The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Learning analytics dashboards are increasingly becoming about learning and not just analytics - A systematic review
Open accessAbstract
This systematic review explores the emerging themes in the design and implementation of student-facing learning analytics dashboards in higher education. Learning Analytics has long been criticised for focusing too much on the analytics, and not enough on the learning. The review is then guided by an interest in whether these dashboards are still primarily analytics-driven or if they have become pedagogically informed over time. By mapping the identified themes of technological maturity, informing frameworks, affordances, data sources, and analytical levels over publications per year, the review identifies an emerging trajectory towards student-focused dashboards. These dashboards are informed by theory-oriented frameworks, designed to incorporate affordances that supporting student learning, and realised through integration of more than just activity data from learning management systems – allowing the dashboards to better support students' learnings processes. Based on this emerging trajectory, the review provides a series of design recommendations for student-focused dashboards that are connected to learning sciences as well as analytics.
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“Studies frequently equated engagement with observable online behaviours, such as the number of clicks or time logged in, which predominantly captures only the behavioural aspect (Algayres & Triantafyllou, 2019;Paulsen & Lindsay, 2024).”
From Unpacking student engagement in higher education learning analytics: a systematic review (2024) · cites this paper as Paulsen & Lindsay, 2024
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“However, the reliance on static questionnaires has been increasingly questioned, as selfreported learning styles may not accurately reflect learners' actual study behaviors in digital environments.”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [9]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Importantly, discrepancies between self-reported and behavior-derived learning styles were explicitly identified, highlighting the fluid and context-dependent nature of learner preferences [8][9][10][11].”
From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [9]
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Metadata
- Authors
- Paulsen, L., & Lindsay, E.
- Affiliations
- Aalborg University (DK)
- Corresponding author
- Paulsen, Lucas
- Venue
- Education and Information Technologies
- Published
- 2024-01-05
- Volume
- 29
- Issue
- 11
- Pages
- 14279-14308
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W4390616983
- Cited by
- 111
- References
- 69
- Citations by year
- 2026: 68 · 2025: 34 · 2024: 9
- FWCI
- 8.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- VBN Forskningsportal (Aalborg Universitet) (acceptedVersion, cc-by)
- Funders
- Aalborg University
- 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 › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- learning analytics dashboards, higher education, learning sciences, virtual learning environments, learning analytics framework, affordances, data sources, levels of analysis, student learning support
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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