Abstract:
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, be-
havioural, and afective processes during the embodied learning activity. However,
the use of eye-tracking data presenting visual attention to understand students’ be-
haviours and learning performance during engagement with tangible learning activ-
ity 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 experi-
ment, students’ learning experience was monitored by collecting their eye-tracking
data, learning profles, 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 ofered in the intersection of eye-gaze tracking with learning
traces, and its possible contribution to investigating students’ behaviour within an
embodied learning context.
