|Title||Engagement Detection with Multi-Task Training in E-Learning Environments|
|Publication Type||Conference Paper|
|Year of Publication||2022|
|Authors||Çopur O, Nakip M, Scardapane S, Slowack J|
|Conference Name||International Conference on Image Analysis and Processing (ICIAP)|
|Keywords||activity recognition, e-learning, Engagement detection, multi-task training, triplet loss|
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.