Student classroom behavior recognition and evaluation system based on YOLOX

Abstract

Most schools rely on human observation for student learning evaluation and teachers to observe and evaluate students' classroom behaviors, giving each student a usual grade, which is inaccurate and too subjective, and there is no public dataset of students' classroom behaviors yet, so we constructed a classroom teaching video library and a library of students' classroom behaviors. First, the classroom monitoring video is extracted regularly in combination with the cv2 library to get a real-time picture stream of each student; then the spatiotemporal features of each student's behavior are learned by using convolutional neural networks, so as to achieve real-time recognition of classroom behavior in classroom teaching scenarios oriented to multi-student targets. In addition, an intelligent teaching assessment model is constructed and an intelligent teaching assessment system based on student classroom behavior recognition is designed and implemented to help improve teaching quality in order to realize smart education. Through experimental comparison and analysis on classroom teaching video dataset, it is verified that the proposed real-time multi-person student classroom behavior recognition model in classroom teaching video can achieve 70% accuracy rate, and the constructed intelligent teaching assessment system based on classroom behavior recognition has also achieved better operation results on classroom teaching video dataset.