Motivation and Emotion in Learning and Teaching Across Educational Contexts 9781032301099, 9781000998276, 9781032301105, 9781003303473

Table of contents :
Cover
Half Title
Series Page
Title Page
Copyright Page
CONTENTS
List of Contributors
Preface
SECTION I: Theoretical Reflections and Perspectives
1. The Relevance of Situated Expectancy-Value Theory to Understanding Motivation and Emotion in Different Contexts
2. Exploring Interest Theory and Its Reciprocal Relation to Achievement Goals, Self-Efficacy, and Self-Regulation
3. Achievement Goals: The Past, Present, and Possible Future of Achievement Goal Research in the Context of Learning and Teaching
4. Explaining the Context-Specificity of Student Motivation: A Self-Determination Theory Approach
5. The Roots and Fruits of Self-Efficacy in Diverse Academic Contexts
6. How Universal Are Academic Emotions? A Control-Value Theory Perspective
7. Motivation and Emotion Regulation in Collaborative Learning Contexts
8. Teacher and Student Well-Being: Theoretical Reflections and Perspectives
9. Teachers’ Motivation to Teach: A Review Through the Lens of Motivational Theories
10. On the Context- and Situation-Specificity of Motivation and Emotion: Which Contexts and Situations Matter?
SECTION II: Methodological Reflections and Perspectives
11. Mixed Methods in Research on Motivation and Emotion
12. The Experience Sampling Method in the Research on Achievement-Related Emotions and Motivation
13. Modelling Development and Change of Motivational Beliefs
14. Intervening on Students’ Motivation to Learn: Promises and Pitfalls of Intervention Studies
15. Affective Processes in Collaborative Learning Contexts: Examining Affordances and Challenges of Video and Multi-Channel Data
16. Where Ethnic and Cultural Identity Meet Situational Demands: Implications for Methodologies Used to Study Motivation
17. Using Heart Rate to Tap into Motivational and Emotional Processes During Teaching and Learning
18. An Epistemological Shift Forward: The Methodological Zone of Proximal Research on Motivation and Emotion in Learning and Teaching
Index

Citation preview

MOTIVATION AND EMOTION IN LEARNING AND TEACHING ACROSS EDUCATIONAL CONTEXTS

Motivation and Emotion in Learning and Teaching across Educational Contexts brings together current theoretical and methodological perspectives as well as examples of empirical implementations from leading international researchers focusing on the context specificity and situatedness of their core theories in motivation and emotion. The book is compiled of two main sections. Section I covers theoretical reflections and perspectives on the main theories on emotion and motivation in learning and teaching and their transferability across different educational contexts illustrated with empirical examples. Section II addresses the methodological reflections and perspectives on the methodology that is needed to address the complexity and context specificity of motivation and emotion. In addition to general reflections and perspectives regarding methodology, concrete empirical examples are provided. All cutting-edge chapters include current empirical studies on emotions and motivation in learning and teaching across different contexts (age groups, domains, countries, etc.), making them applicable and relevant to a wide range of contexts and settings. This high-quality volume with contributions from leading international experts will be an essential resource for researchers, students and teacher trainers interested in the vital role that motivation and emotions can play in education. Gerda Hagenauer is Professor of Educational Science and Head of the School of Education at the University of Salzburg, Austria. Rebecca Lazarides is a Full Professor of Research on Schools and Instruction at the University of Potsdam, Germany. Hanna Järvenoja is Professor of the Learning Sciences and Education at the University of Oulu, Finland.

EARLI New Perspectives New Perspectives on Learning and Instruction

Editor in Chief – Mien Segers (Leiden University and Maastricht University – The Netherlands) Assistant Editor – Isabel Raemdonck (Leiden University – The Netherlands) Editorial Board members David Gijbels (University of Antwerp – Belgium) Sanna Jävelä (University of Oulu – Finland) Margareta Limon (Autonoma University of Madrid – Spain) Karen Littleton (The Open University – UK) Wolff-Michael Roth (University of Victoria – Canada) Advisory Board Members Costas Constantinou (University of Cyprus – Cyprus) Veléria Csépe (Hungarian Academy of Sciences – Hungary) Sibel Erduran (University of Bristol – UK) Sylvia Rojas-Drummond (UNAM – Mexico) Martin Valcke (Ghent University – Belgium) Lieven Verschaffel (Katholieke Universiteit Leuven – Belgium) Kate Wall (Newcastle University – UK) Marold Wosnitza (Murdoch University – Australia) New Perspectives on Learning and Instruction is published by Routledge in conjunction with EARLI (European Association for Research on Learning and Instruction). This series publishes cutting edge international research focusing on all aspects of learning and instruction in both traditional and non-traditional educational settings. Titles published within the series take a broad and innovative approach to topical areas of research, are written by leading international researchers and are aimed at a research and post-graduate student audience. Also available: Re-theorizing Learning and Research Methods in Educational Research Edited by Crina Damşa, Antti Rajala, Giuseppe Ritella and Jasperina Brouwer Motivation and Emotion in Learning and Teaching across Educational Contexts: Theoretical and Methodological Perspectives and Empirical Insights Edited by Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja For a full list of titles, please visit: https://www.routledge.com/ New-Perspectives-on-Learning-and-Instruction/book-series/EARLI

MOTIVATION AND EMOTION IN LEARNING AND TEACHING ACROSS EDUCATIONAL CONTEXTS Theoretical and Methodological Perspectives and Empirical Insights

Edited by Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja

First published 2024 by Routledge 4 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2024 selection and editorial matter, Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja; individual chapters, the contributors The right of Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data Names: Hagenauer, Gerda, editor. | Lazarides, Rebecca, editor. | Järvenoja, Hanna, editor. Title: Motivation and emotion in learning and teaching across educational contexts : theoretical and methodological perspectives and empirical insights / edited by Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja. Description: Abingdon, Oxon ; New York, NY : Routledge, 2024. | Series: New perspectives on learning and instruction | Includes bibliographical references and index. | Identifiers: LCCN 2023026025 (print) | LCCN 2023026026 (ebook) | ISBN 9781032301105 (paperback) | ISBN 9781032301099 (hardback) | ISBN 9781003303473 (ebook) Subjects: LCSH: Motivation in education. | Emotions and cognition. Classification: LCC LB1065 .M66918 2024 (print) | LCC LB1065 (ebook) | DDC 370.15/4--dc23/eng/20230811 LC record available at https://lccn.loc.gov/2023026025 LC ebook record available at https://lccn.loc.gov/2023026026 ISBN: 978-1-032-30109-9 (hbk) ISBN: 978-1-032-30110-5 (pbk) ISBN: 978-1-003-30347-3 (ebk) DOI: 10.4324/9781003303473 Typeset in Sabon LT Pro by KnowledgeWorks Global Ltd.

CONTENTS

List of Contributors Preface

viii xiv

SECTION I

Theoretical Reflections and Perspectives 1 The Relevance of Situated Expectancy-Value Theory to Understanding Motivation and Emotion in Different Contexts Allan Wigfield and Jacquelynne S. Eccles 2 Exploring Interest Theory and Its Reciprocal Relation to Achievement Goals, Self-Efficacy, and Self-Regulation K. Ann Renninger, Suzanne E. Hidi and Arijit De 3 Achievement Goals: The Past, Present, and Possible Future of Achievement Goal Research in the Context of Learning and Teaching Martin Daumiller 4 Explaining the Context-Specificity of Student Motivation: A Self-Determination Theory Approach Barbara Flunger and Julien Chanal

1

3

19

35

54

vi

Contents

5 The Roots and Fruits of Self-Efficacy in Diverse Academic Contexts Ellen L. Usher

69

6 How Universal Are Academic Emotions? A Control-Value Theory Perspective Reinhard Pekrun and Thomas Goetz

85

7 Motivation and Emotion Regulation in Collaborative Learning Contexts Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä and Tatiana Shubina

100

8 Teacher and Student Well-Being: Theoretical Reflections and Perspectives Tina Hascher and Julia Mori

114

9 Teachers’ Motivation to Teach: A Review Through the Lens of Motivational Theories Helen M. G. Watt and Paul W. Richardson

128

10 On the Context- and Situation-Specificity of Motivation and Emotion: Which Contexts and Situations Matter? Fani Lauermann

143

SECTION II

Methodological Reflections and Perspectives 11 Mixed Methods in Research on Motivation and Emotion Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, Melanie Stephan and Michaela Gläser-Zikuda

161

163

12 The Experience Sampling Method in the Research on Achievement-Related Emotions and Motivation Julia Moeller, Julia Dietrich and Jessica Baars

178

13 Modelling Development and Change of Motivational Beliefs Rebecca Lazarides and Burkhard Gniewosz

197

Contents vii

14 Intervening on Students’ Motivation to Learn: Promises and Pitfalls of Intervention Studies Hanna Gaspard

213

15 Affective Processes in Collaborative Learning Contexts: Examining Affordances and Challenges of Video and Multi-Channel Data Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola and Cheryl Jones

228

16 Where Ethnic and Cultural Identity Meet Situational Demands: Implications for Methodologies Used to Study Motivation Tim Urdan 17 Using Heart Rate to Tap into Motivational and Emotional Processes During Teaching and Learning Monika Donker, Selma van Aken and Tim Mainhard 18 An Epistemological Shift Forward: The Methodological Zone of Proximal Research on Motivation and Emotion in Learning and Teaching Alexander Minnaert Index

244

258

274

284

CONTRIBUTORS

Sara Ahola is a doctoral researcher at the University of Oulu, Finland. Her

research interests are in self-efficacy and group-level regulation in collaborative learning. Jessica Baars is a behavioural and neuroscientist working at the Institute of

Educational Sciences at Leipzig University, Germany. Her research interests are in motivation and emotion captured with intensive longitudinal studies and the generalisability and replicability of intensive longitudinal studies. Julien Chanal is a Senior Lecturer at University of Geneva and Distance

Learning University in Switzerland. His research focuses mainly on SelfDetermination Theory in education and health-related physical activity domains. In education, advanced statistical methods allow studying the differentiation of motivation across academic subjects in an innovative developmental perspective. Concerning physical activity, individual motivational trajectories and their determinants are studied by relying on newly developed methods. Martin Daumiller is an Assistant Professor at the University of Augsburg,

Germany. His research interests include motivation in educational settings, academic dishonesty and learning with digital media as well as experiences, behaviours and performance of teachers and researchers. Arijit De is a doctoral candidate in the Department of Psychology, University

of Toronto Scarborough, Canada. His main research interests are in using behavioural, computational and neuroimaging paradigms to investigate selfperception and to connect the findings to pedagogy.

Contributors ix

Julia Dietrich works at the Institute of Educational Science at Friedrich Schil-

ler University Jena, Germany, as a post-doctoral researcher. Her research interests are in heterogeneity in learning and in developmental processes, in individualised digital learning and teaching, and in academic motivation and emotion. Monika Donker is an Assistant Professor at the Department of Youth and

Family, Utrecht University (the Netherlands). She is interested in studying interaction and emotion (regulation) using behavioural observation and physiological measures. Jacquelynne S. Eccles is a Distinguished Professor in the School of Education

at the University of California, Irvine. Her research is on academic motivation and achievement, school and family influences on adolescent development, and gender and ethnicity in STEM fields. Barbara Flunger is an Assistant Professor of Education at Utrecht University,

the Netherlands. Her research focuses on the question of how students’ motivation and learning behaviour can be improved. She is also interested in interindividual differences in students’ motivation, and their association with students’ academic outcomes. Hanna Gaspard is an Associate Professor at TU Dortmund University, Ger-

many. Her research focuses on the development of motivation, interventions to promote motivation and learning, and associations between teachers’ motivation, teaching quality and students’ outcomes. Michaela Gläser-Zikuda is a Professor and chair holder of School Education

with a focus on instructional research at the Friedrich-Alexander-University Erlangen-Nürnberg, Germany. Her main research interests are in emotion, self-regulated learning and social relationships in school, higher education and teacher education, as well as in mixed-methods research. Burkhard Gniewosz is a Full Professor for quantitative empirical research

methods at the Paris Lodron University of Salzburg, Austria. His main fields of research concern adolescent development as well as longitudinal modelling. He mostly focuses on contextual (family and school) influences on adolescents’ social, political and academic development. Thomas Goetz is a Professor of Psychology in the Faculty of Psychology at

the University of Vienna, Austria. His main research interests relate to the antecedents of academic emotions, the domain specificity of emotional experiences, boredom at school, improving self-regulated learning in students

x

Contributors

and teaching quality. He has published 17 books and more than 200 articles and book chapters. Gerda Hagenauer is a Professor of Educational Sciences at the University of

Salzburg, Austria. Her main research interests are in emotion, motivation and social relationships in school, higher education and teacher education. She is also interested in Mixed-Methods research. Tina Hascher is a Professor of Educational Science at the University of Bern,

Switzerland. Her main research interests are in emotion, motivation and learning in school as well as teacher education. She is also interested in health education, longitudinal research and intervention research. Suzanne E. Hidi is an Adjunct Professor of Applied Psychology and Human

Development, OISE, University of Toronto, Canada. Her research interests include neuroscientific implications of interest as well as of rewards for educational and social psychological theory and practice. Sanna Järvelä is a Professor of Learning Sciences and Educational Technol-

ogy and head of the LET lab at the University of Oulu, Finland. Her research interests deal with socially shared regulation in learning, computer-supported collaborative learning and multimodal learning process analytics. Hanna Järvenoja is a Professor of Educational Sciences in the Learning and

Educational Technology Research (LET) lab at the University of Oulu, Finland. Her research interest concerns motivation and emotion regulation in collaborative learning contexts, on both individual and social levels. Cheryl Jones is a researcher at the University of Canberra. Her doctoral re-

search explored the role of affect in group dynamics and group interaction processes. Clara Kuhn is a Research Associate and PhD candidate at the University of

Salzburg, Austria. Her main research interests include professional development in the school practicum, motivational factors in school and teacher education with a focus on mentoring and support. Fani Lauermann is a Professor of Empirical Educational Research and Edu-

cational Psychology at the University of Bonn and a research affiliate at the Center for Research on Education and School Development at TU Dortmund University, Germany. Her current research focuses on teacher and student motivation, teachers’ professional competence, and students’ educational and occupational choices.

Contributors xi

Rebecca Lazarides is a Professor of Research on Schools and Instruction at

the University of Potsdam, Germany, and Principal Investigator in the Cluster of Excellence “Science of Intelligence.” Her research interests include the motivational-affective development of students and teachers, effective teaching and teacher education. Tim Mainhard is a Full Professor of Educational Sciences at Education and

Child Studies, Leiden University, the Netherlands. He studies social dynamics in education and how these are related to student and teacher socialemotional outcomes. Kristiina Mänty is a University Lecturer at the University of Oulu, Finland. Her

research interests are in emotional and motivational aspects of learning, social interactions in learning and young children’s socio-emotional development. Alexander Minnaert is a Full Professor of Inclusion and Special Needs Edu-

cation and Clinical Educational Sciences at the University of Groningen, the Netherlands. Besides, he is a scientific advisory member of the Academy of Finland. His main areas of research comprise motivational, emotional, social and (meta)cognitive processes in learning and teaching at all levels of education. Moreover, he has a strong focus on methodological reasoning and challenges. Julia Moeller is an Assistant Professor of Educational Psychology with a

focus on Development under Risk Conditions at the University of Leipzig, Germany. She studies motivation and emotions in learning and achievement contexts and specialises in the use of intensive longitudinal data. She uses and develops the intra-individual statistical methods necessary for this work. Julia Mori is an advanced post-doc researcher in Educational Science at the University of Bern, Switzerland. Her main research interests are in wellbeing, alienation, motivation and learning in education. She is also interested in longitudinal research and intervention research. Franziska Muehlbacher is a University Assistant and PhD candidate at the

University of Salzburg, Austria. Her main research interests include emotions in the school context and collaborative teaching practices (team teaching). Reinhard Pekrun is a Professor of Psychology at the University of Essex, the

United Kingdom, and a Professorial Fellow at the Institute of Positive Psychology and Education, Australian Catholic University, Sydney, Australia. He is a highly cited scientist who pioneered research on emotions in education, originated the Control-Value Theory of Achievement Emotions and authored more than 350 articles, books and chapters.

xii

Contributors

Deborah Pino-Pasternak is an Associate Professor at the University of Can-

berra, Australia. Her main areas of research include metacognition and selfregulation at individual and group levels in formal and informal learning environments. K. Ann Renninger is the Dorwin P. Cartwright Professor of Social Theory

and Social Action, and Professor of the Department of Educational Studies, Swarthmore College, Swarthmore, PA USA. Her research addresses the role of interest in learning and its implications for instructional practice. Paul W. Richardson is a Professor of Education at Monash University. He is

engaged in a longitudinal study of the career choice motivations of teachers, teacher self-efficacy, the career trajectories of different types of beginning and mid-career teachers (www.fitchoice.org), and teacher health and well-being across the career lifespan. Tatiana Shubina is a doctoral researcher in the LET lab at the University of

Oulu, Finland. Her research focuses on secondary school students’ interest in collaborative learning settings. Melanie Stephan is a post-doctoral researcher at the Chair of Education with

a focus on media education at the Friedrich-Alexander-University ErlangenNürnberg, Germany. She coordinates the Laboratory for Digital Teaching and Learning (DigiLLab) there. Her focus of research lies in the area of teacher education, emotions, motivation and media education as well as mixed-methods research. Tiina Törmänen is a post-doctoral researcher in the LET lab at the University

of Oulu, Finland. Her research focuses on students’ and groups’ emotions, and socially shared regulation in collaborative learning. Tim Urdan is a Professor in the Department of Psychology at Santa Clara Uni-

versity in California. He conducts research on student motivation, classroom contexts and teacher identity. He serves on the editorial boards of several journals and is the co-editor of the Advances in Motivation and Achievement book series. He is a fellow of the American Psychological Association and lives in Berkeley, California. Ellen L. Usher is an Education Scientist in the Office of Applied Research and

Education Science at the Mayo Clinic in Rochester, Minnesota, USA. Her research focuses on understanding the development and influence of the selfsystem in a variety of teaching, learning and decision-making contexts.

Contributors xiii

Selma van Aken combines doing her PhD at Leiden University (the Nether-

lands) with her work as a primary school teacher. Her project focuses on physiology, appraisals and emotions of preservice teachers in the classroom. Helen M. G. Watt is a Professor of Educational Psychology and Director of

Research Development (Social Sciences) at The University of Sydney, Australia. Her longitudinal research is on gendered educational and occupational pathways in STEM fields (www.stepsstudy.org), and the evolution of motivations, professional engagement and well-being through teachers’ careers (www.fitchoice.org). Allan Wigfield is a Professor Emeritus in the Department of Human Develop-

ment and Quantitative Methodology at the University of Maryland, USA, and Honorary Professor of Psychology at the University of Heidelberg, Germany. He studies the development of academic motivation and interventions to enhance it in school settings.

PREFACE

In the late 1980s, some influential researchers argued more about the need to consider motivation and emotions as essential parts of complex human learning (e.g. Brown, 1992; Sorrentino & Higgins, 1986; Weiner, 1990). After the millennium, the discussion considered more strongly the role of context and implemented methods and the ecological validity of motivation and emotion research in the learning sciences (e.g. Boekaerts & Corno, 2005; Patrick & Middleton, 2002; Pintrich, 2003; Volet & Järvelä, 2001). While researchers have started to consider the complexity and context specificity of motivation and emotion, this complexity simultaneously challenges the development and transfer of theories in the field and the methodologies and measures used to capture accurately the phenomena of motivation and emotions. Reconceptualising motivation and emotion in respect of their context specificity has raised new issues concerning theory and methodology (e.g. Dirk & Nett, 2022; Eccles, 2022; Nolen et al., 2015; Pekrun & Marsh, 2022). Well-established motivation and emotion theories, such as expectancy value theory (EVT), self-determination theory and achievement goal theory, all addressed in this book, may work well in some educational contexts (e.g. students’ learning process, achievement-related behaviours and choices) but may need to be adapted when discussing other contexts related to education. How do we make this adaptation? What contextual, cultural or conceptual issues must be acknowledged? How do we adjust the well-established theories to these variations – or do we even need to? Methodologically, research on motivation and emotions in learning now uses multiple methods, including implementing quantitative, qualitative and mixed-methods approaches. The variation in implemented methods has emerged rapidly with the evolution of technology and its possibilities

Preface

xv

(e.g. in implementing process-oriented data; Järvelä & Bannert, 2019; Lajoie et al., 2020). The new methodological approaches allow detecting and examining the context specificity of motivational-affective constructs in a highly differentiated manner (e.g. Goetz et al., 2021). More concretely, the high complexity (e.g. different layers in education reflecting the micro-, mesoand macro-perspective and their constant interaction; interactivity in teaching and learning contexts; motivation and emotions as a part of complex learning systems) and context specificity (cultural-educational dependency, situated and processual nature) of emotions and motivation in teaching and learning challenges the methodology that is applied to assess these phenomena across contexts and target groups. Therefore, the potentials, challenges and boundaries of the used methods in the field need in-depth discussion and reflection. Against this backdrop, researchers have proposed the situatedness and context specificity of their core theoretical frameworks (e.g. Eccles & Wigfield, 2020) and the need for adequate methods to capture this complexity. However, a systematic, updated overview of the potentials, challenges and boundaries of such adaptations and transfers is missing. This desideratum is addressed in the present volume that brings together current theoretical and methodological perspectives and examples of empirical implementations from leading researchers in the field, focusing on the context specificity and situatedness of their core theories in motivation and emotion. This volume contributes to current theory development by discussing the potentials and challenges of the proposed context specificity and situatedness of core theories in research on motivation and emotion. Leading scholars in the field provide an overview of the theoretical and methodological potentials and challenges of the context specificity and situatedness of their theoretical frameworks. They focus on the transferability of theories, methods and empirical designs across different educational contexts. These considerations are enriched by an in-depth discussion of illustrative, cutting-edge empirical studies. Considering the burgeoning need for life-long learning, the need for motivation and emotion research, which produces a theory-based understanding of motivated learning that can be related back to the learners in different contexts, is more topical than ever. Based on this background, the following chapters provide an overview of the addressed issues of context specificity and situatedness of theories on motivation and emotions and the methodological implications. Wigfield and Eccles (Chapter 1) describe and discuss the main theoretical foundations of situated expectancy value theory (SEVT). They argue that all factors in the model are situative and culturally bound. Considering this situatedness was one of the major drivers for further developing Eccles’ EVT to SEVT. Many different avenues for future research are proposed, such as the investigation of the situative nature of the elements of SEVT and its

xvi Preface

influencing factors and the exploration of the interplay of the constructs. Wigfield and Eccles also argue that more research is needed on the role of SEVT in life transitions, accentuating in particular transitions that lead to “life choices” in the form of occupational choices. They make an intriguing argument that a decline in success expectations and subjective task values in a particular field (e.g. science, technology, engineering and mathematics [STEM]) does not necessarily need to be considered a harmful development if a good occupational fit (in another area of interest) is achieved in the end. In Chapter 2, Renninger, Hidi and De aim to explain the dynamic nature of interest and its context specificity and relate it to other motivational constructs, such as self-efficacy, students’ goals and self-regulation. To illustrate these relations, they give an overview of recent empirical studies. One intriguing finding of their analyses is that interest and other motivational concepts are increasingly connected once the interest develops. In terms of interest theory, they argue that the phases of interest development are universal across contexts, contents and individuals; however, the sociocultural context is of crucial importance when promoting the development of interests. They also argue that in future research, demographic variables should be considered less as control variables; instead, they should be explicitly included in the respective research questions. For example, it would be appropriate to test interventions to see for which groups of students certain intervention elements are relevant for their interest development or to test for which groups of learners certain motivational factors are related to their interest. Such questions would allow testing for interindividual differences by simultaneously addressing the respective context. Additionally, the processual nature of interest development would be accounted for appropriately. Daumiller (Chapter 3) reports on the development of achievement goal theory over the past decades. His explanations make clear that the context specificity and temporal variability of achievement goals pose great challenges to researchers in this field, as, for example types of goal orientations vary across contexts. Further, the research efforts to transfer the theory to different contexts must be acknowledged. A question that remains open and closely linked to the issue of context specificity is the transferability of the achievement goal theory to other cultures, especially collectivist cultures, as previous research was conducted primarily in more individualistic (i.e. “Western”) countries. In their theoretical reflections, Flunger and Chanal (Chapter 4) introduce the theoretical foundations of self-determination theory and provide insights into contemporary empirical research findings. Based on Vallerand’s (1997) hierarchical model of extrinsic and intrinsic motivation distinguishing between motivation at the global, contextual and situational level, they emphasise that student motivation can only be understood and explained in detail by including thoughts on its context specificity. More concretely, they

Preface xvii

raise the highly relevant question of distinguishing which forms of student motivation are more context-specific and which are more context-general (see also Pekrun & Marsh, 2022). Flunger and Chanal summarise empirical findings that indicate that controlled forms of motivation are rather contextindependent while context and situation more strongly influence autonomous forms. It seems worthwhile to take these considerations on the degree of context-specific versus context-general constructs more into account in future studies. Usher (Chapter 5) traces the “roots and fruits of self-efficacy in diverse academic domains” (p. 69). In so doing, she summarises and discusses the main premises of Bandura’s social-cognitive theory and relates it to the motivational concept of self-efficacy. Usher argues convincingly that research in self-efficacy must also consider the context to better understand its effects and conditions of influence; she thus advocates for a “contextualized understanding of self-efficacy.” It is also important to consider the specificity of the measures of self-efficacy (e.g. a teacher’s general self-efficacy in teaching, in particular teaching tasks or in teaching a particular topic to a particular group of students), which depends on the respective research objective. However, Usher also points out that not all instruments assessing self-efficacy possess face validity, contributing to the ambiguity of the research results in the field. The quality of the measurements is decisive in determining whether empirical results can be generated validly in different contexts, which makes cross-context comparisons possible in the first place. Pekrun and Goetz (Chapter 6) discuss academic emotions. Relying on the theoretical foundations of control-value theory, they argue for a “relative universality” of academic emotions. Basic functions have been found to be valid across situations and contexts (e.g. the relation between high control and high-value cognitions and students’ or teachers’ emotions is typically positive), albeit the effect size may vary (e.g. across cultures or students’ or teachers’ gender). However, other factors, such as the situational antecedents or the intensity of the experienced emotions, are likely to vary across situations and contexts. Pekrun and Goetz present empirical evidence that supports their statements. For further research, they emphasise the need for within-person designs and intervention studies. Järvenoja, Törmänen, Järvelä and Shubina (Chapter 7) consider motivation and emotion regulation in the framework where self-regulated learning is situated in a collaborative learning context. Järvenoja et al. introduce a concept of socially shared regulation from the motivation and emotion point of view. By situating motivation and emotion regulation in the collaborative learning context and in conjunction with the group interaction, they adopt a broader perspective on motivation and emotion regulation than is typical in individual-focused research. They explain how the conditions, variation and temporal manifestation all contribute to collaborative learning groups

xviii Preface

realising socially shared motivation and emotion regulation. Through examples from empirical studies and discussion, Järvenoja et al. draw links to the current methodological development that enables more situated and processoriented analyses (Section II of this book provides several examples). Hascher and Mori (Chapter 8) introduce the concepts of teacher and student well-being. They argue that heterogeneity in the research landscape poses a special challenge for researchers. More concretely, there is a lack of clear conceptualisations of teacher and student well-being in the field. The authors argue that well-being is, in any case, to be conceptualised multidimensionally and should embrace negative and positive components, particularly cognitive, emotional and physical elements. They also advocate for future research that considers the situatedness and context specificity of wellbeing more. Similar to Pekrun and Goetz, they emphasise the need to develop specific interventions to promote the well-being of students and teachers. Watt and Richardson (Chapter 9) explain how theories, constructs and concepts from the well-established literature concerning students’ motivations to learn were adapted and translated to the study of teachers’ motivations to teach. The authors provide an overview of core motivation theories and their recent reinterpretation in relation to teachers. Thus, the context specificity of motivational theories is described in regard to the field of research on teachers and related topics such as the need to further theoretical development, measurement, and practical implications are highlighted. Empirical findings from the “FIT-Choice” research programme, grounded in expectancy-value theory (www.fitchoice.org) and work that applies motivational theories in teacher research, are discussed. A section commentary by Lauermann (Chapter 10) completes this Section I of the volume. Lauermann raises three main questions that also provide the structure of her discussion. First, she poses the question in which context and situations researchers should study motivation and emotion. She further includes several sub-questions, for example what exactly constitutes a situation or a context. The answer to this question also relates to the question of contextualised interventions. Second, she asks which social phenomena researchers try to explain and in which context and situations these phenomena are of interest. Finally, she raises the critical question of who benefits from motivation and emotion research. This is followed by the question of the extent to which we succeed in our designs in taking sociocultural and historical differences into account. In Section II, specific methodologies are presented and discussed. Hagenauer, Muehlbacher, Kuhn, Stephan and Gläser-Zikuda (Chapter 11) introduce the basic premises of mixed-method approaches and give examples of how mixed methods may be utilised fruitfully in research on emotions and motivation in education. They provide insights into recent and current mixed-methods research projects of their research programme, which

Preface xix

underline how mixed-method approaches help us trace general relations between constructs and consider their situatedness and context specificity. As an outlook for future research, the authors mention that the potential of mixed methods could be used even more, as so far mainly specific method combinations dominate (e.g. qualitative interviews and quantitative questionnaires) while other possibilities, for example ethnographic approaches, have been rarely implemented in mixed-method projects on emotions and motivation. However, it is precisely such approaches that would be important to gain as holistic an understanding as possible of the context and the subjective and situational attribution of meaning by students and teachers (see, e.g. Nolen et al., 2015). Moeller, Dietrich and Baars (Chapter 12) address the situational change in motivational and emotional beliefs and processes. They introduce the experience sampling method (ESM) to assess the situation and context specificities and discuss its methodological possibilities to capture the variation and fluctuation of emotions and motivation. The authors bring together different viewpoints that can challenge beginners and more advanced researchers interested in unpacking complex intra-individual variations of motivation and emotions to consider the insights that the ESM can provide and the challenges the field is facing when shifting towards situated models of motivation and emotions. Lazarides and Gniewosz (Chapter 13) review key methods of change modelling and bring together variable-centred approaches (e.g. growth modelling and true intraindividual change models) and person-centred approaches (e.g. latent transition and growth mixture models). The authors illustrate the value of these statistical methods for the analysis of contextspecific motivational changes. Their focus is on research grounded in situated expectancy-value theory as a core theory in motivational research. The authors discuss both person- and variable-centred methods in relation to their application to examining inter- and intra-individual differences in motivational change. Gaspard (Chapter 14) takes up the theme of intervention studies in motivation research. As proposed in many chapters in Section I of the book, intervention studies are important for educational practice and theory development. However, such intervention studies are still scarce, especially if one considers that motivational interventions are always context-specific and that effectiveness studies must be conducted in different contexts and based on different populations. For example, Gaspard argues that single studies do not provide sufficient evidence about their effectiveness due to the context-bound nature of students’ motivation. Therefore, researchers need to gain more insight into the mechanism of why an intervention works or not and have to apply those interventions across different contexts. She also calls for adaptive interventions to be developed to meet learners’ individual

xx

Preface

needs as best as possible. Such interventions would be highly context- and situation-specific. Mänty, Pino-Pasternak, Ahola and Jones (Chapter 15) target the possibilities of video data in research on affect, particularly their manifestation, in group learning contexts. In their chapter, Mänty et al. argue for the usefulness of video when complemented with other forms of data. They showcase how video serves as a versatile data source that enables addressing the multilevel nature of affect by providing access to the individual, sub-group and whole group displays of emotions. With growing technological and analytical possibilities, video is a continuous data channel for actualised, temporally varying affective processes. Stimulated by the nature of the video data and other process data, Mänty et al. invite research into the role of affect in learning as multilayered and multidimensional, providing actual study examples of how video data with other data sources has enabled the analysis between individual and group-level data, behavioural and physiological data and uncovering temporal sequences and patterns. Urdan (Chapter 16) discusses how ethnic and cultural identity should be considered in motivation research in a more adequate manner. He critically challenges the universal nature of motivation theories, which also has implications for the methodologies used to study motivation. An increasing number of empirical studies have impressively shown that people’s cultural and ethnic backgrounds affect our understanding of motivational dynamics. Moreover, complex methodologies are needed to address this complexity, rooted in the context-dependency and situatedness of motivation due to cultural factors. Urdan argues for a complex systems approach that considers the dynamic and situated nature of motivation. Advanced technologies and the increasing number of mixed-method studies in the field can support this methodological implication. Finally, Donker, van Aken and Mainhard (Chapter 17) take up the dynamic nature of emotion and motivation and present current methods for capturing the situational physiological experience of emotion as accurately as possible. They impressively show how new technologies can help us better understand emotional experience on the physiological level. Such approaches complement well the approaches that have so far focused strongly on introspection (i.e. self-reports). They enable a more holistic view of emotions and motivation and provide a relatively high ecological validity as they can be used in natural classroom settings. As a concrete physiological assessment, they introduce the measurement of the heart rate of learners as an indicator of the physiological component of a learner’s emotional experience. A section commentary by Minnaert (Chapter 18) completes this Section II of the volume. Minnaert argues convincingly that approaches on situated phenomena with reference to multiple (mixed) methods were already propagated in the 13th century; the current “shift” to consider the situatedness

Preface xxi

and context specificity of emotions and motivation is therefore not entirely new – but nevertheless contemporary. Building on these introductory philosophical and epistemological reflections, he further provides a detailed discussion on how the chapters contribute to the methodological debate. In the final section, Minnaert raises a thought-provoking question: “Are we at the forefront (or even in the middle) of an epistemological crisis in the domain of motivation and emotion?” (p. 280). He argues for an “epistemological shift forward” (p. 274). Methods and methodologies need to be developed that succeed in doing justice to the situatedness and complexity of motivation and emotion as social phenomena embedded in diverse contexts. We hope that all readers will enjoy the contributions in this book and that this edited volume will stimulate further discussion in the field. Finally, we thank all authors and reviewers for their important contributions. We would also like to thank the series editors – Isabel Raemdonck and Mien Segers – for the opportunity to publish in this series. Gerda Hagenauer, Rebecca Lazarides and Hanna Järvenoja

References Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231. https://doi. org/10.1111/j.1464-0597.2005.00205.x Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178. https://doi.org/10.1207/s15327809jls0202_2 Dirk, J., & Nett, U. E. (2022). Uncovering the situational impact in educational settings: Studies on motivational and emotional experiences. Learning and Instruction, 81, Article 101661. https://doi.org/10.1016/j.learninstruc.2022.101661 Eccles, J. S. (2022). Comments on within-person designs and motivational science. Learning and Instruction, 81, Article 101662. https://doi.org/10.1016/j.learninstruc. 2022.101662 Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Goetz, T., Bieleke, M., Gogol, K., van Tartwijk, J., Mainhard, T., Lipnevich, A. A., & Pekrun, R. (2021). Getting along and feeling good: Reciprocal associations between student-teacher relationship quality and students’ emotions. Learning and Instruction, 71, Article 101349. https://doi.org/10.1016/j.learninstruc.2020.101349 Järvelä, S., & Bannert, M. (2019). Temporal and adaptive processes of regulated learning – What can multimodal data tell? Learning and Instruction, 72, Article 101268. https://doi.org/10.1016/j.learninstruc.2019.101268 Lajoie, S. P., Pekrun, R., Azevedo, R., & Leighton, J. P. (2020). Understanding and measuring emotions in technology-rich learning environments. Learning and Instruction, 70, Article 101272. https://doi.org/10.1016/j.learninstruc.2019.101272

xxii Preface

Nolen, S. B., Horn, I. S., & Ward, C. J. (2015). Situating motivation. Educational Psychologist, 50(3), 234–247. https://doi.org/10.1080/00461520.2015.1075399 Patrick, H., & Middleton, M. J. (2002). Turning the kaleidoscope: What we see when self-regulated learning is viewed with a qualitative lens. Educational Psychologist, 37(1), 27–39. https://doi.org/10.1207/00461520252828537 Pekrun, R., & Marsh, H. W. (2022). Research on situated motivation and emotion: Progress and open problems. Learning and Instruction, 81, Article 101664. https:// doi.org/10.1016/j.learninstruc.2022.101664 Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. https://doi.org/10.1037/0022-0663.95.4.667 Sorrentino, R. M., & Higgins, E. T. E. (1986). Handbook of motivation and cognition: Foundations of social behavior. Guilford Press. Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 271–360). Academic Press. https://doi.org/10.1016/S0065-2601(08)60019-2 Volet, S. E., & Järvelä, S. E. (2001). Motivation in learning contexts: Theoretical advances and methodological implications. Pergamon Press. Weiner, B. (1990). History of motivational research in education. Journal of Educational Psychology, 82(4), 616–622. https://doi.org/10.1037/0022-0663.82.4.616

SECTION I

Theoretical Reflections and Perspectives

1 THE RELEVANCE OF SITUATED EXPECTANCY-VALUE THEORY TO UNDERSTANDING MOTIVATION AND EMOTION IN DIFFERENT CONTEXTS Allan Wigfield and Jacquelynne S. Eccles

Abstract We discuss our view of the key areas for future research based on Eccles and colleagues’ expectancy-value theory of achievement, performance and choice. Eccles and Wigfield recently renamed situated expectancyvalue theory in order to capture its dynamic, contextual, and situated nature – characteristics of the theory that make it quite relevant to the topic of this volume. We first present the basic tenets of the model, define key constructs, and discuss major research findings reported in the literature. We then turn to the “socialization” part of the model, which focuses on how children’s expectancies and values are impacted by the experiences they have at home and at school. Then we discuss several directions for future research: (a) focusing on the “situated” nature of expectancies and values and their relations to outcomes; (b) new research on expectancies, academic self-concepts, and perceived task difficulty; (c) new research on the task value construct; (d) investigating the interplay of expectancies, academic self-concept, and task value in determining outcomes; (e) studying impact of other school transitions on the key beliefs and values; (f) new directions for Situated Expectancy-Value Theory (SEVT) based intervention research and (g) culture, gender, ethnicity, and SEVT.

Eccles and Wigfield (2020) proposed that the name of Eccles’ ExpectancyValue Theory (EEVT) be changed to Situated Expectancy-Value Theory (SEVT) in order to emphasise more clearly that the processes underlying the SEVT model occur over time and are very much influenced by the immediate situation in which each decision is taking place. We also did so to emphasise the contextualised nature of the constructs in the model, linkages among them, and influences on them. More specifically, we discussed how individuals’ expectancies, self-concepts of ability, and subjective task values (STVs), DOI: 10.4324/9781003303473-2

4

Allan Wigfield and Jacquelynne S. Eccles

along with their goals and identities, depend on the specific current situation in which both conscious and non-conscious choices are being made. This also means that the range of options being considered by the individual in any given choice situation is limited by prior experience and by the cultural values, norms, and characteristics that surround individuals as they mature and move through time and space. That is, we stated that SEVT “is both situationally specific and culturally bound.” Thus, the theory is particularly relevant to a volume focused on motivation in different educational contexts. In Eccles and Wigfield (2020), we made some broad suggestions for future research. In this chapter, we unpack many of those suggestions, and add others, to provide guidelines for the next decades of research based on SEVT and to emphasise its relevance in different contexts, such as different phases of development and different school settings. We begin by discussing the original model and key findings from research testing different aspects of it. Eccles-Parsons et al.’s Expectancy-Value Theory

The Eccles-Parsons et al.’s (1983) expectancy-value model of achievementrelated choices, persistence and performance was originally designed to elaborate and extend the classic expectancy-value models that were prominent in several social science fields, in particular, psychology and economics. As the name implies, expectancy-value theories focus on two key constructs for predicting performance and choice: expectancies (or probabilities) of success in a given activity, and its value or worth to the person. Eccles-Parsons and colleagues updated and expanded this general theoretical framework with ideas from social cognition, developmental sciences, and sociocultural perspectives (see Figure 1.1 for the most recent version). Eccles, Wigfield, and their colleagues have continued to update the model in light of research findings based on it as well as advances in the above-mentioned fields and others. We believe there have been several fundamental contributions from this model to the field of motivation (see also Eccles & Wigfield, 2020). First is the elaboration of the task value construct. Second is the elucidation of ways in which socialisers impact children’s developing motivation; this includes how specific beliefs, values, and behaviours of parents impact their children’s developing motivation, and how different kinds of classroom and school experiences can impact motivation. Third is the elaboration of the constructs and processes that underlie both within- and between-persons’ differentiated expectancies for success (ESs) in different tasks and their valuing of them. Fourth is the inclusion of important social cognitive concepts derived from motivation theory and self-theories that explain individual differences in decision-making outcomes. For instance, the model includes constructs from attribution theory, personality theory, family socialisation theories, and identity theories, to name just a few. An implication of doing

5

Eccles and colleagues’ expectancy-value model of performance and choice

The Relevance of Situated Expectancy-Value Theory

FIGURE 1.1

6

Allan Wigfield and Jacquelynne S. Eccles

this is that the theory is more integrative than many motivation theories; we return to this point later. Fifth, in recent writings, in particular because the model was developed to explain both within- and between-individual achievement-related choices, we have discussed the importance of considering not just beliefs and values about individual activities but also the hierarchical nature of those beliefs and values within the individual and how they influence performance and choice. Research on the impact of such hierarchies is just beginning, however. Sixth, the model explicitly grounded the social and psychological components within the culture of the actors. Eccles-Parsons et al. (1983) designed the model so that each box in Figure 1.1 represented a general category or level of constructs. The specific examples listed within each box were not an exhaustive list. Similarly, we did not and do not currently assume that each will be activated equally in any given instance or over time. We also believe that the key belief and value constructs and the relative weights of each potential influence on them are impacted by developmental processes, situational processes, individual differences, and individual by context processes (see Eccles, 1993; Eccles & Midgley, 1989). The arrows in the figure represent hypothesised processes and links that occur over time when both general and specific task choices are being made. Although, overall, the influences flow from the left to the right side of the model, we also posited the iterative nature of our model over time. As Eccles and Wigfield (2020) put it, “Today’s choices and performances become tomorrow’s past experience.” Much research has now shown that performance and choice are influenced by and influence subsequent expectancies and values (e.g. Gaspard et al., 2020; Meece et al., 1990). Eccles-Parsons et al. (1983) posited that individuals’ ESs and STVs are the most proximal psychological determinants of task and activity choice, performance, and engagement in the chosen activities. They defined ESs as individuals’ beliefs about how well they are doing on an upcoming task. They distinguished conceptually ESs and individuals’ more stable beliefs about their current ability, or academic self-concept (ASC). Empirically, however, these constructs are closely related. However, we think that more nuanced measures of each construct should be developed, and such measures might have reduced the multicollinearity problem; see Eccles and Wigfield (2020) for more discussion as well as discussion in the future directions section. Eccles-Parsons et al. (1983) proposed that the overall value of a given task would be composed at least of four main person-task characteristics/ constructs: intrinsic value, attainment value, utility value, and cost (see Wigfield & Eccles, 2020 and Wigfield et al., 2017 for further discussion of STV). Intrinsic value (which we sometimes call interest value) is the anticipated enjoyment one expects to gain from doing the task for purposes of making choices and the enjoyment one gets when doing the task. Utility value or

The Relevance of Situated Expectancy-Value Theory

7

usefulness concerns how well a particular task fits into an individual’s present or future plans, for instance, taking a math class to fulfil a requirement for a science degree. Attainment value is the relative personal/identity-based importance attached by individuals to engaging in various tasks or activities; it has been measured mostly in terms of the perceived personal importance of the activity and so the ties to identity have not been adequately captured; see Eccles (2005, 2009) for discussion of attainment value and identity. Eccles-Parsons et al. (1983) suggested three different types of costs of doing an activity that reduce the likelihood the person will do the task. These are: (1) Effort cost – how much effort individuals perceive is needed to complete a task and whether it is worth doing so; (2) Opportunity cost – the extent to which doing one task takes away from one’s ability or time to do other valued tasks and (3) Emotional cost – the emotional or psychological costs of pursuing the task, particularly anticipated anxiety and the emotional and social costs of failure. Eccles-Parsons et al. included the cost in the STV box of the model rather than separate from it. In SEVT, we continue to include it in the STV “box” because: (1) We conceptualise STV as a net value derived from both the relative benefits and costs of the various available task or activity options, (2) The other three aspects of STV have positive and negative poles and so can have a negative impact on the net STV of any specific task. That is, we do not view intrinsic, utility, and attainment value as “positive” and “negative” influences on overall value and (3) Costs can also both increase and decrease the relative STV of various perceived options being considered. It has been said, for example the harder something is to obtain (meaning the more costly it is), the more valuable it is (see Wigfield & Eccles, 2020, for an extended discussion). Researchers have both proposed new dimensions of cost and developed new measures of it (e.g. Flake et al., 2015; Gaspard et al., 2015; Gaspard et al., 2017; Perez et al., 2014; Watt et al., 2019; see Wigfield & Eccles, 2020, for further discussion of this work and several of the new measures). However, the new measures of cost sometimes use the same labels to describe very different sets of items. We need more work to find agreed-upon measures. Central Findings Concerning the Right Side of the Model

We have reviewed these findings elsewhere recently, so we will only mention them briefly here. Relations of ESs and STVs to Performance and Choice

Much evidence supports the predictions on the right side of the model regarding how expectancies and values predict performance and choice (see Wigfield & Eccles, 2020, for review). Interestingly, in some studies, domainspecific STVs were more predictive of task choices after controlling for prior

8

Allan Wigfield and Jacquelynne S. Eccles

achievement, whereas domain-specific ASCs and ESs were more predictive of changes in subsequent performance (e.g. Meece et al., 1990). Further, these relations extend over time; for example, Durik et al. (2006) found that the importance children gave to reading in fourth grade predicted the number of English classes they took in high school (see also Simpkins et al., 2006). In an interesting recent study, Rosenzweig and colleagues (2021) interviewed college STEM majors about why they opted out of STEM or changed to a different STEM field. Results showed that it was the value students attached to the major that was the strongest predictor of change; competence-related beliefs were a significant predictor as well, but not as strongly so. Researchers have also shown that the interactions of individuals’ ESs and STVs influence their achievement and that these interactions add small but reliable increments in predictive validity (e.g. Nagengast et al., 2011; Trautwein et al., 2012). Other researchers using person-centred data analytic approaches to investigate these relations find that meaningful patterns in individuals’ expectancies and values can be identified and that they relate to achievement choices (e.g. Conley, 2012; Wang et al., 2013). Development of Children’s Expectancies and Subjective Task Values

There are several findings regarding the development of students’ STVs and domain-specific ASCs across the elementary and secondary school years (see Wigfield et al., 2015 for further discussion). The first is that even first graders make domain-specific distinctions in both ASCs and STVs and within domain distinctions between ASC and STV (Eccles et al., 1993), suggesting early differentiation in both of these beliefs. Eccles and Wigfield (1995) showed that the attainment, intrinsic, and utility components of task value formed separate but highly related factors at least by the late elementary school years. Second, several studies in different Western countries show how ESs and STVs change across the school years. Eccles and Wigfield’s longitudinal Childhood and Beyond (CAB) study examined changes in children’s ASCs and STVs across the k–12 school years. They initially reported a normative pattern of decline across different school subjects across the elementary school years and into the high school years (Jacobs et al., 2002; Wigfield et al., 1997; see Wigfield et al., 2015, for review). Researchers in other countries also reported declines and/or age differences in children’s ASCs and STVs (e.g. Gaspard et al., 2015; Watt, 2004). More recently, researchers analysing the CAB data with growth mixture modelling analyses have shown that there are a variety of patterns of change in children’s STVs and expectancy beliefs across time (e.g. Archambault et al., 2010; Gaspard et al., 2020; Musu-Gillette et al., 2015), including increases for some children. Finally, cultural differences have been found in these patterns of change. For example, Wang and Pomerantz (2009) found declines in early adolescent American students’ but not in Chinese students’ beliefs.

The Relevance of Situated Expectancy-Value Theory

9

The Socialisation of Expectancies and Subjective Task Values: Families and Schools

The boxes at the far left of the model focus on the role of social experiences in SEVT, and the child’s own characteristics. Both the parent and school aspects of this part of the model have received extensive theoretical and empirical attention. Eccles (1993) elaborated on the parent socialisation aspect of the model and presented the expanded model illustrated in Figure 1.2. Simpkins et al. (2015) provided the most comprehensive empirical test of this aspect of the SEVT model, documenting the associations of the kinds of opportunities parents provide their children (among other things) with developmental changes in their children’s ASCs and STVs. They also reviewed the support from other family socialisation studies for the hypotheses inherent in this figure. Furthermore, Harackiewicz et al. (2012) demonstrated experimentally that teaching parents about the value of STEM courses leads to an increased likelihood of their daughters taking high school STEM classes. Thus, there is general support for the importance of the family socialisation processes outlined in Figure 1.2. However, Simpkins et al.’s (2015) findings show that the strength of parental influences varies across activity areas. The situated nature of these processes needs much more work. For example, families may have greater influence in more traditional and familycentric societies than in more individual-focused and westernised societies. The impact of family economic resources on the opportunities children have to develop expectancies and STVs need much more work. Eccles (1993) also laid out theoretical ideas regarding the role of schools in the SEVT. Our work related to school contexts has focused on identifying those classroom characteristics that facilitate students’ confidence in their ability to master academic material and the STV they attach to their academic courses. Like self-determination theorists (Ryan & Deci, 2017), we focused on those characteristics of classrooms that support feelings of competence, connectedness, and autonomy (e.g. Wang et al., 2013). By and large, our results support the importance of these aspects of classrooms in supporting engagement, high ASC, and high academic STV (see Wigfield et al., 2015 for review). In addition, Eccles (1993) and Eccles and Midgley (1989) focused on the extent to which systematic changes in students’ motivational beliefs and engagement over years in school might be linked to systematic changes in the fit between classroom/school characteristics and the developing needs of the students themselves; they referred to this as “stage-environment fit.” Researchers have shown associations between changing experiences in schools/classrooms and both declines and increases in students’ ASCs, ESs, and STV across the school years, at least in U.S. schools (see Eccles & Roeser, 2010; Wigfield et al., 2012, 2019). This work influenced educational policymakers to recommend that traditional junior high schools

10 Allan Wigfield and Jacquelynne S. Eccles

Parent socialisation model FIGURE 1.2

The Relevance of Situated Expectancy-Value Theory

11

be changed to middle schools that focus on early adolescence as a unique phase in development. Promising Future Research Directions for Work Based on SEVT

There are many topics that researchers interested in SEVT can address in the coming years and even decades. In this section, we provide suggestions for what we think are the most promising ones to begin pursuing. The Situative Nature of SEVT

Situated views of motivation that emphasise the importance and (in some cases) the primacy of the situation’s impact on individuals’ in-the-moment motivation are increasingly prominent in the motivation field (see Nolen, 2020). In introducing the name change to SEVT, Eccles and Wigfield (2020) made clear that all aspects of the model are situated, even if the model in Figure 1.1 does not fully capture that. For example, parents are influenced by the sex of the focal child, the race of the focal child, the sexual identity of the child etc. In addition, as Nolen (2020) has discussed, individuals both learn from and co-create contexts as they participate in them. Researchers are beginning to look at “situated” aspects of ESs and SVTs by examining how they change weekly over a semester in different college courses (e.g. Benden & Lauermann, 2022; Beymer and Rosenzweig, 2022; Robinson & Lee, 2022). This work has not examined what produces such changes, however, which is an important next step. New methods of both study and data analysis need to be developed to capture the situated nature and complex, self-organising systems that underlie SEVT. Researchers are also beginning to investigate situated expectancies and values and how they relate to broader or more general expectancies and values, and other outcomes. Dietrich et al. (2019) measured college students’ “dispositional” expectancies and values at the beginning and end of the semester. Over a ten-week period, they had students complete a brief questionnaire on their expectancies and values three times during a class period. They identified different profiles of students based on their responses to the “dispositional” questionnaire, and these were associated with students’ responses to the situated measures. For example, students who reported higher expectancies and values on the situated measures had higher expectancies entering the class and at the end of the semester. More such research is needed. New Research on Expectancies, Academic Self-Concepts, and Perceived Difficulty

We noted earlier that our items measuring expectancies and ASCs load together when we have factor analysed them, and so we generally create a combined expectancy-ASC variable. To test the links of ESs and ASCs,

12

Allan Wigfield and Jacquelynne S. Eccles

researchers could develop more nuanced measures of them, with expectancy items focusing on the future. In addition, Eccles-Parsons et al. (1983) included an extended discussion of perceptions of task difficulty and their relations with choice, noting that in academic settings in particular task/course difficulty should relate negatively to choices such as enrolling in the next level of a particular course sequence. Task difficulty has not often been examined, and re-introducing it and examining its relations to activity choice and to STVs would enrich what we know about the right side of the model. New Research on Subjective Task Values

We believe that there are several important research questions regarding the nature of STVs. As noted earlier, we have not specified exactly how the various components would aggregate to form either the STV of an individual achievement-related task or the relative STVs across several different task or activity options available to an individual at one time or over time. We also think researchers should focus on the social, contextual, and psychological factors that influence which specific aspects of each STV component are weighted most heavily as individuals assess the relative STV of various shortand long-term activity options. We predict that both across time withinperson and more immediate between-person differences in the STV will be influenced by personal characteristics and immediate context characteristics linked to the salience of various aspects of the options being considered, the type of choice being made (immediate decisions versus long-term choices), cultural beliefs, and the social and personal resources individuals can bring to the various choice options. We propose that the subjective attainment value of various options will be weighted quite strongly when deciding which lifedefining activities (like careers or avocations) to pursue. Interplay among the Constructs

Investigating the interplay of the different aspects of a task in determining overall STV and the factors influencing the formation of STV and ASC and hierarchies is a key area needing research, and one we are perhaps most excited about. As with the first suggestion above, researchers doing such work need to attend carefully to developmental and contextual influences. We have focused primarily on the impact of different parenting practice and school environmental factors on children’s developing STVs and ASCs. Many other influences need attention. Hierarchies of ESs and STVs

We noted earlier that although we have been emphasising the importance of hierarchies of STV and ASC hierarchies and not just individual ones, empirical work on these hierarchies is just beginning. Among other

The Relevance of Situated Expectancy-Value Theory

13

things, we need more work on the ways in which STV and ASC hierarchies change in response to feedback while engaged in specific tasks over varying periods of time. What happens to the relative STV attached to a specific course over a semester and both why and for whom? For example, students regularly juggle time spent in any one course with the demands of workflow in other courses over the course of a semester. Today they might not put any time into course A because they have an exam in course B sooner, even though they place a higher long-term STV on performance in course A. Dietrich et al.’s (2019) study is a good beginning to this line of work.

School Transitions and SEVT

Much work has been done on school transitions, particularly from elementary to middle school. This work needs to be extended to the transitions associated with tertiary schooling. A great deal of attention is now being paid to the role of past secondary education in individuals’ life trajectories, particularly those associated with occupational choices. Particular concern has been placed on the dropout of females and minoritised groups of students from STEM fields. Some of this work is grounded in processes articulated in SEVT such as declines in success expectations, perceived costs associated with social stereotypes, and perceived STV. Much of this work explicitly assumes that such declines are bad. But one could approach this transition as an opportunity to study the iterative processes that underlie the development of these motivational beliefs and their role in life choices. Experiences in college courses provide new information about individuals’ relative abilities and relative STVs across various academic subject areas linked to high-level occupational choices. If such experiences are part of an adaptive system through which individuals can find the most optimal occupation for themselves in terms of the fit between their abilities and STVs and their future occupations, then both opting in and opting out are good. Little research has addressed this.

Interventions to Enhance Students’ Motivation

Over the last decade, researchers have developed and tested EVT/SEVTbased interventions to improve students’ motivation; most have focused on utility value. Rosenzweig et al. (2022) made a variety of proposals for extending this work to other central constructs in the model and combinations of constructs. They also made suggestions for how to address the new emphasis on the situated, dynamic aspects of the model. We recommend researchers interested in doing new SEVT-based intervention research to refer to this chapter.

14

Allan Wigfield and Jacquelynne S. Eccles

Culture, Gender, and Ethnicity and SEVT

Eccles and colleagues (1983) began their work with a focus on gender differences in motivation for and choice of STEM courses and careers, and there has been an increasing amount of work on ethnic group differences in motivation (e.g. Diemer et al., 2016; Peck et al., 2014). Yet more work needs to be done on how culture, ethnicity, gender’s interactions impact the development of individuals’ expectancies and values (see Tonks et al., 2018; Wigfield & Gladstone, 2019 for discussion of culture and ethnicity’s impact on the development of children’s expectancies and values). When studying ethnicity, researchers basing their work on SEVT should address how experiences of racism, discrimination, and oppression influence children’s developing ASCs and STVs. In conclusion, we are gratified that SEVT continues to guide much research and that scholars have embraced the change in our characterisation of the theory from EVT to EEVT to SEVT. We look forward to participating in the exciting new work based in SEVT as well as learning from the younger scholars who are taking up its mantle. References Archambault, I., Eccles, J. S., & Vida, M. N. (2010). Ability self-concepts and subjective value in literacy: Joint trajectories from grades 1–12. Journal of Educational Psychology, 102(4), 804–816. https://doi.org/10.1037/a0021075 Benden, D. R., & Lauermann, F. (2022, April). Short-term relations between expectancies and subjective task values in math: A random intercept cross lagged panel approach. In D. K. Benden, P. N. Beymer, & F. Lauermann (Chairs), Students’ expectancies and values following the transition to higher education: A situated perspective [Symposium]. American Educational Research Association Annual Meeting, San Diego, United States. Beymer, P., & Rosenzweig, E. Q. (2022, April). Examining trajectories of situated expectancy-value constructs in a college calculus course. In D. K. Benden, P. N. Beymer, & F. Lauermann (Chairs), Students’ expectancies and values following the transition to higher education: A situated perspective [Symposium]. American Educational Research Association Annual Meeting, San Diego, United States. Conley, A. M. (2012). Patterns of motivation beliefs: Combining achievement goals and expectancy-value perspectives. Journal of Educational Psychology, 104, 32–47. https://doi.org/10.1037/a0026042 Diemer, M. A., Marchand, A. D., Mckellar, S. E., & Malanchuk, O. (2016). Promotive and corrosive factors in African American students’ math beliefs and achievement. Journal of Youth and Adolescence, 45(6), 1208–1225. https://doi.org/ 10.1007/s10964-016-0439-9 Dietrich, J., Moeller, J., Guo, J., Viljaranta, J., & Kracke, B. (2019). In-the-moment profiles of expectancies, values, and costs. Frontiers in Psychology, 10, Article: 1662. https://doi.org/10.3389/fpsyg.2019.01662

The Relevance of Situated Expectancy-Value Theory

15

Durik, A. M., Vida, M., & Eccles, J. S. (2006). Task values and ability beliefs as predictors of high school literacy choices: A developmental analysis. Journal of Educational Psychology, 98(2), 382–393. https://doi.org/10.1037/00220663.98.2.382 Eccles, J. S. (1993). School and family effects on the ontogeny of children’s interests, self-perceptions, and activity choice. In J. Jacobs (Ed.), Nebraska symposium on motivation, 1992: Developmental perspectives on motivation (pp. 145–208). University of Nebraska Press. Eccles, J. S. (2005). Subjective task values and the Eccles et al. model of achievementrelated choices. In A. J. Elliot, & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). Guilford. Eccles, J. S. (2009).Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist, 44(2), 78–89. https://doi.org/10.1080/00461520902832368 Eccles, J. S., & Midgley, C. (1989). Stage/environment fit: Developmentally appropriate classrooms for early adolescents. In R. Ames & C. Ames (Eds.), Research on motivation in education (Vol. 3, pp. 139–181). Academic Press. Eccles, J. S., & Roeser, R. W. (2010). An ecological view of schools and development. In J. L. Meece, & J. S. Eccles (Eds.), Handbook of research on schools, schooling, and human development (pp. 6–21). Routledge. Eccles, J. S., & Wigfield, A. (1995). In the mind of the actor: The structure of adolescents’ achievement task values and expectancy-related beliefs. Personality and Social Psychology Bulletin, 21(3), 215–225. https://doi.org/10.1177/ 0146167295213003 Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Eccles, J. S., Wigfield, A., Harold, R., & Blumenfeld, P. B. (1993). Age and gender differences in children’s self- and task perceptions during elementary school. Child Development, 64, 830–847. https://doi.org/10.1111/j.1467-8624.1993. tb02946.x Eccles-Parsons, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation (pp. 75–146). W. H. Freeman. Flake, J. K., Barron, K. E., Hulleman, C., McCoach, D. B., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. https://doi.org/10.1016/j.cedpsych. 2015.03.002 Gaspard, H., Dicke, A.-L., Flunger, B., Schreier, B., Häfner, I., Trautwein, U., & Nagengast, B. (2015). More value through greater differentiation: Gender differences in value beliefs about math. Journal of Educational Psychology, 107(3), 663–677. https://doi.org/10.1037/edu0000003 Gaspard, H., Häfner, I., Parrisius, C., Trautwein, U., & Nagengast, B. (2017). Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject.

16

Allan Wigfield and Jacquelynne S. Eccles

Contemporary Educational Psychology, 48, 67–84. https://doi.org/10.1016/j. cedpsych.2016.09.003 Gaspard, H., Lauermann, F., Rose, N., Wigfield, A., & Eccles, J. S. (2020) Crossdomain trajectories of students’ ability self-concepts and intrinsic value in math and language arts. Child Development, 91(5), 1800–1818. https://doi.org/10.1111/ cdev.13343 Harackiewicz, J. M., Rozek, C. S., Hulleman, C. S., & Hyde, J. S. (2012). Helping parents to motivate adolescents in mathematics and science: An experimental test of a utility-value intervention. Psychological Science, 43, 899–906. https:/doi. org/10.1177/0956797611435530 Jacobs, J., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self-competence and values: Gender and domain differences across grades one through twelve. Child Development, 73(2), 509–527. https://doi. org/10.1111/1467-8624.00421 Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of math anxiety and its consequences for young adolescents’ course enrollment intentions and performances in mathematics. Journal of Educational Psychology, 82, 60–70. https:// doi.org/10.1037/0022-0663.82.1.60 Musu-Gillette, L. E., Wigfield, A., Harring, J., & Eccles, J. S. (2015). Trajectories of change in student’s self-concepts of ability and values in math and college major choice. Educational Research and Evaluation, 21, 343–370. https://doi.org/10.10 80/13803611.2015.1057161 Nagengast, B., Marsh, H. W., Scalas, L. F., Xu, M., Hau, K. T., & Trautwein, U. (2011). Who took the X out of expectancy-value theory? A psychological mystery, a substantive-methodological synergy, and a cross-national generalization. Psychological Science, 22, 1058–1066. https://doi.org/10.1177/ 0956797611415540 Nolen, S. (2020). A situated turn in the conversation about motivation theory. Contemporary Educational Psychology, 61, Article 101866. https://doi.org/ 10.1016/j.cedpsych.2020.101866 Peck, S. C., Brodish, A. B., Malanchuk, O., Banerjee, M., & Eccles, J. S. (2014). Racial/ethnic socialization and identity development in Black families: The role of parent and youth reports. Developmental Psychology, 50(7), 1897–1909. https:// doi.org/10.1037/a0036800 Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106(1), 315–329. https://doi.org/10.1037/a0034027 Robinson, K. A., & Lee, S. O. (2022, April). Development in context: Short-term trajectories of expectancy and values in a chemistry and computer science. In D. K. Benden, P. N. Beymer, & F. Lauermann (Chairs), Students’ expectancies and values following the transition to higher education: A situated perspective [Symposium]. American Educational Research Association Annual Meeting, San Diego, United States. Rosenzweig, E. Q., Hecht, C. A., Priniski, S. J., Canning, E. A., Asher, M. W., Tibbetts, Y., Hyde, J. S., & Harackiewicz, J. M. (2021). Inside the STEM pipeline: Changes in students’ biomedical career plans across the college years. Science Advances, 7(18), Article eabe0985. https://doi.org/10.1126/sciadv.abe0985 Rosenzweig, E. Q., Wigfield, A., & Eccles, J. S. (2022). Beyond utility value interventions: Recommendations for next steps in expectancy-value intervention research.

The Relevance of Situated Expectancy-Value Theory

17

Educational Psychologist, 57(1), 11–30. https://doi.org/10.1080/00461520. 2021.1984242 Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. Simpkins, S. D., Davis-Kean, P. E., & Eccles, J. S. (2006). Math and science motivation: A longitudinal examination of the links between choices and beliefs. Developmental Psychology, 42(1), 70–83. https://doi.org/10.1037/0012-1649. 42.1.70 Simpkins, S. D., Fredricks, J., & Eccles, J. S. (2015). The role of parents in the ontogeny of achievement-related motivation and behavioral choices. Monographs of the Society for the Study of Child Development, 80(2), 1–22. https://doi.org/10.1111/ mono.12157 Tonks, S. M., Wigfield, A., & Eccles, J. S. (2018). Expectancy value theory in crosscultural perspective: What have we learned in the last 15 years? In G. A. D. Liem & D. McInerney (Eds.), Recent advances in sociocultural influences on motivation and learning: Big theories revisited (2nd ed). Information Age Publishers. Trautwein, U., Marsh, H. W., Nagengast, B., Ludtke, O., Nagy, G., & Jonkmann, K. (2012). Probing for the multiplicative term in modern expectancy-value theory: A latent interaction modeling study. Journal of Educational Psychology, 104, 763–777. https://doi.org/10.1037/a0027470 Wang, M., Eccles, J. S., & Kenny, S. (2013). Not lack of ability but more choice: Individual and gender difference in choice of careers in sciences, technology, engineering, and mathematics. Psychological Sciences, 24(5), 770–775. https://doi. org/10.1177/0956797612458937 Wang, Q., & Pomerantz, E. M. (2009). The motivational landscape of early adolescence in the United States and China: A longitudinal investigation. Child Development, 80(4), 1272–1287. https://doi.org/10.1111/j.1467-8624.2009.01331.x Watt, H. M. G. (2004). Development of adolescents’ self-perceptions, values, and task perceptions. Child Development, 75, 1556–1574. https://doi.org/10.1111/ j.1467-8624.2004.00757.x Watt., H. M. G., Bucich, M., & Dacosta, L. (2019). Adolescents’ motivational profiles in mathematics and science: Associations with achievement striving, career aspirations, and psychological well-being. Frontiers in Psychology, 10, Article 990. https://doi.org/10.3389/fpsyg.2019.00990 Wigfield, A., Cambria, J., & Eccles, J. S. (2012). Motivation in education. In R. C. Ryan (Ed.), Oxford handbook of motivation (pp.463–478). Oxford University Press. Wigfield, A., & Eccles, J. S. (2020). 35 years of research on students’ subjective task values and motivation: A look back and a look forward. In A. Elliot (Ed.), Advances in motivation science (Vol. 7, pp. 162–193). Elsevier. Wigfield, A., Eccles, J. S., Fredricks, J., Simpkins, Roeser, R., & Schiefele, U. (2015). Development of achievement motivation and engagement. In R. Lerner (Series Ed.) and M. Lamb (Vol. Ed.), Handbook of child psychology and developmental science (7th ed., Vol. 3, pp. 657–700). Wiley. Wigfield, A., Eccles, J. S., Yoon, K. S., Harold, R. D., Arbreton, A., FreedmanDoan, C., & Blumenfeld, P. C. (1997). Changes in children’s competence beliefs and subjective task values across the elementary school years: A three-year study. Journal of Educational Psychology, 89, 451–469. https://doi.org/10.1037/ 0022-0663.89.3.451

18

Allan Wigfield and Jacquelynne S. Eccles

Wigfield, A., & Gladstone, J. (2019). What does expectancy-value theory have to say about motivation and achievement in times of change and uncertainty. In E. N. Gonida & M. Lemos (Eds.), Motivation in education at a time of global change: Theory, research, and implications for practice (Advances in motivation and achievement, Vol. 20, pp. 15–32). Emerald. Wigfield, A., Rosenzweig, E., & Eccles, J. (2017). Achievement values. In Elliot, A. J., Dweck, C. S., & Yeager, D. S. (Eds.), Handbook of competence and motivation: Theory and application (2nd ed., pp. 116–134). Guilford Press. Wigfield, A., Turci, L., Cambria, J., & Eccles, J. S. (2019). Motivation in education. In R. M. Ryan (Ed.), Oxford handbook of motivation (2nd ed., pp. 443–462). Oxford University Press.

2 EXPLORING INTEREST THEORY AND ITS RECIPROCAL RELATION TO ACHIEVEMENT GOALS, SELF-EFFICACY, AND SELF-REGULATION K. Ann Renninger, Suzanne E. Hidi and Arijit De

Abstract Interest benefits learners’ attention, feelings, sustained engagement, strategy use, and learning. It does not occur in a vacuum and is reciprocally related to other variables related to motivation. In the early phases of interest development, these variables are not necessarily related; however, in later phases, they are likely to be. In this chapter, we discuss the implications of relationships that researchers have identified between interest and achievement goals, self-efficacy, and self-regulation, paying attention to for whom and when these relationships make a difference. We provide background information about interest, noting that the process of its development is characterised by positive feelings, on-going information search, and increasing coordination of individuals’ knowledge of and value for content. We note that it is the physiological basis of interest that explains its universality and ensures the potential for its development, a course that is also informed by sociocultural influences. Following this, we describe findings from a review of research undertaken to consider the reciprocal relationships that have been identified between interest and achievement goals, self-efficacy, as well as self-regulation. Understanding such relationships may be helpful for fostering an optimal learning environment and could benefit educational theory and research.

Introduction

Interest describes individuals’ meaningful engagement with particular content (e.g. writing, football). This engagement is sociocultural, as interest exists and develops through interactions with other people (peers, parents, educators), as well as by the design of the environment (e.g. assignments, software).1 Interest refers to individuals’ psychological state during DOI: 10.4324/9781003303473-3

20

K. Ann Renninger, Suzanne E. Hidi and Arijit De

engagement, as well as to the likelihood that they voluntarily return to reengage. Their psychological state informs their experience, and feelings, of interest. The re-engagement with content enables them to begin acquiring and developing value and related knowledge. The human brain is hardwired physiologically for interested engagement, and tends to involve information search (e.g. exploration, searching for additional resources, asking questions) (e.g. Gottlieb et al., 2013; Murayama et al., 2019). All typically functioning individuals are likely to experience new interest(s) on a regular basis and to continue to engage with existing interest(s), as long as they find meaning and/or challenge in doing so. The presence of interest facilitates sustained engagement (e.g. Azevedo, 2013; Renninger & Hidi, 2002), guides attention (e.g. Hidi, 1995; Renninger & Wozniak, 1985), and strategy use (e.g. O’Keefe & Linnenbrink-Garcia, 2014; Renninger et al., 2022), as well as learning (Crouch et al., 2018; Jansen et al., 2016). Its development follows an established course of four phases (Hidi & Renninger, 2006; Renninger & Hidi, 2022b). In this chapter, we first provide background information about interest and its development. Following this, we describe findings and implications of a systematic review conducted to consider the similarities and differences between interest and three variables that have been associated with motivation and previously discussed as both distinct from and having the potential to be reciprocally related to interest: achievement goals (e.g. Harackiewicz et al., 2008), self-efficacy (Hidi et al., 2002; Nuutila et al., 2020), and self-regulation (Sansone & Thoman, 2005; Sansone et al., 2015). The associations among these variables seem to be similar: in the early phases of their development, interest and each of these variables appears to be unrelated, whereas in later phases of interest, they become associated. In addition, each of these variables has been shown to have unique relationships within each phase of interest development (Lipstein & Renninger, 2007). Findings such as these, together with current calls to account for the synergies that exist among motivational variables (e.g. Hidi & Renninger, 2019b; Linnenbrink-Garcia & Wormington, 2019), suggest that it is important for both theory and practice to revisit the potential relations between interest and other variables related to motivation, with specific consideration of for whom and when they make a difference. Interest Development

The process of interest development is defined by individuals’ search for meaningful connections to particular content. It is characterised by positive feelings and involves individuals’ ongoing expansion of their value and knowledge. The learning environment has a central and supporting role in this process especially when a new interest is triggered, as assistance in the form of explicit scaffolding to find meaningful ways to make connections

Exploring Interest Theory and Its Reciprocal Relation 21

to content may be essential (see Alexander et al., 2019; Bergin, 2016). In particular, individuals may benefit from support to make self-related connections to the content that they need to process (e.g. Renninger & Hidi, 2022a). Neuroscientific research has demonstrated that the process of engaging in self-related information processing and self-prioritising enables learners to make connections that benefit the quality of their developing understanding (Sui & Humphreys, 2015). It positions learners to begin to ask their own questions and find answers (e.g. Hidi et al., 2018; Renninger & Hidi, 2022a). The Four-Phase Model

Studies of the four-phase model of interest development show that interest follows a similar developmental course for individuals of any age, across a wide range of contents and contexts, including the workplace. The initial triggering of a new interest (triggered situational interest) always begins with individuals’ attention to content. Whether or not a triggered interest is then sustained and progresses to the second phase (maintained situational interest) is dependent on the connections that the individuals are able to make to the new interest. These connections may occur serendipitously or be promoted by other people and intentional design. Such connections may require multiple opportunities and explicit scaffolding by others (e.g. Skalstad & Munkebye, 2022; Xu et al., 2012). When the triggering of interest is sustained and individuals begin seeking more content-related information, they are likely to progress to the third phase (emerging individual interest). In this process, seeking information is intrinsically motivating; it has been associated with the activation of the reward circuitry (Gottlieb et al., 2013; Panksepp, 1998), which results in positive feelings (e.g. Tang et al., 2022), joy (e.g. Zosh et al., 2017) and learning (e.g. Crouch et al., 2018; Jansen et al., 2016). An individual’s psychological state during engagement with interest sets in motion the possibility of ongoing and deepening engagement, and may shift them to the fourth phase (welldeveloped interest). In this final phase, individuals not only seek information but also want feedback that enables them to continue to deepen the connections they have made to their interest. Similarities and Differences among the Four Phases

Each phase of interest involves the development of positive feelings, value, and knowledge, although their relative proportions may vary. Feelings and value often dominate the two earlier phases of interest, as individuals may only be starting to acquire knowledge about their interest. On the other hand, knowledge is likely to assume a more prominent role in the two later phases of interest development. Shifts in the relative importance of the feelings, value,

22

K. Ann Renninger, Suzanne E. Hidi and Arijit De

and knowledge components of interest reflect the extent to which learners are in the position to make meaningful connections to the content and how much extrinsic support they may need. As learners begin engaging in information search based on their own ideas, their self-generated activity is relatively more intrinsically motivated. However, we want to emphasise the use of the word “relative” in this context. Both earlier and later phases of interest can be supported by extrinsic and intrinsic motivators, although how much extrinsic support is required varies (e.g. Hidi & Renninger, 2006; Renninger & Hidi, 2016). For Whom and When

To summarise, all normally functioning individuals

• are hardwired to be able to develop interest, • can make meaningful connections to content, • may proceed to develop interest, once an interest is triggered and sustained. Conclusions from Xu et al.’s (2012) study of eight exemplary African-American teachers in the United States provide corroboration of these points, and have implications for all types of classrooms. In their study, the importance of instructional practices such as teachers’ (a) expressing interest in the subject matter that they teach, (b) providing explicit scaffolding to support students’ meaningful engagement with the content, as well as (c) extending opportunities for them to re-engage the same concepts in multiple ways was demonstrated. Regardless of geographical location, age of student or subject matter addressed, findings regarding interest and instructional practice are consistent. More specific details about scaffolding were offered by Skalstad and Munkebye’s (2022) study of the needs of young Norwegian children. This work extended that of Rotgans and Schmidt (2011), who analysed instructional practices of instructors of post-secondary students in Singapore. Skalstad and Munkebye argued that teachers should focus on encouraging social congruence (e.g. taking part in exploration with their students), supporting cognitive engagement (e.g. encouraging exploration), and enabling their students’ knowledge to develop (e.g. offering additional information). They also noted that these practices needed to take into consideration the students’ phase of interest development. Educators’ instructional practices are critical. There are at least two basic approaches that promote self-related information processing (as described earlier) for supporting students to develop interest in content. One that requires only minor revisions to an existing curriculum asks students to reflect on the work they have been doing and share that information. An example is provided by the ICAN Intervention, in which middle school students were asked to write responses to prompts such as: “I can tell you which liquid had a greater surface tension in the penny experiment (soapy water or pure

Exploring Interest Theory and Its Reciprocal Relation 23

water), and how I know that liquid had a stronger surface tension” (e.g. Hanke-Smith & Renninger, under review). Another approach involves purposefully employing topics and tasks that reference students’ own experiences and/or culturally relevant content. For example Clark (2017) found that culturally relevant texts benefitted AfricanAmerican students who were struggling readers (see also Anderson et al., 1987). Clark’s study indicated that “a dosage” of cultural relevance can trigger topic interest, as well as improve performance. Similar findings were reported in studies of English as a second language (e.g. Goodman, 1982; Jiménez, 1997). This approach has also been shown to enhance interest and performance in technology applications designed to match students’ interest (e.g. Bernacki et al., 2021). It might be expected that there would be some variations in interest development associated with group membership such as gender, race/ethnicity or socio-economic status. However, in most studies, these demographic variables were not examined, and when they were, they focused almost exclusively on gender. The overall findings concerning gender are inconsistent, although there are a few conclusions that can be drawn. First, males and females tend to differ in the topics that are of interest to them. Gender differences among individuals in their interest for particular occupations have been well established in the studies of vocational interest (e.g. Su et al., 2009), and suggest that boys typically prefer thing-oriented topics (e.g. mechanics), whereas girls prefer people-oriented topics (e.g. nursing) (e.g. Stoet & Geary, 2022; Su & Rounds, 2015). In a now classic example of gender differences in topic interest, Hoffmann and Häussler (1998) reported that seventh grade boys in physics classes did not need support for processing information that addressed the mechanics of pumps in an oil-fired heating plant, whereas girls in the same classes did. When the context of the lesson was changed to focus on pumping blood using an artificial heart pump, the girls’ interest (and performance) improved, whereas for boys, no such change occurred. Second, the research literature has also suggested that boys may benefit more from the presence of interest than girls, and/or that boys’ performance is particularly supported by interest, whereas girls’ performance is less influenced by their interests. Asher and Markell (1974), for example, reported that only when fifth grade boys were interested in a text did they read as well as girls, noting that boys had the ability to read and comprehend if they were motivated. Similarly, Ainley et al. (2002) found that boys were less persistent in reading than girls when they were not interested in the topic. Interest and Other Related Variables Associated with Motivation

We noted at the outset of this chapter that interest does not exist in a vacuum. Students’ interests and their beliefs about their experiences influence how and why they participate. More specifically, their interests and beliefs, such as

24

K. Ann Renninger, Suzanne E. Hidi and Arijit De

achievement goals and self-efficacy, inform their engagement, and whether or not they follow through to self-regulate. Here, given previously established findings, we undertook a systematic review of the literature from the last four years to identify empirical research that addressed the relations between interest and achievement goals, self-efficacy, and self-regulation. Our search terms included interest, achievement goals, self-efficacy, self-regulation, gender, race/ethnicity, and socio-economic status. In earlier studies, interest has been described as having a reciprocal relationship to achievement goals (e.g. Harackiewicz et al., 2008), self-efficacy (e.g. Hidi et al., 2002; Nuutila et al., 2020), and self-regulation (Sansone & Thoman, 2005), and that understanding this coordination may be critical especially for practice. However, our review of the literature also reveals that most recent investigations of these variables have not assessed interest. The studies that have included interest have typically operationalised interest in terms of feelings and value, and did not include knowledge and engagement. Moreover, demographic factors such as gender, age, race/ethnicity, and socio-economic status have often been controlled, rather than examined, in the analyses. Similar to interest research, when demography was considered, the focus has been almost exclusively on gender. We note that there have been many studies that reported gender differences in relation to the three other variables (e.g. Tetering et al., 2020; Usher et al., 2019; Wirthwein et al., 2020); however, none of the studies included interest as an additional variable. Achievement Goals and Interest

Achievement goals refer to individuals’ plans for and approaches to an activity, such as a biology class (Urdan & Kaplan, 2020). They guide individuals’ behaviour to realise a desired level of competence to which the individual is committed (Hulleman et al., 2010). They are influenced by interest (e.g. Bandura & Schunk, 1981), and may be relatively short-term (proximal) or long-term (distal). Two of the most frequently studied goal types are mastery achievement goals in which individuals are focused on improving skills and learning new things, and performance goals in which individuals’ efforts are directed towards establishing or demonstrating ability (Nolen, 2019). However, goal types have been further differentiated as mastery-approach, masteryavoidance, performance-approach, and performance-avoidance (e.g. Elliot & McGregor, 2001), and both task- (e.g. doing the task correctly) and self-based achievement goals (e.g. doing better than before) (Elliot et al., 2011). Findings related to different goal types have shown that mastery achievement goals are associated with interest (e.g. Middleton & Midgley, 1997). For example, Harackiewicz et al. (2008) highlighted the reciprocal relationships among mastery achievement goals and interest. They measured college students’ achievement goals, situational interest, and performance at the

Exploring Interest Theory and Its Reciprocal Relation 25

beginning and the end of a psychology course and seven semesters later, and demonstrated that mastery achievement goals are significant predictors of continued interest in a given academic domain. They reported that students’ initial interest in psychology predicted the likelihood of adopting mastery achievement goals, situational interest during the course, and ongoing interest (signalled by enrolment in a psychology course seven semesters later). Moreover, Harackiewicz et al. showed that the influence of the students’ initial interest on their continued interest was mediated by mastery achievement goals. As they explained, individuals’ interest continues to deepen over time, which motivates their desire to learn more and leads to the development of mastery achievement goals. Thus, mastery achievement goals are both a result and a precursor of interest, as well as a possible mediating factor for the continued development of interest. Harackiewicz et al. (2008) also hypothesised that performanceapproach achievement goals may impact future course selections and major choices, as the performance of the college students was predicted by performance-approach achievement goals. They suggested that higher grades in a subject may lead to an increase in perceived value which, in turn, may drive students to seek out similar subjects/courses through which they can develop and deepen interest in their topic of choice. While individuals may have achievement goals without having interest, findings demonstrate that goals change as interest develops, and that interest may be a critical facilitator for realising goals (Durik & Renninger, 2019). In addition, individual characteristics such as gender, race/ethnicity, and socioeconomic status may influence the association of achievement goals and interest. For example, Jagacinski (2013) examined achievement goals, interest, and competence perceptions of women and men in college-level engineering and psychology courses. Performance-approach achievement goals predicted the women’s – but not the men’s – grades in engineering. For psychology courses, not only were women more interested in psychology than men, but gender also had an indirect effect on grades as mastery achievement goals predicted their grades. In early reviews of the achievement goal literature, Midgley et al. (2001) and Anderman (2002) concluded that boys were more likely to adopt performance achievement goals than girls. More recently, Yu and McLellan (2019) suggested that there is a gender gap in school achievement that favours girls. They reported that boys are more likely than girls to adopt social demonstration goals, performance-approach and performanceavoidance goals, and to engage in self-handicapping behaviours. Yet, in our review of studies for this chapter, we found that almost all of the studies in which achievement goals and interest were considered, differences based on demographic characteristics, including gender, were controlled rather than examined.

26

K. Ann Renninger, Suzanne E. Hidi and Arijit De

Self-Efficacy and Interest

Self-efficacy describes individuals’ beliefs about whether they can successfully complete a particular activity. Feelings of self-efficacy have been found to affect individuals’ choices about engagement, interest, effort, learning strategies, persistence, self-regulation, and achievement (e.g. Ahn & Bong, 2019). As Bandura (1997) pointed out, self-efficacy tends to increase as individuals recognise their competence. Feedback that supports individuals to reflect on what they are doing has been shown to contribute to developing competence and increases in self-efficacy and interest (Hidi et al., 2002). Although some studies addressing interest and self-efficacy suggest that these variables are independent (e.g. Lee et al., 2014), interest and self-efficacy have been shown to be positively related in a wide range of studies (Bong et al., 2015; Zimmerman & Kitsantas, 1999). Whereas some researchers have argued that self-efficacy precedes interest (e.g. Perez et al., 2019), others have demonstrated that interest precedes the development of self-efficacy (e.g. Chen et al., 2016; Fryer & Ainley, 2019). When self-efficacy is studied in relation to phases in the development of interest, they have been shown to be increasingly related as each develops (Lipstein & Renninger, 2007). In a study of fourth graders’ work with computerised inductive reasoning tasks, Nuutila et al. (2020) provided details about the relation between learner’s feelings of self-efficacy and different phases of interest development. They found that students’ pre-existing interest in math predicted their situational interest at the beginning and during the task. In addition, situational interest partly facilitated students’ self-efficacy and performance. In a longitudinal study of middle school mathematics students (6th–8th grades), Grigg et al. (2018) concluded that multidimensional interventions that include both interest and self-efficacy should be considered, especially when selection and achievement outcomes are at stake. Among their findings was evidence that mathematics interest predicted mathematics selfefficacy and assessed achievement, but mathematics self-efficacy did not predict interest in mathematics. They also found that although no significant differences were identified for gender and interest, girls had significantly lower self-efficacy in mathematics. Similarly, in a study of middle and high school students, Bong et al. (2015) showed that interest led to the development of self-efficacy, the relation between interest and self-efficacy was stronger in science and mathematics than in language arts, and that interest in mathematics was more stable than self-efficacy in mathematics. Moreover, they found that boys were both more interested in and had higher levels of self-efficacy than girls in mathematics and science, though these differences were not identified in language arts. The researchers concluded that the reluctance of girls to pursue science and mathematics is not due to differences of ability, but to differences in perceived abilities – something that may be addressed with support for interest development.

Exploring Interest Theory and Its Reciprocal Relation 27

Self-Regulation and Interest

Self-regulation describes individuals’ abilities to generate and effectively employ strategies to achieve their goals (e.g. Sansone & Harackiewicz, 1996; Schunk & Zimmerman, 1994), and has been shown to benefit from the presence of interest. Sansone and her colleagues’ (Sansone & Thoman, 2005; Sansone et al., 2019) Self-Regulation Model has demonstrated that individuals need to have at least some interest in order to self-regulate. For example, O’Keefe and Linnenbrink-Garcia (2014) reported that the presence of interest optimised the abilities of undergraduate students working with anagram-based experimental tasks to self-regulate and persist even when the tasks were difficult. They also showed that having interest increased the chance that students would meet their achievement goals. Similarly, in a study of middle school students’ engagement in four subject areas, Lee et al. (2014) found that although individual interest was independent of self-regulation, it not only correlated with it but was also its predictor. In addition, for boys, academic self-regulation was more likely to occur if an individual interest was already established, whereas for girls, having an individual interest was not necessary for effective self-regulation. As self-regulation impacts goal attainment, support to develop these skills in conjunction with interest may be needed. Perry (1998), for example in a study of second and third grade students’ writing, found that classrooms that provided opportunities for students to deliberate with each other and make choices about the type of tasks to engage in, and the challenges that this involved, were classrooms that supported the development of self-regulated learning. Perry’s study demonstrated that even for young children, self-regulation could be promoted. A key feature was the use of instructional practices that encouraged self-related information processing and, as such, promoted interest development. In a related study, Nolen (2007) reported on findings from a study of two second grade classrooms, one of which encouraged students to take responsibility for their learning, and one which was teacher-directed. Students developed their writing skills and an interest in writing only in the classroom that promoted students to engage in meaning-making by taking responsibility for their learning. Similar to interest development, the development of self-regulation as described in Zimmerman’s (2000a, 2000b) multi-level model includes four levels or phases (observation, emulation, self-control, self-regulation) (Hidi & Ainley, 2008). As Lipstein and Renninger (2007) reported, like the earlier phases of interest development, the earliest levels of self-regulation, initially benefit from external scaffolding and educators’ attention to the design of the environment (e.g. software or instructional practices). They explained that individuals’ need for assistance changes as they self-generate their own forms of support as their self-regulation and interest develop (e.g. Hidi & Ainley, 2008). When interest is developed, self-regulation also includes the ability to employ strategies that enable the attainment of goals and requires

28

K. Ann Renninger, Suzanne E. Hidi and Arijit De

meta-awareness. This information appears to be essential for practice, but we found no new studies on this subject. Although we could not find studies that examined interest, self-regulation, and demographic differences, numerous studies have addressed self-regulation and gender differences. For example, in a large-scale cross-sectional study assessing males’ and females’ self-perceived self-regulation, it was found that during mid-adolescence, females rated their attention as higher than males, and also reported higher levels of both self-control and self-monitoring (Tetering et al., 2020). The open question is how these findings would be modified by considering the effects of interest. Concluding Thoughts

Developing at least some interest in content to be learned may not only be useful but also essential to facilitate students’ meaningful engagement and learning. Moreover, research findings demonstrate how the sociocultural context, including instructional practices, makes a crucial contribution to interest development. Although we acknowledge that a person may set achievement goals, have self-efficacy, and/or self-regulate without having interest, the empirical relationships that have been identified between interest and these variables indicate that they are increasingly coordinated as they develop. Such coordination suggests that future research addressing the relation of interest and achievement goals, self-efficacy, as well as self-regulation is critical. In particular, it would benefit both theory and practice if interest development would be examined in studies of other variables associated with motivation. It also would be informative if these investigations were to consider demographic variables, and positioned to provide more information about for whom and when relationships between interest and other variables related to motivation make a difference. Acknowledgements

We gratefully acknowledge the suggestions of two anonymous reviewers, editorial suggestions provided by Melissa Emmerson, and financial support provided by both the Swarthmore College Faculty Research Fund and the Senior College at the University of Toronto for our work on this chapter. Note 1 Due to space limitations, throughout this chapter we draw on theoretical discussions provided by Hidi and Renninger in other publications describing interest measurement and other approaches to studying interest (Renninger & Hid, 2011), motivation and engagement (Renninger & Hidi, 2016), interest and curiosity (Hidi & Renninger, 2019a; Renninger et al., 2023); self-related information processing (Hidi et al., 2018; Renninger & Hidi, 2022a), and reward (Hidi & Renninger, 2023).

Exploring Interest Theory and Its Reciprocal Relation 29

References Ahn, H., & Bong, M. (2019). Self-efficacy in learning. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 63–86). Cambridge University Press. Ainley, M. D., Hillman, K., & Hidi, S. (2002). Gender and interest processes in response to literary texts: Situational and individual interest. Learning and Instruction, 12(4), 411–428. https://doi.org/10.1016/S0959-4752(01)00008-1 Alexander, J. M., Johnson, K. E., & Neitzel, C. (2019). Multiple points of access for supporting interest in science. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 312–352). Cambridge University Press. Anderman, E. M. (2002). The development of goal orientation. In A. Wigfield & J. Eccles (Eds.), Development of achievement motivation (pp. 197–220). Academic Press. https://doi.org/10.1016/B978-012750053-9/50010-3 Anderson, R. C., Shirey, L. L., Wilson, P. T., & Fielding, L. G. (1987). Interestingness of children’s reading material. In R. E. Snow & M. E. Farr (Eds.), Aptitude, learning, and instruction, Vol. III: Cognitive and affective process analyses (pp. 287–299). Erlbaum. Asher, S. R., & Markell, R. A. (1974). Sex differences in comprehension of highand low-interest reading material. Journal of Educational Psychology, 66(5), 680–687. https://doi.org/10.1037/h0037483 Azevedo, F. S. (2013). Knowing the stability of model rockets: An investigation of learning in interest-based practices. Cognition and Instruction, 31(3), 345–374. https://doi.org/10.1080/07370008.2013.799168 Bandura, A. (1997). Self-efficacy: The exercise of control. WH Freeman. Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality & Social Psychology, 41(3), 586–598. https://doi.org/10.1037/0022-3514.41.3.586 Bergin, D. A. (2016). Social influences on interest. Educational Psychologist, 51(1), 7–22. https://doi.org/10.1080/00461520.2015.1133306 Bernacki, M. L., Greene, M. J., & Lobczowski, N. G. (2021). A systematic review of research on personalized learning: Personalized by whom, to what, how, and for what purpose(s)? Educational Psychology Review, 33(4), 1675–1715. https://doi. org/10.1007/s10648-021-09615-8. Bong, M., Lee, S. K., & Woo, Y. K. (2015). The roles of interest and self-efficacy in the decision to pursue mathematics and science. In K. A. Renninger, M. Nieswandt, & S. Hidi (Eds.), Interest in mathematics and science learning (pp. 33–48). American Educational Research Association. https://doi.org/10.3102/978-0935302-42-4_2 Chen, J. A., Tutwiler, M. S., Metcalf, S. J., Kamarainen, A., Grotzer, T., & Dede, C. (2016). A multi-user virtual environment to support students’ self-efficacy and interest in science: A latent growth model analysis. Learning and Instruction, 41, 11–22. https://doi.org/10.1016/j.learninstruc.2015.09.007 Clark, K. F. (2017). Investigating the effects of culturally relevant texts on African American struggling readers’ progress. Teachers College Record, 119(5), 1–30. https://doi.org/10.1177/016146811711900503 Crouch, C. H., Wisittanawat, P., Cai, M., & Renninger, K. A. (2018). Life science students’ attitudes, interest, and performance in introductory physics for life sciences (IPLS): An exploratory study. Physical Review Physics Education Research, 14(1), 010111. https://doi.org/10.1103/PhysRevPhysEducRes.14.010111

30

K. Ann Renninger, Suzanne E. Hidi and Arijit De

Durik, A. M., & Renninger, K. A. (2019). Achievement strivings: Motives and goals that promote competence. In D. P. McAdams, R. L. Shiner, & J. L. Tackett (Eds.), The handbook of personality development (pp. 295–312). Guilford. Elliot, A. J., & McGregor, H. A. (2001). A 2×2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–519. https://doi.org/10.1037/ 0022-3514.80.3.501 Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3 × 2 achievement goal model. Journal of Educational Psychology, 103(3), 632–648. https://doi.org/10.1037/ a0023952 Fryer, L. K., & Ainley, M. (2019). Supporting interest in a study domain: A longitudinal test of the interplay between interest, utility-value, and competence beliefs. Learning and Instruction, 60(1), 252–262. https://doi.org/10.1016/j. learninstruc.2017.11.002 Goodman, Y. M. (1982). Retellings of literature and the comprehension process. Theory into Practice, 21(4), 301–307. https://doi.org/10.1080/0040584820 9543022 Gottlieb, J., Oudeyer, P. Y., Lopes, M., & Baranes, A. (2013). Information seeking, curiosity, and attention: Computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–593. https://doi.org/10.1016/j.tics.2013.09.001 Grigg, S., Perera, H. N., McIlveen, P., & Svetleff, Z. (2018). Relations among math self-efficacy, interest, intentions, and achievement: A social cognitive perspective. Contemporary Educational Psychology, 53, 73–86. https://doi.org/10.1016/j. cedpsych.2018.01.007 Hanke-Smith, K., & Renninger, K. A. (under review). Using evidence-based ICANs to help middle- and high school students see that science is “cool”. Harackiewicz, J. M., Durik, A. M., Barron, K. E., Linnenbrink, L., & Tauer, J. M. (2008). The role of achievement goals in the development of interest: Reciprocal relations between achievement goals, interest, and performance. Journal of Educational Psychology, 100(1), 105–122. https://doi.org/10.1037/0022-0663.100. 1.105 Hidi, S. (1995). A re-examination of the role of attention in learning from text. Educational Psychology Review, 7(4), 323–350. https://doi.org/10.1007/BF022 12306 Hidi, S., & Ainley, M. (2008). Interest and self-regulation: Relationships between two variables that influence learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and application (pp. 77–109). Erlbaum. Hidi, S., Berndorff, D., & Ainley, M. (2002). Children’s argument writing, interest and self-efficacy: An intervention study. Learning and Instruction, 12(4), 429–446. https://doi.org/10.1016/S0959-4752(01)00009-3 Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep 4102_4 Hidi, S. E., & Renninger, K. A. (2019a). Interest development and its relation to curiosity: Needed neuroscientific research. Educational Psychology Review, 31(4), 833–852. https://doi.org/10.1007/s10648-019-09491-3 Hidi, S. E., & Renninger, K. A. (2019b). Introduction: Motivation and its relation to learning. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 1–11). Cambridge University Press.

Exploring Interest Theory and Its Reciprocal Relation

31

Hidi, S. E., & Renninger, K. A. (2023). Interest and its relation to rewards, reward expectations, and incentives. In M. Bong, S. Kim, & J. Reeve (Eds.), Motivation science: Controversies and insights (pp. 159–164). Oxford University Press. Hidi, S. E., Renninger, K., & Northoff, A. (2018). The development of interest and self-related processing. In F. Guay, H. W. Marsh, D. M. McInerney, & R. G. Craven (Eds.), International advances in self research. Vol. 6: SELF – Driving positive psychology and well-being (pp. 51–70). Information Age Press. Hoffmann, L., & Häussler, P. (1998). An intervention project promoting girls’ and boys’ interest in physics. In L. Hoffmann, A. Krapp, K. A. Renninger, & J. Baumert (Eds.), Interest and learning (pp. 301–316). IPN. Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422–449. https://doi.org/10.1037/a0018947 Jagacinski, C. M. (2013). Women engineering students: Competence perceptions and achievement goals in the freshman engineering course. Sex Roles: A Journal of Research, 69(11–12), 644–657. https://doi.org/10.1007/s11199-013-0325-9 Jansen, M., Lüdtke, O., & Schroeders, U. (2016). Evidence for a positive relation between interest and achievement: Examining between-person and within-person variation in five domains. Contemporary Educational Psychology, 46, 116–127. https://doi.org/10.1016/j.cedpsych.2016.05.004 Jiménez, R. T. (1997). The strategic reading abilities and potential of five low-literacy Latina/o readers in middle school. Reading Research Quarterly, 32(3), 224–243. https://doi.org/10.1598/RRQ.32.3.1 Lee, W., Lee, M. J., & Bong, M. (2014). Testing interest and self-efficacy as predictors of academic self-regulation and achievement. Contemporary Educational Psychology, 39(2), 86–99. https://doi.org/10.1598/RRQ.32.3.1 Linnenbrink-Garcia, L., & Wormington, S. V. (2019). An integrative perspective for studying motivation relation to engagement and learning. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 739–758). Cambridge University Press. Lipstein, R., & Renninger, K. A. (2007). “Putting things into words”: The development of 12–15-year-old students’ interest for writing. In P. Boscolo & S. Hidi (Eds.), Motivation and writing: Research and school practice (pp. 113–140). Elsevier. https://doi.org/10.1163/9781849508216_008 Middleton, M. J., & Midgley, C. (1997). Avoiding the demonstration of lack of ability: An underexplored aspect of goal theory. Journal of Educational Psychology, 89(4), 710–718. https://doi.org/10.1037/0022-0663.89.4.710 Midgley, C., Kaplan, A., & Middleton, M. (2001). Performance-approach goals: Good for what, for whom, under what circumstances, and at what cost? Journal of Educational Psychology, 93(1), 77–86. https://doi.org/10.1037/0022-0663. 93.1.77 Murayama, K., FitzGibbon, L., & Sakaki, M. (2019). Process account of curiosity and interest: A reward-learning perspective. Educational Psychology Review, 31, 875–895. https://doi.org/10.1007/s10648-019-09499-9 Nolen, S. B. (2007). The role of literate communities in the development of children’s interest in writing. In G. Rijlaarsdam (Series Ed.) & S. Hidi, & P. Boscolo (Vol. Eds.), Studies in writing: Vol. 19. Writing and motivation (1st ed., pp. 241–255). Elsevier. https://doi.org/10.1108/s1572-6304(2006)0000019015

32

K. Ann Renninger, Suzanne E. Hidi and Arijit De

Nolen, S. B. (2019). Conceptualizing goals in motivation and engagement. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 547–565). Cambridge University Press. Nuutila, K., Tapola, A., Tuominen, H., Kupiainen, S., Páásztor, A., & Niemivirta, M. (2020). Reciprocal predictions between interest, self-efficacy, and performance during a task. Frontiers in Education, 5, Article 36. https://doi.org/10.3389/ feduc.2020.00036 O’Keefe, P. A., & Linnenbrink-Garcia, L. (2014). The role of interest in optimizing performance and self-regulation. Journal of Experimental Social Psychology, 53, 70–78. https://doi.org/10.1016/j.jesp.2014.02.004 Panksepp, J. (1998). Affective neuroscience: The foundations of human and animal emotion. Oxford University Press. Perez, T., Dai, T., Kaplan, A., Cromley, J. G., Brooks, W. D., White, A. C., Mara, K. R., & Balsai, M. J. (2019). Interrelations among expectancies, task values, and perceived costs in undergraduate biology achievement. Learning and Individual Differences, 72, 26–38. https://doi.org/10.1016/j.lindif.2019.04.001 Perry, N. E. (1998). Young children’s self-regulated learning and contexts that support it. Journal of Educational Psychology, 90(4), 715–729. https://doi.org/ 10.1037/0022-0663.90.4.715 Renninger, K. A., Gantt, A. L., & Lipman, D. A. (2022). Comprehension of argumentation in math text: What is the role of interest? ZDM- Mathematics Education. https://doi.org/10.1007/s11858-022-01445-4 Renninger, K. A., & Hidi, S. (2002). Interest and achievement: Developmental issues raised by a case study. In A. Wigfield & J. Eccles (Eds.), Development of achievement motivation (pp. 173–195). Academic Press. Renninger, K. A., & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168–184. https://doi. org/10.1080/00461520.2011.587723 Renninger, K. A., & Hidi, S. (2016). The power of interest for motivation and engagement. Routledge. Renninger, K. A., & Hidi, S. E. (2022a). Interest development, self-related information processing, and practice. Theory into Practice, 61(1), 23–34. https://doi.org/ 10.1080/00405841.2021.1932159 Renninger, K. A., & Hidi, S. E., (2022b). Interest: A unique affective and cognitive motivational variable that develops. In A. Elliot (Ed.), Advances in motivation science (Vol. 9, pp. 179–239). Elsevier. https://doi.org/10.1016/bs.adms.2021.12.004 Renninger, K. A., Qiu, F. W., & Hidi, S. E. (2023). Curiosity and interest: What should educators know and consider? In R. Tierney, F. Rizvl, & K. Ercikan (Eds). International Encyclopedia of Education, I. Wilkinson & J. Parr (vol. eds.), Learning, cognition, and human development. (Vol. 6, pp. 141–148). Elsevier. https:// dx.doi.org/10.1016/B978-0-12-818630-5.14027-8 Renninger, K. A., & Wozniak, R. (1985). Effect of interest on attentional shift, recognition, and recall in young children. Developmental Psychology, 21(4), 624–632. https://doi.org/10.1037/0012-1649.21.4.624 Rotgans, J. I., & Schmidt, H. G. (2011). The role of teachers in facilitating situational interest in an active-learning classroom. Teaching and Teacher Education, 27(1), 37–42. https://doi.org/10.1016/j.tate.2010.06.025 Sansone, C., Geerling, D. M., Thoman, D. B., & Smith, J. L. (2019). Self-regulation of motivation: A renewable resource for learning. In K. A. Renninger & S. E. Hidi

Exploring Interest Theory and Its Reciprocal Relation 33

(Eds.), The Cambridge handbook of motivation and learning (pp. 87–110). Cambridge University Press. Sansone, C., & Harackiewicz, J. M. (1996). “I don’t feel like it”: The function of interest in self-regulation. In L. M. Martin & A. Tesser (Eds.), Striving and feeling: Interactions among goals, affect, and self-regulation (pp. 203–228). Erlbaum. https://doi.org/10.4324/9781315806396 Sansone, C., & Thoman, D. B. (2005). Interest as the missing motivator in selfregulation. European Psychologist, 10(3), 175–186. https://doi.org/10.1027/10169040.10.3.175 Sansone, C., Thoman, D., & Fraughton, T. (2015). The relation between interest and self-regulation in mathematics and science. In K. A. Renninger, M. Nieswandt, & S. Hidi (Eds.), Interest in mathematics and science learning (pp. 111–131). American Educational Research Association. Schunk, D. H., & Zimmerman, B. J. (1994). Self-regulation of learning and performance: Issues and educational applications. In G. Rijlaarsdam (Series Ed.) & S. Hidi & P. Boscolo (Vol. Eds.), Studies in writing: Vol. 19. writing and motivation (1st ed., pp. 241–255). Elsevier. Skalstad, I., & Munkebye, E. (2022). How to support young children’s interest development during exploratory natural science activities in outdoor environments. Teaching and Teacher Education, 114, Article 103687. https://doi.org/10. 1016/j.tate.2022.103687 Stoet, G., & Geary, D. C. (2022). Sex differences in adolescents’ occupational aspirations: Variations across time and place. PLoS One, 17(1), Article e0261438. https://doi.org/10.1371/journal.pone.0261438 Sui, J., & Humphreys, G. W. (2015). The integrative self: How self-reference integrates perception and meaning. Trends in Cognitive Science, 19(12), 719–728. https://doi.org/10.1016/j.tics.2015.08.015 Su, R., & Rounds, J. (2015). All STEM fields are not created equal: People and things interests explain gender disparities across STEM fields. Frontiers in Psychology, 6, Article 189. https://doi.org/10.3389/fpsyg.2015.00189 Su, R., Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests. Psychological Bulletin, 135(6), Article 859. https://doi.org/10.1037/a0017364 Tang, X., Renninger, K. A., Hidi, S. E., Murayma, K., Lavonen, J., & Salmela-Aro, K. (2022). The differences and similarities between curiosity and interest: Metaanalysis and network analyses. Learning and Instruction, 80, Article 101628. https://doi.org/10.1016/j.learninstruc.2022.101628 Tetering, M. A. J. V., Laan, A. M. V., Kogel, C. H., Groot, R. H. M., & Jolles, J. (2020). Sex differences in self-regulation in early, middle and late adolescence: A large-scale cross-sectional study. PLoS One, 15(1), Article e0227607. https://doi. org/10.1371/journal.pone.0227607 Urdan, T., & Kaplan, A. (2020). The origins, evolution, and future directions of achievement goal theory. Contemporary Educational Psychology, 61, Article 101862. https://doi.org/10.1016/j.cedpsych.2020.101862 Usher, E. L., Li, C. R., Butz, A. R., & Rojas, J. P. (2019). Perseverant grit and selfefficacy: Are both essential for children’s academic success? Journal of Educational Psychology, 111(5), 877–902. https://doi.org/10.1037/edu0000324 Wirthwein, L., Sparfeldt, J. R., Heyder, A., Buch, S. R., Rost, D. H., & Steinmayr, R. (2020). Sex differences in achievement goals: Do school subjects matter? European

34

K. Ann Renninger, Suzanne E. Hidi and Arijit De

Journal of Psychology of Education, 35(2), 403–427. https://doi.org/10.1007/ s10212-019-00427-7 Xu, J., Coats, L. T., & Davidson, M. L. (2012). Promoting student interest in science: The perspectives of exemplary African American teachers. American Educational Research Journal, 49(1), 124–154. https://doi.org/10.3102/00028 31211426200 Yu, J., & McLellan, R. (2019). Beyond academic achievement goals: The importance of social achievement goals in explaining gender differences in self-handicapping. Learning and Individual Differences, 69, 33–44. https://doi.org/10.1016/j.lindif. 2018.11.010 Zimmerman, B. J. (2000a). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–40). Academic Press. https://doi.org/10.1016/b978-012109890-2/50031-7 Zimmerman, B. J. (2000b). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82–91. https://doi.org/10.1006/ceps.1999.1016. Zimmerman, B. J., & Kitsantas, A. (1999). Acquiring writing revision skill: Shifting from process to outcome self-regulatory goals. Journal of Educational Psychology, 91(2), 241–250. https://doi.org/10.1037/0022-0663.94.4.660 Zosh, J. M., Hopkins, E. J., Jensen, H., Liu, C., Neale, D., Hirsh-Pasek, K., Solis, S. L., & Whitebread, D. (2017). Learning through play: A review of the evidence. The LEGO Foundation. https://cms.learningthroughplay.com/media/wmtlmbe0/ learning-through-play_web.pdf

3 ACHIEVEMENT GOALS The Past, Present, and Possible Future of Achievement Goal Research in the Context of Learning and Teaching Martin Daumiller

Abstract The achievement goal approach is a particularly prominent approach to study differences in motivation. Rooted in the works developed by seminal achievement goal theorists in the 1980s, a wealth of knowledge has been gained about the characteristics, antecedents, and consequences of goal pursuit. Beyond this, achievement goal research is continuously expanding, with multiple recent theoretical developments and accompanying empirical realisations. This chapter presents an overview of the origins of achievement goal research and noteworthy theoretical developments to explain (1) how achievement goals can be conceptualised, (2) which types of goals are distinguishable and relevant, and (3) how they are pursued. Within this chapter, a focus is placed on students and teachers, including systematic differences between different learning and teaching contexts and how they can contribute to answer open questions in achievement goal research. Special attention is also paid to key emerging lines of research in the literature, including reasons behind goals and goal complexes, multiple goal pursuit and goal profiles, and generality and specificity of goal pursuit – paired with a discussion of the relevance of these issues for the design of empirical studies and the operationalisation of achievement goals.

Introduction

Why do some students look forward to lessons while others dread them? Why is it that certain teachers spend hours searching for the best ways to begin their lessons while others simply begin without a second thought? Understanding the goals that individuals hold can serve to answer such questions, as they represent the motivational underpinnings of different thoughts and behaviours. Goals guide us towards or away from DOI: 10.4324/9781003303473-4

36

Martin Daumiller

future-directed states. They form a central component of human motivation and can be studied from different perspectives (Austin & Vancouver, 1996). Within research on learning and teaching, the achievement goal approach has become an especially prominent approach to study differences in motivation (Elliot & Hulleman, 2017; Murayama & Elliot, 2019; Senko, 2016; Urdan & Kaplan, 2020). In particular, achievement goals have been studied extensively in students and lately also teachers (see Butler, 2014; Daumiller et al., 2020b), forming a powerful approach for describing motivational differences and processes within learning and teaching contexts. This approach stems from research interests of similar-minded scholars in the 1980s who sought to better understand the roots of differences in students’ learning patterns during challenges and setbacks (Ames, 1992; Covington & Omelich, 1984; Dweck, 1986; Maehr, 1984; Nicholls, 1984). These roots consisted of motivational orientations that explain, for example which content, to what extent, and with what strategies a person learns. Research on the development of ability self-concepts (John Nicholls), different reactions to failure (Carol Dweck), and incentive structures (Carol Ames) concluded that these motivational orientations can be described as learners’ perceived purposes for achievement actions. While coming from different perspectives, they agreed that students can define success in various ways and that there are multiple ways to be highly motivated, leading to different motivational styles that affect how one approaches a task. This created appealing approaches to explain differences in learning and performance that extended beyond stable personality traits, ability, or quantity of motivation. Since then, the achievement goal approach has undergone considerable development. Different goal models have been proffered, and extensive knowledge has been gained about the characteristics, antecedents, and consequences of goal pursuit. Specifically, many studies addressed how achievement goals can be leveraged to better understand and support effective learning and teaching processes. Building on the outlined origins and noteworthy theoretical developments, this chapter discusses how achievement goals can be conceptualised, which goals are distinguishable and how they are pursued. Within this, a focus is placed on students and teachers, including systematic differences between different contexts in learning and teaching and how they can contribute to answering open questions in achievement goal research. What are Achievement Goals?

Achievement goal research does not originate from a single theory and its further refinement, but rather from different views and ways of thinking that developed more or less simultaneously. Therefore, the term achievement goal theory may be less appropriate to describe this research tradition compared to speaking about an (or multiple) achievement goal approach(es) (Elliot,

Achievement Goals 37

2005; Murayama & Elliot, 2019). There are excellent narratives detailing how the notion of achievement goals has developed conceptually across the last decades since the original works in the 1980s (e.g. Elliot, 2005; Elliot & Hulleman, 2017). These provide the context for the theoretical and empirical issues surrounding the different understanding and measurement of achievement goals in the literature. Specifically, how exactly achievement goals are conceptualised makes a critical difference in the design of studies and the interpretation of results (Hulleman et al., 2010). There are differences regarding how broadly they are conceived, how exactly goals are defined, and what is considered to be their conceptual core. Achievement Goals or Goal Orientations

Following a conceptual integration of the original perspectives on achievement goals (Ames & Archer, 1988), in the 1990s, researchers often characterised achievement goals broadly as a set of coherent beliefs and feelings about ability, effort, errors, standards, and feedback (Ames, 1992; Kaplan & Maehr, 2007; Pintrich, 2000). This comprehensive account of many achievementrelevant variables into a single construct is typically labelled achievement goal orientation. While helpful to unite the different original perspectives on achievement goals, such an omnibus agglomeration of multiple diverse aspects within a single construct reduces conceptual clarity and impairs the distinguishment of antecedents and consequences from actual goal pursuit. Therefore, Elliot and colleagues proposed to focus on achievement goals per se instead of their broader network of achievement-related beliefs and feelings (Elliot, 2005; Elliot & Thrash, 2001). But what exactly are goals? Goals as Aims or Reasons

Goals are cognitively represented anticipations of action consequences that relate to future, desired action outcomes. They induce actions directed towards the outcome contained in the goal, structure the use of knowledge and skills, and function as a benchmark for judging success of actions. Most achievement goal researchers agree that goals essentially represent the purposes of behaviour (see Dweck, 1986; Nicholls, 1989); however, there are two primary perspectives on how to conceive these purposes (Elliot, 2005): as an aim or end state that an individual’s behaviours are directed at, or as an underlying reason for an individual engaging in certain behaviours. Accordingly, achievement goals are considered as aims, reasons, or a combination of both (Elliot & Hulleman, 2017). Importantly, this is not always an explicit decision made by researchers, and measures often vary widely to which extent they encompass aims, reasons, or both (Korn et al., 2019). For conceptual clarity, researchers need to make their goal conceptualisation explicit and carefully align empirical realisations with it. Aims and

38

Martin Daumiller

reasons likely play different roles for cognition and behaviour, and should therefore be considered as separate constructs (Elliot & Murayama, 2008). While focusing goals on reasons can foster deeper insights into the underlying psychological roots that give meaning to task engagement (see Kaplan & Maehr, 2007), conceptualising goals as aims allows for a more focused and fine-grained conceptualisation of achievement goals. In particular, one aim (e.g. striving to outperform others) may be rooted in different reasons (e.g. wanting to prove oneself, foreseeing potential benefits) – with the combination of aims and reasons potentially creating additional dynamic interactions (Elliot & Thrash, 2001; Urdan & Mestas, 2006). Such goal-reason combinations, labelled goal complexes, form a relevant direction to more comprehensively address not only the direction of human goal pursuit but also how this is moderated by its energetic basis (Sommet & Elliot, 2017). Note that besides considering such moderators inherent to goal pursuit, relevant surrounding moderators may also need to be considered to understand how goals translate into actual behaviours (e.g. when seeking to outperform others, individuals may or may not resort to academic dishonesty, depending on what they think others do in such a situation; Daumiller & Janke, 2020). This illustrates the necessity of considering the context that an individual is in to more comprehensively understand why individuals pursue goals and how these affect their cognitions and behaviours (“for whom and under what circumstances do goals matter?”; see Midgley et al., 2001). Goals Based on Competence or within Achievement Contexts

Irrespective of goals being considered as aims or reasons, what exactly are they focused on? Achievement goal theorists commonly agree that competence is the conceptual core of achievement (Elliot, 2005). By extension, achievement goals may be defined as the purposes (i.e. aims/reasons) for engaging in competence-related behaviour – a definition embraced by most achievement goal theorists (Elliot & Fryer, 2008). Such a definition can be narrower, by explicitly grounding the goals on the standards or standpoints that individuals may use to gauge their own competence (Korn et al., 2019) or broader, by including the different self-related goals that are relevant and active within achievement contexts, as discussed in the next section. Achievement contexts themselves are situations in which competence becomes visible and evaluated, entailing potentially positive and negative outcomes (Elliot, 1999). School forms a prototypical achievement context not only for students but also for teachers and instructors (Butler, 2007). Therein, teachers, like students, demonstrate competencies, conceal deficits, and develop their knowledge and skills. Considering the context can therefore be deemed as highly relevant to understand what competence means

Achievement Goals 39

for the individuals therein and how achievement goals are defined. This also implies that we cannot assume that a given theoretical conceptualisation of achievement goals applies to everyone, especially when extending this concept to novel contexts, but that it is worthwhile to inquire about the actual goals that individuals articulate in these contexts (Daumiller & Dresel, 2020; Lee & Bong, 2016; Lüftenegger et al., 2019). Which Types of Goals Can Be Distinguished?

The self-related goals that individuals pursue in achievement contexts can be consolidated in terms of their characteristics and foci as different types (or sets or classes) of achievement goals. These, in turn, are posited to create a framework for how individuals interpret, experience, and behave in achievement situations (Dweck, 1986; Elliot & Hulleman, 2017; Nicholls, 1984). Theoretical and empirical works proffered several different models, distinguishing goals with different degrees of differentiation and with partly different definitions. There is an on-going debate regarding the number and content of achievement goals that should be meaningfully distinguished. Therefore, an overview model is presented to help summarise the different types of goals and their further differentiations (Figure 3.1). Two Fundamental Types of Goals

Originally, two types of goals were distinguished, based on two different definitions of success. The first type, mastery goals (also labelled “learning” or “task” goals), is focused on fulfilling task-based requirements and developing personal competence. The second type, performance goals (also labelled “ego” or “ability” goals), is focused on one’s own performance relative to others and as perceived by others (Ames, 1992). While further goals were already mentioned (e.g. social goals, extrinsic goals, and work-avoidance goals; Maehr, 1984; Nicholls, 1989), mastery and performance goals received primary attention – likely due to their parsimonious duality and intertwinement with duelling philosophical ideologies regarding the benefits of competition versus promotion of individual strengths (Urdan & Kaplan, 2020). After all, mastery and performance goals tapped into the broad philosophical question of “should schools be based on comparison and competition among students or on internal success standards and individual development?”. Empirically, findings attested the benefits of mastery goals for a variety of learning processes and results; however, performance goals exhibited mixed results. Some studies connected them to harmful processes and outcomes, some to beneficial ones, and others failed to identify any discernible pattern (Harackiewicz et al., 1998). This prompted scholars to ponder this construct and what it entails further, leading to the consideration of goal valence.

40

Martin Daumiller

FIGURE 3.1

Overview model of theoretically distinguishable types of achievement goals (Daumiller et al., 2019). The model is based on a combination of the valence dimension (approach and avoidance) with the different definitional components of mastery goals (task goals and learning goals) and performance goals (appearance goals and norm goals) while considering relationship goals and work avoidance goals as further types of relevant goals

Approach and Avoidance Valence

Following the approach-avoidance distinction developed in preceding motivation theories (McClelland et al., 1953), goals were distinguished orthogonally in terms of their orientation (valence) into approach goals (striving to achieve desired states) and avoidance goals (striving to avoid specific outcomes; see Elliot & Harackiewicz, 1996; Middleton & Midgley, 1997). Based on the work of Elliot and Harackiewicz (1996), performance goals were bifurcated into performance approach (striving to outperform others and appear competent) and performance avoidance goals (striving to avoid being outperformed or appearing incompetent). This proved helpful

Achievement Goals 41

for explaining the mixed patterns of results reported for performance goals. Empirically, performance approach and performance avoidance goals were associated with distinct types of self-regulation and learning (Elliot & Hulleman, 2017): Procrastination, help-avoidance, worry, low intrinsic motivation, and a variety of other negative processes and outcomes were associated with performance avoidance goals, whereas performance approach goals were linked to some positive processes and outcomes (such as challenge appraisal, effort, persistence, and high performance), along with some negative processes and outcomes (e.g. emotionality, unwillingness to seek help; see Senko, 2016; Urdan & Kaplan, 2020). A similar division was subsequently also put forth for mastery goals (Elliot, 1999; Pintrich, 2000), distinguishing mastery approach (e.g. striving to learn and grow) and mastery avoidance goals (e.g. striving to avoid failures to learn or declines in skill). While conceptually very straightforward, this distinction has not yet been broadly accepted within achievement goal literature. One argument against mastery avoidance goals is that they appear to be rather uncommon among regular student demographics (e.g. Ciani & Sheldon, 2010). While they might form relevant motivations in older individuals that begin to notice a decline in their skills (e.g. athletes at the end of their career; Daumiller et al., 2022b), they are only little investigated and supported in younger students (Hulleman et al., 2010; Lee & Bong, 2016; Wirthwein et al., 2013). Accordingly, opposed to students, this type of goal may be more relevant in teachers; however, research has yet to document meaningful patterns with their cognitions and behaviours at work. As such, it remains an open question for future research to examine when and in which contexts mastery avoidance goals are a salient and relevant motivation. Facets of Mastery and Performance Goals

The aforementioned goals are critically discussed regarding the necessity of more finely differentiating them (Brophy, 2005; Elliot, 2005; Grant & Dweck, 2003; Hulleman et al., 2010). As contradicting results have been reported for the different types of goals (especially performance approach goals) and differences regarding their conceptualisation and measurement have been noted, a finer differentiation may be necessary to clarify their various effects for experiences and behaviours. Regarding the conceptual core of mastery goals, two aspects are distinguished (e.g. Elliot et al., 2011): A task component according to which the standard for evaluating one’s own competence lies in the task itself (Barron & Harackiewicz, 2001) as well as a learning component that represents an active striving towards personal development and growth of competence (Grant & Dweck, 2003). While task goals focus on task mastery through striving to do task rights or not wrong, learning goals focus on one’s intrapersonal

42

Martin Daumiller

trajectory, which may be conceptualised based on one’s past performance or one’s potential as an evaluation standard as well as learning and competence development itself (Elliot et al., 2015; Hulleman et al., 2010). Although there has not yet been much systematic research following up on these different aspects of learning goals, studies have clearly documented that task goals can be separated from learning goals and suggest that they might function differently. Specifically, task approach goals should encourage intrinsic motivation and task immersion while learning approach goals may lead to perseverance and enthusiasm through optimal challenge (Elliot & Hulleman, 2017). Focusing on the task at hand should be straightforward and demand less cognitive processing while self-based standards are challenging and more complex in pursuit, as each person has their own baseline. These differences have been well-documented in teaching populations, where learning approach goals have been primarily associated with quantity and quality of one’s own professional learning, while task goals have more strongly been linked to emotional experiences, teaching practices, and teaching quality (Daumiller et al., 2022a, 2023; Mascret et al., 2015). In students, differences between task and learning goals may be more difficult to detect: In schools, the tasks that students engage in are mostly learning tasks, whereas for teachers, the task of teaching does not involve personal learning (which often mostly takes place outside of the classroom) to the same extent. This highlights that context matters for the different effects of goals to become visible, and emphasises the need to examine other achievement contexts outside of traditional classroom settings, where the main objectives may not revolve primarily around learning. Regarding performance goals, two central components have also been identified (Elliot, 1999, 2005; Hulleman et al., 2010; Lee & Bong, 2016; Urdan & Mestas, 2006): an appearance component in which performance is defined by demonstration and affirmation of competence to an audience (wanting to be perceived as competent, or not wanting to be perceived as incompetent, irrespective of personal performance) and a normative component in which performance is defined based on normative social comparisons (wanting to be more competent than others, or not wanting to be worse than others, concerning actual performance). These two aspects have frequently been combined or even used interchangeably since the original works on achievement goals (Ames, 1992; Nicholls, 1984). While strongly correlated, they are empirically distinct and have been found to be differently related to educational outcomes as indicated by meta-analytic, correlational, and experimental research (e.g. Chung et al., 2020; Daumiller et al., 2019; Grant & Dweck, 2003; Hulleman et al., 2010; Senko & Dawson, 2017; Wirthwein & Steinmayr, 2021). Most studies within this line of research have investigated performance approach goals, presumably a function of the unclear effects typically reported for performance approach compared

Achievement Goals 43

to performance avoidance goals. In students, normative approach goals have yielded positive and appearance approach goals negative links with performance outcomes (Hulleman et al., 2010). For other outcomes, the effects are not only less clear but also speak for appearance approach goals yielding worse effects than normative goals (e.g. Chung et al., 2020; Grant & Dweck, 2003; Senko & Dawson, 2017). This has been extended by research on teaching populations. Regarding teaching performance, appearance goals have been found to be relevant (but not normative goals), with appearance approach goals enfolding positive links, and appearance avoidance goals negative links (e.g. Daumiller et al., 2019, 2022a). These findings were also mirrored in faculty members and their achievement goals for teaching; interestingly, regarding achievement goals for research, normative goals but not appearance goals have linked with research performance (Daumiller & Dresel, 2023). This shows that appearance and normative goals may not only differ in terms of their adaptability but fundamentally give rise to different psychological processes that become visible depending on the surrounding context. Following up on these further differentiated components of mastery and performance goals can therefore be considered as a helpful avenue not only for increased conceptual clarity but also for more fully understanding the psychological mechanisms underlying achievement goal pursuit and resolving the question of why performance goals, even when split between approach and avoidance goals, continue to demonstrate mixed relationships with performance outcomes. When explicitly considering different contexts within this, particular attention should be paid to when these types of mastery and performance goals become separable in the first place (for younger students, goal separability may be less clear, see Bong, 2009) and in which contexts they take effect. Further Types of Goals

Besides the aforementioned types of goals, further goals have been proposed (see Butler, 2014). These follow a wider understanding of achievement goals, as they are not grounded in individual definitions of competence based on mastery or performance (Elliot & Hulleman, 2017; Murayama et al., 2012). Still, they constitute relevant drivers in achievement situations: they represent additional, complementing purposes of achievement behaviour and the evaluation criteria used to describe whether an action was successful or not (Urdan & Kaplan, 2020). Work avoidance goals are strivings to get through the day with little effort (Duda & Nicholls, 1992). Such goals are common and useful in achievement settings that include multiple tasks and responsibilities in order to explain possibly unhealthy motivational dynamics (e.g. leading to disengagement or wellbeing; see King & McInerney, 2014). In students, such goals can frequently be

44

Martin Daumiller

attributed to boredom or indifference (Jarvis & Seifert, 2002), but in teachers, they might be unavoidable as teachers must set personal priorities to manage the wide range of tasks and responsibilities at work (Butler, 2007). Relational goals entail strivings to create close and caring relationships with others. They follow the logic that achievement contexts are usually also social contexts. Similar to academic competence, goals can analogously be based on social competence (Bardach et al., 2022). Accordingly, multiple different social goals can be distinguished (Ryan & Shim, 2006; Urdan & Maehr, 1995; Wentzel, 1994). As a superordinated category of these strivings, relational goals are focused on relationships with other people. Particularly for teachers who are responsible for the learning of others, there is also a strong, job inherent obligation to care about all individual students and the relationships shared with them (Butler, 2012). These additional types of goals are often neglected (Lee & Bong, 2016) and their antecedents and particular outcomes are still little researched (cf. King & McInerney, 2014). However, they can be considered to be relevant for particular contexts such as teaching (Butler, 2014). Therein, work avoidance goals have been linked to a host of unfavourable processes while relational goals have been linked to favourable instruction and teaching outcomes (Butler, 2012, 2014; Daumiller et al., 2019, 2020a). To summarise, different types of goals can be distinguished in contemporary achievement goal research. These goals are theoretically supported and help account for more variation in outcomes and explain additional phenomena (Vansteenkiste et al., 2014). However, this does not imply that empirical studies should assess all of these goals (leading to a potentially uneconomic number of items), but rather that their definition and assessment need to be made explicit and that researchers should consciously decide which types of goals to assess, depending on the research aims and the specific context (Mascret et al., 2015). How Are Achievement Goals Pursued?

While examining how the discussed conceptualisations of goals and the further distinctions of achievement goals matter across different contexts and populations is a promising direction for future achievement goal research, there has already been plenty of research on the general linkages of achievement goals (Hulleman et al., 2010; Senko & Dawson, 2017). These findings attest that achievement goals matter for how one feels, thinks, and behaves in achievement situations1 and shed light on how exactly achievement goals are pursued. Due to space restrictions, it is not possible to comprehensively address all aspects characterising goal pursuit; this chapter therefore focuses on three key issues: multiple goal pursuit, generalisability across different cultures, and temporal stability and domain specificity.

Achievement Goals 45

Multiple Goal Pursuit

Against the background of the original dichotomy between mastery and performance goals that aligned with duelling ideologies regarding the benefits of competition versus development, it may not be surprising that these two goals were originally conceived as mutually exclusive. However, empirically, mastery and performance goals were not negatively but positively correlated, and performance goals were often also positively linked to achievement. This led to a multiple-goal perspective that posits that achievement goals are not mutually exclusive but that the different achievement goals can be pursued to different extents (Harackiewicz et al., 2002). Based on this, there have been debates as to whether certain goal combinations exist that lead to more effective learning. For example, performance approach goals are often considered as especially beneficial when adopted alongside strong mastery goals (Pintrich, 2000; Senko et al., 2011). To follow up on such views, person-centred research approaches can be used to identify subgroups of individuals that differ from one another regarding their pursued achievement goals (e.g. through Cluster Analyses or Latent Profile Analyses) and to analyse how these goal combinations relate to differences in experiences and behaviours (Niemivirta et al., 2019). Such approaches can provide a complementary perspective to traditional variablecentred approaches for describing goal pursuit (multiple goals pursued at the same time). Generalisability Across Diverse Contexts

After establishing legitimacy, research must be put to the generalisability test. Most achievement goal research has been conducted in Western, educated, industrialised, rich, and democratic countries (Henrich et al., 2010). In other contexts, however, achievement goals can be pursued to different extents (mean level differences), and may potentially also differ in their predictive power (structural effects). Many researchers have argued that the effects of goals on achievement outcomes are universal across cultures (Zusho & Clayton, 2011) and most meta-analyses show that the goals’ basic effects persist across cultures, genders, and ethnic groups (Senko, 2016). Yet, cross-cultural psychological research indicates that depending on the specific contexts, goals may also enfold different effects (e.g. Cheng & Lam, 2013). For example, in collectivist cultures, the self is conceptualised more interdependently, with people placing a stronger emphasis on “fitting in” rather than “standing out” (Markus & Kitayama, 1991). Therefore, compared to individualistic societies, goals focused on one’s performance may not be as relevant of a motivator as opposed to other types of goals. It is therefore important to explicitly systematically consider not only educationally but also culturally diverse contexts (Zusho & Clayton, 2011).

46

Martin Daumiller

Temporal Stability and Context Specificity

Achievement goals are influenced by multiple factors bound to dispositional characteristics as well as aspects of the context or the situation (Kaplan & Maehr, 2007). While differences can exist between individuals, there is also substantial variability in goal pursuit within individuals. Specifically, preferences for achievement goals are partly stable as well as variable over time (retest correlations ranging between r = .40–.70; e.g. Bürger & Schmitt, 2017; Fryer & Elliot, 2007; Praetorius et al., 2014). Moreover, they are considered partly general and partly context-specific (correlations between different subjects and domains around r = .41–.79; e.g. Bong, 2001, 2004; Daumiller & Dresel, 2020; Sparfeldt et al., 2015). As Figure 3.2 illustrates, this yields two dimensions of temporal stability and context stability (conceptually similar to Kelley, 1967). In empirical investigations, these two sources are often conflated (e.g. when studying retest correlations over time without considering the respective context), yet they represent distinct aspects of variability. Specifically, as the example of teachers illustrates, besides between-person differences, differences in goal pursuit can also be attributed to temporally variable and context overarching aspects (e.g. work load or mood on a given day), temporally stable and context-specific aspects (e.g. different format or knowledge regarding the different courses of the teachers), as well as temporally variable and context-specific aspects (e.g. feelings and behaviours when teaching a particular topic). Research starting to disentangle these different sources of variability in goal pursuit becomes possible through within-person designs,

FIGURE 3.2

Conceptual framework for disentangling temporal variability and context specificity of goal pursuit (Daumiller et al., 2019). The model is illustrated with the example of teachers that can differ in their goals pursuit over time as well as between different contexts (e.g. different courses that they teach)

Achievement Goals 47

including, for example ambulatory assessments. Besides informing research decisions and practical application (Murphy & Alexander 2000), such research is also necessary to better understand how the individual goals are pursued – for example knowing that individuals change their pursuit of mastery goals more than other goals (Muis & Edwards, 2009), it would be illuminating to see if this is due to this goal being less stable due to different contexts (which may offer different task and learning affordances) or temporal changes within a given individual. While the exact components of variation therefore deserve future research, this importantly shows that achievement goal pursuit is malleable. That is, an individual’s adoption of achievement goals can be influenced by factors in the context they engage in (perceived goal structures, see Bardach et al., 2020). This in turn is the basis for fostering more adaptive achievement goal pursuit in individuals in the first place, with can be facilitated through direct interventions as well as environmental emphases. In the laboratory, researchers have demonstrated that situated achievement goals can be manipulated (e.g. Dweck & Leggett, 1988; Elliot & Harackiewicz, 1996), but results of interventions directed at achievement goals in actual classrooms and schools have generally been rather modest (Urdan, 2010). Fostering goal pursuit is a challenging endeavour as one needs to comprehensively consider the different practices, policies, and procedures of the respective environment (Ames, 1992; Dickhäuser et al., 2021; Lüftenegger et al., 2014). All in all, more research is therefore needed to create achievement goal interventions that are effective in complex teaching and learning settings. Conclusion

The achievement goal approach started more than four decades ago with a relatively simple dichotomy between mastery and performance goals. With increasing research, the debate over the definition and nature of achievement goals has intensified and more differentiated goal distinctions have been provided. As shown, this led to different approaches that provide complimentary perspectives and ever-more-detailed, distinct, uni-dimensional constructs (Elliot & Murayama, 2008). While this might be criticised as running the danger of reducing the theory’s parsimony and obscuring its central message (Brophy, 2005), the original purpose of achievement goal research should not be forgotten: to explain differences between individuals in their experiences and cognitions in achievement settings. To this end, this chapter highlighted the important role that context plays: for different individuals, such as students and teachers, achievement and competence can have different meanings, leading to goals differing in their salience and necessity to be distinguished. Further, the context needs to be considered to understand which achievement goals are pursued and how they matter,

48

Martin Daumiller

and to support individuals in their goal pursuit. Looking forward, considering context more fully is therefore necessary to exploit the full potential of the achievement goal approach for understanding and supporting effective learning and teaching processes. Note 1 Achievement goal research has focused primarily on unidirectional effects of goals on outcomes while reciprocal relationships have largely been neglected. However, the dynamic nature of goal pursuit (DeShon & Gillespie, 2005) and emerging empirical results also imply reciprocal influences across time (Daumiller & Dresel, 2023; King & McInerney, 2016). Through longitudinal designs, the direction of the described linkages therefore needs to be addressed more systematically in future research.

References Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261–271. https://doi.org/10.1037/0022-0663.84.3.261 Ames, C., & Archer, J. (1988). Achievement goals in the classroom. Journal of Educational Psychology, 80(3), 260–267. https://doi.org/10.1037/0022-0663.80.3.260 Austin, J. T., & Vancouver, J. B. (1996). Goal constructs in psychology. Psychological Bulletin, 120(3), 338–375. https://doi.org/10.1037/0033-2909.120.3.338 Bardach, L., Daumiller, M., & Lüftenegger, M. (2022). Multiple social and academic achievement goals. The Journal of Experimental Education, 91(4), 655–673. https://doi.org/10.1080/00220973.2022.2081959 Bardach, L., Oczlon, S., Pietschnig, J., & Lüftenegger, M. (2020). Has achievement goal theory been right? Journal of Educational Psychology, 112(6), 1197–1220. https://doi.org/10.1037/edu0000419 Barron, K. E., & Harackiewicz, J. M. (2001). Achievement goals and optimal motivation. Journal of Personality and Social Psychology, 80(5), 706–722. https://doi. org/10.1037/00223514.80.5.706 Bong, M. (2001). Between-and within-domain relations of academic motivation among middle and high school students. Journal of Educational Psychology, 93(1), 23–34. https://doi.org/10.1037/0022-0663.93.1.23 Bong, M. (2004). Academic motivation in self-efficacy, task value, achievement goal orientations, and attributional beliefs. The Journal of Educational Research, 97(6), 287–298. https://doi.org/10.3200/JOER.97.6.287-298 Bong, M. (2009). Age-related differences in achievement goal differentiation. Journal of Educational Psychology, 101(4), 879–896. https://doi.org/10.1037/a0015945 Brophy, J. (2005). Goal theorists should move on from performance goals. Educational Psychologist, 40(3), 167–176. https://doi.org/10.1207/s15326985ep4003_3 Bürger, K., & Schmitt, M. (2017). Students’ multiple state goals as a function of appraisals, trait goals, and their interactions. Contemporary Educational Psychology, 51, 464–481. https://doi.org/10.1016/j.cedpsych.2017.09.006 Butler, R. (2007). Teachers’ achievement goal orientations and associations with teachers’ help seeking. Journal of Educational Psychology, 99(2), 241–252. https:// doi.org/10.1037/0022-0663.99.2.241 Butler, R. (2012). Striving to connect. Journal of Educational Psychology, 104(3), 726–742. https://doi.org/10.1037/a0028613

Achievement Goals 49

Butler, R. (2014). What teachers want to achieve and why it matters. In P. Richardson, S. Karabenick, & H. Watt (Eds.), Teacher motivation (pp. 20–35). Routledge. Cheng, R. W. Y., & Lam, S. F. (2013). The interaction between social goals and selfconstrual on achievement motivation. Contemporary Educational Psychology, 38(2), 136–148. https://doi.org/10.1016/j.cedpsych.2013.01.001 Chung, Y., Bong, M., & Kim, S.-i. (2020). Performing under challenge. Journal of Educational Psychology, 112(4), 823–840. https://doi.org/10.1037/edu0000393 Ciani, K. D., & Sheldon, K. M. (2010). Evaluating the mastery-avoidance goal construct. Psychology of Sport and Exercise, 11(2), 127–132. https://doi.org/10.1016/j. psychsport.2009.04.005 Covington, M. V., & Omelich, C. L. (1984). Task-oriented versus competitive learning structures. Journal of Educational Psychology, 76(6), 1038–1050. https:// doi.org/10.1037/0022-0663.76.6.1038 Daumiller, M., Bieg, S., Dickhäuser, O., & Dresel, M. (2020a). Humor in university teaching. Studies in Higher Education, 45(12), 2619–2633. https://doi.org/10.1080/ 03075079.2019.1623772 Daumiller, M., Dickhäuser, O., & Dresel, M. (2019). University instructors’ achievement goals for teaching. Journal of Educational Psychology, 111(1), 131–148. https://doi.org/10.1037/edu0000271 Daumiller, M., & Dresel, M. (2020). Researchers’ achievement goals. Contemporary Educational Psychology, 61. Article 101843. https://doi.org/10.1016/j.cedpsych. 2020.101843 Daumiller, M., & Dresel, M. (2023). Temporal dynamics between faculty goals, burnout/engagement, and performance in teaching and research: A latent change score approach. Contemporary Educational Psychology, 72, Article 102124. https://doi. org/10.1016/j.cedpsych.2022.102124 Daumiller, M., & Janke, S. (2020). Effects of performance goals and social norms on academic dishonesty in a test. British Journal of Educational Psychology, 90(2), 537–559. https://doi.org/10.1111/bjep.12310 Daumiller, M., Janke, S., Rinas, R., Dickhäuser, O., & Dresel, M. (2022a). Need satisfaction and achievement goals of university faculty. Higher Education, 83(6), 1183–1206. https://doi.org/10.1007/s10734-021-00736-1 Daumiller, M., Janke, S., Rinas, R., Hein, J., Dickhäuser, O., & Dresel, M. (2023). Different time and context = different goals and emotions? Contemporary Educational Psychology, 72, Article 102139. https://doi.org/10.1016/j.cedpsych. 2022.102139 Daumiller, M., Rinas, R., & Breithecker, J. (2022b). Elite athletes’ achievement goals, burnout levels, psychosomatic stress symptoms, and coping strategies. International Journal of Sport and Exercise Psychology, 20(2), 416–435. https://doi.org/ 10.1080/1612197X.2021.1877326 Daumiller, M., Stupnisky, R., & Janke, S. (2020b). Motivation of higher education faculty. International Journal of Educational Research, 99, Article 101502. https://doi.org/10.1016/j.ijer.2019.101502 DeShon, R. P., & Gillespie, J. Z. (2005). A motivated action theory account of goal orientation. Journal of Applied Psychology, 90(6), 1096–1127. https://doi. org/10.1037/0021-9010.90.6.1096 Dickhäuser, O., Janke, S., Daumiller, M., & Dresel, M. (2021). Motivational school climate and teachers’ achievement goal orientations. British Journal of Educational Psychology, 91(1), 391–408. https://doi.org/10.1111/bjep.12370

50

Martin Daumiller

Duda, J. L., & Nicholls, J. G. (1992). Dimensions of achievement motivation in schoolwork and sport. Journal of Educational Psychology, 84(3), 290–299. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048. https://doi.org/10.1037/0003-066x.41.10.1040 Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95(2), 256–273. https://doi.org/10.1037/ 0033-295x.95.2.256 Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34(3), 169–189. https://doi.org/10.1207/s15326985 ep3403_3 Elliot, A. J. (2005). A conceptual history of the achievement goal construct. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 52–72). Guilford. Elliot, A. J., & Fryer, J. W. (2008). The goal construct in psychology. In J. Y. Shah & W. L. Gardner (Eds.), Handbook of motivation science (Vol. 18, pp. 235–250). Guilford. Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation. Journal of Personality and Social Psychology, 70(3), 461–475. https://doi.org/10.1037/0022-3514.70.3.461 Elliot, A. J., & Hulleman, C. S. (2017). Achievement goals. In A. J. Elliot, C. S. Dweck, & D. S. Yeager (Eds.), Handbook of competence and motivation (pp. 43–60). Guilford. Elliot, A. J., & Murayama, K. (2008). On the measurement of achievement goals. Journal of Educational Psychology, 100(3), 613–628. https://doi.org/10.1037/ 0022-0663.100.3.613 Elliot, A., Murayama, K., Kobeisy, A., & Lichtenfeld, S. (2015). Potential-based achievement goals. British Journal of Educational Psychology, 85(2), 192–206. https://doi.org/10.1111/bjep.1205 Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3 × 2 achievement goal model. Journal of Educational Psychology, 103(3), 632–648. https://doi.org/10.1037/ a0023952 Elliot, A. J., & Thrash, T. M. (2001). Achievement goals and the hierarchical model of achievement motivation. Educational Psychology Review, 13(2), 139–156. https://doi.org/10.1023/A:1009057102306 Fryer, J. W., & Elliot, A. J. (2007). Stability and change in achievement goals. Journal of Educational Psychology, 99(4), 700–714. https://doi.org/10.1037/ e633962013-763 Grant, H., & Dweck, C. S. (2003). Clarifying achievement goals and their impact. Journal of Personality and Social Psychology, 85(3), 541–553. https://doi. org/10.1037/0022-3514.85.3.541 Harackiewicz, J. M., Barron, K. E., & Elliot, A. J. (1998). Rethinking achievement goals. Educational Psychologist, 33(1), 1–21. https://doi.org/10.1207/ s15326985ep3301_1 Harackiewicz, J. M., Barron, K. E., Pintrich, P. R., Elliot, A. J., & Thrash, T. M. (2002). Revision of achievement goal theory. Journal of Educational Psychology, 94(3), 638–645. https://doi.org/10.1037/0022-0663.94.3.638 Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29–29. https://doi.org/10.1037/14805-007

Achievement Goals 51

Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures. Psychological Bulletin, 136(3), 422–449. https://doi.org/10.1037/a0018947 Jarvis, S., & Seifert, T. (2002). Work avoidance as a manifestation of hostility, helplessness, and boredom. Alberta Journal of Educational Research, 48(2), 174–187. Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141–184. https://doi. org/10.1007/s10648-006-9012-5 Kelley, H. H. (1967). Attribution theory in social psychology. In Nebraska symposium on motivation. University of Nebraska Press. King, R. B., & McInerney, D. M. (2014). The work avoidance goal construct. Contemporary Educational Psychology, 39(1), 42–58. https://doi.org/10.1016/j. cedpsych.2013.12.002 King, R. B., & McInerney, D. M. (2016). Do goals lead to outcomes or can it be the other way around? British Journal of Educational Psychology, 86(2), 296–312. https://doi.org/10.1111/bjep.12107 Korn, R. M., Elliot, A. J., & Daumiller, M. (2019). Back to the roots: The 2 × 2 standpoints and standards achievement goal model. Learning and Individual Differences, 72, 92–102. https://doi.org/10.1016/j.lindif.2019.04.009 Korn, R., Elliot, A., & Daumiller, M. (2019). Back to the roots. Learning and Individual Differences, 72, 92–102. https://doi.org/10.1016/j.lindif.2019.04.009 Lee, M., & Bong, M. (2016). In their own words. Journal of Educational Psychology, 108(2), 274–294. https://doi.org/10.1037/edu0000048 Lüftenegger, M., Bardach, L., Bergsmann, E., Schober, B., & Spiel, C. (2019). A citizen science approach to measuring students’ achievement goals. International Journal of Educational Research, 95, 36–51. https://doi.org/10.1016/j.ijer.2019.03.003 Lüftenegger, M., Van de Schoot, R., Schober, B., Finsterwald, M., & Spiel, C. (2014). Promotion of students’ mastery goal orientations. Educational Psychology, 34(4), 451–469. https://doi.org/10.1080/01443410.2013.814189 Maehr, M. L. (1984). Meaning and motivation. In C. Ames & R. Ames (Eds.), Research on motivation in education (Vol. 1, ch. 4, pp. 115–144). Academic. Markus, H. R., & Kitayama, S. (1991). Culture and the self. Psychological Review, 98(2), 224–253. https://doi.org/10.1037/0033-295X.98.2.224 Mascret, N., Elliot, A., & Cury, J., F. (2015). The 3 × 2 achievement goal questionnaire for teachers. Educational Psychology, 37(3), 346–361. https://doi.org/10. 1080/01443410.2015.1096324 McClelland, D., Atkinson, J., Clark, R., & Lowell, E. (1953). Toward a theory of motivation. In D. McClelland, J. Atkinson, R. Clark, & E. Lowell (Eds.), The achievement motive (pp. 6–96). Appleton-Century-Crofts. Middleton, M. J., & Midgley, C. (1997). Avoiding the demonstration of lack of ability. Journal of Educational Psychology, 89(4), 710–718. https://doi.org/10. 1037/0022-0663.89.4.710 Midgley, C., Kaplan, A., & Middleton, M. (2001). Performance-approach goals. Journal of Educational Psychology, 93(1), 77–86. https://doi.org/10.1037/00220663.93.1.77 Muis, K. R., & Edwards, O. (2009). Examining the stability of achievement goal orientation. Contemporary Educational Psychology, 34(4), 265–277. https://doi. org/10.1016/j.cedpsych.2009.06.003

52

Martin Daumiller

Murayama, K., & Elliot, A. J. (2019). Achievement goals. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (Vol. 2, pp. 227–245). Oxford University. Murayama, K., Elliot, A. J., & Friedman, R. (2012). Achievement goals. The Oxford handbook of human motivation (pp. 191–207). Murphy, P. K., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25(1), 3–53. https://doi. org/10.1006/ceps.1999.1019 Nicholls, J. G. (1984). Achievement motivation. Psychological Review, 91(3), 328–346. https://doi.org/10.1037/0033-295x.91.3.328 Nicholls, J. G. (1989). The competitive ethos and democratic education. Harvard University. Niemivirta, M., Pulkka, A.-T., Tapola, A., & Tuominen, H. (2019). Achievement goal orientations. In K. A. Renninger & S. E. Hidi (Eds.), The Cambridge handbook of motivation and learning (pp. 566–616). Cambridge University. Pintrich, P. R. (2000). Multiple goals, multiple pathways. Journal of Educational Psychology, 92(3), 544–555. https://doi.org/10.1037/0022-0663.92.3.544 Praetorius, A. K., Nitsche, S., Janke, S., Dickhäuser, O., Drexler, K., Fasching, M., & Dresel, M. (2014). Here today, gone tomorrow. Contemporary Educational Psychology, 39(4), 379–387. https://doi.org/10.1016/j.cedpsych.2014.10.002 Ryan, A. M., & Shim, S. S. (2006). Social achievement goals. Personality and Social Psychology Bulletin, 32(9), 1246–1263. https://doi.org/10.1177/01461672 06289345 Senko, C. (2016). Achievement goal theory. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (Vol. 2, pp. 75–95). Routledge. Senko, C., & Dawson, B. (2017). Performance-approach goal effects depend on how they are defined. Journal of Educational Psychology, 109(4), 574–598. https://doi. org/10.1037/edu0000160 Senko, C., Hulleman, C. S., & Harackiewicz, J. M. (2011). Achievement goal theory at the crossroads. Educational Psychologist, 46(1), 26–47. https://doi.org/10. 1080/00461520.2011.538646 Sommet, N., & Elliot, A. J. (2017). Achievement goals, reasons for goal pursuit, and achievement goal complexes as predictors of beneficial outcomes. Journal of Educational Psychology, 109(8), 1141–1162. https://doi.org/10.1037/edu0000199 Sparfeldt, J. R., Brunnemann, N., Wirthwein, L., Buch, S. R., Schult, J., & Rost, D. H. (2015). General versus specific achievement goals. Learning and Individual Differences, 43, 170–177. https://doi.org/10.1016/j.lindif.2015.08.022 Urdan, T. (2010). The challenges and promise of research on classroom goal structures. In J. L. Meece & J. S. Eccles (Eds.), Handbook of research on schools, schooling and human development (pp. 92–108). Routledge. Urdan, T., & Kaplan, A. (2020). The origins, evolution, and future directions of achievement goal theory. Contemporary Educational Psychology, 61, Article 101862. https://doi.org/10.1016/j.cedpsych.2020.101862 Urdan, T. C., & Maehr, M. L. (1995). Beyond a two-goal theory of motivation and achievement. Review of Educational Research, 65(3), 213–243. https://doi. org/10.3102/00346543065003213 Urdan, T., & Mestas, M. (2006). The goals behind performance goals. Journal of Educational Psychology, 98(2), 354–365. https://doi.org/10.1037/0022-0663. 98.2.354

Achievement Goals 53

Vansteenkiste, M., Lens, W., Elliot, A. J., Soenens, B., & Mouratidis, A. (2014). Moving the achievement goal approach one step forward. Educational Psychologist, 49(3), 153–174. https://doi.org/10.1080/00461520.2014.928598 Wentzel, K. R. (1994). Relations of social goal pursuit to social acceptance, classroom behavior, and perceived social support. Journal of Educational Psychology, 86(2), 173–183. https://doi.org/10.1037/0022-0663.86.2.173 Wirthwein, L., Sparfeldt, J. R., Pinquart, M., Wegerer, J., & Steinmayr, R. (2013). Achievement goals and academic achievement. Educational Research Review, 10, 66–89. https://doi.org/10.1016/j.edurev.2013.07.001 Wirthwein, L., & Steinmayr, R. (2021). Performance-approach goals. European Journal of Psychology of Education, 36(4), 1199–1220. https://doi.org/10.1007/ s10212-020-00520-2 Zusho, A., & Clayton, K. (2011). Culturalizing achievement goal theory and research. Educational Psychologist, 46(4), 239–260. https://doi.org/10.1080/0046 1520.2011.614526

4 EXPLAINING THE CONTEXT-SPECIFICITY OF STUDENT MOTIVATION A Self-Determination Theory Approach Barbara Flunger and Julien Chanal

Abstract Self-Determination Theory (SDT) is a theoretical framework that is useful for explaining students’ behaviours, motivations, and academic outcomes in educational settings. We highlight the main premises of SDT and clarify how the context-specificity of student motivation can be explained using the Hierarchical Model of Intrinsic and Extrinsic Motivation (HMIEM). We review findings from SDT research on within-subject processes underlying students’ motivation and academic outcomes, and on between-subject differences in student motivation. Moreover, we draw attention to critical areas for future research on the context-specificity of motivation in educational settings.

The Main Premises of Self-Determination Theory

The Self-Determination Theory (SDT; e.g. Deci & Ryan, 1985) is a theoretical framework that evolved out of the Cognitive Evaluation Theory (CET), which aimed to explain individuals’ intrinsic motivation (i.e. “the motivation to engage in an activity out of interest and enjoyment,” Reeve & Cheon, 2021, p. 57) and its antecedents (e.g. Gagné et al., 2018). SDT defines universal mechanisms that can be used to describe people’s motivation, its underlying factors and consequences across domains and life contexts (e.g. Vallerand et al., 2008) in six mini-theories (e.g. Gagné et al., 2018). In SDT’s metatheory, the self has the vital role in determining how external (e.g. teacher support) or internal (particularly psychological needs) stimuli are regulated to achieve well-being and other positive outcomes (Ryan & Deci, 2019). In this chapter, we aim to address the utility of SDT for explaining the contextspecificity of motivational and emotional processes in the learning context, DOI: 10.4324/9781003303473-5

Explaining the Context-Specificity of Student Motivation

55

focusing on two mini-theories, the Organismic Integration Theory (OIT) and Basic Psychological Need Theory (BPNT). The premises of SDT have been confirmed in educational research in numerous educational contexts and different age groups, such as elementary and middle school students (Conesa et al., 2022). Several reviews and metaanalyses summarise the main findings on student motivation (Howard et al., 2021) and its antecedents (Bureau et al., 2022). SDT’s mini-theory OIT conceptualises distinct types of motivation arranged on a continuum, which define the regulation styles driving specific actions. Intrinsic motivation refers to doing a task out of the enjoyment derived from engaging in the task (e.g. Reeve & Cheon, 2021). Extrinsic motivation relates to feelings of obligation, contingent self-worth or external outcomes. If a person’s behaviour is extrinsically motivated, the aim is to achieve a reward or avoid an undesired outcome through performing the activity (Ryan & Deci, 2000). If individuals realise that external pressures, norms or values correspond with their own personal values and goals, extrinsic reasons can be more or less internalised. If individuals execute a task for an outcome but perceive the value of the task or the outcome to be fully in line with their sense of self, their extrinsic motivation is described as integrated regulation. If individuals can identify with the value of an activity and adopt external reasons for performing it as personally relevant, their regulation style is defined as identified regulation (e.g. Ryan & Deci, 2000). Behaviours can also be enacted to meet internal pressures concerning self-evaluation, perceived guilt or self-esteem, which reflects (partially internalised) introjected motivation; introjected motivation can be further differentiated into positive introjection (undertaking a behaviour out of approach motivation to achieve a certain outcome) and negative introjection (undertaking a behaviour out of avoidance motivation to avoid a certain outcome) (e.g. Sheldon et al., 2017). Finally, if behaviours are performed to receive rewards or avoid punishment, they are categorised as external regulation (Sheldon et al., 2017). By contrast, amotivation reflects a state of not knowing the reasons and consequences of actions, or having no reason to execute them (e.g. Deci & Ryan, 1985). SDT research distinguishes between controlled and autonomous motivation: extrinsic and introjected regulation are mainly reflecting external forces; consequently, they have been labelled as controlled motivation (e.g. Sheldon et al., 2017). Autonomous motivation is used as an umbrella term for identified, integrated regulation and intrinsic motivation because these motivations are more strongly determined by internal forces like voluntary choices, or self-related (or self-determined) ones (Sheldon et al., 2017). SDT postulates that the process of internalisation reflects people’s motivation to grow and achieve (Stone et al., 2009). The BPNT explains how internationalisation depends on three universal and basic psychological needs (e.g. Deci & Ryan, 1985): Competence (the need to feel capable), relatedness

56

Barbara Flunger and Julien Chanal

(the need to be connected to other people and belong), and autonomy (the need to be free from control and have optional choices; Assor, 2012). Psychological needs can be defined “as experiential outcomes that are affected by contexts (…) [and as] internal motives that can direct behaviour” (Sheldon & Gunz, 2009, p. 1468) and motivation. Namely, the satisfaction of the needs for autonomy, competence and relatedness can influence the degree to which extrinsically motivated behaviours can become internalised to selfrelevant (identified) or fully accepted (integrated) behaviours (e.g. Sheldon et al., 2017). By contrast, the frustration of these psychological needs (feeling controlled, like a failure, or excluded) would lead to disengagement and illbeing (e.g. Vansteenkiste & Ryan, 2013). Explaining the Context-Specificity of Student Motivation: The Hierarchical Model of Intrinsic and Extrinsic Motivation

In this chapter, we focus on the question how SDT can help to understand the context-specificity of student motivation and factors underlying the stability and variability of student motivation, such as students’ need satisfaction and teachers’ need support. Motivation is assumed to have an “open architecture” (Reeve, 2016, p. 32) and can be changed through the context or situation. The Hierarchical Model of Intrinsic and Extrinsic Motivation (HMIEM) proposed by Vallerand (1997), in which intrinsic motivation and extrinsic motivation are conceptualised at a global, contextual, and a situational level, helps to explain how student motivation is affected by context-specific factors, such as teacher behaviours (see Figure 4.1). More specifically, motivation at the global level is proposed to represent a general motivational orientation, or a trait (Vallerand, 1997), reflecting a general motivational tendency (see DeCharms, 1968). Global motivation is supposed to be mainly affected by global factors, such as cultural values (Chanal & Guay, 2015), and is socialised by out-of-school factors. Motivation at the contextual level is conceptualised to refer to the motivation in a specific context, such as the academic domain (Vallerand, 1997). Motivation at the situational level refers to the motivation in a specific activity of the domain (Chanal & Guay, 2015). Therefore, students’ situational motivation, for example in a specific lesson, can be affected by situational factors, such as lesson-specific need support. School subjects, allocated at a lower level of generality than the overall academic domain, may be best categorised at an intermediate level, in between the contextual and situational level, or at the situational level (Paumier & Chanal, 2018). The HMIEM, based on SDT, is useful for explaining students’ behaviours, motivations, as well as further antecedents and outcomes in a specific environment. External incentives, such as performing well in school, might be driving factors at a higher conceptual level than intrinsic stimuli, as their appeal can operate across different

Explaining the Context-Specificity of Student Motivation

57

The Hierarchical Model of Motivation FIGURE 4.1

58

Barbara Flunger and Julien Chanal

contexts and situations. Because characteristics of activities can trigger intrinsic stimuli, intrinsic motivation may depend more on situational factors than extrinsic motivation. The HMIEM (Vallerand, 1997) claims a motivational sequence (antecedents → motivations → outcomes) of antecedents (social or intra-personal characteristics) underlying motivation that drives affective, cognitive, and behavioural consequences. Antecedents → Motivations

There is a large body of evidence that underlines the effectiveness of need support in predicting intrinsic motivation and further outcomes (see reviews by Núñez & León, 2015; Su & Reeve, 2011). In educational research, autonomy support has received widespread attention, for example because educational contexts impose behavioural limits that have to be followed and students’ need for autonomy can easily be frustrated. Several autonomy-supportive practices can be used (e.g. Reeve & Cheon, 2021), such as offering options or providing rationales, which implies explaining the relevance of a task or a rule (Su & Reeve, 2011), or the meaning of the content of a task, for example, for future life plans. Acknowledging students’ emotions and accepting their frustration is another autonomysupportive strategy (Su & Reeve, 2011). Moreover, students’ autonomy is supported if their interests are stimulated, for example, by creating interesting materials (Su & Reeve, 2011). Teachers’ behaviours can also affect students’ need for competence and relatedness. In a Delphi study (Ahmadi et al., 2023), an expert panel of international scholars highlighted 57 motivational behaviours that teachers can apply. Out of these behaviours, 35 are assumed to satisfy students’ needs for autonomy, competence, and relatedness, and 22 instructional styles to potentially thwart the three psychological needs. Findings from studies in lower and higher secondary education show that students’ motivation is closely intertwined with their achievement emotions (e.g. Sutter-Brandenberger et al., 2018). Accordingly, achievement emotions are often conceptualised as central antecedents of students’ motivation (e.g. Pekrun & Perry, 2014). Emotions can be characterised as affective episodes (Mulligan & Scherer, 2012) which “signal the relevance and meaning of events relative to a person’s needs, aims, or goals” (Roth et al., 2019, p. 2). Achievement emotions are positive or negative, activating (e.g. joy) or deactivating (e.g. boredom) emotions referring to learning activities or achievement outcomes (Pekrun & Perry, 2014). Achievement emotions and motivation might also affect each other bidirectionally and reciprocally, which implies that students’ motivation could underlie their achievement emotions. Accordingly, in a recent meta-analysis considering student samples from different age groups (Howard et al., 2021), anxiety, boredom, negative and positive affect, as well as enjoyment were defined as adaptive and maladaptive well-being outcomes of distinct

Explaining the Context-Specificity of Student Motivation

59

motivation types. It needs to be kept in mind that “emotion can comprise motivation, and motivation can comprise emotion, then there is construct overlap, and measures of emotion and motivation may overlap as well” (Pekrun & Marsh, 2022, p. 3). Thus, it might be a relevant question for future research whether and how motivations and emotions can be empirically distinguished. Another important consideration is that not all emotions may be easily malleable by teachers. In a sample with Dutch secondary students and their teachers, Flunger et al. (2022, Supplementary Material, Table S7) revealed that students’ perceptions of teachers’ lesson-specific autonomy support had no statistically significant association with their lesson-specific anxiety. However, for example in lessons in which German teachers self-reported to have provided rationales, students reported less lesson-specific anxiety. By contrast, German teachers’ self-reported acknowledgement of students’ emotions and feelings or perceived relatedness with the class was positively associated with students’ lesson-specific anxiety in German. More research is needed if and how teachers can support distinct negative emotions in their students. Motivation → Outcomes

Concerning subject-specific motivation, intrinsic motivation and identified regulation have been identified in several studies as having strong positive associations with academic achievement, persistence, well-being, and selfevaluation concerns (see Howard et al., 2021). Diverging results on the associations between different types of motivation and academic achievement (for an overview, see Chanal & Paumier, 2020) may result from (a) the neglect of the consideration of the hierarchical level considered (situational, contextual, or global), (b) how motivation was measured and/or modelled (e.g. as a composite score), or (c) how academic achievement was assessed (a test score, grades or more complex skills and knowledge). For example, Lohbeck et al. (2022) found that German children characterised by high intrinsic motivation and identified regulation outperformed their peers with relatively lower intrinsic motivation and identified regulation in a set of motor-skills tests. Stability of Findings across Contexts and the Role of Psychological Needs

A plethora of studies confirms the theoretical premises of SDT in different contexts and age groups. The consistency of findings can be interpreted as validating the theory’s claims on universal processes (e.g. Vallerand et al., 2008). For example, distance learning, which, for example was necessary due to the COVID-19 pandemic, might negatively affect need satisfaction and intrinsic motivation. Empirical findings from multi-country studies suggest that generally high need satisfaction might buffer against a negative trend

60

Barbara Flunger and Julien Chanal

in academic outcomes (e.g. Holzer et al., 2021). An educational implication would be to incorporate tools that allow for interaction also in digital learning situations (Holzer et al., 2021) or create opportunities for challenges to promote the satisfaction of distinct psychological needs. That is, if motivational support is provided and students’ psychological needs are satisfied, students on average may benefit from it in terms of higher interest and positive emotions (e.g. Flunger et al., 2019). However, offering motivational support that is appropriate in terms of content may be more difficult in some school subjects. For instance, Math teachers might find it hard to provide meaningful rationales for the relevance of the content for students’ lives (e.g. Gainsburg, 2008). Accordingly, between-subject differences in the mean levels of student motivation have been revealed in middle and high school students (e.g. Gaspard et al., 2017). Students from different age groups tend to report lower motivation for subjects with (relative to other subjects) higher task difficulty, such as mathematics (Baten et al., 2020) or physics (Gaspard et al., 2017). Conforming to SDT, the more difficult subjects may frustrate students’ psychological need for competence, which is a driving factor of students’ intrinsic motivation (Vansteenkiste & Ryan, 2013). In several meta-analyses in the physical education setting, competence need satisfaction was shown to have stronger associations with intrinsic and identified motivation than autonomy need satisfaction (Bureau et al., 2022; Vasconcellos et al., 2020). This was also confirmed in mathematics, in a study with Dutch elementary school students, in which competence need satisfaction was more strongly associated with intrinsic motivation than autonomy need satisfaction (Baten et al., 2020). Moreover, in the school subject physics, secondary school students’ situational interest was shown to depend on their need for competence (Flunger et al., 2013). It can seem as if these findings highlight that the need for competence is the key predictor of student motivation in educational settings, and if students perceive that their need for competence is thwarted, lower intrinsic motivation is the consequence. However, in a review on the role of the three basic psychological needs in elementary and middle school students, it was revealed that competence need satisfaction did not consistently show stronger associations with outcomes than autonomy and/or relatedness satisfaction in all studies (Conesa et al., 2022). For example, in a study with Japanese elementary school students learning English, Carreira et al. (2013) found that autonomy need satisfaction had somewhat stronger associations with intrinsic motivation than competence and relatedness need satisfaction. And, in case teachers provide autonomy support, it has been shown to be effective in promoting student motivation and engagement also in the subjects characterised by high task difficulty, such as physics (Flunger et al., 2019). Consequently, the negative associations of task difficulty with students’ motivation can be weakened

Explaining the Context-Specificity of Student Motivation

61

by teachers’ need support (e.g. in mathematics, Baten et al., 2020). In line with these findings, in a sample of German secondary school students, Tsai et al. (2008) showed that teachers’ lesson-specific autonomy support was associated with students’ experienced interest in German, a second language (English), and Math lessons. Likewise, Flunger et al. (2022) confirmed the associations of different autonomy-supportive strategies with different student outcomes in both a second language (German) and mathematics. Finally, it is noteworthy that reviews and meta-analyses align in demonstrating that relatedness need satisfaction is less strongly associated with student motivation than autonomy and competence need satisfaction in distinct subjects (Bureau et al., 2022; Conesa et al., 2022; Vasconcellos et al., 2020). The relative weaker associations of students’ relatedness need satisfaction with their outcomes has been attributed to the primary importance of achievement in the education context (Niemiec & Ryan, 2009). In a study with secondary school students from Singapore, Wang et al. (2019) found that relatedness need satisfaction (e.g. “In this class I feel valued/listened to”) was more positively associated with autonomous motivation and more negatively associated with controlled motivation than autonomy and competence need satisfaction. Wang et al. (2019) targeted the classroom in their measures, which can reflect an overall assessment of relatedness need satisfaction with classmates, a teacher, or even several teachers. Thus, it is important to clarify which reference group and level is targeted when measuring student motivation and need satisfaction (individual students versus whole class, situational, contextual, or global), in order to understand the (inconsistent) meaning of findings in school research. Within a classroom, the distinct types of motivation, needs, and achievement emotions are not exhibited by all students to the same degree. Therefore, it is a critical question whether the effectiveness of teachers’ need support is conditional on student characteristics or other classroom-specific factors. It is important to note that the findings on differential effects of motivational support depend on the moderator considered. If students come from families in which parents have low interest in math, relevance instruction in the classroom can provide them with novel information on the importance of the learning material, and the applicability of the content taught (“Robin Hood effects”; Häfner et al., 2017). Concerning students’ personal characteristics, such as their prior motivation and grades, few differential effects of autonomy support or need satisfaction have been identified. In case two-way interactions are confirmed, findings corroborate so-called “Matthew-effects” implying that students with higher need strength (Flunger et al., 2013; Katz et al., 2009), higher general autonomy need satisfaction and higher grades (e.g. Flunger et al., 2019) or with higher initial motivation (Flunger et al., 2022), can benefit more from need satisfaction or autonomy support than students with lower need strength, motivation and lower grades, respectively.

62

Barbara Flunger and Julien Chanal

However, a motivational intervention may be less effective for extreme values of a construct. Mayer et al. (2017) showed that when analysing two-way interactions, self-efficacy was no significant moderator of the effects of an autonomy-supportive intervention on students’ boredom. When a quadratic term was added to the interaction analysis, the autonomy-supportive intervention was revealed to be most effective in reducing boredom for medium values of self-efficacy. Thus, the interplay of constructs can be more complex and solely analysing two-way interactions might fail to fully uncover differential effects. Between-Subject Differentiation in Student Motivation: The School-Subject-Specificity Hypothesis

SDT, and particularly the HMIEM, have great potential when examining subject-specific (i.e. situational) differences in students’ motivation (see Chanal & Paumier, 2020). Unexpectedly, in a study with French-Canadian children, Guay et al. (2010) showed that the correlations between the intrinsic motivations for three school subjects (mathematics, reading, and writing) were lower than those among identified motivations, whereas the correlations between controlled motivations for mathematics, reading and writing were higher than the correlations found for identified motivation. Likewise, Guay and Bureau (2018) found that introjected regulation and external regulation showed high intercorrelations across Math, French, and English. Therefore, the degree to which student motivation is determined by internal or external forces, compared to the inherent characteristics of the activity (i.e. fully self-determined, intrinsic motivation), may determine how differentiated motivation is across school subjects. According to the schoolsubject-specificity hypothesis (Chanal & Guay, 2015), the more trait-like (e.g. influential across various activities) the impulse that regulates motivation is, the less domain-specific and situational is the resulting motivation (Chanal & Paumier, 2020). Therefore, introjected regulation should be less differentiated than identified regulation, which should be less differentiated than intrinsic motivation since these regulations would be less specific to the school subject, situational level (cf. Chanal & Paumier, 2020). And indeed, Chanal and Paumier (2020) confirmed that controlled motivation in a given school subject was more strongly related to a global trait than autonomous motivation, which was found to be more strongly bound to school-subjectspecific outcomes (the situational level). When testing the motivational sequence in a sample of Swiss university students, regarding the link antecedent → motivations, Paumier and Chanal (2022) confirmed that students’ perceptions of their professors’ autonomy support were positively associated with types of autonomous motivation in corresponding courses (statistics and social psychology), but mostly not with controlled motivation. Concerning motivations → outcomes, autonomous types of motivation had more

Explaining the Context-Specificity of Student Motivation

63

significant associations with achievement emotions than controlled types of motivation in corresponding courses (regarding statistics, social, and clinical psychology). In sum, using the HMIEM (Vallerand, 1997) can help to better detail the differences in student motivation regarding a trait-like (global), schoolspecific versus non-academic (contextual), or a school-subject-specific intermediate or situational level. Future Directions for Studying Context-Specific Processes Using SDT

The objective of this chapter was to give a brief overview on the main premises of Self-Determination Theory and its applications in the academic domain in order to explain antecedents and consequences of distinct types of students’ motivation and achievement emotions. Finally, we seek to summarise empirical findings that enable to derive some potentially fruitful avenues for future research when aiming for greater context-specificity in SDT-based educational research. In the following, we draw attention to topics, which we believe are critical areas for future research on the context-specificity of SDT in educational settings. Distinguishing between Context-Specific and Context-General Constructs

When summarising the literature, it becomes clear that, as Pekrun and Marsh (2022) highlighted, there may be a need to distinguish between contextspecific and context-general constructs. For example, the relative effectiveness of the satisfaction of the three distinct psychological needs may depend on context-specific processes. That is, prior evidence suggests that teachers might have greater impact on satisfying or frustrating students’ need for autonomy and competence while peers may have stronger influence on students’ feelings of relatedness (Vasconcellos et al., 2020). Moreover, intrinsic and identified motivation may be more contextspecific, whereas introjected and external regulation may reflect more contextgeneral constructs. A study with French-Canadian high school students by Guay and Bureau (2018) in French, Math, and English revealed that subject-specific intrinsic motivation was positively associated with grades in the same subject (in French and English) but not with intrinsic motivation for the other school subjects. By comparison, subject-specific external regulation was negatively associated with grades in all three school subjects, and introjected motivation in Math and English also was negatively associated with academic achievement in French, Math, and English. Guay and Bureau (2018) explained these findings with the assumption that the underlying

64

Barbara Flunger and Julien Chanal

proximal relationships that are tied to a given school subject, particularly teachers’ behaviours, might be relatively stronger associated with students’ intrinsic motivation and identified motivation than with the more controlled types of motivation. Both intrinsic and identified motivations are supposed to be regulated by classroom processes at the situational level, for example teachers’ need support. By contrast, introjected and external regulations might be influenced by processes and relationships that are not subjectspecific, such as need support by parents or friends (see also findings by Paumier & Chanal, 2022; with a sample of Swiss university students). Consequently, the distinction between context-specific and context-general constructs has great potential for future research to help explain which factors drive classroom processes in distinct school subjects and why some outcomes (e.g. anxiety or feelings of belonging in a class) are less strongly influenced by teacher behaviour. This could enable future research to shed light on inconsistent findings concerning the situatedness and context-specificity of SDT. Considering the Multi-Level Classroom Context

In the classroom context, teachers can either provide need support to the whole class, for example, through preparing need-supportive materials for the whole class (e.g. Patall et al., 2013), or they can address individual students (see e.g. Skinner & Belmont, 1993), for instance by explaining why learning a specific topic is relevant for the future plans of a student. Therefore, students’ motivations, and emotions as well as teachers’ need support, can both refer to an individual-level (student) and a group-level (study group or class) construct. Flunger et al. (2023) found that this “us/class” versus “me/I” distinction can matter for elementary school students’ perception of their teachers’ autonomy support: Teachers’ autonomy support directed at students or at the whole class could be distinguished in student perceptions as two distinct approaches regarding several autonomy-supportive strategies. Both class-directed and individual autonomy support may contribute to an overall autonomy-supportive atmosphere (a class-level construct, Flunger et al., 2023). If multiple teachers instruct students, it is likely that the need-supportive climate in a classroom is affected by several teachers’ instructional styles, and the whole-class motivation and emotions may be affected by the overall support that a class receives by different teachers in distinct school subjects. However, students can perceive to be treated unequally by the teacher relative to classmates, and this perceived relative lack of autonomy support may be positively associated with extrinsic regulation (Flunger et al., 2023). Thus, there seem to be conceptual differences between class-directed and individual support, and perceptions of (un-)equal autonomy support (e.g. Chatzisarantis et al., 2019). Therefore, it seems advisable for researchers interested in classroom processes to measure need support at the respective level they are interested in.

Explaining the Context-Specificity of Student Motivation

65

References Ahmadi, A., Noetel, M., Parker, P., Ryan, R., Ntoumanis, N., Reeve, J., Beauchamp, M., Dicke, T., Yeung, A., Ahmadi, M., Bartholomew, K., Chiu, T.K.F., Curran, T., Erturan, G., Flunger, B., Frederick, C., Froiland, J.M., González-Cutre, D., Haerens, L. … Lonsdale, C. (2023). A classification system for teachers’ motivational behaviours recommended in self-determination theory interventions. Journal of Educational Psychology. Advance online publication. https://doi.org/10.1037/ edu0000783. Assor, A. (2012). Allowing choice and nurturing an inner compass: Educational practices supporting students’ need for autonomy. In S. L. Christenson, C. Wylie, & A. L. Reschly (Eds.), Handbook of research on student engagement (pp. 421–439). Springer. https://doi.org/10.1007/978-1-4614-2018-7 Baten, E., Vansteenkiste, M., De Muynck, G. J., De Poortere, E., & Desoete, A. (2020). How can the blow of Math difficulty on elementary school children’s motivational, cognitive, and affective experiences be dampened? The critical role of autonomy-supportive instructions. Journal of Educational Psychology, 112(8), Article 1490. https://doi.org/10.1037/edu0000444 Bureau, J. S., Howard, J. L., Chong, J. X., & Guay, F. (2022). Pathways to student motivation: A meta-analysis of antecedents of autonomous and controlled motivations. Review of Educational Research, 92(1), 46–72. https://doi.org/ 10.3102/00346543211042426 Carreira, J. M., Ozaki, K., & Maeda, T. (2013). Motivational model of English learning among elementary school students in Japan. System, 41(3), 706–719. https:// doi.org/10.1016/j.system.2013.07.017 Chanal, J., & Guay, F. (2015). Are autonomous and controlled motivations schoolsubjects-specific? PloS One, 10(8), Article e0134660. https://doi.org/10.1371/ journal.pone.0134660 Chanal, J., & Paumier, D. (2020). The school-subject-specificity hypothesis: Implication in the relationship with grades. PloS One, 15(4), Article e0134660. https:// doi.org/10.1371/journal.pone.0230103 Chatzisarantis, N. L., Ada, E. N., Ahmadi, M., Caltabiano, N., Wang, D., ThogersenNtoumani, C., & Hagger, M. S. (2019). Differential effects of perceptions of equal, favourable and unfavourable autonomy support on educational and wellbeing outcomes. Contemporary Educational Psychology, 58, 33–43. https://doi. org/10.1016/j.cedpsych.2019.02.002 Conesa, P. J., Onandia-Hinchado, I., Dunabeitia, J. A., & Moreno, M. Á. (2022). Basic psychological needs in the classroom: A literature review in elementary and middle school students. Learning and Motivation, 79, Article 101819. https://doi. org/10.1016/j.lmot.2022.101819 DeCharms, R. C. (1968). Personal causation: The internal affective determinants of behavior. Academic Press. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press. Flunger, B., Hollmann, L., Hornstra, L., & Murayama, K. (2022). It’s more about a lesson than a domain: Lesson-specific autonomy support, motivation, and engagement in Math and a second language. Learning and Instruction, 77, Article 101500. https://doi.org/10.1016/j.learninstruc.2021.101500 Flunger, B., Mayer, A., & Umbach, N. (2019). Beneficial for some or for everyone? Exploring the effects of an autonomy-supportive intervention in the real-life

66

Barbara Flunger and Julien Chanal

classroom. Journal of Educational Psychology, 111(2), 210–234. https://doi. org/10.1037/edu0000284 Flunger, B., Pretsch, J., Schmitt, M., & Ludwig, P. (2013). The role of explicit need strength for emotions during learning. Learning and Individual Differences, 23, 241–248. https://doi.org/10.1016/j.lindif.2012.10.001 Flunger, B., Verdonschot, A., Zitzmann, S., Hornstra, L., & vanGog, T. (2023, resubmitted). A Bayesian Approach to Students’ Perceptions of Teachers’ Autonomy Support. Learning and Instruction. Gagné, M., Deci, E. L., & Ryan, R. M. (2018). Self-determination theory applied to work motivation and organizational behavior. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Organizational psychology (pp. 97–121). Sage Reference. Gainsburg, J. (2008). Real-world connections in secondary Math teaching. Journal of Mathematics Teacher Education, 11, 199–219. https://doi.org/10.1007/ s10857- 007-9070-8 Gaspard, H., Häfner, I., Parrisius, C., Trautwein, U., & Nagengast, B. (2017). Assessing task values in five domains during secondary school: Measurement structure and mean level differences across grade level, gender, and academic domain. Contemporary Educational Psychology, 48, 67–84. https://doi.org/10.1016/j. cedpsych.2016.09.003 Guay, F., & Bureau, J. S. (2018). Motivation at school: Differentiation between and within school subjects matters in the prediction of academic achievement. Contemporary Educational Psychology, 54, 42–54. https://doi.org/10.1016/j. cedpsych.2018.05.004 Guay, F., Chanal, J., Ratelle, C. F., Marsh, H. W., Larose, S., & Boivin, M. (2010). Intrinsic, identified, and controlled types of motivation for school subjects in young elementary school children. British Journal of Educational Psychology, 80(4), 711–735. https://doi.org/10.1348/000709910X499084 Häfner, I., Flunger, B., Dicke, A. L., Gaspard, H., Brisson, B. M., Nagengast, B., & Trautwein, U. (2017). Robin Hood effects on motivation in math: Family interest moderates the effects of relevance interventions. Developmental Psychology, 53(8), 1522–1539. https://doi.org/10.1037/dev0000337 Holzer, J., Korlat, S., Haider, C., Mayerhofer, M., Pelika, E., Schober, B., Spiel, C., Toumazi, T., Salmela-Aro, K., Käser, U., Schultze-Krumbholz, A., Wachs, S., Dabas, M., Verma, S., Iliev, D., Andonovska-Trajkovska, D., Plichta, P., Pyżalski, J., Walter, N., … Lüftenegger, M. (2021). Adolescent well-being and learning in times of COVID-19—A multi-country study of basic psychological need satisfaction, learning behavior, and the mediating roles of positive emotion and intrinsic motivation. PloS One, 16(5), Article e0251352. https://doi.org/10.1371/journal. pone.0251352 Howard, J. L., Bureau, J., Guay, F., Chong, J. X., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta-analysis from self-determination theory. Perspectives on Psychological Science, 16(6), 1300–1323. https://doi.org/10.1177 %2F1745691620966789 Katz, I., Kaplan, A., & Gueta, G. (2009). Students’ needs, teachers’ support, and motivation for doing homework: A cross-sectional study. The Journal of Experimental Education, 78(2), 246–267. https://doi.org/10.1080/002209709032 92868

Explaining the Context-Specificity of Student Motivation

67

Lohbeck, A., Hohmann, A., von Keitz, P., & Daseking, M. (2022). Children’s motivation profiles in sports and physical activities: A latent profile analysis and selfdetermination theory approach. Journal of Sport and Exercise Psychology, 44(4), 251–262. https://doi.org/10.1123/jsep.2021-0279 Mayer, A., Umbach, N., Flunger, B., & Kelava, A. (2017). Effect analysis using nonlinear structural equation mixture modeling. Structural Equation Modeling: A Multidisciplinary Journal, 24(4), 556–570. https://doi.org/10.1080/10705511.20 16.1273780 Mulligan, K., & Scherer, K. R. (2012). Toward a working definition of emotion. Emotion Review, 4(4), 345–357. https://doi.org/10.1177/1754073912445818 Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom: Applying self-determination theory to educational practice. Theory and Research in Education, 7(2), 133–144. https://doi.org/10.1177/147787850 9104318 Núñez, J. L., & León, J. (2015). Autonomy support in the classroom: A review from self-determination theory. European Psychologist, 20(4), 275–283. https://doi. org/10.1027/1016-9040/a000234 Patall, E. A., Dent, A. L., Oyer, M., & Wynn, S. R. (2013). Student autonomy and course value: The unique and cumulative roles of various teacher practices. Motivation and Emotion, 37(1), 14–32. https://doi.org/10.1007/s11031-012-9305-6 Paumier, D., & Chanal, J. (2018). Motivation autodéterminée, conséquences motivationnelles et hypothèse de spécificité en contexte scolaire: état des lieux, implications et perspectives. Revue Française de Pédagogie, 203, 111–130. https://doi. org/10.4000/rfp.8160 Paumier, D., & Chanal, J. (2022). The antecedents and consequences of autonomous and controlled motivation: Domain specificity and motivational sequence at the situational level. Frontiers in Psychology, 13, Article 987582. https://doi. org/10.3389/fpsyg.2022.987582 Pekrun, R., & Marsh, H. W. (2022). Research on situated motivation and emotion: Progress and open problems. Learning and Instruction, 81, Article 101664. https:// doi.org/10.1016/j.learninstruc.2022.101664 Pekrun, R., & Perry, R. P. (2014). Control-value theory of achievement emotions. In R. Pekrun, & L. Linnenbrink-Garcia (Eds.), International handbook of emotions in education (pp. 120–141). Taylor & Francis. Reeve, J. (2016). A grand theory of motivation: Why not? Motivation and Emotion, 40, 31–35. https://doi.org/10.1007/s11031-015-9538-2 Reeve, J., & Cheon, S. H. (2021). Autonomy-supportive teaching: Its malleability, benefits, and potential to improve educational practice. Educational Psychologist, 56(1), 54–77. https://doi.org/10.1080/00461520.2020.1862657 Roth, G., Vansteenkiste, M., & Ryan, R. M. (2019). Integrative emotion regulation: Process and development from a self-determination theory perspective. Development and Psychopathology, 31(3), 945–956. https://doi.org/10.1017/ S0954579419000403 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Ryan, R. M., & Deci, E. L. (2019). Brick by brick: The origins, development, and future of self-determination theory. In Elliot, A. J. (Ed.), Advances in motivation science (Vol. 6, pp. 111–156). Elsevier. https://doi.org/10.1016/bs.adms.2019.01.001

68

Barbara Flunger and Julien Chanal

Sheldon, K. M., & Gunz, A. (2009). Psychological needs as basic motives, not just experiential requirements. Journal of Personality, 77(5), 1467–1492. https://doi. org/10.1111/j.1467-6494.2009.00589.x Sheldon, K. M., Osin, E. N., Gordeeva, T. O., Suchkov, D. D., & Sychev, O. A. (2017). Evaluating the dimensionality of self-determination theory’s relative autonomy continuum. Personality and Social Psychology Bulletin, 43(9), 1215–1238. https:// doi.org/10.1177/0146167217711915 Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571–581. https://doi.org/10.1037/00220663.85.4.571 Stone, D. N., Deci, E. L., & Ryan, R. M. (2009). Beyond talk: Creating autonomous motivation through self-determination theory. Journal of General Management, 34(3), 75–91. https://doi.org/10.1177/030630700903400305 Su, Y.-L., & Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23(1), 159–188. https://doi.org/10.1007/s10648-010-9142-7 Sutter-Brandenberger, C. C., Hagenauer, G., & Hascher, T. (2018). Students’ selfdetermined motivation and negative emotions in mathematics in lower secondary education—Investigating reciprocal relations. Contemporary Educational Psychology, 55, 166–175. https://doi.org/10.1016/j.cedpsych.2018.10.002 Tsai, Y.-M., Kunter, M., Lüdtke, O., Trautwein, U., & Ryan, R. M. (2008). What makes lessons interesting? The role of situational and individual factors in three school domains. Journal of Educational Psychology, 100(2), 460–472. https://doi. org/10.1037/0022-0663.100.2.460 Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology (pp. 271–360). Academic Press. Vallerand, R. J., Pelletier, L. G., & Koestner, R. (2008). Reflections on self-determination theory. Canadian Psychology/Psychologie Canadienne, 49(3), 257–262. https:// doi.org/10.1037/a0012804 Vansteenkiste, M., & Ryan, R. M. (2013). On psychological growth and vulnerability: Basic psychological need satisfaction and need frustration as a unifying principle. Journal of Psychotherapy Integration, 23, 263–280. https://doi.org/10.1037/ a0032359 Vasconcellos, D., Parker, P. D., Hilland, T., Cinelli, R., Owen, K. B., Kapsal, N., Lee, J., Antczak, D., Ntoumanis, N., Ryan, R. M., & Lonsdale, C. (2020). Selfdetermination theory applied to physical education: A systematic review and metaanalysis. Journal of Educational Psychology, 112(7), 1444–1469. https://doi.org/ 10.1037/edu0000420 Wang, C. J., Liu, W. C., Kee, Y. H., & Chian, L. K. (2019). Competence, autonomy, and relatedness in the classroom: Understanding students’ motivational processes using the self-determination theory. Heliyon, 5(7), Article e01983. https://doi. org/10.1016/j.heliyon.2019.e01983

5 THE ROOTS AND FRUITS OF SELF-EFFICACY IN DIVERSE ACADEMIC CONTEXTS Ellen L. Usher

Abstract Most contemporary theories of motivation acknowledge the central role that personal capability beliefs play as predictors of behaviour. This chapter focuses on self-efficacy – a social cognitive construct theorised by Albert Bandura. Academic self-efficacy is theoretically situated and defined with examples from teaching and learning contexts. Next, the four theorised sources (i.e. “roots”) of self-efficacy – mastery experience, vicarious experience, social persuasion, and physiological and affective states – are described along with findings from education research describing how these factors influence selfefficacy. The chapter then provides an overview of the outcomes (i.e. “fruits”) of academic self-efficacy. Recommendations for investigating self-efficacy in ways that better capture its contextual and dynamic nature are offered.

The Roots and Fruits of Academic Self-Efficacy

“Believe you can and you’re halfway there.” I chuckled when this maxim appeared on my work productivity app as I opened my computer to write this chapter. This aphorism gets at the heart of this chapter: People’s belief in their capability to accomplish desired outcomes (i.e. self-efficacy) is a fundamental component of human growth and development. Over the past four decades, important advances have been made in understanding how self-efficacy develops and the role it plays in teaching, learning, and motivation in different settings. Nevertheless, the roots and fruits of academic self-efficacy should be understood with greater attention to context. I begin this chapter by situating self-efficacy within social cognitive theory. In Part 2, I define and contextualise academic self-efficacy, emphasising its centrality in personal agency. Part 3 describes the “roots” of self-efficacy – DOI: 10.4324/9781003303473-6

70

Ellen L. Usher

the mechanisms through which academic self-efficacy is theorised to develop and change. In Part 4, I consider self-efficacy’s “fruits,” the ways in which selfefficacy affects how people function in academic settings. The chapter’s final section offers areas for new inquiry for a contextualised understanding of selfefficacy and its contribution to individual and collective growth and well-being. Part 1: Self-Efficacy’s Theoretical Underpinnings Triadic Reciprocality

Opposing behaviouristic theories that placed the onus of teaching and learning on the reinforcing environment as the sole guide of human behaviour, psychologist Albert Bandura (1986) proposed that people, through their own cognition, affect, and abilities, can respond to environmental events in different ways. People’s behaviours, he noted, are not always outcomes of social and environmental influences. In fact, people can even select and modify their environments based on what they think and believe (i.e. cognition). Personal influence therefore plays an important role in people’s choices and actions. According to social cognitive theory, human functioning can be understood as a process of triadic reciprocality in which personal, behavioural, and environmental influences dynamically interact (Bandura, 1986). These interacting influences were evident in schools during the first year of the COVID-19 pandemic. In response to the health crisis, many institutions urged teachers and students to wear masks. Some even required masks, sanctioning those who did not comply, which led to changes in behaviour (environment → behaviour). But this was not always the case. Parents and school personnel in certain pockets of the United States began objecting to mask-wearing directives, citing infringement on personal liberty, obstruction to daily activities, or government conspiracy. Their strong beliefs led them decline wearing masks (personal → behaviour). Some even began campaigning to others about the dangers of mask-wearing, creating social pressure to avoid masks in certain locales (behaviour → environment). This recent example illustrates reciprocal determinism in action: the causes of human behaviour are both internal and external, but they are largely mediated by personal beliefs that are contextually situated. In academic settings and beyond, people exercise control over their environment and behaviour through their own agency. This personal freedom can have lasting consequences. As Bandura (1997) so aptly put it, “Because of the capacity for selfinfluence, people are at least partial architects of their own destinies” (p. 8). Personal Agency and Self-Efficacy

Social cognitive theory positions personal agency as a central guiding force in daily life, hence its helpfulness in understanding human motivation. What endows people with agency? First, people have the capacity for forethought

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

71

and self-reflection – two core processes that enable them to manage their actions under ever changing circumstances. Both processes are central to selfregulation. People are also able to think symbolically rather than having to rely solely on their own direct experiences. These agentic capacities enable people to intentionally override environmental influences by making plans to realise desired futures. Agency in action in academic settings means that teachers and learners do not simply respond to the environmental conditions in which they find themselves; they proactively select and construct their environments to achieve desired goals. As people reflect on what has happened to them and on their social environments, they develop beliefs about their own capability to manage their circumstances and to accomplish their goals, or self-efficacy. This chapter focuses broadly on self-efficacy in academic settings. Learners’ academic self-efficacy refers to their perceived capability to accomplish given learning tasks. Teaching self-efficacy refers to teachers’ perceptions of their capabilities to bring about desired outcomes with their students. Self-efficacy guides what people do with the skills and knowledge they possess (Bandura, 1997). Those who feel capable of achieving desired outcomes through personal effort naturally have greater incentive to try. Selfefficacy is therefore an important facet of human motivation. Part 2: Self-Efficacy in Context

Academic self-efficacy is best understood in terms of particular learning contexts. Within the framework of triadic reciprocality, self-efficacy (a personal factor) can be influenced by, and can influence, (a) other personal factors, (b) factors in the sociocultural environment, and (c) one’s own and others’ behaviours. This section offers guiding questions that can be useful for considering the contextual nature of learners’ and teachers’ self-efficacy. Self-Efficacy for What?

A self-efficacy judgement is a response to the question, “Can I do x?” where x could be defined in any number of ways. This is because evaluations of personal capabilities vary within and across contexts and in response to situational demands and constraints. Efficacy beliefs also vary in generality (“I can converse in a second language with friends about pop music” vs. “I can deliver a persuasive argument in a mock debate in the second language”). What is the optimal level of specificity at which to assess learners’ selfefficacy? This depends on the goals of inquiry. For example, Peura et al. (2019) assessed Finnish elementary students’ reading self-efficacy at various levels of specificity to see which best predict children’s reading fluency. They found that self-efficacy assessed at an intermediate level of specificity (i.e. neither too general nor too specific) proved most useful.

72

Ellen L. Usher

The tasks required to develop academic and professional competence are complex and multifaceted; so too are people’s judgements of their capabilities to perform them effectively. For instance, teachers may hold different beliefs about their capabilities to deliver effective instruction, to manage classroom interactions, to assess learning, and to motivate learners. They may adjust their efficacy beliefs with particular learners in mind (Zee & Koomen, 2016). Yet in many schools, teachers are responsible for teaching different content to different groups of learners each day. A global teaching self-efficacy measure may not capture the complexity of their beliefs. Whose Self-Efficacy?

Thinking about the target audience for a self-efficacy assessment is another way to reach greater contextualisation. Self-efficacy can vary between learners at different developmental stages or levels of proficiency. Researchers have had some difficulty assessing the academic self-efficacy of young children, for instance, in part because children have not developed sophisticated metacognitive skills and have difficulty distinguishing between desired levels of competence and their current capability level (Harter, 2008). Concretising the task at hand might be useful when assessing self-efficacy for this group. Level of experience also plays a role in people’s self-efficacy judgements. A first-year medical student and an intern in the first year of postgraduate medical training can both rate their efficacy for delivering expert care, but their judgements must be understood with their skill levels in mind. Aggregating the self-efficacy ratings of preservice and in-service professionals might misrepresent the beliefs of either group. When designing studies or interpreting self-efficacy levels, researchers might likewise consider the homogeneity or heterogeneity of respondents’ social group memberships, social roles, prior learning, developmental level, and ability level. Self-Efficacy When Measured How?

Bandura (2006) emphasised that self-efficacy measures should be crafted with both specificity and correspondence in mind. Self-efficacy can be assessed at different levels of specificity. For example, a learner who has recently begun an intensive writing workshop can evaluate their “writing self-efficacy,” but they can also rate their self-efficacy for writing poems in different styles, crafting op-ed essays, or developing complex characters in short stories. Determining the optimal level of specificity for assessing people’s self-efficacy depends on the target outcomes and the goals of the inquiry. A second consideration in self-efficacy measurement is how well the self-efficacy measure corresponds to the outcome of interest. When the

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

73

correspondence between self-efficacy and the criterial task measures is weak, so too will be their relationship. For instance, one might expect a weak or modest relationship between students’ self-efficacy in mathematics and their overall grade point average in school, the latter of which comprises other subject areas. If students’ overall school performance is the outcome of greatest interest, assessing students’ beliefs in their capabilities for handling the myriad academic tasks required for success would offer better predictive power. This might include both learners’ subject-specific self-efficacy and their self-efficacy for self-regulating their academic tasks (Usher & Schunk, 2018). Self-Efficacy Under What Conditions?

People must carry out their tasks in a variety of conditions that can be externally and/or internally imposed or selected. A student working with a partner on a chemistry lab project who feels certain that they can submit an error-free lab report might feel differently about their capability of answering questions correctly on a high-stakes chemistry exam. The teacher-intraining may have high self-efficacy for delivering a planned math lesson to a well-behaved class of ten-year-olds but not for teaching advanced algebra to a raucous class of 15-year-old students. The same trainee might even report lower or higher teaching self-efficacy for these lessons at different points in time, as was found recently in a study of Swiss student teachers (Rupp & Becker, 2021). Indeed, the contexts of learning, teaching, and performance vary considerably. Changes to the external conditions might involve the psychological climate, working conditions, level of social support, instructional modality, help availability, time pressure, level of competitiveness, and sociohistorical context. The conditions within people’s hearts, minds, and bodies also vary, even as they are susceptible to external influence. Internal conditions affecting self-efficacy include recency of learning, level of prior knowledge, emotional state, perceived threat, personality, cognitive framing, and preconceptions about ability. The conditions under which self-efficacy judgements are made have rarely been a focus of inquiry. Part 3: The Roots of Self-Efficacy

Bandura (1997) hypothesised that people primarily rely on information from primary sources when judging what they can do. Researchers have suggested that selection, weighting rules, and cognitive biases likely mediate the relationship between these informational sources and self-efficacy and that these are affected by the broader sociocultural context (Usher & Weidner, 2018). Each hypothesised “root” of self-efficacy is described next.

74

Ellen L. Usher

Enactive Experience

When assessing whether they can perform a certain task, people naturally look first to their own past experiences with similar tasks. Having experienced successes or mastery of related skills typically supports a strong sense of efficacy, whereas previous difficulties or failures undermine it (Usher & Pajares, 2008). Interpretations of one’s experiences, and not the outward performances themselves, ultimately mediate the effects of experience on self-efficacy. As noted in the previous section, the conditions under which performances take place can affect how self-efficacy is influenced. A student who earned a top score on a science project but who received extensive help completing it may not necessarily feel more capable in science. On the other hand, even marginal success on an extremely challenging task may strengthen one’s self-efficacy. The ratio of perceived successes to failures may also become part of the calculus that affects the relationship between enactive experiences and self-efficacy. Cognitive mastery (i.e. knowledge) is sometimes considered a component of enactive experience. The first astronauts could not rely on having been to space on a successful mission, but their simulated experience and mastery of flight control knowledge certainly boosted (pun intended) their selfefficacy for accomplishing the mission. Similarly, increasing one’s knowledge about how to be a good teacher can boost teaching self-efficacy (Morris et al., 2017). In domains in which people have little or no experience, learning all that can be learned about a particular topic can help support self-efficacy. Vicarious Experience

Although direct experiences can be a powerful source of self-efficacy, they require time and opportunity. Modelled experiences also help observers develop a sense of what they can and cannot do, especially when they have little direct experience in or familiarity with a domain. Numerous contextual factors play a role in whether and how vicarious experience informs an observer’s self-efficacy. These include whether the model is selected or imposed, how similarly the observer feels to the model, the model’s status and skill level, the modelled strategies used, and the model’s attitudes (see Gladstone & Cimpian, 2021, for a review). Being a member of a group that has been underrepresented or stigmatised in an area can lead even the heartiest of souls to question whether they can be successful. In such cases, seeing someone like one’s self who has been successful can serve as a powerful source of self-efficacy. During the recent unveiling of their portraits in the White House, former U.S. First Lady Michelle Obama said of she and her husband, “If the two of us can end up on the walls of the most famous address in the world, then again it is important for every young kid who is doubting themselves to believe that they can too.” People can also play an active role in selecting models. Advances in technology make it possible for learners to select symbolic models from around

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

75

the world who can perform nearly any task imaginable. People can proactively seek out models who help them feel more capable, such as when they join social groups or take part in extracurricular activities. Seeing others accomplishing goals, particularly those slightly beyond what people believe they can do, helps them envision what they could accomplish with similar effort. Models can also help boost coping self-efficacy by normalising struggles, putting challenging tasks within psychological reach (even if they were already within actual reach). Social Persuasion

Self-efficacy can also be supported (or undermined) by the appraisals of others. Messages that convey a strong faith in one’s capabilities can help a learner cast aside self-doubt. Disparaging comments can shatter a sense of one’s personal efficacy. Social appraisals can be made explicitly, such as when a teacher offers verbal encouragement that shows certainty in a student’s preparation and skill, or implicitly, such as when an instructor never calls on a student to answer a question. To assess social persuasions, researchers typically ask people to rate whether or how often they receive encouraging messages from others. However, people are not always aware of the messages they receive, making implicit messages difficult to study. Social cognitive processes involve selecting and weighting information from different social contexts. Bandura (1997) asserted that factors that learners use to appraise social information include the sincerity and status of the messenger and their familiarity with the person and task at hand. For example, when learners feel a trusting relationship with their teachers or supervisors, they are more likely to interpret feedback in ways that build their self-efficacy (e.g. Eva et al., 2012). Questions remain about why certain types of social messages have a more lasting effect on one’s self-efficacy. Physiological and Affective States

People read their own physiological and affective states as another sign of what they can do. An increased heart rate or profuse sweating when thinking about a task might signal that one is incapable. Learners and teachers who experience high levels of stress or anxiety could interpret such feelings as evidence that they do not have what it takes to succeed. Mood states can likewise affect self-efficacy by biasing what people pay attention to as they approach various tasks. A good mood can activate positive memories that support self-efficacy, whereas a bad mood can lead to self-doubt. Temporal patterns of emotion, cognitive construal of physiological arousal, and the intensity of arousal can influence the degree to which arousal affects self-efficacy. People register their internal emotional states across

76

Ellen L. Usher

performance conditions and make inferences about what their emotions might convey about their abilities. Repeated or extreme negative arousal during examinations, for example may undermine self-efficacy and performance in the future, contributing to the poor outcomes people fear. The more intense the arousal felt during the task, the stronger its effects on self-efficacy (Bandura, 1997). A Complex Root System: Integration of Efficacy-Relevant Information

Just as a tree uses its root system to supply nutrients that sustain growth, people select different efficacy-relevant experiences to inform their self-efficacy in different situations (Bandura, 1997). The “uptake” of information depends in part on environmental circumstances and internal selection processes. For example, when secondary science learners were surveyed about their exposure to the four sources of information, a subset of students reported having mastery experience in science but perceived little other efficacy-relevant information. Other students reported experiences related to all four sources of self-efficacy. Still another group of students perceived little efficacy-relevant information at all. Some evidence suggests that women tend to pay more attention to social sources of information in certain contexts (e.g. engineering) than do men (Chen et al., 2023). Contextual and sociocultural features may affect how people experience and interpret efficacy-relevant information (see Usher & Weidner, 2018). Part 4: The Fruits of Self-Efficacy

Empirical studies across diverse spheres of life have shown that people’s evaluations of their personal capabilities affect them in numerous ways. Selfefficacy affects human functioning through cognitive, motivational, affective, and selection processes (Bandura, 1997). In this section, I examine the way that these processes comprise the many “fruits” of self-efficacy and enable people to be agents of their own development. Cognitive Fruits

Self-efficacy affects the mental representations people make of their lives. Those who feel capable of handling the complex challenges they face will imagine better futures for themselves and act accordingly. That is, self-efficacy’s influence on behaviour is explained in part by intermediary cognitions. When people believe they can effectively cope with what is ahead, they are more likely to expect positive outcomes; when they doubt their capability to cope, they will be more apt to conjure up images of catastrophe or dread. This

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

77

partly explains why skill, prior performance, and knowledge are inadequate predictors of how people will behave. People’s beliefs in their capacity to use their skills guide how they interpret what happens to them and how they will respond. Self-efficacy can affect memory processes (Beaudoin & Desrichard, 2011), creativity (Beghetto et al., 2011), and academic performance (Richardson et al., 2012). Motivational Fruits

Self-efficacy activates many motivational processes because “unless people believe they can produce desired effects by their actions, they have little incentive to act” (Bandura, 1997, pp. 3–4). Learners who feel more capable in their academic skills are more likely to adopt goals focused on learning and less likely to engage in their work to avoid looking incompetent (Huang, 2016). Klassen et al. (2008) found that learners with higher self-efficacy for self-regulation employ more effective study skills and avoid procrastinating. Such learners are also more likely to attribute setbacks or failures to faulty strategies or insufficient effort than to internal stable causes, such as lack of ability (Schunk & DiBenedetto, 2020). Those with a firm belief in their own efficacy are less likely to give up when facing challenges and set higher aspirations for achievement (Honicke & Broadbent, 2016). Teachers with higher self-efficacy show greater job satisfaction and commitment (Zee & Koomen, 2016). Affective Fruits

Another means by which self-efficacy influences human flourishing is by activating positive emotions. When people feel capable of managing the tasks before them, they hold a more optimistic outlook and experience less stress. Conversely, a sense of personal inefficacy gives way to stress, depression, and hopelessness. Self-efficacy thus can serve a regulatory function in emotion regulation. Those with high self-efficacy are better able to cope with difficulties. Students who doubt their capabilities to manage academic challenges are most likely to experience anxiety when facing their school tasks. Low professional self-efficacy has been associated with greater burnout among healthcare professionals and teachers, although this relationship is likely reciprocal (e.g. Wang et al., 2015). Selection Processes

The final means by which self-efficacy affects human functioning is through the choices people make. People tend to avoid difficult situations in which task demands seem insurmountable. They accept challenges that

78

Ellen L. Usher

they believe they are capable of handling. As learners navigate a sea of career possibilities, their self-efficacy serves as one guide for decisionmaking. Those who feel capable further their preparation by engaging in opportunities to become more familiar with possible life paths and professions. Those who doubt their capabilities forego such opportunities. Self-efficacy therefore plays a key role in career decision-making (Brown & Lent, 2019). Cascading Effects

Bandura’s (1986) model of reciprocal determinism helps explain how, once activated, any of the four processes above can lead to cascading effects on a person’s life trajectories and outcomes. For instance, a learner who has developed high science self-efficacy applies for a scholarship to attend a summer science camp. This new environment offers exposure to new career ideas and social influences that spawn possibilities never dreamed of and motivate the learner to enrol in a high school course on forensics. The learner begins to follow social media channels about how to apply to medical school. It is likewise easy to imagine the devastating effects of self-doubt. For example, a person who suffers from low social self-efficacy feels anxious in social situations, imagines social mishaps, and avoids social encounters. As a result, social skill development is thwarted, as are social opportunities. Of course, people do not live their lives in isolation. Their sociocultural and sociohistorical environments and skill-building opportunities make these realities more or less likely. Even so, the beliefs people hold can guide their life circumstances in powerful ways that are not always captured in brief selfefficacy investigations. Part 5: Towards Greater Contextualisation of Self-Efficacy in Teaching and Learning

Despite advances in self-efficacy research, the sources and effects of selfefficacy have not been thoroughly explored in many teaching and learning contexts. Moreover, much of the extant research has not adequately considered the contextual nature of self-efficacy and its development. In this final section, I offer several possibilities for how self-efficacy research might become more contextualised. Self-Efficacy in Changing Educational Contexts

Social cognitive theory accords a primary role to the social environment in influencing the self-system. Nevertheless, surprisingly little research has

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

79

examined how changes to the educational context affect learners’ and teachers’ self-efficacy. Consider the following questions:

• When learners move from general education courses to highly selective settings, what happens to their self-efficacy? Why?

• How does self-efficacy affect cognition during routine vs. non-routine tasks? What happens when the cost of errors on tasks is high?

• When team interests or goals are prioritised over individual goals, what role does self-efficacy play in motivation and performance?

• When teachers must develop lessons to be delivered online to learners unfamiliar with their content and the technological platform, what most affects their self-efficacy? • When individuals must perform under psychologically challenging conditions, which sources of self-efficacy are most salient? • Many individuals, particularly those who are members of social groups historically underrepresented in a field, may fear that they will fulfil negative stereotypes about their group or be exposed as less capable than others. How, if at all, do learners maintain a robust belief in their personal efficacy despite stereotype threat or impostor feelings? • To what extent do social and professional identities affect the roots and fruits of learners’ self-efficacy? The discerning reader will find even these questions too broad or too limited. They offer just some of the ways that researchers could offer a more contextualised understanding of self-efficacy in diverse contexts. Exploring the Counterintuitive

In certain situations, and for certain groups of people, self-efficacy’s sources and effects may not operate as theorised. As McNulty and Fincham (2012) observed, “whether [psychological processes] have positive or negative implications depends on the context in which they operate” (pp. 107–108). Such cases offer excellent opportunities for understanding the relation of circumstances, both internal and external, that might give rise to patterns different from those observed predominantly in Western settings based on Bandura’s (1997) theorising. Is more self-efficacy always a good thing? In its extreme forms, selfefficacy can yield undesirable outcomes. When self-efficacy is high and poorly calibrated to requisite skills, people can harm themselves and others. Novice learners and those who lack sufficient knowledge of task demands may overestimate their capabilities and fail to employ effective study strategies (Osterhage et al., 2019). Unreasonably low self-efficacy is also dangerous and may lead people to withdraw from opportunities for which they are

80

Ellen L. Usher

capable. However, some individuals with diminished self-efficacy still manage to learn and perform beyond expectations. Approaches that could help identify cases of extreme self-efficacy, particularly when not aligned with expected outcomes, would open the door to a more nuanced understanding of where self-efficacy originates and possible interventions to enhance people’s self-knowledge and well-being. Construct Ambiguity in Self-Efficacy Research

One persistent problem in self-efficacy research is that much of what is published under the name of “self-efficacy,” is not, at the operational level, reflective of self-efficacy as theorised (i.e. a judgement of personal capability). Some popular self-efficacy scales include items with poor face validity (e.g. “I expect to do well in this class,” Pintrich et al., 1991; “It is easy for me to stick to my aims and accomplish my goals,” Schwarzer & Jerusalem, 1995). Operational problems contribute to noise in the literature and complicate the task for researchers trying to navigate it (e.g. Klassen & Klassen, 2018). An opposite challenge is that many researchers indeed measure self-efficacy but use other labels to describe it (e.g. perceived competence, ability selfconcept). For example, Collie (2022) recently showed that learners’ beliefs about their social-emotional capabilities predict their social-emotional behaviours in school both directly and by influencing other facets of motivation. This interesting contribution would not be retrieved by an individual looking for “self-efficacy” research. Many contemporary theories of motivation emphasise the role of selfreferential beliefs. Much ink has been spilled on differentiating these different aspects of the self-system (e.g. Bong & Skaalvik, 2003; Marsh et al., 2019; Pajares & Schunk, 2002; Usher, 2015). Moving self-efficacy theory forward requires an understanding of its basic theoretical conceptions as well as a high level of discernment when evaluating “self-efficacy” research. Methods

The methodologies that researchers select necessarily rest on their philosophical assumptions, which frame their understanding of a problem. Research is inherently reductionistic, but quantitative approaches have been especially limited in offering a contextualised portrayal of self-efficacy and its sources. Most studies rely on convenience sampling and what might be called convenience measuring of self-efficacy and its sources. Researchers rarely report (and perhaps do not inspect) the distribution of self-efficacy scores, proceeding to linear analyses of any of the previously identified roots and fruits. Full sample results are reported, and, on occasion, results are compared across categories of people (e.g. gender, race). The resulting body of research has

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

81

enabled summaries such as the one provided above, which reinforces decontextualised typologies (see Barrett, 2022). Although a beginning, these approaches are insufficient for exploring the complexity of social cognitive processes, some of which certainly take place outside of conscious awareness (Nolen, 2020; Urdan & Bruchmann, 2018). A contextualised understanding of self-efficacy must better address for whom, under what conditions, to what extent, and why self-efficacy develops and operates (Pajares, 2007). Self-Efficacy in the Self-Regulation of Learning

More than ever in human history, learners must sift through a vast field of information and select what is meaningful to advance their personal and shared goals. Simultaneously, learners increasingly use “intelligent” systems that are often designed to circumvent their agentic engagement. Successful lifelong learning is primarily self-directed, which requires a robust belief in one’s self-regulatory capabilities (Usher & Schunk, 2018). Research can help illuminate the role that self-efficacy plays in guiding self-regulatory skill development and use. Coda

Psychology lost one of its prominent voices and champions of personal and collective agency with the passing of Albert Bandura in 2021. In her tribute to him, his former student wrote of the optimism inherent in Bandura’s theory of human agency, which “instills belief in our ability to navigate life’s challenges to alter life and societal trajectories for the better” (Ozer, 2022, p. 484). To all who continue to explore the sources and effects of self-efficacy in the lives of teachers and learners, may we collectively strive to follow in his footsteps. References Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall. Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman. Bandura, A. (2006). Guide for constructing self-efficacy scales. In F. Pajares & T. Urdan (Eds.), Adolescence and education, Vol. 5: Self-efficacy and adolescence (pp. 307–337). Information Age. Barrett, L. F. (2022). Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science. American Psychologist, 77(8), 894–920. https://doi.org/10.1037/amp0001054 Beaudoin, M., & Desrichard, O. (2011). Are memory self-efficacy and memory performance related? A meta-analysis. Psychological Bulletin, 137(2), 211–241. https://doi.org/10.1037/a0022106

82

Ellen L. Usher

Beghetto, R. A., Kaufman, J. C., & Baxter, J. (2011). Answering the unexpected questions: Exploring the relationship between students’ creative self-efficacy and teacher ratings of creativity. Psychology of Aesthetics, Creativity, and the Arts, 5(4), 342–349. https://doi.org/10.1037/a0022834 Bong, M., & Skaalvik, E. M. (2003). Academic self-concept and self-efficacy: How different are they really? Educational Psychology Review, 15, 1–40. https://doi. org/10.1023/A:1021302408382 Brown, S. D., & Lent, R. W. (2019). Social cognitive career theory at 25: Progress in studying the domain satisfaction and career self-management models. Journal of Career Assessment, 27(4), 563–578. https://doi.org/10.1177/106907271 9852736 Chen, X.-Y., Usher, E. L., Roeder, M. L., Johnson, A. R., Kennedy, M. S., & Mamaril, N. A. (2023). Mastery, models, messengers, and mixed emotions: A qualitative approach to assessing the sources of engineering self-efficacy. Journal of Engineering Education. Advance online publication. https://doi.org/10.1002/jee.20494 Collie, R. J. (2022). Perceived social-emotional competence: A multidimensional examination and links with social-emotional motivation and behaviors. Learning and Instruction, 82, Article 101656. https://doi.org/10.1016/j.learninstruc. 2022.101656 Eva, K. W., Armson, H., Holmboe, E., Lockyer, J., Loney, E., Mann, K., & Sargeant, J. (2012). Factors influencing responsiveness to feedback: On the interplay between fear, confidence, and reasoning processes. Advances in Health Sciences Education, 17, 15–26. https://doi.org/10.1007/s10459-011-9290-7 Gladstone, J. R., & Cimpian, A. (2021). Which role models are effective for which students? A systematic review and four recommendations for maximizing the effectiveness of role models in STEM. International Journal of STEM Education, 8(1), 59–79. https://doi.org/10.1186/s40594-021-00315-x Harter, S. (2008). The developing self. In W. Damon & R. M. Lerner (Eds.), Child and adolescent development: An advanced course (pp. 216–260). Wiley. Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84. https://doi.org/10.1016/j.edurev.2015.11.002 Huang, C. (2016). Achievement goals and self-efficacy: A meta-analysis. Educational Research Review, 19, 119–137. http://dx.doi.org/10.1016/j.edurev.2016.07.002 Klassen, R. M., & Klassen, J. R. L. (2018). Self-efficacy beliefs of medical students: A critical review. Perspectives on Medical Education, 7, 76–82. https://doi.org/ 10.1007/s40037-018-0411-3 Klassen, R. M., Krawchuck, L. L., & Rajani, S. (2008). Academic procrastination of undergraduates: Low self-efficacy to self-regulate predicts higher levels of procrastination. Contemporary Educational Psychology, 33, 915–931. https://doi. org/10.1016/j.cedpsych.2007.07.001 Marsh, H. W., Pekrun, R., Parker, P. D., Murayama, K., Guo, J., Dicke, T., & Arens, A. K. (2019). The murky distinction between self-concept and self-efficacy: Beware of lurking jingle-jangle fallacies. Journal of Educational Psychology, 111(2), 331–353. https://doi.org/10.1037/edu0000281 McNulty, J. K., & Fincham, F. D. (2012). Beyond positive psychology? Toward a contextual view of psychological processes and well-being. American Psychologist, 67(2), 101–110. https://doi.org/10.1037/a0024572

Roots and Fruits of Self-Efficacy in Diverse Academic Contexts

83

Morris, D. B., Usher, E. L., & Chen, J. (2017). Reconceptualizing the sources of teaching self-efficacy: A critical review of emerging literature. Educational Psychology Review, 29, 795–833. https://doi.org/10.1007/s10648-016-9378-y Nolen, S. B. (2020). Challenging research norms in educational psychology. Educational Psychologist, 55(4). 267–272. https://doi.org/10.1080/00461520.2020. 1810043 Osterhage, J., Usher, E. L., Douin, T. A., & Bailey, W. M. (2019). Opportunities for self-evaluation increase student calibration in an introductory biology course. CBE-Life Sciences Education, 18(2), Article 16. https://doi.org/10.1187/ cbe.18-10-0202 Ozer, E. (2022). In memoriam: Albert Bandura. American Psychologist, 77(3), 483–484. https://doi.org/10.1037/amp0000981 Pajares, F. (2007). Culturalizing educational psychology. In F. Salili & R. Hoosain (Eds.), Culture, motivation, and learning (pp. 19–42). Information Age. Pajares, F., & Schunk, D. H. (2002). Self-efficacy and self-belief in psychology and education: A historical perspective. In J. Aronson & D. Cordova (Eds.), Psychology of education: Personal and interpersonal forces (pp. 5–25). Academic Press. Peura, P. I., Aro, T. J. K., Viholainen, H. J. K., Räikkönen, E. M., Usher, E. L., Sorvo, R. A., & Aro, M. T. (2019). Reading self-efficacy and reading fluency development among primary school children: Does specificity of self-efficacy matter? Learning and Individual Differences, 73, 67–78. https://doi.org/10.1016/j. lindif.2019.05.007 Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). University of Michigan. Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138, 353–387. http://doi.org/10.1037/a0026838 Rupp, D., & Becker, E. S. (2021). Situational fluctuations in student teachers’ selfefficacy and its relation to perceived teaching experiences and cooperating teachers’ discourse elements during the teaching practicum. Teaching and Teacher Education, 99, Article 103252. https://doi.org/10.1016/j.tate.2020.103252 Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, Article 101832. https://doi. org/10.1016/j.cedpsych.2019.101832 Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy Scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35–37). NFER-NELSON. Urdan, T., & Bruchmann, K. (2018). Examining the academic motivation of a diverse student population: A consideration of methodology. Educational Psychologist, 53(2), 114–130. https://doi.org/10.1080/00461520.2018.1440234 Usher, E. L. (2015). Personal capability beliefs. In L. Corno & E. H. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 146–159). Taylor & Francis. Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78, 751–796. https://doi.org/10.3102/0034654308321456

84

Ellen L. Usher

Usher, E. L., & Schunk, D. H. (2018). A social cognitive theoretical perspective of selfregulation. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 19–35). Routledge. Usher, E. L., & Weidner, B. L. (2018). Sociocultural influences on self-efficacy development. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2 (pp. 141–164). Information Age. Wang, H., Hall, N. C., & Rahimi, S. (2015). Self-efficacy and causal attributions in teachers: Effects on burnout, job satisfaction, illness, and quitting intentions. Teaching and Teacher Education, 47, 120–130. https://doi.org/10.1016/j.tate. 2014.12.005 Zee, M., & Koomen, H. M. Y. (2016). Teacher self-efficacy and its effects on classroom processes, student academic adjustment, and teacher well-being: A synthesis of 40 years of research. Review of Educational Research, 86(4), 981–1015. https:// doi.org/10.3102/0034654315626801

6 HOW UNIVERSAL ARE ACADEMIC EMOTIONS? A Control-Value Theory Perspective Reinhard Pekrun and Thomas Goetz

Abstract Control-value theory (CVT) posits that emotions show diversity – their situational triggers, objects, frequency, and intensity vary widely across persons and contexts. Due to this variation, patterns of emotions can be unique for each individual student and teacher. Nevertheless, basic functional relations of emotions with outcomes and origins are thought to be universal, across persons as well as contexts. CVT uses the term “relative universality” to denote this combination of uniqueness and universality. We first provide an overview of CVT, summarise the theory’s propositions on relative universality, and outline general advantages of relative universalism in psychological and educational science. We then review existing evidence, which supports both the diversity of academic emotions and the generalisability of their links with control-value appraisals and achievement across genders, ethnic groups, academic domains, learning environments, and cultural contexts. In conclusion, we discuss implications for practice and directions for future inquiry, highlighting the need for context-sensitive within-person research and intervention studies.

Learning and achievement are critically important for students. Similarly, teaching is a fundamentally important task for teachers. Given personal importance, learning, achievement, and teaching can instigate intense emotions in students and teachers alike. Both positive (i.e. pleasant) and negative (i.e. unpleasant) emotions play a role, such as enjoyment of learning and teaching, hope for success, curiosity about a new problem, anger about task demands that seem unreasonable, anxiety before an exam, or boredom during a monotonous lecture. These emotions are not mere epiphenomena of learning and teaching; rather, they direct students’ and teachers’ motivation, thought, and action. DOI: 10.4324/9781003303473-7

86

Reinhard Pekrun and Thomas Goetz

Control-value theory (CVT) explains these emotions. In its original version, the theory considered students’ and teachers’ achievement emotions, defined as emotions related to achievement activities and their outcomes (Pekrun, 2006, 2018; Pekrun, Marsh, Elliot, et al., 2023). The recent generalised version of the theory also explains epistemic, social, and health-related emotions (Pekrun, 2021). The theory considers the origins of these emotions; their functions for learning, performance, and health; their regulation; and related interventions and educational practices (see Figure 6.1 for an overview). In this chapter, we first provide an overview of CVT. We then summarise the theory’s propositions on universal principles explaining emotions, on the one hand, and variation across persons and contexts, on the other (together called “relative universality” of emotions). Next, we review existing evidence on variation of students’ and teachers’ emotions and the universal nature of their links with origins and outcomes across genders, learning environments, and cultural contexts. In conclusion, we discuss implications for practice and directions for future research. Overview of Control-Value Theory

CVT is built on the premise that objective circumstances alone cannot explain human emotions. Rather, it is the subjective perception of the situation that shapes our emotions. For example, the objective demands of an exam are not sufficient to explain students’ anxiety before the exam – rather, it is students’ appraisal of the difficulty of these demands, and the personal relevance of the exam, which determine if they are anxious or not. CVT proposes that two types of appraisals are especially important: perceptions of one’s control over activities and their outcomes, and perceptions of the value (or importance) of these activities and outcomes. Different types and combinations of these appraisals are thought to instigate different emotions. For example, students can enjoy learning if they feel competent to master the material (high control) and if the material is interesting (high value). If they feel unable to understand the material, or are disinterested, learning is not enjoyable. When preparing for a test, students may be fearful if they doubt whether they can pass it (low control) and if the test is deemed important (high value). If success is subjectively certain, or if the test does not matter, then there is no reason to be nervous. Similarly, teachers can enjoy teaching a class if they feel competent to manage the class and deem the class important, and they may be afraid of teaching if the class is important but seems not manageable. Students’ and teachers’ control-value appraisals are considered as proximal antecedents of their emotions. Factors in the person and the environment that influence these appraisals also influence the emotions. Gender, personal goals, and learning-related beliefs are examples at the person side; the classroom environment, family context, and cultural values are

How Universal Are Academic Emotions?

FIGURE 6.1

87

Control-value theory: basic propositions

examples at the side of the environment (Figure 6.1). Control-value theory uses a socio-ecological perspective to conceptualise environments, with immediate learning environments being embedded in broader institutional, economic, and socio-cultural contexts (Bronfenbrenner, 1979). Emotions, in turn, influence students’ and teachers’ learning and performance. The cognitive-motivational model of emotion effects that is part of CVT can be used to explain these influences. For example, effects of students’ anxiety on their achievement are thought to be due to the impact of anxiety on task-irrelevant thinking, intrinsic and extrinsic motivation, use of learning strategies, and the regulation of learning, with overall effects varying across tasks and individuals (Pekrun, 2006). Emotions, their outcomes, and their antecedents can be linked by reciprocal effects over time. For example, enjoyment of learning can promote

88

Reinhard Pekrun and Thomas Goetz

successful performance, and success, in turn, further strengthens enjoyment. The reciprocal nature of these links implies that emotions can be regulated by targeting any of the elements of the resulting cyclic feedback processes (Pekrun & Stephens, 2009). Similarly, emotion-oriented educational interventions and classroom practices can target various components of these processes (Figure 6.1). Relative Universality and the Role of Context

From these propositions of CVT, it follows that situational factors – from the immediate social environment to broader cultural contexts – play a crucial role in shaping emotions. However, does this mean that the processes generating emotions and determining their effects also depend on context? CVT proposes that the basic psychological functions that relate emotions to their origins and outcomes are universal within our species. Nevertheless, the composition of these functions, and the objects of emotions, are thought to vary widely across persons, environments, socio-cultural contexts, and historical times. It is this duality of the role of context – context-general nature of basic functions combined with context-specific variation – which is called “relative universality” in CVT (see Pekrun, 2009, 2018). More specifically, CVT proposes that the links between perceived control, perceived value, and emotions are universal. For example, high perceived value should generally enhance enjoyment of learning and reduce boredom, across students and contexts. Similarly, the functions of emotions for basic processes mediating learning are thought to be universal, such as enjoyment broadening attention and enhancing flexible thinking. It is conceivable that moderators, such as physiological properties of the brain systems of affective working memory, influence the strengths of these relations. Nevertheless, the general functional form of the relations (positive, negative, nonlinear) is thought to be universal. From a statistical perspective, this implies that effect sizes may vary, but that the direction of effects is the same across persons and contexts. In contrast, situational triggers of emotions, their person- and situationspecific objects, and process parameters such as frequency, intensity, and decay rates are thought to differ. For example, some students are afraid of math, others are afraid of language classes, and still others rarely experience anxiety, or are afraid of school generally – depending on their socialisation and personal profile of control and value beliefs. Similarly, some educational contexts are prone to be enjoyable, such as well-designed learning games, whereas others may exacerbate students’ fears, such as environments emphasising performance goals. On a broader scale, levels of academic emotions are expected to differ between cultural contexts, depending on the variation of values, epistemic activities, or styles of teaching across these contexts.

How Universal Are Academic Emotions? 89

Furthermore, given that the strength of basic functions can differ across individuals, relations with origins and outcomes that are based on the interplay of several single mechanisms can also vary. The effects of students’ emotions on their academic achievement are a case in point. For example, CVT proposes that anxiety generates irrelevant thinking and undermines intrinsic motivation, which should reduce overall performance, but can prompt strong motivation to invest effort to avoid failure, which can enhance performance. These oppositional effects may be balanced differently in different students, explaining why overall effects on achievement can vary (although they may be negative in the vast majority of students). Finally, relations with origins and outcomes that depend on the interplay of task demands and psychological mechanisms can vary across task contexts. Again, the effects of anxiety on performance can serve as an example. Irrelevant thinking caused by anxiety reduces the working memory resources available for task performance, resulting in negative effects of anxiety on performance at difficult or complex tasks that require many resources. In contrast, performance on easy tasks need not be affected, or can even be enhanced if anxiety leads to increased effort. From this perspective, universal basic functions of emotions are seen as the building blocks of more complex functional relations, with the latter including several mediating processes that link emotions to origins and outcomes. These complex relations can vary across persons, tasks, and contexts, due to person-, task-, and context-specific combinations of the basic functions that generate them. Advantages of Relative Universalism

All things being equal, parsimonious explanations are more useful than complex ones. From this perspective, explaining phenomena by use of universal, generalisable laws should be preferable to local theories that only apply to a limited number of phenomena in specific socio-cultural contexts. However, while universal laws offer the advantage of explaining many phenomena (provided they are valid), it may be that they do not describe any of these phenomena in sufficient depth. As such, there may be a trade-off between parsimony and depth of explanation. Furthermore, there may be phenomena for which universal laws do not apply at all. Accordingly, where should the science of emotion in education be located on the continuum from universal (or nomothetic or etic) to local (or idiographic or emic)? CVT’s answer is that principles of relative universality hold, as outlined above. Although the concrete form of these principles in CVT is specific to explaining emotions, their general premise (universality of functions combined with situational variation) is not unique. CVT shares principles of relative universality with other theories in the field. For example,

90

Reinhard Pekrun and Thomas Goetz

expectancy-value theories of student motivation posit that motivation is generally a positive function of expectancy and value, but that an individual student’s expectancies and values depend on the specifics of the situation as well as the student’s socialisation (e.g. Eccles & Wigfield, 2020). Selfdetermination theory proposes that need satisfaction universally promotes positive development, and need frustration undermines development, while the triggers for satisfaction and frustration vary across persons and contexts (e.g. Vansteenkiste et al., 2020). Similarly, Marsh’s theories of academic self-concept, such as his big-fish-little-pond effect model, posit that the mechanisms of self-concept change are universal across educational institutions and cultures (e.g. Marsh et al., 2019). From a meta-theoretical perspective, relative universalism avoids the fallacies of (extreme) idiographic and cultural relativism, suggesting that all persons and cultures function differently to the extent that general explanatory principles are invalid and useless. It also avoids the fallacies of the other possible extreme stance, that is a deterministic perspective positing that all individuals function in exactly the same way across all contexts (see also Richters, 2021). Rather, relative universalism bridges the gap between idiographic and nomothetic perspectives. As such, we consider relative universalism as a cornerstone of a science of psychology and education that acknowledges both generality and specificity. Empirical Evidence

From the existing empirical evidence on emotions in education, we can infer whether principles of relative universality hold. However, there are two caveats. First, most of the existing findings pertain to students’ anxiety, implying that conclusions about other emotions are preliminary. Second, to examine generalisability, it is important to establish equivalence of constructs and measures across persons and contexts. This is less of a problem for objective measures, such as physiological parameters of emotional arousal, but a potential problem for self-report measures. Self-report items can acquire different meanings for different persons, and even for the same person in different situations. Fortunately, there is evidence that equivalence can be established. Loderer, Gentsch, et al. (2020) examined profiles of emotion concepts across samples of students from different countries. The findings suggest that concepts of achievement emotions are largely consistent across languages, as are basic concepts of emotions more generally (Fontaine et al., 2013). In addition, large-scale student assessments, such as the Organisation for Economic Co-Cooperation and Development’s (OECD) Programme for International Student Assessment (PISA), established measurement equivalence of self-report instruments across countries (see OECD, 2010, 2013, 2016, 2017). Given equivalence of constructs and measures, it seems possible to reach conclusions about relative universality.

How Universal Are Academic Emotions?

91

Variation Across Persons and Contexts

Studies that report empirical distributions of emotion scores document broad variation across persons. An example is studies that used the Achievement Emotions Questionnaire (AEQ). The instrument provides 5-point Likert scales to respond to items. Across emotions, standard deviations of item responses are typically around 1.0, and scores are distributed across the full range of response options (e.g. Pekrun et al., 2011, Pekrun, Marsh, Elliot, et al., 2023). Emotions also vary across students and teachers differing in gender or ethnicity. For example, female students typically report higher levels of anxiety. In the 2012 cycle of PISA which focused on mathematics, average math anxiety was significantly higher for females in most countries (OECD, 2013; see also Sarfo et al., 2020). In addition, female students report lower positive emotions in mathematics. Frenzel et al. (2007) found that girls reported more anxiety, hopelessness, and shame, but less enjoyment and pride in this subject. CVT provides an explanation for these differences: Female students differ from male students not only in these emotions but also in their controlvalue beliefs in mathematics – even if their performance is similar, they often show less confidence in their abilities, likely due to gender stereotypes about math-related competencies and the lack of competent female role models in math (see also Goetz et al., 2013). In contrast, girls may enjoy languagerelated activities more than boys do. For example, reading for enjoyment was reported more frequently by girls in 64 out of 65 countries participating in the PISA 2009 assessments (OECD, 2010). Similarly, emotions can vary widely across cultural contexts. The PISA assessments found particularly high levels of math and science anxiety in East Asian students (OECD, 2004, 2013, 2017; see also Fan et al., 2019). Again, these differences extend beyond anxiety. For example, Frenzel et al. (2007) investigated Chinese and German students’ emotions in mathematics, using the Achievement Emotions Questionnaire-Mathematics (AEQ-M; Pekrun et al., 2011) after establishing cross-cultural measurement equivalence. On average, the Chinese students reported substantially higher levels of enjoyment, pride, anxiety, and shame, but lower levels of anger than the German students. The findings are consistent with evidence that anger is more avoided in collectivistic cultures, as compared with individualistic cultures (e.g. Grimm et al., 1999). For differences across contexts as well, CVT provides an explanation. Learning environments differ in features that shape emotion-generating control and value appraisals, thus explaining differences in emotions. Critical features include the cognitive activation provided by classroom instruction, which influences competence perceptions and the perceived value of learning; task demands that define difficulty and, therefore, perceptions of control; teachers’ displayed enthusiasm, which signals value; the provision of autonomy support and social interaction (e.g. through group learning),

92

Reinhard Pekrun and Thomas Goetz

which promotes perceived values by meeting students’ needs for autonomy and relatedness; feedback about achievement, which shapes competence perceptions; and the composition of classrooms (e.g. Pekrun et al., 2019). Similarly, differences in emotions across cultural contexts can be explained by the variation of beliefs about ability, effort, and the value of achievement across societies (see Pekrun, 2018). Universality of Functional Relations

Given the evidence, it may be considered trivial that emotions vary across students, teachers, and contexts. In contrast, the proposition that functional relations are universal is non-trivial and contentious (see, e.g. Richters, 2021). Furthermore, there are fewer studies that investigated the universality of these relations. However, the evidence from these studies is consistent and clearly supports universality. Effects of moderators are small at best and do not change the direction of effects. Gender and Ethnicity

As described earlier, girls and boys can differ considerably in their emotions in subjects such as mathematics. Nevertheless, it appears that the relations of these emotions with control-value appraisals and achievement do not differ between genders. For example, in the study by Frenzel et al. (2007), we assessed students’ competence beliefs in mathematics, the intrinsic value of this domain, and the value of achievement in math. In multi-group analysis, the relations between these appraisals and the emotions had the same sign, were equally significant, and had similar size across genders, thus supporting the universality of relations between appraisals and emotions across genders. The meta-analysis by Barroso et al. (2021) examined the influence of gender and ethnicity on the relation between anxiety and achievement in mathematics. Neither gender nor ethnicity was a significant moderator of this relation. Average correlations were r = −.24 and −.28 for male and female students, respectively (based on a total of 90 independent samples). Similarly, ethnicity or race did not significantly moderate the anxiety-achievement relation (based on 176 samples). These findings suggest that relations between emotions and students’ achievement are also equivalent across genders, and across ethnicity. Similarly, in the meta-analysis by Zhang et al. (2019), gender was not a significant moderator of the negative relation between math anxiety and math achievement. Academic Domains

The available evidence suggests that relations of emotions with appraisals and achievement are also equivalent across school subjects. In the study by Goetz et al. (2007), German secondary school students’ levels of enjoyment,

How Universal Are Academic Emotions? 93

pride, boredom, anger, and anxiety clearly differed across subjects (i.e. mathematics, physics, German, and English). In contrast, the relations among these emotions and their links with students’ achievement proved to be very similar across domains. In all four domains, enjoyment showed positive correlations with pride, the negative emotions also showed positive intercorrelations, and the correlations between positive and negative emotions were negative. Furthermore, the correlations of enjoyment and pride with achievement were consistently positive, and the correlations of the negative emotions with achievement were consistently negative, within all four domains. Similarly, in the meta-analysis by Camacho-Morles et al. (2021), relations of students’ enjoyment, anger, and boredom with their achievement were equivalent across four domains including math, science, psychology, and literacy (57 independent samples; total N = 31,868, 11,153, and 28,410 students for enjoyment, anger, and boredom, respectively). Domain was not a significant moderator of these relations. The average correlations with achievement were positive for enjoyment and negative for anger and boredom in all four domains. Learning Environments

Loderer, Pekrun, et al. (2020) reviewed 186 studies examining emotions in technology-based learning environments (TBLEs), such as learning with computer tools (e.g. text annotation software), intelligent tutoring systems, or virtual and augmented realities. The authors analysed relations between emotions (enjoyment, curiosity/interest, anxiety, anger/frustration, confusion, boredom) and their antecedents (control-value appraisals, prior knowledge, gender, TBLE characteristics) and outcomes (engagement, learning strategies, achievement). The findings show that the levels of the emotions differed across TBLEs, but that their functional relations with appraisals and outcomes were equivalent across environments. For example, variables of perceived control related positively to enjoyment and negatively to anxiety; type of TBLE was not a significant moderator for these relations. Similarly, enjoyment related positively, and anxiety related negatively to learning outcomes across types of TBLs. The observed relations were also equivalent to the relations reported in traditional classroom-based studies, at least in terms of the direction of effects. As such, the findings support the robustness of links between emotions, control-value appraisals, and achievement across different types of learning environments. Cultural Context

Evidence on the cross-cultural generality of relations between emotions and appraisals in education is not only sparse but also suggests universality. In the study by Frenzel et al. (2007), students’ enjoyment of math related

94

Reinhard Pekrun and Thomas Goetz

positively to their perceptions of competence and of the value of this domain, whereas anxiety related negatively to perceived competence and positively to the perceived importance of achievement in math, in both Chinese and German students. The findings are consistent with evidence for general control and value beliefs. For example, Luszczynska et al. (2005) reported negative relations between general self-efficacy and anxiety that were equivalent across samples from various countries, and Brown and Cai (2010) have shown that attributions to ability predicted pride after success in both Chinese and US samples. More evidence is available for relations between students’ emotions and their achievement. Meta-analyses have shown that the direction of these relations is consistent across continents and countries. In Barroso et al.’s (2021) analysis, the negative relation between students’ anxiety and achievement in math was consistent across continents, including North America, South America, Europe, Asia, Africa, and Oceania. Continent was not a significant moderator of this relation. Camacho-Morles et al. (2021) examined country (i.e. United States, Canada, Australia, United Kingdom, and Germany) as a possible moderator. Some of the relations of emotions with achievement were moderated by country (relations for enjoyment were stronger in German than Canadian samples, and for boredom stronger in German than the US samples). However, moderation pertained only to the strength of the relations, but not their direction which was consistent across countries. The between-country differences in the strength of relations may have been due to methodological differences, as suggested by additional findings for type of measurement as a moderator. Meta-analyses share advantages and disadvantages with the original studies they are based on – they cannot be better than the original data. Most original studies on emotions in education used samples from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries, thus limiting generalisability. Large-scale student assessments including a broader range of countries, such as PISA, may be better suited to examine generalisability. Findings from PISA confirm that negative relations between anxiety and achievement are generalisable. In the PISA 2012 assessment, students’ anxiety and achievement in math correlated negatively in all of the 64 participating countries, and all of these correlations but one were significant (OECD, 2013). Similarly, in the PISA 2015 assessment, students’ schoolwork-related anxiety showed negative correlations with their science performance in 52 of 55 countries participating in the assessment of anxiety (OECD, 2016). The robustness of relations with achievement also extends to positive emotions. In the PISA 2015 assessment, the relation between students’ enjoyment of science (based on items adapted from the AEQ; Pekrun et al., 2011) and their performance in science was positive in all of the 68 countries for which this relation was examined (see also Guo et al., 2022).

How Universal Are Academic Emotions? 95

Conclusion

As predicted by CVT, distributional parameters such as mean levels of emotions differ widely across individual students and teachers. Nevertheless, the links of these emotions with control-value appraisals and achievement are remarkably consistent across genders, ethnicities, academic domains, learning environments, and cultural contexts (as represented by different countries). Interestingly, generalisability is found not only for the links with appraisals that represent basic functional relations but also for the relations with achievement that are presumably generated by a more complex interplay of different mechanisms as explained earlier. Implications and Future Directions

Relative universality implies that educators, administrators, policymakers, and parents should consider both the diversity and the generality of the emotions occurring in education. Students and teachers vary widely in the emotions they experience, in terms of the types, objects, frequency, and intensity of different emotions. Teachers, parents, and principals need to acknowledge the specificity of individual patterns of emotions. Nevertheless, there are regularities in the links of these emotions with antecedents and outcomes. These regularities make it possible to derive conclusions about average effects of emotions and modal types of origins. For example, the evidence indicates that teachers around the world need to know that anxiety is detrimental for academic learning in the average student, be the student female or male, White or non-White, in a traditional classroom or a technology-based environment, and in a Western, Asian, or African country. However, as noted, much of the available evidence is preliminary and derived from only a few studies. To make further headway, the following developments may be especially important. First, the extant evidence needs to be broadened to include not only achievement emotions but also epistemic and social emotions, such as curiosity, confusion, or compassion. Second, we need more studies with samples beyond the WEIRD countries that dominate current research on generalisability, even in large-scale student assessments such as PISA, which primarily focus on OECD countries. Third, most of the extant research used between-person designs. From between-person studies, we cannot conclude how individual students and teachers function. Between- and within-person relations can differ substantially (Hamaker et al., 2015; Murayama et al., 2017). We need within-person research to better understand students’ and teachers’ emotions and more fully examine their relative universality. Intensive data with multiple assessments across time and contexts are needed to this end. Recent methodological developments make it possible to examine these data (see Hamaker et al.,

96

Reinhard Pekrun and Thomas Goetz

2015; Pekrun, Marsh, Elliot, et al., 2023; Pekrun, Marsh, Suessenbach, et al., 2023). Random-effect approaches, such as dynamic structural equation modelling, are especially promising because they allow to examine not only aggregate within-person relations but also the generalisability of these relations across persons and contexts. Finally, as yet researchers have focused on investigating emotions as they occur in existing educational contexts. These investigations, and the foundational knowledge they generated, represent crucially important steps. We now need to examine how these insights can be translated into educational interventions and practices. We need studies that answer the question: How can we support students and teachers in promoting adaptive academic emotions and preventing or reducing maladaptive emotions? Successful interventions are available to reduce students’ test anxiety (e.g. Putwain et al., in press), suggesting that it should be possible to change other academic emotions as well. From a CVT perspective, a promising avenue is to modify control and value beliefs, especially in terms of enhancing perceived control, increasing intrinsic value, and decreasing excessive importance of success and failure (control-value intervention; Hoessle et al., 2021). For translational research as well, it will be important to consider both universality and diversity. It seems likely that not all students or teachers benefit from any given intervention. For change to be effective, it is crucial to know who benefits, and for whom an intervention is not beneficial or even detrimental. For example, a treatment enhancing perceived control may be promising for students who do not believe in their existing competencies, but may be ineffective in those who are already sufficiently confident, or those who are best supported by changing the educational institution. Withinperson research on change may be a fruitful avenue to develop personalised interventions and emotion-oriented educational practices that adequately consider the diversity of students and teachers. References Barroso, C., Ganley, C. M., McGraw, A. L., Geer, E. A., Hart, S. A., & Daucourt, M. C. (2021). A meta-analysis of the relation between math anxiety and math achievement. Psychological Bulletin, 147(2), 134–168. https://doi.org/10.1037/ bul000030 Bronfenbrenner, U. (1979). The ecology of human development. Harvard University Press. Brown, J. D., & Cai, H. (2010). Thinking and feeling in the People’s Republic of China: Testing the generality of the “laws of emotion”. International Journal of Psychology, 45(2), 111–121. https://doi.org/10.1080/00207590903281104 Camacho-Morles, J., Slemp, G. R., Pekrun, R., Loderer, K., Hou, H., & Oades, L. G. (2021). Activity achievement emotions and academic performance: A metaanalysis. Educational Psychology Review, 33(3), 1051–1095. https://doi.org/ 10.1007/s10648-020-09585-3

How Universal Are Academic Emotions?

97

Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Fan, X., Hambleton, R. K., & Zhang, M. (2019). Profiles of mathematics anxiety among 15-year-old students: A cross-cultural study using multi-group latent profile analysis. Frontiers in Psychology, 10, Article 1217. https://doi.org/10.3389/ fpsyg.2019.01217 Fontaine, J. J. R., Scherer, K. R., & Soriano, C. (Eds.). (2013). Components of emotional meaning: A sourcebook. Oxford University Press. Frenzel, A. C., Pekrun, R., & Goetz, T. (2007). Girls and mathematics – A “hopeless” issue? A control-value approach to gender differences in emotions towards mathematics. European Journal of Psychology of Education, 22(4), 497–514. https:// doi.org/10.1007/BF03173468 Frenzel, A. C., Thrash, T. M., Pekrun, R., & Goetz, T. (2007). Achievement emotions in Germany and China: A cross-cultural validation of the Academic Emotions Questionnaire-Mathematics (AEQ-M). Journal of Cross-Cultural Psychology, 38(3), 302–309. https://doi.org/10.1177/0022022107300276 Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., & Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24(10), 2079–2087. https://doi.org/10.1177/0956797613486989 Goetz, T., Frenzel, A. C., Pekrun, R., Hall, N. C., & Lüdtke, O. (2007). Between- and within-domain relations of students’ academic emotions. Journal of Educational Psychology, 99(4), 715–733. https://doi.org/10.1037/0022-0663.99.4.715 Grimm, S. D., Church, A. T., Katigbak, M. S., & Reyes, J. A. S. (1999). Self-described traits, values, and moods associated with individualism and collectivism. Journal of Cross-Cultural Psychology, 30(4), 466–500. https://doi.org/10.1177/0022022 199030004005 Guo, J., Hu, X., Marsh, H. W., & Pekrun, R. (2022). Relations of epistemic beliefs with motivation, achievement, and aspirations in science: Generalizability across 72 societies. Journal of Educational Psychology, 114(4), 734–751. https://doi. org/10.1037/edu0000660 Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/ 10.1037/a0038889 Hoessle, C., Loderer, K., & Pekrun, R. (2021, August). Piloting a control-value intervention promoting adaptive achievement emotions in university students. Paper presented at the 19th biennial conference of the European Association for Research on Learning and Instruction (EARLI), online. Loderer, K., Gentsch, K., Duffy, M. C., Zhuc, M., Xie, X., Chavarria, J. A., Vogl, E., Soriano, C., Scherer, K., & Pekrun, R. (2020). Are concepts of achievement-related emotions universal across cultures? A semantic profiling approach. Cognition and Emotion, 34(7), 1480–1488. https://doi.org/10.1080/02699931.2020.1748577 Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70, Article 101162. https://doi.org/10.1016/j. learninstruc.2019.101272 Luszczynska, A., Gutiérrez-Doña, B., & Schwarzer, R. (2005). General selfefficacy in various domains of human functioning: Evidence from five countries.

98

Reinhard Pekrun and Thomas Goetz

International Journal of Psychology, 40(2), 80–89. https://doi.org/10.1080/00207590 444000041 Marsh, H. W., Parker, P. D., & Pekrun, R. (2019). Three paradoxical effects on academic self-concept across countries, schools, and students: Frame-of-reference as a unifying theoretical explanation. European Psychologist 24(3), 231–242. https:// doi.org/10.1027/1016-9040/a000332 Murayama, K., Goetz, T., Malmberg, L.-E., Pekrun, R., Tanaka, A., & Martin, A. J. (2017). Within-person analysis in educational psychology: Importance and illustrations. In D. W. Putwain & K. Smart (Eds.), British Journal of Educational Psychology Monograph Series II: Psychological aspects of education – Current trends: The role of competence beliefs in teaching and learning (pp. 71–87). Wiley. Organization for Economic Cooperation and Development [OECD]. (2004). Learning for tomorrow’s world: First results from PISA 2003. Author. Organization for Economic Cooperation and Development [OECD]. (2010). PISA 2009 results (Volume 3): Learning to learn—student engagement, strategies and practices. Author. Organization for Economic Cooperation and Development [OECD]. (2013). PISA 2012 results (Volume 3): Ready to learn. Students’ engagement, drive and selfbeliefs. Author. Organization for Economic Cooperation and Development [OECD]. (2016). PISA 2015 results (Volume 1): Excellence and equity in education. Author. Organization for Economic Cooperation and Development [OECD]. (2017). PISA 2015 results (Volume 3): Students’ well-being. Author. Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9 Pekrun, R. (2009). Global and local perspectives on human affect: Implications of the control-value theory of achievement emotions. In M. Wosnitza, S. A. Karabenick, A. Efklides, & P. Nenniger (Eds.), Contemporary motivation research: From global to local perspectives (pp. 97–115). Hogrefe. Pekrun, R. (2018). Control-value theory: A social-cognitive approach to achievement emotions. In G. A. D. Liem & D. M. McInerney (Eds.), Big theories revisited 2: A volume of research on sociocultural influences on motivation and learning (pp. 162–190). Information Age Publishing. Pekrun, R. (2021). Self-appraisals and emotions: A generalized control-value approach. In T. Dicke, F. Guay, H. W. Marsh, R. G. Craven, & D. M. McInerney (Eds.), Self – A multidisciplinary concept (pp. 1–30). Information Age Publishing. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010. 10. 002 Pekrun, R., Marsh, H. W., Elliot, A. J., Stockinger, K., Perry, R. P., Vogl, E., Goetz, T., van Tilburg, W., Lüdtke, O., & Vispoel, W. (2023). A three-dimensional taxonomy of achievement emotions. Journal of Personality and Social Psychology, 124(1), 145–178. https://doi.org/10.1037/pspp0000448 Pekrun, R., Marsh, H. W., Suessenbach, F., Frenzel, A. C., & Goetz, T. (2023). School grades and students’ emotions: Longitudinal models of within-person reciprocal effects. Learning and Instruction, 83, Article 101626. https://doi.org/10.1016/j. learninstruc.2022.101626

How Universal Are Academic Emotions? 99

Pekrun, R., Murayama, K., Marsh, H. W., Goetz, T., & Frenzel, A. C. (2019). Happy fish in little ponds: Testing a reference group model of achievement and emotion. Journal of Personality and Social Psychology, 117(1), 166–185. https://doi. org/10.1037/pspp0000230 Pekrun, R., & Stephens, E. J. (2009). Goals, emotions, and emotion regulation: Perspectives of the control-value theory of achievement emotions. Human Development, 52(6), 357–365. https://doi.org/10.1159/000242349 Putwain, D. W., Pekrun, R., Rainbird, E. C., & Roberts, C. M. (in press). Cognitivebehavioural intervention for test anxiety: Could teachers deliver the STEPS prgram and what training would they require? In L. R. V. Gonzaga, L. L. DellazzanaZanon, & A. M. B. Silva (Eds.), Handbook of stress and academic anxiety. Springer. https://doi.org/10.1007/978-3-031-12737-3_25 Richters, J. E. (2021). Incredible utility: The lost causes and causal debris of psychological science. Basic and Applied Social Psychology, 43(6), 366–405. https://doi. org/10.1080/01973533.2021.1979003 Sarfo, J. O., García-Santillán, A., Adusei, H., Molchanova, V. S., Drushlyak, M., Semenikhina, O., Donyeh, P. S., & Vally, Z. (2020). Gender differences in mathematics anxiety across cultures: A univariate analysis of variance among samples from twelve countries. European Journal of Contemporary Education, 9(4), 878–885. https://doi.org/10.13187/ejced.2020.4.878 Vansteenkiste, M., Ryan, R. M., & Soenens, B. (2020). Basic psychological need theory: Advancements, critical themes, and future directions. Motivation and Emotion, 44, 1–31. https://doi.org/10.1007/s11031-019-09818-1 Zhang, J., Zhao, N., & Kong, Q. P. (2019). The relationship between math anxiety and math performance: A meta-analytic investigation. Frontiers in Psychology, 10, Article 1613. https://doi.org/10.3389/fpsyg.2019.01613

7 MOTIVATION AND EMOTION REGULATION IN COLLABORATIVE LEARNING CONTEXTS Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä and Tatiana Shubina

Abstract This chapter frames the theoretical underpinnings of motivation and emotion regulation in collaborative learning contexts. Self-regulated learning theories provide the chapter a theoretical grounding for considering the motivation and emotions related to learning in groups. The chapter begins by defining the types of regulation present in collaborative learning contexts – namely, self-, co-, and socially shared regulation – and the role of motivation and emotion regulation in them. Next, it elaborates on the function of motivation and emotions as social forms of regulation. The focus is on (1) motivational and emotional conditions, (2) situational and contextual variations, and (3) the temporal manifestation of motivation and emotion regulation in interaction. Prior studies have indicated that although the occurrence of co- and socially shared regulation of motivation and emotions is relatively rare, they are meaningful to group collaborations. Groups can activate socially shared regulation of learning throughout the collaborative learning process to establish a stage for high-level cognitive processes. It is claimed that motivation and emotions are integral parts of social and cognitive functioning and should be studied in relation to the situation, context and other group processes.

Introduction

With the expansion of collaborative learning in various educational contexts, the complex influence of motivation and emotions on it has become increasingly clear. Instead of just “being motivated or not”, motivation and emotions take part in the learning process of members of a collaborative group in multiple ways and layers (Järvelä & Renninger, 2014). In this chapter, we focus on the role of motivation and emotion regulation in successful collaborative DOI: 10.4324/9781003303473-8

Motivation and Emotion Regulation in Collaborative Learning

101

learning by introducing the socially shared regulation of learning (SSRL) framework and considering it from the motivation and emotion regulation perspective. We begin by briefly framing the theoretical underpinnings of motivation and emotion regulation in collaborative learning contexts. Specifically, self-regulated learning (SRL) theories (Winne & Hadwin, 2008; Zimmerman, 2002) provide us with the theoretical grounds to consider regulation of motivation and emotions in the context of learning in collaborative groups. Initially, the conception of SRL focused on the individual aspects of learning while its social aspects were merely acknowledged as a contextual component (Panadero, 2017). However, since the 2000s, SRL theories have been extended to acknowledge circumstances where groups of learners construct knowledge in collaboration (Järvenoja et al., 2015). These highly interactive situations led to an in-depth exploration of the social forms of regulation, and the social learning context became a more central feature in regulation of learning theories. Following this line of theoretical argumentation and its related empirical evidence, we define the types of regulation present in collaborative learning contexts – namely, self-, co-, and socially shared regulation – and the role of motivation and emotion regulation in them. Next, we elaborate on the function of motivation and emotion regulation as part of social forms of regulation through empirical examples, which draw attention to (1) the conditions for motivation and emotion regulation in collaborative learning, (2) the situational variations in these conditions and related regulation and (3) the temporal manifestation of regulation in groups’ interaction. Types of Regulation in Collaborative Learning

Motivation and emotions have been considered targets for self-regulation already from the early developments of different SRL models (Boekaerts & Pekrun, 2015; Pintrich, 2000). SSRL theory expands SRL theory to include regulation processes that take place through interactions among collaborating group members (Hadwin et al., 2018; Järvenoja et al., 2015). In an SSRL framework, motivation and emotion regulation function as learning mechanisms not only for individual learners but also for collaborative groups to initiate, maintain or restore motivational and emotional grounds for interactions (Hadwin et al., 2018). Socially shared motivation regulation can be used to purposefully maintain and restore a favourable motivational state during the learning process to achieve learning goals (Boekaerts & Pekrun, 2015; Järvenoja et al., 2015). Specifically, motivation regulation is activated when there is a perceived disruption in one’s pursuit of a goal (Hadwin et al., 2018; Winne & Hadwin, 2008). It can be used to initiate, restore, strengthen or redirect attributions made of learning situations or events, efficacy beliefs, interests or (outcome) utility and perceived benefits associated with a task (Wolters & Benzon, 2013). Through socially shared emotion regulation, in turn, group

102

Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

members together ensure emotionally solid (social) grounds for completing academic tasks (Boekaerts & Pekrun, 2015; Järvenoja et al., 2019). Socially shared emotion regulation involves several group members together engaging in regulatory interactions with the intent of releasing negative affect, dissolving emotional tension, unravelling emotional experiences or reducing negative emotional responses to socio-emotionally challenging situations that could hamper their learning and collaboration (Hadwin et al., 2018; Harley et al., 2019; Järvenoja et al., 2015). The need for motivation and emotion regulation is realised through metacognitive monitoring of the self, task, social context, learning progress or product. What makes both motivation and emotion regulation socially shared is the coordinated and complementary efforts of members to contribute to regulating the group’s motivational and emotional states. While there are different theories defining both emotion regulation (e.g. Gross, 2014) and motivation regulation (e.g. Wolters, 2003) as well as related regulation strategies in academic contexts (e.g. Harley et al., 2019), especially the theories emphasising contextuality of learning have often considered motivation and emotion regulation as connected, even as two sides of the coin. Pintrich (2000, p. 461), for example defined motivation regulation as follows: “Regulation of motivation and affect includes attempts to regulate various motivational beliefs that have been discussed in the achievement motivation literature (Pintrich & Schunk, 1996; Wolters, 1998) such as goal orientation (purposes for doing a task) and self-efficacy (judgments of competence to perform a task), as well as task value beliefs (beliefs about the importance, utility, and relevance of the task) and personal interest in the task (liking the content area, domain).” Furthermore, if emotions are considered as one component of motivation, emotion regulation can be even seen as one of the motivation regulation strategies (Wolters, 2003). This is both motivation and emotion regulation on a group level aim to maintain or increase a positive socio-emotional atmosphere and decrease the negative situational influences that derive from motivational and emotional challenges, and thus, are very much intertwined in practice. Recent research has shown that when learners engage in working collaboratively, at least three types of regulation come into play regarding shaping motivation and emotions (Hadwin et al., 2018). First, each group member takes responsibility for regulating his/her own learning (SRL). SRL refers to an individual learner’s goal-directed process of planning, monitoring and adapting cognitions and behaviours as well as their motivation and emotions to accomplish learning goals (Pintrich, 2000; Zimmerman, 2002). Second, each group member supports their peers in regulating their learning (co-regulated learning, CoRL). CoRL refers to individuals or groups temporarily supporting or influencing the regulatory processes of one or more team members. Of the three types of regulation, CoRL plays a mediating role between SRL and SSRL. It is applied to either support other group members’ SRLs through interpersonal

Motivation and Emotion Regulation in Collaborative Learning

103

interactions or to shift the group towards a more productive SSRL (Hadwin et al., 2018). One can think of CoRL as a system of beliefs and actions that weave within SRL and SSRL, fading in and out and serving as a mechanism to support or thwart those modes of regulation. Since CoRL cannot exist in the absence of either SRL or SSRL, its influence can only be understood when the nuances of the learner, task and situation are considered. Third, group members collectively regulate learning processes in a synchronised manner through SSRL, which refers to group members’ transactive and deliberate strategic adaptation during the phases of collaborative planning, task enactment, and reflection. In SSRL, group members together coordinate, monitor and control their cognition, motivation, affect and behaviour through iterative negotiations, realignment and adaptation (Hadwin et al., 2018). Accordingly, genuine SSRL involves multiple individual perspectives for regulating the cognitive, behavioural, motivational and emotional aspects of learning. While these definitions of regulation pose empirical challenges for researchers who want to work with clean constructs that share no overlap with others, the influence of SRL, CoRL, and SSRL can only be understood when the nuances of learner, task, and situation are considered (Järvelä et al., 2019; McCaslin, 2004). The regulation of motivation and emotion in collaborative learning is still an under-represented area in SSRL research, even though group members can experience a range of emotions and motivational hurdles that are unique and different from cognitive challenges but are potentially as detrimental for group processes as cognitive challenges (Bakhtiar et al., 2018; Järvenoja et al., 2019). Research has shown that motivation and emotions are essential in facilitating or hindering the collaborative process and group work outcomes (Rogat & Linnenbrink-Garcia, 2011; Volet et al., 2009). Therefore, a more detailed definition and conceptualisation of the unique conditions and processes behind motivation and emotion regulation in collaborative learning contexts, and their relation to the wider framework of SSRL, will help the field progress in terms of empirical research and practical support for collaborative learning. Motivation and Emotion Regulation in the Collaborative Learning Context

While the CoRL and SSRL of emotions and motivation seem to be relatively rare in many collaborative learning contexts, they are still meaningful for group collaborations to maintain positive atmosphere, engaged working and especially when the groups face emotional or motivational hurdles, and groups activate regulation throughout collaborative learning processes (Bakhtiar & Hadwin, 2020; Järvenoja et al., 2020a; Rogat et al., 2022). To understand the conditions for the (lack of) SSRL, the motivational and emotional antecedents and consequences at the individual and group levels should be addressed, both theoretically and empirically. Motivation and

104

Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

emotion regulation do not emerge in isolation; they are relative to the wider motivational structures as well as the cognitive and collaboration processes of group members (Järvenoja et al., 2018; Lajoie et al., 2015; Winne, 2019). Previous research has indicated that the emergence of socially shared motivation and emotion regulation in collaborative learning is very situation specific. For example, Bakhtiar and Hadwin (2020) showed that motivation regulation should not only match the demands of the situation but also involve varied strategic responses so that the group can continue to collaborate effectively. Accordingly, understanding motivation and emotion regulation also involves understanding the collaborative learning context and processes that create the conditions for the regulation to take place (Järvenoja et al., 2015; Nolen, 2020). Moreover, understanding the conditions for regulation requires a proper comprehension of how it unfolds over time. Next, we frame motivation and emotion regulation related to collaborative learning processes from three distinct viewpoints. To this end, we first focus on the motivational and emotional conditions for regulation in collaborative learning and then move to situational variations in these conditions and the related regulation. Finally, we argue in favour of the temporal analysis of regulation in relation to the other two aspects. The examples derived from a research project aimed at studying motivation and emotion regulation in collaboration (Järvenoja et al., 2020b, 2018). The ecologically valid research design enables to identify the actual dynamics of motivation and emotion regulation, including their antecedents and consequences on individual and group levels. On two rounds of data collection, multiple data sources were administered to produce a multimodal data corpus. The participants in the first data collection (Järvenoja et al., 2021) were sixth-grade primary school students while those in the second data collection (Järvelä et al., 2021) were seventh-grade secondary school students. Before data collection, questionnaire data measuring the students’ SRL skills and interests were collected. During the learning period, the groups’ collaboration was videotaped and students’ electrodermal activity (EDA) was recorded. Additionally, situation-specific motivational, emotional and cognitive interpretations were repeatedly collected (Järvenoja et al., 2020b, 2018). Building Premises for Motivation and Emotion Regulation – Understanding Motivational and Emotional Conditions in Collaborative Learning Context

The conditions for SSRL are multifaceted and multi-layered. Group members enter a collaborative learning situation with a set of internal and external conditions, some of which are motivational or emotional in nature (Winne & Hadwin, 2008). Motivational and emotional aspects are a part of internal conditions. Each group member brings their internal conditions into the collaborative learning situation. The learners’ motivational and emotional

Motivation and Emotion Regulation in Collaborative Learning

105

appraisals, expectancies, values, beliefs and goals together form the (pre)conditions for motivation and emotion regulation during collaborative learning (Harley et al., 2019). They became targets for SSRL and subsequently influence also the groups’ behaviours and interactions (Lobczowski, 2020). In a recent study, we investigated these internal motivational and emotional conditions, aiming to explore secondary school students’ emotional states as well as individual and situational interest in a collaborative learning context, and the changes they underwent after collaboration (Shubina et al., 2021). Situational interest was considered as temporary interest that arises due to environmental factors such as task instructions or social interaction (Renninger & Hidi, 2011). The emotional state was conceptualised as a situation-specific emotional response to a changing environment (Rosenberg, 1998) characterised by two dimensions, emotional valence and activation (Linnenbrink-Garcia et al., 2016). Specifically, we asked how students’ situational interest and emotional valence vary before and after collaborative learning tasks and how they relate to individual interest. The study was based on the second dataset from the above-mentioned project, in which students’ situational interest and emotional valences were measured by situational single-item self-reports while their individual interests were measured using a questionnaire. In this way, we sought to capture students’ motivational and emotional conditions, both on the trait and state levels. The aim was to uncover the role of motivational and emotional factors in collaborative learning where SSRL is manifested at different time frames. The results echo the prior understanding that emotions are more situationspecific compared to motivation; the students demonstrated variation within collaborative learning tasks. Situational interest instead showed changes between the first and following tasks. The results imply that collaborative learning shapes students’ internal emotional conditions at the state level, setting situational stages for the socially shared regulation of emotions and motivation. Furthermore, the students’ situational interest and emotional valence at the state level were positively related to their individual interests at the trait level. Interestingly, this relationship typically decreased or even disappeared towards the end of the five-lesson course, suggesting the multilayered nature of motivational and emotional conditions (Shubina et al., 2021). However, to unpack the drivers of regulation in the collaborative learning context, we need to understand not only the individual conditions but also the situational variations of collaborative learning. Shifting the Focus on the Process – Situational Variations in Motivation and Emotion Regulation

The study explained above addresses the conditions for motivation and emotion regulation within learning groups. It demonstrated how interests, as a motivational factor, create conditions that influence how learners approach

106

Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

the collaborative task at hand. However, these conditions are also built on emotional reactions to a situation. Students experience a variety of emotions and show intra-individual fluctuations in them during learning (e.g. Ketonen et al., 2018; Moeller et al., 2020). Previous studies provide evidence that emotional conditions for learning are not static but highly variable and constantly reshaped by the collaborative learning activities that students engage in (Dietrich et al., 2020; Pietarinen et al., 2019). The control-value theory of achievement emotions posits that these emotional experiences are derived from students’ feelings of being in or out of control as well as from the perceived value of the learning activity or outcome (Pekrun, 2016). A combination of control and value appraisals gives rise to emotional reactions towards a situation, creating varying situational conditions for the regulation in groups. The following examples provide evidence of this situational variation concerning collaborative groups’ motivation and emotion regulation. By targeting variation, it is possible to focus on how motivation and emotion regulation actively shape the collaboration process. We captured the situational variation in the group members’ emotional conditions through two situationally reactive dimensions: valence and activation (Linnenbrink, 2007; Russell & Barrett, 1999). The dimension of valence captures variations in the positive versus negative states, whereas activation captures the activation level of the state. In practice, variation in affective states was measured by capturing these dimensions at multiple times during the learning process. First, we aimed to understand what type of learning situations trigger emotional states in collaborative groups. The study was carried out using video observations (valence) and EDA (activation) data. Multichannel data enabled us to identify both the expressive and physiological components of group members’ emotional states (Törmänen et al., 2021b) and define the variations in them as positive and activated (e.g. enjoyment, pride), negative and activated (e.g. anxiety, anger), positive and deactivated (e.g. relief), or negative and deactivated (e.g. boredom, hopelessness) states (Pekrun, 2016). When these data were aggregated at a group level, emotionally “mixed” states, where the affective states group members exhibit divergences, were revealed (Barsade & Knight, 2015; Törmänen et al., 2022b). The various emotional states emerged as reactions to situational triggers deriving from both task and socially related aspects of collaboration. They serve as triggers to activate socially shared motivation and emotion regulation. Accordingly, we next related these emotional states to the initiation of emotion regulation during the collaborative learning process and asked how the valence and activation dimensions are associated with the manifestation of group-level emotion regulation (Järvenoja et al., 2022). While further research is still needed, the results of this study indicate that specifying the dimensions of emotional states can help to unpack what triggers the activation

Motivation and Emotion Regulation in Collaborative Learning

107

of motivation and emotion regulation. For example, we observed that when negative valence is coupled with increased physiological emotional activation, the group is more likely to initiate emotion regulation. In contrast, if the affective state in the group is mixed, the group’s ability to initiate emotion regulation may face challenges. These findings are consistent with those of Törmänen et al. (2021a), who studied individual group members’ conditions prior to regulation in relation to their participation in socially shared emotion regulation. The results revealed that if there was no personal need for emotion regulation, the students did not actively participate in emotion regulation and vice versa. Moreover, CoRL helped the target students to change their emotional state and subsequently continue collaboration. These results demonstrate that the situational variation in conditions and related motivation and emotion regulation shape the nature of the collaborative learning process. Moving Forward – Focus on the Temporal Manifestation of Motivation and Emotion Regulation in Collaborative Interactions

The target of the studies presented above was to understand the conditions and timely variations of motivation and emotion regulation. Such an analysis reveals how motivational and emotional conditions and grouplevel motivation and emotion regulation intertwine. However, the variations and fluctuations in situational motivational and emotional conditions within and across persons or the relationship between individual experiences and regulation are relational to the overall interactions and learning processes within the group (e.g. Bakhtiar et al., 2018; Goetz et al., 2016; Ketonen et al., 2018; Moeller et al., 2018; Törmänen et al., 2022b). Questions such as how motivation and emotion regulation function reciprocally with cognitive processing cannot be properly answered by focusing only on a moment or at the sequence level. The temporal manifestation of motivation and emotion regulation should be also considered in relation to the other collaborative learning processes that evolve temporally and dynamically through changes in sequences of interactions (Azevedo, 2014; Bannert et al., 2014; Molenaar & Järvelä, 2014). To this end, our studies have targeted longer episodes of socio-emotional interactions in collaborative groups. This makes it possible to explore how conditions for motivation and emotion regulation are continually constituted in group members’ interactions and how these conditions set the stage not only for motivation and emotion regulation but for SSRL in general in the longer run. For example, a study by Törmänen et al. (2021b) explored what types of socio-emotional interaction episodes can be identified during collaborative learning and how groups’ emotional valence and activation fluctuate with the overall regulation of learning during these episodes. Four different types of socio-emotional interaction episodes were identified using multimodal

108

Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

multichannel data (video & EDA), together with multichannel sequence mining and clustering using mixture hidden Markov models. The found episodes revealed how the emotional valence and activation in groups fluctuate in line with socio-emotional interactions and how these interactions are further connected with SSRL. Consistent with previous studies, recurrent positive interactions seemed to facilitate the groups’ ability to engage in SSRL when needed. Recurring negative interactions, in turn, seemed to challenge the groups’ regulation of learning. In another study, we utilised a person-centred approach to identify individual differences in the group members’ conditions for collaboration based on their participation in socio-emotional interactions (Törmänen et al., 2022a). We were able to identify three different and relatively stable socio-emotional interaction profiles that proved to be related to students’ participation in group-level regulation. In the future, future empirical research is challenged to investigate these different levels on different granularities and relate them with the overall learning and collaboration process. Conclusions

Recent empirical research on motivation and emotion regulation in collaborative learning contexts has implications for conceptual and theoretical progress. Extending the focus from individual SRL to CoRL and SSRL has already increased the emphasis on contextualised premises to consider the consequences of motivation and emotion regulation on both short-term and long-term collaborative learning processes. To date, strong existing theories (Hadwin et al., 2018; Järvenoja et al., 2015) coupled with recent novel methods (Järvelä et al., 2022; Sharma & Giannakos, 2020) have contributed to developing an understanding of short-term situational variations in motivation and emotion regulation, as well as contextualised conditions for regulation. However, how motivation and emotion regulation dynamically and temporarily influence and are influenced by group learning and interaction processes in the long term is yet to be studied. Current SSRL models could be improved to better explain the development of motivation and emotions in short and also long-term. Such a theoretical development challenges us to explore how different motivational and emotional theories fit in explaining the empirically proven situational and temporal variation in motivation and emotion regulation. This calls for not only theoretical but also methodological advancements (Pekrun & Marsh, 2022). Recent methodological advancements can provide the means to analyse the motivational, emotional and social aspects of collaborative learning at a level of detail and accuracy that has not been possible before. Multimodal data enable the collection of high-granularity data at different levels, enabling a more systematic analysis when these analyses are (Järvelä et al., 2021). High-quality video recordings, for example, can capture learners’ emotional

Motivation and Emotion Regulation in Collaborative Learning

109

states continuously and in real-time, while simultaneously enabling the contextualisation of the collaborative learning situation and its integration with other methods, such as facial expression recognition. In addition, video recordings enable the observation of motivation and emotion regulation as shared through group members’ interactions, emphasising how individualand group-level processes intertwine (Jones et al., 2021). Adding more data channels to understand a phenomenon, such as physiological measures, has the potential to increase the level of explanation and offer new insights into changes in motivational, emotional and cognitive processes, as well as the interplay between them during learning (Sharma & Giannakos, 2020; Taub et al., 2019). Furthermore, the development of novel analysis methods to account for dynamic and temporal relations and processes can enable an in-depth understanding of motivation and emotion regulation in shaping the progress of collaborative learning. The regulation of learning fundamentally assumes metacognitive awareness and strategic intentions to change the current learning situation when required. However, research has shown that learners are not always accurate in their interpretations, especially regarding the motivational and emotional hurdles hindering their learning and performance (Koivuniemi et al., 2018). While we have many theories on motivation and emotions in learning, the collaborative group members might not have the same lenses to interpret and analyse the situational challenges on the spot and accommodate their regulation accordingly to fit the situational needs of the group. Conceptual advancements and methodological means can together inform us to create solutions that make these phenomena more visible for teachers and learners themselves. In the future, a more detailed theoretical model of SSRL has the potential to inform adaptive technologies, digital data traces and artificial intelligence-based methods to utilise this understanding to offer personalised as well as predictive support for SRL and SSRL (Järvelä et al., 2022). Acknowledgements

This work was supported by the Academy of Finland (Grant Number 348765) and carried out with the support of LeaF Research Infrastructure, University of Oulu, Finland. References Azevedo, R. (2014). Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning. Metacognition and Learning, 9(2), 217–228. https://doi.org/10.1007/s11409-014-9123-1 Bakhtiar, A., & Hadwin, A. F. (2020). Dynamic interplay between modes of regulation during motivationally challenging episodes in collaboration. Frontline Learning Research, 8(2), 1–34. https://doi.org/10.14786/flr.v8i2.561

110

Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2018). Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning, 13(1), 57–90. https://doi.org/10.1007/s11409-017-9178-x Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and Learning, 9(2), 161–185. https://doi.org/10.1007/s11409-013-9107-6 Barsade, S. G., & Knight, A. P. (2015). Group affect. Annual Review of Organizational Psychology, 2, 21–46. https://doi.org/10.1146/annurev-orgpsych-032414-111316 Boekaerts, M., & Pekrun, R. (2015). Emotions and emotion regulation in academic settings. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 76–90). Routledge. Dietrich, E., Fields, C., Hoffman, D. D., & Prentner, R. (2020). Editorial: Epistemic feelings: Phenomenology, implementation, and role in cognition. Frontiers in Psychology, 11, Article 606046. https://doi.org/10.3389/fpsyg.2020.606046 Goetz, T., Sticca, F., Pekrun, R., Murayama, K., & Elliot, A. J. (2016). Intraindividual relations between achievement goals and discrete achievement emotions: An experience sampling approach. Learning and Instruction, 41, 115–125. https://doi. org/10.1016/j.learninstruc.2015.10.007 Gross, J. J. (2014). Emotion regulation: Conceptual and empirical foundations. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 3–20). The Guilford Press. Hadwin, A. F., Bakhtiar, A., & Miller, M. (2018). Challenges in online collaboration: Effects of scripting shared task perceptions. International Journal of Computer-Supported Collaborative Learning, 13, 301–329. https://doi.org/10. 1007/s11412-018-9279-9 Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). Routledge. Harley, J. M., Pekrun, R., Taxer, J. L., & Gross, J. J. (2019). Emotion regulation in achievement situations: An integrated model. Educational Psychologist, 54(2), 1–21. https://doi.org/10.1080/00461520.2019.1587297 Järvelä, S., Järvenoja, H., & Malmberg, J. (2019). Capturing the dynamic and cyclical nature of regulation: Methodological progress in understanding socially shared regulation in learning. International Journal of Computer-Supported Collaborative Learning, 14, 425–441. https://doi.org/10.1007/s11412-019-09313-2 Järvelä, S., Järvenoja, H., & Malmberg, J. (2021). CLEVER – Making complex learning processes visible for enabling regulation: Change human behavior for learning success (Version 1). https://doi.org/10.23729/fa61790c-1929-4408-986947d236255880 Järvelä, S., Nguyen, A., Vuorenmaa, E., Malmberg, J., & Järvenoja, H. (2023). Predicting regulatory activities for socially shared regulation to optimize collaborative learning. Computers in Human Behavior, 144, Article 107737. https://doi. org/10.1016/j.chb.2023.107737 Järvelä, S., & Renninger, K. A. (2014). Designing for learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 668–685). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.040 Järvenoja, H., Järvelä, S., & Malmberg, J. (2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50(3), 204–219. https://doi.org/10.1080/00461520.2015.1075400

Motivation and Emotion Regulation in Collaborative Learning

111

Järvenoja, H., Järvelä, S., & Malmberg, J. (2020a). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, Article 101090. https://doi.org/10.1016/j.learninstruc.2017.11.004 Järvenoja, H., Järvelä, S., Törmänen, T., Näykki, P., Malmberg, J., Kurki, K., Mykkänen, A., & Isohätälä, J. (2018). Capturing motivation and emotion regulation during a learning process. Frontline Learning Research, 6(3), 85–104. https:// doi.org/10.14786/flr.v6i3.369 Järvenoja, H., Malmberg, J., Törmänen, T., Mänty, K., Haataja, E., Ahola, S., & Järvelä, S. (2020b). A collaborative learning design for promoting and analyzing adaptive motivation and emotion regulation in the science classroom. Frontiers in Education, 5, Article 111. https://doi.org/10.3389/feduc.2020.00111 Järvenoja, H., Mänty, K., & Törmänen, T. (2021). EmReg - Emotion regulation in secondary school pupils’ learning. http://urn.fi/urn:nbn:fi:att:78e3a149-040f-49ff8e1f-3b85dc10f230 Järvenoja, H., Näykki, P., & Törmänen, T. (2019). Emotional regulation in collaborative learning: When do higher education students activate group level regulation in the face of challenges? Studies in Higher Education, 44(10), 1747–1757. https:// doi.org/10.1080/03075079.2019.1665318 Järvenoja, H., Törmänen, T., & Mänty, K. (2022). Affective conditions for collaborative learning: When do groups engage in emotion regulation? In Törmänen, T. (2023). Emotion regulation in collaborative learning: students’ affective states as conditions for socially shared regulation. Oulu University: Acta UNIVERSITATIS OULUENSIS E220. 1796-2242 Jones, C., Volet, S., & Pino-Pasternak, D. (2021). Observational research in face-to-face small group work: Capturing affect as socio-dynamic interpersonal phenomena. Small Group Research, 52(3), 341–376. https://doi.org/10.1177/1046496420985920 Ketonen, E. E., Dietrich, J., Moeller, J., Salmela-Aro, K., & Lonka, K. (2018). The role of daily autonomous and controlled educational goals in students’ academic emotion states: An experience sampling method approach. Learning and Instruction, 53, 10–20. https://doi.org/10.1016/j.learninstruc.2017.07.003 Koivuniemi, M., Järvenoja, H., & Järvelä, S. (2018). Teacher education students’ strategic activities in challenging collaborative learning situations. Learning, Culture and Social Interaction, 19, 109–123. https://doi.org/10.1016/j.lcsi.2018. 05.002 Lajoie, S. P., Lee, L., Poitras, E., Bassiri, M., Kazemitabar, M., Cruz-panesso, I., Hmelo-silver, C., Wiseman, J., Ki, L., & Lu, J. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. Computers in Human Behavior, 52, 601–616. https://doi.org/ 10.1016/j.chb.2014.11.073 Linnenbrink, E. A. (2007). The role of affect in student learning: A multi-dimensional approach to considering the interaction of affect, motivation, and engagement. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education (pp. 107–124). Academic Press. https://doi.org/10.1016/B978-012372545-5/50008-3 Linnenbrink-Garcia, L., Patall, E. A., & Pekrun, R. (2016). Adaptive motivation and emotion in education. Policy Insights from the Behavioral and Brain Sciences, 3(2), 228–236. https://doi.org/10.1177/2372732216644450 Lobczowski, N. G. (2020). Bridging gaps and moving forward: Building a new model for socioemotional formation and regulation formation and regulation. Educational Psychologist, 55(2), 1–16. https://doi.org/10.1080/00461520.2019.1670064

112 Hanna Järvenoja, Tiina Törmänen, Sanna Järvelä, et al.

McCaslin, M. (2004). Coregulation of opportunity, activity, and identity in student motivation. In D. M. McInerney & S. Van Etten (Eds.), Big theories revisited (pp. 249–274). Information Age. Moeller, J., Brackett, M. A., Ivcevic, Z., & White, A. E. (2020). High school students’ feelings: Discoveries from a large national survey and an experience sampling study. Learning and Instruction, 66, Article 101301. https://doi.org/10.1016/j. learninstruc.2019.101301 Moeller, J., Ivcevic, Z., Brackett, M. A., & White, A. E. (2018). Mixed emotions: Network analyses of intra-individual co-occurrences within and across situations. Emotion, 18(8), 1106–1121. https://doi.org/10.1037/emo0000419 Molenaar, I., & Järvelä, S. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning, 9(2), 75–85. https://doi. org/10.1007/s11409-014-9114-2 Nolen, S. B. (2020). A situative turn in the conversation on motivation theories. Contemporary Educational Psychology, 61, Article 101866. https://doi.org/10. 1016/j.cedpsych.2020.101866 Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 1–28. https://doi.org/10.3389/ fpsyg.2017.00422 Pekrun, R. (2016). Academic emotions. In K. R. Wentzel & D. B. Miele (Eds.), Handbook of motivation at school (2nd ed., pp. 120–144). Routledge. Pekrun, R., & Marsh, H. W. (2022). Research on situated motivation and emotion: Progress and open problems. Learning and Instruction, 81, Article 101664. https:// doi.org/10.1016/j.learninstruc.2022.101664 Pietarinen, T., Volet, S., Lehtinen, E., & Vauras, M. (2019). Affect in peer group learning during virtual science inquiry: Insights from self-reports and video observations. Frontiers in Psychology, 10, Article 2981. https://doi.org/10.3389/ fpsyg.2019.02981 Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulated learning (pp. 451–502). Academic Press. Pintrich, P. R., & Schunk, D. H. (1996). Motivation in education: Theory, research and applications. Prentice Hall Merrill. Renninger, K. A., & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168–184. https://doi. org/10.1080/00461520.2011.587723 Rogat, T. K., Hmelo-Silver, C. E., Cheng, B., Traynor, A., Adeoye, A. F., Gomoll, A., & Downing, B. K. (2022). A multidimensional framework of collaborative groups’ disciplinary engagement. Frontline Learning Research, 10(2), 1–21. https://doi. org/10.14786/flr.v10i2.863 Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29(4), 375–415. https://doi.org/10. 1080/07370008.2011.607930 Rosenberg, E. L. (1998). Levels of analysis and the organization of affect. Review of General Psychology, 2(3), 247–270. https://doi.org/10.1037/1089-2680.2.3.247 Russell, J. A., & Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant. Journal of Personality and Social Psychology, 76(5), 805–819. https://doi.org/10.1037/0022-3514.76.5.805

Motivation and Emotion Regulation in Collaborative Learning

113

Sharma, K., & Giannakos, M. (2020). Multimodal data capabilities for learning: What can multimodal data tell us about learning? British Journal of Educational Technology, 51(5), 1450–1484. https://doi.org/10.1111/bjet.12993 Shubina, T., Järvenoja, H., Mänty, K., Peltonen, J., & Järvelä, S. (2021). The changes in lower secondary school students’ interest during collaborative learning. Scandinavian Journal of Educational Research, 66(7), 1127–1140. https://doi.org/10.10 80/00313831.2021.1958377 Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2019). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? Learning and Instruction, 72, Article 101200. https://doi.org/10.1016/j.learninstruc.2019.04.001 Törmänen, T., Järvenoja, H., & Mänty, K. (2021a). All for one and one for all – How are students’ affective states and group-level emotion regulation interconnected in collaborative learning? International Journal of Educational Research, 109, Article 101861. https://doi.org/10.1016/j.ijer.2021.101861 Törmänen, T., Järvenoja, H., & Mänty, K. (2021b). Exploring groups’ affective states during collaborative learning: What triggers activating affect on a group level? Educational Technology Research and Development, 69(5), 2523–2545. https:// doi.org/10.1007/s11423-021-10037-0 Törmänen, T., Järvenoja, H., Saqr, M., Malmberg, J., & Järvelä, S. (2022a). A person-centered approach to study students’ socio-emotional interaction profiles and regulation of collaborative learning. Frontiers in Education, 7, Article 866612. https://doi.org/10.3389/feduc.2022.866612 Törmänen, T., Järvenoja, H., Saqr, M., Malmberg, J., & Järvelä, S. (2022b). Affective states and regulation of learning during socio-emotional interactions in secondary school collaborative groups. The British Journal of Educational Psychology, Article e12525. https://doi.org/10.1111/bjep.12525 Volet, S., Vauras, M., & Salonen, P. (2009). Self- and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44(4), 215–226. https://doi.org/10.1080/00461520903213584 Winne, P. H. (2019). Paradigmatic dimensions of instrumentation and analytic methods in research on self-regulated learning. Computers in Human Behavior, 96, 285–289. https://doi.org/10.1016/j.chb.2019.03.026 Winne, P. H., & Hadwin, A. F. (2008). The weave of motivation and self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Motivation and selfregulated learning: theory, research, and applications (pp. 297–314). Routledge. https://doi.org/10.4324/9780203831076 Wolters, C. A. (1998). Self-regulated learning and college students’ regulation of motivation. Journal of Educational Psychology, 90(2), 224–235. https://doi.org/ 10.1037/0022-0663.90.2.224 Wolters, C. A. (2003). Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38(4), 189–205. https://doi.org/10.1207/S15326985EP3804_1 Wolters, C. A., & Benzon, M. B. (2013). Assessing and predicting college students’ use of strategies for the self-regulation of motivation. The Journal of Experimental Education, 81(2), 199–221. https://doi.org/10.1080/00220973.2012.699901 Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102

8 TEACHER AND STUDENT WELL-BEING Theoretical Reflections and Perspectives Tina Hascher and Julia Mori

Abstract Among a wide range of educational research topics in motivation and emotion, well-being has attracted growing interest during the last decades. Researchers from various disciplines aim to understand predictors of teacher and student well-being or the impact of well-being on learning and teaching. Also, political action plans that address the fundamental and far-reaching function of schools and academic learning for individual and societal development encourage research in well-being in education (e.g. UNESCO’s sustainable development goals). Although there seems consensus about the important and beneficial role of teacher and student wellbeing for educational processes and outcomes, less agreement can be found regarding the conceptualisation and theoretical underpinning of wellbeing. A plethora of approaches and operationalisations can be seen either as a source of richness of theoretical ideas and inspiration for practical implications or as a source of difficulties for the research field advancement and improvement of educational practices, due to its arbitrariness and ambiguity. This chapter, thus, will give an overview of theoretical outlines on teacher and student well-being, considering discipline- and domain-specific aspects, to inform future research and practice in education.

Well-Being

Empirical research on well-being can be dated back to the year 1948 when the World Health Organisation (WHO, 1948, p. 1) has defined health as “a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” Diener (1984) was among the first who introduced a multidimensional concept of well-being into the field of DOI: 10.4324/9781003303473-9

Teacher and Student Well-Being

115

psychology. He gave an overview of well-being research that was primarily related to adult well-being and identified three categories of definitions: well-being defined through “external criteria such as virtue or holiness,” a positive life evaluation resulting in life satisfaction, or a predominance of positive affective experiences over negative (Diener, 1984, p. 543). By defining well-being as “a person’s cognitive and affective evaluations of his or her life” (Diener et al., 2002, p. 63), one of his major contributions was to conceptualise subjective well-being as a multidimensional construct that cannot be represented by a single construct such as satisfaction or the absence of complaints. Instead, various dimensions must be included into the broad construct of well-being. This multidimensional approach was also supported by researchers who highlighted the role of subjective affective and cognitive dimensions (e.g. Ryff & Keyes, 1995). Several dimensions are also represented in the prominent Seligman’s PERMA model of flourishing (2012) that includes Positive emotion, Engagement, Relationships, Meaning, and Accomplishment and in the widely used Job Demands-Resources (JD-R) model (Demerouti et al., 2001) that explains work-related well-being. Since the beginning of well-being research, scientists have aimed at identifying distinct forms or domains of well-being. One topic is the differentiation of state well-being as a situation-specific experience with intraindividual variation across situations and trait well-being that represents inter-individual differences in dispositional well-being experiences (e.g. Kim-Prieto et al., 2005). Others identified various types of well-being such as subjective and objective well-being (Veenhoven, 1991) or emotional, psychological, or social well-being (Keyes, 2002). Differences regarding its functions can be found in the distinction of hedonic well-being that represents positive affect experience due to need fulfilment, whereas eudemonic well-being is related to positive experience based on a meaningful life (Ryan & Deci, 2000). Well-being can also be related to various age groups such as children, youth, or adults and related to the roles in the education system such as students or teachers (Hascher, 2022). Similarly, context- and domain-specific forms of well-being such as workplace well-being (Burns & Machin, 2013) or school-related well-being (Hascher, 2004) are considered. Accordingly, research on well-being has led to a huge variety of approaches and instruments for measuring well-being in general as well as in its context- and situation-specific forms. Along with this variety of approaches and instruments, the question about the core elements of well-being is still an issue. Generally, it can be said that well-being represents a predominantly positive, emotional, and cognitive evaluation of a situation, a context, or of life. It acknowledges a co-existence of positive and negative factors with the prevalence of positive experiences and, thus, can be described as a “positive imbalance” (Hascher, 2022), with a bigger difference between the positive and negative dimensions representing

116

Tina Hascher and Julia Mori

higher well-being. It can be influenced by many factors (Diener, 1984) and, in turn, can play a role for individual fulfilment in life such as positive adjustment, self-efficacy, and performance (e.g. Wood & Joseph, 2010).

Teacher Well-Being

Teacher well-being (TeachWB) has advanced to the scientific and political agenda during the last two decades and research on TeachWB is impressively growing. Reasons for this increasing attention can be found in teacher shortage, attrition, high prevalence of mental and physical health issues, as well as the important role of TeachWB for student academic success and school outcomes (e.g. Education Support, 2019; Sutton & Wheatley, 2003; Viac & Fraser, 2020). TeachWB is related to the research on general well-being and, thus, investigated in a broader frame of mental health (e.g. Capone & Petrillo, 2018). However, this implies that TeachWB is not explicitly connected to characteristics of the teaching profession and measured with general scales addressing life satisfaction, emotions experienced, and health but with the benefit that the well-being of teachers can be compared to well-being of other professions (e.g. Pretsch et al., 2012). Other researchers conceptualise TeachWB context-specific in relating dimensions of well-being to the profession such as enjoyment in teaching (Yin et al., 2018), teacher stress (Cook et al., 2017), school connectedness (De Biagi et al., 2017), or so-called student interaction well-being (Collie et al., 2015). This approach can be based on the adaption of general questionnaires on well-being to the profession (e.g. Lavy & Eshet, 2018) or might lead to the development of new instruments for assessing TeachWB against the background of profession-specific characteristics such as the value of the teaching profession or social support in schools (e.g. Tang et al., 2018). It can be concluded that despite the high agreement of TeachWB as an important predictor for teacher successful functioning and professional development at work as well as educational success of students and schools, the plethora of approaches and studies illustrates that there is little consensus about its specific nature. Often, TeachWB is used as an umbrella term that serves to cover a wide range of constructs such as facets of burnout, stress, emotions, motivation and health factors, and/or their combination. One reason for this heterogeneity might be related to the observation that TeachWB research is inspired from various disciplines. Just recently, a literature review by Hascher and Waber (2021) identified five main research fields of TeachWB clustered by the theoretical underpinning of the reviewed studies (see Table 8.1). They found a high engagement in quantitative and qualitative research aiming at a deeper understanding of causes and consequences of TeachWB. This clustering allows to illustrate the richness of TeachWB research by acknowledging the variety of different approaches and

Teacher and Student Well-Being

117

TABLE 8.1 Main branches of research on TeachWB and StudWB and well-being dimensions

Branch of research

Exemplary study

Positive psychology Teachers “Stressors, personality, and wellbeing among language teachers” (MacIntyre et al., 2019) Students “A multidimensional approach to measuring well-being in students: Application of the PERMA framework” (Kern et al., 2015) Health science Teachers “Resilience predicts well-being in teachers, but not in non-teaching employees” (Pretsch et al., 2012) Students

“Growing up unequal: Gender and socioeconomic differences in young people’s health and well-being” (Inchley et al., 2016) Psychology of well-being Teachers “The quality of school life and burnout as predictors of subjective well-being among teachers” (Cenkseven-Önder & Sari, 2009) Students “Preliminary development and validation of a multidimensional life satisfaction scale for children” (Huebner, 1994)

Well-being in education Teachers “Teacher’s beliefs about the determinants of students achievement predict job satisfaction and stress” (Heyder, 2019) Students “Well-being in school” (Hascher, 2004)

Psychology in work and organisation Teachers “A multilevel analysis of job characteristics, emotion regulation, and teacher well-being: A job demand-resources model” (Yin et al., 2018)

WB dimensions

• • • • • • • •

Life satisfaction Positive affect Negative affect Engagement Perseverance Optimism Connectedness Happiness

• • • • • • • •

Medical outcomes health Job satisfaction Physical illness Exhaustion Individual resources Social resources Health behaviour Health outcomes

• Life satisfaction • Positive affect • Negative affect • Satisfaction with • • • • •

family friends school living environment self

• Job satisfaction • Stress

• • • • • •

Positive attitudes towards school Enjoyment in school Positive academic self-concept Worries in school Physical complaints in school Social problems in school

• • • •

Contentment Enthusiasm Anxiety Depression

Note. For the five main branches of research on TeachWB, see Hascher and Waber (2021, p. 6).

118 Tina Hascher and Julia Mori

operationalisations. It suggests understanding the research field on TeachWB from a theoretical perspective as well as the level of domain specificity of the well-being construct. For example, studies from the perspective of well-being psychology as introduced by Diener (1984) predominantly see TeachWB as a combination of life satisfaction, positive and negative affect in life (e.g. Janovská et al., 2016) or equal well-being with mental health (e.g. Kim & Lim, 2016). Others follow the ideas of positive psychology and Seligman’s PERMA model by investigating affect, social relations, work engagement, and presence of meaning (Kern et al., 2014). Studies in health science, in turn, show a strong interest in examining health issues when investigating TeachWB and apply, for instance, a general health questionnaire (Thakur et al., 2018). Whereas studies related to the psychology in work and organisation conceptualise TeachWB as general work-related affect and work engagement (e.g. Tadić Vujčić et al., 2017), profession-related studies on well-being acknowledge the specific features of the teaching profession such as school connectedness, enjoyment of teaching, or teaching efficacy (Collie et al., 2015; Renshaw et al., 2015). Along with a variety of approaches to TeachWB, research unsurprisingly has revealed a plethora of factors that are related with TeachWB and can contribute to it (for an overview, see Hascher & Waber, 2021). Predominantly cross-sectional studies identify associations with TeachWB that range from individual objective characteristics such as age, gender, or tenure (e.g. Kaur & Singh, 2019) and individual subjective factors such as self-efficacy or resilience (e.g. Brouskeli et al., 2018) to profession-related factors such as teacherstudent relationship (e.g. Turner & Thielking, 2019) and societal factors such as the political situation (e.g. Veronese et al., 2018). It must be noted, however, that most studies revealed moderate associations with TeachWB and the predominantly applied cross-sectional designs do not allow causal interpretation. Rather, reciprocal associations seem reasonable, for instance between TWB and trust in principals (Berkovich, 2018) or teacher self-efficacy (Capone & Petrillo, 2018). Among the variety of factors that are associated with TeachWB, one factor has proved to be of utmost importance to TeachWB, that is social relationships within schools (e.g. Jones et al., 2019; Renshaw et al., 2015; Wong & Zhang, 2014). Relatedness with colleagues, students, and principals, as well as parents supports teachers in their professional role and nourishes their need for social relatedness at work (Deci & Ryan, 2002). Student Well-Being

Well-being of students is becoming an increasingly important concept in the educational milieu and a national priority in a number of countries (Govorova et al., 2020). Student well-being (StudWB) can be viewed as a resource for facilitating academic achievement and not only as a protective

Teacher and Student Well-Being

119

factor in confronting learning difficulties and problems in school (Hascher, 2012) but also as an outcome of successful learning and school experiences (Yang et al., 2019). Despite the widely recognised importance of addressing StudWB, there is little consensus regarding its core aspects (Powell & Graham, 2017). Similar to the concept of general well-being, most scholars support the multidimensional nature of StudWB, but definitions and operationalisations of StudWB vary substantially across disciplines. Similar to our work on TeachWB (Hascher & Waber, 2021), we conducted a systematic literature review of papers on StudWB, published between 2000 and 2022, using the following databases: EBSCOHost, Scopus, Web of Science, PsycInfo. Search strings consisted of a combination of the terms “student well-being” and “student wellbeing” and a specific research branch, including positive psychology, health science, education, and the well-being of children and adolescence (see Table 8.1). The four main branches of StudWB research are interrelated and not completely isolated. For example, the EPOCH model of adolescent wellbeing that includes the dimensions of Engagement, Perseverance, Optimism, Connectedness, and Happiness (Kern et al., 2015) was recently adapted to the school context (Holzer et al., 2022). This line of research, which specifically addresses StudWB or well-being in school, is of particular interest for educational research. It represents a domain-specific approach to StudWB and suggests viewing StudWB within a broader school context. However, also within a branch, several definitions and frameworks of StudWB can be found, with varying conceptualisations and operationalisations. To illustrate, while Fraillon (2004, p. 6) described StudWB as “the degree to which a student is functioning effectively in the school community,” comprising an interpersonal (e.g. empathy) and an intrapersonal dimension (e.g. resilience), De Fraine et al. (2005, p. 297) addressed StudWB as “the degree to which a student feels good in the school environment,” including four aspects of StudWB (e.g. interest in learning tasks, liking the school). These two conceptualisations of StudWB reflect eudemonic and hedonic well-being, respectively. Engels et al. (2004) described StudWB as a positive emotional life resulting from harmony between environmental factors and students’ personal needs and expectations vis-à-vis school, whereas Kaya and Erdem (2021, p. 1761) argue that well-being should be “holistically interpreted.” Thus, it remains unclear what exactly is meant by StudWB. Based upon grounded conceptualisations of general well-being (Grob et al., 1996; Mayring, 1991), Hascher (2004, 2012) suggested a comprehensive definition of StudWB and accentuated a co-existence of positive and negative factors. StudWB has been conceptualised as the prevalence of students’ positive emotions and cognitions towards school, persons in school, and the school context over the negative ones. The ensuing multidimensional model of StudWB in school binds aspects crucial for well-being in school and

120

Tina Hascher and Julia Mori

includes three positive and three negative dimensions, as well as emotional, cognitive, and physical dimensions related to the school context. Similarly, Noble et al. (2008) characterised StudWB as the predominance of students’ positive feelings and attitudes and suggested seven well-being pathways. The Programme for International Student Assessment (PISA) of the Organisation for Economic Co-operation and Development (OECD) was the first large-scale study to examine StudWB in its 2015 cycle. In their definition, StudWB encompasses multiple dimensions of students’ lives, such as cognitive, psychological, physical, social, and material (Borgonovi & Pál, 2016). Each of the five dimensions of StudWB was measured by a set of variables (e.g. overall life satisfaction for the psychological dimension; belongingness at school for the social dimension). Discussion and Future Research

In identifying main research branches on TeachWB and StudWB, which are based on theoretical approaches, a substantial accordance can be found. Four out of five branches show an overlap of TeachWB and StudWB research, and this intersection helps to understand the basic notions that are represented in the diverse empirical studies. Another benefit of this structure into main research branches lies in the possibility of integrating high-topical research. As can be shown, the temporary increase in research interest in TeachWB during and after the COVID-19 pandemic can be included into this structure. For example, research on TeachWB was related to Diener’s multi-dimensional model (1984) by focusing on satisfaction and positive and negative affect amid the pandemic (e.g. Alves et al., 2020), to Bakker and Demerouti’s JD-R model (2007) by identifying positive and negative job characteristics during the pandemic (e.g. Collie, 2022; Stang-Rabrig et al., 2022), and to Seligman’s PERMA model (2012) in analysing how TeachWB can be fostered in the face of the pandemic (e.g. Billett et al., 2022). However, while most scholars agree upon a multidimensional approach to well-being, definitions vary substantially and studies on TeachWB and StudWB differ regarding the choice and number of subdimensions. This disparity is intensified through inconsistencies within approaches. To illustrate, the PISA framework utilises a domain-specific approach to StudWB. In PISA 2015, cognitive well-being, for example, refers to “the skills and foundations students have to participate effectively in today’s society [… and] comprises students’ proficiency in academic subjects, their ability to collaborate with others to solve problems and their sense of mastery in-school subjects […].” (Borgonovi & Pál, 2016, p. 10), but is measured by subject specific skills, competencies, and self-beliefs (OECD, 2017). In PISA 2018, however, other indicators were used to measure StudWB, such as growth mindset for the cognitive dimension or students’ life satisfaction and meaning in life,

Teacher and Student Well-Being

121

students’ feelings, students’ self-efficacy and fear of failure for the psychological dimension (OECD, 2019). Similarly, research related to the JD-R model (Bakker & Demerouti, 2007) differs regarding the definition of demands and resources and lacks discussing the relevance, role, and balance of specific resources and demands. In line with earlier work (Hascher & Waber, 2021), we propose several strategies to advance research on TeachWB and StudWB. First, research on TeachWB and StudWB could benefit from a more concise definition of both constructs through a theoretical alignment with grounded conceptualisations of general well-being. A concise definition could contribute to a deeper understanding and more valid operationalisation of the constructs, and in turn, support the development of intervention programs designed to promote TeachWB and StudWB. A common understanding of TeachWB and StudWB, respectively, and an agreement on central dimensions would contribute to a coherent view of what constitutes TeachWB and StudWB. Second, in terms of the multidimensional nature of well-being, the number and selection of dimensions vary substantially across the studies, which lead to some crucial theoretical questions. Do researchers investigate the same TeachWB when they operationalise it, for example, through health complains, or affect-balance, work-commitment, vitality, or combinations thereof? How could well-being research be aligned when StudWB is, for instance, measured by affective variables, or sense of belonging, satisfaction, engagement, or as combinations thereof? We believe that at least the rationale for the selection as well as the relationships between the dimensions have to be more clearly described. Based on the premise that well-being is not merely the absence of anxiety, burnout, disease, or burden, we suggest considering both positive and negative dimensions of well-being (e.g. enjoyment and complaints) and define TeachWB and StudWB as a positive imbalance, with positive dimensions clearly outweighing the negative ones (Hascher, 2022). We further recommend to consider TeachWB and StudWB from a broader perspective and include cognitive, emotional, and physical elements in the measurement. Such a comprehensive approach may provide guidance to future research in developing TeachWB and StudWB intervention programmes. Third, an increasing body of empirical evidence suggests that TeachWB and StudWB are linked (e.g. Bilz et al., 2022; McCallum & Price, 2010; Roffey, 2012; Thomas et al., 2016). However, there is still a paucity of research examining both constructs within one study. Findings regarding the interrelatedness of TeachWB and StudWB are important because schools are highly social settings and the intense interaction between students and teachers seems to be a key to educational outcomes. We suggest advancing the field at least from two perspectives: (1) First, more research is needed that aims at understanding how TeachWB and StudWB are related and how levels of TWB influence levels of SWB, and vice versa. (2) The second

122

Tina Hascher and Julia Mori

perspective is represented in a so-called whole-school approach aiming at fostering TeachWB and StudWB simultaneously. The basic idea is that within a school context, TeachWB and StudWB are linked to another factor that can be more generally defined as school quality. Hence, we encourage future studies to include both student and teacher perspectives and conduct longitudinal investigations of the relationship between TeachWB and StudWB. (3) Moreover, modelling reciprocal relationships between the constructs could be beneficial for interventions based on the whole-school approaches, focusing on social and emotional well-being among all members of the school community (Waters, 2011). (4) Fourth, more research is needed that identifies the situation specificity and context specificity of TeachWB and StudWB. How do individual well-being experiences differ across situations (e.g. across subjects, contents, or settings) and contexts (e.g. learning and teaching in the formal school context versus informal learning context) and how are they interrelated? Accordingly, spill-over effects among life domains that have been found in stress research (e.g. Bakker & Demerouti, 2013) might deserve more attention in future well-being research. (5) Fifth, due to the paucity of longitudinal and intervention research, we still know less about the variables that foster or deteriorate TeachWB and StudWB. Regarding TeachWB, the majority of implemented programmes are only loosely related to the teaching profession. Rather, they address relaxation (e.g. Tamilselvi & Thangarajathi, 2016) or gratitude (e.g. Chan, 2013), general stress coping (Beshai et al., 2016), or cognitive training (Taylor, 2018). Few studies analysed the impact of profession-related programmes such as coaching support (Naghieh et al., 2015) or a teaching effectiveness training (Talvio et al., 2013). The need of supporting TeachWB as well as StudWB was impressively accentuated during the pandemic (e.g. García-Álvarez et al., 2021; Steinmayr et al., 2022) and future research may elucidate the crucial factors that foster well-being in school.

References Alves, R., Lopes, T., & Precioso, J. (2020). Teachers’ well-being in times of COVID-19 pandemic: Factors that explain professional well-being. International Journal of Educational Research and Innovation, 15, 203–217. https://doi.org/10.46661/ ijeri.5120 Bakker, A. B., & Demerouti, E. (2007). The Job Demands-Resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10. 1108/02683940710733115 Bakker, A. B., & Demerouti, E. (2013). The spillover-crossover model. In J. G. Grzywacz, & E. Demerouti (Eds.), New frontiers in work and family research (pp. 54–70). Psychology Press. Berkovich, I. (2018). Typology of trust relationships: Profiles of teachers’ trust in principal and their implications. Teachers and Teaching, 24(7), 749–767. https:// doi.org/10.1080/13540602.2018.1483914

Teacher and Student Well-Being

123

Beshai, S., McAlpine, L., Weare, K., & Kuyken, W. (2016). A non-randomised feasibility trial assessing the efficacy of a mindfulness-based intervention for teachers to reduce stress and improve well-being. Mindfulness, 7(1), 198–208. https://doi. org/10.1007/s12671-015-0436-1 Billett, P., Turner, K., & Li, X. (2022). Australian teacher stress, well-being, selfefficacy, and safety during the COVID-19 pandemic. Psychology in the School, 1–21. https://doi.org/10.1002/pits.22713 Bilz, L., Fischer, S. M., Hoppe-Herfurth, A.-C., & John, N. (2022). A consequential partnership: The association between teachers’ well-being and students’ well-being and the role of teacher support as a mediator. Zeitschrift für Psychologie, 230(3), 264–275. https://doi.org/10.1027/2151-2604/a000497 Borgonovi, F., & Pál, J. (2016). A framework for the analysis of student well-being in the PISA 2015 Study: Being 15 in 2015 (OECD Education Working Papers No. 140). OECD Publishing. https://doi.org/10.1787/5jlpszwghvvb-en Brouskeli, V., Kaltsi, V., & Loumakou, M. (2018). Resilience and occupational wellbeing of secondary education teachers in Greece. Issues in Educational Research, 28(1), 43–60. http://www.iier.org.au/iier28/brouskeli.pdf Burns, R. A., & Machin, M. A. (2013). Employee and workplace well-being: A multilevel analysis of teacher personality and organizational climate in Norwegian teachers from rural, urban and city schools. Scandinavian Journal of Educational Research, 57(3), 309–324. https://doi.org/10.1080/00313831.2012.656281 Capone, V., & Petrillo, G. (2018). Mental health in teachers: Relationships with job satisfaction, efficacy beliefs, burnout and depression. Current Psychology, 39(5), 1757–1766. https://doi.org/10.1007/s12144-018-9878-7 Cenkseven-Önder, F., & Sari, M. (2009). The quality of school life and burnout as predictors of subjective well-being among teachers. Educational Sciences: Theory and Practice, 9(3), 1223–1235. Chan, D. W. (2013). Counting blessings versus misfortunes: Positive interventions and subjective well-being of Chinese school teachers in Hong Kong. Educational Psychology, 33(4), 504–519. https://doi.org/10.1080/01443410.2013.785046 Collie, R. J. (2022). Job demands and resources, teacher well-being, and turnover intentions: An examination during COVID-19. Educational Psychology, 1–20. https://doi.org/10.1080/01443410.2022.2036323 Collie, R. J., Shapka, J. D., Perry, N. E., & Martin, A. J. (2015). Teacher wellbeing: Exploring its components and a practice-oriented scale. Journal of Psychoeducational Assessment, 33(8), 744–756. https://doi.org/10.1177/07342829 15587990 Cook, C. R., Miller, F. G., Fiat, A., Renshaw, T. L., Frye, M., Joseph, G., & Decano, P. (2017). Promoting secondary teacher’s well-being and intentions to implement evidence-based practices: Randomized evaluation of the ACHIEVER resilience curriculum. Psychology in the Schools, 54(1), 13–28. https://doi.org/10.1002/ pits.21980 De Biagi, N. B., Celeri, E. H., & Renshaw, T. L. (2017). Technical adequacy of the teacher subjective wellbeing questionnaire with Brazilian educators. Journal of Psychoeducational Assessment, 36(8), 850–855. https://doi.org.10.1177/ 0734282917714904 De Fraine, B., Van Landeghem, G., Van Damme, J., & Onghena, P. (2005). An analysis of wellbeing in secondary school with multilevel growth curve models and multilevel multivariate models. Quality and Quantity, 39(3), 297–316. https://doi. org/10.1007/s11135-004-5010-1

124

Tina Hascher and Julia Mori

Deci, E. L., & Ryan, R. M. (Eds.). (2002). Handbook of self-determination research. University of Rochester Press. Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499 Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. https://doi.org/10.1037/0033-2909.95.3.542 Diener, E., Lucas, R. E., & Oishi, S. (2002). Subjective well-being: The science of happiness and life satisfaction. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 63–73). Oxford University Press. Education Support (2019). Teacher Wellbeing Index. Education Support. Engels, N., Aelterman, A., Petegem, K. V., & Schepens, A. (2004). Factors which influence the well-being of pupils in flemish secondary schools. Educational Studies, 30(2), 127–143. https://doi.org/10.1080/0305569032000159787 Fraillon, J. (2004). Measuring student well-being in the context of Australian schooling: Discussion paper. Ministerial Council on Education, Employment, Training and Youth Affairs. García-Álvarez, D., Soler, M. J., & Achard-Braga, L. (2021). Psychological well-being in teachers during and post-COVID-19: Positive psychology interventions. Frontiers in Psychology, 12, Article 769363. https://doi.org/10.3389/fpsyg.2021.769363 Govorova, E., Benítez, I., & Muñiz, J. (2020). How schools affect student well-being: A cross-cultural approach in 35 OECD countries. Frontiers in Psychology, 11, Article 431. https://doi.org/10.3389/fpsyg.2020.00431 Grob, A., Little, T. D., Wanner, B., & Wearing, A. J. (1996). Adolescents’ well-being and perceived control across 14 sociocultural contexts. Journal of Personality and Social Psychology, 71(4), 785–795. https://doi.org/10.1037/0022-3514.71.4.785 Hascher, T. (2004). Wohlbefinden in der Schule. Waxmann. Hascher, T. (2012). Well-being and learning in school. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 3453–3456). Springer. Hascher, T. (2022). Well-being and learning. In I. Wilkinson & J. M. Par (Eds.), International encyclopedia of education (4th ed., Chapter 14082). Elsevier. Hascher, T., & Waber, J. (2021). Teacher well-being. A systematic review. Educational Research Review, 34, Article 100411. https://doi.org/10.1016/j.edurev.2021.100411 Heyder, A. (2019). Teachers’ beliefs about the determinants of student achievement predict job satisfaction and stress. Teaching and Teacher Education, 86, Article 102926. https://doi.org/10.1016/j.tate.2019.102926 Holzer, J., Bürger, S., Lüftenegger, M., & Schober, B. (2022). Revealing associations between students’ school-related well-being, achievement goals, and academic achievement. Learning and Individual Differences, 95, Article 102140. https://doi. org/10.1016/j.lindif.2022.102140 Huebner, E. (1994). Preliminary development and validation of a multidimensional life satisfaction scale for children. Psychological Assessment, 6(2), 149–158. https://doi.org/10.1037/1040-3590.6.2.149 Inchley, J., Currie, D., Young, T., Samdal, O., Torsheim, T., Augustson, L., Mathison, F., Aleman-Diaz, A., Molcho, M., Weber, M., & Barnekow, V. (2016). Growing up unequal: Gender and socioeconomic differences in young people’s health and wellbeing. Health Behaviour in School-aged Children (HBSC) study: International report from the 2013/2014 survey (Health Policy for Children and Adolescents No. 7). WHO Regional Office for Europe.

Teacher and Student Well-Being

125

Janovská, A., Orosová, O., & Janovský, J. (2016). Head teacher’s social support, personality variables and subjective well-being of Slovak primary teachers. Orbis Scholae, 10(3), 71–87. https://doi.org/10.14712/23363177.2017.11 Jones, C., Hadley, F., Waniganayake, M., & Johnstone, M. (2019). Find your tribe! Early childhood educators defining and identifying key factors that support their workplace wellbeing. Australasian Journal of Early Childhood, 44(4), 326–338. https://doi.org/10.1177/1836939119870906 Kaur, M., & Singh, B. (2019). Teachers’ well-being: An overlooked aspect of teacher development. Образование и саморазвитие, 14(3), 25–33. https://doi.org/10. 26907/esd14.3.03 Kaya, M., & Erdem, C. (2021). Students’ well-being and academic achievement. Child Indicators Research, 14, 1743–1767. https://doi.org/10.1007/s12187-02109821-4 Kern, M. L., Waters, L., Adler, A., & White, M. (2014). Assessing employee wellbeing in schools using a multifaceted approach: Associations with physical health, life satisfaction, and professional thriving. Psychology, 5(6), 500–513. https://doi. org/10.4236/psych.2014.56060 Kern, M. L., Waters, L., Adler, A., & White, M. A. (2015). A multidimensional approach to measuring well-being in students: Application of the PERMA framework. The Journal of Positive Psychology, 10(3), 262–271. https://doi.org/10.1080/ 17439760.2014.936962 Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207–222. https://doi. org/10.2307/3090197 Kim, S.-Y., & Lim, Y.-J. (2016). Virtues and well-being of Korean special education teachers. International Journal of Special Education, 31(1), 114–118. Kim-Prieto, C., Diener, E., Tamir, M., Scollon, C., & Diener, M. (2005). Integrating the diverse definitions of happiness: A time-sequential framework of subjective well-being. Journal of Happiness Studies, 6(3), 261–300. https://doi.org/10.1007/ s10902-005-7226-8 Lavy, S., & Eshet, R. (2018). Spiral effects of teachers’ emotions and emotion regulation strategies: Evidence from a daily diary study. Teaching and Teacher Education, 73, 151–161. https://doi.org/10.1016/j.tate.2018.04.001 MacIntyre, P. D., Ross, J., Talbot, K., Mercer, S., Gregersen, T., & Banga, C. A. (2019). Stressors, personality and wellbeing among language teachers. System, 82, 26–38. https://doi.org/10.1016/j.system.2019.02.013 Mayring, P. (1991). Psychologie des Glücks. Kohlhammer. McCallum, F., & Price, D. (2010). Well teachers, well students. Journal of Student Wellbeing, 4(1), 19–34. https://doi.org/10.21913/jsw.v4i1.599 Naghieh, A., Montgomery, P., Bonell, C. P., Thompson, M., & Aber, J. L. (2015). Organisational interventions for improving wellbeing and reducing work-related stress in teachers. Cochrane Database of Systematic Reviews, 4(4), 1–70. https:// doi.org/10.1002/14651858.cd010306.pub2 Noble, T., Wyatt, T., McGrath, H., Roffey, S., & Rowling, L. (2008). Scoping study into approaches to student wellbeing. Department of Education, Employment and Workplace Relations. Organisation for Economic Co-operation and Development (OECD). (2017). PISA 2015 results volume III: Students’ well-being. OECD Publishing. https://doi.org/10. 1787/9789264273856-en

126

Tina Hascher and Julia Mori

Organisation for Economic Co-operation and Development (OECD). (2019). PISA 2018 results (Volume III): What school life means for students’ lives. OECD Publishing. https://doi.org/10.1787/acd78851-en Powell, M. A., & Graham, A. (2017). Wellbeing in schools: Examining the policy– practice nexus. The Australian Educational Researcher, 44(2), 213–231. https:// doi.org/10.1007/s13384-016-0222-7 Pretsch, J., Flunger, B., & Schmitt, M. (2012). Resilience predicts well-being in teachers, but not in non-teaching employees. Social Psychology of Education, 15(3), 321–336. https://doi.org/10.1007/s11218-012-9180-8 Renshaw, T. L., Long, A. C. J., & Cook, C. R. (2015). Assessing adolescents’ positive psychological functioning at school: Development and validation of the Student Subjective Wellbeing Questionnaire. School Psychology Quarterly, 30(4), 534–552. https://doi.org/10.1037/spq0000088 Roffey, S. (2012). Pupil wellbeing – Teacher wellbeing: Two sides of the same coin? Educational & Child Psychology, 29(4), 8–17. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68 Ryff, C. D., & Keyes, C. L. M. (1995). The structure of psychological well-being revisited. Journal of Personality and Social Psychology, 69(4), 719–727. https://doi. org/10.1037/0022-3514.69.4.719 Seligman, M. E. (2012). Flourish: A visionary new understanding of happiness and well-being. Simon and Schuster. Stang-Rabrig, J., Brüggemann, T., Lorenz, R., & McElvany, N. (2022). Teachers’ occupational well-being during the COVID-19 pandemic: The role of resources and demands. Teaching and Teacher Education, 117, Article 103803. https://doi. org/10.1016/j.tate.2022.103803 Steinmayr, R., Paschke, P., & Wirthwein, L. (2022). Elementary school students’ subjective well-being before and during the COVID-19 pandemic: A longitudinal study. Journal of Happiness Studies, 23, 2985–3005. https://doi.org/10.1007/ s10902-022-00537-y Sutton, R. E., & Wheatley, K. F. (2003). Teachers’ emotions and teaching: A review of the literature and directions for future research. Educational Psychology Review, 15(4), 327–358. https://doi.org/10.1023/A:1026131715856 Tadić Vujčić, M., Oerlemans, W. G., & Bakker, A. B. (2017). How challenging was your work today? The role of autonomous work motivation. European Journal of Work and Organizational Psychology, 26(1), 81–93. https://doi.org/10.1080/135 9432X.2016.1208653 Talvio, M., Lonka, K., Komulainen, E., Kuusela, M., & Lintunen, T. (2013). Revisiting Gordon’s teacher effectiveness training: An intervention study on teachers’ social and emotional learning. Electronic Journal of Research in Educational Psychology, 11(3), 693–716. https://doi.org/10.14204/ejrep.31.13073 Tamilselvi, B., & Thangarajathi, S. (2016). Subjective well-being of school teachers after yoga – An experimental study. Journal on Educational Psychology, 9(4), 27–37. https://doi.org/10.26634/jpsy.9.4.5974 Tang, Y., He, W., Liu, L., & Li, Q. (2018). Beyond the paycheck: Chinese rural teacher well-being and the impact of professional learning and local community engagement. Teachers and Teaching, 24(7), 825–839. https://doi.org/10.1080/135 40602.2018.1470972

Teacher and Student Well-Being

127

Taylor, M. J. (2018). Using CALMERSS to enhance teacher well-being: A pilot study. International Journal of Disability, Development and Education, 65(3), 243–261. https://doi.org/10.1080/1034912X.2017.1394985 Thakur, M., Chandrasekaran, V., & Guddattu, V. (2018). Role conflict and psychological well-being in school teachers: A cross-sectional study from Southern India. Journal of Clinical & Diagnostic Research, 12(7), 1–6. https://doi.org/10.7860/ jcdr/2018/31776.11738 Thomas, N., Graham, A., Powell, M. A., & Fitzgerald, R. (2016). Conceptualisations of children’s wellbeing at school: The contribution of recognition theory. Childhood, 23(4), 506–520. https://doi.org/10.1177/0907568215622802 Turner, K., & Thielking, M. (2019). Teacher well-being: Its effects on teaching practice and student learning. Issues of Educational Research, 29(3), 938–960. http:// www.iier.org.au/iier29/turner2.pdf Veenhoven, R. (1991). Questions on happiness: Classical topics, modern answers, blind spots. In F. Strack, I. Argyle, & N. Schwarz (Eds.), Subjective well-being (pp. 7–26). Pergamon Press. Veronese, G., Pepe, A., Dagdukee, J., & Yaghi, S. (2018). Teaching in conflict settings: Dimensions of subjective wellbeing in Arab teachers living in Israel and Palestine. International Journal of Educational Development, 61, 16–26. https://doi. org/10.1016/j.ijedudev.2017.11.009 Viac, C., & Fraser, P. (2020). Teachers’ well-being: A framework for data collection and analysis (OECD Education Working Papers No. 213). OECD Publishing. https://doi.org/10.1787/19939019 Waters, L. (2011). A review of school-based positive psychology interventions. The Educational and Developmental Psychologist, 28(2), 75–90. https://doi.org/10. 1375/aedp.28.2.75 Wong, Y.-H. P., & Zhang, L.-F. (2014). Perceived school culture, personality types, and wellbeing among kindergarten teachers in Hong Kong. Australasian Journal of Early Childhood, 39(2), 100–108. https://doi.org/10.1177/18369391140 3900213 Wood, A. M., & Joseph, S. (2010). The absence of positive psychological (eudemonic) well-being as a risk factor for depression: A ten year cohort study. Journal of Affective Disorders, 122(3), 213–217. https://doi.org/10.1016/j.jad.2009.06.032 World Health Organisation (WHO). (1948). Constitution of the World Health Organization. World Health Organization. Yang, Q., Tian, L., Huebner, E. S., & Zhu, X. (2019). Relations among academic achievement, self-esteem, and subjective well-being in school among elementary school students: A longitudinal mediation model. School Psychology, 34(3), 328–340. https://doi.org/10.1037/spq0000292 Yin, H., Huang, S., & Lv, L. (2018). A multilevel analysis of job characteristics, emotion regulation, and teacher well-being: A job demands-resources model. Frontiers in Psychology, 9, Article 2395. https://doi.org/10.3389/fpsyg.2018.02395

9 TEACHERS’ MOTIVATION TO TEACH A Review through the Lens of Motivational Theories Helen M. G. Watt and Paul W. Richardson

Abstract The field of teacher motivation has burgeoned in the last 15 years. Although the study of teacher motivation itself is not new, until then, researchers had not drawn upon motivation theories in a concentrated way to develop theoretically grounded programmes of research to address questions concerning the nature, development and influence of teachers’ motivations. Theories, constructs and concepts from the well-established literature concerning students’ motivations to learn were adapted and translated to the study of teachers’ motivations to teach. Transposing the theoretical concepts highlighted the potentials, challenges and boundaries in their application to the domain of teaching, both conceptually and methodologically. We overview each of the major motivation theories (expectancy-value, achievement goal, and self-determination) that have recently been reinterpreted in relation to teachers. In this endeavour, motivation researchers have asked first what kinds of motivations are relevant for teachers; second, whether and how we can measure them and third, whether and how they matter for teachers and teaching. We will present empirical findings from our own work grounded in expectancy-value theory (the “FIT-Choice” research programme; www.fitchoice.org) and from the work of colleagues whose research is underpinned by other theoretical lenses. We conclude with implications and suggestions for future research.

Why Teach, and Does It Matter? A Review through the Lens of Motivational Theories

Deciding on a career to pursue can be a complex decision-making process, involving perceptions, values, beliefs and expectations derived from experiences, observations and actual knowledge of the nature of the career. Teaching DOI: 10.4324/9781003303473-10

Teachers’ Motivation to Teach

129

is a “messy job” (Bernstein-Yamashiro & Noam, 2013, p. 46), enacted in a specific “social world” in which teachers and young people are “tied together in a complex maze of social interactions” (Waller, 1932, p. 1), whose increasing heterogeneity requires more resources from the teacher. Teachers necessarily need well-developed “soft skills” in social communication and cultural awareness, analytical thinking and active learning strategies, and “hard” skills and expertise in content knowledge. An enduring interest in learning and helping others acquire new knowledge would also seem important. Although teaching can be seen as a relatively secure career offering the possibility of balancing family and work commitments, it provides for a modest salary, and according to TALIS 2018 data (OECD, 2019), only 26% of teachers on average consider their profession to be valued by society. Although the proportion was higher in Australia, still fewer than half of teachers agreed (OECD, 2019). Why teachers choose the career, stay in it, and invest in their development is central to understanding how they can be supported to flourish, amid the challenges created by the “messiness” of the job. Structure of the Review

Almost two decades ago, motivation researchers who had previously focused their attention on student motivation turned the spotlight on the other actor in the classroom, the teacher. Theories, constructs and concepts from the well-established literature concerning students’ motivations to learn were translated to the study of teachers’ motivations to teach. (Situated) expectancy-value theory ((S)EVT; Eccles & Wigfield, 2020) was initially developed to explain students’ enrolment choices, adapted to understand the reasons people choose teaching and remain in the career. Achievement goal theory (AGT; Dweck, 1986; Elliot & Harackiewicz, 1996; Nicholls, 1979) was developed to explain why students are motivated to succeed, adapted to understand teachers’ goals in their daily practice. Self-determination theory (SDT; Ryan & Deci, 2000) was developed to understand how people’s basic psychological needs promote their degree of self-determined motivation and flourishing, adapted to understand teachers’ needs satisfaction, controlled versus autonomous motivations, as well as school and broader professional context influences on well-being. In turn, teachers’ level of work commitment, professional identification and satisfaction have important implications for how they teach and thereby student outcomes. These major theoretical frameworks more or less map to three “waves” of policy press. First, the need to meet the demand for suitably qualified teachers can be informed by understanding teachers’ motivations to enter and remain in the career, using the lens of (S)EVT. Second, the impetus to improve teaching quality can be illuminated by understanding what goals teachers

130

Helen M. G. Watt and Paul W. Richardson

strive to achieve in their daily practice, using an AGT lens. Third, teachers’ high levels of work-related distress, burnout and reduced job satisfaction have more recently garnered attention. Using the lens of SDT, the extent to which their basic needs and self-determined motives are met or frustrated can provide levers to support them to thrive in the profession. Although self-efficacy researchers had turned their attention to teachers earlier, the three broader motivational frameworks have subsequently proven fruitful in researchers’ understanding of the nature of teachers’ motivations and consequences for their plans, practice and wellbeing. These theories include constructs akin to self-efficacy such as perceived competence and success expectancies, alongside other motivational constructs within broader theoretical frameworks with each shining a spotlight on particular outcomes of theoretical and pragmatic policy concern. In this chapter, we describe and review in turn, each of the adaptations of major motivational theories to understand what kinds of motivations are relevant for teachers, how we can measure them, and whether and how they matter for teachers and for their students. We critically review the key findings from each of these programmes of research, and highlight the conceptual and methodological challenges and boundaries in the application of each motivational theory to teachers in their workplace contexts. Expectancy-Value Theory: The FIT-Choice Approach to Teacher Motivation Impetus

Drawing initially from expectancy-value theory (EVT), prompted by concern regarding teacher recruitment and retention, we developed the “FITChoice” framework (Factors Influencing Teaching Choice; www.fitchoice. org) to study teachers’ initial choice to become a teacher, and continuing choice whether or not to remain in teaching. Our programme of research began with the development of the FIT-Choice scale (Richardson & Watt, 2006; Watt & Richardson, 2007) to measure teacher motivations in a theoretically grounded and systematic way. Eccles et al.’s (1983) EVT of student motivation offered a fruitful framework within which to map the identified informative motivations from the teaching literature while also suggesting additional motivations. In this theory, supported by a wealth of empirical work (for reviews, see Watt, 2010, 2016; Wigfield & Cambria, 2010), students’ choices and performance are shaped by their expectancies and different kinds of values. Expectancies tap how well an individual expects to perform at a task and are shaped over time by experience. Values are comprised of a number of dimensions, such as whether the individual enjoys the task (intrinsic value; similar to intrinsic motivation as defined in SDT), if it will be instrumental

Teachers’ Motivation to Teach

131

for any long- or short-term goals (utility value; resembling extrinsic motivation in SDT) if it is seen to fulfil identity-related needs (attainment value), and be worth the effort or other sacrifices required to succeed (cost value). Most studies focused on the three positive values, sometimes combining attainment and utility into “importance value.” More recently, the cost facet has been empirically examined (e.g. Flake et al., 2015; Perez et al., 2014; Watt et al., 2019). EVT provided a fruitful integrative framework to organise the findings about motivations to teach from the teaching literature, by mapping these to theoretical constructs. Measurement

The FIT-Choice scale has been demonstrated to be psychometrically sound across multiple samples and settings where it has been applied (Watt & Richardson, 2007, 2012), facilitating comparisons across settings and investigation of correlates and consequences of different kinds of motivations to teach. The 12 primary FIT-Choice motivation factors are depicted in Figure 9.1 (see Richardson & Watt, 2006; Watt & Richardson, 2007 for reviews). All elements of the model are assumed to operate in concert to influence the choice to enter and remain in a teaching career.

FIGURE 9.1

The FIT-Choice framework

132

Helen M. G. Watt and Paul W. Richardson

Social utility values to shape the future of youth, enhance social equity, make a social contribution, and work with children/adolescents tap “altruistic” motivations. Personal utility values tap quality-of-life issues that include having time for family, job security and job transferability. Such personal factors had been previously nominated as extrinsic, although that fails to distinguish identity-related factors from extrinsic social influences from friends, family or work colleagues, or perceptions of task rewards and demands. Prior teaching and learning experiences had previously been linked to choosing to teach. Intrinsic value and perceived ability were less a focus in the teacher education literature although they are key factors in several motivation theories including EVT, and ability-related beliefs have been a focus in the career choice literature. “Fallback” career was based on claims that people may default into teaching. Later additions were the motivations of subject interest and professional autonomy. Measured perceptions regarding the teaching profession tapped each of task demand (perceived expertise and difficulty) and task return (social status and salary), whose discrepancy we conceptualise as a “cost.” Experiences of social dissuasion were measured, and satisfaction with the choice of teaching as a career. Which Motivations Drive the Choice of Teaching Career Across Different Contexts?

Teacher motivation is an issue of concern around the world, indicated by at least 23 language translations of our FIT-Choice scale of which we are aware, and 1,740 studies have drawn on it according to Google Scholar. For the first time, a robust scale offered a common platform to measure initial and subsequent motivations to teach. Across this literature, teachers’/future teachers’ highest-rated motivations are very positive; but, teaching is understood to be high on demands and low on external rewards. In the first Australian validation sample (N = 1,651 preservice teachers in their first term of teacher education; Watt & Richardson, 2007), highestrated motivations for teaching were perceived teaching abilities, intrinsic value, and social utility values. Recruitment campaigns have tended to focus on a limited subset of individuals’ motivations to teach, predominantly relating to the opportunity to make a social contribution and the opportunity to work with children, likely limiting their audience and effectiveness. Reassuringly, the lowest-rated motivation to teach was as a “fallback,” revealing teaching to be a career of choice, followed by social influences of others encouraging them to become teachers. The stereotype of especially women choosing to teach because it offers family-flexibility was found to be only moderately rated, relative to competing motivations in a comprehensive framework. Interestingly, motivations

Teachers’ Motivation to Teach

133

appeared similarly among women and men, primary and secondary teachers, secondary teachers of different subject specialisations (Watt et al., 2017), and undergraduate and graduate-entry teacher education students (Richardson & Watt, 2006). Future teachers perceived teaching as highly demanding in terms of a heavy workload, high emotional demand and requiring specialised and technical knowledge. They perceived the external rewards in terms of salary and social status as rather low, and reported experiences of social dissuasion from choosing a teaching career. Thus, even at the start of teacher education, these future teachers perceived a “cost” to the career choice yet rated their career choice satisfaction highly. We would not expect the FIT-Choice instrument to function without variation across different contexts. For instance, the possibility of teaching providing an easily transferable job across states and countries was endorsed by Australian teachers who can and do seek employment in English-speaking countries such as the United Kingdom and the United States, and in “international” schools around the world. However, this is not the case for teachers educated in parts of Europe, Turkey or China where there is little possibility of moving outside a specific country for reasons of language and registration requirements. Additionally, higher relative salary and status in countries such as Taiwan, Norway, Finland and Germany should reduce the “cost” dimension. A first cross-cultural comparison (Watt et al., 2012) demonstrated scalar invariance of the FIT-Choice measure, with the exception of job transferability and teaching as a fallback career that were not equally relevant across settings. In a collation of findings across different contexts (Watt & Richardson, 2012b1), fallback career motivations were uniformly low except in China and Turkey, likely reflecting work availability in those job markets. Ability and intrinsic motivations were highly rated except in those samples from China and Turkey, in which collectivist culture’s career choice may derive less from individuals’ interests and abilities. Social utility values were lowest in the Chinese sample, which may be more taken for granted in collectivistic culture. Personal utility values were similar and moderately rated across samples, presumably reflecting basic needs in contemporary society (Watt & Richardson, 2012a). Antecedents and Outcomes

To date, ours is the only research programme in which the FIT-Choice scale has been administered to future teachers initially in their teacher education, followed longitudinally into beginning teaching, and through most recently into their midcareer (Watt & Richardson, 2023). Motivations to enter and stay in teaching moderately correlated over time, with significant decreases following professional entry for four motivations: “job security,” “time for family,” “work with children/adolescents” and “fallback career,” meaning that teachers were less motivated by these during

134

Helen M. G. Watt and Paul W. Richardson

their early career than at the outset of their teacher education. On the other hand, they became more motivated by three motivations: “intrinsic value,” “enhance social equity” and “shape future of youth.” There were no significant changes for the other five motivations. Changes in beginning teachers’ motivations to teach were associated with contextual supports, among which leadership support and sense of belonging showed the most relationships. That belonging was central to supporting teachers’ motivations echoes its significance in studies of students’ motivation (see metaanalysis by Korpershoek et al., 2020). Strikingly, experienced excessive demands during early career did not undermine any motivations, although perceived demands of teaching did increase, widening the “cost” gap between perceived demands and rewards. Highest-rated motivations during teacher education and across the transition to teaching were not necessarily the strongest predictors of later outcomes. Those that we have studied to date include teachers’ professional engagement and career development aspirations, reported instructional practices (Richardson & Watt, 2014) and burnout/wellbeing (Watt & Richardson, 2019). Although the high-rated motivations – intrinsic value, perceived teaching abilities, prior teaching and learning experiences and social utility values – did predict professional engagement at the end of teacher education (planned persistence, professional development, effort and leadership) and positive teacher-reported teaching behaviours during early career teaching (structure, positive expectations, and relatedness with their students), so did the low-endorsed fallback motivation (negatively), showing that even small amounts of this motivation can be harmful. Not predictive were the personal utility values, counter to earlier researchers’ concerns that such motivations may prove “unworthy” (e.g. Sparkes, 1988; Woods, 1981; Yong, 1995). The most adaptive motivations, in terms of predicting positive outcomes, were ability, intrinsic and social values – consistent with Butler’s (2012) findings through an AGT lens, of teachers’ adaptive mastery and relational goal orientations. Interestingly, social influences predicted later reported negative teaching behaviour. We interpret this using the lens of SDT, as choosing to teach based on others’ influence suggests a “controlled” rather than “autonomous” motivation. These identified positive motivations additionally functioned as personal resources that protected against the development of teachers’ burnout from early to midcareer; in contrast, fallback and social influence motivations promoted burnout (Watt & Richardson, 2019). Policy Recommendations

Having established the enduring effect of initial teaching motivations for teachers several years later, it may be important to ensure that those who are less than enthusiastic about working with youth or who defaulted into

Teachers’ Motivation to Teach

135

teaching as a “fallback,” do not find their way into the teaching profession. However, before recommendations for policy can be made, it is necessary to discern which of the influential motivations appear fixed versus malleable. Non-malleable motivations, such as “fallback,” may be important to consider at selection into teacher education, whereas malleable motivations could be targeted during teacher education or early career induction. For example, both “ability” and “positive teaching and learning” motivations may increase as a result of direct or vicarious experiences (Bandura, 1997). “Social utility” motivations may be able to be enhanced by experiences in less advantaged educational settings, such as one-on-one peer tutoring offered by some teacher education programmes with school students who experience disadvantage or have various disabilities. Drawing on the theoretical framework of (S)EVT (Eccles & Wigfield, 2020) has advanced understanding of what motivates people to want to become and remain teachers, how to measure their motivations and compare them across samples and settings, examine how they are realised or not in particular school contexts, and consequent impacts on teachers’ development. The Translation of Achievement Goal and Self-Determination Theories to the Study of Teachers Achievement Goal Theory: Teachers’ Goals and Daily Practice

The decision to explore whether, like students, teachers pursued different types of goals stemmed from Butler’s (2007) observation that schools may be an “achievement arena” for teachers as much as it is for students. This led her to adapt achievement goal theory (AGT) to develop the “Goal Orientation for Teaching” (GOT; Butler, 2007) survey instrument with which to measure the work-related goals of practising teachers. Her programme of research has shown that teachers pursue a range of goals similar to those pursued by students: mastery to strive to learn and acquire more competence as a teacher, ability-approach to demonstrate high teaching ability relative to other teachers, ability-avoidance to avoid demonstrating inferior performance as a teacher, and work-avoidance to exert as little effort as possible. Subsequently, based on the insight that teaching (unlike learning) is an inherently interpersonal endeavour, Butler (2012) developed a new class of “relational” goal to develop close and caring relationships with students with whom teachers strive to connect. According to the results from the TALIS survey (OECD, 2014), positive teacher-student relationships and constructive collaborations with work colleagues bolster teachers’ work satisfaction. Relational goals can “make their curricula vital and real; the human connection gives visible meaning and tangible purpose to their work”

136

Helen M. G. Watt and Paul W. Richardson

(Bernstein-Yamashiro & Noam, 2013; p. 56). Butler (2012) has demonstrated relational goals predict to teachers’ social and emotional support for their students, and cognitively activating instruction. Mastery goals positively associate with students’ positive perceptions of seeking help and frequency of help seeking. Ability-approach and ability-avoidance goals predicted teachers’ performance-focused instructional practices; ability-avoidance also predicted students’ negative perceptions, avoidance of help-seeking and cheating on their schoolwork. Like other workers, the degree of commitment and effort teachers exert varies considerably. Some people are happy to do as little as possible to meet their obligations while others want to master their “craft” and be constantly learning and developing. For some, work is a platform to outshine others and be recognised for their superior performance. By contrast, others strive to avoid revealing their lack of skills or poorer quality work. Teachers can adopt more than one goal simultaneously. A study of teachers’ goal profiles showed that mastery and relational goals tended to co-occur and associated with positive professional engagement and instructional practice, whereas profiles characterised by higher ability than mastery/relational goals showed poorest professional engagement and reported negative teaching behaviour (Watt et al., 2021). That study provoked the intriguing suggestion that lower ability-approach goals may prove beneficial for teachers’ supportive instructional practice, presumably due to less focus on their own “teacher self” heightening their positive focus on students. The translation and adaptation of AGT from the study of students’ learning to teachers’ teaching, illustrates that an additional class of relational goals is needed when conceptualising the classroom as an achievement arena for teachers. Further, even goals such as ability-approach that could be directly transposed may play a different role in teachers’ interactions with students than students’ engagement with their studies. Self-Determination Theory: Teachers’ Basic Needs Satisfaction and Level of Self-Determination

Self-determination theory (SDT) is referred to as a “macrotheory” of motivation in that it assumes all human beings possess inherent growth propensities and innate psychological needs, which are the foundation for autonomous motivation and psychological flourishing. Motivations can be classified along a continuum of self-determination, from externally controlled to autonomous. Autonomously motivated teachers enjoy a sense of personal accomplishment, enact autonomy-supportive teaching behaviours, promote students’ autonomous learning motivation and show lower levels of burnout (e.g. Pelletier et al., 2002; Roth et al., 2007). In contrast, controlled motivations associate with negative outcomes for teachers and their students.

Teachers’ Motivation to Teach

137

SDT proposes that if any of individuals’ basic needs are thwarted, autonomous motivation and wellbeing will be impaired; the theory focuses on etic and emic contextual factors that can support versus undermine people’s motivational resources (Ryan & Deci, 2000). The three basic needs are for autonomy, competence, and relatedness/belonging. Autonomy is supported by individuals’ inner resources of interest and value, but derailed by external control, pressure and lack of perceived choice, often in the form of punishments and rewards. A sense of competence emerges from experiences of exercising one’s capabilities, seeking out and mastering challenges and experiencing success and growth. Relatedness, the third pillar of SDT (also discussed as fundamental to teachers’ goals and initial motivations in AGT and EVT), refers to the need to form close and secure relationships which provide a sense of belonging. School contexts in which teachers are pressured to teach in certain ways that may not sit well with their beliefs and values, or that focus on accountability measures by means of high-stakes testing, undermine teachers’ needs for competence and autonomy. Management models that take teachers away from their work in classrooms and interactions with students likely sit at odds with their need for relatedness. Undermining teachers’ basic needs reduces their self-determined autonomous motivations and professional satisfaction (Pelletier et al., 2002), promoting emotional exhaustion (Roth, 2014) and burnout (Fernet et al., 2012). Work overload and behaviourally disruptive students are other factors found to reduce teachers’ autonomous motivation, undermine their sense of competence and accomplishment, and promote emotional exhaustion (Fernet et al., 2012). Contextual pressures, whether from “above” or “below” that impinge on teachers’ sense of autonomy, in turn, lead them to exert greater control over their students, who become less positively engaged in their learning. Policy measures introduced to monitor and improve teaching quality therefore “backfire,” intensifying teachers’ administrative work rather than allowing them to focus on the actual teaching work they are motivated to do. Outlook and Future Directions

We conclude with pressing gaps in theorisation, particularly regarding context specificity and empirical gaps in the field. The challenge in deciding the level of specificity is to strike the right balance between sufficient context sensitivity to be meaningful and sufficient generality to usefully inform policy and fashion effective interventions. Information yielded at different “grain sizes” (e.g. teaching in general, per class, for particular groups of students or individual students) will have utility for different stakeholder groups such as employers, teacher educators and policymakers. Point-in-time context means

138

Helen M. G. Watt and Paul W. Richardson

that earlier explanations may not remain adequate or relevant. The changing role of teachers may clash with initial motivations based mainly on a desire to work with young people. Increased external regulation of teachers’ work may make the profession less appealing to talented individuals who have a range of career possibilities. The extent to which positive motivations can be altered and negative ones nurtured remains an open question. Positive working environments can help breed motivational resources and vice versa. Positive motivations and emotions increase our capacity to generate new ideas and handle difficulties (Frederickson, 2001), whereas negative emotions interfere with our capacity for processing information (Eysenck & Calvo, 1992). More engaged workers can experience a positive “gain spiral” due to self-determined positive changes to their work such as seeking new challenges or job control (De Lange et al., 2008). To date, few studies involve longitudinal data that allow for testing such bidirectional processes. The major theories of motivation, which have so far been translated to the study of teachers, appear to us to each fruitfully shed light on key outcomes of concern in different aspects of teachers’ careers. Across the teaching career “lifespan,” (S)EVT illuminates initial and continuing career choice, AGT teachers’ daily work, and SDT their psychological growth and flourishing. Such outcomes map onto policy presses concerning each of teacher recruitment and retention, teaching quality, and teachers’ wellbeing, central to teachers’ positive development in the career. It may seem self-evident that motivations from the different theoretical perspectives ought to work together in some way, to shape teachers’ outcomes and thereby students’, given that the differently theorised motivations co-occur within same individuals. Some of the constructs across theories are rather similar, for example intrinsic value ((S)EVT), mastery goal orientation (AGT) and intrinsic motivation (SDT) all tap the interest and enjoyment dimension of teaching. Yet, we lack a developmental, integrative framework to connect these major theories. We have elsewhere (Richardson & Watt, 2018) proposed the Selection, Optimisation and Compensation model (SOC; Baltes & Baltes, 1990) of lifespan developmental psychology as a potentially integrative framework to encompass these theories targeted to different points during the career “lifespan.” Author Note

Authors contributed equally to the manuscript. The FIT-Choice project (www.fitchoice.org) is supported by sequential Australian Research Council Discovery Projects DP140100402 (2014-2016; Richardson and Watt), DP0987614 (2009-2012; Watt & Richardson) and DP0666253 (20062009; Richardson, Watt and Eccles). Watt additionally holds an honorary Professorship in the Faculty of Education at Monash University; Richardson

Teachers’ Motivation to Teach

139

holds an honorary Professorship in the Centre for Educational Measurement and Assessment at The University of Sydney, and was sponsored to work at the Sydney Social Sciences and Humanities Advanced Research Centre (SSSHARC) by the award of a fellowship funded by the Ernest Athelstan James Bequest during the preparation of this manuscript. Note 1 The United States and China: Lin et al., 2012; Croatia: Jugović et al., 2012; Germany: König & Rothland, 2012; and Turkey: Kılınç et al., 2012.

References Baltes, P. B., & Baltes, M. M. (1990). Psychological perspectives on successful aging: The model of selective optimization with compensation. In P. B. Baltes & M. M. Baltes (Eds.), Successful aging: Perspectives from the behavioral sciences (pp. 1–34). Cambridge University Press. https://doi.org/10.1017/CBO9780511665684.003 Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman. Bernstein-Yamashiro, B., & Noam, G. G. (2013). Teacher-student relationships: Toward personalized education. new directions for youth development, 137. Wiley Periodicals. Hoboken. Butler, R. (2007). Teachers’ achievement goals and associations with teachers’ helpseeking: Examination of a novel approach to teacher motivation. Journal of Educational Psychology, 99(2), 241–252. https://doi.org/10.1037/0022-0663.99.2.241 Butler, R. (2012). Striving to connect: Extending an achievement goal approach to teacher motivation to include relational goals for teaching. Journal of Educational Psychology, 104(3), 726–742. https://doi.org/10.1037/a0028613 De Lange, A. H., De Witte, H., & Notelaers, G. (2008). Should i stay or should i go? Examining longitudinal relations among job resources and work engagement for stayers versus movers. Work & Stress, 22(3), 201–223. https://doi.org/ 10.1080/02678370802390132 Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048. https://doi.org/10.1037//0003-066x.41.10.1040 Eccles (Parsons), J., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motivation: Psychological and sociological approaches (pp. 75–146). Freeman. Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Elliot, A. J., & Harackiewicz, J. M. (1996). Approach and avoidance achievement goals and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 70, 461–475. https://doi.org/10.1037//0022-3514.70.3.461 Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition & Emotion, 6(6), 409–434. https://doi.org/10. 1080/02699939208409696

140

Helen M. G. Watt and Paul W. Richardson

Fernet, C., Guay, F., Senécal, C., & Austin, S. (2012). Predicting intraindividual changes in teacher burnout: The role of perceived school environment and motivational factors. Teaching and Teacher Education, 28(4), 514–525. https://doi. org/10.1016/j.tate.2011.11.013 Flake, J. K., Barron, K. E., Hulleman, C., McCoach, B. D., & Welsh, M. E. (2015). Measuring cost: The forgotten component of expectancy-value theory. Contemporary Educational Psychology, 41, 232–244. https://doi.org/10.1016/j.cedpsych. 2015.03.002 Frederickson, B. L. (2001). The role of positive emotions in positive psychology: The broadening-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218 Jugović, I., Marušić, I., Pavin Ivanec, T., & Vizek Vidović, V. (2012). Motivation and personality of preservice teachers in Croatia. Asia-Pacific Journal of Teacher Education, 40(3), 271–287. https://doi.org/10.1080/1359866X.2012.700044 Kılınç, A., Watt, H. M., & Richardson, P. W. (2012). Factors influencing teaching choice in Turkey. Asia-Pacific Journal of Teacher Education, 40(3), 199–226. https://doi.org/10.1080/1359866X.2012.700048 König, J., & Rothland, M. (2012). Motivations for choosing teaching as a career: Effects on general pedagogical knowledge during initial teacher education. AsiaPacific Journal of Teacher Education, 40(3), 289–315. https://doi.org/10.1080/ 1359866X.2012.700045 Korpershoek, H., Canrinus, E. T., Fokkens-Bruinsma, M., & de Boer, H. (2020). The relationships between school belonging and students’ motivational, socialemotional, behavioural, and academic outcomes in secondary education: A metaanalytic review. Research Papers in Education, 35(6),641–680, https://doi.org/10. 1080/02671522.2019.1615116 Lin, E., Shi, Q., Wang, J., Zhang, S., & Hui, L. (2012). Initial motivations for teaching: Comparison between preservice teachers in the United States and China. AsiaPacific Journal of Teacher Education, 40(3), 227–248. https://doi.org/10.1080/13 59866X.2012.700047 Nicholls, J. G. (1979). Quality and equality in intellectual development. American Psychologist, 34, 1071–1084. https://doi.org/10.1037/0003-066X.34.11.1071 OECD (2014). New insights from TALIS 2013: Teaching and learning in primary and upper secondary education. TALIS, OECD Publishing. http://dx.doi. org/10.1787/9789264226319-en OECD (2019). TALIS 2018 results (Volume II): Teachers and school leaders as valued professionals. Retrieved from https://www.oecd.org/education/talis-2018-resultsvolume-ii-19cf08df-en.htm. Pelletier, L. G., Séguin-Lévesque, C., & Legault, L. (2002). Pressure from above and pressure from below as determinants of teachers’ motivation and teaching behaviors. Journal of Educational Psychology, 94(1), 186–196. https://doi. org/10.1037/0022-0663.94.1.186 Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106(1), 315–329. https://doi.org/10.1037/a0034027 Richardson, P. W., & Watt, H. M. G. (2006). Who chooses teaching and why? Profiling characteristics and motivations across three Australian universities. AsiaPacific Journal of Teacher Education, 34(1), 27–56. https://doi.org/10.1080/ 13598660500480290

Teachers’ Motivation to Teach

141

Richardson, P. W., & Watt, H. M. G. (2014). Why people choose teaching as a career: An expectancy-value approach to understanding teacher motivations. In P. W. Richardson, S. Karabenick, & H. M. G. Watt (Eds.), Teacher motivation: Theory and practice (pp. 3–19). Routledge. Richardson, P. W., & Watt, H. M. G. (2018). Teacher professional identity and career motivation: A lifespan perspective. In P. A. Schutz, J. Y. Hong, & D. C. Francis (Eds.), Research on teacher identity and motivation: Mapping challenges and innovation (pp. 37–48). Springer. Roth, G. (2014). Antecedents and outcomes of teachers’ autonomous motivation: A self-determination theory analysis. In P. W. Richardson, S. Karabenick, & H. M. G. Watt (Eds.), Teacher motivation (pp. 36–51). Routledge. Roth, G., Assor, A., Kanat-Maymon, Y., & Kaplan, H. (2007). Autonomous motivation for teaching: How self-determined teaching may lead to self-determined learning. Journal of Educational Psychology, 99(4), 761–774. https://doi.org/10. 1037/0022-0663.99.4.761 Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68. Sparkes, A. (1988). Strands of commitment within the process of teacher initiated innovation. Educational Review, 40, 301–317. https://doi.org/10.1080/001319188 0400304 Waller, W. (1932/2014). The sociology of teaching. Russell & Russell. Watt, H. M. G. (2010). Gender and occupational choice. In J. C. Chrisler & D. R. McCreary (Eds.), Handbook of gender research in psychology (pp. 379–400). Springer. Watt, H. M. G. (2016). Gender and motivation. In K. Wentzel & D. Miele (Eds.), Handbook of motivation at school (2nd. ed., pp. 320–339). Routledge, Taylor & Francis. Watt, H. M. G., Bucich, M., & Dacosta, L. (2019). Adolescents’ motivational profiles in mathematics and science: Associations with achievement striving, career aspirations and psychological wellbeing. Frontiers in Psychology, 10, 244–266. https://doi.org/10.3389/fpsyg.2019.00990 Watt, H. M. G., Butler, R., & Richardson, P. W. (2021). Antecedents and consequences of teachers’ goal profiles in Australia and Israel. Learning and Instruction, 76, Article 101491. https://doi.org/10.1016/j.learninstruc.2021.101491 Watt, H. M. G., & Richardson, P. W. (2007). Motivational factors influencing teaching as a career choice: Development and validation of the FIT-Choice scale. The Journal of Experimental Education, 75(3), 167–202. https://doi.org/10.3200/JEXE. 75.3.167-202 Watt, H. M. G., & Richardson, P. W. (2012a). An introduction to teaching motivations in different countries: Comparisons using the FIT-Choice scale. Asia-Pacific Journal of Teacher Education, 40(3), 185–197. https://doi.org/10.1080/13598 66X.2012.700049. Watt, H. M. G., & Richardson, P. W. (Eds.). (2012b). Teaching motivations in different countries: Comparisons using the FIT-Choice scale. Asia-Pacific Journal of Teacher Education, 40(3), 185–197. https://doi.org/10.1080/1359866X.2012.700049 Watt, H. M. G., & Richardson, P. W. (2019, 12-16 August). Influences on beginning teachers’ motivations, reported instruction and burnout until mid-career. In H. M. G. Watt (Ed.), Contextual influences on teacher motivations, self-efficacy,

142

Helen M. G. Watt and Paul W. Richardson

and instructional and wellbeing outcomes [Symposium]. European Association for Research on Learning and Instruction (EARLI) biennial conference, Aachen, Germany. Watt, H. M. G., & Richardson, P. W. (2023). Supportive school workplaces for beginning teachers’ motivations and career satisfaction. In T. Urdan & E. Gonida (Eds.), Remembering the life, work, and influence of Stuart Karabenick: A legacy of research on self-regulation, help-seeking, teacher motivation, and more; (Advances in Motivation and Achievement, Vol. 22, pp. 115–138). Emerald. Watt, H. M. G., Richardson, P. W., Klusmann, U., Kunter, M., Beyer, B., Trautwein, U., & Baumert, J. (2012). Motivations for choosing teaching as a career: An international comparison using the FIT-Choice scale. Teaching and Teacher Education, 28(6), 791–805. https://doi.org/10.1016/j.tate.2012.03.003 Watt, H. M., Richardson, P. W., & Morris, Z. A. (2017). Divided by discipline? Contrasting motivations, perceptions, and background characteristics of beginning Australian English and mathematics teachers. In H. M. G. Watt, P. W. Richardson, & K. Smith (Eds.), Global perspectives on teacher motivation (pp. 349–376). Cambridge University Press. Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30(1), 1–35. https://doi.org/10.1016/j.dr.2009.12.001 Woods, P. (1981). Strategies commitment and identity: Making and breaking the teacher role. In L. Burton & S. Walker (Eds.), Schools, teachers and teaching. Falmer Press. Yong, B. C. S. (1995). Teacher trainees’ motives for entering into a teaching career in Brunei Darussalam. Teaching and Teacher Education, 11(3), 275–280. https://doi. org/10.1016/0742-051X(94)00023-Y

10 ON THE CONTEXT- AND SITUATION-SPECIFICITY OF MOTIVATION AND EMOTION Which Contexts and Situations Matter? Fani Lauermann

Abstract This chapter will focus on three overarching themes and refrain from discussing the definition of motivational and affective constructs described in previous chapters (e.g. self-concept of ability, success expectancy, task values, interest, achievement goals, anxiety, and well-being). Illustrative examples mentioned in the following sections reference different theoretical frameworks reviewed in the previous chapters rather than focus on one particular framework. Three main questions guide the discussion including which dimensions can help us to identify and describe relevant contexts and situations in research on motivations and emotions in educational settings? What social phenomena do we aim to understand better by examining the effects of contextual and situated factors on individuals’ motivations and emotions in educational settings, and what role do different contexts and situations play in explaining these phenomena? And who can or should benefit from this research?

I am grateful for the opportunity to learn from and discuss nine excellent chapters that focus on the contextual and situated nature of learning- and teaching-related motivations and emotions through the lens of different theoretical frameworks. The featured work expanded and, in some ways, challenged my ideas about why and how motivation and emotion researchers should account for contextual and situated influences in their theories and empirical research. Due to length constraints, I will focus on three overarching themes and refrain from discussing the definition of motivational and affective constructs described in previous chapters (e.g. self-concept of ability, success expectancy, task values, interest, achievement goals, anxiety, and well-being). Illustrative examples mentioned in the following DOI: 10.4324/9781003303473-11

144

Fani Lauermann

sections reference different theoretical frameworks reviewed in the previous chapters rather than focus on one particular framework. Three main questions guide the discussion. First, which dimensions can help us to identify and describe relevant contexts and situations in research on motivations and emotions in educational settings? I will outline different approaches to defining a given context or situation, discuss the importance of differentiating objective and subjective characteristics of relevant contexts and situations, and outline some of the challenges associated with designing contextualised interventions. Second, what social phenomena do we aim to understand better by examining the effects of contextual and situated factors on individuals’ motivations and emotions in educational settings, and what role do different contexts and situations play in explaining these phenomena? Similar to people’s motivations and emotions, the outcomes of the motivational and affective processes we aim to explain, such as achievement gaps and societal disparities, can vary significantly across contexts and over time. In some contexts, the social phenomena we study (e.g. gender-specific achievement gaps) may not exist or may not be significant. Understanding what factors contribute to this context-specific variability can help us generate new hypotheses and challenge existing theories and research (e.g. why do gender disparities in math and science exist only in some countries but not others). Third, who can or should benefit from this research? Most research in educational psychology has focused on students rather than teachers and is based on so-called WEIRD samples – that is samples from White, Educated, Industrialised, Rich, and Democratic societies. We need to expand the scope of our research and examine the validity and relevance of central theoretical assumptions across diverse contexts and target populations. Which Contexts and Situations Should We Study and How Can We Describe Them?

Motivational and affective processes are typically context-dependent and tend to vary across situations. As the chapters in this edited volume demonstrate, research from various educational settings and using diverse theoretical frameworks shows significant within-person variation in achievement-related motivations and emotions across different contexts and time points. This research highlights “the crucial importance of attending to the role of the situation” in learning and instruction (Pekrun & Marsh, 2022, p. 2). A deeper understanding of which contextual and situational factors drive this variability is needed to explain corresponding variability in achievement-related choices and behaviours (i) across educational settings, (ii) over time, and (iii) for different target populations, as well as (iv) to inform contextualised interventions.

On the Context- and Situation-Specificity of Motivation and Emotion

145

For instance, in a daily diary study on the effectiveness of different emotion regulation strategies, Brockman et al. (2023) observed that no single strategy was universally effective or ineffective for promoting positive or preventing negative affect. Instead, their effectiveness depended on people’s psychological needs in a given situation (i.e. on a given day). Accordingly, the effectiveness of emotion regulation strategies was context- and situation-dependent. Similarly, an experimental study on the differential effects of performancegoal orientations on students’ intrinsic motivation and achievement found that these effects varied across contexts (evaluative vs. non-evaluative) and individuals (high vs. low in achievement orientation; Senko & Harackiewicz, 2002). As theorised by the authors, the goal to outperform others undermined students’ intrinsic motivation relative to a no-goal condition, but only in evaluative contexts (where students expected an external evaluation) and only for individuals with low habitual achievement orientation (individuals who tended to avoid challenges and feared failure). Accordingly, the activation of performance goals was not universally maladaptive. Context- and situation-specific characteristics not only activate different goals and needs in achievement settings (e.g. the goal to collaborate versus outperform others) but can also moderate their effects on relevant academic outcomes (e.g. in evaluative vs. non-evaluative contexts). What Dimensions Describe a Given Situation or Context?

There is some ambiguity and conceptual overlap between the terms “situation” and “context” in the educational literature. Recently, two excellent commentaries by Pekrun and Marsh (2022) and Eccles (2022) defined the term “situation” based on the dimensions of time and context and distinguished it from momentary experiences. Pekrun and Marsh (2022) noted that research on the situation-specific variability of motivations and emotions – such as individuals’ expected success, intrinsic interest, or achievement-related anxiety – typically refers to their variability over time (e.g. longitudinal observations of the same students), across contexts (e.g. cross-sectional comparisons of students in different educational and sociocultural settings), or both (e.g. longitudinal research across different educational contexts). Similarly, Eccles (2022) emphasised that situations differ from momentary experiences in that the term “situated” refers not only to a given moment in time but also to the context in which a person is making achievement-related choices or displaying achievement-related behaviours (e.g. in different classes, school subjects, and social settings). Accordingly, context is integral to the definition of situated motivational and affective processes. How do you know a context when you see one? Decades ago, Maehr and Midgley (1996) described how motivation theories, such as achievement goal theory, can be used to transform a school’s culture, which is a critical

146

Fani Lauermann

contextual factor shaping students’ and teachers’ motivations and emotions. Somewhat provocatively, they asked: “How do you know a culture when you see one?” and explained that We make no claim to study culture in its holistic richness. Our intention is to use culture as a variable, to think of it as a specifiable and ultimately manageable antecedent of motivation and learning. (Maehr & Midgley, 1996, p. 67) To an extent, the chapters of this volume took a similar approach to defining context by focusing on environmental variables that influence students’ and teachers’ motivations and emotions. Important contextual variables include different cultural norms (e.g. what is considered appropriate behaviour for different individuals), family and school settings (e.g. authoritative structures and reward systems), classroom characteristics (e.g. autonomy-supportive or controlling instructional practices), and task characteristics (e.g. difficulty, interestingness). Contextual factors such as cultural norms, authority structures in the home and school environment, reward systems, feedback practices, teaching practices, the objective difficulty of different tasks, and the interestingness of study materials can have powerful effects on people’s motivations and emotions in achievement situations. These contextual variables can affect the salience of different choice options (e.g. what are acceptable options of action for someone like me in my cultural context) and activate different goals and needs (e.g. to collaborate or compete against peers). An inherent challenge of this variable-focused approach to the definition of context is that a broad range of contextual variables can affect people’s motivations and emotions. Therefore, it is often challenging to identify which variables matter most, for whom, and under what conditions, as a seemingly endless list of variables could be relevant. I return to this point in the discussion of contextualised interventions. Several chapters of this volume further point out that contextual influences on individuals’ motivations and emotions can be described in relation to different (i) subjects (whose motivation/emotions?) and (ii) targets (motivation for what/emotional reactions to what or in anticipation of what). First, the same construct – motivational or affective – can have different interpretations or salience for different subjects, such as younger versus older children and students versus teachers. For instance, younger children tend to display far less differentiated self-concepts of ability than older ones (Wan et al., 2021, 2023). Young children often evaluate their abilities positively across various domains, have high self-efficacy, and report high interest in different subjects. Over time, as students mature, receive domain-specific performance feedback, and specialise across domains, their self-perceptions become more nuanced and increasingly reflect their perceived domain-specific strengths and weaknesses (Gaspard et al., 2020; Wan et al., 2021, 2023). These developmental differences contribute to context- and time-specific differences

On the Context- and Situation-Specificity of Motivation and Emotion

147

in motivations and emotions (e.g. self-concept assessments in elementary vs. secondary school; Wan et al., 2021). In addition, these differences can cause misalignments between young children’s self-perceptions and their externally evaluated levels of ability and thus diminish the predictive effects of these self-perceptions on achievement-related outcomes (Davis-Kean et al., 2008, 2009). Indeed, Davis-Kean et al. (2008) found substantially weaker and partly nonsignificant correlations between students’ ability self-concepts and their school grades in elementary relative to secondary school. The definition of achievement goals from the perspective of students versus teachers is another pertinent example discussed in this volume to illustrate how focusing on different subjects and target populations can contribute to greater contextualisation. In her adaptation of achievement goal theory to the context of teaching, Ruth Butler argued that schools are “an achievement arena not only for students but also for teachers who presumably strive to succeed at their job but who may differ in the ways they define success, in the goals they strive to attain, and, thus, in their personal achievement goal orientations for teaching” (Butler, 2007, p. 242). Thus, even though much of the achievement goal literature is limited to students’ goals and their implications for students’ learning and well-being, teachers’ achievement goals also warrant attention due to their potential to influence teachers’ professional decision-making and well-being (Butler, 2007, 2012; Butler & Shibaz, 2014). However, the types of achievement goals students versus teachers pursue and the achievements they strive to attain can be vastly different, as different goals are likely to be salient in the context of learning versus teaching (e.g. to learn math versus to develop meaningful relationships and connections with students; see Butler, 2012). Second, motivations and emotions in educational settings can have different targets, resulting in target-dependent variability or stability across contexts and over time (e.g. interest in math in general vs. interest in a specific topic; generalised math anxiety vs. anxiety before an exam). Evidence suggests that relatively general assessments of motivations and emotions, such as students’ self-concepts of math ability or math interest (e.g. “How much do you like math?”) tend to be remarkably stable over long (e.g. years; Rieger et al., 2017) or short periods (e.g. months; Steinmayr & Spinath, 2008). In contrast, topic- and lesson-specific assessments (e.g. “I liked the topic”) are more sensitive to situational influences and tend to reveal much greater variability over time and across educational settings (e.g. different classes; Benden & Lauermann, 2022, 2023; Tsai et al., 2008). The appropriate level of specificity and necessary time lags for capturing meaningful changes in motivations and emotions depend on the developmental processes of interest (Eccles, 2005). Some changes occur over long periods and concern individuals’ generalised beliefs (e.g. the differentiation of ability beliefs over time; Wan et al., 2021) while others unfold over relatively short periods and concern context-specific beliefs (e.g. periods of adaptation after the transition to a new educational context and corresponding changes in course- or

148

Fani Lauermann

lesson-specific beliefs; Benden & Lauermann, 2022). Careful consideration of (i) the appropriate level of specificity of motivational/affective assessments (e.g. lesson-specific vs. generalised), (ii) the necessary time lags for meaningful changes (e.g. lessons, weeks, months, or years), and (iii) the underlying developmental processes of interest (e.g. identity development over many years vs. short-term adaptations to a new learning environment or task) is needed to avoid overlooking important developmental or context-driven changes in individuals’ motivations and emotions and corresponding implications for their academic success and well-being. Notably, the target of individuals’ motivations and emotions can be dynamic rather than static. Dynamic tasks involve a series of interrelated decisions and the distribution of limited resources such as time and effort (Vancouver & Kendall, 2006; Vancouver et al., 2002, 2008). The more resources an individual invests towards a task (e.g. studying for an exam), the fewer resources are available for other tasks (e.g. time to prepare for other exams). Evidence suggests that the links between motivational constructs such as self-efficacy and relevant performance outcomes can change dramatically over time when individuals engage in dynamic tasks with limited resources. For instance, Vancouver et al. (2008) conducted a laboratory study on the effects of self-efficacy on dynamic decision-making, which asked students to play a novel computer game across multiple trials and to manage their limited time across trials. Self-efficacy positively predicted the participants’ engagement in each trial (whether they allocated any time at all); however, self-efficacy negatively predicted how much time the participants allocated to each trial, likely because those with high self-efficacy believed they would need fewer resources to be successful. Additional studies suggest that participants whose self-efficacy and performance are misaligned (e.g. due to overconfidence) tend to allocate more resources to subsequent trials, and their alignment of self-efficacy and performance improves (Vancouver & Kendall, 2006; Vancouver et al., 2002, 2008). As a result, the direction and strength of associations between self-efficacy and task performance can vary over time (for a discussion of teaching as a dynamic task, see Lauermann & ten Hagen, 2021). These dynamic aspects of teachers’ and students’ decisionmaking are often neglected in empirical research but seem essential for understanding the sources of contextual and situated variability in the links between motivations (or emotions) and performance outcomes. Why Is It Important to Differentiate Objective and Subjective Characteristics of Contexts and Situations?

Sociocognitive theories suggest that contextual factors (or variables) typically do not affect individuals’ motivations and emotions directly but rather indirectly through their subjective perceptions and interpretations of the (social)

On the Context- and Situation-Specificity of Motivation and Emotion

149

environment (e.g. Bandura, 1997; Eccles et al., 1983, 1993). Accordingly, different individuals may interpret the same context differently, and the same environmental stimuli may elicit different reactions. For instance, a fundamental assumption in self-determination theory is that people have a basic need for autonomy and self-determination and that social environments that support this basic need foster students’ intrinsic motivation, self-regulation, and psychological well-being (Deci & Ryan, 2000; Flunger et al., 2019). One of the most frequently used strategies for supporting learners’ need for autonomy is the provision of choice, for instance, regarding the tempo, the social setting, or the content of learning tasks (Patall et al., 2008, 2010). In fact, some assessments of autonomy support seem to equate autonomy with choice by using self-report items that focus solely or primarily on this strategy (Bieg et al., 2011). However, the provision of choice – as an objective characteristic of the social environment – is not necessarily autonomy-supportive. Studies show that too many choices can be overwhelming and often fail to support learners’ self-determination, intrinsic motivation, and performance (Iyengar & Lepper, 1999; Patall et al., 2008; Scheibehenne et al., 2010). Furthermore, choices that are not personally meaningful – for example the option to choose between two learning tasks without understanding what these tasks entail – generally fail to support students’ goal engagement and achievement and may even elicit adverse effects (Flowerday et al., 2004). Moreover, learners may make choices that minimise effort rather than optimise their performance (Flowerday & Schraw, 2003; Flowerday et al., 2004; Schraw et al., 2001). Finally, when teachers consider students’ preferences or provide compelling rationales for assigning learning tasks, the provision of choice (vs. no choice) over these tasks does not seem to make a difference in students’ motivation and self-determination (Katz & Assor, 2007). It is, therefore, critical to separate the objective characteristics of the learning context, such as the provision of choice, from their psychological impacts, such as the perception of autonomy support and self-determination. Perhaps it is for this reason that many studies operationalise the impact of contextual factors on students’ or teachers’ motivations and emotions by focusing only on their presumed psychological impacts. For instance, many studies examine whether students or teachers perceive a given learning environment as autonomy-supportive without assessing which objective characteristics contribute to this perception (e.g. Lauermann & Berger, 2021). Students may feel autonomy-supported because their teacher provides them with meaningful choices about learning activities, because the teacher provides a compelling rationale for the assigned learning activities, or because the teacher assigns engaging learning materials that students perceive as intrinsically valuable. However, objective assessments of

150

Fani Lauermann

contextual characteristics and subjective assessments of their psychological impact are needed to inform the design of contextualised interventions. What Are Contextualised Interventions, and How Can We Design Them?

As noted at the beginning of this chapter, the effects of motivationally and emotionally supportive interventions and coping strategies are rarely universal, which has led to the conceptualisation of contextualised interventions targeting specific subpopulations and contexts. In a research programme of remarkable scope, Kizilcec et al. (2020) implemented an iterative research paradigm of cyclically preregistering new hypotheses in between waves of data collection to test the effects of three established psychological interventions in the context of massive online courses serving diverse populations. These included plan-making interventions that guide students to develop detailed action plans for their course (Yeomans & Reich, 2017) and value-relevance interventions that support students’ sense of belonging and perceived utility of the course (Kizilcec et al., 2017). Previously published research with relatively large samples had identified contexts and subpopulations for whom these interventions were effective, with medium-to-high effect sizes. For instance, across two large online courses, the value-relevance intervention increased course completion rates in developing countries from 17% to 41% and closed the gap in course completion rates between participants from more- versus less-developed countries (Kizilcec et al., 2017). Unfortunately, contrary to the authors’ expectations, most preregistered analyses failed to replicate the expected subgroup-specific effects, with only two exceptions (Kizilcec et al., 2020). Plan-making interventions showed short-term positive effects on course activity (inferred from click-stream data) but no overall effect on course completion. Value-relevance interventions reduced the course completion gap between participants from more- versus less-developed countries but only in courses that closely matched the original studies that informed the preregistered hypotheses. The authors concluded that predicting where and for whom a given intervention is likely to work presents significant challenges. Even though data were collected in the same educational institutions across different waves, relevant characteristics of the student populations varied significantly from cohort to cohort (e.g. achievement gaps between more- vs. less-developed countries). Accordingly, the educational needs of each cohort may have varied as well, potentially diminishing the anticipated intervention effects. More studies like this are needed to identify which context characteristics are likely to affect the effectiveness of educational interventions. In addition, more work should focus on identifying relevant educational needs and choosing appropriate interventions to

On the Context- and Situation-Specificity of Motivation and Emotion

151

address these context- and situation-specific needs. To borrow a metaphor from the medical field: prescribing headache medication to individuals who do not have headaches will tell us little about its effectiveness. Adaptive interventions follow a similar rationale to identify “what works for whom” in educational settings but use within-person intervention designs rather than cyclical interventions with different cohorts (Chow & Hampton, 2022; Wozny et al., 2018). For instance, sequential multiple-assignment randomised trials (SMARTs) administer a series of interventions to the same individuals, depending on how an individual responds to previous interventions (e.g. intensifying the dosage of a reading intervention only for students who fail to demonstrate progress). Such interventions account for the fact that the educational needs of different students vary, so the needed intervention – its dosage and type – is likely to vary as well. However, this type of research is severely underused in education. A recent systematic review found only 13 SMARTs conducted in educational contexts, and only a few of them examined direct educational impacts such as student achievement, engagement, or school attendance (Chow & Hampton, 2022). Adaptive interventions and within-person randomised trials warrant more attention in the education literature as they can advance our understanding of designing optimal learning environments for diverse groups of students and teachers. Which Social Phenomena Are We Trying to Explain?

Analyses of the context- and situation-specificity of individuals’ motivations and emotions in educational settings should also consider the context- and situation-specificity of the outcomes we aim to predict and explain. First, motivations and emotions generally have stronger predictive effects when the predictor and its presumed outcomes have similar levels of specificity or generality (Pajares, 1996). This correspondence principle can refer to the specificity of a given task, domain, or level of analysis (e.g. individual, classlevel, or school-level effects). For instance, teachers’ student-specific (relative to general) self-efficacy is more closely related to student-specific academic outcomes, such as their achievement and motivation (ten Hagen et al., 2022; Zee & Koomen, 2019; Zee et al., 2018). Similarly, teachers’ class-specific (relative to general) self-efficacy is more closely related to class-specific outcomes, such as student-rated teaching quality in a given classroom (Lauermann & ten Hagen, 2021; Thommen et al., 2022). Thus, motivational or emotional predictors are generally more strongly correlated with relevant outcomes when their levels of specificity match. Second, some of the social phenomena we study imply significant shifts in context, such as the transition to secondary school (Eccles et al., 1993; Midgley et al., 1989), higher education (Benden & Lauermann, 2022, 2023), or between learning tasks, lessons, and teachers (Benden &

152

Fani Lauermann

Lauermann, 2022; Tsai et al., 2008). Examining these shifts can provide insight into how different contexts shape the psychology of teachers and students. For instance, Midgley et al. (1989) found that low-efficacy teachers were more prevalent in secondary than elementary schools and that students’ motivation decreased significantly after transitioning to secondary school. Students who transitioned from a high- to a low-efficacy math teacher experienced greater declines in their math-related competence beliefs than students taught by a low-efficacy teacher before and after the transition. Similarly, Ross et al. (2001) found that transitioning from a low- to a high-efficacy teacher in computer science within the same school predicted positive changes in elementary students’ computer self-efficacy and learning of advanced content. Changes in the learning environment were thus linked to changes in students’ motivation and learning gains. Examining such contextual shifts and transitions can aid in isolating the impact of contextual influences on students’ motivations and emotions. Unfortunately, this research strategy remains underutilised, and existing research on contextual shifts from school to school, class to class, and lesson to lesson is often exploratory rather than confirmatory. Finally, some phenomena of interest may occur only in some contexts and situations but not others, which can inform hypotheses about which contextspecific features are essential for their existence. For instance, much research in the motivation and emotion literature has focused on why women are less likely to participate in math- and science-related educational and occupational fields than men (Eccles et al., 1983; Stoet & Geary, 2018). Gender differences in motivational and affective constructs, such as ability-related beliefs, task values, and (math) anxiety, have been identified as contributing factors. However, gender disparities in math and science are not universal. These disparities can be substantial in Western societies but small or nonexistent in other countries, such as Eastern Europe. In fact, in some European countries, there are more women than men scientists (e.g. in Lithuania, Bulgaria, and Latvia; Thornton, 2019). The lack of gender disparities in these countries has been attributed to context-specific factors, such as economic pressures that may lead to greater interest in well-paying jobs in math and science among men and women, past gender quotas for obtaining a degree in math and science, and targeted policies designed to combat gender inequality (Economist, 2019; Stoet & Geary, 2018; Thornton, 2019). In fact, contextual factors may sometimes constrain or even overrule personal preferences and interests. Cross-country comparisons indicate that context characteristics, such as economic prosperity and gender equality on the country level, go along with greater rather than smaller gender disparities in STEM participation (Stoet & Geary, 2018) and gender-typical preferences (e.g. altruism; Falk & Hermle, 2018). These findings are consistent with the hypothesis that the availability of social and economic resources in

On the Context- and Situation-Specificity of Motivation and Emotion

153

economically developed and egalitarian societies allows individuals to make choices that reflect their gender-specific strengths, expectancies, and values, leading to more gender-typical educational and career choices. Moreover, Stoet and Geary (2018) found that subject-specific achievement gaps vary across countries such that boys outperform girls in math and science in some countries, but girls outperform boys in others. However, there were nearly universal gender disparities in students’ intraindividual academic strengths across subjects. On average, boys had a larger relative strength in math and science than reading, whereas girls had a larger relative strength in reading in all 67 countries. That is, in countries where girls outperformed or performed similarly to boys in math, girls were, on average, even better in reading. Notably, the correlations between these gender differences in intraindividual academic strengths (i.e. boys being more likely to have an intraindividual strength in math or science over reading, and vice versa for girls) and corresponding graduation rates in science, technology, engineering, and math fields were larger in more economically advanced and genderequal countries. Economic prosperity and equality thus seem to imply a higher likelihood of making individual choices that reflect intraindividual strengths and gender-typical preferences. Moreover, the degree of universality of students’ academic profiles seems to vary depending on the operationalisation of gender disparities as objective performance differences versus relative academic strengths. In international samples, not objective performance differences but relative strengths have emerged as a key to explaining the underrepresentation of women in math-intensive fields (Breda & Napp, 2019). Analyses of relative universality can thus point to the sources of achievement gaps. Who Benefits from This Research?

The final overarching topic I would like to draw attention to is the target populations for research on situated motivational and affective processes in learning and teaching, which thus far has focused primarily on students rather than teachers (or other socialisers) and samples from WEIRD countries (White, Educated, Industrialised, Rich, and Democratic). Traditionally, educational psychologists have focused on studying the psychology of students rather than teachers, school administrators, parents, or other agents in the education system (Lauermann & Butler, 2021; Richardson et al., 2014). Research that does exist often studies teachers’ and other socialisers’ beliefs and behaviours as “context factors” affecting the learning environment and typically aims to identify those factors likely to benefit students’ academic outcomes (e.g. adaptive instructional approaches). However, in recent years, there has been a growing interest in exploring teachers’ motivations, emotions, and self-regulation and how

154

Fani Lauermann

they impact teachers’ decision-making, instructional practices, and professional well-being, in addition to students’ educational outcomes. Over the past decade, widely used motivation and emotion theories were adapted to capture the perspectives of teachers rather than students (Richardson et al., 2014). This burgeoning field of research has linked teachers’ motivations, emotions, and self-regulation to their work engagement, job satisfaction, and preferred teaching approaches. However, a reliable link between teachers’ psychology and students’ educational outcomes has failed to emerge (see special issue edited by Lauermann & Butler, 2021). Strengthening the interconnectedness between motivation and emotion theories, learning theories, and instructional design frameworks is likely needed to clarify these inconclusive results, and constitutes an important avenue for future research. Furthermore, as previous chapters emphatically show, a significant limitation of current research is its overreliance on WEIRD samples to test central theoretical assumptions about the nature of human motivations and emotions in education. A cursory glance at systematic reviews in educational psychology shows that most of the research we rely on to accumulate knowledge is based on relatively affluent, educated, and primarily White samples. Increasingly, motivation and emotion researchers are calling for broadening and diversifying the scope of our research by considering the educational needs of diverse target populations and communities (Kumar & DeCuir-Gunby, 2023; Usher, 2018), although the main focus of this discourse is thus far on US-centred sociocultural and historical perspectives. Importantly, this expansion should not be limited to testing assumptions about generalisability and relative universality in diverse samples; it is critical to ask who can or should benefit from this research. Are the target populations we study informants or also potential beneficiaries of this research? Are our study designs sufficiently sensitive to sociocultural and historical differences that shape the subjective interpretations of motivational and emotional constructs, the design of learning environments, and the sources of achievement-related choices in diverse contexts? Does the same construct have the same meaning to different individuals? Do our analyses account for intra-group variability in comparative research? These questions are critical for moving away from decontextualised studies of the mind and towards more contextualised analyses of the psychology of learners, educators, and other key agents in educational settings. Outlook

This discussion focused on three overarching themes related to all nine chapters in this section of the volume. First, which dimensions can help us to identify and describe relevant contexts and situations in research on motivations

On the Context- and Situation-Specificity of Motivation and Emotion

155

and emotions in educational settings? There is some ambiguity in the education literature about the defining characteristics of situations, contexts, and momentary experiences. Situations can be defined based on varying time points and contexts (e.g. educational systems, schools, classrooms, subjects). Contexts are typically described based on a subset of variables capturing objective (e.g. the provision of choice) or subjective (e.g. the perception of autonomy support) characteristics of the learning environment. A theorydriven approach to the selection of relevant variables and greater conceptual clarity is needed to advance research on situated motivations and emotions and to study their implications for achievement-related choices and behaviours (i) across educational settings, (ii) over time, and (iii) for different target populations, as well as (iv) to inform contextualised interventions. Researchers also need to be purposeful in selecting the appropriate level of specificity for their motivational or emotional assessments and the necessary time lags to capture meaningful changes in these assessments, depending on which developmental processes are of interest. Second, what social phenomena do we aim to understand better by examining the effects of contextual and situated factors on individuals’ motivations and emotions in educational settings? Analyses of the context- and situation-specificity of individuals’ motivations and emotions in educational settings require careful consideration of the context- and situation-specificity of the outcomes we aim to predict and explain and the contexts in which these outcomes are likely to occur (or not). Analyses of contextual shifts (e.g. transitions across schools, classes, lessons, and tasks) and internationally comparative research can help us identify relevant contextual influences shaping achievement gaps and contributing to societal disparities. Finally, who can or should benefit from this research? Educational psychologists need to broaden the scope of their research on situated motivational and affective processes beyond students and include teachers and other agents in the education system. Moving away from decontextualised analyses of the mind further requires us to consider sociocultural and historical differences that shape subjective interpretations of motivational and emotional constructs, the design of learning environments, and the sources of achievement-related choices in diverse contexts.

References Bandura, A. (Ed.). (1997). Self-efficacy: The exercise of control. Freeman & Co. Benden, D. K., & Lauermann, F. (2022). Students’ motivational trajectories and academic success in math-intensive study programs: Why short-term motivational assessments matter. Journal of Educational Psychology, 114(5), 1062–1085. https://doi.org/10.1037/edu0000708, https://doi.org/10.1037/edu0000708.supp (Supplemental).

156

Fani Lauermann

Benden, D. K., & Lauermann, F. (2023). Relative importance of students’ expectancy– value beliefs as predictors of academic success in gateway math courses. Annals of the New York Academy of Sciences, 1–8. https://doi.org/10.1111/nyas.14961 Bieg, S. R., Backes, S., & Mittag, W. (2011). The role of intrinsic motivation for teaching, teachers’ care and autonomy support in students’ self-determined motivation. Journal for Educational Research Online, 3(1), 122–140. Breda, T., & Napp, C. (2019). Girls’ comparative advantage in reading can largely explain the gender gap in math-related fields. Proceedings of the National Academy of Sciences, 116(31), 15435–15440. https://doi.org/10.1073/pnas.1905779116 Brockman, R., Ciarrochi, J., Parker, P., & Kashdan, T. B. (2023). Behaving versus thinking positively: When the benefits of cognitive reappraisal are contingent on satisfying basic psychological needs. Journal of Contextual Behavioral Science, 27, 120–125. https://doi.org/10.1016/j.jcbs.2023.01.005 Butler, R. (2007). Teachers’ achievement goal orientations and associations with teachers’ help seeking: Examination of a novel approach to teacher motivation. Journal of Educational Psychology, 99(2), 241–252. https://doi.org/10.1037/00220663.99.2.241 Butler, R. (2012). Striving to connect: Extending an achievement goal approach to teacher motivation to include relational goals for teaching. Journal of Educational Psychology, 104(3), 726–742. https://doi.org/10.1037/a0028613 Butler, R., & Shibaz, L. (2014). Striving to connect and striving to learn: Influences of relational and mastery goals for teaching on teacher behaviors and student interest and help seeking. International Journal of Educational Research, 65(0), 41–53. https://doi.org/10.1016/j.ijer.2013.09.006 Chow, J. C., & Hampton, L. H. (2022). A systematic review of sequential multipleassignment randomized trials in educational research. Educational Psychology Review, 34(3), 1343–1369. https://doi.org/10.1007/s10648-022-09660-x Davis-Kean, P. E., Huesmann, L. R., Jager, J., Collins, W. A., Bates, J. E., & Lansford, J. E. (2008). Changes in the relation of self-efficacy beliefs and behaviors across development. Child Development, 79(5), 1257–1269. https://doi.org/10.1111/ j.1467-8624.2008.01187.x Davis-Kean, P. E., Jager, J., & Collins, W. A. (2009). The self in action: An emerging link between self-beliefs and behaviors in middle childhood. Child Development Perspectives, 3(3), 184–188. https://doi.org/10.1111/j.1750-8606.2009.00104.x Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268. https://doi.org/10.1207/S15327965PLI1104_01 Eccles, J. S. (2005). Studying the development of learning and task motivation. Learning and Instruction, 15(2), 161–171. https://doi.org/10.1016/j.learninstruc. 2005.04.012 Eccles, J. S. (2022). Commentary on within-person designs and motivational science. Learning and Instruction, 81, Article 101662. https://doi.org/10.1016/j. learninstruc.2022.101662 Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J. L., & Midgley, C. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Expectancies, values, and academic behaviors (pp. 75–146). W. H. Freeman. Eccles, J. S., Midgley, C., Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). The impact of stage-environment fit on young adolescents’

On the Context- and Situation-Specificity of Motivation and Emotion

157

experiences in schools and in families. American Psychologist, 49, 90–101. https:// doi.org/10.1037/0003-066X.48.2.90 Economist. (2019). Why half the scientists in some Eastern European countries are women. The Economist. https://www.economist.com/europe/2019/07/18/why-halfthe-scientists-in-some-eastern-european-countries-are-women Falk, A., & Hermle, J. (2018). Relationship of gender differences in preferences to economic development and gender equality. Science, 362 (6412). https://doi.org/ 10.1126/science.aas9899 Flowerday, T., & Schraw, G. (2003). Effect of choice on cognitive and affective engagement. Journal of Educational Research, 96(4), 207–215. https://doi.org/10. 1080/00220670309598810 Flowerday, T., Schraw, G., & Stevens, J. (2004). The role of choice and interest in reader engagement. Journal of Experimental Education, 72(2), 93–114. https:// doi.org/10.3200/JEXE.72.2.93-114 Flunger, B., Mayer, A., & Umbach, N. (2019). Beneficial for some or for everyone? Exploring the effects of an autonomy-supportive intervention in the real-life classroom. Journal of Educational Psychology, 111(2), 210–234. https://doi.org/ 10.1037/edu0000284 Gaspard, H., Lauermann, F., Rose, N., Wigfield, A., & Eccles, J. S. (2020). Crossdomain trajectories of students’ ability self-concepts and intrinsic values in math and language arts. Child Development, 91(5), 1800–1818. https://doi.org/10.1111/ cdev.13343 Iyengar, S. S., & Lepper, M. R. (1999). Rethinking the value of choice: A cultural perspective on intrinsic motivation. Journal of Personality and Social Psychology, 76(3), 349–366. https://doi.org/10.1037/0022-3514.76.3.349 Katz, I., & Assor, A. (2007). When choice motivates and when it does not. Educational Psychology Review, 19(4), 429–442. https://doi.org/10.1007/s10648-0069027-y Kizilcec, R. F., Reich, J., Yeomans, M., Dann, C., Brunskill, E., Lopez, G., Turkay, S., Williams, J. J., & Tingley, D. (2020). Scaling up behavioral science interventions in online education. Proceedings of the National Academy of Sciences, 117(26), 14900–14905. https://doi.org/10.1073/pnas.1921417117 Kizilcec, R. F., Saltarelli, A. J., Reich, J., & Cohen, G. L. (2017). Closing global achievement gaps in MOOCs. Science, 355(6322), 251–252. https://doi.org/10.1126/ science.aag2063 Kumar, R., & DeCuir-Gunby, J. (2023). What is the role of race in educational psychology? A review of research in Educational Psychologist. Educational Psychologist, 58(1), 1–17. https://doi.org/10.1080/00461520.2022.2137804 Lauermann, F., & Berger, J.-L. (2021). Linking teacher self-efficacy and responsibility with teachers’ self-reported and student-reported motivating styles and student engagement. Learning and Instruction, 76, Article 101441. https://doi. org/10.1016/j.learninstruc.2020.101441 Lauermann, F., & Butler, R. (2021). The elusive links between teachers’ teachingrelated emotions, motivations, and self-regulation and students’ educational outcomes. Educational Psychologist, 56(4), 243–249. https://doi.org/10.1080/00461 520.2021.1991800 Lauermann, F., & ten Hagen, I. (2021). Do teachers’ perceived teaching competence and self-efficacy affect students’ academic outcomes? A closer look at

158

Fani Lauermann

student-reported classroom processes and outcomes. Educational Psychologist, 56(4), 265–282. https://doi.org/10.1080/00461520.2021.1991355 Maehr, M. L., & Midgley, C. (Eds.). (1996). Transforming school cultures. Westview Press. Midgley, C., Feldlaufer, H., & Eccles, J. S. (1989). Change in teacher efficacy and student self- and task-related beliefs in mathematics during the transition to junior high school. Journal of Educational Psychology, 81(2), 247–258. https://doi. org/10.1037/0022-0663.81.2.247 Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578. https://doi.org/10.3102/00346543066004543 Patall, E. A., Cooper, H., & Robinson, J. C. (2008). The effects of choice on intrinsic motivation and related outcomes: A meta-analysis of research findings. Psychological Bulletin, 134(2), 270–300. https://doi.org/10.1037/0033-2909. 134.2.270 Patall, E. A., Cooper, H., & Wynn, S. R. (2010). The effectiveness and relative importance of choice in the classroom. Journal of Educational Psychology, 102(4), 896–915. https://doi.org/10.1037/a0019545 Pekrun, R., & Marsh, H. W. (2022). Research on situated motivation and emotion: Progress and open problems. Learning and Instruction, 81, Article 101664. https:// doi.org/10.1016/j.learninstruc.2022.101664 Richardson, P. W., Karabenick, S. A., & Watt, H. M. G. (2014). Teacher motivation: Theory and practice. Routledge, Taylor & Francis. Rieger, S., Göllner, R., Spengler, M., Trautwein, U., Nagengast, B., & Roberts, B. W. (2017). Social cognitive constructs are just as stable as the Big Five between grades 5 and 8. AERA Open, 3(3), 1–9. https://doi.org/10.1177/2332858417717691 Ross, J. A., Hogaboam-Gray, A., & Hannay, L. (2001). Effects of teacher efficacy on computer skills and computer cognitions of Canadian students in grades K-3. Elementary School Journal, 102(2), 141–156. www.jstor.org/stable/1002205 Scheibehenne, B., Greifeneder, R., & Todd, P. M. (2010). Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research, 37, 409–425. https://doi.org/10.1086/651235 Schraw, G., Flowerday, T., & Lehman, S. (2001). Increasing situational interest in the classroom. Educational Psychology Review, 13(3), 211–224. https://doi.org/ 10.1023/A:1016619705184 Senko, C., & Harackiewicz, J. M. (2002). Performance goals: The moderating roles of context and achievement orientation. Journal of Experimental Social Psychology, 38(6), 603–610. https://doi.org/10.1016/S0022-1031(02)00503-6 Steinmayr, R., & Spinath, B. (2008). Sex differences in school achievement: What are the roles of personality and achievement motivation? European Journal of Personality, 22(3), 185–209. https://doi.org/10.1002/per.676 Stoet, G., & Geary, D. C. (2018). The gender-equality paradox in science, technology, engineering, and mathematics education. Psychological Science, 29(4), 581–593. https://doi.org/10.1177/0956797617741719 ten Hagen, I., Lauermann, F., Wigfield, A., & Eccles, J. S. (2022). Can i teach this student?: A multilevel analysis of the links between teachers’ perceived effectiveness, interest-supportive teaching, and student interest in math and reading. Contemporary Educational Psychology, 69, Article 102059. https://doi.org/10.1016/j. cedpsych.2022.102059

On the Context- and Situation-Specificity of Motivation and Emotion

159

Thommen, D., Grob, U., Lauermann, F., Klassen, R., & Praetorius, A.-K. (2022). Different levels of context-specificity of teacher self-efficacy and their relations with teaching quality. Frontiers in Psychology, 13., Article 857526. https://doi. org/10.3389/fpsyg.2022.857526 Thornton, A. (2019). Gender equality in STEM is possible. These countries prove it. World Economic Forum. https://www.weforum.org/agenda/2019/03/genderequality-in-stem-is-possible Tsai, Y.-M., Kunter, M., Lüdtke, O., Trautwein, U., & Ryan, R. M. (2008). What makes lessons interesting? The role of situational and individual factors in three school subjects. Journal of Educational Psychology, 100(2), 460–472. https://doi. org/10.1037/0022-0663.100.2.460 Usher, E. L. (2018). Acknowledging the Whiteness of motivation research: Seeking cultural relevance. Educational Psychologist, 53(2), 131–144. https://doi.org/10. 1080/00461520.2018.1442220 Vancouver, J. B., & Kendall, L. N. (2006). When self-efficacy negatively relates to motivation and performance in a learning context. Journal of Applied Psychology, 91(5), 1146–1153. https://doi.org/10.1037/0021-9010.91.5.1146 Vancouver, J. B., More, K. M., & Yoder, R. J. (2008). Self-efficacy and resource allocation: Support for a nonmonotonic, discontinuous model. Journal of Applied Psychology, 93(1), 35–47. https://doi.org/10.1037/0021-9010.93.1.35 Vancouver, J. B., Thompson, C. M., Tischner, E. C., & Putka, D. J. (2002). Two studies examining the negative effect of self-efficacy on performance. Journal of Applied Psychology, 87(3), 506–516. https://doi.org/10.1037/0021-9010.87.3.506 Wan, S., Lauermann, F., Bailey, D. H., & Eccles, J. S. (2021). When do students begin to think that one has to be either a ‘math person’ or a ‘language person’? A metaanalytic review. Psychological Bulletin, 147(9), 867–889. https://doi.org/10.1037/ bul0000340 Wan, S., Lauermann, F., Bailey, D. H., & Eccles, J. S. (2023). Developmental changes in students’ use of dimensional comparisons to form ability self-concepts in math and verbal domains. Child Development, 94(1), 272–287. https://doi.org/10.1111/ cdev.13856 Wozny, N., Balser, C., & Ives, D. (2018). Low-cost randomized controlled trials in education. AEA Papers and Proceedings, 108, 307–311. https://doi.org/10.1257/ pandp.20181054 Yeomans, M., & Reich, J. (2017). Planning prompts increase and forecast course completion in massive open online courses. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, Canada. Zee, M., & Koomen, H. M. Y. (2019). Engaging children in the upper elementary grades: Unique contributions of teacher self-efficacy, autonomy support, and student-teacher relationships. Journal of Research in Childhood Education, 34(4), 477–495. https://doi.org/10.1080/02568543.2019.1701589 Zee, M., Koomen, H. M. Y., & Jong, P. F. d. (2018). How different levels of conceptualization and measurement affect the relationship between teacher self-efficacy and students’ academic achievement. Contemporary Educational Psychology, 55, 189–200. https://doi.org/10.1016/j.cedpsych.2018.09.006

SECTION II

Methodological Reflections and Perspectives

11 MIXED METHODS IN RESEARCH ON MOTIVATION AND EMOTION Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, Melanie Stephan and Michaela Gläser-Zikuda

Abstract The investigation of emotions and motivation as individual processes interacting with the (socio-historical) context is a complex and highly dynamic endeavour that requires an adequate methodology. Mixed-methods research has represented a promising and increasingly popular approach in this context since the late 1990s. In general, mixed-methods studies aim to explore (educational) phenomena by meaningfully combining qualitative and quantitative methods to paint a more comprehensive and complete picture of the phenomenon under study. The complementary function of quantitative and qualitative approaches is frequently addressed as a key advantage of mixedmethods projects, relying on different types of parallel or sequential research designs. In this contribution, we first examine why mixed methods may be fruitful in research on emotion and motivation in education. We then introduce and discuss our recent empirical studies applying a mixed-methods design to explore emotion and motivation in different educational contexts. We conclude with a general reflection on future agendas for mixed-methods research on motivation and emotion.

Introduction

Education as a field of inquiry is characterised by high complexity. On the one hand, contextual factors on the micro, meso and macro levels (Fend, 1980) impact learning processes and educational outcomes. These contextual factors can be (rather) stable (e.g. the prevailing school system at the macro level), but they also vary across contexts and situations, leading to variations of emotions and motivation dependent on contextual preconditions. On the other hand, the learner or teacher brings various individual DOI: 10.4324/9781003303473-13

164

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

characteristics, such as prior knowledge and motivational and emotional traits (e.g. students’ individual interest; Renninger & Hidi, 2011), which influence how the situation is perceived and used. This complexity is mirrored in core theories on motivation and emotion as one specific research area in education. Consequently, methodological approaches are needed (GläserZikuda & Järvelä, 2008) that can account for this complexity by tracing general relations between variables and detecting general differences between individuals as well as changes within individuals over time (see Lazarides & Gniewosz in this volume). At the same time, the (subjective) meaning attributed to situations and contexts by people should be considered. One way to achieve this goal is to use mixed methods (MM), a methodological approach that attempts to develop a better understanding of the research subject through the application of qualitative (Qual) and quantitative (Quan) research approaches (Creswell & Plano Clark, 2018; Johnson & Onwuegbuzie, 2004; Johnson et al., 2007). In this chapter, we briefly describe the core premises of MM and discuss how motivation and emotion research can benefit from the use of MM. We then present concrete examples of MM studies of motivation and emotion and conclude by addressing avenues for future research. A Brief Introduction to Mixed Methods

The term “mixed methods” refers to the combination and application of quantitative and qualitative approaches for gathering and/or analysing data. According to Johnson et al. (2007) mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration. (p. 123) Researchers not only select a qualitative, quantitative or MM study to conduct but also decide on a type of strategy of inquiry. Strategies of inquiry are types of qualitative, quantitative and MM designs or models that provide a specific direction for procedures in a research design (Creswell, 2009). Qualitative and quantitative approaches may be defined as paradigms (Lincoln & Guba, 2000). Creswell (2009) calls them worldviews and distinguishes four, which apply qualitative and quantitative methods differently: postpositivism, constructivism, advocacy/participatory and pragmatism. Quantitative approaches and methods were the dominant forms of research in the social sciences from the late 19th century up until the mid-20th

Mixed Methods in Research on Motivation and Emotion

165

century. They mainly follow the postpositivist tradition (Smith, 1983). Thus, the quantitative approach starts with a theory and then collects data, which either supports or refutes it. Interest in qualitative research methods grew during the second half of the 20th century (Creswell, 2009) primarily with a constructivist orientation. Strauss and Corbin (1990) notably defined the procedures of grounded theory aiming at inductively developing a pattern of meaning or a theory. Wolcott (1999) summarised ethnographic procedures, and Merriam (1998) suggested case-study research methods. In contrast, MM are less well known than the methods associated with the quantitative and qualitative paradigms. MM studies are often connected to worldviews of advocacy/participatory and pragmatism. Recognising that all methods have limitations, Campbell and Fiske (1959) argued that seeking convergence between qualitative and quantitative methods could neutralise or cancel the biases of each method (on triangulation, see Flick, 2018). In the 1990s, the idea of MM moved from seeking convergence to actually integrating or connecting quantitative and qualitative data (Tashakkori & Teddlie, 1998). As qualitative and quantitative data can be merged (e.g. qualitative case analyses supporting statistical results; Creswell & Plano Clark, 2007), numerous MM studies were carried out, and a growing number of publications focused on MM strategies of inquiry, such as multimethod, convergence, integrated and combined models (Creswell & Plano Clark, 2007) using different types of research procedures. In a highly recognised publication, Plano Clark and Ivankova (2016) described different rationales for conducting MM studies. In the next section, we explain these rationales in more detail based on examples from research on emotion and motivation. The Benefits of Mixed-Methods Approaches for Motivation and Emotion Research

Research on motivation and emotion can benefit from MM approaches in myriad ways. These benefits are closely linked to the rationale behind why someone assumes that an MM design may be useful and appropriate for answering a specific research question. Generally, MM studies are based on different rationales (for an overview, see Greene et al., 1989; Plano Clark & Ivankova, 2016). When researchers apply a concurrent parallel MM design (Qual + Quan; Creswell & Plano Clark, 2018), they frequently aim to develop a more complete understanding of the phenomenon by using the complementary strengths (and, thus, reciprocally reducing the weaknesses) of quantitative and qualitative approaches. Occasionally, the validation of findings is also mentioned as a reason for conducting concurrent parallel MM studies. For example, Jiang et al. (2016) explored students’ perception of their teachers’ emotions (Quan) and interviewed teachers about their emotions and emotion regulation (Qual).

166

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

They found that teachers’ suppressed emotions were still observed by their students, concluding that the suppression of emotions should be avoided. In this case, an MM approach was useful to explore different perceptions of the same phenomenon (namely, teachers’ emotions). Regarding sequential MM designs, two main types can be distinguished. The exploratory sequential MM design starts with a qualitative approach followed by a quantitative approach (Qual → Quan). Typically, measurement development and generalisation are rationales for exploratory sequential designs. Conversely, in the explanatory sequential MM design (Quan → Qual), the quantitative approach is followed by the qualitative approach. Rationales for this design are, for example, to construct an in-depth understanding of the findings or follow up on unexpected results. Exploratory sequential MM designs are very popular in motivation and emotion research as they ultimately aim at generalising findings based on larger and representative samples. This is particularly attractive for researchers who mainly follow a postpositivist research paradigm. In the last couple of years, exploratory sequential MM designs have been particularly prominent in research on teachers’ emotions as this area is among the youngest in emotion and motivation research. Several authors have utilised this specific MM design. For example, based on a multicomponent approach to teacher emotions, Burić et al. (2018) conducted semi-structured interviews with teachers to gain an in-depth understanding of the emotions they experience while teaching and their antecedents (Qual). Based on the interview findings and the teachers’ statements, the researchers developed the Teacher Emotion Questionnaire (TEQ), which was then administered to a larger sample of teachers (Quan). The authors explain that “statements delivered by teachers were used to construct an item pool for TEQ. We believe that such statements could appear more authentic and have greater face validity than those created by researchers who rely primarily on theoretical considerations” (p. 328). Similarly, Stupnisky et al. (2016) conducted focus-group interviews with new faculty about their emotions related to teaching, followed by a quantitative study “to test the quality of measurements and generalizability of qualitative findings” (p. 1175). These examples suggest that an exploratory sequential MM research design may be appropriate in research on emotion and motivation if core theories are to be transferred to specific educational contexts. For example, control-value theory has been proposed to explain students’ emotions related to achievement (Pekrun, 2006), but whether this theory also applies to higher-education academics’ emotions related to teaching and research remains to be explored (see Stupnisky et al., 2016). This question is particularly important when theories that were initially developed to explain learners’ motivation and emotion are transferred to the field of teacher motivation and emotion (e.g. achievement goal theory applied to

Mixed Methods in Research on Motivation and Emotion

167

teachers in higher education; Daumiller & Dresel, 2020). In addition, the applicability of theories to different cultures needs to be tested carefully (e.g. Chen, 2019). Exploratory sequential MM designs may support this process by allowing the researcher to first explore the cultural context. If similarities are observed in the subjective descriptions of motivations and emotions, quantitative approaches should follow, aiming, for example, at translating instruments into other languages (see Urdan in this volume). Explanatory sequential MM designs are used less frequently than exploratory ones. However, they have several benefits as they help to better understand quantitative findings, in particular if these findings are unexpected. For example, Carmignola et al. (2021) found, in their quantitative questionnairebased study on student teachers’ needs satisfaction and vitality during the COVID-19 pandemic (self-determination theory; Ryan & Deci, 2017), that student teachers’ vitality did not decrease but rather increased slightly during the pandemic. This surprising result was further explored via qualitative interviews. The results showed that the student teachers succeeded in maintaining or even slightly increasing their vitality by focusing on leisure time (e.g. spending time in nature), although the fulfilment of their need for competence in their studies deteriorated. Thus, being able to follow up on unexpected results is a major advantage of explanatory sequential MM designs. Furthermore, this design is also used for participant selection in the qualitative part of the research. For example, Sharp et al. (2018) investigated higher-education students’ academic boredom quantitatively, compiling boredom-proneness scores. To collect rich reflections on students’ boredom experiences, follow-up semi-structured interviews were conducted with students who had high and low boredom proneness scores and compared. This example shows how the purposive selection of participants for the qualitative part can be informed by the quantitative results. The possibility to follow up on unexpected results or better understand the processes that are responsible for changes are the main benefits of MM intervention studies. In the field of emotion and motivation, many scholars aim to develop interventions that foster motivation and emotions in learners and/or teachers. The quantitative methodology allows researchers to test the effectiveness of the intervention; however, these results do not provide insights into how students and/or teachers experience and interpret the (elements of the) intervention. Qualitative approaches can thus complement quantitative approaches by providing in-depth insights into the experiences of the targets of the intervention. These insights are especially important if interventions turn out to be ineffective because qualitative approaches can help to find out why they failed. In addition, qualitative approaches can support the development of interventions, for instance, in exploring which intervention elements are perceived as attractive and motivating by students. To date, most intervention studies in the field of motivation and emotion

168

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

have applied a (quasi-)experimental design with quantitative measures prevailing (e.g. Hascher & Schmitz, 2010). MM intervention studies in the field of emotion and motivation, however, are still rare. One example in emotion research is the “Emotional and Cognitive Learning” or ECOLE study (GläserZikuda et al., 2005), which aimed to foster students’ positive emotions and achievement in the domains of physics, biology and German. In this study, a quasi-experimental MM design was applied, combing questionnaires and achievement tests with classroom observation, semi-structured interviews and student and teacher diaries. As a result, the effectiveness of the intervention could be tested (Quan), the implementation process could be monitored and the students’ and teachers’ experiences during the intervention could be explored in depth (Qual) simultaneously. The benefits of this approach are obvious: the combination of quantitative and qualitative approaches makes it possible to understand the outcomes/effects of the intervention (Quan) as well as the processes (Qual) and to develop a rich understanding of the subjective experiences of the actors involved in the intervention. Consequently, to not only ascertain the effectiveness of an intervention in fostering motivation and/or emotion but also trace the processes (e.g. Hascher et al., 2019) and the subjective (situated and contextualised) experiences that it evokes, qualitative elements integrated into a quasi-experimental design are crucial. Empirical Examples of Mixed-Methods Studies in the Field of Motivation and Emotion

In the following sections, we provide insights into three current MM studies to illustrate in depth how an MM approach can help us to better understand specific questions in research on motivation and emotion. We will also address the role of MM studies for the examination of the context-specificity and situatedness of emotions and motivation. Online and Face-to-Face Teaching from Student Teachers’ Perspective

Online teaching has been very popular at least since the COVID-19 pandemic. At a German university, an online course is used to prepare student teachers for the state examination in school pedagogy. Based on the theoretical foundations of control-value theory (Pekrun, 2006), self-determination theory (Ryan & Deci, 2017) and the technology-acceptance model of Venkatesh and Bala (2008), the study aimed to explore whether a face-to-face course and an e-learning course with the same concept and content had different effects on students’ emotion and motivation (Stephan, 2021). As the researchers were interested in the general change in emotions, motivation and performance of the student teachers over time (Quan) and their contextualised subjective experiences in the online course (Qual), a convergent

Mixed Methods in Research on Motivation and Emotion

FIGURE 11.1

169

MM design in a study comparing student teachers’ emotions and motivation during an online and a face-to-face seminar at a university

parallel design (see Figure 11.1) was applied. Three sub-studies – a quantitative questionnaire study (N = 186 pre, 89 post), a specifically developed performance test (N = 172 pre, 92 post) and qualitative guided interviews (N = 11) – were conducted. The data collection was carried out independently for each. The separate data analyses of the three parts of the research were merged interpretatively at the end. The research topic, which is complex and determined by latent and, at the same time, disturbance-prone variables, suggested a comparatively designed MM study. Other reasons justify the methodological approach (Kuckartz, 2014, p. 58). First, triangulation enables the validation of the results. Consistently, regardless of the methodological approach, it was found that for student teachers, a major goal of attending the preparatory course for the state examination in school pedagogy is to learn as much as possible and acquire a deep understanding of the content. Second, in terms of complementarity, qualitative data from the guided interviews provided explanations for the quantitative findings. Of interest, for example, was the elucidation of significant differences in performance in favour of the face-to face course. Third, contradictions provide clues about further research needs (initiation). The emotion of hope proved far more important for the student teachers surveyed in the face-to-face course than for those in the online course. This difference did not appear in the quantitative data. The quantitative results also revealed a stronger experience of boredom in the online course, a difference that was not reflected in the qualitative data. Finally, MM approaches have the potential to contribute to the continued development of research methods. In this

170

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

respect, the qualitative data provide indications for a possible further development of the Academic Emotions Questionnaire (AEQ) used in the study (Pekrun et al., 2005). The AEQ is already a very complex survey instrument, with eight queried academic emotions. However, not all emotions relevant to the learning context are captured here. Familiarity, insecurity, admiration and nervousness, among others, were reported in the interviews in connection with the face-to-face and/or online course for preparing for the state examination. Thus, the AEQ could be further developed based on the results of the interviews. In conclusion, the interrelation of qualitative and quantitative data enabled a comprehensive gain in knowledge about the motivation and emotions experienced by student teachers during an online and a face-to-face course in teacher education. While the quantitative study allowed for testing general changes in emotions over time – but in a specific context – the qualitative data allowed for insight into students’ situational experiences. This also allowed for a more in-depth mapping of the dynamics of student teachers’ emotional experience. Teacher Emotions and Emotion Regulation during Team-taught Lessons from the Teachers’ Perspective

In Austrian low-track, lower secondary schools, teachers of the main subjects engage in team teaching, that is collaborative planning, instruction and evaluation of the lesson. Teachers experience an array of emotions while teaching. Relying on Frenzel’s (2014) model of teacher emotions and Gross’ (2015) model of emotion regulation, this project aims to explore which discrete emotions team teachers experience due to their team partner’s behaviour in the classroom, why they experience them (antecedents) and to what extent these emotions influence the teacher’s well-being and instructional practices (effects). Furthermore, this study aims to examine how team teachers handle their emotions in the classroom (emotion regulation). An exploratory sequential MM design was chosen (see Figure 11.2). First, a qualitative cross-sectional interview study (N = 30 teachers) was conducted to get a preliminary understanding of teachers’ emotions and emotion regulation during team teaching as this field is unexplored so far. This first study has been completed. Currently, a longitudinal daily diary study among team teachers is carried out to produce generalisable findings about the relationships between experienced emotions, emotion regulation and teaching behaviour (N = 46 teachers). At the end of the project, the results of the interviews and the diaries will be compared, merged and interpreted. Utilising an MM design in this study has several advantages. First, the combination of qualitative and quantitative measures enables an enriched and elaborate investigation of teacher emotions and compensates for the

Mixed Methods in Research on Motivation and Emotion

FIGURE 11.2

171

MM design in a study of teachers’ emotions and emotion regulation during team teaching

potential drawbacks of single types of research paradigms (complementarity). For example, the qualitative interviews generated in-depth contextualised, individualised and situated insights into team teachers’ emotional lives (see Muehlbacher et al., 2022), which a standardised, quantitative scale would not have captured. However, the team teachers’ recollections of past emotional events during the interviews might have been characterised by an intensity or recall bias (Frenzel, 2014). To counter this methodological issue, we opted for a follow-up quantitative diary study, which measures team teachers’ emotions in real time on the day of the team teaching, thereby recording state emotions. Moreover, the diary study allows for the generalisation of the results. Second, the interview data were used to develop the quantitative diary study (development). For example, team teachers reported experiencing certain emotions because of their team partner (e.g. gratefulness, shame), which are usually neglected in studies that focus on studenttriggered teacher emotions. For our diary study, we expanded the 20-item Positive and Negative Affect Schedule (PANAS; Breyer & Bluemke, 2016) with emotions that emerged from the interview data. Based on the rich accounts in the interviews, the ecological validity of the item development for the diary study is confirmed. Overall, the application of an MM approach allowed us, on the one hand, to explore teachers’ emotions and emotion regulation in their context in a hitherto unexplored area and to gain an initial understanding of them. On the other hand, the diary study enables us to understand relationships between variables and generalise findings beyond their context. In addition, the diary study will also make it possible to survey the emotions in the situation and thus also to better understand the fluctuations of emotions between situations and their (situational) conditions of influence.

172

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

The Role of Mentor Teachers’ Motivations

Mentor teachers – teachers who supervise student teachers in the practical school phases – play an important role in the process of professionalisation of student teachers and are central to stimulating their learning processes (Clarke et al., 2014). However, little is known about what motivates teachers to become mentor teachers and what their goals are in mentoring student teachers. Therefore, based on expectancy-value theory (EVT; Eccles, 2005) and goal-orientation theory (GOT, Butler, 2012), the objective was to develop a theoretical framework that allows a systematic investigation of mentor teachers’ motivations. A sequential exploratory MM approach was chosen. This MM design seems appropriate because a transfer of EVT and GOT to the mentoring context is being sought. To reach this goal, first, the “new” context for applying EVT and GOT – namely, mentoring – needs to be explored (Qual), followed by an effort to achieve generalisability of the findings (Quan). In a first step, semi-structured interviews were conducted with 23 Austrian mentor teachers (Kuhn et al., 2022). Based on the interview data, a questionnaire was developed for the quantitative cross-sectional study and distributed among a larger sample of mentor teachers. In this study,189 mentor teachers across Austria participated. Finally, the results from both the qualitative and quantitative studies are discussed in an integrated manner (Figure 11.3). The MM approach used seems advantageous because combining qualitative and quantitative methods can provide a more in-depth, differentiated and elaborate perspective on the motivation of mentor teachers

FIGURE 11.3

MM design in a study of mentor teachers’ motivations

Mixed Methods in Research on Motivation and Emotion

173

(complementarity). Instead of first looking at existing instruments and adapting them to the mentoring context for a quantitative study, starting with interviews provided us with a differentiated insight into aspects relevant to mentors. For instance, we found that in the mentoring context, personal utility value in EVT needs to be further divided into an extrinsic and an intrinsic component. This finding was considered in the construction of the questionnaire for the quantitative study (development). More generally, the knowledge gained from the interviews enabled us to develop a questionnaire that is based on the views of the mentors and, thus, is hoped to better fit the mentoring context. We adapted existing scales from other contexts (e.g. Daumiller et al., 2019; Watt et al., 2012) to the mentoring context, and added self-developed items and scales derived directly from the interview data. In sum, the main advantage of the MM approach in this case is that it allows for a precise context-specific transfer of core motivation theories to the topic of student-teacher mentoring in the school practicum by building on the authentic voices and experiences of the mentor teachers as reflected in the rich interview data. Conclusion and Future Directions

In the present contribution, we have discussed why MM approaches can be beneficial for studies of motivation and emotion. Some may argue that MM studies have always been important in this field and that nothing is novel in the MM approach. We do not agree with this argument as the MM approach has triggered an elaborate reflection on how to combine quantitative and qualitative approaches during all steps of the research process and provided arguments why this combination may be fruitful. As emotions and motivation are complex and multi-layered phenomena that are situated in a cultural-historical context (see also Schutz et al., 2016), both approaches that can capture this situational specificity and approaches that enable the generalisation of statements across situations and contexts are necessary. Nowadays, sophisticated quantitative measures can also account for situational specificities, for instance, by applying experience-sampling modelling. However, what is missing in such an approach is an in-depth understanding of the meaning that a person attributes to a situation. This is where qualitative methods within an MM approach can add value if motivation and emotion are to be understood in context holistically. Avenues for future studies are manifold. First, we believe that emotion and motivation research in education has not yet fully exploited the potential of MM approaches. A frequent combination in MM research in previous studies is the use of (quantitative) questionnaire studies with (qualitative) interview studies. This is also mirrored in the examples that

174

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

we have given in this chapter. Future research should in particular embrace the breadth of qualitative approaches more significantly, for instance, by making greater use of ethnographic studies and interpretative or reconstructive methods of data analysis. Second, in training our young academics, we must ensure that they develop an openness to and an understanding of both approaches and that they also acquire basic competencies in both areas. The bottom line, however, is that strengthening (interdisciplinary) collaborations in which scientists bring different methodological competencies to the table is essential. Both quantitative and qualitative research methods are evolving rapidly, and it will be difficult for individuals to keep up with the latest developments in both approaches. If we succeed in establishing this (complementary) cooperation, in which the possibilities and limits of MM for motivation and emotion research are consciously reflected upon and discussed, we can assume that numerous sophisticated MM projects will be implemented in the future. Finally, a challenge connected to MM studies is the publication of their results. Some researchers decide to publish the results of their quantitative and qualitative parts of their MM project separately. Therefore, MM projects are often overlooked because the papers are not explicitly linked, and metainferences (i.e. inferences drawn from all parts of the study) are (largely) missing. Those researchers who publish the MM study in one paper often have difficulties with the word count allowed by the respective journal. Some reviewers are also not familiar with the standards associated with MM research. However, more and more journals welcome MM articles. This positive development in terms of the acceptance of MM studies is also reflected in the fact that the American Psychological Association (APA) publication manual 7th edition has incorporated mixed-methods publication standards explicitly (APA, 2020; Levitt et al., 2018). We expect that this will also have a positive impact on the publication of MM projects in the field of motivation and emotion. References American Psychological Association (APA). (2020). Publication manual of the American Psychological Association: The official guide to APA style (7th ed.). American Psychological Association. Breyer, B., & Bluemke, M. (2016). Deutsche Version der Positive and Negative Affect Schedule PANAS (GESIS Panel) [German Version of the Positive and Negative Affect Schedule PANAS (GESIS Panel)]. https://doi.org/10.6102/zis242 Burić, I., Slišković, A., & Macuka, I. (2018). A mixed-method approach to the assessment of teachers’ emotions: Development and validation of the Teacher Emotion Questionnaire. Educational Psychology, 38(3), 325–349, https://doi.org/10.1080/ 01443410.2017.1382682

Mixed Methods in Research on Motivation and Emotion

175

Butler, R. (2012). Striving to connect: Extending an achievement goal approach to teacher motivation to include relational goals for teaching. Journal of Educational Psychology, 104(3), 726–742. https://doi.org/10.1037/a0028613 Campbell, D. T., & Fiske, D. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56, 81–105. https://doi. org/10.1037/h0046016 Carmignola, M., Martinek, D., & Hagenauer, G. (2021). ‘At first I was overwhelmed, but then—I have to say—I did almost enjoy it’. Psychological need satisfactions and vitality of student teachers during Covid19 lockdown. Social Psychology of Education, 24, 1607–1641. https://doi.org/10.1007/s11218-021-09667-2 Chen, J. (2019). Research review on teacher emotion in Asia between 1988 and 2017: Research topics, research types, and research methods. Frontiers in Psychology, 10, Article 1628. https://doi.org/10.3389/fpsyg.2019.01628 Clarke, A., Triggs, V., & Nielsen, W. (2014). Cooperating teacher participation in teacher education: A review of the literature. Review of Educational Research, 84(2), 163–202. https://doi.org/10.3102/0034654313499618 Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). Sage. Creswell, J. W., & Plano Clark, V. L. (2007). Designing and conducting mixed methods research. Sage. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage. Daumiller, M., Dickhäuser, O., & Dresel, M. (2019). University instructors’ achievement goals for teaching. Journal of Educational Psychology, 111(1), 131–148. https://doi.org/10.1037/edu0000271 Daumiller, M., & Dresel, M. (2020). Teaching and research: Specificity and congruence of university faculty achievement goals. International Journal of Educational Research, 99, Article 101460. https://doi.org/10.1016/j.ijer.2019.08.002 Eccles, J. S. (2005). Subjective task value and the Eccles et al. model of achievementrelated choices. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 105–121). The Guilford Press. Fend, H. (1980). Theorie der Schule [School theory]. Urban & Schwarzenberg. Flick, U. (2018). Doing triangulation and mixed methods. Sage. Frenzel, A. C. (2014). Teacher emotions. In E. A. Linnenbrink-Garcia & R. Pekrun (Eds.), International handbook of emotions in education (pp. 494–519). Routledge. Gläser-Zikuda, M., Fuß, S., Laukenmann, M., Metz, K., & Randler, C. (2005). Promoting students’ emotions and achievement – Instructional design and evaluation of the ECOLE-approach. Learning and Instruction, 15(5), 481–495. https:// doi.org/10.1016/j.learninstruc.2005.07.013 Gläser-Zikuda, M., & Järvelä, S. (2008). Application of qualitative and quantitative methods to enrich understanding of emotional and motivational aspects of learning. International Journal of Educational Research, 47(2), 79–83. https://doi. org/10.1016/j.ijer.2007.11.009 Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274. https://doi.org/10.2307/1163620

176

Gerda Hagenauer, Franziska Muehlbacher, Clara Kuhn, et al.

Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26, 1–26. https://doi.org/10.1080/1047840X.2014.940781 Hascher, T., Fries, S., & Hagenauer, G. (2019). Grundlagen der Förderung von Motivation [Basics of promoting motivation]. In H. Gaspard, U. Trautwein, & M. Hasselhorn (Eds.), Diagnostik und Förderung von Motivation und Volition im Schulkontext (pp. 145–160). Hogrefe. Hascher, T., & Schmitz, B. (2010). Pädagogische Interventionsforschung [Pedagogical intervention research]. Juventa. Jiang, J., Vauras, M., Volet, S., & Wang, Y. (2016). Teachers’ emotions and emotion regulation strategies: Self and students’ perceptions. Teaching and Teacher Education, 54, 22–31. https://doi.org/10.1016/j.tate.2015.11.008 Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. https:// doi.org/10.3102/0013189x033007014 Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a definition of mixed methods research. Journal of Mixed Methods Research, 1, 112–133. https:// doi.org/10.1177/1558689806298224 Kuckartz, U. (Ed.). (2014). Mixed methods. Springer. https://doi.org/10.1007/9783-531-93267-5 Kuhn, C., Hagenauer, G., & Gröschner, A. (2022). “Because you always learn something new yourself!” An expectancy-value-theory perspective on mentor teachers’ initial motivations. Teaching and Teacher Education, 113, Article 103659. https:// doi.org/10.1016/j.tate.2022.103659 Levitt, H. M., Bamberg, M., Creswell, J. W., Frost, D. M., Josselson, R., & SuárezOrozco, C. (2018). Journal article reporting standards for qualitative primary, qualitative meta-analytic, and mixed methods research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 26–46. https://doi.org/10.1037/amp0000151 Lincoln, Y. S., & Guba, E. G. (2000). Paradigmatic controversies, contradictions, and emerging confluences. In Y. S. Lincoln & E. G. Guba (Eds.), Handbook of qualitative research (pp. 163–188). Sage. Mayring, P. (2015). Qualitative Inhaltsanalyse: Grundlagen und Techniken (12th ed.) [Qualitative content analysis: Foundations and techniques]. Beltz. Merriam, S. B. (1998). Qualitative research and case study applications in education. Jossey-Bass. Muehlbacher, F., Hagenauer, G., & Keller, M. M. (2022). Teachers’ emotion regulation in the team-taught classroom: Insights into teachers’ perspectives on how to regulate and communicate emotions with regard to the team teaching partner. Frontiers in Education, 7, Article 787224. https://doi.org/10.3389/feduc.2022. 787224 Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9 Pekrun, R., Goetz, T., & Perry, P. (2005). Academic Emotions Questionnaire (AEQ): User’s manual. University of Munich, Department of Psychology; University of Manitoba. https://de.scribd.com/doc/217451779/2005-AEQ-Manual# Plano Clark, V. L., & Ivankova, N. V. (2016). Mixed methods research. A guide to the field. Sage.

Mixed Methods in Research on Motivation and Emotion

177

Renninger, K. A., & Hidi, S. (2011). Revisiting the conceptualization, measurement, and generation of interest. Educational Psychologist, 46(3), 168–184. https://doi. org/10.1080/00461520.2011.587723 Ryan, R. M., & Deci, E. L. (2017). Self-determination-theory. The Guilford Press. Schutz, P. A., DeCuir-Gunby, J. T., & Williams-Johnson, M. R. (2016). Using multiple and mixed methods to investigate emotions in educational contexts. In M. Zembylas & P. A. Schutz (Eds.), Methodological advances in research on emotion and education (pp. 217–229). Springer. Sharp, J. G., Hemmings, B., Kay, R., & Atkin, C. (2018). Academic boredom, approaches to learning and the final-year degree outcomes of undergraduate students. Journal of Further and Higher Education, 42(8), 1055–1077. https://doi. org/10.1080/0309877X.2017.1349883 Smith, J. K. (1983). Quantitative versus qualitative research: An attempt to clarify the issue. Educational Researcher, 12(3), 6–13. https://doi.org/10.2307/1175144 Stephan, M. (2021). Online- und Präsenzlehre aus Sicht von Lehramtsstudierenden: Eine Mixed Methods Studie zu emotionalen und motivationalen Effekten [Dissertation, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen]. Deutsche Nationalbibliothek. urn:nbn:de:bvb:29-opus4-165515 Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques (1st ed.). Sage. Stupnisky, R. H., Pekrun, R., & Lichtenfeld, S. (2016). New faculty members’ emotions: A mixed-methods study. Studies in Higher Education, 41(7), 1167–1188. https://doi.org/10.1080/03075079.2014.968546 Tashakkori, A., & Teddlie, C. (1998). Mixed methodology: Combining qualitative and quantitative approaches. Sage. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://ai2-s2-pdfs. s3.amazonaws.com/d112/d71f9dcd74cf1a44df50dee44bc48c6a9217.pdf Watt, H. M. G., Richardson, P. W., Klusmann, U., Kunter, M., Beyer, B., Trautwein, U., & Baumert, J. (2012). Motivations for choosing teaching as a career: An international comparison using the FIT-Choice scale. Teaching and Teacher Education, 28(6), 791–805. https://doi.org/10.1016/j.tate.2012.03.003 Wolcott, H. T. (1999). Ethnography: A way of seeing. AltaMira.

12 THE EXPERIENCE SAMPLING METHOD IN THE RESEARCH ON ACHIEVEMENT-RELATED EMOTIONS AND MOTIVATION Julia Moeller, Julia Dietrich and Jessica Baars

Abstract Motivation and emotions fluctuate across learning situations and contexts. That makes it necessary to assess and analyse their processes of change, along with its situated and context-specific sources of variation. A method to assess the situation- and context-specificity and fluctuation of emotions and motivation is the experience sampling method (ESM). The ESM produces intensive longitudinal data with many measurement time points per person. Typically, participants are surveyed repeatedly during their day about their current emotions or motivational states with self-report surveys on portable devices, such as smartphones. This chapter discusses the ESM for both beginners and advanced researchers. It starts with an introduction to the ESM, referencing useful resources to researchers interested in applying the ESM in their own studies of emotion and motivation in education. Then, the contribution of the ESM to the recent and expected future theoretical shifts towards situated models of motivation and emotions are discussed. We give an overview of current innovations in the research with the ESM and address current challenges in this field, including limitations to the replicability and generalisability of ESM studies across contexts. First, solutions to these challenges are proposed; second, directions for future research are presented.

What Is the Experience Sampling Method and How Does It Contribute to the Research on Motivation and Emotion in Learning and Instruction?

This chapter introduces insights that research on motivation and emotions can gain from the experience sampling method (ESM). It gives an overview of resources helping readers getting started with ESM data collections and DOI: 10.4324/9781003303473-14

ESM in the Research on Achievement-Related Emotions and Motivation

179

analyses. We discuss how the ESM contributes to current paradigmatic changes in psychological research, including methodological debates about how to best examine (in-)variance of research findings across time, individuals, contexts, studies, methods, and other boundary conditions. Crucial learning-related processes, such as sensing academic emotions, subjectively weighing motivating and demotivating aspects of a learning task, paying attention, processing new information, or giving up on a task all happen in and change between specific learning situations and contexts. Understanding these processes therefore requires us to study them in the moments in which they occur. The ESM can capture situated, fluctuating aspects of emotion and motivation. With the ESM, researchers ask participants in real-time via mobile devices about their current psychological states. The individual surveys typically last only a few minutes and are repeated multiple (typically at least > 10) times. ESM surveys aim to capture fluctuating psychological experiences (motivation and emotions) as well as their context information (the current task, current company, current location, etc.). Such data enable researchers to disentangle situational, contextual, and personal determinants because the same students or the same teachers are assessed many times in different situations (see Figure 12.1). Statistical methods to collect and analyse ESM data are rapidly evolving and research employing these methods is currently booming (see Gates et al., 2023; Hamaker & Wichers, 2017; Myin-Germeys & Kuppens, 2022). Giving a comprehensive overview exceeds the scope of this chapter, but readers getting started with ESM data collections and analyses find helpful resources in Fritz et al. (in prep.) and Piccirillo et al. (in prep.). Hall et al. (2021) reviewed ESM research designs for the study of emotions. An overview of ESM data collection software was provided by Arslan et al. (2018). Theoretical and Methodological Innovations: How the ESM Contributes to Paradigmatic Shifts in the Research on Motivation and Emotions

ESM data make it possible to study short-term affective processes and in-themoment experiences in learning and achievement contexts. One example of a theoretical shift and new empirical research programme resulting from such a situation- and context-aware perspective is the recent focus on situated determinants of achievement motivation in the research on the (now situated) expectancy-value theory of achievement motivation (Eccles & Wigfield, 2020). While this theory had been widely established for many years, the byword “situated” was added recently (Eccles & Wigfield, 2020). The availability of situated measures contributed to this theoretical shift (Dietrich et al., 2017; Moeller et al., 2020). The new focus provided new insights into relations between expectancy and value experiences, their correlates and

180

Julia Moeller, Julia Dietrich and Jessica Baars

FIGURE 12.1

The complex multilayered nature of ESM data illustrated with the example of time points nested in individual students nested in classrooms nested in schools, influenced by characteristics of time, context, and persons1

outcomes (Edwards & Taasoobshirazi, 2022; Parrisius et al., 2021). The situation focus enhanced our understanding of the influence of context, time, and person-specific factors on achievement motivation. It inspired research on intra-individual trajectories in achievement motivation from one learning situation to the next, and on heterogeneity between situations and between individuals (Moeller et al., 2022b). While the situation focus is rather new in research on the SEVT, other theories used situational assessments earlier: the research on flow states has piloted, used, and further developed the ESM for decades (Csikszentmihalyi, 1975; Engeser & Rheinberg, 2008). Mihaly Csikszentmihalyi, author of the flow theory, has been an early adopter of the ESM and has ploughed a path for the ESM research on achievement-related motivation and emotions (for overviews, see, e.g. Larson & Csikszentmihalyi, 2014). A theoretical framework and methodological innovations for research on situation- and context-specific determinants of learning-relevant emotions was provided by the control-value theory (Pekrun, 2021; see also Berweger et al., 2022; Frenzel et al., 2020; Goetz et al., 2020).

ESM in the Research on Achievement-Related Emotions and Motivation

181

ESM is increasingly used not only in Educational Psychology (Wieland et al., 2022) but also in many other areas including Personality Psychology (Fleeson, 2007), Clinical Psychology (Fried et al., 2020), Developmental Psychology (Sonnenberg et al., 2012), and Work and Organizational Psychology (Kühnel et al., 2017). The time- and context-specificity and manifold sources of variation captured with ESM data make research questions about heterogeneity more salient and testable. Table 12.1 gives an overview TABLE 12.1 Paradigmatic changes in research on motivation and emotion enabled,

accelerated and in part inspired by intensive longitudinal data Tradition

New perspectives

Theory-guided, hypothesis-guideddeductive: Deriving hypotheses from theories and falsifying them on data

Open to exploratory, inductive approaches because many theories do not yet make explicit predictions; new methods and unexpected findings lead to theory development From idiographic, person-specific models to nomothetic models, clarifying which findings can be generalised under which boundary conditions Intra-individual: Analysis of variance within and between individuals

Nomothetic: In search of general laws of experience and behaviour

Inter-individual: In search of differences between individuals, focus on the analysis of variance between individuals and groups Assumption of ergodicity: Findings obtained with inter-individual procedures are interpreted as describing structures and processes within individuals Controlled: Efforts to control the influence of boundary conditions (laboratory or control variables) Sequential understanding of causality: Search for causal sequences: Clear distinction between independent & dependent variables Focus on stationary effects: Assuming that an effect is stable over time. E.g. describing one effect between two variables, instead of this coefficients’ change over time Focus on linear developmental trajectories & correlations: Most of our methods only examine linear correlations

Openness to lacking ergodicity: Exploration of possibly different structures and processes at different levels of analysis Research interest in complex interactions in complex systems (Holism); Attempt to model all relevant influences rather than control them Interest in iterative feedback processes in which one variable can be both an outcome and a predictor of another variable Openness to non-stationarity: Effects can change over time. E.g. the relation between two variables can get stronger or weaker. Openness to non-linearity: Not all correlations are linear. Higher exploration and testing of the form of associations

182

Julia Moeller, Julia Dietrich and Jessica Baars

of paradigmatic changes in the psychological research on motivation and emotion that ESM has enabled, accelerated and in part inspired. Some of these paradigmatic shifts are discussed in detail below.

Insight 1: Intra-Individual Analyses Reveal Differences between Situations

Intensive longitudinal data enable us to study the within-person variation and co-variation of psychological constructs (Murayama et al., 2017), whereas much of the previous research has relied on between-person analyses. Such within-person methods are often more suited than the commonly used between-person methods to answer research questions about psychological processes (Moeller, 2021; Molenaar, 2004). Between-person and within-person analyses can reveal very different results and phenomena (e.g. Voelkle et al., 2014). Factor structures, relations among constructs, and trajectories over time can differ within- versus between individuals (e.g. Hamaker, 2012). Figure 12.2 illustrates how the often-examined meanlevel stability differs from individual trajectories (here Anna’s and Bernd’s). ESM examined with cluster or latent profile analyses can reveal distinct types of in-the-moment profiles of motivation or emotional experiences, used to identify states of flow or situational engagement (Inkinen et al., 2020), or to describe distinct situational profiles of task values, success expectancies, and costs (Dietrich et al., 2019).

FIGURE 12.2

ESM data revealing intra- and inter-individual differences within and between situations, and differences between nomothetic group statistics (e.g. mean score, mean-level stability) and idiographic statistics (e.g. individual scores, individual trajectories)

ESM in the Research on Achievement-Related Emotions and Motivation

183

Insight 2: Predicting One Moment by the Previous One

The ESM enables us to study moment-to-moment changes and stabilities in motivation and emotions, for instance by asking whether students’ or teachers’ experiences in a certain moment are predicted by experiences in a preceding moment (for analytical methods, see e.g. Asparouhov et al., 2018; Reitzle & Dietrich, 2019). Typical are autoregressive models examining carry-over effects from one situation to the next (Malmberg & Martin, 2019) or one moment to the next (e.g. Theobald et al., 2021). Defining the time span for such a moment-to-moment analysis is theoretically and empirically challenging, as change can occur within minutes, half an hour, from a previous lesson to the next one, or from one day to the next (Malmberg & Martin, 2019). Insight 3: Heterogeneity: How People, Time Points and Contexts Differ from One Another

Because of the many hierarchically clustered and uncontrolled sources of variation (e.g. within and across time, contexts, individuals), ESM data sharpen the view for problems such as unknown boundary conditions, heterogeneity versus generalisability, and Simpson’s paradox, as well as for the role of discovery and exploration in a research process (see Table 12.1). Therefore, using ESM data provides insights that are unconventional in a discipline influenced by an epistemology and methods developed for hypothetico-deductive, nomothetic, highly controlled lab experiments. These insights are relevant beyond the use of ESM data, but they are more salient and obvious in ESM studies. The multilayered nature of ESM (see Figure 12.1) makes it possible not only to study how individuals differ from each other in their momentary psychological states but also how time and context influence moment-tomoment changes in these experiences. While some in-the-moment correlations differ vastly between students (e.g. task difficulty and effort; Murayama et al., 2017), others are more universal across individuals (e.g. associations between emotions and their antecedents and outcomes; Berweger et al., in press). With ESM, such heterogeneity vs. universality in moment-to-moment processes can explicitly be tested (e.g. Neubauer et al., 2019). Insight 4: Developmental Dynamics

The above-mentioned features of ESM studies make it possible to study concepts of development proposed by dynamic systems theories, which we expect to be a major driver of innovation in the research on learning-related motivation and emotions (see Dietrich et al., 2022; Moeller et al., 2022b). The conceptual underpinnings of the dynamic systems theory are detailed

184

Julia Moeller, Julia Dietrich and Jessica Baars

for instance in Granic and Patterson (2006) and Reitzle and Dietrich (2019). For example, dynamical systems concepts help us study whether repeated situational experiences (of situational success or interest) ultimately lead to the emergence of stable person characteristics (e.g. stable personal interest; Dietrich et al., 2017). Technological Innovations in the ESM Research

The ESM is used and combined with other methods in innovative ways which are rapidly developing and seem likely to heavily influence the research on motivation and emotions in learning and achievement contexts in the upcoming years. In-the-Moment Personalised Interventions

ESM can be used to tailor psychological interventions to an individual’s needs in exactly the moments in which individuals need them (Schmitz & Wiese, 2006). ESM surveys can administer personalised instruction tailored to previously assessed characteristics of the person, time point or context. This can be used to send individuals personalised experimental instructions (Schmiedek & Neubauer, 2020), personalised (mental or other) health treatments (Rodebaugh et al., 2020), or personalised instruction within personalised learning programmes (Azevedo et al., 2022; Harley et al., 2016). Sensor-Augmented ESM

Researchers increasingly connect ESM data with other sources of information, such as sensor data automatically recorded by the smartphones used to administer ESM surveys (location data, surrounding noise levels, close-by devices, app usage and others) or psychophysiological measurement devices (heart rate variability, electrodermal resistance, eye tracking data, recorded facial expressions, etc.). Such sensor-augmented ESM (Ferreira et al., 2015; Krkovic et al., 2018) can be used to validate ESM self-reports by triangulating them with objective behavioural data. It could also be used to trigger ESM surveys in certain kinds of situations (e.g. when the phone GPS realises the participant just entered their workplace). Artificial Intelligence

The personalised interventions and sensor-augmented ESM data collections mentioned above seem likely to benefit from innovations in machine learning and artificial intelligence. We define machine learning here as a machine’s ability to find patterns in data (relations, clusters) and artificial intelligence

ESM in the Research on Achievement-Related Emotions and Motivation

185

as a computer’s ability to make decisions and simulate human reasoning and productivity based on patterns previously identified in data (e.g. Dobrev, 2005). Considering the huge and growing role of AI in emotion detection, we expect that ESM and sensor data will be used in the future to automatically identify people’s emotions and to automatically suggest a personalised treatment tailored to the individual, time, and context (as in intelligent tutoring systems, see Azevedo et al., 2022). Gamification

Being interrupted by ESM surveys many times a day can feel annoying and therefore reduce participants’ compliance and response rates. Gamification has been suggested as a way to increase the participation and retention in ESM studies by making responding to ESM surveys more enjoyable (van Berkel et al., 2017). However, more research is needed to study whether gamification in ESM studies may change the studied emotions or motivation, which could limit the validity of gamified ESM surveys. Current Challenges in the ESM Research and Directions for Future Research

ESM research is particularly affected by challenges to replicability, generalisability, and other criteria of trustworthy research (see Table 12.2). A more detailed discussion of the challenges to replicability and generalisability affecting ESM studies and available solutions can be found in Moeller et al. (2022a). For overviews of currently on-going discussions among ESM researchers about directions for future research, see for instance Fritz et al. (in prep.) and Piccirillo et al. (in prep.). Directions for future research include, among others, the need for further theory development describing the situated, contextualised and person-specific aspects of academic emotions and achievement motivation, the need to develop and validate better measurement instruments, and the need for systematic research and improvements to the replicability and generalisability of ESM studies. This can only be an incomplete list of the promising avenues currently being discussed. To address the challenges mentioned in Table 12.2, we propose that future research should focus on theory development as well as on the validation of operational definitions and measurement instruments. Theory

In ESM research, new methods and unexpected findings often precede theoretical explanations, which conflicts with the widely accepted epistemological foundation of theory-led deductive hypothesis testing. Few theories

Challenge

Theory-method gaps in many ESM studies; many ESM studies appear more datadriven than theory-driven; few theories specify effects on the levels on which ESM data are analysed (few theories specifying situation- and context-specificity and intra-individual structures and processes). Construct validity Since ESM measures need to be brief, they typically capture only few aspects crisis of multifaceted motivational constructs. Jingle and jangle fallacies plaguing Schimmack (2021) the research on motivation lead to confusion over definitions, overlaps and differences between constructs and the measures supposed to represent them. Measurement crisis Information about psychometric properties are lacking for most employed ESM Flake and Fried (2020) measures. Many items are created ad hoc for a new study without being validated. The measurement models used to measure emotions and motivations may be theoretically implausible and this debate is only starting (Lange et al., 2020). Normativity crisis Due to the norms of how to write and publish, many ESM studies use exploratory Lundh (2019) approaches but pretend their approaches were hypothetico-deductive. Unexpected findings are either not reported or declared to be the result of deductive hypothesistesting. At the same time, researchers dedicated to a deductive research logic may shy away from a much-needed systematic exploration of the boundary conditions that may limit the generalisability of ESM findings. ESM studies are often limited to relatively few individuals from only a handful of Inference crisis schools or organisations in few countries, rarely including enough data from Starns et al. (2019) different cultural or ethnic groups. Their results are often interpreted as if they and Syed (2021); were universally valid. ESM data include many complex, multilayered, and not yet see also the well understood sources of variation that unknown boundary conditions (hidden Generalisability moderators) seem likely. Therefore, generalisability is unknown and hard to crisis establish. Yarkoni (2022) Replicability crisis Due to rapid innovations in this quickly developing field, many ESM studies are the Ioannidis (2005, first of their kind and warrant replications. 2012) and Nosek et al. (2022)

(Partial) solutions Theory development with reference to time-, context- and person specificity and heterogeneity Testing and discussing construct validity, robustness checks to rule out method artefacts; Multiverse analyses (Weermeijer et al., 2022) Systematic item validation studies; ESM item repositories (Kirtley et al., 2018) ESM pre-registration template (Kirtley et al., 2021); Hypository to registering unexpected findings for later tests (Moeller et al., 2022a)

Epistemological discussions of replicability & generalisability and systematic study of boundary conditions (e.g. Moeller et al., 2022a)

Systematic replications in many-lab collaborations, many-analysts studies (e.g. Bastiaansen et al., 2020)

Julia Moeller, Julia Dietrich and Jessica Baars

Theory crisis Eronen and Bringmann (2021)

Relevance in the ESM research on learning-related emotions & motivation

186

TABLE 12.2 Challenges to research trustworthiness and their relevance to ESM studies

ESM in the Research on Achievement-Related Emotions and Motivation

187

are yet sophisticated enough to address the heterogeneity between situations, individuals, and contexts in as much detail as ESM findings often reveal. Hence, there is a need to update theories on academic motivational and emotional processes to address variability and generalisability within and between situations, persons, and contexts. Perspectives enabled by recent methodological advances that we expect to shape future theory development are, for instance, (1) iterative feedback loops, referring to the possibility that a construct may be at the same time a predictor and an outcome of another construct in self-reinforcing processes (vicious circles or virtuous circles of motivational development, e.g. Murayama, 2022; Pekrun, 2021; Vu et al., 2022); (2) the relation between momentary experiences and stable dispositions, including the question how traits determine the levels and frequencies of situational emotions and motivational states, or whether the frequency and intensity of situational experiences leads to the emergence of stabilising emotional or motivational dispositions and (3) heterogeneity and lacking ergodicity (discrepancies in findings between different levels of analyses, within-person versus between-person) requiring theoretical explanations. Integrating these perspectives into the research on motivation and emotions will benefit from the dynamic systems thinking mentioned above (Moeller et al., 2022a). Construct Validity and Measurement

ESM data are affected by the typical limitations of self-report data, including response styles and subjective perceptions. The hope is that asking participants about their experiences in the moments in which they occur will reduce memory errors (Takarangi et al., 2006) as well as stereotypical response biases related to identity, such as gender (Goetz et al., 2013). The limitations of self-report data can be ameliorated by triangulating ESM data with objective observations of behaviour, for instance in sensor-augmented ESM (Ferreira et al., 2015) or through behavioural data in online learning environments (Azevedo et al., 2022). An alternative is to survey groups of individuals at the same time in the same context and then use statistical methods to disentangle subjective perceptions of a situation (a person’s deviation from the group trend) from objective situation characteristics (indicated by the group trend; see Moeller et al., 2020). While there is a hope that integrating ESM data with psychophysiological assessments of emotions will help to validate ESM measures, it has also been argued that the different channels of information (self-report, psychophysiological measures) assess different, not entirely exchangeable aspects of emotions (Barrett, 2017), suggesting that the subjectivity of a self-report can be considered to be a strength as much as a weakness. More research is needed to find out which exact components of emotions and motivation and which exact outcomes are best indicated by which

188

Julia Moeller, Julia Dietrich and Jessica Baars

combination of ESM self-reports psychophysiological measures and other sensor data. There is a lack of knowledge about the psychometric properties of ESM measures, because many item sets are developed ad hoc by researchers and because methods to ascertain the reliability of the typical single-item measures are still being developed (Gogol et al., 2014). More systematic validation studies in combination with repositories of validated ESM measures (see Kirtley et al., 2018) are needed to obtain trustworthy information about their reliability, validity, objectivity, measurement invariance across time and across contexts, translations, and the generalisability of these information. Analytical Methods

The complex structure of ESM data with their multilayered sources of variation (see Figure 12.1) poses challenges to the analysis of ESM data. Ever new analytical methods become available at rapid speed, enabling researchers to address new types of research questions (such as feedback loops, heterogeneity, see above). Still, there are statistical challenges, for example in disentangling the variation between time points, individuals, and contexts even in complex analyses. For example, consider how the nestedness of students in classrooms is ignored in some DSEM analyses (Neubauer et al., 2022). Many novel analytical approaches are relatively complicated and require a level of expertise that is rare due to the novelty of the methods (e.g. hierarchical time dynamic structural equation models; Asparouhov et al., 2018). Keeping up with the methodological developments can be challenging even for experts in the field. There is a need for accessible tutorials to build the required expertise, which is addressed in recent publications (Fritz et al., in prep; Piccirillo et al., in prep; Gates et al., 2023; Myin-Germeys & Kuppens, 2022). Missing data are a particular problem in ESM studies, since missingness is frequent due to the intrusiveness of ESM measures and the need for participants to respond multiple times. Since many ESM-measured constructs are situation- and context-specific with much variation from one moment to the next, it is questionable whether the responses of a missed ESM survey should be imputed or otherwise estimated based on existing data, thus limiting the usual kludges of estimate missing data based on available observations. In the research on learning-relevant motivation and emotions, it often seems plausible to assume a missingness not at random, if for instance characteristics of the context, time, or person determine how likely a person is to respond (think of nonresponse due to being totally absorbed in an exciting activity, or due to being too depressed, frustrated or stressed

ESM in the Research on Achievement-Related Emotions and Motivation

189

to answer, see Wilson et al., 1992). There is a need for more research on how to avoid missingness via better instructions, fewer or better-timed surveys, or measures ensuring optimal participant compliance. There also is a need to reflect the limitations of given missing data in the interpretation of results. Inference and Generalisability

ESM studies pose known and less known, unique challenges to the trustworthiness of research findings (for overviews, see Moeller et al., 2022b; Moeller et al., in prep.). A challenge to ascertaining the replicability and generalisability of ESM findings is that ESM is used to study phenomena that are expected to greatly vary between contexts, time points, and individuals, while very little theoretical knowledge and hypotheses about the exact sources of variation and their mechanisms of influencing motivation and emotion are available. That ESM studies are typically conducted in natural every-day life settings makes it harder to control and monitor sources of variation than in typical lab experimental studies. At the same time, we expect ESM data to be influenced by many unknown and uncontrolled sources of variation which are multilayered and nested, implying potentially limited generalisability. There are so far few systematic replication ESM studies while many studies are the first of their kind, warranting replications. Multilab data collections and multi-lab data analyses have been introduced as possible solutions to these challenges (Bastiaansen et al., 2020; Moeller et al., in prep.). With lacking specific hypotheses and missing empirical knowledge about sources of variation, there is a high likelihood that unknown boundary conditions (also called hidden moderators) may limit the generalisability of many ESM findings. Even if a large number of previous replication studies had found an effect to be replicable and generalisable across the previously considered contexts, we can therefore never rule out that a previously unconsidered person or context characteristic may limit the generalisability of an ESM finding. This implies that an ESM finding may look generalisable in regard to known factors when it is non-generalisable in regard to not yet considered factors, or that an ESM finding looks like it may be non-replicable when in fact it would have been replicable but differed between two studies due to unknown boundary conditions (which is a lack of generalisability, not replicability, for the definition see Nosek et al., 2022 and Moeller et al., 2022b).This requires us to newly define and determine replicability and generalisability in the face of multilayered complex heterogeneity (see Moeller et al., 2022b). Until systematic knowledge about the generalisability of an ESM finding is available, researchers need to hypothesise about possible boundary conditions of their findings Might the findings be limited to particular teachers or a

190

Julia Moeller, Julia Dietrich and Jessica Baars

particular school, etc.? Are there hypotheses about possible boundary conditions that future studies need to address more systematically? A template for pre-registering ESM studies was proposed to increase the deductive research process and to increase replicability by preventing problematic practices such as HARKing or p-hacking (Kirtley et al., 2021). Another challenge to ascertaining the replicability and generalisability of ESM findings is the problem that a lack of repeatability, reproducibility, or robustness (for the difference, see Moeller et al., 2022b) can look deceivingly similar to a lack of replicability and generalisability. ESM researchers have manifold degrees of freedom in the analytics choices they make when preparing and analysing their data (Bastiaansen et al., 2020). It has therefore been suggested that robustness checks (i.e. sensitivity analyses) be applied across different choice options (like which data to exclude from the analysis) – a procedure called multiverse analysis (Weermeijer et al., 2022). Funding

This work was funded by a Jacobs Foundation Early Career Research Fellowship and a grant by the German Research Foundation (DFG; #451682742) to Julia Moeller and Julia Dietrich. Note 1 This figure is an illustration of the example of student data. There can be crossclassifications, in terms of one unit (one student) belonging to multiple units of a higher level (one student attending different classes throughout one day), depending on the research design and research question. There can also be additional sources of variation between or beyond the levels specified in this figure (multiple teachers cross-classified teaching multiple classes in each of which they have multiple measurement time points); that could, and sometimes should, be studied as additional levels, or, alternatively, as predictors (dummy variables) in a regression model or as group variables in other models.

References Arslan, R. C., Tata, C. S., & Walther, M. P. (2018). ESM & mobile sensing solutions: Feature table. https://comparison-to.formr.org. R.C. Arslan, C.S. Tata, & M.P. Walther (Eds.), formr: A study framework allowing for automated feedback generation and complex longitudinal experience sampling studies using R (version v0.18.3). https://doi.org/10.3758/s13428-019-01236-y Asparouhov, T., Hamaker, E., & Muthén, B.. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25(3), 359–388. https://doi.org/10.1080/10705511.2017.1406803 Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., Cloude, E., Dever, D., Wiedbusch, M., Wortha, F., & Cerezo, R. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold

ESM in the Research on Achievement-Related Emotions and Motivation

191

self-regulated learning with an intelligent tutoring system. Frontiers in Psychology, 13, Article 813632. https://doi.org/10.3389/fpsyg.2022.813632 Barrett, L. F. (2017). How emotions are made: The secret life of the brain. Pan Macmillan. Bastiaansen, J. A., Kunkels, Y. K., Blaauw, F. J., Boker, S. M., Ceulemans, E., Chen, M., Chow, S. M., de Jonge, P., Emerencia, A. C., Epskamp, S., Fisher, A. J., Hamaker, E. L., Kuppens, P., Lutz, W., Meyer, M. J., Moulder, R., Oravecz, Z., Riese, H., Rubel, J., Ryan, O., & Bringmann, L. F. (2020). Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology. Journal of Psychosomatic Research, 137, Article 110211. https://doi.org/10.1016/j. jpsychores.2020.110211 Berweger, B., Born, S., & Dietrich, J. (2022). Expectancy-value appraisals and achievement emotions in an online learning environment: Within- and between-person relationships. Learning & Instruction, 77, Article 101546. https://doi.org/10.1016/j. learninstruc.2021.101546 Berweger, B., Kracke, B., & Dietrich, J. (2023). Preservice teachers’ epistemic and achievement emotions when confronted with common misconceptions about education. Journal of Educational Psychology. Advance online publication. https://doi. org/10.1037/edu0000792 Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. Jossey-Bass Publishers. Dietrich, J., Moeller, J., Guo, J., Viljaranta, J., & Kracke, B. (2019). In-the-moment profiles of expectancies, task values, and costs. Frontiers in Psychology, 10, 1662. https://doi.org/10.3389/fpsyg.2019.01662 Dietrich, J., Schmiedek, F., & Moeller, J. (2022). Academic motivation and emotions are experienced in learning situations, so let’s study them. Introduction to the special issue. Learning & Instruction, 81, Article 101623. https://doi.org/10.1016/j. learninstruc.2022.101623 Dietrich, J., Viljaranta, J., Moeller, J., & Kracke, B. (2017). Situational expectancies and task values: Associations with students’ effort. Learning and Instruction, 47, 53–64. https://doi.org/10.1016/j.learninstruc.2016.10.009 Dobrev, D. (2005). A definition of artificial intelligence. Mathematica Balkanica, New Series, 19, 67–74. https://doi.org/10.48550/arXiv.1210.1568 Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Edwards, O. V., & Taasoobshirazi, G. (2022). Social presence and teacher involvement: The link with expectancy, task value, and engagement. The Internet and Higher Education, 55, Article 100869. https://doi.org/10.1016/j.iheduc.2022. 100869 Engeser, S., & Rheinberg, F. (2008). Flow, moderators of challenge-skill-balance and performance. Motivation and Emotion, 32, 158–172. https://doi.org/10.1007/ s11031-008-9102-4 Eronen, M. I., & Bringmann, L. F. (2021). The theory crisis in psychology: How to move forward. Perspectives on Psychological Science, 16(4), 779–788. https://doi. org/10.1177/174569162097058 Ferreira, D., Kostakos, V., & Dey, A. K.. (2015). AWARE: Mobile context instrumentation framework. Frontiers in ICT, 2, 6. https://doi.org/10.3389/fict.2015.000

192

Julia Moeller, Julia Dietrich and Jessica Baars

Flake, J. K., & Fried, E. I. (2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465. https://doi.org/10.1177/25152459 20952393 Fleeson, W. (2007). Using experience sampling and multilevel modeling to study person-situation interactionist approaches to positive psychology. In A. D. Ong & M. H. M. van Dulmen (Eds.), Oxford handbook of methods in positive psychology (pp. 501–514). Oxford University Press. Frenzel, A. C., Fiedler, D., Marx, A. K. G., Reck, C., & Pekrun, R. (2020). Who enjoys teaching, and when? Between- and within-person evidence on teachers’ appraisal-emotion links. Frontiers in Psychology, 11, Article 1092. https://doi. org/10.3389/fpsyg.2020.01092 Fried, E. I., Papanikolaou, F., & Epskamp, S. (2020). Mental health and social contact during the COVID-19 pandemic: An ecological momentary assessment study. Clinical Psychological Science, 10, 340–354. https://doi.org/10.1177/ 21677026211017839 Fritz, J., Piccirillo, M. L., Cohen, Z. D., Frumkin, M., Kirtley, O., Moeller, J., Neubauer, A. B., Norris, L. A., Schuurman, N. K., Snippe, E., & Bringmann, L. F. (in prep.). So you want to do ESM? A brief overview on ESM design and implementation. Manuscript in preparation. Gates, K. M., Chow, S., & Molenaar, P. C. M. (2023). Intensive longitudinal analysis of human processes (1st ed.). Chapman and Hall/CRC. Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R., & Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24(10), 2079–2087. https://doi.org/10.1177/0956797613486989 Goetz, T., Keller, M. M., Lüdtke, O., Nett, U. E., & Lipnevich, A. A. (2020). The dynamics of real-time classroom emotions: Appraisals mediate the relation between students’ perceptions of teaching and their emotions. Journal of Educational Psychology, 112(6), 1243–1260. https://doi.org/10.1037/edu0000415 Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Fischbach, A., & Preckel, F. (2014). ‘My questionnaire is too long!’ The assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39, 188–205. https://doi.org/10.1016/j. cedpsych.2014.04.002 Granic, I., & Patterson, G. R.. (2006) Toward a comprehensive model of antisocial development: A dynamic systems approach. Psychological Review, 113(1), 101–131. https://doi.org/10.1037/0033-295x.113.1.101 Hall, M., Scherner, P. V., Kreidel, Y., & Rubel, J. A. (2021). A systematic review of momentary assessment designs for mood and anxiety symptoms. Frontiers in Psychology, 12, Article 642044. https://doi.org/10.3389/fpsyg.2021.642044 Hamaker, E. L. (2012). Why researchers should think “within-person”: A paradigmatic rationale. In M. R. Mehl & T. S. Conner (Eds.), Handbook of research methods for studying daily life (pp. 43–61). Guilford. Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10–15. https://doi.org/10.1177/0963721416666518 Harley, J., Lajoie, S., Frasson, C., & Hall, N. (2016). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International

ESM in the Research on Achievement-Related Emotions and Motivation

193

Journal of Artificial Intelligence in Education, 27(2), 268–297. https://doi.org/10.1007/ s40593-016-0126-8 Inkinen, J., Klager, C., Juuti, K., Schneider, B., Salmela-Aro, K., Krajcik, J. S., & Lavonen, J. (2020). High school students’ situational engagement associated with scientific practices in designed science learning situations. Science Education, 104, 667–692. https://doi.org/10.1002/sce.21570 Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), Article e124. https://doi.org/10.1371/journal.pmed.0020124 Ioannidis, J. P. A. (2012). Why science is not necessarily self-correcting. Perspectives on Psychological Science, 7(6), 645–654. https://doi.org/10.1177/17456916 12464056 Kirtley, O., Hiekkaranta, A. P., Kunkels, Y. K., Verhoeven, D., van Nierop, M., & Myin-Germeys, I. (2018). The Experience Sampling Method (ESM) Item Repository. https://doi.org/10.17605/OSF.IO/KG376 (see also https://www.esmitemre pository.com) Kirtley, O. J., Lafit, G., Achterhof, R., Hiekkaranta, A. P., & Myin-Germeys, I.. (2021) Making the black box transparent: A template and tutorial for registration of studies using experience-sampling methods. Advances in Methods and Practices in Psychological Science, 4(1), 1–16. https://doi.org/10.1177/2515245920924686 Krkovic, K., Krink, S., & Lincoln, T. (2018). Emotion regulation as a moderator of the interplay between self-reported and physiological stress and paranoia. European Psychiatry, 49, 43–49. https://doi.org/10.1016/j.eurpsy.2017.12.002 Kühnel, J., Sonnentag, S., Bledow, R., & Melchers, K. G. (2017). The relevance of sleep and circadian misalignment for procrastination among shift workers. Journal of Occupational and Organizational Psychology, 91(1), 110–133. https://doi. org/10.1111/joop.12191 Lange, J., Dalege, J., Borsboom, D., van Kleef, G. A., & Fischer, A. H. (2020). Toward an integrative psychometric model of emotions. Perspectives on Psychological Science, 15(2), 444–468. https://doi.org/10.1177/1745691619895057 Larson, R., & Csikszentmihalyi, M. (2014). The experience sampling method. In M. Csikszentmihalyi (Ed.), Flow and the foundations of positive psychology. The collected works of Mihaly Csikszentmihalyi (pp. 21–34). Springer. https://doi. org/10.1007/978-94-017-9088-8_1 Lundh, L. G. (2019). The crisis in psychological science, and the need for a personoriented approach. In J. Valsiner (Ed.), Social philosophy of science for the social sciences (pp. 203–233). Springer. https://doi.org/10.1007/978-3 Malmberg, L. E., & Martin, A. J. (2019). Processes of students’ effort exertion, competence beliefs and motivation: Cyclic and dynamic effects of learning experiences within school days and school subjects. Contemporary Educational Psychology, 58, 299–309. https://doi.org/10.1016/j.cedpsych.2019.03.013 Moeller, J. (2021). Averting the next credibility crisis in psychological science: Within-person methods for personalized diagnostics and intervention. Journal for Person-Oriented Research, 7(2), 53–77. https://doi.org/10.17505/jpor.2021. 23795 Moeller, J., Bergmann, C., Bringmann, L., Bastiaansen, J., Schmiedek, F., Loderer, K., Riediger, M., & Pekrun, R., & the ManyMoments Consortium (in prep.). ManyMoments – Improving replicability of experience sampling method studies in multi-lab collaborations. Manuscript in preparation.

194

Julia Moeller, Julia Dietrich and Jessica Baars

Moeller, J., Dietrich, J., Neubauer, A., Brose, A., Kühnel, J., Baars, J., Dehne, M., Jähne, M., Schmiedek, F., Bellhäuser, H., Malmberg, L.-E., Stockinger, K., Riediger, M., & Pekrun, R. (2022a). Generalizability crisis meets heterogeneity revolution: Determining under which boundary conditions findings replicate and generalize. Pre-print: https://doi.org/10.31234/osf.io/5wsna Moeller, J., Viljaranta, J., Kracke, B., & Dietrich, J. (2020). Disentangling objective characteristics of learning situations from subjective perceptions thereof, using an experience sampling method design. Frontline Learning Research, 8(3), 63–84. https://doi.org/10.14786/flr.v8i3.529 Moeller, J., Viljaranta, J., Tolvanen, A., Kracke, B., & Dietrich, J. (2022b). Introducing the DYNAMICS framework of moment-to-moment development in achievement motivation. Learning and Instruction, 81(10), Article 101653. https://doi. org/10.1016/j.learninstruc.2022.101653 Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2(4), 201–218. https://doi. org/10.1207/s15366359mea0204_1 Murayama, K. (2022). A reward-learning framework of knowledge acquisition: An integrated account of curiosity, interest, and intrinsic–extrinsic rewards. Psychological Review, 129(1), 175–198. https://doi.org/10.1037/rev0000349 Murayama, K., Goetz, T., Malmberg, L.-E., Pekrun, R., Tanaka, A., & Martin, A. J.. (2017). Within-person analysis in educational psychology: Importance and illustrations. In D. W. Putwain & K. Smart (Eds.), British journal of educational psychology monograph series II: Psychological aspects of education—Current trends: The role of competence beliefs in teaching and learning, 12, 71–87. Wiley. Myin-Germeys, I. & Kuppens, P. (Eds.). (2022). The open handbook of experience sampling methodology: A step-by-step guide to designing, conducting, and analyzing ESM studies (2nd ed.). Center for Research on Experience Sampling and Ambulatory Methods Leuven. Neubauer, A. B., Dirk, J., & Schmiedek, F. (2019). Momentary working memory performance is coupled with different dimensions of affect for different children: A mixture model analysis of ambulatory assessment data. Developmental Psychology, 55(4), 754–766. https://doi.org/10.1037/dev0000668 Neubauer, A. B., Schmidt, A., Schmiedek, F., & Dirk, J. (2022). Dynamic reciprocal relations of achievement goals with daily experiences of academic success and failure: An ambulatory assessment study. Learning and Instruction, 81, Article 101617. https://doi.org/10.1016/j.learninstruc.2022.101617 Nosek, B. A., Hardwicke, T. E., Moshontz, H., Allard, A., Corker, K. S., Dreber, A., Fidler, F., Hilgard, J., Kline Struhl, M., Nuijten, M. B., Rohrer, J. M., Romero, F., Scheel, A. M., Scherer, L. D., Schönbrodt, F. D., & Vazire, S. (2022). Replicability, robustness, and reproducibility in psychological science. Annual Review of Psychology, 73(1), 719–748. https://doi.org/10.1146/ annurev-psych-020821-114157 Parrisius, C., Gaspard, H., Zitzmann, S., Trautwein, U., & Nagengast, B. (2021). The “situative nature” of competence and value beliefs and the predictive power of autonomy support: A multilevel investigation of repeated observations. Journal of Educational Psychology, 114(4), 791–814. https://doi.org/10.1037/ edu0000680

ESM in the Research on Achievement-Related Emotions and Motivation

195

Pekrun, R. (2021). Self-appraisals and emotions: A generalized control-value approach. In T. Dicke, F. Guay, H. W. Marsh, R. G. Craven, & D. M. McInerney (Eds.), Self – A multidisciplinary concept (pp. 1–30). Information Age Publishing. Piccirillo, M. L., Fritz, J., Cohen, Z. D., Frumkin, M. R., Kirtley, O., Moeller, J., Neubauer, A. B., Norris, L. A., Schuurman, N. K., Snippe, E., & Bringmann, L. F. (in prep.). So you want to do ESM? Part II: A momentary assessment on the future of ESM research. Manuscript in preparation. Reitzle, M., & Dietrich, J. (2019). From between-person statistics to within-person dynamics. Diskurs Kindheits- und Jugendforschung, 14, 323–342. https://doi. org/10.3224/diskurs.v14i3.06 Rodebaugh, T. L., Frumkin, M. R., & Piccirillo, M. L. (2020). The long road from person-specific models to personalized mental health treatment. BMC Medicine, 18(1), 1–2. https://doi.org/10.1186/s12916-020-01838-w Schimmack, U. (2021). The validation crisis in psychology. Meta-Psychology, 5, 1–9. https://doi.org/10.15626/MP.2019.1645 Schmiedek, F., & Neubauer, A. B. (2020). Experiments in the wild: Introducing the within-person encouragement design. Multivariate Behavioral Research, 55, 256–276. https://doi.org/10.1080/00273171.2019.1627660 Schmitz, B., & Wiese, B. S.. (2006). New perspectives for the evaluation of training sessions in self-regulated learning: Time-series analyses of diary data. Contemporary Educational Psychology, 31, 64–96. https://doi.org/10.1016/J. CEDPSYCH.2005.02.002 Sonnenberg, B., Riediger, M., Wrzus, C., & Wagner, G. G. (2012). Measuring time use in surveys–concordance of survey and experience sampling measures. Social Science Research, 41(5), 1037–1052. https://doi.org/10.1016/J. SSRESEARCH.2012.03.013 Starns, J. J., Cataldo, A. M., Rotello, C. M., Annis, J., Aschenbrenner, A., Bröder, A., Cox, G., Criss, A., Curl, R. A., Dobbins, I. G., Dunn, J., Enam, T., Evans, N., Farrell, S., Fraundorf, S. H., Gronlund, S. D., Heathcote, A., Heck, D. W., Hicks, J. L., & Wilson, J.. (2019). Assessing theoretical conclusions with blinded inference to investigate a potential inference crisis. Advances in Methods and Practices in Psychological Science, 2(4), 335–349. https://doi.org/10.1177 %2F2515245919869583 Syed, M. (2021). Reproducibility, diversity, and the crisis of inference in psychology. Pre-print: https://doi.org/10.31234/osf.io/89buj Takarangi, M. K., Garry, M., & Loftus, E. F. (2006). Dear diary, is plastic better than paper? I can’t remember: Comment on Green, Rafaeli, Bolger, Shrout, and Reis (2006). Psychological Methods, 11(1), 119–125. https://doi.org/10.1037/ 1082-989X.11.1.119 Theobald, M., Breitwieser, J., Murayama, K., & Brod, G. (2021). Achievement emotions mediate the link between goal failure and goal revision: Evidence from digital learning environments. Computers in Human Behavior, 119, Article 106726. https://doi.org/10.1016/j.chb.2021.106726 van Berkel, N. V., Gonçalves, J., Hosio, S. J., & Kostakos, V.. (2017). Gamification of Mobile experience sampling improves data quality and quantity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 1, 1–21. https://doi.org/10.1145/3130972

196

Julia Moeller, Julia Dietrich and Jessica Baars

Voelkle, M. C., Brose, A., Schmiedek, F., & Lindenberger, U. (2014). Toward a unified framework for the study of between-person and within-person structures: Building a bridge between two research paradigms. Multivariate Behavioral Research, 49(3), 193–213. https://doi.org/10.1080/00273171.2014. 889593 Vu, T., Magis-Weinberg, L., Jansen, B. R., van Atteveldt, N., Janssen, T. W., Lee, N. C., van der Maas, H. L. J., Raijmakers, M. E. J., Sachisthal, M. S. M., & Meeter, M. (2022). Motivation-achievement cycles in learning: A literature review and research agenda. Educational Psychology Review, 34(1), 39–71. https://doi. org/10.1007/s10648-021-09616-7 Weermeijer, J., Lafit, G., Kiekens, G., Wampers, M., Eisele, G., Kasanova, Z., Vaessen, T., Kuppens, P., Myin-Germeys, I., & Myin-Germeys, I. (2022). Applying multiverse analysis to experience sampling data: Investigating whether preprocessing choices affect robustness of conclusions. Behavior Research Methods, 54, 2981–2992. https://doi.org/10.3758/s13428-021-01777-1 Wieland, L. M., Hoppe, J. D., Wolgast, A., & Ebner-Priemer, U. W. (2022). Task ambiguity and academic procrastination: An experience sampling approach. Learning and Instruction, 81, Article 101595. https://doi.org/10.1016/j. learninstruc.2022.101595 Wilson, K. C. M., Hopkins, R., de Vries, M. W., & Copeland, J. R. M. (1992). Research alliance and the limit of compliance: Experience sampling with the depressed elderly. In M. W. deVries (Ed.), The experience of psychopathology: Investigating mental disorders in their natural settings (pp. 339–346). Cambridge University Press. Yarkoni, T. (2022). The generalizability crisis. Behavioral and Brain Sciences, 45, 1–37. https://doi.org/10.1017/S0140525X20001685

13 MODELLING DEVELOPMENT AND CHANGE OF MOTIVATIONAL BELIEFS Rebecca Lazarides and Burkhard Gniewosz

Abstract This chapter provides an overview of methods to capture developments and changes in motivational beliefs. Motivational research has recently begun to venture beyond just examining average developmental trends in motivational variables by starting to investigate how developmental changes in motivational variables differ between and within individuals in different learning situations and across contexts. Although studies have started to uncover differences in motivational changes, a systematic overview of suitable methods for capturing motivational differences in developmental processes is still missing. In this chapter, we review key methods of change modelling, bringing together variable-centred approaches, such as growth modelling and true intraindividual change (TIC) models, and person-centred approaches, such as latent transition and growth mixture models. We illustrate the value of the reviewed statistical methods for the analysis of context-specific motivational changes by reviewing recent empirical studies that identify different patterns and trajectories of such motivational beliefs across time. Our focus is thereby on research grounded in situated expectancy-value theory as a core theory in motivational research.

Introduction

Theoretical work in motivational research proposes that the development of individuals’ motivational characteristics is influenced by both learning contexts and learning situations (Eccles & Wigfield, 2020; Heckhausen & Heckhausen, 2018; Hidi & Renninger, 2006). Motivational variables are thus conceptualised as context- and time-sensitive constructs (Pintrich, 2003; Schiefele, 2009; Urdan & Bruchmann, 2018). In this chapter, we review both DOI: 10.4324/9781003303473-15

198

Rebecca Lazarides and Burkhard Gniewosz

variable-centred and person-centred research methods that can be applied to examine whether, how, and to what extent contextual and situational factors shape individuals’ idiosyncratic motivational development. The main premise of the methods we review is that motivational characteristics of individuals vary across time and, thus, across situations (intraindividual difference), and that changes in motivational characteristics can vary across individuals (interindividual difference). Two important distinctions are thus made in this chapter: (1) variable vs. person-centred methods and (2) inter- vs. intraindividual differences in changes. Variable-centred methods assume that the motivational development across time is, on average, similar for all individuals within a population (average developmental trend), although individuals may vary from this average trend (Hamaker et al., 2015b; Steyer et al., 1997). Person-centred methods assume that there are different groups of individuals within a population that share characteristics in terms of their motivational development or changes (Bergman & El-Khouri, 1999; Muthen, 2004; Nylund, 2007). Both person- and variable-centred methods can capture inter- and intraindividual differences in change. Interindividual differences in change are examined when researchers are interested, for example, in how developmental trajectories differ between individuals. Intraindividual differences in change, in turn, refer to the question of to what extent changes between time points are constant within individuals or vary across time. Further, intraindividual change is examined when one is interested in the question of whether and how patterns of motivational characteristics change over time for an individual. Applying variable-centred methods, average change trajectories are modelled and variations of the average trajectory of the population are explained by situational or individual factors. For example, researchers might be interested in the question of whether social categories, such as being considered a boy or a girl, may explain differences in the developmental trend of motivational beliefs within a population. For girls, for example, it might be assumed that motivational beliefs in the domain of mathematics decline more strongly than for boys. Thus, in this case, interindividual differences (differences between individuals) in changes are examined with variable-centred methods. However, one might also be interested in whether motivational beliefs change consistently between multiple timepoints. For example, researchers may assume that students experience on average a high level of interest at the beginning of a learning unit, which is then followed by a decline in interest in the lesson content, resulting in a low level of interest at the end of the learning unit. In this case, intraindividual differences in changes across timepoints are examined (differences within individuals across several time points) with variable-centred methods. In a person-centred framework, not one average change trajectory is assumed, but rather a finite number of groups of individuals sharing change characteristics would be expected. For example, researchers might be

Modelling Development and Change of Motivational Beliefs

199

interested in different types of changes in motivational beliefs (trajectories). For some individuals, motivational beliefs might decline, whereas for others motivational beliefs might remain stable across time. Researchers might be interested in whether the gender of the participant explains group membership (decline vs. stability). Thus, in this case, interindividual differences (differences between individuals) in changes are examined using person-centred approaches. However, one might also be interested in the patterns of motivational beliefs of individuals at different time points. For example, one might assume that some students report high levels of interest combined with low competence beliefs at the beginning of a learning unit in mathematics, and then change to a pattern of low interest and low competence beliefs during the course of the learning unit. Thus, in this case, intraindividual differences (differences within individuals) are examined using person-centred methods. In this chapter, we aim to provide a systematic overview of variable-centred and person-centred methods that capture inter- and intraindividual motivational differences in developmental processes. In each section, we illustrate our methodological considerations with empirical examples. Our examples mostly – but not exclusively – focus on SEVT-based research because situated expectancy-value theory (SEVT; Eccles & Wigfield, 2020) is an established motivational theory that conceptualises both the contextual and situational influences on inter- and intraindividual motivational changes across time. Finally, we describe future research fields and practical implications of the presented theoretical and empirical considerations. Variable-Centred Methods

Thinking about development usually implies change in a certain outcome over time. Theoretically, this change is conceptualised as within-person development, for instance, in math interest. Methodologically, modelling and predicting change is a fundamental tool for testing causal hypotheses. In recent years, it has become clear that the traditional means of longitudinal analysis, such as cross-lagged panel models (CLMP), are not suitable for modelling and predicting within-person changes for various methodological reasons (e.g. Rogosa, 1995). Three alternative approaches shall be briefly presented in the following paragraphs. First, the Random Intercept Cross-Lagged Panel Models address a major problem with classical CLPM, namely the lacking disentanglement of intraindividual change variance and interindividual difference components in this prediction: the individual students’ math interest may change differently across time (intraindividual change), and the students in the sample generally differ in their levels of interest (interindividual difference). Therefore, a clear-cut interpretation of the change predictions is impossible. With

200

Rebecca Lazarides and Burkhard Gniewosz

FIGURE 13.1

Random Intercept Cross-Lagged Panel Model

RICLPM, Hamaker and colleagues proposed an important generalisation of the CLPM (Hamaker et al., 2015a; Hamaker et al., 2015b; Mulder & Hamaker, 2021). Here, it is possible to model reciprocal effects between two variables over time and, at the same time, arrive at a clear-cut interpretation of the predictions of both within-person changes and between-person differences (see Figure 13.1). Both components, level differences in changes (between-person) in aspects such as interest, for example as well as intraindividual (withinperson) changes, can be predicted by characteristics such as gender. Thus, gender differences in the general level of a variable (i.e. interest) as well as the changes over time can be modelled, which means that interindividual differences in timepoint-specific changes are addressed while the changes on average are not depicted. Applying RICLPM to motivational research, Buchmann et al. (2022) investigated reciprocal effects between parental educational aspirations and children’s academic self-concept. In a representative Swiss sample of students in late childhood through mid-adolescence, results showed positive associations between parental aspirations and self-concept at both the between- and within-person level. Thus, on average, higher levels of parental aspiration went along with better self-concepts for the students in the sample (betweenperson level). Within students, parental aspirations were likewise positively associated with within-person changes in academic self-concept across a three-year interval. This means that students whose parents reported high levels of their children’s aspired achievement in comparison to the other students in class at a given time point reported an increase in their individual

Modelling Development and Change of Motivational Beliefs

201

self-concept across time. Moreover, a reciprocal effect of the children’s selfconcept on changes in parental aspirations was found. Thus, generally, parental aspirations and academic self-concepts are positively associated (between-person); parents have higher aspirations for students with high selfconcepts. But also, higher aspirations predicted the individual changes across time within-person. Second, Latent Change Modelling (McArdle, 2009; Steyer et al., 2000) is a flexible tool for modelling short-term and long-term changes in longitudinal data. Here, it can be tested, for instance, whether short-term and long-term changes in interest differ between gender categories in terms of shape, timing, and persistence. The basic idea of this class of models (also known as Latent Change Score Models, True Intraindividual Change Models) is to decompose differences between measurement occasions into the latent intercept and particular latent change variables. These changes refer to certain measurement occasions and therefore do not imply a certain shape of the change. The flexibility of this way of modelling arises from the opportunity to select reference points of the changes to depict the changes. Two standard options are the baseline models and the neighbour models see Figure 13.2, representing True Intraindividual Change Models by Steyer et al. (2000). In the baseline models, the first point of measurement is the reference point for the changes. That means that all changes are specified in relation to the very first measurement occasions. Depending on the timeframe of the longitudinal study, this enables the researcher to investigate long-term changes over time and to predict these changes by, for instance, gender. In the neighbour models, the changes are specified relative to the preceding point of measurement. Therefore, it is possible to model short-term changes between neighbouring measurement occasions and thus these models may serve as a tool to depict and predict situation-related changes, that is, intraindividual differences in change rates across timepoints, as well as to determine the timing of an effect. The latent intercept and change variables can serve as both outcome variables and predictor variables. Thus, interindividual differences in changes can be depicted and explained. Applying neighbour models to motivational research, Gniewosz and Watt (2017) used the neighbour models to predict changes in intrinsic and utility values in mathematics between grades seven and eight, eight and nine, and nine and ten by student-perceived parents’ and teachers’ overestimation of the students’ own perceived mathematical ability. In an Australian longitudinal sample, it was shown that perceived parental overestimations predicted intrinsic task value changes between all neighbouring measurement occasions, whereas teacher influences commenced after the transition to Grade 8 (only changes eight to nine and nine to ten). Third, Latent Growth Curve Modelling (Willett & Sayer, 1994) is an interesting tool if researchers have substantial hypotheses as to the shape

202 Rebecca Lazarides and Burkhard Gniewosz

Latent Change Models FIGURE 13.2

Modelling Development and Change of Motivational Beliefs

FIGURE 13.3

203

Latent Growth Curve Model

of the changes in a certain outcome, be it linear, quadratic, cubic, etc. (see Figure 13.3). It provides the researcher with the possibility to test whether the general changes follow the expected polynomial shape, for instance if the development of math interest over time can be considered as linear or whether the quadratic term better describes the developmental trajectory, that is to test the assumption that there is accelerated growth over time (intraindividual differences). By constraining the factor loadings to a certain pattern following the polynomial shape to test, latent growth variables representing linear, quadratic, cubic changes, etc., can be specified. The interindividual differences in these change variables can be explained by predictor variables, such as gender. Moreover, the change variables themselves can be used as predictors, as well. Applying Latent Growth Curve Modelling to motivational research, Frenzel et al. (2010) investigated students’ trajectories of mathematics interest and predicted these changes by, for instance, gender. Results of this German longitudinal study showed decreases in students’ mathematics interest that plateaued in later years for all students. Boys reported higher mathematics interest than girls, but similar downward growth trajectories (between-person differences).

204

Rebecca Lazarides and Burkhard Gniewosz

All three proposed ways of investigating within-person or intraindividual changes provide distinct features that correspond to the specific research questions. If the researchers are interested in differentiating within-person changes from between-person differences, the Random Intercept Cross-lagged Panel Models are a good choice. If the research question calls for fine-grained, long-term or short-term within-person change modelling, the Latent Change Models become an option. If the theoretical basis allows for the assumption that certain shapes of developmental trajectories should be investigated, the Latent Growth Curve Models are a great tool. All three model classes described in this section follow a variable-centred approach. Interindividual differences in the intraindividual changes can be modelled, but the change patterns are assumed to be valid for all participants in the study. This is a very strong assumption that, for good reasons, is questioned by the proponents of person-centred approaches. In many cases, it is likely that there are homogeneous subgroups of participants with different developmental trajectories or even different prediction patterns of these developmental changes over time. Therefore, a combination of these latent change models and person-centred analytical tools is a very useful combination to investigate questions of situatedness and context specificity. Person-Centred Approaches

Person-centred approaches to longitudinal data allow researchers to describe and test assumptions about interindividual (between-person) differences in intraindividual (within-person) change (Bergman & El-Khouri, 1999; Muthén, 2006; Nylund-Gibson & Choi, 2018; Ram & Grimm, 2009). In this section, two person-centred analytical approaches are presented that examine interindividual differences in changes of variables over time – Latent Transition Analysis (LTA) and Growth Mixture Modelling (GMM). Such models are also summarised under the term “finite mixture models.” Both LTA and GMM can be applied when researchers assume that populations are heterogeneous in such a way that different groups of individuals exist for which changes occur similarly, and for which variables are interrelated in similar ways (Bergman et al., 2003). For example, both the Latent Growth Curve Model (LGCM) reviewed above and the GMM examine changes in variables. However, the LGCM follows a variable-centred approach and thus is suitable when the goal is to examine the average shape of the changes of variables. The person-centred GMM approach would then be suitable if one assumes that there are different types of changes in variables. Person-centred methods have a long history and were often applied in the early 20th century, but then lost their importance when researchers started to become more interested in examining generalisable developmental trends and

Modelling Development and Change of Motivational Beliefs

205

interrelations among variables (Laursen & Hoff, 2006). However, in the last two decades, questions that pertain to interindividual differences in motivationalaffective characteristics as well as to adaptive social and instructional behaviours have gained increasing relevance, and person-centred methods are again in the focus of research (Bergman & El-Khouri, 1999; Laursen & Hoff, 2006). Latent Transition Analyses (LTA; Collins & Lanza, 2009; Lanza et al., 2003; Nylund, 2007) can be applied if one is interested in how individuals change their group membership across time. When using this method, in the first step the optimal number of profiles (groups) is selected at each specific time point, followed by an examination of profile similarity across time points with regard to the number of groups (configural similarity), the shape of the groups (structural similarity), the within-group variability (dispersion similarity), and the relative size of the profiles (distributional similarity) (Morin & Litalien, 2017). Finally, covariates and distal outcomes – so-called “auxiliary information” – can be included in the mixture model, for example, to examine whether and how individual, situational, and contextual factors are related to or are affected by motivational transitions of individuals across time. In Figure 13.4 an example is depicted in which the

FIGURE 13.4

Latent Transition Model with covariate

206

Rebecca Lazarides and Burkhard Gniewosz

gender of the individuals in the sample directly relates to their membership in motivational belief groups at Times 1 and 2 (bold arrow from x to c1 and c2), and at the same time moderates the stability of group membership across time (dashed arrow). The annotations “c1” and “c2” in Figure 13.4 represent the respective latent class models per time point. For example, it might be the case that one assumes that girls belong to a group of students with high interest in mathematics but low domain-related competence beliefs, whereas boys belong to a group with high interest in mathematics and high domain-related competence beliefs – in this case, gender (x) relates to group membership at Time 1 (c1). Further, one might assume that being a girl increases the likelihood of changing from the group with high interest and low competence beliefs in mathematics at Time 1 to a group with low interest and low competence beliefs in mathematics at Time 2 (c2) – this assumed effect is depicted by the dashed arrow in Figure 13.4. Recently, the literature has thereby recommended a three-step approach when including covariates and distal outcomes in LTA models because, in contrast to previously used one-step approaches, the three-step method avoids situations where once the covariates or distal outcomes are included in the model, the measurement parameters of the latent class model shift, which could lead to a different number or kind of groups in the model with and without covariates (Nylund-Gibson et al., 2014; Vermunt, 2010). LTA in the context of motivational research is often used when aiming to examine the stability of certain groups representing specific configurations or “patterns” of motivational characteristics of individuals, the changes of individuals across such groups, and external variables that relate to transitions across groups over time. This approach addresses the intraindividual differences over time. Applying the LTA approach to motivational research, Lazarides et al. (2021) found five profiles of motivational beliefs (subjective task values and academic self-concept) for students in Grade 10 in the domains of mathematics, English, biology, and physics, which were partially stable over time, resulting in four profiles in Grade 10. Transitions occurred in some cases because students lost their motivation in a specific domain and increased their motivation in another domain – often, such transitions indicated processes of inner specialisation because students transitioned from profiles characterised by high motivation in two contrasting domains at Grade 10 to profiles with high motivation in only one of these domains in Grade 12 – such shifts occurred mainly into the domain of English. Consequently, Latent Transition Analyses showed that motivational beliefs became more hierarchical over time. Group membership in Grade 12 was related to subsequent college majors after finishing high school and to occupational skill sets in adulthood. Whereas latent transition analyses allow researchers to track specific motivational changes in groups of individuals across two or more time points, they do not allow an examination of whether different trajectories of growth

Modelling Development and Change of Motivational Beliefs

207

exist for different groups of individuals. For such research questions, growth mixture modelling is applicable. Growth Mixture Models (GMM; Muthén, 2006; Petras & Masyn, 2010) are used to examine the heterogeneity in growth trajectories over time and thus assume that using a single growth trajectory to describe the growth of the entire population is an oversimplification of the complex growth patterns that describe developmental trends among members of different groups within that population (Jung & Wickrama, 2008). The different group differences in change are identified post hoc, meaning that there is no a priori knowledge of individuals’ different growth classes (Ram & Grimm, 2009). For example, one might assume that some students in school experience a decline in motivational beliefs, such as their interest and competence beliefs in mathematics, for example, whereas others experience an increase in their interest and competence beliefs in mathematics, and yet another group might have stable interest and competence beliefs in mathematics across the course of the different school years. Thus, inter-individual differences in change are investigated. The membership in those trajectories might be related to social categories, such as the gender of the child. The described modelling approach has a high level of flexibility, as different individual growth trajectories vary around different means (with the same or different forms), with each growth model having unique variance and unique relations to covariates (Jung & Wickrama, 2008; Muthén, 2006). The specific value of GMM for motivational research is that these modelling approaches allow the researcher to examine how the intraindividual (within-person) and interindividual (between-person) changes in motivational beliefs relate to different individual and contextual variables, which allow, for example, the identification and description of subgroups with particularly high risk of motivational decreases or particularly high potential for motivational increases in academic settings. Only a few studies have addressed such questions in the EVT framework by examining different inter-individual growth trajectories of domain-specific task values and/or competence perceptions (Gaspard et al., 2020; Guo et al., 2018; Lee & Ju, 2021). Applying GMM to motivational research, Gaspard et al. (2020), for example focused on different inter-individual growth trajectories of academic self-concept and intrinsic value in mathematics and language arts from Grade 1 to Grade 12. The authors identified two different trajectory classes for students’ academic self-concept in mathematics and language arts (Moderate Math Decline/Stable High Language Arts; Moderate Math Decline/Strong Language Arts Decline) and three trajectory classes for students’ intrinsic value (Strong Math Decline/Language Arts Decline Levelling Off; Moderate Math Decline/Strong Language Arts Decline; Stable Math and Language Arts Trajectories). Student gender was related to membership in the “Stable Math and Language Arts Trajectories” group, with boys being more likely to

208

Rebecca Lazarides and Burkhard Gniewosz

be in that group than girls compared to the “Strong Math Decline/Language Arts Decline Levelling Off” and to the “Moderate Math Decline/Strong Language Arts Decline” group. Students in the “Stable Math and Language Arts Trajectories” class in turn had higher math-related career aspirations than students in the “Strong Math Decline/Language Arts Decline Leveling Off” class, but did not differ from the “Moderate Math Decline/Strong Language Arts Decline” class. Conclusion and Outlook

The last sections have highlighted that different up-to-date variable- and person-centred approaches exist that are applicable when motivational researchers are interested in intra- (within-person) and inter-individual (between-person) differences in motivational characteristics and developments. The up-to-date methods reviewed above allow researchers to capture inter-individual differences in levels and changes in motivational constructs (RICLPM), describe the shape of developmental trajectories of motivational characteristics (Growth Models), or describe the change rates in motivational constructs over time (Latent Change Models). For an analysis of the situatedness and context specificity of motivation, such methods are useful because they allow the identification of average developmental trajectories of motivational characteristics across learning situations and can provide information about generalisable relations among characteristics of the learning context and motivational characteristics over time. The reviewed person-centred methods are applicable when the question of interest is how developmental trends of motivational characteristics vary across groups of individuals, or how relations between constructs vary across groups of individuals. For an analysis of the situatedness and context specificity of motivation, the reviewed person-centred methods are useful because they inform the researcher about different patterns of motivation and their changes across learning situations (LTA), about different groups of individuals with different developmental trajectories of motivational characteristics within a population (GMM), and about whether and how relations between the learning context and motivational constructs vary across different groups of individuals. Although rarely done so far, RICLPM and latent change models can be adapted to a person-centred perspective as well. In mixture-RICLPM, groups of individuals could be identified who differ in their intraindividual change patterns over time (e.g. group 1: no changes at all, group 2: increases, group 3: decreases, group 4: no changes in the early measurement occasions, but later, etc.) and/or in their interindividual level differences. There could also be a group of individuals characterised by variable A (e.g. interest) predicting variable B (e.g. academic self-concept) and another group with the opposite

Modelling Development and Change of Motivational Beliefs

209

effect pattern. In Latent Change Mixture Models, groups with different change patterns can be identified, for example no short-term/only long-term versus only short-term or early short-term versus late short-term changes (intraindividual differences in change rates). This could, for instance, be applied to detect different sleeper effects in motivational intervention studies, if the treatment affects some students later. Moreover, differing intervention effects can be detected by investigating heterogeneity (group differences) in treatment effects on latent changes. Some students might change with respect to their math utility values over time following an intervention, but others may not. This heterogeneity may be explained by contextual features like the intervention conditions, such as the trainer or materials. It is important to note that researchers have emphasised that personcentred and variable-centred methods are complementary rather than competing methods (Marsh et al., 2009). If the research interest is to describe general developments and interrelations that are valid for the majority of the members in a population, variable-centred research is applicable. If the research interest is to identify group differences in developmental trends and in associations among variables, person-centred approaches are applicable. It is important to note that those groups do not have to be defined beforehand, such as differences between boys and girls, but are the result of the person-centred analyses. The combination of both approaches then allows a holistic picture of the phenomenon under investigation. When examining the situatedness and context specificity of motivation, person-centred methods help the researcher to better understand how situational and contextual characteristics are differently important for the motivational changes of different groups. Such questions can be addressed in various ways. Generally, the methods we review here can be applied in different fields of motivational research whenever questions arise concerning the development trends of motivational constructs and their interrelations with characteristics of the learning context – including teacher education, learning and instruction, or teacher research. One field of motivational research in which such questions are highly relevant is, for example, work on adaptive learning and teaching strategies (Lazarides, Dicke, et al., 2019; Lazarides, Dietrich, et al., 2019). There are several avenues for future studies in motivational research that involve the application of the methods reviewed here: (1) Motivational research can focus more strongly on the reciprocal nature of relations between motivational constructs and characteristics of the learning and teaching context, thereby also disentangling intra- from interindividual changes (Ehm et al., 2019). (2) Motivational research can also use the reviewed methods to gain more knowledge about the developmental trends and dynamics of change for different groups of individuals. (3) Researchers can thereby address the question of how characteristics of the learning and teaching context differently relate to such developmental trends and changes for different

210

Rebecca Lazarides and Burkhard Gniewosz

groups within a population, and use this knowledge to plan tailored instructions, teacher education courses, and interventions. Acknowledgement

This chapter is supported by the German Research Foundation (DFG: LA 3522/5-1). References Bergman, L. R., & El-Khouri, B. M. (1999). Studying individual patterns of development using I-States as objects analysis (ISOA). Biometrical Journal, 41(6), 753–770. https:// doi.org/10.1002/(SICI)1521-4036(199910)41:63.0.CO;2-K Bergman, L. R., Magnusson, D., & El-Khouri, B. M. (2003). Studying individual development in an interindividual context: A person-oriented approach. Psychology Press. Buchmann, M., Grütter, J., & Zuffianò, A. (2022). Parental educational aspirations and children’s academic self-concept: Disentangling state and trait components on their dynamic interplay. Child Development, 93(1), 7–24. https://doi.org/10.1111/ cdev.13645 Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons. Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Ehm, J.-H., Hasselhorn, M., & Schmiedek, F. (2019). Analyzing the developmental relation of academic self-concept and achievement in elementary school children: Alternative models point to different results. Developmental Psychology, 55(11), 2336–2351. https://doi.org/10.1037/dev0000796 Frenzel, A. C., Goetz, T., Pekrun, R., & Watt, H. M. G. (2010). Development of mathematics interest in adolescence: Influences of gender, family, and school context. Journal of Research on Adolescence, 20(2), 507–537. https://doi.org/10.1111/ j.1532-7795.2010.00645.x Gaspard, H., Lauermann, F., Rose, N., Wigfield, A., & Eccles, J. S. (2020). Crossdomain trajectories of students’ ability self-concepts and intrinsic values in math and language arts. Child Development, 91(5), 1800–1818. https://doi.org/10.1111/ cdev.13343 Gniewosz, B., & Watt, H. M. G. (2017). Adolescent-perceived parent and teacher overestimation of mathematics ability: Developmental implications for students’ mathematics task values. Developmental Psychology, 53(7), 1371–1383. https:// doi.org/10.1037/dev0000332 Guo, J., Wang, M.-T., Ketonen, E. E., Eccles, J. S., & Salmela-Aro, K. (2018). Joint trajectories of task value in multiple subject domains: From both variableand pattern-centered perspectives. Contemporary Educational Psychology, 55, 139–154. https://doi.org/10.1016/j.cedpsych.2018.10.004

Modelling Development and Change of Motivational Beliefs

211

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. (2015a). A critique of the crosslagged panel model [Research Support, Non-U.S. Gov’t]. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889 Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. (2015b). A critique of the crosslagged panel model. Psychological Methods, 20(1), 102–155. https://doi.org/ 10.1037/a0038889 Heckhausen, J., & Heckhausen, H. (2018). Motivation and action: Introduction and overview. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (pp. 1–14). Springer. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127. https://doi.org/10.1207/s15326985ep 4102_4 Jung, T., & Wickrama, K. A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social Personality Psychology Compass, 2(1), 302–317. https://doi.org/10.1111/j.1751-9004.2007.00054.x Lanza, S. T., Flaherty, B. P., & Collins, L. M. (2003). Latent class and latent transition analysis. In I. B. Weiner (Ed.), Handbook of psychology (pp. 663–685). Wiley. Laursen, B. P., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly, 52(3), 377–389. https://doi.org/ 10.1353/mpq.2006.0029 Lazarides, R., Dicke, A.-L., Rubach, C., & Eccles, J. (2019). Profiles of motivational beliefs in math: Exploring their development, relations to student-perceived classroom characteristics and impact on future career aspirations and choices. Journal of Educational Psychology, 112(1), 70–92. https://doi.org/10.1037/edu0000368 Lazarides, R., Dicke, C., Rubach, C., Oppermann, E., & Eccles, J. S. (2021). Motivational profiles across domains and academic choices within Eccles et al.’s situated expectancy–value theoretical framework. Developmental Psychology, 57(11), 1893–1909. https://doi.org/10.1037/dev0001250 Lazarides, R., Dietrich, J., & Taskinen, P. H. (2019). Stability and change in students’ motivational profiles in mathematics: The role of perceived teaching. Teaching and Teacher Education, 79, 164–175. https://doi.org/10.1016/j.tate.2018.12.016 Lee, Y.-k, & Ju, U.. (2021). Joint trajectories of extrinsic motivation and competence beliefs: A parallel-process growth mixture modeling approach. Contemporary Educational Psychology, 65, Article 101944. https://doi.org/10.1016/j. cedpsych.2021.101944 Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 191–225. https://doi.org/ 10.1080/10705510902751010 McArdle, J. J. (2009). Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605. https://doi.org/10.1146/ annurev.psych.60.110707.163612 Morin, A. J. S., & Litalien, D. (2017). Webnote: Longitudinal tests of profile similarity and latent transition analyses. http://smslabstats.weebly.com/uploads/1/0/0/ 6/100647486/lta_distributional_similarity_v100647402.pdf. Mulder, J. D., & Hamaker, E. L. (2021). Three extensions of the random intercept cross-lagged panel model. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 638–648. https://doi.org/10.1080/10705511.2020.1784738

212 Rebecca Lazarides and Burkhard Gniewosz

Muthen, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345–368). Sage. Muthén, B. (2006). The potential of growth mixture modelling. Infant Child Development, 15(6), 623–625. https://doi.org/10.1002/icd.482 Nylund, K. L. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. University of Los Angeles. http://www.statmodel.com/ download/Nylund%20dissertation%20Updated1.pdf Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. Translational Issues in Psychological Science, 4(4), 440–461. https://doi.org/10.1037/tps0000176 Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014). A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 439–454. https://doi.org/10.1080/10705511.20 14.915375 Petras, H., & Masyn, K. (2010). General growth mixture analysis with antecedents and consequences of change. In A. R. Piquero & D. Weisburd (Eds.), Handbook of quantitative criminology (pp. 69–100). Springer. Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. https://doi.org/10.1037/0022-0663.95.4.667 Ram, N., & Grimm, K. J. (2009). Methods and measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33(6), 565–576. https://doi.org/10.1177/0165025409343765 Rogosa, D. R. (1995). Myths and methods: “Myths about longitudinal research”. In J. M. Gottman (Ed.), The analysis of change (pp. 3–65). Lawrence Erlbaum Associates. Schiefele, U. (2009). Situational and individual interest. In K. R. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 197–222). Routledge. Steyer, R., Eid, M., & Schwenkmezger, P. (1997). Modeling true intraindividual change: True change as a latent variable. Methods of Psychological Research Online, 2(1), 21–33. Steyer, R., Partchev, I., & Shanahan, M. (2000). Modeling true intra-individual change in structural equation models: The case of poverty and children’s psychosocial adjustment. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple-group data: Practical issues, applied approaches, and specific examples (pp. 109–126). Erlbaum. Urdan, T., & Bruchmann, K. (2018). Examining the academic motivation of a diverse student population: A consideration of methodology. Educational Psychologist, 53(2), 114–130. https://doi.org/10.1080/00461520.2018.1440234 Vermunt, J. K. (2010). Latent class modeling with covariates: Two improved threestep approaches. Political Analysis, 18(4), 450–469. https://doi.org/10.1093/pan/ mpq025 Willett, J. B., & Sayer, A. G. (1994). Using covariance structure-analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116(2), 363–381. https://doi.org/10.1037/0033-2909.116.2.363

14 INTERVENING ON STUDENTS’ MOTIVATION TO LEARN Promises and Pitfalls of Intervention Studies Hanna Gaspard

Abstract In the past two decades, there has been a large increase in intervention studies targeting students’ motivation to learn. Such research has shown that even brief motivation interventions can have surprisingly large and long-lasting effects. Although promising, these interventions are contextdependent and thus need to be investigated across different school settings and student populations. The chapter provides considerations for designing and evaluating interventions in practice that result from the contextspecificity of motivation. To start with, interventions should always be developed carefully for the target population or adapted correspondingly. To develop an intervention that works in practice, it is also necessary to investigate the mechanisms through which the intervention works, implement it in several steps under conditions that subsequently get closer to educational practice, and test for moderating variables across these steps. Finally, I discuss evidence from several studies that tested the Motivation in Mathematics intervention in ninth-grade classrooms in Germany as an illustrative example of the promises and pitfalls of intervention studies.

Introduction

In the past two decades, the number of intervention studies in the area of motivation has increased (for overviews, see Harackiewicz & Priniski, 2018; Lazowski & Hulleman, 2016; Rosenzweig & Wigfield, 2016). Such research draws on motivation theory and evidence from correlational research and attempts to apply motivational principles in educational practice. Theory-driven intervention studies can be categorised as use-inspired basic research (Pintrich, 2003) that generates important insights for both motivational theory DOI: 10.4324/9781003303473-16

214

Hanna Gaspard

and educational practice. From a theoretical perspective, intervention studies can provide stronger evidence of causal relationships than correlational studies can, as intervention studies investigate what happens when an independent variable (e.g. a particular motivational construct) is manipulated, especially when randomised study designs are used (Shadish et al., 2002). Intervention studies can thus be seen as a strong test of motivation theory in an educational context. From a practical perspective, intervention studies develop and test materials that can help improve educational outcomes in a particular context and can therefore directly inform educational practice. This chapter provides an overview of the development of intervention studies targeting students’ motivation in recent years. Hereby, a particular focus is on the need to consider the context-specificity of motivation in designing and evaluating interventions. First, intervention studies are described with respect to different intervention approaches and design features. Second, a brief overview of some central findings of recent intervention studies is given as a basis for discussing the promises and pitfalls of intervention studies. Finally, I provide insights from a research project targeting students’ motivation in mathematics classrooms as an illustrative example of these promises and pitfalls. Intervention Studies Targeting Student Motivation

Intervention researchers who aim to foster students’ motivation need to make a number of decisions: first, they need to consider the target population and context in which motivation is to be fostered. Second, they can draw on different theoretical backgrounds and decide to target a specific motivational construct. Third, they need to decide which specific intervention approach to use to promote motivation. Finally, when it comes to evaluating the intervention, researchers can make use of different study designs. These decisions do not necessarily follow upon each other linearly, but they can be intertwined or made in a different order (e.g. when using intervention research to test motivation theory, the theory comes first). Target Population and Context

The motivational needs of the target population and the context should be considered when developing the intervention and all the choices that come with it, including the targeted motivational construct and the intervention approach. Such a priori considerations are central because different populations and contexts may face specific problems that can then be addressed through an intervention (Harackiewicz & Priniski, 2018; Yeager & Walton, 2011). Prior motivation interventions have been conducted in different student populations and academic contexts. A relatively large body of research has focused on higher education (for a review, see Harackiewicz & Priniski,

Intervening on Students’ Motivation to Learn

215

2018), but interventions have also been implemented in middle and high school contexts. Motivation interventions have been tested less frequently with younger student populations, such as elementary school students (see Lazowski & Hulleman, 2016). Moreover, interventions can focus on academic motivation at a general level, motivation in a specific domain, or even motivation in a specific course. Many interventions have been conducted in the domains of mathematics and science (for a review, see Rosenzweig & Wigfield, 2016), whereas much less research has been conducted in other domains. Research has shown that students’ perceive the characteristics of academic domains quite differently and thus report different affectivemotivational experiences across domains (Gaspard et al., 2017; Goetz et al., 2014). Accordingly, students might benefit from different types of motivation interventions in these domains. For instance, performing well in mathematics and sciences is often associated with high talent, and thus growth mindset interventions, which target these beliefs directly, might work particular well in these domains. Finally, researchers may decide to target the motivation of the entire population of students (e.g. to prevent a decline in motivation over the school years as has been observed across student populations) or to focus on specific subpopulations at risk for low motivation and poor academic outcomes. For example, researchers have focused on students from groups traditionally underrepresented in college (Harackiewicz & Priniski, 2018) and on female students in mathematics and science (Lesperance et al., 2022). Theoretical Background and Targeted Motivational Construct

Motivation interventions can draw on different motivation theories, from which specific motivational principles can be derived and applied in interventions. For example, intervention researchers drawing on situated expectancy-value theory (Eccles & Wigfield, 2020) have focused on students’ utility value as a motivational construct and thus attempted to increase the perceived usefulness of the learning material. Another prominent example are growth mindset interventions, which draw on implicit theories of intelligence (Dweck & Leggett, 1988) and are aimed at promoting the belief that intelligence can be increased. The applied theoretical background has direct implications for the intervention. Whereas interventions drawing on expectancy-value theory, which focuses on motivation for specific tasks, have typically been implemented within particular domains or courses, growth mindset interventions can also be implemented at a more general level. Researchers may also decide to use several motivation theories as a backdrop for developing an intervention. In any way, a theoretical grounding of an intervention can be seen as an important criterion for its quality (Rosenzweig & Wigfield, 2016). Motivation interventions can be further differentiated into targeted

216

Hanna Gaspard

interventions, such as utility value interventions or growth mindset interventions, which address a specific motivational construct, and multicomponent interventions, which combine different strategies to foster several motivational constructs (drawing on one or multiple theories). An example for a multicomponent intervention is Concept-Oriented Reading Instruction (CORI), which draws on self-determination theory and interest theory and combines different motivational principles to foster reading engagement such as providing choices, supporting relevance, and providing interesting texts (Guthrie et al., 2004). Both approaches have their advantages and disadvantages. Targeted motivation interventions can provide stronger insights for theory, as the outcomes of the intervention can be more clearly linked to the manipulation of a single motivational construct. Multicomponent interventions, on the other hand, may be more effective in fostering students’ educational outcomes because their effect can unfold through different targeted mechanisms at the same time. Intervention Approach

Independent of the targeted motivational constructs, intervention researchers can then apply different approaches to foster students’ motivation. An important decision is whether the intervention targets students directly or indirectly. Direct interventions involve activities for the students themselves, whereas indirect interventions are focused on changing the beliefs and behaviours of other individuals in the students’ environment (e.g. teachers or parents) to affect the students’ motivation through the behaviour of these others. For example, most growth mindset interventions have targeted the students directly by telling them about the malleability of their intelligence (e.g. Yeager et al., 2016, 2019), but they have also been applied indirectly through the students’ parents who were told how to support their child by praising their effort rather than their performance (e.g. Andersen & Nielsen, 2016). On the one hand, indirect interventions can multiply intervention effects when, for example teachers apply the knowledge they have gained to foster their students’ motivation. Hence, they may reach a larger group of students with lower costs and fewer resources. They can also represent an opportunity to target younger students who might not yet have the necessary abilities to understand the content of the intervention themselves. On the other hand, such indirect interventions entail the risk that the intervention message might get lost. For example, the intervention may be unsuccessful in changing teachers’/ parents’ beliefs or behaviours, or the teachers/parents in turn might not deliver the intervention as intended, all of which may undermine the effects of the intervention. Motivation interventions can also vary in length or dosage. Previous successful interventions range from one-time interventions consisting of brief

Intervening on Students’ Motivation to Learn

217

tasks that take only a few minutes for students to complete over repeated brief intervention activities to extensive programmes that last for several weeks. Importantly, the longest programmes do not seem to produce the strongest intervention effects (for a meta-analysis on autonomy-supportive interventions, see Su & Reeve, 2011). Instead, it seems important to “change students’ mind-sets to help them take greater advantage of available learning opportunities” (Yeager & Walton, 2011, p. 274). Motivation interventions can then generate long-lasting effects on students’ academic outcomes, as they trigger recursive processes that evolve in the educational context (Harackiewicz & Priniski, 2018; Yeager & Walton, 2011). Another important question, especially when it comes to direct interventions, is the framing of the intervention (Yeager & Walton, 2011; Yeager et al., 2016). Many of the brief interventions that have been developed in recent years do not tell the students that they receive an intervention that was developed to help them (a “direct” framing), as this might lead to unwanted effects (e.g. stigmatisation or reactance). Instead, the students participating in an intervention are often asked to evaluate and contribute content for future students (an “indirect” framing). The interventions thereby rely on the “saying-is-believing” mechanism studied in social psychology. Yeager et al. (2016) found that this indirect framing was more effective in changing students’ mindsets than direct framing was. Evaluating the Intervention

To evaluate the effects of an intervention, researchers typically compare the development of motivation in the intervention group with a control group. In some circumstances, researchers may also focus on changes in motivation after receiving an intervention for practical or ethical reasons, which, however, limits the conclusions about intervention effects as compared with the development under control conditions (“treatment as usual”). Studies with a control group can further use either random assignment to intervention and control groups or a quasi-experimental design, in which assignment is based on the study participants’ decisions, organisational reasons, or other reasons. The study design affects internal validity, with randomised studies providing the strongest tests of causality (Shadish et al., 2002). In recent years, intervention studies on motivation have increasingly relied on randomised designs, with randomised designs yielding smaller (and more realistic) effects than quasi-experimental designs (Lazowski & Hulleman, 2016). Intervention effects can be determined for different outcomes assessed from the participating students themselves or relying on other sources. Intervention studies targeting students’ motivation typically rely on self-reports to investigate changes in students’ motivation. As these interventions are typically aimed at improving more distal outcomes as the ultimate goal, performance

218

Hanna Gaspard

measures or behavioural outcomes (e.g. academic engagement or choices) are often considered in addition (Lazowski & Hulleman, 2016). To reach conclusions with external validity, it is important that these outcomes represent authentic educational outcomes that are meaningful beyond the context of the study (e.g. motivation in class, grades, course choices). Maybe even more importantly, external validity is affected by the setting in which the intervention is implemented and evaluated, with field studies having a higher degree of external validity than laboratory studies. Because the level of control that researchers can exercise is lower in field studies, the intervention strength that is achieved is often reduced, which can lead to smaller intervention effects (e.g. Hulleman & Cordray, 2009). Finally, to yield realistic estimates of an intervention’s effects, the sample in which the intervention is tested and evaluated needs to be large enough. This issue has often been neglected in intervention studies targeting motivation, in which power analyses have rarely been reported so far. Because educational outcomes are influenced by multiple factors and because students are nested in classes and schools, large sample sizes are often necessary for testing effects of educational interventions with sufficient power (e.g. Spybrook et al., 2020). Whereas most earlier studies relied on relatively small ad hoc samples, some recent studies have accounted for these issues by evaluating motivation interventions in large samples. An exemplary study by Yeager et al. (2019) tested a growth mindset intervention in a nationally representative sample of 12,490 students from 65 high schools in the United States. Promises and Pitfalls of Intervention Research: How to Design and Test Motivation Interventions in Educational Practice

Overall, previous intervention studies focusing on students’ motivation have yielded promising results. Lazowski and Hulleman’s (2016) meta-analysis, which focused on studies with a randomised or quasi-experimental design that measured an authentic educational outcome, reported a mean effect size of d = 0.49 across 74 studies. These findings are particularly promising, as some of the interventions are very brief, and effects have been found to last over long periods of time, sometimes years after the initial intervention (Yeager & Walton, 2011). However, some studies also failed to replicate positive effects or even reported adverse effects in a different setting (e.g. Canning et al., 2019). Such findings have led to discussions about the robustness of the effects of these interventions, with the growth-mindset intervention as the most prominently discussed one. A meta-analysis that focused on growth-mindset interventions (Sisk et al., 2018) reported an average effect size of d = 0.08 for academic achievement across 43 studies. However, larger effects were found for students with low socioeconomic status or students who were academically at risk, findings that are in line with the theoretical assumptions underlying the intervention.

Intervening on Students’ Motivation to Learn

219

Even seemingly small effects can be practically meaningful if they contribute to solving educational problems in practice. Randomised studies conducted in the educational field typically produce effect sizes that would be judged as small by conventional standards (Kraft, 2020). Therefore, on the basis of the available evidence from such trials, Kraft proposed new benchmarks for evaluating effects on student achievement so that effects between 0.05 and 0.20, for instance, would be judged as medium. Moreover, when implementing these interventions in practice, he suggested that the costs of implementation and the scalability should also be considered. Under these premises, the effects that have been reported in prior meta-analyses on motivation interventions (Lazowski & Hulleman, 2016; Sisk et al., 2018) can be judged to be practically meaningful overall. However, it has also become obvious that not all motivation interventions developed and tested by researchers are effective in educational practice. In the following, I describe several typical pitfalls of intervention research along with recommendations for how to prevent them. Pitfall #1: Positive Effects Are Not Replicable Across Contexts

Concerning replicability, intervention researchers have repeatedly stated that motivation interventions are context-specific, and direct replications, in which one attempts to reproduce intervention effects using the same procedures, should therefore be difficult or even impossible (Harackiewicz & Priniski, 2018; Yeager & Walton, 2011). Instead, conceptual replications, in which one tries to test the same hypothesis using different materials, might be possible. Such replications should consider the meaning of an intervention in a specific context so that the same theoretical constructs are still targeted (e.g. Why might the learning material be relevant for students’ lives in this particular context?). One way to make the intervention meaningful in a specific context is to customise the intervention materials for the target group and the context. Getting feedback on students’ responses to the intervention materials in pilot studies or focus groups can then prove useful for developing or adapting intervention materials for a specific context. Pitfall #2: Intervention Studies Do Not Provide Insights into Mechanisms

Many intervention studies provide limited insights into how an intervention works. In order to open this “black box” of the intervention, it is important to develop a change model (Murrah et al., 2017). Such a model should include several elements. First, it must specify the core components of the intervention and the intervention processes elicited through these components.

220

Hanna Gaspard

Second, the psychological processes (e.g. increased motivation) targeted by these intervention processes should be made explicit. Finally, a change model needs to include the more distal outcomes that can be triggered through these psychological processes. The model can then also be used to understand the mechanisms that explain why an intervention works or why it fails to produce the desired effects in a specific context, which can contribute to test and further develop motivation theory. To test the change model, it is important to assess implementation fidelity (i.e. the degree to which the intervention was implemented as intended) using indicators that align with the change model and tap the different core components. Pitfall #3: Single Studies Yield Limited Evidence About an Intervention’s Effectiveness

Many interventions are tested in only one field trial in a specific population and setting under relatively standardised conditions, so-called efficacy trials (Gottfredson et al., 2015). However, to provide evidence that an intervention works and under which conditions, there is a need for several studies in more diverse populations and settings with a decreasing level of control by the researchers across studies. Efficacy trials should therefore be followed by effectiveness trials, which test an intervention under more varying “realworld” educational conditions. The reduced level of control by the researchers in these trials often leads to more variation in how the intervention is implemented, which can help explain why effects are often reduced at this point (Kim, 2019). Assessing implementation fidelity is therefore even more important in such studies. In the next step, the intervention can be scaled up further. At this stage, it is important to also evaluate whether or under which conditions such an implementation at scale leads to the desired effects. Figure 14.1 presents the phases of development, stepwise implementation, and evaluation of an intervention in educational practice. In each phase, it might be necessary to further develop the intervention on the basis of the findings in this phase, similar to the iterative cycles used in design-based research (The Design-Based Research Collective, 2002).

FIGURE 14.1

Development, stepwise implementation, and evaluation of an intervention in educational practice (adapted from Herbein et al., 2018)

Intervening on Students’ Motivation to Learn

221

Pitfall #4: Interventions Produce Heterogeneous Effects Across Students and Contexts

Across the different steps of evaluating an intervention in practice (i.e. efficacy and effectiveness trials and scale-up research), it is important to investigate heterogeneity in intervention effects. Moderation analyses in the earlier phases, where there is little variation in context, may focus on student factors. Indeed, the effects of motivation interventions have been found to vary depending on student factors such as gender, family background or prior motivation (Lesperance et al., 2022; Rosenzweig & Wigfield, 2016). To understand the mechanisms driving larger intervention effects for some subgroups of students, it can be useful to investigate differences in intervention processes. For instance, Harackiewicz et al. (2016) showed that firstgeneration underrepresented minority students, who benefitted the most from the intervention in their study, also showed the highest cognitive engagement in the intervention. Studies in which the intervention is implemented in more diverse contexts also make it possible to test whether the intervention effects vary by context. For example, Yeager et al. (2019) showed that a growth-mindset intervention worked best in schools with peer norms that supported the intervention message. Understanding why interventions work under specific conditions is crucial to develop interventions that are effective in practice, but can also contribute to gaining further insights for theory (e.g. the contexts in which specific motivational principles apply).

Illustrative Example: MoMa Intervention

In this paragraph, I describe evidence from several studies that evaluated the Motivation in Mathematics (MoMa) intervention as an illustrative example of a motivation intervention. I also describe the promises and pitfalls discovered in testing this intervention. The MoMa intervention draws on situated expectancy-value theory (Eccles & Wigfield, 2020) and targets students’ perceived relevance of mathematics. The intervention was developed for ninth-grade students in academic track schools in Germany. Students in these schools have been found to show a pronounced decline in their utility value in mathematics across the secondary school years (e.g. Gaspard et al., 2017). The intervention can therefore be seen as a “universal prevention” against further decreases in perceived usefulness. Drawing on prior intervention studies with high school and college students in the United States (e.g. Hulleman & Harackiewicz, 2009), different individual tasks were developed and piloted in a few math classes. One of these tasks (i.e. writing a text about the usefulness of what students learn in class) was already tested in prior studies, whereas another task (i.e. reading and evaluating relevance-related quotations from young adults as potential

222

Hanna Gaspard

FIGURE 14.2

Change model of the MoMa intervention (adapted from Gaspard et al., 2021)

role models) was newly developed in this study. However, different from prior studies (e.g. Hulleman & Harackiewicz, 2009), these tasks did not focus on the usefulness of specific topics currently taught in class but on mathematics as a more general domain. In the pilot study, it became obvious that the targeted adolescents struggled to write elaborate texts on this topic. The research team therefore decided to provide some more scaffolding. Consequently, a 90-min intervention was subsequently implemented, which consisted of a psychoeducational presentation for the whole class followed by individual tasks for the students. To inoculate students against potential negative effects of highlighting the relevance of mathematics for students with low expectancies of success, the presentation began with research results on the importance of effort and self-concept for math achievement and frameof-reference effects in the classroom. This part targeted students’ mindsets and their expectancies, whereas the later parts targeted students’ utility value (see the change model in Figure 14.2). The examples that followed to highlight the usefulness of mathematics in different careers were targeted towards typical aspirations in the academic track. For instance, because the majority of academic-track students attend university later (Autor:innengruppe Bildungsberichterstattung, 2022), students were presented with a list of different study majors in which mathematics is a part of the curriculum. This 90-min intervention was tested in two large, randomised trials with 82 and 78 ninth-grade classrooms from 25 and 28 academic track schools, respectively, in the German state of Baden-Württemberg. A minimum sample size of 75 classrooms resulted from a power analysis with the goal to be able to also detect smaller, realistic effect sizes of 0.20 (for more details, see Gaspard et al., 2015, 2021). In both studies, the classrooms were randomly assigned to one of two intervention conditions or a waitlist control condition within each school. As an efficacy trial, the MoMa 1 study tested whether students’ value beliefs could be promoted through such an intervention in the classroom under relatively optimal, standardised conditions. Doctoral

Intervening on Students’ Motivation to Learn

223

students who were involved in its development delivered the intervention in the classrooms. The study was also designed to investigate which intervention approach (i.e. text or quotations approach) was more efficacious in promoting students’ value beliefs (Brisson et al., 2017; Gaspard et al., 2015). The quotations task proved to be more successful in this target group. Although students in both intervention conditions reported higher utility value compared with the waitlist control condition six weeks and five months after the intervention, respectively, the effects in the quotations condition (d = 0.30/0.26) were larger than in the text condition (d = 0.14/0.16). In the quotations condition, positive effects were also observed for attainment value, intrinsic value, self-concept, a standardised math test, and teacher-reported effort. Furthermore, analyses of different student moderators indicated that girls (Gaspard et al., 2015) and students from families with lower interest in mathematics (Häfner et al., 2017) showed comparatively stronger intervention effects. As these groups were found to be at risk for low motivation in mathematics in this context, the intervention contributed to reducing gaps in motivation related to gender and family background. Finally, students’ writing in both intervention conditions was coded to gain an indicator of their compliance with the respective instructions. Particularly in the text condition, students who complied with the instructions showed stronger intervention effects compared with noncompliant students (Nagengast et al., 2018). In the next step, MoMa 2 as an effectiveness trial tested whether these effects could be replicated under conditions that are closer to educational practice (Gaspard et al., 2021). To this end, the intervention was delivered through the regular math teachers or through education science master’s students, all of whom were trained for this purpose. In both intervention conditions, the intervention was an optimised and more manualised version of the quotations condition in MoMa 1. To assess implementation fidelity more extensively, two observers were present in each intervention and rated the adherence to the script, the behaviour of the participating students, and the quality of delivery. Although the master’s students showed greater adherence than the teachers, there were no substantial differences in the effectiveness of the two intervention conditions. In both the master’s student and the teacher condition, positive intervention effects were again observed on utility value four weeks (d = 0.15/0.18) and three months (d = 0.10/0.09) after the intervention, even though these effects were smaller than in MoMa 1. Across the two intervention conditions, more positive changes in utility value were associated with higher adherence, a lack of discipline problems, and autonomy support during the intervention. In the master’s student condition, an additional increase in the importance of effort and a decrease in the importance of talent for math achievement as well as higher performance on a standardised math test were observed compared with the control condition. Unexpectedly, students in both intervention conditions also reported

224

Hanna Gaspard

higher perceived cost related to studying mathematics in school after the intervention compared with students in the control condition. These effects on cost could be due to changes in the intervention materials compared with MoMa 1 (e.g. overemphasising the importance of effort in mathematics) or the quality of delivery through master’s students and teachers (e.g. a less autonomy-supportive and more controlling style). Overall, based on a rigorous scientific evaluation, the results from the two MoMa studies show that classroom-based relevance interventions can have long-lasting effects, even when delivered by the teachers. However, the effects in the effectiveness study were reduced in size, could not be observed on all targeted outcomes, and were accompanied by undesired effects (i.e. an increase in cost). Therefore, the intervention does not yet appear to be ready to be implemented at scale. However, the two studies provided many insights into the validity of the theoretical assumptions in educational practice and began to provide answers to the questions of how, for whom, and under which conditions expectancy-value beliefs can be promoted. As a next step, there is a plan to use these insights to adapt the intervention materials to individual students’ needs and interests. Conclusions and Steps for Future Research

Intervention studies can provide important insights for theory and educational practice. Over the past two decades, the number of intervention studies in the field of motivation has increased. This research has shown that even brief motivation interventions can have surprisingly large and longlasting effects. Although these findings are promising, the interventions are context-dependent, and it is thus not surprising that effects do not always replicate across settings. Interventions should therefore always be carefully developed or adapted for the target population. More research is needed to understand the processes through which motivation interventions work and under which conditions. It will therefore be important for future intervention studies to systematically explicate the hypothesised change models and to measure intervention and psychological processes to be able to test these models. Moreover, student and context factors as potential moderators of intervention effects need to be tested more systematically. This research will be important not only to develop effective interventions for practice, but also for further theory development. Finally, it might be necessary to adapt motivation interventions to the needs of individual students instead of applying “one-size-fits-all” programmes. Such adaptations might also involve providing an additional dose of an intervention to students who need it on the basis of an assessment of proximal outcomes of the intervention. Evaluating such adaptive intervention approaches might then require researchers to make use of different study designs. For instance, sequential multiple assignment randomised trial (SMART) designs randomly assign individuals to conditions

Intervening on Students’ Motivation to Learn

225

several times and can thereby provide evidence of the best tailoring variables and decision rules for adaptive interventions (Collins et al., 2007). References Andersen, S. C., & Nielsen, H. S. (2016). Reading intervention with a growth mindset approach improves children’s skills. Proceedings of the National Academy of Sciences of the United States of America, 113(43), 12111–12113. https://doi. org/10.1073/pnas.1607946113 Autor:innengruppe Bildungsberichterstattung. (2022). Bildung in Deutschland 2022 Ein indikatorengestützter Bericht mit einer Analyse zum Bildungspersonal [Education in Germany 2022 An indicator-supported report with an analysis on educational personnel]. wbv Media. https://doi.org/10.3278/6001820hw Brisson, B. M., Dicke, A.-L., Gaspard, H., Häfner, I., Flunger, B., Nagengast, B., & Trautwein, U. (2017). Short intervention, sustained effects: Promoting students’ math competence beliefs, effort, and achievement. American Educational Research Journal, 54(6), 1048–1078. https://doi.org/10.3102/0002831217716084 Canning, E. A., Priniski, S. J., & Harackiewicz, J. M. (2019). Unintended consequences of framing a utility-value intervention in two-year colleges. Learning and Instruction, 62, 37–48. https://doi.org/10.1016/j.learninstruc.2019.05.001 Collins, L. M., Murphy, S., & Strecher, A., V. (2007). The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART). New methods for more potent ehealth interventions. American Journal of Preventive Medicine, 32(5S), 112–118. https://doi.org/10.1016/j.amepre.2007.01.022 Dweck, C. S., & Leggett, E. L. (1988). A social–cognitive approach to motivation and personality. Psychological Review, 95, 256–273. https://doi.org/10.1037/ 0033-295X.95.2.256 Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Gaspard, H., Dicke, A.-L., Flunger, B., Brisson, B. M., Häfner, I., Nagengast, B., & Trautwein, U. (2015). Fostering adolescents’ value beliefs for mathematics with a relevance intervention in the classroom. Developmental Psychology, 51(9), 1226–1240. https://doi.org/10.1037/dev0000028 Gaspard, H., Häfner, I., Parrisius, C., Trautwein, U., & Nagengast, B. (2017). Assessing task values in five subjects during secondary school: Measurement structure and mean level differences across grade level, gender, and academic subject. Contemporary Educational Psychology, 48, 67–84. https://doi.org/10.1016/j. cedpsych.2016.09.003 Gaspard, H., Parrisius, C., Piesch, H., Kleinhansl, M., Wille, E., Nagengast, B., Trautwein, U., & Hulleman, C. S. (2021). The potential of relevance interventions for scaling up: A cluster-randomized trial testing the effectiveness of a relevance intervention in math classrooms. Journal of Educational Psychology, 113(8), 1507–1528. https://doi.org/10.1037/edu0000663 Goetz, T., Haag, L., Lipnevich, A. A., Keller, M. M., Frenzel, A. C., & Collier, A. P. M. (2014). Between-domain relations of students’ academic emotions and their judgments of school domain similarity. Frontiers in Psychology, 5, Article 1153. https://doi.org/10.3389/fpsyg.2014.01153

226

Hanna Gaspard

Gottfredson, D. C., Cook, T. D., Gardner, F. E. M., Gorman-Smith, D., Howe, G. W., Sandler, I. N., & Zafft, K. M. (2015). Standards of evidence for efficacy, effectiveness, and scale-up research in prevention science: Next generation. Prevention Science, 16(7), 893–926. https://doi.org/10.1007/s11121-015-0555-x Guthrie, J. T., Wigfield, A., Barbosa, P., Perencevich, K. C., Taboada, A., Davis, M. H., Scafiddi, N. T., & Tonks, S. (2004). Increasing reading comprehension and engagement through concept-oriented reading instruction. Journal of Educational Psychology, 96(3), 403–423. https://doi.org/10.1037/0022-0663.96.3.403 Häfner, I., Flunger, B., Dicke, A. L., Gaspard, H., Brisson, B. M., Nagengast, B., & Trautwein, U. (2017). Robin Hood effects on motivation in math: Family interest moderates the effects of relevance interventions. Developmental Psychology, 53(8), 1522–1539. https://doi.org/10.1037/dev0000337 Harackiewicz, J. M., Canning, E. A., Tibbetts, Y., Priniski, S. J., & Hyde, J. S. (2016). Closing achievement gaps with a utility-value intervention: Disentangling race and social class. Journal of Personality and Social Psychology, 111(5), 745–765. https://doi.org/10.1037/pspp0000075 Harackiewicz, J. M., & Priniski, S. J. (2018). Improving student outcomes in higher education: The science of targeted intervention. Annual Review of Psychology, 69, 409–435. https://doi.org/10.1146/annurev-psych-122216-011725 Herbein, E., Golle, J., Tibus, M., Zettler, I., & Trautwein, U. (2018). Putting a speech training program into practice: Its implementation and effects on elementary school children’s public speaking skills and levels of speech anxiety. Contemporary Educational Psychology, 55, 176–188. https://doi.org/10.1016/j. cedpsych.2018.09.003 Hulleman, C. S., & Cordray, D. S. (2009). Moving from the lab to the field: The role of fidelity and achieved relative intervention strength. Journal of Research on Educational Effectiveness, 2(1), 88–110. https://doi.org/10.1080/19345740802539325 Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410–1412. https://doi. org/10.1126/science.1177067 Kim, J. S. (2019). Making every study count: Learning from replication failure to improve intervention research. Educational Researcher, 48(9), 599–607. https:// doi.org/10.3102/0013189X19891428 Kraft, M. A. (2020). Interpreting effect sizes of education interventions. Educational Researcher, 49(4), 241–253. https://doi.org/10.3102/0013189X20912798 Lazowski, R. A., & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational Research, 86(2), 602–640. https:// doi.org/10.3102/0034654315617832 Lesperance, K., Hofer, S., Retelsdorf, J., & Holzberger, D. (2022). Reducing gender differences in student motivational-affective factors: A meta-analysis of schoolbased interventions. British Journal of Educational Psychology, 92(4), 1502–1536. https://doi.org/10.1111/bjep.12512 Murrah, W. M., Kosovich, J., & Hulleman (2017). A framework for incorporating intervention fidelity in educational evaluation studies. In G. Roberts, S. Vaugn, S. N. Beretvas, & V. Wong (Eds.), Treatment fidelity in studies of educational intervention (pp. 39–60). Routledge. Nagengast, B., Brisson, B. M., Hulleman, C. S., Gaspard, H., Häfner, I., & Trautwein, U. (2018). Learning more from educational intervention studies: Estimating

Intervening on Students’ Motivation to Learn

227

complier average causal effects in a relevance intervention. Journal of Experimental Education, 86(1), 105–123. https://doi.org/10.1080/00220973.2017.1289359 Pintrich, P. R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95(4), 667–686. https://doi.org/10.1037/0022-0663.95.4.667 Rosenzweig, E. Q., & Wigfield, A. (2016). STEM motivation interventions for adolescents: A promising start, but further to go. Educational Psychologist, 51(2), 146–163. https://doi.org/10.1207/s15326985ep2003 Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasiexperimental designs for generalized causal inference. Houghton, Mifflin and Company. Sisk, V. F., Burgoyne, A. P., Sun, J., Butler, J. L., & Macnamara, B. N. (2018). To what extent and under which circumstances are growth mind-sets important to academic achievement? Two meta-analyses. Psychological Science, 29(4), 549–571. https://doi.org/10.1177/0956797617739704 Spybrook, J., Zhang, Q., Kelcey, B., & Dong, N. (2020). Learning from cluster randomized trials in education: An assessment of the capacity of studies to determine what works, for whom, and under what conditions. Educational Evaluation and Policy Analysis, 42(3), 354–374. https://doi.org/10.3102/0162373720929018 Su, Y. L., & Reeve, J. (2011). A meta-analysis of the effectiveness of intervention programs designed to support autonomy. Educational Psychology Review, 23(1), 159–188. https://doi.org/10.1007/s10648-010-9142-7 The Design-Based Research Collective. (2002). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8. https://doi. org/10.3102/0013189X032001005 Yeager, D. S., Hanselman, P., Walton, G. M., Murray, J. S., Crosnoe, R., Muller, C., Tipton, E., Schneider, B., Hulleman, C. S., Hinojosa, C. P., Paunesku, D., Romero, C., Flint, K., Roberts, A., Trott, J., Iachan, R., Buontempo, J., Man Yang, S., Carvalho, C. M., & Dweck, C. S. (2019). A national experiment reveals where a growth mindset improves achievement. Nature, 573(7774), 364–369. https://doi. org/10.1038/s41586-019-1466-y Yeager, D. S., Romero, C., Paunesku, D., Hulleman, C. S., Schneider, B., Hinojosa, C., Yeon Lee, H., O’Brien, J., Flint, K., Roberts, A., Trott, J., Greene, D., Walton, G. M., & Dweck, C. S. (2016). Using design thinking to improve psychological interventions: The case of the growth mindset during the transition to high school. Journal of Educational Psychology, 108(3), 374–391. https://doi.org/10.1007/ s13398-014-0173-7.2 Yeager, D. S., & Walton, G. M. (2011). Social-psychological interventions in education: They’re not magic. Review of Educational Research, 81(2), 267–301. https:// doi.org/10.3102/0034654311405999

15 AFFECTIVE PROCESSES IN COLLABORATIVE LEARNING CONTEXTS Examining Affordances and Challenges of Video and Multi-Channel Data Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola and Cheryl Jones Abstract The possibilities of using video to explore learning and interactions in context have grown significantly in recent decades. At the same time, the role of affect in collaborative learning has been increasingly recognised and examined. However, studies in this area have largely relied on self-reported data. When video data have been used, findings have been reported in the form of descriptive accounts of illustrative cases as a way of dealing with complex and large datasets. This chapter focuses on recent research developments in collaborative learning, stressing the value of video as a versatile data source in itself and when used in conjunction with other forms of data. It is argued that video captures the socially dynamic and evolving nature of affect incontext: Specifically, (a) the multi-level nature of affect, capturing individual, sub-group and whole-group displays of emotions; (b) the temporal ebbs and flows of affective processes; and (c) the specific affordances of observational data when combined with self-reported and physiological data. In conclusion, this chapter acknowledges the advances made so far in this field while alerting the reader to current challenges and future developments.

Introduction

The study of human interaction captured through video has a long history, starting from the development of video technology (Jordan & Henderson, 1995). However, its use in the examination of emotions in authentic social contexts has gained prominence only in the past decades (Boekaerts & Corno, 2005). In the learning sciences, emotion-related research has traditionally used self-reported data to study motivation, test anxiety, and different DOI: 10.4324/9781003303473-17

Affective Processes in Collaborative Learning Contexts

229

learning-related emotions (Pekrun, 2017). However, more recently, research has progressed towards a situated approach that acknowledges emotions’ complex connections with social environments (Jones et al., 2021, 2022; Lajoie et al., 2015; Rogat & Adams-Wiggins, 2015). Video data are particularly valuable in capturing emotions in social environments as emotions naturally emerge in group contexts (Järvenoja et al., 2019; Lobczowski, 2022). Fine-grained analysis of video complemented with other data sources has potential to enhance the understanding how emotions drive interpersonal interaction and vice versa. This theoretical understanding can be used for pedagogical purposes to promote individual and social awareness and create learning designs and tools that consider affective aspects of interactions in a sensitive and focused manner. In present times, as almost no wicked problem faced by humanity can be solved by the efforts of individuals, we argue that unpacking the critical elements leading to constructive, productive, and diverse-embracing affective dynamics of collaboration is a worthwhile contribution researchers can make. In this chapter, we approach this argument by providing examples of recent research, including our own, to illustrate how affective processes have been studied in the intersection of individual and group-level processes in collaborative learning. For clarity, we will use the term affective processes (Scherer, 2005) when we refer to various affect and emotion-related individual and social constructs from recent research. This is because “affect” as an umbrella construct covers various emotion related phenomena such as emotions, moods, and stress responses (Gross, 2015; Scherer, 2005). In the examples, we showcase how video data alone and combined with other data forms has the capacity to capture individual, social and temporal dimensions of affective processes in situ. In looking into the future, we consider some issues, potential, and prospects of AI supported video analysis in exploring affective processes, and their analytical possibilities. Affective Processes in Collaborative Learning: Key Concepts and Theories

Research shows that when collaborative aspects of learning function well, group outcomes can be of better quality than those reached by individuals (Howe & Zachariou, 2019; Khosa & Volet, 2014). However, collaborative learning requires skills to manage cognitive, behavioural, motivational, and affective processes individually and within the group. This is because collaboration is not only about “getting along” or making sure each member does their share. It is about building shared understanding and reflections, managing learning-related and social challenges, and making joint decisions throughout the learning process (Dillenbourg, 1999; Järvenoja et al., 2020; Roschelle & Teasley, 1995). All this requires mutual trust and safety to

230

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

exchange viewpoints and engage in productive argumentation (Kreijns et al., 2003; Rogat & Adams-Wiggins, 2015). Hence, for a favourable atmosphere to be created and sustained, group members require awareness of and skills to manage group’s affective processes (Järvenoja et al., 2020). Recent research has addressed affective processes that contribute to group’s socioemotional functioning, focusing on both individual- and grouplevel analytic units. Examples of individual level (or individual-in-group) concepts are affective states (Törmänen et al., 2021), emotional experiences (Mänty et al., 2020) and interpersonal affect behaviours (Jones et al., 2022). Group-level concepts, in turn, are for example co- and socially shared emotion regulation (Järvenoja et al., 2020; Lobczowski et al., 2021), socioemotional interactions (Mänty et al., 2020), socioemotional challenges (Näykki et al., 2014), socioemotional climate (Bakhtiar et al., 2018), and emotion contagion (for review see Jones et al., 2021). The circumplex model of affect (Russell & Barrett, 1999) has lately been identified as a useful theoretical approach to explore the affective processes in learning (Törmänen et al., 2021). This model captures dimensions of affect, namely valence and activation, that research has linked to learning process and its outcomes, mainly connecting positive activating affect to success in learning and group interaction, and negative deactivating affect to less successful outcomes (Boekaerts & Pekrun, 2015; LinnenbrinkGarcia et al., 2011). Emotions, more specifically, manifest through physical, sensory, expressive, cognitive, and motivational/behavioural components (Boekaerts & Pekrun, 2015). Even though these components can be examined at an individual level, emotions also have a strong social component (Barsade & Knight, 2015; Mesquita & Boiger, 2014). Gross’ (2015) process model of emotions, for instance, emphasises the appraisals of internal or external (social) conditions that generate emotional responses. Finally, emotion regulation involves awareness of emotions and the capacity to exert control over the emotion experienced or displayed (Gross, 2015). The issue in applying these theoretical concepts to affective processes in collaborative learning is, however, that they are originally described as individual level processes. The boundaries of individual and group-level affective processes are not straightforward because they are manifested in sequences of micro-level displays of emotions, gestures, verbal interactions, and regulatory efforts (Jones et al., 2021; Näykki et al., 2014). These individual and group-level emotional processes come together in socioemotional interactions or interpersonal (affective) processes: in exchanges among group members that shape the perceptions and expressions of emotions and socioemotional climate (Kreijns et al., 2003; Marks et al., 2001). Thus, socioemotional interactions provide a window to analyse reciprocal relationship of individual and group level affective processes, and their fluctuation. However, to understand these

Affective Processes in Collaborative Learning Contexts

231

processes, there is a need to grasp how they are triggered, perceived, manifested, and regulated (Bakhtiar et al., 2018; Lobczowski, 2020). Lately, theories of self-regulated learning have been expanded to capture the regulation of emotions in learning as intrapersonal and interpersonal phenomena. For example, Bakhtiar et al. (2018) applied the Conditions, Operations, Products, Evaluation, Standards (COPES) model (Winne & Hadwin, 1998) to understand the micro-level socioemotional elements of collaborative learning. In their research, socioemotional climate was regarded as conditions for collaborative learning, and the products of regulation of learning, which then become new conditions for learning. This happens through operations that take place in socioemotional interactions and regulation that occurs within the group. A similar approach has been used by Törmänen et al. (2021), who utilised the COPES model to understand the temporal connections of individual affective conditions and their role in group’s emotion regulation in collaborative learning settings. In addition, Lobczowski (2020) has considered various theoretical approaches (e.g. Gross, 2015; Hadwin et al., 2018) in her Formation and Regulation of Emotions in Collaborative Learning (FRECL) model, which describes the process of emotion formation and regulation and considers the social dimension of emotions in collaborative learning. Despite these bold theoretical moves to understand the interpersonal correlates of affective processes during group interactions, the nature and role of emotions in the temporal interplay of the individual and the group are only partially understood. The following section describes studies that have used video data to illuminate some of the complexities argued above. Empirical Research on Affective Processes in Collaborative Learning

Early in the history of video research, video-based studies contributed to identifying some core processes of collaboration, such as mutual engagement (Bakeman & Adamson, 1984) and joint problem solving (Barron et al., 2013). With the increase of research on emotions in learning, socioemotional processes in collaborative learning have also gained increasing attention (Näykki et al., 2014; Rogat & Adams-Wiggins, 2015). The benefit of video data is that it can capture affective processes temporally and at multiple levels (individual, sub-group, and group). Video analysis can (1) capture expressed emotions; (2) connect these to the responses they elicit in other group members (Jones et al., 2022; Törmänen et al., 2021); (3) identify the temporal coordination of these expressions and behaviours and (4) identify the triggers and regulation activated on a social plane (Järvenoja et al., 2019). Furthermore, these cues can be used for (5) analysing more stable socioemotional interactions such as socioemotional climate or group mood (Lobczowski et al.,

232

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

2021), and (6) detecting changes and patterns in group level phenomena during the learning process (Törmänen et al., 2021). Further, by combining video data with other data sources, (7) even indications of emotions not visible to the eye, such as emotional experiences or physiological states, can add to the analysis of multiple dimensions of emotional processes (Mänty et al., 2020; Törmänen et al., 2021). In this section, we will use example studies to illustrate how video data with other data sources have enabled the analysis of multi-level (from individual to group-level data), multi-channel (from overt verbal and non-verbal behaviour to covert physiological markers), and temporal affect phenomena (sequences and patterns). We argue that in integrating these analytic dimensions video data can contribute to significant advancements in the understanding of interpersonal affect in genuine collaborative contexts. Overview of Example Studies

The following example studies (Jones et al., 2022; Mänty et al., 2020; Törmänen et al., 2021) illustrate dynamic combinations of individual and group-based video analyses to capture the fluidity of the individual and the collective in affective processes. Mänty et al.’s (2020) and Törmänen et al.’s (2021) studies explored 12-year-old students’ collaborative working in small groups. These studies shared the same data and some basic analysis locating socioemotional interactions and emotion regulation episodes within interactions. In their analysis, Mänty connected the individual group members’ self-reported emotional experiences to groups’ negative socioemotional interactions and explored the temporal interplay of co- and socially shared emotion regulation and valence of interactions at group level (Mänty et al., 2020). Using the COPES architecture of regulated learning (Winne & Hadwin, 1998), Törmänen took a closer look at the interplay between individual students’ co- and socially shared emotion regulation behaviours, and the conditions and products of this regulation, namely emotional valence and activation, and participation in task execution (Törmänen et al., 2021). Törmänen’s analysis differed from Mänty’s in that she focused on individuals’ contributions and affective states within the group activity, whereas Mänty’s analysis of socioemotional interactions and regulation was at group level as the example of socioemotional interaction episode illustrates (Figure 15.1). Jones et al. (2022), in turn, explored interpersonal effect of two groups of university students from the same class that reported divergent group dynamics outcomes in terms of valence. The study traced affect dynamics as sequentially unfolding phenomena in group interactions as they naturally wove on- and off-task. Salient episodes of interpersonal affect behaviours were identified and explored through fine-grained qualitative analysis that unveiled interactive dynamics.

Affective Processes in Collaborative Learning Contexts

FIGURE 15.1

233

An example showcasing the different analysis levels of Törmänen’s and Mänty’s studies. Figure is adapted from Törmänen et al. (2021). Also analysed in Mänty et al. (2020)

Multi-Level: From Individual to Group-Level Data

Törmänen explored how individual students’ affective states and participation set a stage for and was affected by the roles (initiator, contributor, target, observer) students had in group-level emotion regulation (see Figure 15.1). The analysis was tightly connected to the group process, but the focus was on individuals’ affective states and actions. Socioemotional interactions were analysed in detail based on the students’ affective states, participation in interactions and their roles in group level regulation of emotions. Based on the COPES model, affective states and students’ participation to interaction were then considered as an affective condition for group level regulation, which, in turn, was considered as operations that led to potentially changed affective conditions (products of regulation). In their examination of negatively charged interactions, Mänty explored the extent to which groups’ emotional processes were connected to individual

234

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

students’ self-reported emotional experiences. Even though individuals’ emotional experiences were connected to the video analysis of collaborative learning process, the video analysis of socioemotional interactions and their valence as well as activated emotion regulation were analysed at group level (Figure 15.1). Jones explored the function of interpersonal affect that emerged at different systemic levels (individual-in-group; sub-group; group) and the dynamic interplay of the multiple levels as they evolved over time. A conceptually grounded coding scheme captured everyday individual affect behaviours such as humour, laughter, and side conversations that arose during groups’ social interaction. For example, the relatively high frequency of the code side-talk in one group led to the qualitative identification of dyadic subgroup emergence. The example studies showcase the unique affordances of video data for researchers. Even in the same dataset, it is possible to fine-tune the analysis to different and multiple levels while understanding the reciprocal influences of behaviours displayed at each level. This is, video data enables the examination of (1) individual behaviours as triggers, antecedents, or creators of affective group dynamics as well as (2) more general descriptions of such dynamics at sub-group, group, session or multiple session level. Multi-Channel Data: Detecting Emotional Processes from Video and Other Data Sources

A challenge in using video data is that it only captures overt verbal and nonverbal behaviours and leaves thoughts, feelings, physiological changes, intentions, and individual regulatory efforts hidden, if they are not expressed. Our example studies show how additional data sources can assist researchers in unveiling some of those “hidden” aspects of affective processes that shape interpersonal dynamics in collaborative learning. In Törmänen’s study, EDA – physiological arousal data – was used for a more nuanced assessment of individual students’ affective states not visible in the video data. This data source was easy to integrate in the analysis, as both EDA and video were temporal data collected simultaneously. When clustering the affective conditions, EDA showed its additional value in identifying the individuals’ affective conditions in more detail. Figure 15.1 shows how the combination of EDA and video data helped to classify Benjamin’s affective states during group activity. In Mänty’s study, bivariate correlations were used to examine associations between self-reported emotional experiences and the overall time the group spent displaying negative interactions and regulating them. This analysis enabled the identification of complex connections of individual emotional experiences to group-level processes in collaborative learning. It also showcased

Affective Processes in Collaborative Learning Contexts

235

the importance of identifying the target of emotions, to understand how collaborative learning processes are connected to individuals’ experiences. Jones’ study combined video analyses with focus group interviews eliciting participant perceptions of their experiences at the end of all collaborative sessions. Free-flow discussions and video-stimulated dialogues prompted participants’ reflections on group interactive dynamics. Interview transcripts were analysed for evidence of negative or positive affect expressed and for social dynamics as they unfolded during the interviews, for example how participants responded to one another’s reflections. In this way, the analyses of interview data extended the group dynamics picture that emerged through the observational analyses. The value of additional data sources is that they unravel internal affective processes that cannot be analysed merely using video. This can further elucidate affective processes by identifying associations between individual and group responses and by enriching the understanding of complex interpersonal affect. Additional data can also contribute to the reliability of analysis as the coherence of emotion response components (e.g. expressed and experienced emotions) can vary (Mauss et al., 2005). Temporal Data: Examining Sequences and Patterns

Exploring frequencies of affective states, emotions and emotion regulation indicators has been a commonly used approach to examine affective processes using video data, with temporality being addressed either as timebased changing patterns in such frequencies or via qualitative, descriptive (narrative) approaches, where temporal fluctuations have been showcased by in-depth analysis of interactions. Jones, for instance, followed a combination of such approaches to unveil macro- and micro-temporal patterns leading to the emergence and development of interpersonal affect dynamics that became relatively consistent over five group meetings. As shown in Figure 15.2, coding frequencies provided a macro-temporal evolutionary perspective of each group. This revealed specific behaviours that were present to different extents in group meetings, how they evolved over a semester, and the divergent pathways of interpersonal affect across groups over time. The analytical lens was then honed in particular affect behaviours arising in each group during their first meeting, and their influence on group dynamics over the time period examined. The example presented in Figure 15.2 illustrates interpersonal affect dynamics of the group who reported negative affect dynamics outcomes. The macrotemporal perspective showcases higher frequencies of side talk, negative small talk and mobile phone use, when compared to more positive demonstrations of interpersonal affect. The micro-temporal sequence (from the group’s last meeting) shows how two members used side talk and mobile

236

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

FIGURE 15.2

Macro- and micro-temporal perspectives on the analysis of interpersonal affect (Jones et al., 2022)

phone activity as a way distancing themselves from the group’s activity (Jones et al., 2022). The richness of the approaches illustrated above has been further enhanced in the past decade through various quantitative analytic methods. Examples include data mining techniques that can unearth interactional patterns in exploratory ways (Järvenoja et al., 2019; Törmänen et al., 2021) and lag sequential analyses, empirically investigating the probability of specific affective processes to be followed or preceded by other affective, social, or cognitive phenomena (Mänty et al., 2020; Schneider et al., 2018). In Mänty’s study, negative socioemotional interaction episodes with or without group’s emotion regulation were coded from the video data (Figure 15.3). Time lag sequential analysis was conducted for group’s interaction episodes, investigating whether groups’ emotion regulation during negative socioemotional interactions contributed to the valence of the following interactions. This was carried out by locating and establishing the valence of the interactions following a negative interaction with or without regulation

Affective Processes in Collaborative Learning Contexts

FIGURE 15.3

237

Illustration of sequential patterns of the outcomes of negative regulated interactions. Figure also presents self-report results for pre- and post-test of emotional experiences (1–5 scale, negativeneutral-positive)

in one-minute intervals. Figure 15.3 includes a snapshot from Mänty’s analysis timeline using The Observer XT software. This example illustrates that regulated negative interactions were followed by positive or neutral interactions (Mänty et al., 2020). In Törmänen’s study, lag sequential analysis was also used when exploring the connections between students’ different affective conditions (activated, positive/neutral de-activated, negative de-activated, and not participating) and their roles in group’s emotion regulation (initiator, contributor, target, observer) during socioemotional interactions. Törmänen continued to explore the data by running a process analysis with Fluxicon Disco (https://fluxicon. com/disco/), to identify most common patterns of different conditions, emotion regulation behaviour, and products among the students in different clusters. Overall, Törmänen’s analysis brings to the fore processes in which socioemotional interactions are regulated and change at group level, which Mänty’s study examined at more general level. As described in the example studies, video offers possibilities to unravel temporal patterns in collaborative interactions from micro-level emotional expressions and behaviour to semester long changes in interpersonal affect. This increases the theoretical understanding of the flow of affective processes in collaborative learning and identifying patterns of interactions potentially leading to or hindering productive group interactions towards learning goals. However, manual analysis of video data is laborious and time consuming. New technologies for video analysis may

238

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

bring solutions but also new issues to consider in the analysis of affective processes. Future Directions in Video Analysis

It is apparent that in addition to the technologies illustrated in the examples above, video research is on a fast trajectory towards the use of Artificial Intelligence (AI) to address the laborious and interpretative nature of manual coding. This could potentially increase the amount of data to be analysed and, at least partially, minimise interpretation issues in video research. For example, video or audio analysis software based on AI have been tested so far in facial recognition (Lajoie et al., 2021) and speech recognition (Akçay & Oğuz, 2020) to explore discrete emotions (e.g. happy, sad, angry). Deep learning techniques have been used to train the software to detect and classify traces of different emotions from facial movements or waveforms of speech. Even remote physiological measures from video data have been developed to detect emotional reactions from the colour changes reflecting the blood flow under the skin (Yu et al., 2021). However, these tools require optimal conditions to reliably detect affective processes. Background noise, changes in postures and movement, as well as changes in lighting, for example reduce the reliability of the analysis (e.g. Akçay & Oğuz, 2020; Yu et al., 2021). So far, these technologies haven’t been sensitive enough to be applied to authentic settings, such as classrooms and work environments, but they will likely develop in this direction. As promising as these technologies may be, emotions are nested within contexts and cultural conventions that cannot (yet) be captured via the patterns of facial movements or speech alone. For example, reliability issues have been identified in AI-based analytic processes of facial recognition of emotions. While this analysis detects facial muscle movements, the translation of these into emotions is not straightforward. People not only mask their emotions but also express them in a diverse way, depending on individual differences and contextual and cultural expectations (Barrett et al., 2019). Another significant challenge is that the interpretation of emotions or affective processes requires the integration of verbal information with a range of other non-verbal behavioural manifestations such as gazes, nods, and changes in posture as well as object manipulation and management of proxemics. The integration of different automated techniques could be a way to address some of these challenges (Akçay & Oğuz, 2020). Larger datasets including small and simultaneous units of coding can now be analysed via sophisticated statistical methods to explore the complex coordination of interactions (e.g. Hudson et al., 2022). However, the risk may be that statistical modelling removes the phenomena from their context and from the meanings construed in those contexts. Therefore, researchers have also developed tools for statistical methods, for example quantitative

Affective Processes in Collaborative Learning Contexts

239

ethnography (Shaffer, 2017), an approach that provides opportunities for integration of qualitative and quantitative analysis. Epistemic network analysis (ENA) tool (e.g. Tan et al., 2022), for example could potentially enable the analysis of affective processes by automatically forming dynamic network models of coded video episodes that reflect the structures of connection of different phenomena. This tool also provides the possibility to move back and forth from original (coded) data to models, which helps the researchers to analyse the meanings and qualitative connections of the formed models. At best, all these technological developments could have potential to increase the effectiveness, accuracy, and reliability of the analysis. They could be used to analyse various levels of affective processes and their relations to other aspects of collaboration as they occur in authentic settings and find temporal patterns of these relations in even more fine-grained manner. When used in conjunction with additional data sources, this can boost further the theoretical development of understanding multi-level, temporal and multidimensional connections of affective processes in collaborative settings. This is to better understand how individual affect is reciprocally connected to the group’s interactions, how affective processes fluctuate in relation to changes in collaborative conditions and how different dimensions of affect (experiences, behavioural, expressive, and physiological responses) manifest in these collaborative settings. Prospects of video analysis using AI are certainly exciting, but as technologies develop, the ethics of such methodologies need to be an integral part of discussions. Video data with advanced techniques for the detection of affective processes are identifiable, personal, and sensitive data, and a large amount of these sensitive data are required for training machines to detect and classify traces of affect (Akçay & Oğuz, 2020). Furthermore, the potential use of these technologies in the classrooms and other contexts requires careful consideration (Nguyen et al., 2022). In regard of theoretical implications, discussions are needed about conceptual operationalisations that, as illustrated in our first section, remain varied. Cross-disciplinary approaches to the analysis of affective processes, including contributions from organisational psychology, linguistics, ethnomethodology, and affective computing should also inform future approaches to the conceptualisation, operationalisation, and analysis of emotions in collaborative groups. Finally, the translational application of the outcomes of such complex and fine-grained research is an area that merits further discussion and one that we commence in the conclusion of this chapter. Conclusions

Emotion regulation and collaboration skills have strong predictive connections to academic outcomes and socioemotional well-being among children and adolescents (OECD, 2021). Meta-analytic evidence suggests that much like the regulation of learning, these emotion-related skills can be taught,

240

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

leading to beneficial outcomes in individuals (Durlak et al., 2011; Mahoney et al., 2018). However, teaching these skills requires thorough understanding of the affective processes that need supporting. In concluding this chapter, we argue that the type of micro-analytic and finegrained research that is enabled via the analysis of video and other data sources provides two critical affordances for building transferable connections between knowledge, practice, and intervention. Firstly, as illustrated in our example studies, the analysis of video and other sources can enhance the understanding of the processes by which affect drives interpersonal interaction and vice versa. Secondly, the powerful nature of image can be used for pedagogical purposes as a tool to promote individual and social awareness. We are optimistic in seeing how this agenda can be pursued as technological innovations continue to be developed and married to systematic theory-driven empirical studies. References Akçay, M. B., & Oğuz, K. (2020). Speech emotion recognition: Emotional models, databases, features, pre-processing methods, supporting modalities, and classifiers. Speech Communication, 116, 56–76. https://doi.org/10.1016/j.specom.2019. 12.001 Bakeman, R., & Adamson, L. B. (1984). Coordinating attention to people and objects in mother–infant and peer–infant interaction. Child Development, 55, 1278–1289. https://doi.org/10.2307/1129997 Bakhtiar, A., Webster, E. A., & Hadwin, A. F. (2018). Regulation and socioemotional interactions in a positive and a negative group climate. Metacognition and Learning, 13(1), 57–90. https://doi.org/10.1007/s11409-017-9178-x Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930 Barron, B. J., Pea, R., & Engle, R. A. (2013). Advancing understanding of collaborative learning with data derived from video records. In C. Hmelo-Silver, C. Chinn, C. Chan, & A. O’Donnell (Eds.), The international handbook of collaborative learning (pp. 203–219). Routledge. Barsade, S. G., & Knight, A. P. (2015). Group affect. Annual Review of Organizational Psychology, 2(1), 21–46. https://doi.org/10.1146/annurev-orgpsych032414-111316 Boekaerts, M., & Corno, L. (2005). Self-regulation in the classroom: A perspective on assessment and intervention. Applied Psychology, 54(2), 199–231. https://doi. org/10.1111/j.1464-0597.2005.00205.x Boekaerts, M., & Pekrun, R. (2015). Emotions and emotion regulation in academic settings. In L. Corno & E. M. Anderman (Eds.), Handbook of educational psychology (3rd ed., pp. 76–90). Routledge. Dillenbourg, P. (1999). Introduction: What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1–19). Pergamon.

Affective Processes in Collaborative Learning Contexts

241

Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82, 405–432. https://doi.org/10.1111/j.1467-8624.2010.01564.x Gross, J. J. (2015). Emotion regulation: Current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014. 940781 Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). Routledge. Howe, C., & Zachariou, A. (2019). Small-group collaboration and individual knowledge acquisition: The processes of growth during adolescence and early adulthood. Learning and Instruction, 60, 263–274. https://doi.org/10.1016/j. learninstruc.2017.10.007 Hudson, D., Wiltshire, T. J., & Atzmueller, M. (2022). multiSyncPy: A python package for assessing multivariate coordination dynamics. Behavior Research Methods, 1–31. https://doi.org/10.3758/s13428-022-01855-y Järvenoja, H., Järvelä, S., & Malmberg, J. (2020). Supporting groups’ emotion and motivation regulation during collaborative learning. Learning and Instruction, 70, Article 101090. https://doi.org/10.1016/j.learninstruc.2017.11.004 Järvenoja, H., Näykki, P., & Törmänen, T. (2019). Emotional regulation in collaborative learning: When do higher education students activate group level regulation in the face of challenges?. Studies in Higher Education, 44(10), 1747–1757. https:// doi.org/10.1080/03075079.2019.1665318 Jones, C., Volet, S., & Pino-Pasternak, D. (2021). Observational research in face-toface small groupwork: Capturing affect as socio-dynamic interpersonal phenomena. Small Group Research, 52(3), 341–376. https://doi.org/10.1177/1046496420985920 Jones, C., Volet, S., Pino-Pasternak, D., & Heinimäki, O. P. (2022). Interpersonal affect in groupwork: A comparative case study of two small groups with contrasting group dynamics outcomes. Frontline Learning Research, 10(1), 46–75. https://doi. org/10.14786/flr.v10i1.851 Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. The Journal of the Learning Sciences, 4(1), 39–103. https://doi.org/10.1207/ s15327809jls0401_2 Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding?. Metacognition and Learning, 9(3), 287–307. https://doi.org/10.1007/s11409-014-9117-z 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. https://doi. org/10.1016/S0747-5632(02)00057-2 Lajoie, S. P., Lee, L., Poitras, E., Bassiri, M., Kazemitabar, M., Cruz-Panesso, HmeloSilver, C., Wiseman, J., Chank, L.K., & Lu, J. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. Computers in Human Behavior, 52, 601–616. https://doi. org/10.1016/j.chb.2014.11.073

242

Kristiina Mänty, Deborah Pino-Pasternak, Sara Ahola, et al.

Lajoie, S. P., Zheng, J., Li, S., Jarrell, A., & Gube, M. (2021). Examining the interplay of affect and self-regulation in the context of clinical reasoning. Learning and Instruction, 72, Article 101219. https://doi.org/10.1016/j.learninstruc.2019. 101219 Linnenbrink-Garcia, L., Rogat, T. K., & Koskey, K. L. K. (2011). Affect and engagement during small group instruction. Contemporary Educational Psychology, 36(1), 13–24. https://doi.org/10.1016/j.cedpsych.2010.09.001 Lobczowski, N. G. (2020). Bridging gaps and moving forward: Building a new model for socioemotional formation and regulation. Educational Psychologist, 55(2), 53–68. https://doi.org/10.1080/00461520.2019.1670064 Lobczowski, N. G. (2022). Capturing the formation and regulation of emotions in collaborative learning: The FRECL coding procedure. Frontiers in Psychology, Article 1968. https://doi.org/10.3389/fpsyg.2022.846811 Lobczowski, N. G., Lyons, K., Greene, J. A., & McLaughlin, J. E. (2021). Socioemotional regulation strategies in a project-based learning environment. Contemporary Educational Psychology, 65, Article 101968. https://doi. org/10.1016/j.cedpsych.2021.101968 Mahoney, J. L., Durlak, J. A., & Weissberg, R. P. (2018). An update on social and emotional learning outcome research. Phi Delta Kappan, 100, 18–23. https://doi. org/10.1177/0031721718815668 Mänty, K., Järvenoja, H., & Törmänen, T. (2020). Socioemotional interaction in collaborative learning: Combining individual emotional experiences and group-level emotion regulation. International Journal of Educational Research, 102, Article 101589. https://doi.org/10.1016/j.ijer.2020.101589 Marks, M., Mathieu, J., & Zaccaro, S. (2001). A temporally based framework and taxonomy of team processes. The Academy of Management Review, 26(3), 356–376. https://doi.org/10.5465/AMR.2001.4845785. Mauss, I. B., Levenson, R. W., McCarter, L., Wilhelm, F. H., & Gross, J. J. (2005). The tie that binds? Coherence among emotion experience, behavior, and physiology. Emotion, 5(2), 175–190. https://doi.org/10.1037/1528-3542.5.2.175 Mesquita, B., & Boiger, M. (2014). Emotions in context: A sociodynamic model of emotions. Emotion Review, 6(4), 298–302. https://doi.org/10.1177/17540739 14534480 Näykki, P., Järvelä, S., Kirschner, P. A., & Järvenoja, H. (2014). Socioemotional conflict in collaborative learning—A process-oriented case study in a higher education context. International Journal of Educational Research, 68, 1–14. https://doi. org/10.1016/j.ijer.2014.07.001 Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2022). Ethical principles for artificial intelligence in education. Education and Information Technologies, 1–21. https://doi.org/10.1007/s10639-022-11316-w OECD (2021). Beyond academic learning: First results from the survey of social and emotional skills. OECD Publishing. https://doi.org/10.1787/92a11084-en Pekrun, R. (2017). Achievement emotions. In A. J. Elliot, C. S. Sweck, & D. S. Yeager (Eds.), Handbook of competence and motivation (pp. 251–271). The Guilford Press. Rogat, T. K., & Adams-Wiggins, K. R. (2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior, 52, 589–600. https://doi.org/10.1016/j.chb.2015.01.026

Affective Processes in Collaborative Learning Contexts

243

Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In C. O’Malley (Ed.), Computer supported collaborative learning (pp. 69–97). Springer. https://doi.org/10.1007/978-3-64285098-1_5 Russell, J. A., & Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant. Journal of Personality and Social Psychology, 76(5), 805–819. https://doi.org/10.1037/0022-3514. 76.5.805 Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. https://doi.org/10.1177/0539018405 058216 Schneider, B., Sharma, K., Cuendet, S., Zufferey, G., Dillenbourg, P., & Pea, R. (2018). Leveraging mobile eye-trackers to capture joint visual attention in co-located collaborative learning groups. International Journal of Computer-Supported Collaborative Learning, 13, 241–261. https://doi.org/10.1007/s11412-018-9281-2 Shaffer, D. W. (2017). Quantitative ethnography. Cathcart Press. Tan, Y., Hinojosa, C., Marquart, C., Ruis, A. R., & Shaffer, D. W. (2022). Epistemic network analysis visualization. In B. Wasson & S. Zörgő (Eds.), Advances in quantitative ethnography. ICQE 2021 (part of the book series communications in computer and information science (Vol. 1522, pp. 129–143). Springer. https://doi. org/10.1007/978-3-030-93859-8_9 Törmänen, T., Järvenoja, H., & Mänty, K. (2021). All for one and one for all – How are students’ affective states and group-level emotion regulation interconnected in collaborative learning? International Journal of Educational Research, 109, Article 101861. https://doi.org/10.1016/j.ijer.2021.101861 Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Lawrence Erlbaum. Yu, Z., Li, X., & Zhao, G. (2021). Facial-video-based physiological signal measurement: Recent advances and affective applications. IEEE Signal Processing Magazine, 38(6), 50–58. https://doi.org/10.1109/MSP.2021.3106285

16 WHERE ETHNIC AND CULTURAL IDENTITY MEET SITUATIONAL DEMANDS Implications for Methodologies Used to Study Motivation Tim Urdan

Abstract Over the long history of research on motivation, the influences of race, ethnicity, and culture have largely been excluded or inadequately examined. In this chapter, we discuss how recent advances in research methodologies can be combined with more nuanced understandings of identity, experience, and situational factors to develop a better understanding of motivational processes for minoritised students. Specifically, we consider the need for a situated, complex-systems perspective on motivation that centres race, ethnicity, and culture.

Research on motivation, as with much of educational psychology, has arrived at two important inflection points. First, the assumption that broad theories of motivation are universal is coming under increasing scrutiny by a new generation of scholars, many of them scholars of colour. This has created increasing demand for research that examines motivation through ethnic and cultural lenses that question whether motivational processes work the same for all. Second, there seems to be increasing recognition among researchers that some of the most commonly used methods for studying motivation are inadequate. Survey and experimental methodologies, the two most commonly used, especially in the motivation sciences, often ignore the specific situational factors found in achievement contexts, such as classrooms, and therefore fail to capture the dynamic processes influencing motivation in the actual situations where they occur. The purpose of this chapter is to explore two broad themes and how they might be integrated. The first theme is about ethnic and cultural identity, immigration, and education. In the United States and much of Western Europe, DOI: 10.4324/9781003303473-18

Where Ethnic and Cultural Identity Meet Situational Demands

245

there has been a surge in immigration in recent decades. Immigrants and their children are often members of different ethnic and cultural groups than the majority of citizens in the host country, and the experiences of immigrants and their children are often influenced by aspects of ethnic and cultural identity. In the United States, there are important effects of cultural and ethnic identity on the motivation and achievement of many students who are not Caucasian, whether their families have recently immigrated to the United States or have been in the country for many generations (e.g. African American, Chinese American, Hispanic American, and Native American populations). The issues of ethnic and cultural identity and immigration are presented first because it is important to establish definitions for these constructs and discuss some of ways ethnicity and culture influence motivation before critiquing the methods that have been used to study ethnic and cultural influences on motivation. The second theme I explore in this chapter is the movement towards more complex, mixed-methods research methodologies in the study of motivation. I begin this section with a brief history of the research methods used to study motivation, and how those methods may have missed some of the influences that race, ethnicity, culture, and situational factors have on motivation. Although researchers have been discussing the importance of these factors for decades (e.g. Graham, 1992; Maehr, 1974), there is still a relative dearth of research that examines the complex ways situational and cultural factors influence motivation. Recent research, however, has begun to adopt a more systems-oriented perspective on motivation and this recent trend, spurred in part by advances in technology and statistics, is producing a more full and complex picture of how individual, situational and cultural factors combine to affect motivation. The chapter concludes with an attempt at merging the two themes to consider how advances in theory and research methodologies can be leveraged to better understand the motivational process of diverse student populations. Theme I: Ethnicity, Culture, and Generational Status

When discussing race, ethnicity, and culture, clearly defining the terms is critical. Race is often defined as a categorical variable that is assigned to individuals by others and is thought by some to have its basis in genetics. In contrast, ethnicity is rooted more in identity and may be multifaceted.1 A person with ancestors from Ireland and Mexico may perceive their ethnicity to be both Irish and Mexican, or White and Hispanic. Culture overlaps with both race and ethnicity and refers to the shared values, beliefs, customs, and experiences of any group of people. Culture does not, by definition, have to apply to either race or ethnicity. For example, first-, second-, and thirdgeneration immigrant families from the same country of origin often have different cultural attributes, despite a common race and ethnicity (SuarezOrozco & Suarez-Orozco, 1995).

246

Tim Urdan

Research that has examined issues of race, ethnicity, and culture in motivation has often conflated or oversimplified these constructs. One of the most common methods of examining the association between race and student motivation is to simply treat race as a categorical, independent variable and examine group differences. Race is assessed by asking students to select from a limited list of choices (e.g. Black, White, Hispanic, Asian), a task that can be challenging for students with multiple ethnic identifications. This is a method that has often uncovered differences between groups, but are these differences really racial? Might they more likely be due to ethnic or cultural factors? Regardless of whether researchers use the term race, ethnicity, or culture, the influence of each on student motivation depends on each student’s subjective experience. Two students who belong to the same ethnic or cultural group may differ in how salient and central this ethnic or cultural identity is to their overall sense of self in the classroom, and these differences in identity can have important effects on motivation (Armenta, 2010; Hudley & Irving, 2012). In addition, the subjective experience of individuals is likely influenced by how members of one’s group are perceived and treated by others. Because the perceptions and actions of others are often influenced by stereotyped generalisations about entire groups, members of a particular cultural or ethnic group may have some level of shared experience. For example, African Americans have all been subjected to stereotypical thinking and incidents of racism by white people, regardless of differences in socioeconomic status, appearance, and ethnic identity (DeCuir & Dixson, 2004). Perhaps it is this experience that contributes to any observed, group-level differences in academic motivation (Graham & Hudley, 2005). The experience of being minoritised in certain contexts may be a shared experience for members of any group with a history of marginalisation (Stewart, 2013). The task for motivation researchers interested in learning about the motivation of diverse populations is to develop and use methodologies that capture the complex dynamics of ethnicity and culture. How, for example can we understand the unique experiences of individuals who may share experiences and perceptions with their broader ethnic or cultural group (e.g. the frequent experience of microaggressions in school or learning a curriculum that includes few contributions from members of their ethnic group) but may also differ in important ways from other members of their ethnic or cultural group? Simply asking participants to select the ethnic label that fits them best from a list of choices will often miss important differences within ethnic groups, including the strength and salience of ethnic identity, and important identifications with multiple groups, such as ethnic, gender, sexual orientation, and generational status (i.e. intersectionality). Recently, researchers have focused on the intersectionality of race or ethnicity and sexual orientation (e.g. Warner & Shields, 2013); LGBT youth of colour are thought to have unique experiences not just because of the societal prejudices against their race and sexuality, but because they are less likely to be accepted into

Where Ethnic and Cultural Identity Meet Situational Demands

247

their own ethnic or religious groups (Pew Research Center, 2013). When researchers examine the effects of culture or ethnicity on student motivation, it is important to consider the intersectionality of the multiple aspects of identity for many students. Immigration, Generational Status, and Acculturation

Among minoritised students are those who are immigrants, refugees, and the children of immigrants and refugees. Some of the challenges confronting immigrants and refugees, such as lack of fluency in the dominant language or difficulties finding work that pays a living wage, are directly tied to being new residents in a foreign land. But other challenges faced by many immigrant and refugee families may not be directly tied to their immigrant or refugee status. For example, many of these families may belong to ethnic or cultural groups that are stigmatised or about which there are negative stereotypes, regardless of immigrant or refugee status. In the United States, for example there are negative stereotypes about the academic abilities of Mexican American students, and these stereotypes apply whether the students are immigrants to the United States or native-born in the U.S. Immigrants differ in the way they are perceived by the host country, and the political rhetoric that is applied to different groups of immigrants can shape national opinion and policies that subsequently affect the motivation and achievement of students (Portes & Zhou, 1993; Urdan et al., 2019). Students who are immigrants or the children of immigrants simultaneously operate within two distinct cultures: the native culture of their families and the dominant culture of their adopted countries. Researchers have examined how those who are not members of the dominant, majority culture have formed identities that combine their native cultural or ethnic identity with that of the dominant culture (Berry et al., 2006). This research has revealed four patterns of cultural integration: full assimilation with the dominant culture, full separation from the dominant cultural, bicultural identity, and marginalised, in which the ethnic minority individual does not feel like they identify with either their native ethnic culture or the dominant culture in society. Adding further complexity to this picture is the question of with whom immigrant students choose to assimilate. The children of immigrants may have peers who, because of the low wages and limited opportunities that their parents have experienced, have soured on the American dream and devalue academic effort and achievement (Portes & Zhou, 1993). Although research indicates that first- and second-generation students tend to have more positive attitudes about school than their third-generation peers (Coll & Marks, 2012), some students identify more strongly with their disaffected peers and develop negative attitudes about school. These patterns of assimilation among immigrants and the children of immigrants can have profound influences on their academic motivation.

248

Tim Urdan

Conclusion for Theme I

To date, much of the research examining ethnic and cultural differences in student motivation, and the effects of culture and ethnicity on motivation, has failed to capture the dynamic processes through which ethnicity and culture may influence motivation. Race, ethnicity, and culture have been ill-defined and have often been treated as simplistic group labels used to compare entire groups. Recent research has questioned the universality of motivation theories and called for a more nuanced understanding of the complex ways that ethnicity and culture can affect motivation. This includes a consideration of differences within ethnic and cultural groups in identity, goals, beliefs, and behaviours that emerge from socialisation and experience. It also includes the examination of some important shared experiences among members of a particular ethnic, racial, or cultural group, such as negative stereotypes about entire groups and shared histories of systemic discrimination in social institutions such as schools. The combination of shared and unique experiences, identities, and social environments will create complex and varied motivational responses in school. The complexity of definitions and experience of ethnicity and culture call for a similarly complex methodological approach to the study of motivation. Theme II: Methodologies Used to Study Motivation

As the cognitive revolution took hold in the 1970s, motivation researchers placed increasing faith in the validity of self-report survey instruments to measure motivation. The argument was that motivated action is guided by our conscious beliefs and thoughts. Therefore, people should be able to accurately report their thoughts and beliefs on survey measures (Maehr, 1974). Over the last 50 years, hundreds of studies using survey or experimental methods have yielded incredible insights about the nature, sources, and functions of motivation. This research has also had some important limitations, particularly for understanding the motivation of minoritised students. One important limitation of much of the research examining student motivation is that each study tends to rely on a single methodology. For example, much of the published research is based solely on student selfreports. Although student perceptions are obviously important, there is a great deal of evidence that student reports are not always valid measures of psychological phenomenon. Reasons for the questionable validity of self-report data range from students misinterpreting the meaning of survey items (Karabenick et al., 2007) to students having limited access to the unconscious processes that affect their motivation and behaviour (Urdan, 2023). The different interpretations of survey items may be an especially important issue for students who are not fluent in the primary language used in schools or familiar with the cultural references and lexicon of the

Where Ethnic and Cultural Identity Meet Situational Demands

249

dominant culture, such as immigrant and refugee children and other minoritised groups. Another important limitation of survey and experimental methods, especially laboratory-based experiments, is that they often do not consider situational and contextual factors that exist in the classroom environment where academic motivation occurs. In real classrooms, the effect of race, ethnicity, and culture on student motivation is influenced by dynamic social processes that might include teacher-student interactions, student-student interactions, the student’s prior experiences with similar assignments or activities, and other characteristics of the student, such as ethnic identity, goals, and valuing of the material being learned. The complex interaction of personal and contextual factors has been noted for many decades in research on motivation processes (Lewin, 1942). Recent years have seen a substantial increase in the use of more complex and mixed methodologies in motivation research. Part of this change has been the result of dissatisfaction with mono-method studies that did not seem to capture the complexity of school and classroom life, particularly for minoritised students. For example, researchers adopting a sociocultural approach have argued that within-person factors, social dynamics within classrooms, and opportunity structures in classrooms communicated through assignments, rules, rewards, and classroom procedures all interact to influence the motivation of students (Hickey, 2003; Nolen et al., 2015; Turner & Patrick, 2008). This approach to the study of motivation in classrooms has included calls for less reliance on surveys and greater use of ethnographic methods, including observations in classrooms and artefact analysis. In addition, advances in technology have allowed researchers to employ advanced statistical techniques, including person-centred analyses, hierarchical linear modelling, and multidimensional scaling that were not available to researchers from previous generations. These statistical methods have allowed researchers to test more complex models than were previously tested. Other technological advances are opening new avenues for research. The use of fMRI technology is enabling researchers to examine motivational processes in real time at the neural level. Computers have allowed researchers to collect reaction-time data to identify implicit biases among teachers (Kumar et al., 2015). And the frequent use of computers by students allows researchers to collect data about situation-specific behaviour, and how that behaviour (and, by extension, motivation, self-regulation, and emotional reactions) is being influenced by contextual factors (Dirk & Schmiedek, 2016; Winne, 2020). These advances in technology, statistics, and conceptualisations regarding the complex, situational nature of motivation processes have ushered in a new era of methodological approaches that has important implications for how we study the influence of race, ethnicity, and culture on students’ motivation. This issue is the focus of the next and final section of this chapter.

250

Tim Urdan

Theme III: Combining Themes I and II

Research on the motivation of minoritised students seems to be at a critical juncture produced by simultaneous developments in research methodologies in general and shifting beliefs about how motivation should be conceptualised and researched among students of colour and immigrant populations. In this final section of the chapter, I combine information about general methodological advances with recent race-reimagined approaches to the study of motivation to suggest how future research in this area might unfold. Our first suggestion is that future research in this area should be careful about assumptions that the definition and function of various theories and constructs in motivation research are universal (Zusho & Clayton, 2011). In recent years, a number of researchers have argued that simply comparing different ethnic and cultural groups on traditional measures of motivation and achievement is not enough to develop a full understanding of the motivation and achievement of an increasingly diverse student population (DeCuirGunby & Schutz, 2014). What is needed is a reconsideration of traditional motivation constructs to consider how race, ethnicity, and culture, within current and historical contexts, affects the meaning and influence of these constructs for different students (DeCuir-Gunby et al., 2017). The last 15 years have seen a rapid increase in the number of studies that have re-imagined the meaning of key motivation constructs through a racial lens. In one example, Gray et al. (2018) reconsidered how belonging is defined and operates in schools for African American students. They argued that existing research on belonging, which depends on the opportunity structures in specific settings like schools to gain a sense of belonging, was developed around notions of whiteness, that is the opportunity structures for white students. Further, these scholars note that for many African American students, pride in being Black and the importance of maintaining strong bonds to that culture can be an important part of the belonging equation, so conceptualisations of belonging that do not include this aspect are lacking in their ability to truly capture the meaning and function of belonging in school for African American (and or minoritised populations) in schools. In another example of race re-imagined motivation research, Lopez (2017) examined teacher-expectancy effects on Latino students. Unlike traditional research approaches that have compared teacher expectancies of students from different cultural and ethnic groups, Lopez examined how teachers’ critical awareness of the experiences of their students, specifically how some groups have been marginalised by biases in society and schools, influenced their expectancies of students and their use of asset-based pedagogy. She found that teachers’ critical awareness raised their expectations of their Latino students, which in turn increased the achievement of these students and fostered the development of positive ethnic and academic identities. These

Where Ethnic and Cultural Identity Meet Situational Demands

251

are just two of many recent studies offering race-reimagined examinations of motivation theories and constructs. Questioning the assumption that the dominant theories and constructs found in research on motivation are universal and culture-free necessitates a reconsideration of the methods that are used to study these constructs. There is no prescription for what methodology works best in race re-imagined research, although some have noted the value of qualitative and mixedmethods research (DeCuir-Gunby & Schutz, 2014). Instead, the focus has been on the adoption of research models and paradigms that place a greater emphasis on understanding the social-historical influences on the identities and experiences of students of various ethnic and cultural backgrounds. Critical Race Theory (CRT) has the specific goal of countering existing narratives created by the dominant culture and creating new narratives that lead to change in both the story and the systems that perpetuate racism in school (Decuir & Dixson, 2004; Ladson-Billings, 1999). Rather than thinking of race or ethnicity as a demographic variable to be used as a moderator in statistical analysis, perspectives like CRT place race, ethnicity, and the social-historical context of racism at the centre of the analysis. This centring of race and racial bias would have profound implications for the methods used to study motivation. For example, it would require that more purposeful sampling that includes larger and more frequent samples of minoritised youth be included in research in these areas. In addition, it highlights the need for greater examination of the meaning of motivation constructs, and the measures used to study them, for students from different groups. Rather than giving all students the same survey and then comparing them by ethnic or cultural group, steps should be taken through methods like cognitive pre-testing (Karabenick et al., 2007) to see whether there are differences in understanding and interpretation of the measures. In addition, there is a clear need to better understand the contexts in which minoritised students attend school. This will most likely require greater use of qualitative methodologies. Although quantitative research has the air of objectivity, it is clear that researchers’ assumptions and biases affect their choices of methodologies and their interpretations of the data. When researchers use surveys to examine differences between ethnic groups, for example they assume that the self-report data are valid, that the questions on the survey will be interpreted the same way by members of different ethnic groups, and that differences found reflect important distinctions between entire groups. As I have illustrated, these assumptions are often called into question by subsequent research that has employed different methods and reflected different assumptions. It would perhaps be a worthwhile exercise for researchers to identify and state their potential biases and assumptions, and how those may have guided their choice of

252

Tim Urdan

methodologies, regardless of whether the research employs qualitative of quantitative methods. A Complex-Systems Approach

Throughout this chapter, I have argued that motivation in schools among minoritised students involve far more complex processes than are often accounted for in research models. Minoritised students, even within the same ethnic or cultural group, often differ from each other in ways that have important implications for their motivational beliefs and experiences in schools. For example, there is considerable evidence that within a single culture (e.g. Mexican Americans), students’ motivational orientations vary significantly as a function of their generational status (Fuligni & Tseng, 1999; Urdan, 2004). In addition, researchers in the field of motivation have argued that there is a need to think of motivation, emotion, and achievement as part of one larger dynamic system rather than as unrelated constructs (Urdan & Kaplan, 2020; Vansteenkiste et al., 2014). As researchers have paid increasing attention in recent years to the dynamic, emergent, non-linear nature of motivation within specific contexts, a greater awareness of the need for methods that can capture the dynamic, situation-specific nature of motivation in classrooms has developed among researchers. I briefly discuss two of these systems approaches here. Sociocultural models represent one systems approach to the study of motivation in the classroom (Hickey, 2003; Nolen, 2020). This approach posits that there are different planes of classroom experience, such as the intraindividual plane (e.g. the student’s achievement history, goals, ethnic identity, feelings of safety in the classroom), the interpersonal plane (e.g. interactions among students, teacher-student relationships, efforts to include or exclude students in the classroom), and the opportunity structure plane (e.g. the tasks that are assigned, the support and encouragement provided to students, how students are rewarded and recognised). At different points during the classroom experience, one plane may come to the foreground, and then recede at a later time as others come to the foreground. For example, a female student who is aware of negative stereotypes about women in science may be focused on her gender and this stereotype when told she will be working on a group project with boys in her physics class. However, when she sees that the assignment is about a topic in which she has deep interest and expertise, her gender identity may recede and her opportunity plane may come to the foreground. To capture the complex and dynamic nature of motivation within specific situations, researchers have often used qualitative, ethnographic methods (Nolen et al., 2015). Another dynamic systems approach to the study of motivation can be found in the work of Kaplan and his colleagues (Kaplan et al., 2019; Urdan

Where Ethnic and Cultural Identity Meet Situational Demands

253

& Kaplan, 2020). They have used the model, called the complex dynamic systems model of role identity, to examine the individual’s situated meaning in the achievement situation. Kaplan and his colleagues have highlighted the system’s unit-of-analysis as the individual’s situated and comprehensive meaning of him or herself (e.g. the meaning of being a student in the situation). The role identity complex system involves salient interdependent elements from four components of the system: students’ working model of their reality and how they know it (e.g. implicit theories of intelligence and personality, environmental goal structures, theories of education), personal purpose and goals/objectives (e.g. intrinsic, extrinsic, social, mastery, performance goals), self-perceptions and self- definitions (e.g. perceived ability, interests, values, social identities, ethnic identity), and perceived action possibilities (i.e. those strategies and actions that the individual considers to be available for pursuing the goals in that particular situation). The content and salience of the elements in the four components, the emotions associated with them, and their relations are continuously forming within the frame of the role identity system’s control parameters: the social context and social interactions that position a person in a particular role and through which beliefs, self- perceptions, and goals are highlighted and negotiated; the culture that provides tangible and semiotic mediating means for these social interactions; the subject domain that frames such meanings and the person’s implicit dispositions (e.g. unconscious motives, emotional conditioning, temperament). The dynamic interplay among the elements from the four components gives rise to action, which, in turn, feeds-back into the continuous iterative formation of the complex role identity motivational system. A complex, dynamic systems approach to the study of motivation may have particular promise for understanding how motivation emerges and influences behaviour among minoritised students in specific contexts. Ethnicity, race, and culture are complex and multidimensional constructs, and their association with motivation in the classroom is dynamic, non-linear, and emergent. Two African American students in the same classroom may have very different phenomenological experiences due to differences in the salience and centrality of their ethnic identities, their goals, their attributions, and the nature of their social interactions with peers and their teacher. However, they may also have some shared experience due to their culture, including an awareness of negative stereotypes about their academic abilities, biased perceptions of their teacher, or the sting of a racist remark from a classmate. Understanding the effects of their experiences on their motivation in the classroom requires a mixed-methods approach that includes some measure of beliefs, attitudes, and emotions internal to the student, observations of the classroom dynamic and interactions in real time, and measures of behaviour. Fortunately, advances in technology, statistics, and theory have created new opportunities for better understanding the classroom and

254

Tim Urdan

individual processes that influence the motivation of minoritised students within classrooms. Conclusion

Research on motivation has had difficulty capturing the complex processes involved in real classrooms. This is particularly true when discussing how race, ethnicity, and culture are associated with motivation. There are several reasons this has been challenging, ranging from assumptions about the universality of motivation constructs across racial, ethnic, and cultural groups to the use of methodologies that tend to be too simplistic to capture the dynamic, systems-oriented processes involved. Recent advances in our thinking about race, ethnicity, and culture, however, can be combined with recent advances in technology, statistics, and the use of mixed methodologies to promote a deeper understanding of the complex interactions between motivation, emotion, achievement, and culture. This includes centring race, ethnicity, and culture in our framing of motivation theories and our understanding of school and classroom contexts (DeCuir-Gunby et al., 2017). Clearly, no single study will include all of the research questions and methodologies necessary to capture a phenomenon as complex as the interplay between race, ethnicity, culture, and motivation. A combination of methodologies employed across a wide range of researchers is needed to understand this phenomenon. Given the history of racial tensions, discrimination, and marginalisation of several ethnic and cultural groups in the United States and Europe, it is important to consider what assumptions about different ethnic groups are being reflected in the research methods used to understand ethnic and cultural influences on motivation and emotion, and how the results of the research conducted with these methodologies may challenge or reinforce existing assumptions and biases. Note 1 There is disagreement and discussion about whether race and ethnicity should be considered interchangeable or separate constructs. See Hudley and Irving (2012) for an excellent discussion of the overlap and distinction between race and ethnicity.

References Armenta, B. E. (2010). Stereotype boost and stereotype threat effects: The moderating role of ethnic identification. Cultural Diversity and Ethnic Minority Psychology, 16, 94–98. https://doi.org/10.1037/a0017564

Where Ethnic and Cultural Identity Meet Situational Demands

255

Berry, J. W., Phinney, J. S., Sam, D. L., & Vedder, P. (2006). Immigrant youth: Acculturation, identity, and adaptation. Applied Psychology: An International Review, 55, 303–332. https://doi.org/10.1111/j.1464-0597.2006.00256.x Coll, C. G., & Marks, A. K. (2012). The immigrant paradox in children and adolescence: Is becoming American a developmental risk? American Psychological Association. DeCuir, J. T., & Dixson, A. D. (2004). “So when it comes out, they aren’t that surprised that it is there”: Using Critical Race Theory as a tool of analysis of race and racism in education. Educational Researcher, 33, 26–31. https://doi.org/10.3102/ 0013189X033005026 DeCuir-Gunby, J. T., & Schutz, P. A. (2014). Researching race within educational psychology contexts. Educational Psychologist, 49, 244–260. https://doi.org/10.1 080/00461520.2014.957828 DeCuir-Gunby, J. T., Schutz, P. A., & Harris, S. D. (2017). A future agenda for researching race and ethnicity in the study of motivation in educational contexts. In J. T. DeCuir-Gunby & P. A. Schutz (Eds.), Race and ethnicity in the study of motivation in education (pp. 201–211). Routledge. Dirk, J., & Schmiedek, F. (2016). Fluctuations in elementary school children’s working memory performance in the school context. Journal of Educational Psychology, 108, 722–739. https://doi.org/10.1037/edu0000076 Fuligni, A. J., & Tseng, V. (1999). Family obligation and the academic motivation of adolescents from immigrant and American-born families. In T. Urdan (Ed.) Advances in motivation and achievement (Vol. 11, pp. 159–183). Emerald. Graham, S. (1992). “Most of the subjects were White and middle class:” Trends in reported research on African Americans in selected APA journals, 1970–1989. American Psychologist, 47, 629–639. https://doi.org/10.1037/0003-066X.47. 5.629 Graham, S., & Hudley, C. (2005). Race and ethnicity in the study of motivation and competence. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 392–413). Guilford Press. Gray, D. L., Hope, E. C., & Matthews, J. S. (2018). Black and belonging at school: A case for interpersonal, instructional, and institutional opportunity structures. Educational Psychologist, 53, 97–113. https://doi.org/10.1080/00461520.2017. 1421466 Hickey, D. T. (2003). Engaged participation versus marginal nonparticipation: A stridently sociocultural approach to achievement motivation. The Elementary School Journal, 103, 401–429. https://doi.org/10.1086/499733 Hudley, C., & Irving, M. (2012). Ethnic and racial identity in childhood and adolescence. In T. Urdan (Ed.) APA educational psychology handbook (Vol. 2). American Psychological Association. Kaplan, A., Garner, J. K., & Brock, B. (2019). Identity and motivation in a changing world: A complex dynamic systems perspective. In E. Gonida & M. Lemos (Eds.), Advances in motivation and achievement (Vol. 20): motivation in education at a time of global change: Theory, research, and implications for practice (pp. 101–127). Emerald. Karabenick, S., Woolley, M. E., Friedel, J. M., Ammon, B. V., Blazevski, J., Bonney, C. R., De Groot, E., Gilbert, M. C., Musu, M., Musu, L., Kempler, T. M., & Kelly, K. L. (2007). Cognitive processing of self-report items in educational research: Do

256

Tim Urdan

they think what we mean? Educational Psychologist, 42, 139–151, https://doi. org/10.1080/00461520701416231 Kumar, R., Karabenick, S. A., & Burgoon, J. N. (2015). Teachers’ implicit attitudes, explicit beliefs, and the mediating role of respect and cultural responsibility on mastery and performance-focused instructional practices. Journal of Educational Psychology, 107, 533–545. https://doi.org/10.1037/a0037471 Ladson-Billings, G. (1999). Just what is critical race theory, and what’s it doing in a nice field like education? In L. Parker, D. Dehle, & S. Villenas (Eds.), Race is… race isn’t: Critical Race Theory and qualitative studies in education (pp. 7–30). Westview Press. Lewin, K. (1942). Field theory and learning. In N. B. Henry (Ed.), The forty-first yearbook of the National Society for the study of education: Part 2, the psychology of learning (pp. 215–242). University of Chicago Press. Lopez, F. (2017). Altering the trajectory of the self-fulfilling prophecy: Asset-based pedagogy and classroom dynamics. Journal of Teacher Education, 68, 193–212. https://doi.org/10.1177/0022487116685751 Maehr, M. L. (1974). Culture and achievement motivation. American Psychologist, 29, 887–896. https://doi.org/10.1037/h0037521 Nolen, S. B. (2020). A situative turn in the conversation about motivation theories. Contemporary Educational Psychology, 61, Article 101866. https://doi.org/ 10.1016/j.cedpsych.2020.101866 Nolen, S. B., Horn, I. S., & Ward, C. J. (2015). Situating motivation. Educational Psychologist, 50, 234–247. https://doi.org/10.1080/00461520.2015.1075399 Pew Research Center. (2013). A survey of LGBT Americans http://www.pewsocialtrends. org/2013/06/13/a-survey-of-lgbt-americans/ Portes, A., & Zhou, M. (1993). The new second generation: Segmented assimilation and its variants among post-1965 immigrant youth. The Annals of the American Academy of Political and Social Science, 530, 74–96. https://doi.org/10.1177/ 0002716293530001006 Stewart, (2013). Racially minoritized students at U.S. 4-year Institutions. Journal of Negro Education, 82, 184–197. https://doi.org/10.7709/jnegroeducation.82. 2.0184 Suarez-Orozco, C., & Suarez-Orozco, M. M. (1995). Transformations: Immigration, family life, and achievement motivation among Latino adolescents. Stanford University Press. Turner, J. C., & Patrick, H. (2008). How does motivation develop and why does it change? Reframing motivation research. Educational Psychologist, 43(3), 119– 131. https://doi.org/10.1080/00461520802178441 Urdan, T. (2023). The uneasy relationship between conscious and non-conscious motivation. In M. Bong, S. Kim, & J. Reeve (Eds.), Motivation science: Controversies and insights (pp. 191–197). Oxford University Press. Urdan, T. (2004). Predictors of academic self-handicapping and achievement: Examining achievement goals, classroom goal structures, and culture. Journal of Educational Psychology, 96, 251–264. https://doi.org/10.1037/0022-0663. 96.2.251 Urdan, T., & Kaplan, A. (2020). The origins, evolution, and future directions of achievement goal theory. Contemporary Educational Psychology, 61, Article 101862. https://doi.org/10.1016/j.cedpsych.2020.101862

Where Ethnic and Cultural Identity Meet Situational Demands

257

Urdan, T., Sharma, N., & Dunn, M. (2019). Motivation and achievement of immigrant students in times of economic and political instability. In E. Gonida & M. Lemos (Eds.), Advances in motivation and achievement (Vol. 20, pp. 169–184). Emerald. Vansteenkiste, M., Lens, W., Elliot, A. J., Soenens, B., & Mouratidis, A. (2014). Moving the achievement goal approach one step forward: Toward a systematic examination of the autonomous and controlled reasons underlying achievement goals. Educational Psychologist, 49(3), 153–174. https://doi.org/10.1080/0046 1520.2014.928598 Warner, L. R., & Shields, S. A. (2013). The intersections of sexuality, gender, and race: Identity research at the crossroads. Sex Roles, 68, 803–810. https://doi. org/10.1007/s11199-013-0281-4 Winne, P. (2020). Construct and consequential validity for learning analytics based on trace data. Computers in Human Behavior, 112, Article 106457. https://doi. org/10.1016/j.chb.2020.106457 Zusho, A., & Clayton, (2011). Culturalizing achievement goal theory and research. Educational Psychologist, 46(4), 239–260. https://doi.org/10.1080/00461520.20 11.614526

17 USING HEART RATE TO TAP INTO MOTIVATIONAL AND EMOTIONAL PROCESSES DURING TEACHING AND LEARNING Monika Donker, Selma van Aken and Tim Mainhard

Abstract A current ambition of research into motivation and emotion in teaching and learning is to investigate motivation and emotion in more holistic ways and to dive deeper into the dynamics of motivation and emotion processes in the classroom setting. Physiological measures have the potential to reach these goals by moving beyond between-person comparisons of habitual, often self-reported, levels of motivation and emotion. For a long time, tracking physiology was only possible in lab settings, which is problematic for studying authentic processes as they occur during teaching and learning. But recent technological innovations have enabled physiological measurement in ambulatory settings, such as the classroom. For many educational researchers interested in motivation and emotion, dealing with these measures can be challenging. This chapter provides a basic introduction to physiological measures in general and heart rate in particular. We also discuss the conceptual meaning of heart rate in studies on motivation and emotion. Furthermore, we present concrete tips for collecting heart rate data (i.e. study preparation, data cleaning, and data analyses). An important conclusion is that physiological measures open up some new aspects of human functioning to educational researchers and can complement (but not replace) behavioural and self-report measures of motivation and emotion.

Research into motivation and emotion currently strives to investigate human functioning in more holistic ways, which means also including bodily functioning and physiology next to widely used measures relying on participant’s introspection. Especially in the field of emotions, investigating the physiological component of emotion (next to cognitive, affective, and motivational components) is well aligned with both theoretical models and empirical DOI: 10.4324/9781003303473-19

Using Heart Rate to Tap into Motivational and Emotional Processes

259

measures (Pekrun et al., 2011; Scherer, 2009). Another, but related, ambition of research into motivation and emotion in learning and teaching is to move beyond between-person comparisons of habitual, often self-reported, levels of motivation and emotion. Physiological measures are a promising way to get insight into moment-to-moment changes associated with motivation and emotion, without solely relying on self-report. For educational researchers, dealing with physiological measures can be challenging. This is not only because of the technicalities of collecting data in classrooms but also because of the different nature of data cleaning, data analysis, and the conceptual interpretation of the findings. The aim of this chapter is to provide the reader with a basic introduction to physiological measures in general, and heart rate in particular. We discuss the conceptual meaning of heart rate in studies on motivation and emotion, and share our personal experience with collecting heart rate data in the context of studying teacher-student interaction and emotions. Studying the Dynamics of Motivational and Emotional Processes

Motivation and emotion have for a long time been studied with methods relying on self-report that compare differences between individuals, like questionnaires and interviews. Because research questions that involve emotions and motivation, however, often involve processes within rather than between individuals, such between-person approaches have received a fair amount of criticism. Between-person designs examine the relationships between variables based on individual differences and thereby fail to consider variation within an individual over time, or the relations between variables within individuals (Murayama et al., 2017). Conclusions drawn from research using a between-person design cannot easily be generalised to the within-person level and thus might inform both theory and practice inadequately (i.e. Simpson’s paradox; for more information and a graphical representation, see Hamaker & Grasman, 2014). In order to test the validity of psychological and learning models and draw conclusions that apply to processes within an individual, it is therefore important to collect repeated measurements of motivation and emotion over time within persons. With the ambition to dive deeper into the situatedness and context specificity of emotional and motivational processes – from moment to moment within persons – scholars have sought tools that allow them to study experiences in specific situations instead of differences in habitual emotions or motivation between persons. Many researchers have incorporated momentary focused self-report using repeated diaries or the experience sampling method (ESM; Csikszentmihalyi & Larson, 1987), also in the classroom setting (e.g. Roos et al., 2020). Notwithstanding its conceptual and psychological importance, such methods are still prone to biases affecting

260

Monika Donker, Selma van Aken and Tim Mainhard

self-report in general, such as recall inaccuracies (Carson et al., 2010) and overestimation of own abilities (Podsakoff et al., 2003). Moreover, asking repeated questions about how you feel right now or about motivational states potentially disrupts the natural flow of behaviour, cognition, and emotions (Scollon et al., 2009), and it can be viewed as targeting only one component of human functioning. To supplement or sometimes even to replace self-report, researchers have looked for directly observable correlates of motivation and emotion. Prominent examples are direct observations in classrooms (Van Braak et al., 2021) or the (automatic) coding of facial expressions to extract emotional states (D’Mello et al., 2017). Such approaches have in common that they are independent of volition and explicit influence of the individual who is studied (Mossink et al., 2015) and that they target objective behaviours (also referred to as motor expressions; Scherer, 2009) rather than subjective feelings. Thus, some of the problems connected to self-report can be circumvented with such measures. However, observations are often labour-intensive, and, although more objective, the perception of the observer might not match the actual feelings of the participants themselves (Donker et al., 2021). Physiological measures could give a more direct insight in the appraisal, motivation, and emotion of a participant and thereby overcome some of the drawbacks of ESM and observational research. Examples are tracking eye movements (Wolff et al., 2015), brain activity (Dikker et al., 2017), skin conductance (Roos et al., 2020), and heart rate (Donker et al., 2020; Scrimin et al., 2018). These measures also offer the possibility to study within-person processes at high frequency, and thus to account for the situatedness and context specificity of emotion and motivation. While ESM often results in a maximum of ten to 50 measures within a person over the course of several days or weeks, physiological measures may result in several hundred or even a thousand data points within an hour of observation. A More Holistic Approach to Motivation and Emotion

Incorporating physiological measures into educational psychology research has also a more substantial purpose. Some have argued that to fully understand human functioning, research on motivation, emotion, and learning must take into account the more implicit evaluations of the self and the environment (Blascovich, 2008; Cacioppo et al., 2017; Schultheiss & Wirth, 2018). The basic assumption, especially behind physiological measures, is that differences in physiological responding underlie differences in behaviours and psychological experiences. Currently, it is also acknowledged that a specific individual history in terms of social interactions and psychological experiences can shape an individual’s physiology. The least that can be said is that psychological aspects as well as behaviour and physiology together form a single system

Using Heart Rate to Tap into Motivational and Emotional Processes

261

that underlies human functioning. A well-known example that reflects this is Scherer’s component process model of emotions (2009), where emotions are conceptualised as including subjective feelings, motor expressions, action tendencies, and physiological reactions. Also, in theoretical models as well as empirical measures of academic emotions, physiology is often seen as one of the components of emotions, next to cognitive, affective, and motivational components (Pekrun et al., 2011; Roos et al., 2020). Thus, it can be argued, if we want to fully understand emotion and motivation in educational settings, we also need to incorporate physiology in our theorising and empirical work. Heart Rate as Measure of Physiological Arousal

This chapter focuses on heart rate as an example of a physiological measure that can complement behavioural measures and self-report of motivation and emotion, because heart rate is one of the most robust, sensitive, and most widely used physiological measures (Kreibig, 2010; Myrtek, 2004). Heart rate encompasses both the sympathetic and parasympathetic branches of the autonomic nervous system (ANS). While the sympathetic system is related to quick responses and mobilisation (“fight or flight”), the parasympathetic system is more slowly activated and is sometimes referred to as more concerned with dampening activation (“rest or digest”). Heart rate can easily be collected ambulatorily and in a very small temporal resolution because the ANS reacts faster to changes and stressors in the environment compared to the hypothalamic-pituitary-adrenal (HPA) axis, which makes it well-suited for within-person analyses of motivation and emotion dynamics. The heart is first and foremost a pump, supplying the body with oxygen by sending blood into the lungs and then to the rest of the body. It has four chambers: two atria and two ventricles. The atria pump blood inside the ventricles, which pump blood to the outside of the heart. The right atrium and ventricle pump blood to the lungs to get oxygenised. The left part of the heart receives the blood from the lungs and pumps it via the arteries into the body. Each heartbeat cycle consists of two phases: systole, during which the ventricles contract and pump blood into the body, and diastole, during which the ventricles relax and get filled with blood again. The pacemaker cells in the sinoatrial node produce an electrical impulse that causes the heart to contract. An electrocardiogram (ECG) is a way to record the electrical activity of the heart. It requires at least two electrodes on the participant’s body, which sense the (changes in) electrical activity during the heartbeat cycle (see Figure 17.1; the well-known “heart rate graph”). The P wave reflects the contraction of the atria, which is followed by the QRS complex, representing ventricular systole, and the T wave representing ventricular diastole, after which the cycle is repeated. Heart rate is calculated based on the number of R-peaks in a minute. For example, a heart rate of

262

Monika Donker, Selma van Aken and Tim Mainhard

FIGURE 17.1

Graphical representation of five repeated heartbeat cycles

70 beats per minute means that there were 70 R-peaks and thus 70 heartbeat cycles during this minute. Conceptual Meaning of Heart Rate in Studies on Motivation and Emotion

The basic idea behind using heart rate in psychological research is that, by controlling heart rate for physical activity levels (which ask for more oxygen being pumped to the muscles), the residual physiological activation can inform us about psychological or cognitive processes and “emotional arousal” (Myrtek, 2004). That is, when people evaluate their self or their environment, their motivational system automatically mobilises the physiological resources needed for anticipated action (i.e. increased sympathetic activation and an increased heart rate; Behnke & Kaczmarek, 2018; Blascovich, 2008). When an individual judges a situation as relevant and potentially harming their goals, their physiological system will get activated to support action. These action-oriented cognitive processes are in essence what influential scholars such as Lazarus and Folkman, Frijda, and Scherer have called primary appraisals (e.g. “this is important” or “not sure whether I can do this”). Appraisals, in turn, form the basis of emotions (e.g. anxiety or enthusiasm; Moors et al., 2013). As such appraisals are not easily accessed cognitively or verbalised, physiological measures might be well-suited to get insight in these processes (Scherer, 2009). Efforts have been made to link specific patterns of physiological activation to specific motivational and emotional states. However, there is no clear one-to-one connection between physiological and emotional changes.

Using Heart Rate to Tap into Motivational and Emotional Processes

263

Although some (lab-based) studies have found a link between physiological arousal and some discrete emotions, it is hard to differentiate between positive versus negative emotions by physiology only (Kreibig, 2010). Kreibig (2010) concludes that, only if several advanced measures of the autonomic nervous system are combined with respiratory measures, some discrete emotions can be distinguished in the laboratory. The Dynamics of Emotional Processes in Teachers (DEPTh) Project

Given this state of affairs, we concluded for our own research on teaching (see the DEPTh project; Donker, 2020) that it would be possible to infer changes in teachers’ emotional arousal from ambulatory heart rate measures (i.e. in the classroom, during teaching), such as identifying moments of personal importance, but that it would be rather unlikely to infer more specific motivational states or even momentary, discrete emotions. The goal of our project was to better understand how teachers’ and students’ self-reported emotions arise from what happens during the classroom (i.e. teacher-student interaction and teachers’ physiological arousal). We combined physiological measurement of teacher’s heart rate with continuous coding of teachers’ interpersonal behaviour based on a video recording of the lesson to add the interpretation of physiological changes. As a coding scheme, we used the interpersonal circle (Wubbels et al., 2006; see Figure 17.3). This circumplex model depicts how prototypical teacher behaviours (such as being directing or understanding) can be seen as a combination of certain levels of agency (i.e. social influence) and communion (i.e. friendliness). Considerations during Data Collection

Collecting heart rate data in ambulatory settings has become more common since the 1990s, following the increased availability of ambulatory devices. Still, the field is in its infancy, and clear guidelines for using heart rate in research on motivation and emotion are hard to find. Below, we give an overview of issues to consider during the collection of heart rate data in the classroom setting (i.e. study preparation, data cleaning, and data analyses). Study Preparation Which Device?

When choosing a device for measuring heart rate, important considerations are how easy the device is to use in school settings as well as the quality and reliability of the data. There are two broad categories of ambulatory devices: devices using photoplethysmography (PPG) and devices using ECG measurement.

264

Monika Donker, Selma van Aken and Tim Mainhard

Examples of devices using PPG are Empatica E4, Fitbit, and other smartwatches. These devices use infrared light to measure changes in blood volume in tissue under the skin due to diastole and systole (for more details, see Moraes et al., 2018). Although these devices are easy to use, inexpensive, and non-invasive, our experience was that the resulting data are heavily affected by movement. When teachers moved their hands, the sensors lost contact with the skin and blood volume changes could not be tracked, resulting in huge amounts of missing data. Data collected using ECG devices are generally more in line with the laboratory standard (Dobbs et al., 2019; Georgiou et al., 2018), but such devices are often also more invasive and expensive. Examples are the Movisens EcgMove 4, Imotions, Shimmer3, and the VU-Ambulatory Monitoring System (VU-AMS). In our study, we used the VU-AMS (www.vu-ams.nl), which has a seven-lead configuration and includes ECG, ICG (impedance cardiography), and physical activity measurements. The VU-AMS was specifically developed for research purposes and dedicated software and an extensive user manual are available. The data are stored on a CompactFlash card during the recording and can be uploaded onto a local computer. A drawback is that electrodes need to be placed on the participant’s chest and back. Teachers or students might therefore be hesitant to participate as they are not familiar with the methodology and placing the electrodes could be experienced as invasive. It is important to provide clear information letters with a good balance between explaining the methodology and not making it too complex. Attaching the device in a separate room is also advised. Teachers who participated reported no problems fitting the device and teaching while wearing the VU-AMS. The device has also been used successfully with younger populations. (What) Baseline?

Experimental studies in lab settings usually include a baseline measurement (e.g. Scrimin et al., 2018). After setting up the device, participants are often instructed to watch a relaxing video during a short period. The heart rate assessed during this period is then used to correct experimental data for differences in individuals’ baseline in order to be able to compare group means. In our study, we were mostly interested in within-teacher changes in physiological arousal (between situations) rather than in mean differences between (groups of) teachers. We therefore did not compare absolute heart rate values, but only within-person deviations from individual mean scores. As such, a teacher’s average heart rate during a lesson served as a benchmark against which situational changes were evaluated. Using a baseline can nonetheless be necessary or desired, but is at the same time challenging in the classroom. First, teachers often have a tight schedule with only short breaks in between lessons, which makes it hard to find a

Using Heart Rate to Tap into Motivational and Emotional Processes

265

good moment for the baseline measurement. Also, teachers might be even more aroused just before or after the lesson than during teaching. Second, the question is how adequate a resting baseline would be for an activity such as teaching, as in our sample the average teacher heart rate resembled values commonly reported for vigorous exercise. As a solution, more differentiated baseline values could be calculated, such as separate averages for whole group lecturing or seatwork (cf., Junker et al., 2021). Data Cleaning Artefact Correction

Before the analysis, the raw data need to be checked for any irregularities caused by external influences, such as connection problems or a noisy signal. Most software programs use an algorithm to identify artefacts automatically, which can then be checked manually by the researcher. It is advised to not rely on the automatic corrections only, but to make any manual corrections with at least two trained assistants and to check their agreement in data adjustments. In our project, less than 1% of the heart rate data needed corrections (mainly moving the identification of the Rpeak) and assistants discussed differences in corrections until agreement was reached. Please note that doing the artefact corrections can be timeintensive when the data collection period is long or when the signal is noisy. Controlling for Physical Activity

To be able to tap into the emotional part of physiological arousal, heart rate changes due to actual movement need to be filtered out. We used the regression-based Additional Heart Rate approach described by Myrtek (2004). This approach is based on the idea that emotional arousal is the arousal that exceeds the arousal that could be expected based on the level of physical activity. A challenge in our case was that physical activity was related not only to heart rate but also to teachers’ interpersonal behaviour (see Figure 17.2). For example, agency (i.e. being imposing or directing) was often associated with higher physical activity (e.g. standing in front of the classroom, walking around). Because we were interested in the association between interpersonal behaviour and heart rate, we did not want to take this substantive overlap already out in the regression analyses. Therefore, we first regressed physical activity on interpersonal behaviour and saved the residual physical activity (i.e. the variability not related to interpersonal behaviour). Second, we regressed heart rate on the residual physical activity score and saved the residuals as our emotional arousal score. Higher values indicated more arousal than could be expected based on physical activity.

266

Monika Donker, Selma van Aken and Tim Mainhard

FIGURE 17.2

Graphical representation of the Additional Heart Rate approach (Donker et al., 2018; Myrtek, 2004)

We used five-second time intervals and did separate regression analyses for each teacher because the correlations between heart rate, interpersonal behaviour, and physical activity level are different per person. Data Analyses

Physiological measurement often results in long time series, also referred to as intense longitudinal data. Such data enable researchers to analyse precisely how the heart rate of a teacher or student fluctuates over time and whether there are any associations with other variables. We present three approaches for data analyses that we used in our studies. Univariate Analyses

Based on the physiological data, we calculated person-specific statistical indicators to characterise specific teachers, and to compare teachers by correlating the indicators with outcome variables, such as teacher or student emotions (Donker et al., 2020; Mainhard et al., 2022). The challenge here is to find a balance between finding overall patterns (which in the end reflect between-person differences) and using the intensive physiological data to its full potential. Lab studies often summarise physiological data into a mean heart rate value to compare groups. However, by using only the mean, information on the changes over time is lost. What could be interesting is to calculate mean levels for shorter time periods or specific teacher activities and compare these within persons to see what is most arousing for a specific teacher (e.g. Junker et al., 2021). To get more insight in variability, we calculated the standard deviation, range, and autocorrelation of the heart rate time series (Donker et al., 2018, 2020). Standard deviations and range mainly tap into the extremeness of the

Using Heart Rate to Tap into Motivational and Emotional Processes

267

heart rate values over the course of the lesson. Autocorrelations additionally give information on how well a value can be predicted by the previous score. Lower standard deviations, smaller ranges, and higher autocorrelations for heart rate represent less dynamic changes over time and might point towards lower reactivity to changing classroom situations and/or less recovery (Houben et al., 2015). It is important to note that for shorter time intervals, the autocorrelation might be automatically higher because data points are more closely connected. However, by using larger time-intervals we might lose the richness of the moment-to-moment data. See Donker et al. (2018) for a graphical representation of how aggregating heart rate data at 10-, 30-, and 60-second intervals fails to capture moment-to-moment changes in heart rate. Stability or predictability measures combine the autocorrelation with the magnitude of the changes (i.e. standard deviation) and is usually calculated as the mean squared successive difference (MSSD; Jahng et al., 2008). Higher values represent more extreme moment-to-moment changes (i.e. instability) in heart rate. Such a pattern could be associated with more extreme emotional arousal at several times during a lesson and/or more demanding situations during the lesson (see Identifying Situations), and thus potentially more negative emotions after the lesson. It is important to note that not only the MSSD but also the standard deviation and range of heart rate are often higher in people with higher (uncorrected) average heart rates (see Donker et al., 2020). Multivariate Analyses

Physiological data in itself is hard to interpret in relation to emotion and motivation. It is therefore advised to combine physiological measures with other measures, such as (video) observation, experience sampling, or eye tracking. In this way, the additional data help to make sense of the physiological arousal. In our case, we used behavioural observation of teacher’s interpersonal behaviour during the lesson. We quantified the strength and direction of the relationship between teacher’s emotional arousal and interpersonal behaviour using cross-correlations. Cross-correlations can be seen as person-specific correlations, and thus as an indication of teachers’ individual action tendencies (Mainhard et al., 2022; Scherer, 2009). Figure 17.3, for example illustrates the crosscorrelations between agency, communion and heart rate for one specific teacher (i.e. Teacher A in Donker et al., 2018). The coloured dots represent the teacher’s heart rate when showing a certain level of agency and communion in class, with darker colours indicating higher heart rates. In our total sample of 75 teachers, the cross-correlation between heart rate and teacher agency ranged from −.43 to .68 (M = .17, SD = .25). The

268

Monika Donker, Selma van Aken and Tim Mainhard

FIGURE 17.3

The Interpersonal Circle for Teachers combined with heart rate values

cross-correlation between heart rate and communion ranged from −.65 to .51 (M = −.08, SD = .21). Thus, overall teachers had a high heart rate when they showed relatively high agency and low communion, but there were large differences between teachers. Regarding the link with emotions, we found that especially teachers who had an increased heart rate while showing high levels of communion reported negative emotions after the lesson (e.g. anger; Donker et al., 2020). Identifying Situations

Heart rate data could also be used to identify specific, potentially demanding, moments during a lesson. In one of our projects, we selected all moments during a lesson where the heart rate of the teacher was two standard deviations above their personal mean (Donker, 2020). The number of peak moments ranged from 0 to 26 per teacher. Interestingly, more experienced teachers had more of these high heart rate situations. All these situations were then coded in terms of the instructional setting. We found that teachers experienced the most extreme heart rate peaks during the lesson start and during teacher-centred activities. Future Directions

Using heart rate in ambulatory classroom settings to get an insight into motivational and emotional processes is promising, but the field is in its early phases and rapidly evolving. Future studies should replicate earlier findings

Using Heart Rate to Tap into Motivational and Emotional Processes

269

and include larger samples to improve the generalisability of the findings. Moreover, also student’s emotional arousal should be included in future research to study for example emotional contagion in the classroom (Järvenoja et al., 2018; Pijeira-Díaz et al., 2018). Applying multilevel modelling is useful to separate between- and within-person associations of physiological measures with other variables such as interpersonal behaviour and emotional outcomes. Statistical methods such as Dynamic Structural Equation Modeling (DSEM; Hamaker et al., 2018) are promising in this regard, but high autocorrelations as we found in our behaviour coding may prevent an efficient use of such applications as yet. Furthermore, it should be considered that there is no clear one-on-one link between physiological arousal and emotions or motivation. Especially in ambulatory settings, there are many variables that could affect the physiological arousal of participants, beyond motivational or emotional processes. Scrimin et al. (2018) for example linked heart rate (variability) to cognitive effort. Including more specific and advanced physiological indicators, such as cardiac output and total peripheral resistance, could help to clarify some of the ambiguity and to differentiate for example between challenge versus threat motivational states (Blascovich, 2008). More in general, we should not expect that it will be possible any time soon to use physiological measures instead of self-report of internal motivational or emotional processes. Instead, combining physiology with more (qualitative) data, such as teachers’ perceptions of specific classroom situations and their self-reported motivation and emotion seems to be a more likely and fruitful path for future research. In our case, we showed that observing teachers’ interpersonal behaviour was helpful in interpreting the physiological patterns. However, specific appraisals of situations cannot be discerned with observational data, beyond identifying potential episodes of personal relevance through heart rate peaks. Therefore, selecting and analysing specific classroom situations could, for example be used as a starting point for physiology-stimulated recall interviews with teachers. Such an approach could provide more information on the idiosyncratic way in which teachers interpret classroom situations and how physiological reactions affect their emotional experiences. Conclusion

Research into motivation and emotion has recently moved from using mainly between-person self-reported data towards including more momentary, within-person measures. One promising avenue is the use of physiological measures, such as heart rate. Due to recent technological advancements, physiological measures can now be collected in ambulatory settings, such as the classroom, which increases the ecological validity of the findings. Using

270

Monika Donker, Selma van Aken and Tim Mainhard

physiological measures can not only open up pathways to assessing withinperson measures but they also enrich research into motivation and emotion in more substantive ways. For example, theorising on emotions often involves componential models that also include physiology next to cognitive, affective, and motivational components. Before physiological data can be included in research designs and collected, however, choices about devices or baseline measurements need careful consideration and more insight into the conceptual meaning of heart rate is needed. We hope that this chapter is helpful for future researchers and encourages them to implement physiology in their studies on motivation and emotion in teaching and learning. Doing this will help us to better understand within-person processes as well as the situatedness and context specificity of emotion and motivation. Funding

This work was supported by the Netherlands Initiative for Education Research [NRO/PROO grant 405-14-300-039], which resides under the Netherlands Organization for Scientific Research (NWO). References Behnke, M., & Kaczmarek, L. D. (2018). Successful performance and cardiovascular markers of challenge and threat: A meta-analysis. International Journal of Psychophysiology, 130, 73–79. https://doi.org/10.1016/j.ijpsycho.2018.04.007 Blascovich, J. (2008). Challenge and threat. In A. J. Elliot (Ed.), Handbook of approach and avoidance motivation (pp. 431–445). Psychology Press. Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (Eds.). (2017). Handbook of psychophysiology (4th ed.). Cambridge University Press. Carson, R. L., Weiss, H. M., & Templin, T. J. (2010). Ecological momentary assessment: A research method for studying the daily lives of teachers. International Journal of Research & Method in Education, 33, 165–182. https://doi.org/10. 1080/1743727x.2010.484548 Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the ExperienceSampling Method. Journal of Nervous and Mental Disease, 175, 526–536. https:// doi.org/10.1007/978-94-017-9088-8 D’Mello, S., Dieterle, E., & Duckworth, A. (2017). Advanced, Analytic, Automated (AAA) measurement of engagement during learning. Educational Psychologist, 52, 104–123. https://doi.org/10.1080/00461520.2017.1281747 Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J. J., Ding, M., & Poeppel, D. (2017). Brain-tobrain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology, 27, 1375–1380. https://doi.org/10.1016/j.cub.2017.04.002 Dobbs, W. C., Fedewa, M. V., MacDonald, H. V., Holmes, C. J., Cicone, Z. S., Plews, D. J., & Esco, M. R. (2019). The accuracy of acquiring Heart Rate Variability

Using Heart Rate to Tap into Motivational and Emotional Processes

271

from portable devices: A systematic review and meta-analysis. Sports Medicine, 49(3), 417–435. https://doi.org/10.1007/s40279-019-01061-5 Donker, M. H. (2020). In DEPTh: Dynamics of emotional processes in teachers – An exploration of teachers’ interpersonal behavior and physiological responses. Utrecht University. Donker, M. H., Van Gog, T., Goetz, T., Roos, A. L., & Mainhard, T. (2020). Associations between teachers’ interpersonal behavior, physiological arousal, and lessonfocused emotions. Contemporary Educational Psychology, 63, Article 101906. https://doi.org/10.1016/j.cedpsych.2020.101906 Donker, M. H., Van Gog, T., & Mainhard, M. T. (2018). A quantitative exploration of two teachers with contrasting emotions: Intra-individual process analyses of physiology and interpersonal behavior. Frontline Learning Research, 6(3), 162–184. https://doi.org/10.14786/flr.v6i3.372 Donker, M. H., van Vemde, L., Hessen, D. J., van Gog, T., & Mainhard, T. (2021). Observational, student, and teacher perspectives on interpersonal teacher behavior: Shared and unique associations with teacher and student emotions. Learning and Instruction, 73, Article 101414. https://doi.org/10.1016/j.learninstruc. 2020.101414 Georgiou, K., Larentzakis, A. V., Khamis, N. N., Alsuhaibani, G. I., Alaska, Y. A., & Giallafos, E. J. (2018). Can wearable devices accurately measure Heart Rate Variability? A systematic review. Folia Medica, 60(1), 7–20. https://doi.org/10.2478/ folmed-2018-0012 Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018). At the frontiers of modeling intensive longitudinal data: Dynamic Structural Equation Models for the affective measurements from the COGITO study. Multivariate Behavioral Research, 53, 820–841. https://doi.org/10.1080/00273171.2018. 1446819 Hamaker, E. L., & Grasman, R. P. P. P. (2014). To center or not to center? Investigating inertia with a multilevel autoregressive model. Frontiers in Psychology, 5, Article 1492. https://doi.org/10.3389/fpsyg.2014.01492 Houben, M., Van den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141, 901–930. https://doi.org/10.1037/a0038822 Jahng, S., Wood, P. K., & Trull, T. J. (2008). Analysis of affective instability in ecological momentary assessment: Indices using successive difference and group comparison via multilevel modeling. Psychological Methods, 13, 354–375. https://doi. org/10.1037/a0014173 Järvenoja, H., Järvelä, S., Törmänen, T., Näykki, P., & Malmberg, J. (2018). Capturing motivation and emotion regulation during a learning process. Frontline Learning Research, 6(3), 85–104. https://doi.org/10.14786/flr.v6i3.369 Junker, R., Donker, M. H., & Mainhard, T. (2021). Potential classroom stressors of teachers: An audiovisual and physiological approach. Learning and Instruction, 75, Article 101495. https://doi.org/10.1016/j.learninstruc.2021.101495 Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84, 394–421. https://doi.org/10.1016/j.biopsycho.2010. 03.010 Mainhard, T., Donker, M. H., & Van Gog, T. (2022). When closeness is effortful: Teachers’ physiological activation undermines positive effects of their closeness

272

Monika Donker, Selma van Aken and Tim Mainhard

on student emotions. British Journal of Educational Psychology, 92, 1384–1402. https://doi.org/10.1111/bjep.12506 Moraes, J. L., Rocha, M. X., Vasconcelos, G. G., Vasconcelos Filho, J. E., de Albuquerque, V. H. C., & Alexandria, A. R. (2018). Advances in photopletysmography signal analysis for biomedical applications. Sensors, 18, Article 1894. https://doi. org/10.3390/s18061894 Mossink, J. C. L., Verkuil, B., Burger, A. M., Tollenaar, M. S., & Brosschot, J. F. (2015). Ambulatory assessed implicit affect is associated with salivary cortisol. Frontiers in Psychology, 6, Article 111. https://doi.org/10.3389/fpsyg.2015. 00111 Murayama, K., Goetz, T., Malmberg, L.-E., Pekrun, R., Tanaka, A., & Martin, A. J. (2017). Within-person analysis in educational psychology: Importance and illustrations. British Journal of Educational Psychology Monograph Series II, 12, 71–87. Myrtek, M. (2004). Heart and emotion. Ambulatory monitoring studies in everyday life. Hogrefe & Huber Publishers. Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., & Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36, 36–48. https:// doi.org/10.1016/j.cedpsych.2010.10.002 Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2018). Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members? Computers in Human Behavior, 92, 188–197. https:// doi.org/10.1016/j.chb.2018.11.008 Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879–903. https://doi. org/10.1037/0021-9010.88.5.879 Roos, A. L., Goetz, T., Krannich, M., Jarrell, A., Donker, M. H., & Mainhard, T. (2020). Test anxiety components: An intra-individual approach testing their control antecedents and effects on performance. Anxiety, Stress and Coping, 34, 279–298. https://doi.org/10.1080/10615806.2020.1850700 Scherer, K. R. (2009). The dynamic architecture of emotion: Evidence for the component process model. Cognition & Emotion, 23, 1307–1351. https://doi. org/10.1080/02699930902928969 Schultheiss, O. C., & Wirth, M. M. (2018). Biopsychological aspects of motivation. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (3rd ed., pp. 407–451). Springer. Scollon, C. N., Kim-Prieto, C., & Diener, E. (2009). Experience sampling: Promises and pitfalls, strengths and weaknesses. In E. Diener (Ed.), Assessing well-being: The collected works of Ed Diener (pp. 157–180). Social Indicators Research Series. https://doi.org/10.1007/978-90-481-2354-4 Scrimin, S., Patron, E., Ruli, E., Pagui, C. E. K., Altoè, G., & Mason, L. (2018). Dynamic psychophysiological correlates of a learning from text episode in relation to reading goals. Learning and Instruction, 54, 1–10. https://doi.org/10.1016/j. learninstruc.2018.01.007 Van Braak, M., Van de Pol, J., Poorthuis, A. M. G., & Mainhard, T. (2021). A micro-perspective on students’ behavioral engagement in the context of teachers’

Using Heart Rate to Tap into Motivational and Emotional Processes

273

instructional support during seatwork: Sources of variability and the role of teacher adaptive support. Contemporary Educational Psychology, 64, Article 101928. https://doi.org/10.1016/j.cedpsych.2020.101928 Wolff, C., Van den Bogert, E., Jarodzka, N., & Boshuizen, H., H. P. A. (2015). Keeping an eye on learning: Differences between expert and novice teachers’ representations of classroom management events. Journal of Teacher Education, 66, 68–85. https://doi.org/10.1177/0022487114549810 Wubbels, T., Brekelmans, M., Den Brok, P., & Van Tartwijk, J. (2006). An interpersonal perspective on classroom management in secondary classrooms in the Netherlands. In C. M. Evertson & C. S. Weinstein (Eds.), Handbook of classroom management: Research, practice, and contemporary issues (pp. 1161–1191). Lawrence Erlbaum Associates.

18 AN EPISTEMOLOGICAL SHIFT FORWARD The Methodological Zone of Proximal Research on Motivation and Emotion in Learning and Teaching Alexander Minnaert

Abstract The author considers all chapters of Section II, highlighting that the most underlying and unequivocal message is the well-determined shift in emotion and motivation research from static, variable-centred, highly controlled, single-method studies (like surveys, randomised-control trials, or laboratory experimental designs) to a holistic, multi-method approach, tapping the dynamics of emotions and motivation and capturing situated within-person next to between-person change in the real habitat of the person. Demonstrating how readers can strive towards this epistemological shift, the author reflects on the chapters first by addressing the major insights and by reasoning on the opportunities and challenges in the methodological zone of proximal research in motivation and emotion.

The need for knowing the three R’s, reading, writing and arithmetic, is well known. But these are not sufficient to cope with uncertainties facing an individual at every moment of his life. He has to make decisions … This requires a different kind of skill, which we may call the fourth R, reasoning under uncertainty, for making decisions in real life. (Rao, 1997, p. 163)

The 13th-century philosopher Roger Bacon, noteworthy for his philosophical accomplishments in the fields of mathematics, natural sciences, and language studies, wrote that “The strongest arguments prove nothing so long as the conclusions are not verified by experience” (Opus Tertium, c. 1267). Besides, he stated that the scientia experimentalis, that is DOI: 10.4324/9781003303473-20

An Epistemological Shift Forward

275

the Medieval Latin wording for experimental science in his chapter “De Scientia experimentorum,” is the queen of sciences. Given the vast and impressive variety of methodological (mixed) methods presented in this book, it is highly doubtful whether the experiment is still the queen of sciences. It has been frequently shown that there are many pitfalls and problems innate to experimental designs. These threats of internal validity, such as sensitisation, history, selection, mortality and maturation (Campbell & Stanley, 1963), are jeopardising the validity of the data collected as well as the interpretation of the data. Even a Solomon Four-Group design (Kerlinger & Lee, 2000; Solomon, 1949) cannot tackle all threats of internal validity, not even mentioning external validity (Van Loon et al., 2015). Did Bacon get it wrong (in the 13th century)? No, I would not dare say so because Bacon firmly stipulated that the dignity of the scientia experimentalis is to aid the process of science’s search for truth and certitude. Besides, the scientia experimentalis serves the purpose of serving the other sciences to their fullest potential to discover hidden truths. Bacon went far beyond the experiment by encompassing observations to explain existing theories and phenomena in the world we live in. Hence, researchers have always been indulged in studying phenomena over time, in a variety of contexts, and in many different ways in order to understand and explain them. Methodological reflections on new ways of tapping the phenomena and going beyond our knowledge and reaching out for truth and certainty are scientific drivers for the zone of proximal research. Accordingly, this concluding chapter is meant as an epistemological shift forwards into the methodological zone of proximal research on motivation and emotion in learning and teaching inspired by the contributions in Section II of this book. The Overall Picture Stemming from the Methodological Contributions in Section II

All chapters of Section II considered the most underlying and unequivocal message is the well-determined shift in emotion and motivation research from static, variable-centred, highly controlled, single-method studies (like surveys, randomised-control trials, or laboratory experimental designs) to a holistic, multi-method approach, tapping the dynamics of emotions and motivation and capturing situated within-person next to between-person change in the real habitat of the person. The mindful and deliberate embarquement in tapping situation- and context-specificity by an Experience Sampling Method (ESM), person-centred next to variable-centred methods, (latent change) mixture models integrating a person- and variablecentred approach, multi-methods and mixed methods, mixed methods intervention studies making use of multi-channel data, the added and

276

Alexander Minnaert

complimentary value of physiological measures like heart rate, and the multifaceted and dynamic approach of ethnical, cultural and generational status of persons (especially those being minoritised) is captivating and overwhelming. The aforementioned transition is often portrayed as recent. Given the Opus Tertium of Bacon in the 13th century, one cannot claim that methodological variation and the focus on situated phenomena in scientific research stems only from the last decades. In the explanation of a rainbow, it was Roger Bacon to introduce a change in the concept of explanation, serving the fertility of new scientific methods. Likewise, a shift appears on the horizon of emotion and motivation in learning and teaching research. The targeted and augmented character of this shift towards complexity and dynamics is to meet the shift in societal demands, the needs of learners, teachers and school managers, situated and contextualised (special needs) problems in learning and teaching, explanations of the rapid technological, environmental, demographical and geopolitical changes in the world (Gaub, 2019) in which we live, learn and teach. We seem triggered to redefine the global order, the education system, the conceptualisations of motivation and emotion, and to embrace the epistemological practice of reason justification (Dreyer, 2022). Paving the way towards this epistemological shift, I will reflect on the chapters first by addressing the major insights and by reasoning on the opportunities and challenges in the methodological zone of proximal research in motivation and emotion. Major Insights

To capture fluctuating motivational and emotional experiences as well as context-related information, Moeller et al. (2023) describe a paradigmatic shift from a nomothetic, inter-individual and linear approach to an idiographic, intra-individual and non-linear approach. Besides, theory and theoretical frameworks shift more and more towards situatedness (e.g. the situated expectancy-value theory by Eccles & Wigfield, 2020) to dynamically incorporate change, non-stationarity, and context specificity in the moment. This requires new methods, as stipulated by Lazarides and Gniewosz (2023), to model development and change, both in variable-centred and person-centred methods. In variable-centred methods, within-person change (and shapes of development) can be modelled next to between-person differences. Basic assumption is, however, that change patterns are assumed to be valid for all participants involved. Homogeneity of the population is the assumption of variable-centred methods, while, conversely, heterogeneity in the population is a key assumption of person-centred methods. Can we still rely on the viewpoint of homogeneity in our (student and teacher)

An Epistemological Shift Forward

277

population? Or should we fully embrace heterogeneity in our students’ and teachers’ population? In accordance with the above, the universality of motivational and emotional theories needs to be tweaked and/or locally adapted to the increasing diversity of our target research group. This augmenting diversity in the classroom and in our society must appeal to more situated demands. It requires more diversified support strategies to include all children in our schools. Accordingly, Urdan (2023) stressed that the intersectionality of ethnicity, race, cultural and generational aspects of an individual should be taken into account. The rather categorical viewpoint on ethnic and cultural identity is not valid anymore in our contemporary society (Eccles & Wigfield, 2020; Urdan, 2023). So, to conduct equitable quality research, a multifaceted approach is (becoming) the golden standard and calls into play complex and mixed methodologies to do justice to diversity, and especially to those who are less heard and seen (in research): minoritised students, students with special educational needs as well as students (and teachers) being bullied and/or getting less motivated and/or more frustrated. Since the rise of new methodologies and laws to protect these students (from discrimination on the basis of, e.g. race, colour, gender, and disability), the voice of these students have been more justified in (motivational and emotional) research: that is every student’s voice matters. There is, however, still a long way ahead of us. Talking about mixed methodologies, Hagenauer et al. (2023) clearly depict various types of mixed method designs, also stressing the benefits of these designs. The integration and connection of quantitative and qualitative data enable researchers to triangulate the data, look and check for complementarity and focus on continued development, next to situated, contextualised, and individualised insights. Intervention studies (and especially mixed methods intervention studies, see Gaspard, 2023; Hagenauer et al., 2023) are prone to internal and external validity threats. Hence, a lot of decisions are to be made prior to the onset of the intervention study. Although intervention studies may provide evidence for causal relationships and unfold direct, indirect and/or sleeper-effects, the power of replication is limited due to cross-situational and cross-temporal changes within the educational context. Hence, Gaspard (2023) rightfully recommends investigating the heterogeneity in intervention effects, thereby taking person-centred as well as person-centred methodological perspectives into account. Two chapters specifically address the rise of relatively new measures within the field of motivation and emotion research. The use of video and multi-channel data is focused on within the context of collaborative learning by Mänty et al. (2023). These data provide a window to analyse the reciprocal relationship of individual and group-level affective processes and

278

Alexander Minnaert

their fluctuations over time. Besides, the level of emotion regulation can be tapped, looking at between- and within-person phenomena. It should be noted that these methodologies are laborious and time-consuming. Heart rate measures tapping moment-to-moment physiological activities and changes over time are the core of the contribution by Donker et al. (2023). Given the basic assumption that there are differences in physiological responses underlying differences in behaviour and psychological experiences, the applicability of heart rate measures (if non-invasive) is promising and smart. Research on teachers’ heart rates in relation to their agency and communion shows, however, a very high variability in correlations. It was recommended to use heart rate measurements in conjunction with other measures. Opportunities and Challenges in the Methodological Zone of Proximal Research

As variable-centred methods focus on the large majority of the population and on characteristics across situations, person-centred methods focus on subgroups and patterns of change across situations (Lazarides & Gniewosz, 2023). In other words, one can focus respectively on the picture as a whole, which poses the risk of taking on too broad of a viewpoint and thereby overlooking salient details, or one can focus on snapshots of the picture, which poses the risk of overaccentuating the details and neglecting the whole picture or “Gestalt.” The combination of both, for example in latent change mixture models (Lazarides & Gniewosz, 2023) or in the different parallel or sequential mixed methods (Hagenauer et al., 2023), is seen as a fruitful way of having a kaleidoscopically oriented view in one. Turning the kaleidoscope could be the way out. But the complex reality is even more challenging to capture and to model. Many chapters in Section II did, however, not (fully) include the nested structure of motivational and emotional data. Despite technological innovations in the area, like sensor-augmented ESM, video and multi-channel data, heart rate measures and facial expression registrations, phenomena in learning and teaching are nested as pupils (and teachers) who are situated in classes, groups/ projects, school locations, schools, neighbourhoods, regions, states and/ or countries. Beyond the multilevel issue, raised by Mänty et al. (2023), of person and group level in collaborative settings, we might also have within-person nested data: that is moment-to-moment experience and interaction, the lesson or meeting, and the student level. This enables the researcher to focus on repeated measurement within a student and on (clinical) subgroups of students as well. So, multilevel techniques, as mentioned by Donker et al. (2023), within- and between-person structural equation modelling, and cross-recurrence quantification analysis (to model

An Epistemological Shift Forward

279

moment-to-moment interactions) can also be part of the methodological (mixed method) kaleidoscope. With regard to intervention studies, the use of effect sizes within one person is in line with the recommendation of Gaspard (2023), namely to focus on heterogeneity in intervention effects. Reliable change indices (for an overview, see Bauer et al., 2004) and non-overlapping data techniques (Parker & Vannest, 2009) are to be recommended. Hence, (sub)group effect sizes as well as individualised effect sizes might complement each other or provide a more diversified view. Next to the effect sizes, Gaspard (2023) recommends more in-depth analyses of the fidelity of implementation, that is to what extent is the intervention implemented according to plan and which are the facilitating and debilitating factors in the implementation efficacy. This is undeniably of added value as teachers’ level and quality of adherence to the intervention have strong positive effects on motivation in case of a strong adherence, but rather negative effects on motivation in case of a weak adherence (Rozendaal et al., 2005). So, checking for the fidelity of implementation is of utmost importance to estimate the true level of heterogeneity in intervention effects. The use of non-invasive heart rate measurements (e.g. by means of smart watches) in the domain of motivation and emotion is still in progress (Donker et al., 2023). Although some artefacts (e.g. external noises) can be automatically identified by software programs, heart rate changes are heavily affected by movement and needs to be filtered out of the raw data (Donker et al., 2023). However, the meta-analysis and literature review of Kim et al. (2018) demonstrated that heart rate variability, as a psychological stress indicator, is affected by a lot of factors, such as physiological factors (breathing, posture), non-modifiable factors (age, genetic factors), modifiable factors (obesity, smoking, drinking, physical activity) and medication. Although heart rate variability is of amplified theoretical and clinical value in the domain of emotions, the interpretation of the data remains, however, very challenging, namely to estimate the (residual) emotional part of physiological arousal within each individual. Multimethod studies looking at within-person change conducted in the domain of emotion and motivation situated within an ecologically valid classroom setting are not new, but still remain scarce and very challenging. The most challenging question is how to integrate or connect (see Hagenauer et al., 2023; Lazarides & Gniewosz, 2023; Mänty et al., 2023; Moeller et al., 2023) the raw survey data, the residual heart rate data, the video and multichannel data, the moment-to-moment experiences, the voices stemming from the interviews, the in-class observations and facial expressions, collected by sequential or parallel mixed methods designs? In the multimethod study of Ahmed et al. (2010) on emotional experiences of students in the classroom, it was examined how the emotional response systems (heart rate

280

Alexander Minnaert

changes, nonverbal/facial expressions and subjective feelings being measured by video stimulated recall interviews and an appraisal questionnaire) work simultaneously to reveal emotional experiences. Remarkable was that the various emotional response systems acted fairly in concert for anger, anxiety and enjoyment, but the nonverbal expressions of boredom, pride and shame were in huge contrast with their subjective feelings. This finding is not in line with the expected emotion response coherence implied in most theories. Besides, the type and frequency of emotion were found to depend on task difficulty and on students’ ability level. All in all, the harmonious or disharmonious interplay between emotional response systems is far more complex than theory presumes (see Cacioppo et al., 2000; Kim et al., 2018) and still needs more in-depth, complex studies with multi-channel and multi-level data to figure out whether the data show true dissonance or whether the theory is obsolete and needs to be revised. An Epistemological Shift Forward

Are we at the forefront (or even in the middle) of an epistemological crisis in the domain of motivation and emotion? As delineated by Moeller et al. (2023), the aforementioned shift might unfold as an epistemological crisis, for example a theoretical, a conceptual (operationalisation), an assessment-related, a normativity, an inference, a replicability crisis. How can we reach full and dynamic integration/connection of multilevel, multichannel, cross-temporal and cross-situational quantitative and qualitative data? In other words, how can we validly grasp the highly contextualised value of emotion and motivation aligned to the global between-person and situated within-person picture, and, equally important, how to decontextualise these moment-to-moment snapshots as feedforward into complex, dynamic theoretical frameworks, in order that adaptive theory improvement can take place? A crisis might also act as a driver for epistemological growth in the zone of proximal research. All chapters in Section II mentioned scaffolds for kaleidoscopical improvement: quantitative ethnography and epistemic network analysis as a step forward, mixture models, complex and mixed methodologies, embracing heterogeneity in populations and effect sizes, paradigmatic changes are requested to realise the full potential of this proximal zone. We have to acknowledge that this crisis goes in sync with areas beyond the domain of emotion and motivation in learning and teaching. Eminent colleagues in the domain of mixed methods have raised the “integration challenge” (see Creamer, 2020; Fetters & Freshwater, 2015), that is the imperative to produce a thorough integration that is greater than the sum of the individual qualitative and quantitative parts. The purposeful interdependence between different sources, methods, and/or approaches is what

An Epistemological Shift Forward

281

distinguishes mixed methods from multi- and single-method research (Bazeley, 2018). In the field of urban health and social work, Mendlinger and Cwikel (2008) developed a DNA double helix as a metaphor to visualise the iterative and interactive way mixed methods research processes unfold. Iterations and recursive bootstrapping (Boekaerts & Minnaert, 2003) are self-evident in the dialectical process of integrating insights and data stemming from different methods over time, namely in the design phase, data collection phase, and parallel/sequential/embedded/integrated data analysis phase (Mendlinger & Cwikel, 2008). Only in this way, one might avert the “Double Trouble” counterargument, as introduced by Newby (2010), due to the absence of a conceptual infrastructure for mixed methods. In doing so, an epistemological shift forward is on the agenda for the years to come. The growing scepticism about the truth in/of science and the aligned positivistic-analytic paradigm of experimental designs emphasises that the scientia experimentalis is not able to meet the situational demands and needs in the contemporary “habitat” of young children, students and their teachers/educators anymore (see also Dreyer, 2022; Hauser-Cram et al., 2000; Urdan, 2023; Van Loon et al., 2015). Bacon already paved the way in the 13th century, but the experiment (“the queen of science”), as addressed by many contributors to this book, is just one of the pieces on the scientific chessboard. Hence, embracing complexity, change, and mixed methodologies is paving the way towards an epistemological practice of reason justification (Dreyer, 2022). Reasoning under uncertainty (Rao, 1997) will always be part of our scientific journey, also in the domain of motivation and emotion. Our domain is complex, and we like it because we want to reach out for truth and certainty, thereby incorporating reasoning under uncertainty.

References Ahmed, W., Werf, G., van der, & Minnaert, A. (2010). Emotional experiences of students in the classroom: A multi-method qualitative study. European Psychologist, 15(2), 142–151. https://doi.org/10.1027/1016-9040/a000014 Bauer, S., Lambert, M. J., & Nielsen, S. L. (2004). Clinical significance methods: A comparison of statistical techniques. Journal of Personality Assessment, 82(1), 60–70. https://doi.org/10.1207/s15327752jpa8201_11 Bazeley, P. (2018). Integrating analyses in mixed methods research. Sage. Boekaerts, M., & Minnaert, A. (2003). Measuring behavioral change processes during an ongoing innovation program: Scope and limits. In E. De Corte, L. Verschaffel, N. Entwistle, & J. van Merriënboer (Eds.), Powerful learning environments: Unravelling basic components and dimensions (pp. 71–87). Pergamon. Cacioppo, J. T., Brentson, G. G., Larsen, G. T., Poehlmann, K. M., & Ito, T. A. (2000). The psychophysiology of emotion. In R. Lewis & J. M. Haviland-Jones (Eds.), Handbook of emotions (2nd ed.) (pp. 173–191). Guilford Press.

282

Alexander Minnaert

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Rand McNally College Publishing Company. Creamer, E. G. (2020). Why integration? Why now? Caribbean Journal of Mixed Methods Research, 1(1), 1–15. Donker, M., van Aken, S., & Mainhard, T. (2023). Using heart rate to tap into motivational and emotional processes during teaching and learning. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 258–273). Routledge. Dreyer, V. M. (2022). The epistemology and science of justified reason. Philosophia, 50(2), 503–532. https://doi.org/10.1007/s11406-021-00399-3 Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859 Fetters, M. D., & Freshwater, D. (2015). The 1 + 1 = 3 integration challenge. Journal of Mixed Methods Research, 9(2), 115–117. https://doi.org/10.1177/ 1558689815581222 Gaspard, H. (2023). Intervening on students’ motivation to learn: Promises and pitfalls of intervention studies. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 213–227). Routledge. Gaub, F. (2019). Global trends to 2030: Challenges and choices for Europe. ESPAS. https://www.iss.europa.eu/sites/default/files/EUISSFiles/ESPAS_Report.pdf Hagenauer, G., Muehlbacher, F., Kuhn, C., Stephan, M., & Gläser-Zikuda, M. (2023). Mixed methods in research on motivation and emotion. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 163–177). Routledge. Hauser-Cram, P., Warfield, M. A., Upshur, C. C., & Weisner, T. S. (2000). An expanded view of program evaluation in early childhood intervention. In S. J. Meisels & J. P. Shonkoff (Eds.), Handbook of early childhood intervention (pp. 487–510). Cambridge University Press. Kerlinger, F. N., & Lee, H. B. (2000). Foundations of behavioral research (4th ed.). Harcourt College Publishers. Kim, H.-G., Cheon, E.-J., Bai, D.-S., Lee, Y. H., & Koo, B.-H. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15(3), 235–245. https://doi.org/10.30773/pi.2017.08.17 Lazarides, R., & Gniewosz, B. (2023). Modelling development and change of motivational beliefs. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 197–212). Routledge. Mänty, K., Pino-Pasternak, D., Ahola, S., & Jones, C. (2023). Affective processes in collaborative learning contexts: Examining affordances and challenges of video and multi-channel data. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 228–243). Routledge.

An Epistemological Shift Forward

283

Mendlinger, S., & Cwikel, J. (2008). Spiraling between qualitative and quantitative data on women’s health behaviors: A double helix model for mixed methods. Qualitative Health Research, 18(2), 280–293. https://doi.org/10.1177/10497323 07312392 Moeller, J., Dietrich, J., & Baars, J. (2023). The Experience Sampling Method in the research on achievement-related emotions and motivation. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 178–196). Routledge. Newby, P. (2010). Research methods for education. Pearson. Parker, R. I., & Vannest, K. J. (2009). An improved effect size for single case research. Non-overlap of all pairs (NAP). Behavior Therapy, 40, 357–367. https:// doi.org/10.1016/j.beth.2008.10.006 Rao, C. R. (1997). Statistics and truth. Putting chance to work (2nd ed.). World Scientific Publishing. Rozendaal, J. S., Minnaert, A., & Boekaerts, M. (2005). The influence of teacher perceived administration of self-regulated learning on students’ motivation and information processing. Learning and Instruction, 15(2), 141–160. https://doi. org/10.1016/j.learninstruc.2005.04.011 Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46, 137–150. https://doi.org/10.1037/h0062958 Urdan, T. (2023). Where ethnic and cultural identity meet situational demands: Implications for methodologies used to study motivation. In G. Hagenauer, R. Lazarides, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 244–257). Routledge. Van Loon, D., Van der Meulen, B., & Minnaert, A. (2015). Effectonderzoek in de gedragswetenschappen [Effectiveness research in the behavioural sciences]. Garant.

INDEX

Note: Page references in italics denote figures, in bold tables and with “n” endnotes. ability 132, 135; perceived 132, 201; -related beliefs 132, 147; self-concepts of 3, 36, 147 ability-avoidance 135–136; goals 136 academic emotions 85–96; empirical evidence 90–95; functional relations 92–95; future directions 95–96; relative universalism 89–90; relative universality of emotions 86, 88–89; role of context 88–89 Academic Emotions Questionnaire (AEQ) 170 academic self-concept (ASC) 3, 6, 8, 11–12, 201 academic self-efficacy 69–72 acculturation 247 achievement contexts 38–39, 42, 44, 179, 184, 244 achievement emotions 58–59, 61, 63, 86, 90–91, 95 Achievement Emotions Questionnaire (AEQ) 91, 170 Achievement Emotions QuestionnaireMathematics (AEQ-M) 91 achievement goals 36–39; defined 24; and interest 24–25; orientation 37; overview 35–36; performance-approach 25; pursuing 44–47; research 48n1

achievement goal theory (AGT) 129, 135–137; teachers’ goals and daily practice 135–136 action tendencies 261, 267 affective processes in collaborative learning 228–240; empirical research on 231–238; key concepts and theories 229–231; overview 228–229; video analysis 238–239 affective states 75–76, 230, 232–235; and self-efficacy 77 Ahmed, W. 279 aims 37–38; see also goal(s) Ainley, M. D. 23 analytical methods, ESM research 188–189 Anderman, E. M. 25 approach goals 40, 42; appearance 43; normative 43; performance 25, 40–42, 45 Arslan, R. C. 179 artefact correction 265 Artificial Intelligence (AI) 184–185, 229, 238–239 Asher, S. R. 23 Asparouhov, T. 188 attainment value 6–7, 12, 131, 223 autonomously motivated teachers 136

Index 285

autonomy 9, 56, 58, 137; professional 132; -supportive interventions 62, 217; supportive practices 58–64, 91–92, 136, 149 avoidance goals 40; mastery 41; performance 40–41; work 43–44 Bacon, R. 274, 276, 281 Bakhtiar, A. 104, 231 Bakker, A. B. 120 Bala, H. 168 Bandura, A. 26, 70, 73, 75, 78 Barroso, C. 92, 94 Basic Psychological Need Theory (BPNT) 55 between-person analyses 182 between-person differences 12, 46, 200, 204, 266, 276; see also interindividual differences Bong, M. 26 Brockman, R. 145 Brown, J. D. 94 Buchmann, M. 200 Bureau, J. S. 62, 63 Butler, R. 135, 136 Cai, H. 94 Camacho-Morles, J. 93, 94 Campbell, D. T. 165 Carmignola, M. 167 Carreira, J. M. 60 Chanal, J. 62 Childhood and Beyond (CAB) study 8 children’s expectancies 8, 14 choice: and expectancies for success 7–8; of methodologies 251–252; and subjective task values 7–8; of teaching career 132–133 Clark, K. F. 23 Clinical Psychology 181 Cognitive Evaluation Theory (CET) 54 cognitive mastery 74; and self-efficacy 77 cognitive-motivational model of emotion 87 cognitive training 122 collaborative learning: motivational and emotional conditions in 104–105; motivation and emotion regulation in 103–109; types of regulation in 101–103 Collie, R. J. 80 complementarity 169, 171, 173

computers 249, 264; ability to make decisions 185; game 148; inductive reasoning tasks 26; self-efficacy 152; tools 93 Concept-Oriented Reading Instruction (CORI) 216 concurrent parallel MM design 165 Conditions, Operations, Products, Evaluation, Standards (COPES) 231, 232, 233 construct validity and measurement 187–188 context(s) 144–151; and academic emotions 88–89; achievement 38–39, 42, 44, 179, 184, 244; changing educational 78–79; cultural and functional relations 93–94; differentiating objective/ subjective characteristics of 148–150; multi-level classroom 64; self-efficacy in 71–73; variation across 91–92; see also situation context specificity 46–47; vs. contextgeneral constructs 63–64; of student motivation 56–59, 57 contextualised interventions 150–151 control-value theory (CVT) 86, 168; basic propositions 87; overview 86–88; relative universality in 88, 89–90 convergent parallel design 168–169, 169 Corbin, J. 165 co-regulated learning (CoRL) 102–103, 107–108 correspondence principle 151 cost: effort 7; emotional 7; opportunity 7; and STV 7 counterintuition 79–80 COVID-19 pandemic 59, 70, 120, 167, 168 Creswell, J. W. 164 Critical Race Theory (CRT) 251 cross-correlations 267 cross-disciplinary approaches 239 cross-lagged panel models (CLMP) 199; generalisation of 200 Csikszentmihalyi, M. 180 cultural norms 146 culture 245–247, 250–253; collectivist 45, 91, 133; dominant 247–249; ethnic 247; individualistic 91; school’s 145–146; and SEVT 14

286

Index

data analyses 266–268 data cleaning 265–266 data collection 263–268; data analyses 266–268; data cleaning 265–266; study preparation 263–265 Daumiller, M.35, 36, 38, 39, 41, 43, 44 Davis-Kean, P. E. 147 De Fraine, B. 119 Demerouti, E. 120 Developmental Psychology 181 Diener, E. 114, 118, 120 Diener’s multi-dimensional model 120 Dietrich, J. 11, 13, 184 differences: gender 23, 200; interindividual 115, 182, 198–201, 203–205, 207–208; intraindividual 198–201, 204, 206–209; between situations and ESM 182 dimensions describing given situation or context 145–148 dispositional expectancies 11 Donker, M. H. 267, 278 Durik, A. M. 8 Dynamics of Emotional Processes in Teachers (DEPTh) project 263 Dynamic Structural Equation Modeling (DSEM) 269 Eccles, J. S. 3–4, 6, 8, 9, 11, 145 Eccles’ Expectancy-Value Theory (EEVT) see Situated ExpectancyValue Theory (SEVT) Eccles-Parsons, J. S. 4–7 educational interventions 96 Educational Psychology 181 efficacy-relevant information 76 efficacy trials 220, 222 effort cost 7 electrocardiogram (ECG) 261, 263–264 Elliot, A. J. 40 emotion(s): academic 85–96; achievement 58–59, 61, 63, 86, 90–91, 95; benefits from research 153–154; contexts and situations 144–151; contextualised interventions 150–151; dimensions 145–148; empirical examples of MM studies 168–173; mixed methods in research on 163–174; negative 85; positive 85; regulation during team-taught lessons 170–171;

relative universality of 86; social phenomena 151–153; temporal patterns of 75 “Emotional and Cognitive Learning” or ECOLE study 168 emotional cost 7 emotional experiences 230, 232, 234, 237, 276, 279–280 emotional processes from video 234–235 emotion contagion 230 emotion-related research 228 empirical examples of mixed-methods studies 168–173 enactive experience 74 Engels, N. 119 epistemic network analysis (ENA) tool 239 epistemological shift forward 280–281 EPOCH model of adolescent wellbeing 119 Erdem, C. 119 ESM research/studies: analytical methods 188–189; artificial intelligence 184–185; challenges to research trustworthiness 186; construct validity and measurement 187–188; current challenges in 185–190; directions for future research 185–190; gamification 185; inference and generalisability 189–190; in-the-moment personalised interventions 184; sensor-augmented ESM 184; technological innovations in 184–185; theory 185–187 ethnicity 245–247, 250; functional relations 92; and SEVT 14 example studies 232, 233 expectancies: children’s 8; dispositional 11; new research on 11–12; socialisation of 9–11, 10 expectancies for success (ESs) 4, 6; and choice 7–8; hierarchies of 12–13; and performance 7–8 expectancy-value theory (EVT) 4–7, 5, 128, 130–132, 172, 179, 215, 221 experience sampling method (ESM) 259–260, 275, 278; developmental dynamics 183–184; heterogeneity 183;

Index

intra-individual analyses and situations 182; paradigmatic shifts in research on motivation/ emotions 179–184; people/time points/contexts 183; predicting one moment by previous one 183; research on motivation/ emotion in learning/instruction 178–179; sensor-augmented 184; theoretical and methodological innovations 179–184 explanatory sequential MM designs 166, 167 exploratory sequential MM designs 166, 167, 170 external regulation 55, 62–64, 138 extrinsic motivation 55; hierarchical model of 56–59, 57 face-to-face teaching 168–170 facial recognition 238 Fincham, F. D. 79 finite mixture models 204 Fiske, D. 165 FIT-Choice approach to teacher motivation 130–132 FIT-Choice framework 131 FIT-Choice scale 130, 132 Flunger, B. 61, 64 Fluxicon Disco 237 fMRI technology 249 Formation and Regulation of Emotions in Collaborative Learning (FRECL) model 231 Fraillon, J. 119 Frenzel, A. C. 91, 92, 93, 170, 203 Fritz, J. 179, 185 functional relations 92–95; academic domains 92–93; cultural context 93–94; gender and ethnicity 92; learning environments 93 gamification 185 Gaspard, H. 207, 277, 279 Geary, D. C. 153 gender: differences 200; differences, and interest development 23; functional relations 92; and SEVT 14; student 207 generalisability 45, 90, 94–96, 154, 183, 185, 187–190 general stress coping 122 generational status 245–247

287

Gniewosz, B. 201, 276 goal(s) 35–38; based on competence 38–39; complexes 38; types of 39–44; see also specific goals “Goal Orientation for Teaching” (GOT) 135 goal-orientation theory (GOT) 172 Goetz, T. 92 Granic, I. 184 gratitude 122 Gray, D. L. 250 Grigg, S. 26 group-level data 233–234 growth-mindset interventions 218, 221 growth mixture modelling (GMM) 8, 204, 207, 208 Guay, F. 62, 63 Hadwin, A. F. 104 Hagenauer, G. 277 Hall, M. 179 Hamaker, E. L. 200 Harackiewicz, J. M. 9, 24–25, 40, 221 HARKing or p-hacking 190 Hascher, T. 116, 119 health, defined 114 heart rate 261–262, 262, 266, 268; noninvasive 279; variability 279 heterogeneity and lacking ergodicity 187 Hierarchical Model of Intrinsic and Extrinsic Motivation (HMIEM) 56–59, 57 Hulleman, C. S. 218 hypothalamic-pituitary-adrenal (HPA) 261 ICAN Intervention 22 identified regulation 55, 59, 62 immigration 244–245, 247 individual data 233–234 inference and generalisability 189–190 instructional practices, of educators 22 integrated regulation 55 integration challenge 280 interest: and achievement goals 24–25; defined 19–20; and engagement 19–20; and motivation 23–28; and self-efficacy 26; and selfregulation 27–28 interest development 20–23; cultural relevance 23; defined 20; fourphase model of 21; gender differences 23; and learning

288

Index

environment 20–21; similarities and differences among phases 21–22 interindividual differences 115, 182, 198–201, 203–205, 207–208 interpersonal affect behaviours 230, 232 interventions: adaptive 151; approach 216–217; autonomy-supportive 62; contextualised 144, 146, 150–151, 155; development of 220; educational 96, 150; to enhance students’ motivation 13; evaluation 217–218; evaluation of 220; growth-mindset 218, 221; heterogeneous effects 221; ICAN 22; MM studies 167–168; MoMa 221–224, 222; motivational 62, 209; motivational construct 215–216; multicomponent 216; multidimensional 26; overview 213–214; personalised 184; plan-making 150; population and context 214–215; promises and pitfalls of 218–221; stepwise implementation of 220; for student motivation 214–218; StudWB 121; targeted 215–216; TeachWB 121; test motivation in educational practice 218–221; theoretical background 215–216; value-relevance 150 in-the-moment personalised interventions 184 intraindividual differences 198–201, 204, 206–209 intrinsic motivation 55; hierarchical model of 56–59, 57 intrinsic value 6, 92, 96, 130, 132, 134, 138, 207 iterative feedback loops 187 Ivankova, N. V 165 Jagacinski, C. M. 25 Järvelä, S. 100, 103, 104, 107–109 Järvenoja, H. 100, 101–104, 108, 109 JD-R model 120, 121 Jiang, J. 165 Job Demands-Resources (JD-R) model 115 Johnson, R. B. 164 Jones, C. 232, 234, 235

Kaya, M. 119 Kim, H.-G. 279 Kizilcec, R. F. 150 Klassen, R. M. 77 Kraft, M. A. 219 Kreibig, S. D. 263 latent change modelling 201, 202, 204, 208, 209 latent growth curve model (LGCM) 201, 203, 203–204 latent transition analysis (LTA) 204–206, 205, 208 Latin, M. 275 Lauerman, F. 143, 147, 149, 151, 153, 154 Lazarides, R. 206, 276 Lazowski, R. A. 218 learning: -related emotions 229; research on motivation and emotion in 178–179; self-efficacy in 78–81; self-regulation of 81 learning environment: emotiongenerating control 91; functional relations 93; and interest development 20–21 Lee, W. 27 LGBT youth of colour 246 Linnenbrink-Garcia, L. 27 Lipstein, R. 27 Lobczowski, N. G. 231 Loderer, Gentsch 90 Loderer, Pekrun 93 Lohbeck, A. 59 Lopez, F. 250 Luszczynska, A. 94 “macrotheory” of motivation 136 Maehr, M. L. 145 Mänty, K. 232–234, 233, 236–237, 277, 278 Markell, R. A. 23 Markov models 108 Marsh, H. W. 63, 145 mastery approach 41 mastery avoidance goals 41 mastery goals 39, 136; facets of 41–43; learning component 41–42; task component 41–42 mathematics: academic self-concept in 207; competence beliefs in 206–207; interest in 26, 203, 206, 223; middle school students

Index 289

26; motivational beliefs in 198; motivations for 62, 214, 223; positive emotions in 91; selfefficacy in 26, 73 Matthew-effects 61 Mayer, A. 62 McLellan, R. 25 McNulty, J. K. 79 mean squared successive difference (MSSD) 267 Mendlinger, S. 281 mentor teachers’ motivations 172–173 Merriam, S. B. 165 methodologies 80–81, 248–249, 250, 254; choice of 251–252; complex 245, 249, 277, 280–281; ethics of 239; experimental 244; mixed 245, 249, 277, 280–281; qualitative 251; survey 244; used to study motivation 248–249 Midgley, C. 9, 25, 145, 152 mixed methods: benefits of 165–168; brief introduction to 164–165; conclusion and future directions 173–174; defined 164; empirical examples of 168–173; in research on motivation and emotion 163–174 mixed-methods (MM) studies: empirical examples of 168–173; in the field of motivation and emotion 168–173; intervention 167–168; mentor teachers’ motivations 172–173; online and face-toface teaching 168–170; teacher emotions and emotion regulation 170–171 Moderate Math Decline/Strong Language Arts Decline group 208 Moeller, J. 185, 276, 280 motivation(s): achievement goals 24–25; autonomy-supportive practices 58–59; benefits from research 153–154; contexts and situations 144–151; contextualised interventions 150–151; dimensions of situation/context 145–148; driving choice of teaching career 132–133; empirical examples of MM studies 168–173; extrinsic

55; and interest 23–28; intrinsic 55; mentor teachers 172–173; mixed methods in research on 163–174; and self-efficacy 26, 77; at situational level 56; social phenomena 151–153 motivational beliefs 197–210; in mathematics 198; overview 197–199; person-centred approaches 204–208; variablecentred methods 199–204 motivational/emotional processes 258–270; conceptual meaning of heart rate in 262–263; data collection 263–268; DEPTh project 263; dynamics of 259–260; heart rate 261–262, 262; holistic approach to 260–261 motivational research 197, 209; benefits of mixed-methods approaches for 165–168; GMM 207; LGCM 203; LTA in context of 206; neighbour models 201; RICLPM 200 motivational theories 128–129 motivational variables 20, 197 motivation/emotion regulation: in collaborative interactions 107–108; in collaborative learning context 103–109; premises for 104–105; situational variations in 105–107; temporal manifestation of 107–108 Motivation in Mathematics (MoMa) intervention 221–224, 222 multi-channel data 234–235 multicomponent interventions 216 multi-level classroom context 64 multiple goal pursuit 45 multivariate analyses 267 Munkebye, E. 22 negative emotions 85 negative introjection 55 Newby, P. 281 Noble, T. 120 non-invasive heart rate 279 non-malleable motivations 135 non-overlapping data techniques 279 Nuutila, K. 26 Observer XT software 237 O’Keefe, P. A. 27

290

Index

online teaching from student teachers’ perspective 168–170 opportunity cost 7 Opus Tertium of Bacon 274 Organisation for Economic Cooperation and Development (OECD) 90, 120 Organismic Integration Theory (OIT) 55 parental aspirations 200–201 Patterson, G. R. 184 Paumier, D. 62 Pekrun, R. 63, 145 perceived difficulty 11–12 performance: and expectancies for success 7–8; and subjective task values 7–8 performance approach goals 25, 40–42, 45 performance avoidance goals 40–41 performance goals 39; appearance component 42–43; facets of 41–43; normative component 42–43 PERMA model 115, 120 Perry, N. E. 27 personal agency 70–71 Personality Psychology 181 personal utility values 132, 173 person-centred methods 198–199, 204–208 Peura, P. I. 71 photoplethysmography (PPG) 263, 264 physical activity 265–266 physiological arousal 261–262 physiological arousal data 234 physiological measures 260 physiological state 75–76 Piccirillo, M. L. 179, 185 Pintrich, P. R. 102 Plano Clark, V. L. 165 Pomerantz, E. M. 8 Positive and Negative Affect Schedule (PANAS) 171 positive emotions 85 positive imbalance 115 Programme for International Student Assessment (PISA) 90, 91, 94, 120 proximal research: challenges in 278–280; epistemological shift forward 280–281; methodological zone of 274–281; opportunities in 278–280

psychological needs 56, 59–62 PsycInfo 119 qualitative methodologies 251 race 23–25, 245–246, 250–251; defined 245 random-effect approaches 96 Random Intercept Cross-Lagged Panel Models (RICLPM) 199–200, 200, 204, 208 reasons 37–38 reciprocal determinism 78 regulation: in collaborative learning 101–103; external 55, 62–64, 138; identified 55, 59, 62; integrated 55; types of 101–103 Reitzle, M. 184 relational goals 44, 135–136 relative universalism 89–90 relative universality 153 relative universality of emotions 86, 88–89 relaxation 122 reliable change indices 279 Renninger, K. A. 27 research: achievement goals 48n1; emotion-related 228; on motivation/emotion in learning/ instruction 178–179; see also specific types Rosenzweig, E. Q. 8, 13 Ross, J. A. 152 Rotgans, J. I. 22 Scherer, K. R. 261–262 Schmidt, H. G. 22 school-related well-being 115 school-subject-specificity hypothesis 62–63 school transitions, and SEVT 13 scientia experimentalis 275, 281 Scopus 119 Scrimin, S. 269 Selection, Optimisation and Compensation model 138 selection processes, and self-efficacy 77–78 self-concepts 3, 11–12, 200–201 self-determination theory (SDT) 129, 135–137, 168; HMIEM 56–59, 57; main premises of 54–56; psychological

Index

needs 59–62; school-subjectspecificity hypothesis 62–63; self-determination of teachers 136–137; teachers’ basic needs satisfaction 136–137 self-efficacy 148; academic 69–70; in changing educational contexts 78–79; conditions for 73; in context 71–73; defined 26; fruits of 76–78; and interest 26; in learning 78–81; measurement 72–73; and motivation 77; and personal agency 70–71; physiological and affective states 75–76; roots of 73–76; in selfregulation of learning 81; target audience for 72; in teaching 78–81; theoretical underpinnings 70–71 self-regulated learning (SRL) theories 101 self-regulation: defined 27; and interest 27–28; of learning 81 Self-Regulation Model 27 Seligman, M. E. 115, 120 sensor-augmented ESM 184 sequential exploratory MM approach 172 sequential multiple-assignment randomised trials (SMARTs) 151, 224 Sharp, J. G. 167 Simpkins, S. D. 9 situated expectancy-value theory (SEVT) 3–4, 7, 9, 199; and culture 14; and ethnicity 14; and gender 14; and school transitions 13; situative nature of 11 situation: achievement 253; collaborative learning 104, 109; dimensions 145–148; dimensions describing given 145–148; political 118; subjective perception of 86; see also context(s) situational variations in motivation and emotion regulation 105–107 situative nature of SEVT 11 Skalstad, I. 22 social influences 78, 132, 134 socialisation of expectancies 9–11, 10 socially shared emotion regulation 101–102, 230, 232

291

socially shared motivation regulation 101 socially shared regulation of learning (SSRL) framework 101 social persuasion 75 social phenomena 151–153 social utility values 132 sociocognitive theories 148 socioemotional challenges 230 socioemotional climate 230, 231 socioemotional interactions 230–234, 237 speech recognition 238 Stable Math and Language Arts Trajectories group 207–208 stage-environment fit 9 Steyer, R. 201 Stoet, G. 153 Strauss, A. 165 Strong Math Decline/Language Arts Decline Levelling Off class 208 student gender 207 student interaction well-being 116 student motivation 214–218 student well-being (StudWB) 117, 118–120; discussion and future research 120–122; main branches of research on 117; and well-being dimensions 117 Stupnisky, R. H. 166 subject differentiation in student motivation 62–63 subject interest 132 subjective task values (STVs) 3, 6–7; and choice 7–8; development of 8; hierarchies of 12–13; new research on 12; and performance 7–8; socialisation of expectancies 9–11, 10 subject-specific motivation 59 TALIS survey 129, 135 task approach goals 41–42 Teacher Emotion Questionnaire (TEQ) 166 teacher emotions: student-triggered 171; and Teacher Emotion Questionnaire (TEQ) 166; during team-taught lessons 170–171 teacher motivation: antecedents and outcomes 133–134; driving choice of teaching career

292

Index

132–133; FIT-Choice approach to 130–132; impetus 130–131; measurement 131–132; policy recommendations 134–135 teachers: basic needs satisfaction 136–137; goals and daily practice 135–136; motivation to teach 128–139; selfdetermination, level of 136–137; translation of achievement goal theory to study of 135–137; translation of self-determination theory to study of 135–137 teacher self-efficacy 118 teacher well-being (TeachWB) 116–118, 117; discussion and future research 120–122; main branches of research on 117; and well-being dimensions 117 teaching 85–86, 88; face-to-face 168–170; learning approach goals 42; online 168–170; performance 43; self-efficacy in 78–81; TeachWB 116–118 technology: advances in 74–75, 249, 253, 254; fMRI 249; innovations in ESM research 184–185 technology-based learning environments (TBLEs) 93 temporal data 235–238, 237 temporal manifestation: of emotion regulation in collaborative interactions 107–108; of motivation in collaborative interactions 107–108 temporal stability 46–47 theory: ESM 185–187; ESM research/ studies 185–187; see also specific theories Törmänen, T. 107, 231–234, 233, 237 triadic reciprocality 70 triangulation 169 True Intraindividual Change Models 197, 201, 202; see also Latent Change Modelling Tsai, Y.-M. 61 univariate analyses 266–267 Urdan, T. 277

Usher, E. L. 69, 73, 74, 76, 80, 81 utility value 6–7 valence 106; approach and avoidance 40–41; dimension of 106; emotional 105, 107–108, 232; negative 107 Vallerand, R. J. 56 value: attainment 6–7, 12, 131, 223; intrinsic 6, 92, 96, 130, 132, 134, 138, 207; utility 6–7 Vancouver, J. B. 148 variable-centred methods 198, 199–204 Venkatesh, V. 168 vicarious experience 74–75 video analysis 231, 238–239 video data 228–229, 232, 234, 239; benefit of 231; manual analysis of 237; physiological measures from 238 VU-Ambulatory Monitoring System (VU-AMS) 264 Waber, J. 116 Wang, Q. 8, 61 Watt, H. M. G. 201 Web of Science 119 WEIRD countries 153 WEIRD samples 144, 154 well-being 114–116 Western societies 152 Wigfield, A. 3–4, 6, 8, 11 Wilson, K. C. M. 189 within-person analyses 182 within-person changes 199, 200, 204; see also interindividual differences Wolcott, H. T. 165 Work and Organizational Psychology 181 work-avoidance 135 work avoidance goals 43–44 World Health Organisation (WHO) 114 Xu, J. 22 Yeager, D. S. 217–218, 221 Yu, J. 25 Zhang, J. 92