Learning analytics in serious games: a review of the literature

Yaelle Chaudy, Thomas Connolly, Thomas Hainey

Research output: Contribution to conferencePaperpeer-review


Learning analytics (LA) is a powerful technology expected to be adopted by institutions in the near future. The 2014 NMC horizon report situates it in the two to three years’ timeframe for primary and secondary level and one year or less for higher education. The use of LA in serious games raises two important questions, what data is gathered and how is the information analysed and transformed to knowledge. This paper presents the preliminary results of a systematic review of the literature on learning analytics in serious games. The 14 relevant studies identified will be outlined and categorised in order to offer a starting point for developers and researchers studying this important subject. The review categorises the studies found according to the type of data monitored by the game, the data processing technique and the target user of the LA tool. The review suggests that there are three perspectives to learning analytics; the student’s, the teacher’s and the researcher’s. After defining learning analytics and serious games and presenting the two main approaches to data analysis, this paper provides an overview of the use of learning analytics within serious games as well as a discussion on existing frameworks. Finally, we will present the model used in our generalised assessment engine taking into account the three perspectives of LA.
Original languageEnglish
Publication statusPublished - Nov 2014
EventEuropean Conference in the Applications of Enabling Technologies 2014 - IET Teacher Building, Glasgow, United Kingdom
Duration: 20 Nov 201421 Nov 2014


ConferenceEuropean Conference in the Applications of Enabling Technologies 2014
Abbreviated titleECAET 2014
Country/TerritoryUnited Kingdom
Internet address


  • Learning analytics
  • framework
  • feedback
  • assessment
  • monitoring tool
  • serious game
  • visualisation
  • data mining
  • data analysis


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