Abstract
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.
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Funding
Funded from the Strategic Investment Board of the IOE, UCL's Faculty of Education and Society. For data collection and game development funding see http://iread-project.eu.
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Rizvi, S., Gauthier, A., Cukurova, M., Mavrikis, M. (2022). Examining Gender Differences in Game-Based Learning Through BKT Parameter Estimation. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_55
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DOI: https://doi.org/10.1007/978-3-031-11644-5_55
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