Students Attention and Engagement Prediction Using Machine Learning Techniques
European Journal of Molecular & Clinical Medicine,
2020, Volume 7, Issue 4, Pages 3011-3017
AbstractReal-time student engagement tracking is an important step towards education. Current approach doesn’t consider student engagement detection using biometric features. In this project, we propose a hybrid architecture invoking student’s eye gaze movements, head movements and facial emotion to dynamically predict student attention and engagement level towards the tutor and based on the output value the content is changed dynamically. Hence this concept has a huge scope in e-learning, class room training, analyse human behaviour. This project covers main process like Eye Ball, facial emotion and head movements Human Beings. For feature extraction step, we used Principal Component Analysis (PCA) for facial emotion recognition, Haar Cascade for pupil detection and Local Binary Patterns for recognizing head movements and OpenCV for machine learning model generation and comparison.
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