DVS-TBA: Research on Classroom Teaching Behavior Analysis Method Based on Dual Video Stream

This paper proposes a dual video stream-based teaching behavior analysis model (DVS-TBA) to enhance teaching quality and student engagement. By deploying dual cameras in classrooms, the system non-invasively captures comprehensive teaching activities, avoiding information loss and reducing manpower costs. Utilizing the M-CPIE model for feature extraction and Bayesian causal discrimination, the system analyzes teacher and student behaviors accurately. Experimental results demonstrate that the DVS-TBA model improves teaching mode recognition accuracy and enhances overall educational effectiveness.

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Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1109/cste64638.2025.11092190
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

DVS-TBA: Research on Classroom Teaching Behavior Analysis Method Based on Dual Video Stream

Jingjing Zhang, Hao Zhang, Yifan Wang, Jian-Jun Chen et al.
Anomaly Detection Techniques and Applications
article

DVS-TBA: Research on Classroom Teaching Behavior Analysis Method Based on Dual Video Stream

Jingjing Zhang, Hao Zhang, Yifan Wang, Jian-Jun Chen, Yaopeng Li, Furong Yu
article en

Abstract

This paper proposes a dual video stream-based teaching behavior analysis model (DVS-TBA) to enhance teaching quality and student engagement. By deploying dual cameras in classrooms, the system non-invasively captures comprehensive teaching activities, avoiding information loss and reducing manpower costs. Utilizing the M-CPIE model for feature extraction and Bayesian causal discrimination, the system analyzes teacher and student behaviors accurately. Experimental results demonstrate that the DVS-TBA model improves teaching mode recognition accuracy and enhances overall educational effectiveness.

Central China Normal University (CN), Wuhan Institute of Technology (CN)
National Natural Science Foundation of China
Quality Education
Openalex Percentile: Top 91%
Anomaly Detection Techniques and Applications
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