Title: Teaching classroom learning behaviour analysis based on support vector machine and CLM

Authors: Chunlin Xiang; Lingtong Li; Yan Jiang

Addresses: Department of Primary Education, Chongqing Preschool Education College, Chongqing, 404047, China ' Department of Primary Education, Chongqing Preschool Education College, Chongqing, 404047, China ' Department of Primary Education, Chongqing Preschool Education College, Chongqing, 404047, China

Abstract: Learning behaviour reflects student engagement and knowledge absorption. However, current automatic recognition methods lack accuracy and efficiency. To enhance teaching quality and provide precise data, a model based on support vector machine and constrained local model is proposed. Visual sensors and cameras capture student motion and expressions. Facial expressions and behavioural features are extracted using local binary patterns and skeleton data. Support vector machines and facial tracking classify behaviours, which provide teachers with a basis for judging the quality of teaching based on the identification results. The experimental results show that the recognition accuracy of the analysis model is 95.23%. The running time of the research design model is 0.25s, which has high real-time performance. From this, the designed model can more accurately capture subtle differences and potential patterns in learning behaviour, providing insights for teachers to optimise teaching strategies and improve students' learning outcomes.

Keywords: support vector machine; SVM; learning behaviour analysis; Kalman filtering; teaching quality; emotional recognition.

DOI: 10.1504/IJWET.2026.155087

International Journal of Web Engineering and Technology, 2026 Vol.21 No.2, pp.168 - 194

Accepted: 05 Oct 2025
Published online: 27 Jul 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article