Privacy-Preserving Learning Analytics: Challenges and Techniques
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Tarih
2017
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE Computer Soc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Educational 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.
Açıklama
Anahtar Kelimeler
Data mining, data privacy, learning analytics, learning management systems, protection
Kaynak
Ieee Transactions on Learning Technologies
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
10
Sayı
1
