论文标题
通过电子学习环境中的多任务培训进行参与检测
Engagement Detection with Multi-Task Training in E-Learning Environments
论文作者
论文摘要
识别用户互动的认识，特别是参与度检测，对于在线工作和学习环境，尤其是在Covid-19爆发期间。这种识别和检测系统通过提供有价值的反馈来显着提高用户体验和效率。在本文中，我们提出了一个新型的参与检测，其中使用多任务培训（ED-MTT）系统，将平方误差和三重态损失最小化，以确定在电子学习环境中学生的参与度。评估该系统的性能并与公开可用数据集中的最先进以及从现实生活中收集的视频进行比较。结果表明，ED-MTT的MSE比最佳最佳性能低6％，并具有高度可接受的训练时间和轻巧的功能提取。
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.
