@article{2922,
  keywords = {Learning Analytics, Education, Data Mining, Games, Literature Review},
  author = {Ahmed Tlili and Maiga Chang and Jewoong Moon and Zhichun Liu and Daniel Burgos and Nian-Shing Chen and Kinshuk},
  title = {A Systematic Literature Review of Empirical Studies on Learning Analytics in Educational Games},
  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.},
  year = {2021},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {7},
  number = {2},
  pages = {250-261},
  month = {12/2021},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_22_0.pdf},
  doi = {10.9781/ijimai.2021.03.003},
}