Student privacy self-management: implications for learning analytics

Prinsloo, Paul and Slade, Sharon (2015). Student privacy self-management: implications for learning analytics. In: Proceedings of the LAK '15 Fifth International Conference on Learning Analytics And Knowledge, ACM pp. 83–92.



Optimizing the harvesting and analysis of student data promises to clear the fog surrounding the key drivers of student success and retention, and provide potential for improved student success. At the same time, concerns are increasingly voiced around the extent to which individuals are routinely and progressively tracked as they engage online. The Internet, the very thing that promised to open up possibilities and to break down communication barriers, now threatens to narrow it again through the panopticon of mass surveillance.
Within higher education, our assumptions and understanding of issues surrounding student attitudes to privacy are influenced both by the apparent ease with which the public appear to share the detail of their lives and our paternalistic institutional cultures. As such, it can be easy to allow our enthusiasm for the possibilities offered by learning analytics to outweigh consideration of issues of privacy.
This paper explores issues around consent and the seemingly simple choice to allow students to opt-in or opt-out of having their data tracked. We consider how 3 providers of massive open online courses (MOOCs) inform users of how their data is used, and discuss how higher education institutions can work toward an approach which engages and more fully informs students of the implications of learning analytics on their personal data.

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