Behavioral Engagement Assessment in University Classrooms via Deep Learning-based Video Object Detection.

This study aims to assess students' learning engagement in university classrooms using deep learning-based video object detection. To do so, via correlation analysis, this research first identified seven classroom behaviors presenting highly positive correlation with learning engagement as indicators to measure students' learning engagement; then it collected 30 synchronized videos of real classroom teaching from 6 classes from Shandong University of Science and Technology (SDUST) and divided them into a training set and a test set. After the seven behaviors were manually annotated in the training data, a machine learning algorithm was then trained in a supervised manner on this set. Once trained, the model generated initial annotations for the remaining unlabeled data. To achieve more accurate and efficient classroom behavior recognition, this study selected two representative algorithms, namely, Faster R-CNN and YOLOv5s, for behavior detection experiments. Based on a comparison of their detection performance in terms of accuracy and time cost, YOLOv5s was selected for classroom behavior detection in this study. Finally, this study used the focus group method to assign scores to each behavior and develop a three-level learning engagement scoring model. Based on automatically measured behavioral data, the model enables real-time, automatic assessment of learning engagement at both the individual and class levels.