Gaze-Based Assessment of Dyslexic Students' Motivation within an E-Learning Environment
Ruijie Wang, Liming Chen, Aladdin Ayesh, Jethro Shell, Ivar Solheim · 2019
This paper proposes a gaze-based motivation analysis approach for evaluation of motivational strategies and assessment of motivational factors for students with dyslexia in an e-learning environment. We first collect real-time eye movement data from dyslexic learners during their e-learning practices. We then use statistic tests to evaluate four typical motivational strategies with the eye-tracking data and use logistic regressions for the assessment of motivation. Initial results show that eye-tracking data is effective at evaluating the effects of the studied motivational strategies, and adding eye-tracking features significantly improves the model accuracy which has reached to a correct prediction rate of between 72.0% and 81.3%.