Application of online teaching-based classroom behavior capture and analysis system in student management

Abstract

Analyzing online learning behavior helps to understand students’ progress, difficulties, and needs during the learning process, making it easier for teachers to provide timely feedback and personalized guidance. However, the classroom behavior (CB) of online teaching is complex and variable, and relying on traditional classroom supervision methods, teachers find it difficult to comprehensively pay attention to the learning behavior of each student. In this regard, a dual stream network was designed to capture and analyze CB by integrating AlphaPose human keypoint detection method and image data method. The experimental results show that when the learning rate of the model parameters is set to 0.001, the accuracy of the model is as high as 92.3%. When the batch size is 8, the accuracy of the model is as high as 90.8%. The accuracy of the fusion model in capturing upright sitting behavior reached 97.3%, but the accuracy in capturing hand raising behavior decreased to only 74.8%. The fusion model performs well in terms of accuracy and recall, with recall rates of 88.3, 86.2, and 85.1% for capturing standing up, raising hands, and sitting upright behaviors, respectively. And the maximum F1 value is 0.931. The dual stream network effectively integrates the advantages of two types of data, improves the performance of behavior capture, and improves the robustness of the algorithm. The successful application of the model is beneficial for teachers’ classroom observation and research activities, providing a favorable path for their professional development, and thereby improving the overall teaching quality of teachers.

Keywords

• online teaching
• keypoint detection method
• rgb images
• double flow network
• behavior capture