Supporting Learning Analytics Adoption: Evaluating the Learning Analytics Capability Model in a Real-World Setting

Justian Knobbout*, Esther van der Stappen, Johan Versendaal, Rogier van de Wetering

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
Original languageEnglish
Article number3236
Number of pages18
JournalApplied Sciences
Issue number5
Publication statusPublished - 3 Mar 2023


  • adoption
  • capabilities
  • design science research
  • evaluation
  • higher education
  • learning analytics


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