The privacy paradox and its implications for learning analytics

Yi-Shan Tsai, Alexander Whitelock-Wainwright, Dragan Gasevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Learning analytics promises to support adaptive learning in higher education. However, the associated issues around privacy protection, especially their implications for students as data subjects, has been a hurdle to wide-scale adoption. In light of this, we set out to understand student expectations of privacy issues related to learn-ing analytics and to identify gaps between what students desire and what they expect to happen or choose to do in reality when it comes to privacy protection. To this end, an investigation was carried out in a UK higher education institution using a survey (N=674) and six focus groups (26 students). The study highlight a number of key implications for learning analytics research and practice: (1) purpose, access, and anonymity are key benchmarks of ethics and privacy integrity; (2) transparency and communication are key levers for learning analytics adoption; and (3) information asymmetry can impede active participation of students in learning analytics.
Original languageEnglish
Title of host publicationLAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Print)9781450377126
Publication statusPublished - 23 Mar 2020
EventThe 10th International Learning Analytics & Knowledge Conference - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10


ConferenceThe 10th International Learning Analytics & Knowledge Conference
Abbreviated titleLAK20
Internet address


  • Learning analytics
  • privacy
  • expectations
  • privacy paradox
  • higher education


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