Engagement Detection with Multi-Task Training in E-Learning Environments

TytułEngagement Detection with Multi-Task Training in E-Learning Environments
Publication TypeConference Paper
Rok publikacji2022
AutorzyÇopur O, Nakip M, Scardapane S, Slowack J
Conference NameInternational Conference on Image Analysis and Processing (ICIAP)
Słowa kluczoweactivity 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.


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Data aktualizacji: 19/10/2022 - 13:32; autor zmian: Mert Nakip (mnakip@iitis.pl)