Quantification Evaluation of Teacher Behaviors Based on Teacher Action Patterns Using ST-GCN

Abstract

This paper aims to improve a teaching quality of a beginner teacher, quantitatively evaluates the difference between beginner teachers and specialist teachers from analyzing behaviors of beginner teachers and specialist teachers. Specifically, this study extracts behavior patterns of two groups using spatial-temporal graph convolution networks, and generates pattern datasets of two groups. As an experiment, we evaluated the teacher’s behavior by calculating the score of the trial lesson based on pattern datasets. As a result, we obtained a classification accuracy of 81.1 percents against beginner and specialist teacher data, were able to analyze teacher’s behavior. Furthermore, beginner teachers’ class consisted of the movement of “writing on the board”, on the other hand, specialist teachers’ class composed the class by combining “standing and speaking” with other behaviors. This visualization shows that beginner teachers can obtain awareness of the differences between experienced teachers and classes.