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Learning analytics in European higher education—Trends and barriers
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“This finding is consistent with research showing that higher education remains slow to adopt learning analytics (Márquez et al., 2023; Tsai et al., 2020).”
From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Tsai et al., 2020
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“This finding is consistent with research showing that higher education remains slow to adopt learning analytics (Márquez et al., 2023; Tsai et al., 2020).”
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Metadata
- Authors
- Tsai, Y.-S., Rates, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Jivet, I., Scheffel, M., Drachsler, H., Delgado Kloos, C., & Gašević, D.
- Affiliations
- University of Edinburgh (GB)
- Universidad Carlos III de Madrid (ES)
- Open University of the Netherlands (NL)
- DIPF | Leibniz Institute for Research and Information in Education (DE)
- Corresponding author
- Tsai, Yi-Shan
- ORCID
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 155
- Pages
- 103933
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W3027748857
- Cited by
- 192
- References
- 80
- Citations by year
- 2026: 26 · 2025: 23 · 2024: 32 · 2023: 58 · 2022: 29 · 2021: 22 · 2020: 2
- FWCI
- 25.35
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Computers & Education (publishedVersion)
- e-Archivo (Carlos III University of Madrid) (submittedVersion, cc-by-nc-nd)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- DSpace (Open University in the Netherlands) (submittedVersion)
- Funders
- European Commission
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- learning analytics, higher education institutions, institutional adoption, barriers to adoption, student engagement, student self-regulation, learning sciences, institutional management, teaching support, European higher education
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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