Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with Crystal Island
Michelle Taub, Nicholas V. Mudrick, Roger Azevedo, Garrett C. Millar, Jonathan Rowe, James Lester
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.
The final publication is available at Springer via https://doi.org/10.1007/978-3-319-39583-8_24.