Examining the impact of feedback based on learning analytics from the perspective of self-regulated learning /
Files
(Published version)
Date
2020
Authors
Lim, Lisa-Angelique
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
thesis
Citation
Statement of Responsibility
Conference Name
Abstract
Learner data collected as a by-product of digital learning environments have created new opportunities for providing personalised feedback and support to students. Learning analytics (LA), an area of research that uses digital learner traces to enable insights into students’ learning processes, has garnered much interest in the development of technologies to scale personalised feedback. However, empirical research is limited regarding the impact of such feedback systems on students’ learning and performance. This doctoral thesis examines the impact of LA-based feedback systems on students’ performance and self-regulated learning (SRL) through four studies. Collectively, the results contribute to theory and practice of feedback based on LA data and provide empirical support to feedback theories explicitly linking feedback with SRL.
School/Discipline
University of South Australia. UniSA Education Futures.
UniSA Education Futures
UniSA Education Futures
Dissertation Note
Thesis (PhD(Education)Curriculum, Education Studies)(DUCIER)--University of South Australia, 2020.
Provenance
Copyright 2020 Lisa-Angelique Lim.
Description
1 ethesis (xiv, 233 pages) :
illustrations (some colour)
Includes bibliographical references (pages 179-199)
illustrations (some colour)
Includes bibliographical references (pages 179-199)
Access Status
506 0#$fstar $2Unrestricted online access