Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation.

This paper presents an experimental evaluation of eye gaze data as a source for modeling user’s learning in Interactive Simulations (IS). We compare the performance of classifier user models trained only on gaze data vs. models trained only on interface actions vs. models trained on the combination of these two sources of user interaction data. Our long-term goal is to build user models that can trigger adaptive support for students who do not learn well with ISs, caused by the often unstructured and open-ended nature of these environments. The test-bed for our work is the CSP applet, an IS for Constraint Satisfaction Problems (CSP). Our findings show that including gaze data as an additional source of information to the CSP applet’s user model significantly improves model accuracy compared to using interface actions or gaze data alone.