01242nas a2200145   4500000000100000000000100001008004100002260001300043100001600056700001500072700002200087700002000109245007700129520089000206       2022                            d  bSpringer1 aOnur Çopur1 aMert Nakip1 aSimone Scardapane1 aJürgen Slowack00aEngagement Detection with Multi-Task Training in E-Learning Environments3 a<p>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-ofthe-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.</p>