Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students' recall

• Published in: ACM International Conference Proceeding Series
• Main Author: Lim L.-A.; Gasevic D.; Matcha W.; Ahmad Uzir N.; Dawson S.
• Format: Conference paper
• Language: English
• Published: Association for Computing Machinery 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103919433&doi=10.1145%2f3448139.3448174&partnerID=40&md5=17c141b4ae4eee574427c3d1deefd66b

Learning analytics (LA) has been presented as a viable solution for scaling timely and personalised feedback to support students' self-regulated learning (SRL). Research is emerging that shows some positive associations between personalised feedback with students' learning tactics and strategies as well as time management strategies, both important aspects of SRL. However, the definitive role of feedback on students' SRL adaptations is under-researched; this requires an examination of students' recalled experiences with their personalised feedback. Furthermore, an important consideration in feedback impact is the course context, comprised of the learning design and delivery modality. This mixed-methods study triangulates learner trace data from two different course contexts, with students' qualitative data collected from focus group discussions, to more fully understand the impact of their personalised feedback and to explicate the role of this feedback on students' SRL adaptations. The quantitative analysis showed the contextualised impact of the feedback on students' learning and time management strategies in the different courses, while the qualitative analysis highlighted specific ways in which students used their feedback to adjust these and other SRL processes. © 2021 ACM.