An intelligent e-learning platform leveraging AI and gamification to deliver personalized learning experiences. Developed as a Software Engineering Capstone Project.
- Key Features
- Technology Stack
- Installation
- Configuration
- API Documentation
- Contributing
- License
- Acknowledgements
- Contact
- Adaptive Learning Paths
Machine learning models (Scikit-learn) analyze student performance to recommend personalized content - Smart Quiz Generation
NLP-powered question generation using Hugging Face transformers - Predictive Analytics
Early identification of at-risk students using regression models
- Dynamic leaderboards with Elo rating system
- Achievement badges (Bronze, Silver, Gold tiers)
- XP points system with daily challenges
- Progress visualization using D3.js charts
- Multi-role access control (Student/Instructor/Admin)
- Course management system with rich text editor
- Real-time discussion forums with WebSocket integration
- Automated certificate generation (PDF/PNG)
- Comprehensive analytics dashboard
| Component | Technologies |
|---|---|
| Frontend | HTML5, CSS3, JavaScript |
| Backend | Python 3.10, Django 4.2, Django REST Framework |
| Database | PostgreSQL 15 |
| AI/ML | Scikit-learn 1.3, Hugging Face Transformers, spaCy 3.7 |
| DevOps | Docker 24.0 |
| Security | JWT Authentication, OWASP ZAP, Let's Encrypt SSL |
We welcome contributions! Please follow these steps:
Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open a Pull Request
See CONTRIBUTING.md for detailed guidelines.
Distributed under the MIT License. See LICENSE for more information.
Md Mahfuzur Rahman Shanto
Proudly developed as part of SE 331 - Software Engineering Design Capstone (February 2025) Team Members:
- Md Mahfuzur Rahman Shanto (221-35-917 E1)
- Md Fahim Abdullah Danial (221-35-864 D1)