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Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems
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“The large-scale collection of learner data raises concerns regarding ethics, privacy, fairness, and trustworthiness [7,22].”
From Federated Learning Analytics: Investigating the Privacy-Performance Trade-Off in Machine Learning for Educational Analytics (2024) · cites this paper as [22]
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“Research is relevant when it addresses “the problems faced and the opportunities afforded by the interaction of people, organisations, and information technology” [25].”
BackgroundInfluentialBut is it rigorous? Trustworthiness and authenticity in naturalistic evaluation
Lincoln, Y. S., & Guba, E. G. (1986) · New Directions for Program Evaluation · doi:10.1002/ev.1427 · Cited by 3,668
Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017) · Computers & Education · doi:10.1016/j.compedu.2016.10.001 · Cited by 1,003
Time will tell: The role of mobile learning analytics in self-regulated learning
Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.08.004 · Cited by 242
SYMBALS: A Systematic Review Methodology Blending Active Learning and Snowballing
van Haastrecht, M., Sarhan, I., Yigit Ozkan, B., Brinkhuis, M., & Spruit, M. (2021) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2021.685591 · Cited by 80
Self-citationUsing trace data to enhance Students' self-regulation: A learning analytics perspective
Ye, D., & Pennisi, S. (2022) · The Internet and Higher Education · doi:10.1016/j.iheduc.2022.100855 · Cited by 69
Counting Clicks is Not Enough: Validating a Theorized Model of Engagement in Learning Analytics
Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović, S., van Staalduinen, J.-P., & Gašević, D. (2019) · Proceedings of the 9th International Conference on Learning Analytics & Knowledge · doi:10.1145/3303772.3303775 · Cited by 57
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Cited by
van Haastrecht, M., Brinkhuis, M., & Spruit, M. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-64299-9_5 · Cited by 13
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Metadata
- Authors
- van Haastrecht, M., Brinkhuis, M., Peichl, J., Remmele, B., & Spruit, M.
- Affiliations
- Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands
- Department of Information and Computing Sciences, Utrecht University, Netherlands
- University of Education Freiburg, Germany
- Department of Public Health and Primary Care, Leiden University Medical Center (LUMC), Netherlands and Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands
- Venue
- LAK23: 13th International Learning Analytics and Knowledge Conference
- Publisher
- ACM
- Published
- 2023-03-13
- Pages
- 552-558
- Cited by
- 3
- References
- 7
- Funders
- Erasmus+ (2022-1-DE02-KA220-VET-000087221)
- Horizon 2020 Framework Programme (883588)
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- van Haastrecht, M., Brinkhuis, M., Peichl, J., Remmele, B., & Spruit, M. (2023). Embracing Trustworthiness and Authenticity in the Validation of Learning Analytics Systems. LAK23: 13th International Learning Analytics and Knowledge Conference. https://doi.org/10.1145/3576050.3576060
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.