View Proposal
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Proposer
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Aigerim Khairullina
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Title
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AI-Powered Student Engagement Monitoring Platform
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Goal
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To develop a localized, privacy-preserving software platform that monitors and classifies student engagement levels in real-time during a simulated learning environment.
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Description
- Using a standard laptop webcam, the system captures live video feeds via OpenCV, tracks multiple participants with unique IDs using the YOLO framework, and extracts detailed facial coordinates (head pose, eye state, and gaze direction) via the MediaPipe Face Mesh library. The core of the project involves building an autonomous pipeline where the student selects and implements an appropriate Machine Learning (e.g., Random Forest, XGBoost) or Deep Learning (e.g., LSTM Neural Networks) classification model using Scikit-learn or PyTorch/TensorFlow to accurately predict focus metrics and render them on an interactive dashboard.
- Resources
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Background
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Url
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Difficulty Level
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High
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Ethical Approval
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Full
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Number Of Students
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1
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Supervisor
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Aigerim Khairullina
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Keywords
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machine learning, mediapipe, yolo, engagement detection, real-time analytics
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Degrees
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Bachelor of Science in Computational Sciences and Software Engineering