Student Performance Prediction with Eye-Gaze Data in Embodied Educational Context

Recent advances in sensor technology, including eye-gaze tracking, have introduced the opportunity to incorporate gaze into student modelling within an embodied learning context. The produced multimodal data is used to uncover cognitive, behavioural, and affective processes during the embodied learning activity. However, the use of eye-tracking data presenting visual attention to understand students’ behaviours and learning performance during engagement with tangible learning activity is rather unexplored. Therefore, this paper explores the integration of eye-gaze features to predict students’ learning performance during an embodied activity. We present an in-situ study where 110 primary school students (aged 8–9 years), solved a tangible learning activity for learning human body anatomy. During the experiment, students’ learning experience was monitored by collecting their eye-tracking data, learning profiles, academic performances, and time to complete the activity. We applied predictive modelling to identify the synergies between eye-gaze features and students’ learning performance. The obtained results suggest that combining eye-gaze tracking with learning traces and behaviour attributes may support an accurate prediction of students’ learning performance. This research sheds light on the opportunities offered in the intersection of eye-gaze tracking with learning traces, and its possible contribution to investigating students’ behaviour within an embodied learning context.