Impact of learning analytics feedback on self-regulated learning: Triangulating behavioural logs with students’ recall
Abstract
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.
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Automated LA-based systems, such as OnTask, have demonstrated potential in enhancing self-regulation of learning through personalised feedback interventions.
“Automated LA-based systems, such as OnTask, have demonstrated potential in enhancing self-regulation of learning through personalised feedback interventions (Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Lim et al., 2021
Empirical studies indicate that feedback informed by LA can enhance self-regulation by offering timely and actionable insights.
“Empirical studies indicate that feedback informed by LA can enhance self-regulation by offering timely and actionable insights (Chen, 2024; Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Lim et al., 2021
LA-informed, automated feedback with different tools can positively influence self-regulation of learning development.
“it is consistent with previous research suggesting that LAinformed, automated feedback with different tools can positively influence self-regulation of learning development (Chen, 2024; Lim et al., 2021; Tsai et al., 2021).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Lim et al., 2021
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- Authors
- Lim, L.-A., Gasevic, D., Matcha, W., Ahmad Uzir, N., & Dawson, S.
- Affiliations
- University of South Australia (AU)
- Monash University, Australia
- Prince of Songkla University (TH)
- Universiti Teknologi MARA, Malaysia
- Venue
- LAK21: 11th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2021-04-12
- Pages
- 364-374
- OpenAlex
- W3148673861
- Cited by
- 47
- References
- 38
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 14 · 2023: 9 · 2022: 11
- FWCI
- 9.92
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- learning analytics, personalized feedback, student self-regulation, time management strategies, learning strategies, learning design, delivery modality
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