Our publication about „De-Identification in Learning Analytics“ got published in the Journal of Learning Analytics.
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.
Reference: Khalil, M. & Ebner, M. (2016) De-Identification in Learning Analytics. Journal of Learning Analytics. 3(1). pp. 129 – 138