Using eye tracking technology to identify visual and verbal learners
- Resource Type
- Conference
- Authors
- Mehigan, Tracey J.; Barry, Mary; Kehoe, Aidan; Pitt, Ian
- Source
- 2011 IEEE International Conference on Multimedia and Expo Multimedia and Expo (ICME), 2011 IEEE International Conference on. :1-6 Jul, 2011
- Subject
- Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Visualization
Tracking
Heating
Electronic learning
Adaptation models
Adaptive systems
Mice
Measurement
Human Factors
Interaction
Eye Tracking
Learner Styles
- Language
- ISSN
- 1945-7871
1945-788X
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.