Student Monitoring System

Overview

This project provides a computational framework for detecting and analyzing students' continuous emotions in a real-time online classroom environment.
It features a video conferencing system developed using WebRTC, combined with a Convolutional Neural Network (CNN) model to predict emotional valence and arousal values of students.
These predicted values are further mapped to five key emotional states — neutral, bored, confused, hopeful, and curious — that are crucial in the context of online learning environments.

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Results

Below are some screenshots of the real-time student emotion monitoring dashboard developed as part of this project:

Real-Time Monitoring

• Displays live emotional states of each student.
• Tracks continuous valence and arousal values per participant.
• Reports five learning centered emotional states: Neutral, Bored, Confused, Hopeful, Curious.

Summary View

• Aggregates emotional states across all students.
• Provides overall classroom emotion trends during the session.
• Useful for instructors to assess collective engagement and adjust teaching strategies.

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The dashboard is designed to assist online instructors by offering both individual real-time tracking and aggregate session analysis to better understand students' emotions during classes.

Installation

1. Clone the repository:

   git clone https://github.com/innocentmchry/studentmonitoring.git
   cd studentmonitoring

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install the project dependencies:

   pip install -r requirements.txt

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Running the Project

1. Development Server (for testing locally)

Start the Django development server:

python manage.py runserver

By default, it runs on http://127.0.0.1:8000/.

Make sure you are inside the directory containing manage.py when you run this.

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2. Production Server (using Gunicorn)

First, ensure Gunicorn is installed.

Then, run the project using Gunicorn:

gunicorn studentmonitoring.wsgi:application --bind 0.0.0.0:8000 --workers 3

• studentmonitoring is the Django project folder name.
• --workers 3 suggests using 3 worker processes (you can adjust based on CPU cores).
• --bind 0.0.0.0:8000 makes the server available publicly if deployed on a remote machine.

You can combine it with Nginx for reverse proxying and Supervisor for process management for production environments (optional).

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Deployment Notes

• Set DEBUG = False in your Django settings.py during production.
• Set up ALLOWED_HOSTS properly in settings.py.
• Collect static files:

  python manage.py collectstatic

• Use a production-grade database (e.g., PostgreSQL) if scaling beyond local use.
• Configure SSL if deploying publicly.

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How to Cite

If you use this code or build upon it, please cite the following:

@ARTICLE{10969593,

  author={Mochahari, Innocent Dengkhw and Brahma, Sanjiu Raja and Maity, Ranjan},

  journal={Complex System Modeling and Simulation}, 

  title={An approach to detect and analyze continuous emotions in a real-time online classroom}, 

  year={2025},

  volume={},

  number={},

  pages={1-16},

  keywords={Real-time systems;Face recognition;Videoconferences;Electronic learning;Education;Computational modeling;Analytical models;Streaming media;Feature extraction;Face detection;Real-time emotion analysis;Dimensional emotion analysis;Classroom analytics;Post-class summary;Emotion-aware video conferencing},

  doi={10.23919/CSMS.2025.0008}}