Learning analytics for online game-Based learning: a systematic literature review

Seyyed Kazem Banihashem, Hojjat Dehghanzadeh, Douglas Clark, Omid Noroozi, Harm J.A. Biemans

Research output: Contribution to journalReview articlepeer-review


Game-based learning researchers have been investigating various means to maximise learning in educational games. One promising venue in recent years has been the use of learning analytics in online game-based learning environments. However, little is known about how different elements of learning analytics (e.g. data types, techniques methods, and stakeholders) contribute to game-based learning practices within online learning environments. There is a need for a comprehensive review to bridge this gap. In this systematic review, we examined the related literature in five major international databases including Web of Science, Scopus, ERIC, IEEE, and compiled Proceedings of the International Conference on Learning Analytics and Knowledge. Twenty relevant publications were identified and analysed. The analysis was conducted using four core elements of learning analytics, namely the types of data that the system collects (what), the methods used for performing analytics (how), the reasons the system captures, analyzes, and reports data (why), and the recipients of the analytics (who). This study synthesises the existing literature, provides a conceptual framework as to how learning analytics can enhance online game-based learning practices in higher education, and sets the agenda for future research.
Original languageEnglish
Number of pages28
JournalBehaviour & Information Technology
Early online date19 Sept 2023
Publication statusE-pub ahead of print - 19 Sept 2023


  • Conceptual framework
  • Game-based learning
  • Higher education
  • Learning analytics
  • Systematic review


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