#EP4LA #LAK15 de-identification in learning analytics 


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. 

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About sharonslade

Dr Sharon Slade is a senior lecturer in the Faculty of Business and Law at the Open University in the UK working to support both tutors and students on Open University distance learning modules and programmes. Her research interests encompass ethical issues in learning analytics and online learning and tuition. Project work includes the development of a student support framework to improve retention and progression and the development of a university wide tool for tracking students and triggering relevant and targeted interventions. She led the development of new policy around the ethical use of learning analytics within the Open University, UK.
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