sis 24(5):

Research Article

Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom

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  • @ARTICLE{10.4108/eetsis.5872,
        author={Yihua Liu and Weirong Wang},
        title={Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={5},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={5},
        keywords={student classroom behavior detection, convolutional neural network, path aggregation network, attention mechanism strategy},
        doi={10.4108/eetsis.5872}
    }
    
  • Yihua Liu
    Weirong Wang
    Year: 2024
    Improved Convolutional Neural Network Algorithm for Student Behavior Detection in the Classroom
    SIS
    EAI
    DOI: 10.4108/eetsis.5872
Yihua Liu1,*, Weirong Wang
  • 1: Xi'an Siyuan University
*Contact email: lyh9912203@163.com

Abstract

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority.