Article: Intelligent recognition and analysis system of students' behaviour in continuing education based on classroom video Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.41 pp.1 - 23 Abstract: Student behaviour recognition is crucial for intelligent education but faces challenges in accuracy, robustness under complex conditions like occlusion and lighting variations, and cross-scenario generalisation. This paper proposes the EAST-GCN-HRNet model, which integrates spatiotemporal features and multimodal data to enhance recognition precision and robustness. The model combines HRNet's high-resolution feature extraction, GCN's temporal joint graph modelling, and the EAST module's feature fusion within an end-to-end, multi-scale framework. Experimental results demonstrate the system achieves 86.5% mAP on the SCB-Dataset3 test set, outperforming HRNet by 3.8%. It also shows strong generalisation, with a PCK@0.2 of 63.8% on the AP-10K animal pose dataset (11.5% higher than Hourglass), and robustness with only 4.8% mAP decay in dynamic occlusion scenarios - half that of baseline models. With a real-time inference speed of 28 FPS and a teacher experience rating of 4.6/5, the model provides a reliable tool for intelligent education. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent recognition and analysis system of students' behaviour in continuing education based on classroom video

Authors: Ye Zhiqun

Addresses: School of Economics and Management, Nanchang Institute of Technology, Nanchang, China

Abstract: Student behaviour recognition is crucial for intelligent education but faces challenges in accuracy, robustness under complex conditions like occlusion and lighting variations, and cross-scenario generalisation. This paper proposes the EAST-GCN-HRNet model, which integrates spatiotemporal features and multimodal data to enhance recognition precision and robustness. The model combines HRNet's high-resolution feature extraction, GCN's temporal joint graph modelling, and the EAST module's feature fusion within an end-to-end, multi-scale framework. Experimental results demonstrate the system achieves 86.5% mAP on the SCB-Dataset3 test set, outperforming HRNet by 3.8%. It also shows strong generalisation, with a PCK@0.2 of 63.8% on the AP-10K animal pose dataset (11.5% higher than Hourglass), and robustness with only 4.8% mAP decay in dynamic occlusion scenarios - half that of baseline models. With a real-time inference speed of 28 FPS and a teacher experience rating of 4.6/5, the model provides a reliable tool for intelligent education.

Keywords: classroom video; students; behaviour recognition; skeleton model.

DOI: 10.1504/IJICT.2025.149986

International Journal of Information and Communication Technology, 2025 Vol.26 No.41, pp.1 - 23

Received: 20 May 2025
Accepted: 18 Aug 2025
Published online: 20 Nov 2025 *

Title: Intelligent recognition and analysis system of students' behaviour in continuing education based on classroom video

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