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  • Autor
    • Khalil, Mohammad
    • Ebner, Martin
  • TitelDe-Identification in Learning Analytics
  • Volltext
  • DOI10.18608/jla.2016.31.8
  • Persistent Identifier
  • Erschienen inJournal of learning analytics [Elektronische Ressource]
  • Band3
  • Erscheinungsjahr2016
  • Heft1
  • Seiten129-138
  • LicenceCC-BY
  • ZugriffsrechteCC-BY
  • Download Statistik852
  • Peer ReviewJa
  • AbstractLearning analytics has reserved its position as an important field in the educational sector. However, the large-scale collection, processing, and analyzing of data has steered the wheel beyond the borders to face an abundance of ethical breaches and constraints. Revealing learners’ personal information and attitudes, as well as their activities, are major aspects that lead to identifying individuals personally. 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 discuss de-identification methods in the context of the learning environment and propose a first prototype conceptual approach that describes the combination of anonymization strategies and learning analytics techniques.