Machine Learning for Enhanced Student Learning and Engagement in Interactive Classrooms

Forecasting student performance is one of the most talked-about concerns in the world of education. Productive educational programme rely on maintaining an adequate comprehension of how students behave and engage in the classroom. This study analyses the use of facial expression and body language recognition for gathering and analyzing student responses to incidents happening in the classroom. The goal is to provide a comprehensive overview of proposed strategies and algorithms applied to create an instant system that can effectively perceive and translate facial and body movements, revealing significant details about student's emotional states and participation. The proposed model, showcase a superior recognition accuracy of 70.01%.