Analysis and research on students' classroom behavior data based on GCN

Minchao Ban; Mingwei Tang; Jie Zhou; MingFeng Zhao; Zhiming Xiao

doi:10.1117/12.2674596

23 May 2023 Analysis and research on students' classroom behavior data based on GCN

Minchao Ban, Mingwei Tang, Jie Zhou, MingFeng Zhao, Zhiming Xiao

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126041T (2023) https://doi.org/10.1117/12.2674596
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

The analysis and study of students' classroom behavior can help develop students' abilities and improve teachers' teaching, and has been one of the key issues closely followed by the education community. In recent years, graph convolutional neural networks (GCN) have been widely used in various fields with outstanding success. Therefore, this paper proposes a GCN-based approach for modeling and analyzing student classroom behavior data. The experimental results show that the method helps to develop students' ability and improve teachers' teaching level.

Citation Download Citation

Minchao Ban, Mingwei Tang, Jie Zhou, MingFeng Zhao, and Zhiming Xiao "Analysis and research on students' classroom behavior data based on GCN", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126041T (23 May 2023); https://doi.org/10.1117/12.2674596

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