🚀 Routex

Personalized learning path system for high school students (Grades 10–12) (Only supported Vietnamese)

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📌 Overview

Routex is a system designed to generate personalized study roadmaps based on user performance and learning goals. It focuses on optimizing study efficiency through adaptive planning and continuous evaluation.

The system supports the following subjects:

• Mathematics
• Physics
• Chemistry
• Biology Works for the following grades
• 10
• 11
• 12

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🧠 Core Features

🎯 Personalized Learning Path

• Generates study plans based on user input (grade, subject, goal, time)
• Prioritizes weak topics using performance data

📝 Mini Test System

• Weekly tests to evaluate understanding
• Questions mapped directly to specific topics

📊 Progress Tracking

• Analyze strengths and weaknesses per topic
• Visual dashboard (planned)

🔁 Adaptive Learning Loop

• Study → Test → Evaluate → Adjust
• Automatically updates learning priorities

🧩 Prerequisite Graph

• Topics linked via dependencies
• Ensures logical learning progression

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🏗️ System Workflow

User Input 
   ↓
Generate Learning Plan
   ↓
Study Topics
   ↓
Weekly Mini Test
   ↓
Performance Analysis
   ↓
Adjust Plan (priority + time)
   ↺ (loop continues)

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⚙️ Tech Stack

Frontend

• React / Next.js
• HTML / CSS

Backend

• Node.js / Express
• REST API

Data Layer

• JSON / Database for:

  • Topics
  • Questions
  • Prerequisite relationships

Core Logic

• Priority scoring algorithm
• Topic dependency resolution

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📂 Project Structure

Routex/
├── frontend/              # UI layer
├── backend/               # API + logic
├── data/                  # Static data (topics, questions)
│   ├── topics.json
│   └── questions.json
├── docs/                  # Documentation
└── README.md

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🚀 Getting Started

1. Clone repository

git clone https://github.com/tamnguyentommy-rgb/Routex.git
cd Routex

2. Install dependencies

npm install

3. Run project

npm run dev

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🧪 Example Use Case

Input:

• Subject: Math
• Grade: 11
• Mode: Exam Preparation
• Study Time: 2 hours/day

System Output:

• Personalized topic list
• Priority ranking based on difficulty
• Weekly study schedule

After 1 week:

• Mini test evaluation
• Weak topics identified
• Learning plan adjusted automatically

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📈 Future Improvements

• AI-powered recommendation engine
• Smarter topic prioritization (ML-based)
• Full analytics dashboard
• Real-time progress tracking
• Integration with external learning resources

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⚠️ Current Limitations

• Limited dataset for questions/topics

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👤 Author

• Tommy (tamnguyentommy-rgb)

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📄 License

This project is licensed under the MIT License.