Preserving Both Privacy and Utility in Learning Analytics
Date
2024
Authors
Zhan, C.
Joksimovi'c, S.
Ladjal, D.
Rakotoarivelo, T.
Marshall, R.
Pardo, A.
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Journal article
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IEEE Transactions on Learning Technologies, 2024; 17:1655-1667
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Abstract
Data is fundamental to Learning Analytics 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 Learning Analytics 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 Learning Analytics models, providing an evaluation of the utility loss. We further explore the balance between preserving data privacy and maintaining data utility in Learning Analytics. The results prove the compatibility between preserving learners’ privacy and Learning Analytics, 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 Learning Analytics technique.
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Copyright 2024 IEEE