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.
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