Analyzing Classroom Behavior to Enhance Student Engagement and Learning Outcomes

This research presents a high-tech smart system that utilizes video records to monitor student behavior in classroom settings and identify specific activities like raising hands and dozing. The uneven frequency of certain behaviors along with variances in student posture, seating arrangements, and video quality make it extremely difficult to identify these behaviors in a live classroom. While some behaviors, like raising one's hand, may happen frequently and in a variety of ways, others, like napping, may happen less frequently but are just as crucial to comprehending student engagement. We use a sizable annotated dataset, in which bounding boxes are constructed around significant objects of interest in the video frames, to get over these difficulties. Our system's core is a convolutional neural network (CNN), which we have specially tuned for behavior identification. The CNN model is made to quickly evaluate the visual data and recognize characteristics and patterns connected to certain student behaviors in real time. We make sure the model can accurately identify actions like raising one's hand and falling asleep even when there are variations in lighting, seating configurations, and video quality by training it with a large dataset. Our model is specifically designed to achieve high accuracy in identifying minute cues that distinguish one movement from another, in contrast to conventional methods that might find it difficult to comprehend the intricacy of these actions.