BlackPearl-SIH

🎓 Early Student Dropout Detection Solution

A Smart India Hackathon Project Aligned with NEP 2020

Revolutionizing education by detecting early signs of student dropouts, providing financial aid, and offering flexible schooling options to empower students and reduce dropout rates.

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🌟 Key Features

1. 🧠 AI Early Dropout Detection System
   Leverages AI to analyze student performance, reviews, and teacher remarks, identifying at-risk students and enabling timely support.

2. 💸 Financial Aid Management
   Connects students with government schemes and efficiently manages funds to ensure every student receives the financial help they need.

3. ⏰ Flexible Schooling
   Allows students to choose flexible scheduling or part-time study options, minimizing constraints and promoting retention.

4. 👪 Parental Engagement Portal
   Keeps parents in the loop by providing insights into their child’s progress, struggles, and achievements, fostering strong family support.

5. 🛠️ Student Support System
   Provides resources and guidance for students' academic and personal growth through interactive support tools and virtual reality modules.

6. Immersive Learning with VR School
   A sub-module of our solution, VR School provides an interactive and engaging way for high school students to learn key subjects through immersive educational content.

Modules Covered:

• Physics, Chemistry, Biology, and Social Studies
  Each module presents topics using high-definition 3D models that students can interact with in real time, deepening their understanding of complex concepts.

Learning Flow:

1. Intro Page: Students begin their journey with an introductory screen.
2. Module Selection: On the index page, students select the module they wish to explore.
3. Immersive Content: Students engage with interactive 3D models, gaining a hands-on understanding of the subject matter.
4. Recommendation Page: After completing a module, students receive suggestions for related topics to continue their learning.

Technologies Used:

• 3D Modeling: Environments created and modified using Blender3D.
• Animation and Interaction: Developed using C# in Unity3D.
• Recommendation System: Powered by Python in Jupyter Notebook.
• Deployment: Application deployed on Oculus Quest 2 using SideQuest's App Lab.

📸 Snapshots of VR School

Intro Page

Index Page

Module Previews

Module 1: Solar System

Module 2: Atoms

Module 3: Chemical Bonding

Module 4: Food Chain

Recommendation Page

🎥 Demonstration Video

Check out our demo video to see the VR School in action:
Watch Video

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🛠️ Technology Stack

• AI & Machine Learning: TensorFlow
• Frontend Development: React, Flutter
• Backend Development: MongoDB, Google Cloud Platform (GCP)
• Virtual Reality: Unity

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🚀 Our Approach

1. AI Early Dropout Detection System

• 📊 Data Collection: Aggregate data on student performance, attendance, teacher feedback, and reviews.
• 🧮 Model Development: Utilize TensorFlow to create a machine learning model that detects dropout risks.
• 🔍 Real-Time Analysis: Deploy the model on GCP for real-time monitoring and early warning alerts.

2. Financial Aid Management

• 📑 Government Integration: Seamless integration with various government schemes using backend technologies (MongoDB and GCP).
• 🏦 Automated Fund Allocation: Smart automation to distribute funds fairly and transparently.

3. Flexible Schooling

• 📅 Adaptive Scheduling: Built with Flutter, allowing dynamic schedule adjustments and part-time study options.
• 📱 User Interface: Intuitive, responsive interfaces using React for easy access on any device.

4. Parental Engagement Portal

• 📞 Communication Hub: A React-based portal for parents to receive updates, notifications, and track student progress.
• 📈 Regular Insights: Automated analytics and progress reports to keep parents informed.

5. Student Support System

• 🤖 AI Chatbot: Powered by TensorFlow and Flutter, helping students resolve queries and providing instant support.
• 🌍 Immersive VR Learning: Unity-based modules for interactive learning experiences, enhancing understanding through VR.

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📝 Getting Started

1. Clone the Repository

   git clone https://github.com/yourusername/early-dropout-detection.git
   cd early-dropout-detection

2. Frontend Setup

   cd frontend
   npm install
   npm start

3. Backend Setup

   cd backend
   npm install
   node server.js

4. AI Model Deployment

   pip install tensorflow
   python model_training.py

5. VR Module Setup

   • Import the project files into Unity from the vr-module directory.

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🤝 Contributing

We ❤️ contributions! Check out our Contributing Guidelines to get started!

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

📧 Contact Us

Have questions? Reach out to us at [email protected]

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🎯 Join us in transforming education and ensuring every student has the opportunity to succeed!