Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning
This addresses the challenge of monitoring student engagement for teachers and policymakers in distance learning, but it is incremental as it combines existing methods for a specific application.
The paper tackles the problem of detecting student engagement in e-learning by developing a system that uses web-camera data on eye tracking, head movement, and facial emotion analysis to classify engagement into three levels, achieving correct identification of engagement periods and correlating higher concentration indexes with better student scores.
With the increase of distance learning, in general, and e-learning, in particular, having a system capable of determining the engagement of students is of primordial importance, and one of the biggest challenges, both for teachers, researchers and policy makers. Here, we present a system to detect the engagement level of the students. It uses only information provided by the typical built-in web-camera present in a laptop computer, and was designed to work in real time. We combine information about the movements of the eyes and head, and facial emotions to produce a concentration index with three classes of engagement: "very engaged", "nominally engaged" and "not engaged at all". The system was tested in a typical e-learning scenario, and the results show that it correctly identifies each period of time where students were "very engaged", "nominally engaged" and "not engaged at all". Additionally, the results also show that the students with best scores also have higher concentration indexes.