The DOI matches its Crossref record; 19 of the 20 references it cites were found in a registry or an index.
Learning analytics in higher education --- challenges and policies: a review of eight learning analytics policies
Abstract
This paper presents the results of a review of eight policies for learning analytics of relevance for higher education, and discusses how these policies have tried to address prominent challenges in the adoption of learning analytics, as identified in the literature. The results show that more considerations need to be given to establishing communication channels among stakeholders and adopting pedagogy-based approaches to learning analytics. It also reveals the shortage of guidance for developing data literacy among end-users and evaluating the progress and impact of learning analytics. Moreover, the review highlights the need to establish formalised guidelines to monitor the soundness, effectiveness, and legitimacy of learning analytics. As interest in learning analytics among higher education institutions continues to grow, this review will provide insights into policy and strategic planning for the adoption of learning analytics.
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“It is possible that stakeholders might not be aware of their textual data (eg, forum posts or conversations) on digital platforms (eg, MOOCs and LMS) being used in LLMs-based innovations for different purposes of automation (eg, automated reply and training chatbots) as the consenting process is often embedded into the enrollment or signing up of these platforms (Tsai & Gasevic, 2017).”
From Practical and ethical challenges of large language models in education: A systematic scoping review (2024) · cites this paper as Tsai & Gasevic, 2017
Citations
Cites
Course signals at Purdue: using learning analytics to increase student success
Arnold, K. E., & Pistilli, M. D. (2012) · Proceedings of the 2nd International Conference on Learning Analytics and Knowledge · doi:10.1145/2330601.2330666 · Cited by 987
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Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan
Macfadyen, L. P., & Dawson, S. (2012) · Cited by 284
Learning Analytics in Higher Education
Lester, J., Klein, C., Rangwala, H., & Johri, A. (2017) · ASHE Higher Education Report · doi:10.1002/aehe.20121 · Cited by 217
Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014) · HAL · Cited by 147
Factors influencing beliefs for adoption of a learning analytics tool: An empirical study
Ali, L., Asadi, M., Gašević, D., Jovanović, J., & Hatala, M. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.10.023 · Cited by 135
Developing a Code of Practice for Learning Analytics
Sclater, N. (2016) · Journal of Learning Analytics · doi:10.18608/jla.2016.31.3 · Cited by 133
Colvin, C., Rogers, T., Wade, A. T., Dawson, S., Gašević, D., Shum, S. B., Nelson, K. J., Alexander, S., Lockyer, L., Kennedy, G. E., et al. (2015) · UTS ePRESS (University of Technology Sydney) · Cited by 116
Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption
Ferguson, R., Macfadyen, L. P., Clow, D., Tynan, B., Alexander, S., & Dawson, S. (2014) · Journal of Learning Analytics · doi:10.18608/jla.2014.13.7 · Cited by 105
Code of practice for learning analytics
Sclater, N., & Bailey, P. M. (2015) · Cited by 74
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Cited by
Sorted by citations
Practical and ethical challenges of large language models in education: A systematic scoping review
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13370 · Cited by 1,435
“…conversations) on digital platforms (e.g., MOOCs and LMS) being used in LLMs-based innovations for different purposes of automation (e.g., automated reply and training chatbots) as the consenting process is often embedded into the enrollment or signing up of these platforms (Tsai and Gasevic, 2017).”
MethodNazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13232 · Cited by 589
“These phenomena might be attributed to the manifestation of fundamental human bias and heuristics of judgement under uncertainty (Tversky & Kahneman, 1974), such as Confirmation Bias and Belief Perseverance (Nickerson, 1998), and they can further lead teachers' resistance to the adoption of…”
BackgroundJin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100348 · Cited by 452
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Barua, P. D., Vicnesh, J., Gururajan, R., Oh, S. L., Palmer, E., Azizan, M. M., Kadri, N. A., & Acharya, U. R. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031192 · Cited by 270
Metadata
- Authors
- Tsai, Y.-S., & Gasevic, D.
- Affiliations
- University of Edinburgh (GB)
- The University of Edinburgh, Edinburgh, United Kingdom
- ORCID
- Tsai, Yi-Shan
- Venue
- Proceedings of the Seventh International Learning Analytics & Knowledge Conference
- Publisher
- ACM
- Published
- 2017-03-13
- Pages
- 233-242
- OpenAlex
- W2593639882
- Cited by
- 211
- References
- 20
- Citations by year
- 2026: 20 · 2025: 22 · 2024: 32 · 2023: 31 · 2022: 28 · 2021: 20 · 2020: 31 · 2019: 14 · 2018: 10 · 2017: 3
- FWCI
- 14.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- HAL (submittedVersion)
- Funders
- European Commission (562080-EPP-1-2015-1-BE-EPPKA3-PIFORW)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Educational and Technological Research
- Keywords
- learning analytics, higher education, barriers to adoption, stakeholder communication, data literacy, strategic planning
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