TY - JOUR
T1 - LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox
AU - Steiner, Christina
AU - Kickmeier-Rust, Michael
AU - Albert, Dietrich
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
U2 - 10.18608/jla.2016.31.5
DO - 10.18608/jla.2016.31.5
M3 - Article
SN - 1929-7750
VL - 3
SP - 66
EP - 90
JO - Journal of Learning Analytics
JF - Journal of Learning Analytics
IS - 1
ER -