Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

Tore Hoel
Weiqin Chen


Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately systems will tend to collapse due to legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focussing on design justifications to allow for an incremental development of solutions. This research is exploratory of nature, and further validation is needed to prove the usefulness of the Learning Analytics Design Space model.

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