LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox
Christina Steiner, Michael Kickmeier-Rust, Dietrich Albert · Journal of Learning Analytics · 2016
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.