A Survey of Live Engagement Detection Using Real-Time Video Processing

Registration ID: IJNRD_304351

Published ID: IJNRD2503120

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Swathi S, Amith Kurian Joseph, Aadhil Muhammed, Arun Kumar S, Adil Bin Anwar

Keywords Real Time Engagement Detection, CNN, ResNet, Jitsi Meet, SMOTE,EfficientNetB0, Online Learning , Facial Feature Analysis , DAiSEE, Deep Learning, Machine Learning, Real time

The rapid growth of online education has necessitated the development of tools to monitor and enhance student engagement during virtual classes. This study focuses on predicting student engagement levels using video data from the DAiSEE dataset, which captures students facial expressions and behaviors. By analyzing these visual cues, the system aims to classify engagement into three distinct categories are Engaged high, Engaged low, and Engaged not listening. The primary objective is to provide real-time feedback to educators, enabling them to address disengagement and improve the overall learning experience. The proposed system leverages a combination of deep learning and machine learning techniques. Video frames are preprocessed and fed into a pretrained EfficientNetB0 model for feature extraction, generating a 1280-dimensional feature vector for each frame. These features are then used to train a custom neural network with a fully connected architecture, consisting of an input layer, a hidden layer with 1024 units, and an output layer with softmax activation for multi-class classification. The dataset is balanced using SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance. The entire pipeline is integrated with the Jitsi Meet platform, enabling real-time engagement prediction during online classes. The model achieved higher accuracy, demonstrating its effectiveness in classifying student engagement levels. These results indicate that the system can reliably identify engagement patterns and provide actionable insights to educators. By integrating this solution into video conferencing platforms, educators can proactively address disengagement, fostering a more interactive and productive learning environment. This study highlights the potential of AI-driven tools in revolutionizing online education and improving student outcomes.

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Swathi S, Amith Kurian Joseph, Aadhil Muhammed, Arun Kumar S, & Adil Bin Anwar (March-2025). A Survey of Live Engagement Detection Using Real-Time Video Processing. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), b170-b176. https://ijnrd.org/papers/IJNRD2503120.pdf

Volume 10 Issue 3, March-2025

Pages : b170-b176

Paper Reg. ID: IJNRD_304351

Published Paper Id: IJNRD2503120

Research Area: Science and Technology

Author Type: Indian Author

Country: Kollam, Kerala, India

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