Khalil and Ebner
Key issues are: Transparency and 3rd party issues, identification, ownership, accuracy, security. Research into how to de identify students. In context of education, tensions between learning analytics and personal information. Students don’t want research into their personal data.
Aim is to achieve privacy, research reasons (key to be able to analyse data for research purposes rather than just the application of learning analytics to students), available to public (data that can be freely released). In the US and EU, there is relevant legislation HIPAA, FERPA, DPD 95/46/EC which allows anonymised data to be used.
Propose approach which analyses de-identified student datasets (blurring, masking, hashing, suppression – remove key personal information, swapping – deliberately assigning characteristics of one student with another’s, noising – amending/adjusting results by x%, say). Despite such techniques de-identification is fraught with problems.