Engagement Detection with Multi-Task Training in E-Learning Environments
| Author | Çopur O.; Nakip M.; Scardapane S.; Slowack J. |
|---|---|
| Title | Engagement Detection with Multi-Task Training in E-Learning Environments |
| Journal | International Conference on Image Analysis and Processing (ICIAP) |
| Year | 2022 |
| Status | Published |
| DOI | 10.1007/978-3-031-06433-3_35 |
| Abstract | <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> |
| Publisher | Springer |
| Engagement_Arxiv.pdf |