CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
This addresses the challenge of monitoring student engagement in online learning for instructors, though it is incremental as it builds on existing engagement detection frameworks.
The authors tackled the problem of poor label quality and data imbalance in online student engagement detection by introducing the CMOSE dataset with high-quality labels and the MocoRank training mechanism, resulting in a 1.32% increase in overall accuracy and 5.05% improvement in average accuracy over prior methods.
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 students 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.