Student engagement measuring mechanism using computer vision — Research Link – Collected and preprocessed real classroom video datasets, applying data augmentation techniques to improve model robustness. – Developed a YOLOv8-based deep learning model to measure engagement through eye movement, gaze points, body posture, and head orientation of students. – Achieved 85% accuracy in engagement detection, validated on 2500+ annotated video frames, providing actionable insights for educators. – Tech Stack: YOLOv8, OpenCV, MediaPipe, ResNet-50, Scikit-learn, TensorFlow, Roboflow.