Preserving Both Privacy and Utility in Learning Analytics

• Published in: IEEE transactions on learning technologies 2024, Vol.17, p.1615-1627
• Main Authors: Zhan, Chen, Joksimovic, Srecko, Ladjal, Djazia, Rakotoarivelo, Thierry, Marshall, Ruth, Pardo, Abelardo
• Format: Article
• Language: English
• Subjects: Data privacy; Education; Ethics; Guidelines; Learning; Learning analytics; Learning analytics (LA); Privacy; privacy and ethics; Protection; Reviews
• ISSN: 1939-1382; 2372-0050
• DOI: 10.1109/TLT.2024.3393766

Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide, this does not guarantee successful implementation in practice. It is necessary to develop practical approaches that would allow for the translation of the existing guidelines into practice. In this study, we examine an initial set of privacy-preserving mechanisms on a large-scale education dataset. The data utility is evaluated before and after privacy-preserving mechanisms are applied by fitting into commonly used LA models, providing an evaluation of the utility loss. We further explore the balance between preserving data privacy and maintaining data utility in LA. The results prove the compatibility between preserving learners' privacy and LA, providing a benchmark of utility loss to practitioners and researchers in the education sector. Our study reminds an imminent concern of data privacy and advocates that privacy preserving can and should be an integral part of the design of any LA technique.