Institution wide information privacy frameworks to support academics in the use of learning analytics


  • Eva Dobozy
  • Jennifer Heath
  • Pat Reynolds
  • Eeva Leinonen



Learning Design, Learning Analytics, information privacy, data-driven education, personal privacy


Lecturers invest time and effort in developing, implementing and evaluating their learning designs. They are also increasingly interested in and engaged with the capture of changes in student engagement and utilization patterns and learning outcomes using learning analytics tools. These new analytics tools make individual student surveillance possible. Given these rapid developments, there is now an urgent need for educators and learning analytics researchers to think about the ethics of learning analytics and the protection of individual privacy. This presentation will consider the importance of an Institution wide privacy framework to support learning analytics. Institution wide frameworks provide protection for both students and academic staff as they engage with learning analytics and should provide the academic staff with clarity regarding ethical matters that often arise in this domain. Key features of institution wide frameworks include: governance structures; responsibility for action; maximum transparency; privacy principles consistent across diverse education delivery methods; legislative requirements met; suitable student consent mechanisms and; a clear secondary use of data policy.