Eye-tracking to model and adapt to user meta-cognition in intelligent learning environments

Abstract

In this paper we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during the interaction with an intelligent learning environment. We describe the probabilistic user model that processes this information, and its formal evaluation. We show that adding eye-tracker information significantly improves the model accuracy on assessing user exploration and self-explanation behaviors.