Using Eye Tracking Data for Enhancing Adaptive Learning Systems

Abstract

Adaptive learning systems analyse a learner's input and respond on the basis of it, for example by providing individual feedback or selecting appropriate follow-up tasks. To provide good feedback, such a system must have a high diagnostic capability. The collection of gaze data alongside the traditional data obtained through mouse and keyboard input seems to be a promising approach for this. We use the example of graphical differentiation to investigate whether and how the integration of eye tracking data into such a system can succeed. For this purpose, we analyse students' eye tracking data and gather empirical understanding about which measures are suitable as decision support for adaptation