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<p>This paper briefly introduces the learning behavior recognition algorithm for smart classrooms. This algorithm first uses You Only Look Once version 5 (YOLOv5) to identify and locate students in classroom surveillance images. It then utilizes OpenPose to extract the student action skeleton within the localization box. Finally, a convolutional neural network (CNN) is used to classify the learning behavior corresponding to each extracted skeleton. A case analysis was conducted to evaluate the algorithm. First, the proposed algorithm was compared with the traditional CNN, YOLOv5, and Faster region-based convolutional neural network (R-CNN) algorithms to verify its performance in recognizing learning behaviors. Then, its effectiveness in Chinese teaching was verified using 60 non-native Chinese speakers. The results indicated that the learning behavior recognition algorithm had better recognition performance compared to the traditional CNN, YOLOv5, and Faster R-CNN algorithms. When it was applied to the teaching of the international Chinese smart classroom, there was a significant improvement in students’ Chinese scores compared to the traditional teaching mode.</p>

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Learning behavior recognition and teaching effectiveness analysis in smart classrooms for international Chinese education

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月3日

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  • Yan Gu

摘要

This paper briefly introduces the learning behavior recognition algorithm for smart classrooms. This algorithm first uses You Only Look Once version 5 (YOLOv5) to identify and locate students in classroom surveillance images. It then utilizes OpenPose to extract the student action skeleton within the localization box. Finally, a convolutional neural network (CNN) is used to classify the learning behavior corresponding to each extracted skeleton. A case analysis was conducted to evaluate the algorithm. First, the proposed algorithm was compared with the traditional CNN, YOLOv5, and Faster region-based convolutional neural network (R-CNN) algorithms to verify its performance in recognizing learning behaviors. Then, its effectiveness in Chinese teaching was verified using 60 non-native Chinese speakers. The results indicated that the learning behavior recognition algorithm had better recognition performance compared to the traditional CNN, YOLOv5, and Faster R-CNN algorithms. When it was applied to the teaching of the international Chinese smart classroom, there was a significant improvement in students’ Chinese scores compared to the traditional teaching mode.