De-Identification in Learning Analytics

Authors

  • Mohammad Khalil Graz University of Technology
  • Martin Ebner Graz University of Technology

DOI:

https://doi.org/10.18608/jla.2016.31.8

Keywords:

Learning analytics, anonymization, de-identification, ethics, privacy

Abstract

Learning Analytics has reserved its position as an important field in the educational sector. However, the large-scale collection, processing and analyzing of data have steered the wheel beyond the border lines and faced an abundance of ethical breaches and constraints. Revealing learners’ personal information and attitudes, as well as their activities, are major aspects that lead to personally identify individuals. Yet, de-identification can keep the process of Learning Analytics in progress while reducing the risk of inadvertent disclosure of learners’ identities. In this paper, the authors talk about de-identification methods in the context of learning environment and propose a first prototype conceptual approach that describes the combination of anonymization strategies and Learning Analytics techniques.

Author Biographies

Mohammad Khalil, Graz University of Technology

PhD Candidate - Social Learning Department

Martin Ebner, Graz University of Technology

Associate Professor - Social Learning Department

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Published

2016-04-23

How to Cite

Khalil, M., & Ebner, M. (2016). De-Identification in Learning Analytics. Journal of Learning Analytics, 3(1), 129–138. https://doi.org/10.18608/jla.2016.31.8

Issue

Vol. 3 No. 1 (2016): Ethics and Privacy in Learning Analytics

Section

Special Section: Ethics and Privacy in Learning Analytics

License

Copyright (c) 2016 Journal of Learning Analytics

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