Classroom Teaching Behaviour Analysis and Teaching Quality Evaluation Design Based on Deep Learning

Authors

  • Yaqi Lian School of Mechanical and Electrical Engineering, Taizhou Vocational & Technical College, Taizhou 318000, China
  • Feng He College of Humanities and Law, Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou 311500, China
  • Shanshan Wang The Eurasian Institute of Language and Culture, Xi’an FanYi University, Xi’an 710000, China

Abstract

With the rapid development of educational technology, the analysis of students’ classroom behavior has been an important focus of educational research. Traditional classroom monitoring methods rely on actual observation and cannot provide accurate and comprehensive analysis. Multimodal data analysis combined with deep learning technology provides a new way to solve this problem. This paper proposes a multimodal classroom behavior analysis system based on video, audio and sensor data, which aims to accurately identify and predict the behavior of teachers and students through deep learning models to improve the quality of classroom teaching. The system includes a data acquisition module, data preprocessing module, feature extraction module, behavior analysis and prediction module, and result feedback module. First, classroom behavior data is collected
through video, audio and sensors, and the data is preprocessed by denoising, normalization and other operations. Then, a convolutional neural network (CNN) is used to extract image features from video, the MFCC method is used to extract spectral features from audio, and the LSTM model is used to extract time series features from sensor data. Then, the system uses deep neural network (DNN) for behavior classification and LSTM to predict learning status and teaching quality. Finally, the analysis results are fed back to teachers and education managers in the form of reports. Experimental results show that the system performs well in terms of behavior classification and regression tasks, with higher accuracy, precision and F1 score than
traditional models, and the system has stable performance in different tasks. This research not only provides new ideas for classroom behavior analysis, but also provides decision support tools for educational administrators.


Keywords: multimodal data, deep learning, behavior analysis, classroom monitoring, educational technology

Cite As

Y. Lian, F. He, S. Wang, "Classroom Teaching Behaviour Analysis and Teaching Quality Evaluation Design Based
on Deep Learning", Engineering Intelligent Systems, vol. 33 no. 6, pp. 689-699, 2025.



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Published

2025-11-01

Issue

Vol. 33 No. 6 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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