Engagement Detection with Multi-Task Training in E-Learning Environments
This addresses engagement recognition for students in online learning environments, but it is incremental as it builds on existing methods with specific improvements.
The paper tackled engagement detection in e-learning by proposing a multi-task training system that minimizes mean squared error and triplet loss, achieving 6% lower MSE than state-of-the-art methods.
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.