CMOSE: Comprehensive Multi-Modality Online Student Engagement Dataset with High-Quality Labels
CoRR(2023)
摘要
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
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.
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