Privacy-Preserving Learning Analytics: Challenges and Techniques

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dc.authoridInan, Ali/0000-0002-3149-1565
dc.contributor.authorGursoy, Mehmet Emre
dc.contributor.authorInan, Ali
dc.contributor.authorNergiz, Mehmet Ercan
dc.contributor.authorSaygin, Yucel
dc.date.accessioned2025-01-06T17:37:21Z
dc.date.available2025-01-06T17:37:21Z
dc.date.issued2017
dc.description.abstractEducational data contains valuable information that can be harvested through learning analytics to provide new insights for a better education system. However, sharing or analysis of this data introduce privacy risks for the data subjects, mostly students. Existing work in the learning analytics literature identifies the need for privacy and pose interesting research directions, but fails to apply state of the art privacy protection methods with quantifiable and mathematically rigorous privacy guarantees. This work aims to employ and evaluate such methods on learning analytics by approaching the problem from two perspectives: (1) the data is anonymized and then shared with a learning analytics expert, and (2) the learning analytics expert is given a privacy-preserving interface that governs her access to the data. We develop proof-of-concept implementations of privacy preserving learning analytics tasks using both perspectives and run them on real and synthetic datasets. We also present an experimental study on the trade-off between individuals' privacy and the accuracy of the learning analytics tasks.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [114E261]
dc.description.sponsorshipThis research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 114E261. Ali Inan is the corresponding author.
dc.identifier.doi10.1109/TLT.2016.2607747
dc.identifier.endpage81
dc.identifier.issn1939-1382
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85027451045
dc.identifier.scopusqualityQ1
dc.identifier.startpage68
dc.identifier.urihttps://doi.org/10.1109/TLT.2016.2607747
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2206
dc.identifier.volume10
dc.identifier.wosWOS:000417993300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE Computer Soc
dc.relation.ispartofIeee Transactions on Learning Technologies
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectData mining
dc.subjectdata privacy
dc.subjectlearning analytics
dc.subjectlearning management systems
dc.subjectprotection
dc.titlePrivacy-Preserving Learning Analytics: Challenges and Techniques
dc.typeArticle

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