Learning Analytics in Open and Distributed Learning -

Learning Analytics in Open and Distributed Learning (eBook)

Potential and Challenges
eBook Download: PDF
2022 | 1. Auflage
VIII, 126 Seiten
Springer Nature Singapore (Verlag)
978-981-19-0786-9 (ISBN)
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This book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the 'data revolution' and the emergence of learning analytics on this increasingly diverse form of educational delivery. Following an introductory chapter that maps the book's conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.

Paul Prinsloo is a Research Professor of Open and Distance Learning at the Department of Business Management, College of Economic and Management Sciences, University of South Africa. His academic background includes fields as diverse as theology, art history, business management, online learning, and religious studies. Paul is an internationally recognised speaker, scholar and researcher and has published numerous articles in the fields of teaching and learning, student success in distance education contexts, learning analytics, and curriculum development. His current research focuses on the collection, analysis and use of student data in learning analytics, graduate supervision and digital identity.

Sharon Slade worked until recently as a Senior Lecturer leading projects focusing on teaching and learning across the Open University in the UK. She led the team which developed the first institutional policy for the ethical use of learning analytics and has worked as an active member of several international teams developing ethical codes of practice for uses of student data. Her work as an academic lead for learning analytics projects within the Open University included work around the operationalisation of predictive analytics and approaches aiming to improve retention and advancement.  Recent research contributions include papers and chapters on student consent, the obligation to act on what is known, examining the concept of educational triage, and broader issues concerning an ethics of care. She now works on data insight issues for an environmental and educational charity in the UK.

Mohammad Khalil is a Senior Researcher and Lecturer in Learning Analytics at the Centre for the Science of Learning & Technology, Department of Psychology, University of Bergen, Norway. Mohammad has published numerous articles on learning analytics in major journals and conference proceedings. His current research focuses on learning analytics in open and distance learning, health, visualizations and gamification, as well as privacy and ethics.


This book explores and further expands on the rich history of theoretical and empirical research in open and distributed learning, and addresses the impact of the "e;data revolution"e; and the emergence of learning analytics on this increasingly diverse form of educational delivery. Following an introductory chapter that maps the book's conceptual rationale, the book discusses the potential, challenges and practices of learning analytics in various open and distributed contexts. A concluding chapter briefly summarises the chapters before providing a tentative future research agenda for learning analytics in open and distributed environments.
Erscheint lt. Verlag 13.5.2022
Reihe/Serie SpringerBriefs in Education
SpringerBriefs in Open and Distance Education
Zusatzinfo VIII, 126 p. 19 illus., 15 illus. in color.
Sprache englisch
Themenwelt Schulbuch / Wörterbuch Unterrichtsvorbereitung Unterrichts-Handreichungen
Sozialwissenschaften Pädagogik Schulpädagogik / Grundschule
Schlagworte Big Data in Education • Distance Education • Distance Learning • distributed learning • Learning Analytics • Open Distance Learning • social network analysis • Student data • Student Success and Retention
ISBN-10 981-19-0786-2 / 9811907862
ISBN-13 978-981-19-0786-9 / 9789811907869
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