students and learning processes smarter (Spirina, 2018). • Pushing classroom limitations: Traditional classrooms, blackboards, and manuals have contributed to a regressive view of the learning process. Technology allows educators and students to go far beyond what was previously thought possible. Hopefully, one day soon, the only thing holding back progress in the educational field will be the desire of one person or another for personal growth. It is possible to create global classrooms with AI-powered tools. Helping visually impaired students and/or speak a different language provides new learning opportunities. It also enables teachers to take different learning styles into account (easing the burden on visual and auditory learners, for example) and personalize learning to help students achieve better academic results. Realtime subtitles are generated using the free PowerPoint Presentation Translator plug-in for presentations. Students can follow along with their teachers in the most convenient way possible thanks to Azure Cognitive Services, AI-powered speech recognition, and similar technologies. • Robots for no learning in isolation: Long-term illnesses prevent many students from attending school worldwide (COVID 19 Pandemic). However, AI is here to provide smart opportunities for distance learning. Real-time presence can be ensured even if it is physically impossible for a student to be present in a classroom, and robots can help alleviate the isolation of a student from their peers and teachers. Microsoft Azure IoT Hub-connected robots give students access to all their video and audio connections, allowing them to participate in class discussions. The market for educational robots is expanding because of the growing demand for low-cost educational hardware and software. However, there is a limited market for this technology due to teachers’ reluctance to use robots in the classroom.

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• Teacher’s assistant: Routine activities are increasingly automated as part of the digital transformation. Moreover, it is no different in the educational sector. Do teachers grade homework and prepare reports for their students? It is absurdly large. What if they had to re-arrange their schedules to make the most of a better opportunity to help their students succeed? It is true if only they had the opportunity to delegate clerical duties to someone else. Software like Gradescope, which utilizes Microsoft Azure and Machine Learning, can help grade student work and submissions while generating and sharing detailed analytics. Teachers can use an AI assistant to assist them in creating lesson plans that cater to the individual needs of their students. For example, CENTURY Tech tracks the progress of each student. When AI analyses student data, it considers the small details that a human teacher might overlook or fail to put together. In the future, AI will predict whether students will succeed or fail based on historical data and identify knowledge gaps, highlight strengths, and suggest areas in which extra tutoring might be beneficial. AI’s real power is its omniscience. AI will be a daily reality for today’s students and graduates in the future. The era of textbooks on paper and chalkboards is over. Students should be exposed to cuttingedge technologies while still in school. The modern learning process necessitates greater interactivity and personalization to make knowledge more widely available, regardless of a person’s location, language, health status, or learning abilities. When needed, personal tutoring and help can quickly become a best friend for students and teachers, thanks to AI. However, unlike a human teacher, AI cannot serve as a source of inspiration for students to motivate learning. Empathy is more important than most people realize in the learning process. Moreover, this is the main drawback of educational AI solutions, so AI is unlikely to replace human teachers completely.

13.12 Usage of AI in Education—Present and Future Even as educators, psychologists, and parents argue over how much screen time is appropriate for children, another emerging technology in the form of AI and machine learning is already beginning to change educational institutions, tools, and what the future of education may look like. According to HolonIQ’s Annual Report (https://www.holoniq.com/notes/2019-artificial-intelligence-globaleducation-report/) on the State of AI in Global Education, AI adoption in education will explode in the coming years, with a global expenditure of $6 billion expected by 2025. According to a report on the US education sector’s artificial intelligence market in 2018–2022, AI is expected to grow by 47.77% in US education (https://www.researchandmarkets.com/reports/4613290/artificial-int elligence-market-in-the-us). Even though most experts believe teachers are indispensable, many changes will be made to a teacher’s job and educational best practices (Marr, 2019).

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• Teacher and AI collaboration: AI has already been used in education, primarily tools that aid in skill development and testing. Teachers and schools can do more now than ever before, thanks to the advancement of AI educational solutions. Because AI can help with efficiency, personalization, and streamlining administrative tasks, teachers will have more time and freedom to teach students understanding and adaptability. The goal of using AI in education is to help students achieve their full potential by combining the strengths of machines and human teachers. Today’s students will be required to work in the AI world in the future, so educational institutions must expose students to and use the technology to prepare them. • Differentiated and individualized learning: Educators have prioritized tailoring learning for years to meet the specific needs of each student, but AI will enable a level of differentiation that is currently impossible for teachers to achieve with 30 students in a class. Students who have difficulty grasping a concept could have lessons tailored to their needs as AI develops. As a result of companies like Content Technologies and Carnegie Learning, which develop AI-based intelligent instruction design and digital platforms for students from pre-K to college level, students get the challenges they are ready for, identify knowledge gaps, and get feedback on their progress. Customizing the curriculum to meet the individual needs of every student is not currently possible, but it will be for machines powered by AI in the future. • Universal access for all students: By using AI tools, it will be possible to provide global classrooms to all students, regardless of their native language or other special needs. With the free PowerPoint plug-in Presentation Translator, subtitles for what the teacher says are generated in real-time as the teacher speaks. Students who cannot attend school because of illness or need to learn at a different level or on a specific subject because it is not offered at their school will now have more options. AI can aid in the dismantling of hierarchies within and between educational institutions. • Automate admin tasks: Grading homework and exams takes an educator’s significant amount of time. With AI, these tasks can be completed quickly while also providing recommendations on closing learning gaps. When it comes to writing assessments, machines are not yet capable of grading multiple-choice tests, but they are getting closer all the time. As AI takes over admin tasks, teachers have more time to spend with each student. AI has much potential for streamlining higher education enrollment and admissions processes. • Tutoring and support outside the classroom: Talk to parents who have struggled to help their teenagers with algebra, and you will hear them rave about the promise of AI in helping their kids when they are having trouble with homework or studying for exams. AI allows tutoring and study programs to advance, which will lead to more programs being available and able to accommodate a broader range of learning styles in the future.

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Education-related applications of AI continue to grow, including AI mentors for students, smarter content development, and a new way for educators to grow personally through participation in virtual global conferences. The adoption of AI and machine learning in education may be a bit slower than in other sectors, but the shifts are already underway and will continue. The Future of Education is AI-Assisted, not AI-Led. Rather than AI-led classrooms, most experts believe that AI-assisted teachers hold the most promise (Goddard, 2020). One student said, “I wish we had more interaction with our human teachers,” in response to a question about how Squirrel AI can improve (Kulkarni, 2019). A teacher, for example, would be unable to design personalized learning plans for more than 20 students at once. However, AI-based learning systems can accomplish this in a matter of seconds. Carnegie Learning, ALO7, and Sana Labs are just a few companies demonstrating how AI can help meet the unique needs of each student, free up teacher time by automating administrative tasks, and more (Kulkarni, 2019; Goddard, 2020). While technology has its place in the classroom, humans still play an essential role. Innovative teaching methods that adapt to each student’s needs can help students who fall behind and have trouble keeping up. AI can help normalize differences between school districts and even traditional grade levels in the long run (Kulkarni, 2019; Goddard, 2020). In a piece for the Australian Association for Research in Education, Neil Selwyn highlights six areas in which humans are still superior to computers when it comes to teaching (Kulkarni, 2019): • • • • • •

Share their knowledge and expertise with the class. Establish cognitive connections. Make friends with people in your community. Speak up loudly. Perform with their bodies. Be flexible and adaptable.

Humans and computers will never compete to see who can teach better with the help of AI in education. AI is not replacing humans as the primary means of education but rather augmenting them to teach and learn more effectively than ever before.

13.13 Case Studies: Examples of Successful AI in Higher Education that Can Serve as Inspiration for Our Future There has been much buzz in higher education over the last few years about the potential of AI on campus, thanks to stories about data-fed virtual teaching assistants and smart enrollment counselor chatbots. Academic institutions expect AI

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to relieve them of time-consuming administrative and academic tasks, increase the efficiency of IT processes, increase enrollment in a time of declining enrollment, and provide students with a better learning experience. A few colleges and universities have begun making these changes. A chatbot named Reggie was launched by Ocean County College in New Jersey in November 2017 in partnership with AdmitHub to assist prospective students with enrollment-related questions. First, Reggie had 1,200 questions in its database, and in its first year, it answered 14,000 questions immediately and around the clock. In the first year, it more than doubled its knowledge base, and by the second year, engagement had risen from 10 to 26% before Reggie took over (Lynch, 2020). When Georgia State University introduced a chatbot named Pounce for smart text messaging students over the summer holidays to entice them back to school in the fall, they saw similarly positive results. Dropout rates fell by 22% due to providing one-on-one attention to first-generation college students from lowincome backgrounds, which was previously impossible to do with human beings (Lynch, 2020). Examity, an AI invigilator used by Penn State and University of California, Davis, is used for online testing. Student identities and content integrity are verified using biometric keystroke analysis, predictive analytics, and a video review (Lynch, 2020). Classcraft and the University of Montreal’s artificial intelligence research team are working together to find new ways to gauge student participation. Classcraft’s EMS (Engagement Management System) reframes students’ school progress as a game they play together throughout the year (Lynch, 2020). In order to help students learn Mandarin, IBM Research and Rensselaer Polytechnic Institute have teamed up on a new approach. A chat agent powered by AI is used in conjunction with an immersive classroom environment to give students the impression that they are in a restaurant in China, the garden, or in a Tai Chi class, where they can practice speaking Mandarin with the AI chat agent. Cognitive Immersive Room was developed by IBM and Rensselaer Polytechnic Institute at the Cognitive and Immersive Systems Lab, a joint research effort (Neelakantan, 2020). Many students in a master’s level AI class at the Georgia Institute of Technology were unaware that Jill Watson, one of their teaching assistants, was not human. An online message board used by the class’s 300 students received about 10,000 messages each semester, making it nearly impossible for a regular assistant to keep up with. After finishing the course, students learned that one of their favorite teaching assistants, Jill Watson, was a robot answering their questions most of the time! The team had fed an AI program that could answer FAQs with 97% accuracy with all the chats generated over a year (Neelakantan, 2020).

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13.14 Conclusion Artificial intelligence (AI) has enormous potential in education. The use of AI is improving the quality of education. A platform for delivering knowledge is being built using AI in education. This is fueling the demand for AI in the education sector. Education institutions, in particular, have made extensive use of AI adoption and implementation. Computers and computer-related systems were the first forms of AI in education; later, web-based and online education platforms became available. It is now possible to use cobots or humanoid robots as teacher colleagues or independent instructors and chatbots to carry out teacher or instructor-like functions, thanks to embedded systems and AI. These platforms and tools have been proven to increase or enhance teacher effectiveness. Student learning experiences have been enhanced due to AI customizing and personalizing learning materials to meet individual students’ specific needs and capabilities. As a whole, AI has had a significant impact on education, particularly in administration, instruction, and learning. Education has never been easier than it is now, thanks to advances in AI.

References Chace, C. (2020). The impact of artificial intelligence on education. Forbes. https://www.forbes. com/sites/calumchace/2020/10/29/the-impact-of-artificial-intelligence-on-education/?sh=50b 0c59450df. Chaudhary, S. (2018). Need of AI in education. Medium.Com. https://medium.com/@thesuperc oder/need-of-ai-in-education-8648b3d9aaa2. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510. Deloitte. (2021). Global development of AI-based education. Deloitte.Com. https://www2.del oitte.com/cn/en/pages/technology-media-and-telecommunications/articles/development-of-aibased-education-in-china.html. Freeze, J. (2020). How to incorporate AI in education. Forbes. https://www.forbes.com/sites/for bescommunicationscouncil/2020/11/03/a-new-class-is-in-session-how-to-incorporate-ai-ineducation/?sh=23715b4b69bc. Getsmarter. (2019). The role of artificial intelligence in the future of education. Getsmarter.Com. https://www.getsmarter.com/blog/market-trends/the-role-of-artificial-intelligence-in-the-fut ure-of-education/. Goddard, W. (2020). AI in education. Itchronicles.Com. https://itchronicles.com/artificial-intellige nce/ai-in-education/. Gupta, J. (2020). 7 real-life examples of AI in education. Wire19. https://wire19.com/real-life-exa mples-of-ai-in-education/. Kahraman, H. T., Sagiroglu, S., & Colak, I. (2010). Development of adaptive and intelligent webbased educational systems. In 2010 4th International Conference on Application of Information and Communication Technologies (pp. 1–5). Kim, Y., Soyata, T., & Behnagh, R. F. (2018). Towards emotionally aware AI smart classroom: Current issues and directions for engineering and education. IEEE Access, 6, 5308–5331. https:// doi.org/10.1109/ACCESS.2018.2791861. Knowledgereview. (2021). Using artificial intelligence in education: Pros and Cons. Theknowledgereview.Com. https://theknowledgereview.com/using-artificial-intelligence-in-educat ion-pros-and-cons/.

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Kulkarni, A. (2019). AI in education: Where is it now and what is the future? Lexalytics.Com. https://www.lexalytics.com/lexablog/ai-in-education-present-future-ethics. LiveTiles. (2021). 15 Pros and 6 cons of artificial intelligence in the classroom. Livetilesglobal.Com. https://livetilesglobal.com/pros-cons-artificial-intelligence-classroom/. Lynch, M. (2017a). 5 examples of artificial intelligence in the classroom. Thetechedvocate.Org. https://www.thetechedvocate.org/5-examples-artificial-intelligence-classroom/. Lynch, M. (2017b). 8 must-have artificial intelligence apps and tools. Thetechedvocate.Org. https:// www.thetechedvocate.org/8-must-artificial-intelligence-apps-tools/. Lynch, M. (2020). Successful AI examples in higher education that can inspire our future. Thetechedvocate.Org. https://www.thetechedvocate.org/successful-ai-examples-in-higher-edu cation-that-can-inspire-our-future/. Marr, B. (2019). How is AI used in education—Real world examples of today and a peek into the future. Forbes. https://www.forbes.com/sites/bernardmarr/2018/07/25/how-is-ai-usedin-education-real-world-examples-of-today-and-a-peek-into-the-future/?sh=32b68ac3586e. Moore, T. (2020). AI-powered apps for students. Newgenapps.Com. https://www.newgenapps.com/ blogs/ai-powered-apps-for-students/. Neelakantan, S. (2020). Successful AI examples in higher education that can inspire our future. Edtechmagazine.Com. https://edtechmagazine.com/higher/article/2020/01/successfulai-examples-higher-education-can-inspire-our-future. RoboticsBiz. (2020). Top 6 AI tools for education—Learning made simple and fun! Roboticsbiz.Com. https://roboticsbiz.com/top-6-ai-tools-for-education-learning-made-simple-and-fun/. Rus, V., Mello, S. D., Hu, X., & Graesser, A. C. (2013). Recent advances in conversational intelligent tutoring systems. AI Magazine, 42–54. Sahu, A. (2021). 8 applications of artificial intelligence in education. Westagilelabs.Com. https:// www.westagilelabs.com/blog/8-applications-of-artificial-intelligence-in-education/. Schmelzer, R. (2019). AI applications in education. Forbes. https://www.forbes.com/sites/cognit iveworld/2019/07/12/ai-applications-in-education/?sh=4c5505f562a3. Schroer, A. (2021). 11 companies using AI in education to enhance the classroom. Builtin.Com. https://builtin.com/artificial-intelligence/ai-in-education. Spirina, K. (2018). Is AI here to replace human teachers or is it a teacher’s assistant? Towardsdatascience.Com. https://towardsdatascience.com/is-ai-here-to-replace-humanteachers-or-is-it-a-teachers-assistant-2db6bd624a45. Tsai, Y., & Gasevic, D. (2017). Learning analytics in higher education—Challenges and policies: A review of eight learning analytics policies. In The Seventh International Learning Analytics & Knowledge Conference (pp. 1–10). https://doi.org/10.1145/3027385.3027400. USM. (2021). Unlock AI mobility solutions for education industry. Usmsystems.Com. https://usm systems.com/unlock-ai-mobility-solutions-for-education-industry/.