Handbook of Learning Analytics and Adaptive Learning in Schools: (Elgar Handbooks in Education)

About the Book

This timely Handbook explores the use of emerging digital technologies in education, demonstrating how learning analytics, adaptive technologies and artificial intelligence can be implemented to achieve pedagogical goals. As digital technologies become integral to education, chapters discuss how the focus is shifting from whether to use these tools, to how they can best support teaching and learning.

Contributors address concerns surrounding data privacy, transparency and practical use, emphasising the importance of professional development and collaboration among educators, leaders and stakeholders. Drawing on empirical insights, chapters highlight the potential of learning analytics and adaptive learning to personalise education and improve outcomes for all students. This insightful Handbook promotes thoughtful policy, robust infrastructure and a thorough understanding of the professional and ethical contexts in which these tools are deployed.

The Handbook of Learning Analytics and Adaptive Learning in Schools is an essential resource for scholars and students interested in educational technology, particularly those exploring the impact of data mining and learning analytics. It is also highly beneficial for educators and policymakers looking to ensure these emerging tools are used purposefully and in alignment with curriculum goals.

Table of Contents:
Contents PART I INTRODUCTION 1 Why do we need a handbook on learning analytics and adaptive learning in schools? 2 Cathrine Edelhard Tømte, Barbara Wasson and Linda Barman PART II UNDERSTANDING LEARNING ANALYTICS AND ADAPTIVE LEARNING: DEFINITIONS, PRINCIPLES AND SCOPES 2 Learning analytics in schools: provocations and pointers 10 Paul Prinsloo and Mohammad Khalil 3 Adaptive learning systems: leveraging generative artificial intelligence for optimal adaptivity 21 Sima Caspari-Sadeghi 4 Designing multimodal collaboration analytics: challenges and opportunities 35 Daniel Spikol, Morten Misfeldt, Zaibei Li and Viktor Holm-Janas 5 Integrating the Science of Learning with EdTech solutions: from frameworks to models of practice 51 Natalia Ingebretsen Kucirkova PART III ETHICAL CONSIDERATIONS AND PRIVACY CONCERNS 6 The General Data Protection Regulation’s right to explanation of algorithmic decision-making in the context of using AI-powered learning analytics in compulsory education 64 Małgorzata Agnieska Cyndecka 7 Smart predictions, unseen bias: navigating the promises and pitfalls of predictive analytics in compulsory education 77 Lin Li 8 When categories matter: attendance data practices in an upper secondary classroom 96 Fride Haram Klykken PART IV PEDAGOGICAL POSSIBILITIES AND CHALLENGES 9 Teacher development with learning analytics: a design narrative 113 Fabio Campos, Ha Nguyen and June Ahn 10 Multi-case teacher inquiry to inform dashboard development 127 Merike Saar 11 Understanding the interrelated nature of academic, social, and emotional competencies through learning analytics 137 Jae H. Paik, Igor Himelfarb, Seung Hee Yoo, Jongtak Lee, Hoyong Ha and Yurim Park 12 Using learning analytics to explore pre-service teachers’ lesson planning 157 Kjersti Nedland Røneid, Gleb Belokrys and Kristin Børte 13 Teaching Learning Analytics as formative assessment for pre-service teachers: report from a first trial 176 Cathrine Edelhard Tømte and Maria Wasmuth Grønvold 14 EssayCritic: enhancing student essays with locally trained AI feedback 191 Anders Mørch and Irina Engeness 15 Adaptive learning in primary mathematics: teachers’ practices and insights 210 Kjetil Egelandsdal 16 Operationalising science competencies to support teachers’ formative assessment and instruction with Inq-ITS AI 230 Janice D. Gobert, Amy Adair, Jessica A. Owens and Rachel Dickler PART V PROMOTING IMPLEMENTATION 17 Navigating learning analytics: challenges, opportunities, and recommendations 248 Marte Blikstad-Balas 18 Implementing learning analytics and adaptive learning in Norwegian schools: lessons learned 259 Barbara Wasson and Cecilie J.S. Hansen 19 Bridging the data divide in Swedish school education: challenges and opportunities in data standards 275 Italo Masiello and Zeynab (Artemis) Mohseni 20 The paradoxes of AI promises in schools: Insights from a multi-sited ethnography 294 Katarina Sperling and Linnéa Stenliden

About the Author :
Edited by Cathrine E. Tømte, Professor, Department of Information Systems, University of Agder and Adjunct Professor, Centre for the Science of Learning & Technology (SLATE), University of Bergen, Norway, Linda Barman, Associate Professor, Department of Education, Stockholm University, Sweden and Barbara Wasson, Professor and Director, Centre for the Science of Learning & Technology (SLATE) and AI LEARN, University of Bergen, Norway