Exploring users' perceived activities in a sketch-based intelligent tutoring system through eye movement data

Abstract

Intelligent tutoring systems (ITS) empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students' progress. Despite the advantages, existing ITS do not automatically assess how students engage in problem solving? How do they perceive various activities? and How much time they spend on each activity leading to the solution? In this research, we present an eye tracking framework that, based on eye movement data, can assess students' perceived activities and overall engagement in a sketch based Intelligent tutoring system, "Mechanix" [Valentine et al. 2012]. Based on an evaluation involving 21 participants, we present the key eye movement features, and demonstrate the potential of leveraging eye movement data to recognize students' perceived activities, "reading, gazing at an image, and problem solving," with an accuracy of 97.12%.