Examining Gender Differences in Game-Based Learning Through BKT Parameter Estimation.
Artificial Intelligence in Education. AIED 2022. Proceedings.
(pp. pp. 600-606).
Springer: Cham, Switzerland.
Access restricted to UCL open access staff until 28 June 2023.
The increased adoption of digital game-based learning (DGBL) requires having a deeper understanding of learners’ interaction within the games. Although games log data analysis can generate meaningful insights, there is a lack of efficient methods for looking both into learning as a dynamic process and how the game- and domain-specific aspects relate to contextual or demographic differences. In this paper, employing student modelling methods associated with Bayesian Knowledge Tracing (BKT), we analysed data logs from Navigo, a collection of language games designed to support primary school children in developing their reading skills. Our results offer empirical evidence on how contextual differences can be evaluated from game log data. We conclude the paper with a discussion of design and pedagogical implications of the results presented.
|Title:||Examining Gender Differences in Game-Based Learning Through BKT Parameter Estimation|
|Event:||AIED 2022: The 23rd International Conference on Artificial Intelligence in Education,|
|Dates:||27 Jul 2022 – 31 Jul 2022|
|Additional information:||This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.|
|UCL classification:||UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE – Culture, Communication and Media
UCL > Provost and Vice Provost Offices > School of Education
Downloads since deposit
Archive Staff Only