Classroom Learning Status Assessment Based on Deep Learning

Student classroom behavior performance is an important part of classroom teaching evaluation, and conducting student classroom behavior recognition is important for classroom teaching evaluation. The article proposes a deep learning-based student classroom behavior recognition method, which extracts...

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Bibliographic Details
Published in:Mathematical problems in engineering 2022-04, Vol.2022, p.1-9
Main Authors: Zhou, Jie, Ran, Feng, Li, Guang, Peng, Jun, Li, Kun, Wang, Zheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classrooms
Deep learning
Educational technology
Efficiency
Enrollments
Human performance
Neural networks
Recognition
Student behavior
Students
Teaching
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Summary:Student classroom behavior performance is an important part of classroom teaching evaluation, and conducting student classroom behavior recognition is important for classroom teaching evaluation. The article proposes a deep learning-based student classroom behavior recognition method, which extracts the key information of the human skeleton from student behavior images and combines a 10-layer deep convolutional neural network (CNN-10) to recognize students’ classroom behavior. To verify the effectiveness of this method, the paper conducts a comparison experiment on the student classroom behavior dataset using CNN-10 and the student classroom behavior recognition method. The experimental results show that the student classroom behavior recognition method can effectively exclude the interference of irrelevant information such as students’ physique, dress, and classroom background, highlight the key effective information, and have higher recognition accuracy and generalization ability. Using the human skeleton and a deep learning-based student classroom behavior detection approach to identify students’ typical classroom behaviors might improve intelligent classroom teaching by reflecting students’ learning status in a timely and effective manner.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/7049458