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EYE-TRACKING STUDENT'S BEHAVIOUR FOR E-LEARNING IMPROVEMENT

Nowadays, there is a shift in society towards an increased use of technological aids, which help us in our daily life tasks. However, very little of these improvements are being used in the education system, specifically to help mitigate common academic problems that affect students, such as poor performance and disengagement issues, that can arise from identifiable symptoms like changes in behaviour and emotions. In this paper an architecture to handle eye tracker data is presented, which analyses the gaze data, captured from students in learning scenarios to predict specific behaviours and issues. A scenario where distractive stimuli was used to emulate deviations in the attention levels is presented. A methodology was defined able to detect gaze patterns for further automatization of the detection of students’ issues as lack of attention and performance. The goal is in the near future to provide Learning Management Systems with solutions able to present insightful real time cognitive data of the users and automatically detect if they are exhibiting any low performance and automatically react.