The DOI matches its Crossref record; 42 of the 45 references it cites were found in a registry or an index.
An elephant in the learning analytics room: the obligation to act
Abstract
As higher education increasingly moves to online and digital learning spaces, we have access not only to greater volumes of student data, but also to increasingly fine-grained and nuanced data. A significant body of research and existing practice are used to convince key stakeholders within higher education of the potential of the collection, analysis and use of student data to positively impact on student experiences in these environments. Much of the recent focus in learning analytics is around predictive modeling and uses of artificial intelligence to both identify learners at risk, and to personalize interventions to increase the chance of success.
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“An experimental philosophical study (McGrath et al., 2023) of university teachers' perceptions of responsibility and AI in higher education found a broad consensus among participants that universities should provide AI educational tools, especially to support first-generation students and those with learning disabilities, reflecting a commitment to equity and fairness, as argued by Prinsloo and Sl”
From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Prinsloo and Slade, 2017
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Cited by
Sorted by citations
The current landscape of learning analytics in higher education
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.07.027 · Cited by 766
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Mah, D.-K., & Groß, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00490-1 · Cited by 218
“…and AI in higher education found a broad consensus among participants that universities should provide AI educational tools, especially to support first-generation students and those with learning disabilities, reflecting a commitment to equity and fairness, as argued by Prinsloo and Slade (2017).”
Metadata
- Authors
- Prinsloo, P., & Slade, S.
- Affiliations
- University of South Africa (ZA)
- The Open University (GB)
- Venue
- Proceedings of the Seventh International Learning Analytics & Knowledge Conference
- Publisher
- ACM
- Published
- 2017-03-13
- Pages
- 46-55
- OpenAlex
- W2592547188
- Cited by
- 171
- References
- 45
- Citations by year
- 2026: 41 · 2025: 26 · 2024: 12 · 2023: 19 · 2022: 17 · 2021: 17 · 2020: 14 · 2019: 14 · 2018: 6 · 2017: 5
- FWCI
- 9.83
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Open Research Online - ORO (The Open University) (acceptedVersion)
- 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, student data, higher education, online learning, predictive modeling, artificial intelligence, at-risk students, personalized interventions, student success, student experience
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