What the Eyes Can Tell: Analyzing Visual Attention with an Educational Video Game

Abstract

3D video games show potential as educational tools that improve learner engagement. Integrating 3D games into school curricula, however, faces various challenges. One challenge is providing visualizations on learning dashboards for instructors. Such dashboards provide needed information so that instructors may conduct timely and appropriate interventions when students need it. Another challenge is identifying contributive learning predictors for a computational model, which can be the core algorithm used to make games more intelligent for tutoring and assessment purposes. Previous studies have found that students' visual-attention is a vital aspect of engagement during gameplay. However, few studies have examined whether attention visualization patterns can distinguish students from different performance groups. Complicating this research is the relatively nascent investigation into gaze metrics for learning-prediction models. In this exploratory study, we used eye-tracking data from an educational game, Mission HydroSci, to examine visual-attention pattern differences between low and high performers and how their self-reported demographics affect such patterns. Results showed different visual-attention patterns between low and high performers. Additionally, self-reported science, gaming, and navigational expertise levels were significantly correlated to several gaze metric features.