Real-time Student Engagement Monitoring in Classroom Environments using Machine Learning and Computer Vision

This study explores the use of machine learning and computer vision technology to monitor student engagement in a classroom setting. The study aims to address the challenges faced by educators in monitoring and improving student participation, attentiveness, and conduct during classroom sessions. The integration of machine learning and computer vision seeks to provide real-time insights and actionable data for educators to enhance the learning environment and student outcomes. The study will involve the development and implementation of a computer vision system capable of capturing and analyzing visual cues, such as pose estimation, body language, and overall activity levels, to gauge student engagement and behaviour. The system will leverage image processing and machine learning algorithms to interpret and categorize these cues, providing a comprehensive assessment of individual and collective student dynamics. In conjunction with the computer vision component, the system will be designed to process the data generated by the visual analysis and provide meaningful feedback to educators. By examining the impact of machine learning and computer vision on student engagement and behaviour, this study aims to contribute to the advancement of educational technology and pedagogical strategies. The findings and outcomes of this study have the potential to inform the development of more effective teaching methodologies and support systems, ultimately benefiting both educators and students in diverse learning environments.