Using eye tracking technology to identify visual and verbal learners

Abstract

Learner style data is increasingly being incorporated into adaptive eLearning (electronic learning) systems for the development of personalized user models. This practice currently relies heavily on the prior completion of questionnaires by system users. Whilst potentially improving learning outcomes, the completion of questionnaires can be time consuming for users. Recent research indicates that it is possible to detect a user's preference on the Global / Sequential dimension of the FSLSM (Felder-Silverman Learner Style Model) through a user's mouse movement pattern, and other biometric technology including eye tracking and accelerometer technology. In this paper we discuss the potential of eye tracking technology for inference of Visual / Verbal learners. The paper will discuss the results of a study conducted to detect individual user style data based on the Visual / Verbal dimension of the FSLSM.