Investigation of Student Engagement Monitoring System using Machine Learning

This research study aims to enhance the intelligent classroom ecosystem by integrating the Local Binary Pattern Histogram (LBPH) algorithm with human expertise to identify students' faces. Monitoring student attentiveness is crucial for effective learning. To support this, this study uses Mediapipe to analyze students' body posture, providing insights into their engagement levels. Additionally, the proposed framework includes the Eye Aspect Ratio (EAR) Algorithm to detect signs of drowsiness and yawning. By continually refining the proposed framework, this study intends to improve intelligent classroom ecosystems, helping educators to create environments that increase student success.