LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox

Christina Steiner, Michael Kickmeier-Rust, Dietrich Albert

    Research output: Contribution to journalArticlepeer-review

    Abstract

    To find a balance between learning analytics research and individual privacy learning analytics initiatives need to appropriately address ethical, privacy and data protection issues and comply with relevant legal regulations. A range of general guidelines, model codes, and principles for handling ethical issues and for appropriate data and privacy protection exist, which may serve the consideration of these topics in a learning analytics context. The importance and significance of data security and protection are also reflected in national and international laws and directives, where data protection is usually considered as a fundamental right. Existing guidelines, approaches and relevant regulations served as a basis for elaborating a comprehensive privacy and data protection framework for the LEA’s BOX project. It comprises a set of eight principles to derive implications for ensuring an ethical treatment of personal data in a learning analytics platform and its services. The privacy and data protection policy set out in the framework is suitable to be used as best practice for other learning analytics projects.
    Original languageEnglish
    Pages (from-to)66-90
    JournalJournal of Learning Analytics
    Volume3
    Issue number1
    DOIs
    Publication statusPublished - 2016

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