Contemporary Privacy Theory Contributions to Learning Analytics

Type: Evidence | Proposition: D: Ethics | Polarity: | Sector: | Country:

This paper provides an overview of privacy and considers the potential contribution contemporary privacy theories can make to learning analytics. It is written from the position that having the technical capability to conduct a particular learning analytics task does not automatically mean that the task should be performed. It reflects on how privacy theories can help advance learning analytics and stresses the importance of hearing the student voice in this space.

“Transmission principles regarding the provision of a student’s personal demographic data in the student application, admission, and administration context do not necessarily apply in any other context. If a student agrees to the flow of student equity‐related data to support admission processes, he or she is not necessarily agreeing to the same terms and conditions of information flow in another context, such as secondary use of data for learning analytics activities.”

This paper is considered to be positive evidence for ‘Proposition D: Learning analytics are used in an ethical way’ because it provides evidence that learning analytics researchers are thinking carefully about these issues.

Citation: Heath, Jennifer. (2014). Contemporary privacy theory contributions to learning analytics. Journal of Learning Analytics, 1(1), 140-149. | Url: http://epress.lib.uts.edu.au/journals/index.php/JLA/issue/view/307

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