Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-of-the-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction.
@article{arxiv.2204.04020,
title = {Engagement Detection with Multi-Task Training in E-Learning Environments},
author = {Onur Copur and Mert Nakıp and Simone Scardapane and Jürgen Slowack},
journal= {arXiv preprint arXiv:2204.04020},
year = {2022}
}