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.
@article{arxiv.1909.12913,
title = {Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning},
author = {Prabin Sharma and Shubham Joshi and Subash Gautam and Sneha Maharjan and Salik Ram Khanal and Manuel Cabral Reis and João Barroso and Vítor Manuel de Jesus Filipe},
journal= {arXiv preprint arXiv:1909.12913},
year = {2023}
}