CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels

Chi-Hsuan Wu, Shih-Yang Liu, Xijie Huang, Xingbo Wang, Rong Zhang, Luca Minciullo, Wong Kai Yiu, Kenny Kwan, Kwang-Ting Cheng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4636-4645

Online learning is a rapidly growing industry. However a major doubt about online learning is whether students are as engaged as they are in face-to-face classes. An engagement recognition system can notify the instructors about the student's condition and improve the learning experience. Current challenges in engagement detection involve poor label quality extreme data imbalance and intra-class variety -- the variety of behaviors at a certain engagement level. To address these problems we present the CMOSE dataset which contains a large number of data from different engagement levels and high-quality labels annotated according to psychological advice. We also propose a training mechanism MocoRank to handle the intra-class variety and the ordinal pattern of different degrees of engagement classes. MocoRank outperforms prior engagement detection frameworks achieving a 1.32% increase in overall accuracy and 5.05% improvement in average accuracy. Further we demonstrate the effectiveness of multi-modality in engagement detection by combining video features with speech and audio features. The data transferability experiments also state that the proposed CMOSE dataset provides superior label quality and behavior diversity.