Theory Informing and Arising from Learning Analytics delves into the dynamic intersection of learning theory and educational data analysis within the field of Learning Analytics (LA). This groundbreaking book illuminates how theoretical insights can revolutionize data interpretation, reshape research methodologies, and expand the horizons of human learning and educational theory. Organized into three distinct sections, it offers a comprehensive introduction to the role of theory in LA, features contributions from leading scholars who apply diverse theoretical frameworks to their research, and explores cutting-edge topics where new theories are emerging. A standout feature is the inclusion of three in conversation chapters, where expert panels dive into the topics of ethics, self-regulated learning, and qualitative computation, enriched by accompanying podcasts that provide fresh, thought-provoking perspectives. This book is an invaluable resource for researchers, sparking debates on the evolving role of theory in LA and challenging conventional epistemological views. Published by Springer, it is an essential read for both aspiring and seasoned scholars eager to engage with the forefront of LA research.
Part I State of the Art Theory and Learning Analytics.- Theory and
learning analytics a historical perspective.- Making bigger waves Automating
theoretical coding to generate educationally.- In Conversation Gulson
Anderson & Prinsloo Examining theoretical approaches and future directions
for ethics in learning analytics.- Part II Theory Application in Practice
Answering Questions and Increasing the Meaningfulness of Learning Analytics
Research.- In Conversation Bannert Molenaar & Winne Multiple perspectives on
researching and supporting self regulated learning via analytics.- Learning
analytics Framework for Analysing Regulation in Collaborative
Learning.- Theory and intermediate level knowledge in Multimodal Learning
Analytics.- Collaborative learning theory and analytics.- Emotion theory and
learning analytics A theoretical framework for capturing emotion regulation
using process data.- Integrating theories of learning and social networks in
learning analytics.- What could learning analytics learn from Human Computer
Interaction theory.- Part III Innovative Theory Uses and Possibilities in
Learning Analytics.- In conversation Baker Järvelä & Shaffer The relationship
between computational methods and theory in learning analytics.- Theories all
the way across The role of theory in learning analytics and the case for
unified methods.- Towards a genealogical critical theory of learning
analytics.
Kathryn Bartimote is Senior Lecturer in the Sydney School of Education and Social Work at The University of Sydney. She supervises research students in educational psychology and assessment. Kath serves as the Chair of the Research Committee for the Centre for Educational Measurement and Assessment and holds leadership roles on two of the University's Human Research Ethics Committees. She is also a member of the Executive Committee of the Society for Learning Analytics Research (SoLAR). Previously, Kath was Head of Quality and Analytics at the University of Sydney, where she led initiatives in quality evaluation, analytics, and data governance. Her research explores key topics such as motivation and choice, epistemic beliefs, student success, and the integration of theory in learning analytics.
Sarah K. Howard is Associate Professor in the School of Education at the University of Wollongong where she is the Head of Research. Sarah is also Group Lead for the Smarter Schools and Digital Technologies group within the SMART Infrastructure Facility at the University. Her research focuses on the use of new technologies and data science to explore classroom practice and teacher change related to technological innovation and integration. Specifically, in her work, Sarah looks at cultural and individual factors of teachers digital technology use, change process and learning design related to subject areas, school culture and underlying principles of teaching and learning.
Dragan Gaevi is Distinguished Professor of Learning Analytics and Director of Research in the Department of Human Centred Computing of the Faculty of Information Technology and the Director of the Centre for Learning Analytics at Monash University. Dragans research interests center around data analytic, AI, and design methods that can advance understanding of self-regulated and collaborative learning. He is a founder and served as the President (2015-2017) of the Society for Learning Analytics Research. He has also held several honorary appointments in Asia, Australia, Europe, and North America. He is a recipient of the Life-time Member Award (2022) as the highest distinction of the Society for Learning Analytics Research (SoLAR) and a Distinguished Member (2022) of the Association for Computing Machinery (ACM). In 2019-2023, he was recognized as the national field leader in educational technology in The Australians Research Magazine that is published annually. He led the EU-funded SHEILA project that received the Best Research Project of the Year Award (2019) from the Association for Learning Technology.
