Students’ sense-making of personalised feedback based on learning analytics
Open accessAbstract
Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students’ sense-making of learning analytics–based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students’ current learning strategies and achievement of associated learning goals. Implications for practice or policy: Personalised feedback based on learning analytics should be informed by an understanding of students’ self-regulated learning. Instructors implementing personalised feedback should align this closely with the course curriculum. Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone. Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.
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Several studies have offered indications that LA can support provision of meaningful and timely feedback to students.
“Several studies have offered indications that LA can support provision of meaningful and timely feedback to students (e.g. Lim et al., 2020; Zheng et al., 2021).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Lim et al., 2020
Information visualization approach was used in 34 studies.
“We discovered that an information visualization approach was used is used in 34 studies (e.g. Lim et al., 2020; Yu et al., 2018).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Lim et al., 2020
LA-based feedback has a reminder system that helps students to be more focused and less procrastinated.
“and also has a reminder system that helps students to be more focused and less procrastinated (e.g. Lim et al., 2020).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Lim et al., 2020
SNA was only used in five LA feedback studies.
“We found that SNA was only used in five LA feedback studies (Choi & Cho, 2020; Iandoli et al., 2014; Lim et al., 2020, 2021c; Misiejuk et al., 2021), though with various goals.”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Lim et al., 2020
Our review revealed that text analytics methods were used for mining similarities and differences among students feedback, emotions, and perceptions.
“Our review revealed that text analytics methods were used for mining similarities, and differences among students feedback, emotions, and perceptions (Lee & Lim. 2016; Lim et al., 2020; Misiejuk et al., 2021).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Lim et al., 2020
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Batita, M. S. R., Setyosarı, P., Kuswandi, D., & Ulfa, S. U. (2024) · International Conference on Education and Multimedia Technology (ICEMT) · doi:10.1145/3678726.3678734
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Mertayasa, I. K., & Saputra, P. W. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1062-6.ch010
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Kemal, S., Süzan, D., Öztüfekçi, A., & Eşfer, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7579-3.ch011
Ghosh, O., & Kumar, B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1235-4.ch001 · Cited by 1 · see citing works
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Mejeh, M., & Fromm, Y. M. (2026) · Learning and Instruction · doi:10.1016/j.learninstruc.2026.102394
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OĞUL, V. G., & Balcıoğlu, Y. S. (2026) · Opus uluslararası toplum araştırmaları dergisi · doi:10.26466/opusjsr.1948539
Connors, I. C., & Korir, V. (2026) · Studies in Higher Education · doi:10.1080/03075079.2026.2710801
Russell, J. M., Smith, A. M., George, S., & Brummett, A. E. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10728-6
- Authors
- Lim, L.-A., Dawson, S., Gašević, D., Joksimović, S., Fudge, A., Pardo, A., & Gentili, S.
- Affiliations
- University of South Australia (AU)
- Monash University (AU)
- ORCID
- Venue
- Australasian Journal of Educational Technology
- Published
- 2020-12-23
- Volume
- 36
- Issue
- 6
- Pages
- 15-33
- ISSN
- 1449-3098 (ISSN-L)
- 1449-5554
- OpenAlex
- W3118139433
- Cited by
- 66
- References
- 42
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 19 · 2023: 10 · 2022: 12 · 2021: 3 · 2020: 1
- FWCI
- 9.85
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Australasian Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc-nd)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- HAL (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc-nd)
- UTS ePRESS (University of Technology Sydney) (submittedVersion, other-oa)
- Funders
- Australian Government
- Office for Learning and Teaching
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- personalized feedback, learning analytics, sensemaking, student self-regulation, goal setting, procrastination, dialogic feedback, instructor feedback, learning strategies, epistemic networks, student perceptions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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