Student Live Behaviour Monitoring System in Virtual Classes

Abstract

The shift from traditional classrooms to virtual learning environments during global health crises has transformed the educational landscape, emphasizing the need for technological adaptation among students and educators. While platforms such as Zoom, Google Meet, Microsoft Teams, and others have enabled the continuity of education, they also present new challenges in monitoring student engagement and behavior. This study introduces a Student Live Behaviour Monitoring System designed to support educators by providing real-time insights into student activity during virtual sessions. By leveraging artificial intelligence techniques, the system tracks indicators such as attention levels, posture, drowsiness, and active participation to assess behavioral patterns. Survey data indicates that although a majority of users did not experience negative academic impact, only a small fraction observed academic improvement, highlighting the need for additional support mechanisms. The proposed system acts as an intelligent assistant, helping educators identify disengaged or distracted students, thus enabling timely intervention. This innovation not only enhances student involvement but also promotes critical thinking and academic performance, ensuring a more effective and interactive remote learning experience.

Introduction

Overview:
The study presents an AI-driven system designed to monitor student behavior in real-time during virtual classes using computer vision and deep learning. It aims to detect levels of engagement and emotional states by analyzing facial expressions and physical cues, helping educators identify inattention and intervene promptly to improve academic outcomes.

Key Objectives:

• Monitor student attentiveness in virtual classrooms.

• Detect behaviors like yawning, drowsiness, distraction, and mobile phone usage.

• Provide real-time feedback to educators for timely intervention.

• Improve the effectiveness and quality of remote education.

Technologies Used:

• Facial Expression Recognition: Central to detecting cognitive and emotional engagement based on academic emotion theory.

• YOLOv3: Used for efficient real-time face detection and tracking.

• Dlib + OpenCV: For facial landmark detection and behavior analysis.

• Deep Learning Models (CNNs, RNNs): For accurate expression recognition and temporal behavior tracking.

• Flask Web App + WebRTC + Socket.IO: Enables real-time video feed processing and user interface integration.

Literature Insights:

• Prior work highlights the role of emotion-aware systems and deep learning in analyzing student engagement.

• Challenges identified include lighting issues, camera quality, and real-time performance.

• Researchers suggest YOLOv3 and lightweight models for efficient processing in virtual environments.

Methodology:

A. Data Collection

• Real-time webcam feeds simulate classroom conditions.

• Behavior labels include yawning, drowsiness, distraction, mobile usage, and “no face detected.”

• Training data sourced from OpenCV, Kaggle, and custom annotations.

B. Preprocessing

• Grayscale conversion, frame resizing, and 68-point facial landmark alignment.

C. Behavior Detection

• Rule-based thresholds:

  • EAR < 0.2 → Drowsy

  • MAR > threshold → Yawning

  • Head angle > 30° → Distraction

  • Detected object near ear → Phone usage

D. Real-Time Processing

• Frames are captured via browser and analyzed on the backend using Flask.

• Detected behaviors are visualized on a dashboard with live alerts and color-coded indicators.

• Events are logged with timestamps for later review.

E. Deployment

• A secure web app enables educators to view live behaviors, alerts, and download behavior logs.

• Includes login authentication and user registration to protect student data.

Results and UI Design:

• Home & Login Screens: Clean, intuitive interfaces for easy access and secure user management.

• Live Camera Screen: Real-time behavior graph with visual cues (e.g., Brow, Yaw, Pos).

• Alerts & Logs: Behavioral alerts shown on teacher dashboards; all data stored for reporting and analysis.

• The system was validated in controlled sessions to ensure stability and accuracy.

Contributions:

This project advances the field of emotion-aware learning environments and intelligent tutoring systems by:

• Providing real-time behavior detection for virtual classrooms.

• Offering educators actionable insights to support student engagement.

• Creating a scalable and deployable framework for remote learning enhancement.

Conclusion

This research presents a real-time AI-driven student monitoring system that combines classical facial landmark analysis with rule-based behavioral inference to enhance attentiveness tracking in virtual learning environments. The proposed architecture achieves high detection accuracy (94%) in identifying behaviors such as drowsiness, yawning, distraction, and mobile phone usage. It demonstrates the effectiveness of lightweight, domain-tuned models for low-latency deployment in real-time educational settings.

References

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Copyright

Copyright © 2025 Koyyada Navadeep, Mohd Irfan, Khushi Chawda, J. Sudhakar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.