Abstract
Studies investigating the effectiveness of game-based learning environments (GBLEs) have reported the effectiveness of these environments on learning and retention. However, there is limited research on using eye-tracking data to investigate metacognitive monitoring with GBLEs. We report on a study that investigated how college students’ eye tracking behavior (n = 25) predicted performance on embedded assessments within the Crystal Island GBLE. Results revealed that the number of books, proportion of fixations on book and article content, and proportion of fixations on concept matrices—embedded assessments associated with each in-game book and article—significantly predicted the number of concept matrix attempts. These findings suggest that participants strategized when reading book and article content and completing assessments, which led to better performance. Implications for designing adaptive GBLEs include adapting to individual student needs based on eye-tracking behavior in order to foster efficient completion of in-game embedded assessments.
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Notes
- 1.
Data from 25 participants were included in this analysis because the other participants were in the No Agency condition (see Results section below).
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This study was supported by funding from the Social Sciences and Humanities Research Council of Canada. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Social Sciences and Humanities Research Council of Canada.
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Taub, M., Mudrick, N.V., Azevedo, R., Millar, G.C., Rowe, J., Lester, J. (2016). Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with Crystal Island . In: Micarelli, A., Stamper, J., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2016. Lecture Notes in Computer Science(), vol 9684. Springer, Cham. https://doi.org/10.1007/978-3-319-39583-8_24
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DOI: https://doi.org/10.1007/978-3-319-39583-8_24
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