De-Identification in Learning Analytics

Main Article Content

Mohammad Khalil
Martin Ebner

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.

Article Details

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
Section
Special Section: Ethics and Privacy in Learning Analytics
Author Biographies

Mohammad Khalil, Graz University of Technology

PhD Candidate - Social Learning Department

Martin Ebner, Graz University of Technology

Associate Professor - Social Learning Department