Real Time Learning Evaluation Based on Gaze Tracking
In this paper, we present a system that extracts the information implied by eye movements and use this information to analyze students' learning behavior. Our system uses a common webcam to capture students' facial image sequences when they are learning in front of monitors. We then process these images and establish sequences of changing location of iris, which represent the movements of eyes. With the eye movement sequences, we train a HMM classifier that can analyze their pattern and generate learning status for any given moment in the lesson. These statuses could help the computers to get a better understanding about the students' intention and behavior during online learning. The status sequences of those who view the same lesson could also be used as a reference for teaching quality assessment.