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


  • Tore Hoel
  • Weiqin Chen University of Bergen



Learning analytics, privacy, data sharing, trust, control of data, privacy by design, interoperability


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.

Author Biography

Tore Hoel

Tore Hoel, Oslo and Akershus University College of Applied Sciences, NO.
Tore Hoel is affiliated with Learning Centre and Library at HiOA, and has been working within the learning technology standardisation community for more than ten years. He started his career in higher education as a director of communication at Oslo University College, and has the last ten years participated in a number of national and European projects. Hoel has a background in journalism, publishing (founder of a number of professional journals), ICT consultancy, public relations and information management, as well as in ICT and learning, and standardisation.

Tore works now primarily with European and Nordic projects within the field of ICT and learning, e.g., the NordicOER project, the Open Discovery Space project, and the Learning Analytics Community Exchange project.




How to Cite

Hoel, T., & Chen, W. (2016). Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability. Journal of Learning Analytics, 3(1), 139–158.



Special Section: Ethics and Privacy in Learning Analytics

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