A systematic literature review of empirical studies on learning analytics in educational games

Ahmed Tlili, Maiga Chang, Jewoong Moon, Zhichun Liu, Daniel Burgos, Nian Shing Chen, Kinshuk

Research output: Contribution to journalJournal Articlepeer-review

8 Citations (Scopus)

Abstract

Learning analytics (LA) in educational games is considered an emerging practice due to its potential of enhancing the learning process. Growing research on formative assessment has shed light on the ways in which students' meaningful and in-situ learning experiences can be supported through educational games. To understand learners' playful experiences during gameplay, researchers have applied LA, which focuses on understanding students' in-game behaviour trajectories and personal learning needs during play. However, there is a lack of studies exploring how further research on LA in educational games can be conducted. Only a few analyses have discussed how LA has been designed, integrated, and implemented in educational games. Accordingly, this systematic literature review examined how LA in educational games has evolved. The study findings suggest that: (1) there is an increasing need to consider factors such as student modelling, iterative game design and personalisation when designing and implementing LA through educational games; and (2) the use of LA creates several challenges from technical, data management and ethical perspectives. In addition to outlining these findings, this article offers important notes for practitioners, and discusses the implications of the study’s results.

Original languageEnglish
Pages (from-to)250-261
Number of pages12
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
Volume7
Issue number2
DOIs
Publication statusPublished - 2021

Keywords

  • Educational Data Mining
  • Educational Games
  • Learning Analytics
  • Systematic Literature Review

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