E-Academy : Secure E-Learning Management System With Content Encryption and Student Performance Prediction Using Neural Networks

Overview

E-Academy is a comprehensive e-learning platform that incorporates advanced security features, content encryption, and student performance prediction capabilities. The platform provides separate interfaces for students and teachers, enabling efficient learning management and performance tracking.

Key Features

• Secure user authentication system
• Role-based access control (Student/Teacher)
• Encrypted content delivery
• Student performance prediction using Neural Networks
• Interactive dashboards for performance visualization
• Course management system
• Assignment tracking and submission
• Secure study material distribution

E-Academy Repositories

• thearmanqureshi : On this GitHub Account the repositories were created to be deployed on Render

  • thearmanqureshi/E-Academy : Contains the main project
  • thearmanqureshi/E-Academy-Proxy : Contains the E-Academy Proxy Webpage
  • thearmanqureshi/E-Academy-Model-Host : Contains the tensorflow-lite model and scaler for the prediction model

• anshh-arora/E-Academy : On this GitHub Account's Repositorie our team members worked on the E-Acaedmy Project

Platform Screenshots

E-Academy Proxy

E-Academy Proxy Webpage that enhances users experience and displays a message until the Render services are started (No longer needed as Render has implemented a browser page load)

Homepage

E-Academy homepage featuring sign-in and registration options

Student Home Page

Student interface showing courses, assignments, and study materials

Teacher's Dashboard

Comprehensive teacher dashboard with student performance metrics

Performance Prediction Model

Neural Network-based performance prediction visualization

Project Architecture

graph TD
    A[User Access] --> B{Authentication}
    B -->|Student| C[Student Home Page]
    B -->|Teacher| D[Teacher Home Page]
    C --> E[Courses]
    C --> F[Assignments]
    C --> G[Study Materials]
    D --> H[PowerBI Dashboard]
    D --> I[Assignment Management]
    D --> J[Student Prediction Model]
    

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Technology Stack

• Frontend: HTML, CSS & JavaScript
• Backend: Python (Flask)
• Database: MongoDB Atlas
• Analytics: PowerBI
• Machine Learning: Neural Networks & TensorFlow Lite
• Security: Flask Bcrypt

Repository Structure

E-Academy/
│
├── static/                 # Static files (CSS, JS, Images)
│   ├── css/                # Stylesheet files
│   ├── js/                 # JavaScript files
│   └── images/             # Image assets
│
├── templates/              # HTML templates
│   ├── index.html          # Home template
│   ├── student.html        # Student Home template
│   ├── teacher.html        # Teacher's Dashboard template
│   └── ...
│
├── app.py                  # Contain python flask app
├── config.py               # Configuration settings
├── wsgi.py                 # WSGI entry point
├── requirements.txt        # Project dependencies
├── LICENSE                 # Project's License
├── Readme.md               # Project's Readme 
└── procfile                # Procfile for Render

Installation and Setup

Prerequisites

• Python 3.8+
• MongoDB
• PowerBI Desktop (for dashboard visualization)

Clone the Repository

https://github.com/thearmanqureshi/E-Academy.git
cd E-Academy

Environment Setup

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Configure MongoDB

1. Create a MongoDB database
2. Update the connection string in config.py

MONGO_URI = "your_mongodb_connection_string"

Running the Application

python wsgi.py

The application will be available at http://localhost:5000

Team Members and Contributions

• Arman: Frontend Development & Project Deployment
• Piyush: Responsive Design Implementation
• Ansh: Neural Network Model Development
• Karan: PowerBI Dashboard and Data Visualization

Contact Information

For any questions or feedback, feel free to reach out:

• Email: thearmanqureshi@gmail.com
• LinkedIn: Connect with me on LinkedIn