Automated Classroom Dynamics Analysis using YOLOv8-based Posture Detection for Enhanced Student Engagement

Abstract

This research introduces a cutting-edge approach to monitoring student engagement and concentration in classroom environments using advanced computer vision techniques. By integrating Head Pose Estimation (HPE) and YOLOv8, the study focuses on analyzing student postures in real-time, offering a precise assessment of concentration levels during lectures. A specialized dataset of annotated classroom images was curated to train the YOLOv8 model, categorizing postures such as 'bow,' 'down,' 'lookup,' 'sit,' and 'sleep.' This model goes beyond traditional face detection or crowd estimation methods by providing a detailed analysis of individual student behaviors, allowing educators to gain deeper insights into student engagement. The system's real-time monitoring capabilities enable continuous assessments and immediate feedback, empowering educators to adapt their teaching strategies dynamically based on observed concentration levels. Integration with live camera feeds enhances classroom management and instructional methods, creating a more responsive and effective learning environment. The potential of this approach to improve educational outcomes is significant, as it allows for tailored interventions and supports the mental well-being and academic success of students. Future research will focus on enhancing model accuracy, scaling the system for larger class sizes, and incorporating facial expression analysis to refine the understanding of student engagement further and optimize teaching practices.