Automated Recognition of Teacher and Student Activities in the Classroom: A Deep Learning Framework

Teacher and student behavior during class is often observed by education professionals to evaluate and develop a teacher’s skill, adapt lesson plans, or monitor and regulate student learning and other activities. Traditional methods rely on accurate manual techniques involving in-person field observations, questionnaires, or the subjective annotation of video recordings. These techniques are time-consuming and typically demand observation and coding by a trained professional. Thus, developing automated tools for detecting classroom behaviors using artificial intelligence could greatly reduce the resources needed to monitor teacher and student behaviors for research, practice, or professional development purposes. This paper presents an automated framework using a deep learning approach to recognize classroom activities encompassing both student and teacher behaviors from classroom videos. The proposed method utilizes a long-term recurrent convolutional network (LRCN), which captures the spatiotemporal features from the video frames. For evaluation purposes, experiments were carried out on a subset of EduNet and an independent dataset composed of classroom videos collected from the internet. The proposed LRCN system achieved a maximum average accuracy (ACC) of 93.17%, precision (PRE) of 94.21%, recall (REC) of 91.76%, and F1-Score (F1-S) of 92.60% on the EduNet dataset when estimated by 5-fold cross-validation. The system provides ACC =83.33%, PRE =89.25%, REC =83.32%, and F1-S =82.14% when applied to independent testing, which ensures reliability. The study has significant methodological implications for the automated recognition of classroom activities and may assist in providing information about classroom behaviors that are worthy of inclusion in the evaluation of education quality.