Recognition and Analysis of Teaching Behavior Based on Multi-scale GCN

In the field of education,classroom teaching evaluation stands as a pivotal element in enhancing teaching quality.With the widespread adoption of digital education,the quest for an intelligent evaluation method becomes increasingly crucial.Therefore,this paper proposes a novel method based on skeleton action recognition and lagged sequence analysis,aiming to more accurately capture and analyze teachers' teaching behaviors while reducing manpower consumption and diminishing the subjectivity of teaching evaluations.Firstly,a multi-scale feature graph convolutional network is proposed and applied to analyze teacher classroom behaviors.This network utilizes a multi-scale semantic feature fusion module to capture features at two scales,skeleton points,and body parts,in the spatial dimension.In the temporal dimension,a multi-scale temporal feature extraction module is employed to extract temporal features of skeleton data from both global and local perspectives.Subsequently,a dataset for analyzing teachers' classroom behaviors is constructed,and the effectiveness of the proposed method is validated on this dataset.Finally,leveraging the proposed skeleton action recognition model and lagged sequence analysis,a system for recognizing and analyzing teaching behaviors is developed.The proposed method demonstrates significant advantages in classroom behavior recognition and analysis when applied to various classroom teaching scenarios.