CodeNexus

A cutting-edge platform designed to empower college students with personalized learning paths, AI-driven assessments, and employability support. The platform offers structured courses, daily challenges, career guidance, and skill-based assessments to enhance student learning and career growth.

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Table of Contents

• Overview
• Features
• Tech Stack
• Installation
• Usage
• User Flow
• API Documentation
• Contributing
• License

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Overview

The Personalized Learning Platform helps college students by providing:

1. Personalized Learning Paths: AI-powered suggestions based on user interests and skill levels.
2. Daily Challenges: Adaptive tasks and exercises tailored to a student's current progress.
3. AI-Driven Assessments: Continuous evaluation of user skills with actionable feedback.
4. Career Guidance: Personalized course recommendations, project suggestions, and career pathways.
5. Employability Support: Tools to improve resumes, prepare for mock interviews, and showcase work on GitHub or LinkedIn.

This platform aims to bridge the gap between academia and the job market by offering an engaging, adaptive learning experience.

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Features

• Personalized Learning Paths:

  • Structured paths with modules, submodules, and lessons.
  • Examples include Full Stack Development, Data Structures and Algorithms (DSA), and DevOps.

• Daily Challenges:

  • AI-generated challenges based on user progress.
  • Difficulty adapts as the user advances through the learning path.

• AI-Powered Assessments:

  • Evaluates user skill level: Beginner, Intermediate, or Advanced.
  • Provides periodic quizzes, coding tests, and performance analysis.

• Career Path Recommendations:

  • Suggests supplementary courses, certifications, or career shifts.
  • Tracks employability progress through practical tasks and soft skills evaluation.

• Employability Support:

  • Resume-building tips.
  • Mock interviews and personalized project suggestions.

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Tech Stack

Frontend

• React.js / Next.js: To build a dynamic and interactive user interface.
• TailwindCSS: For fast styling and responsive design.

Backend

• Node.js with Express: Handles user authentication, data management, and serves API endpoints.
• Django (Optional): For a more scalable solution with robust ORM and admin interface.

Database

• PostgreSQL: A relational database to store user data, learning paths, challenges, and assessments.
• MongoDB: Optional for storing AI model-related data (such as recommendation results and logs).

AI/ML

• TensorFlow / PyTorch: For building and deploying machine learning models for assessments, recommendations, and content generation.
• Scikit-learn: For simpler models and feature engineering.

Authentication

• JWT / OAuth / NextAuth: For user authentication and session management.

Hosting

• Vercel: For hosting the frontend.
• AWS / Heroku: For backend and database hosting.

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Installation

Prerequisites

Before you begin, ensure you have the following installed:

• Node.js (v14 or higher)
• Python (for ML models)
• PostgreSQL or MongoDB for database setup
• Git for version control

1. Clone the Repository

git clone https://github.com/moiz2405/personalized-learning-platform.git
cd personalized-learning-platform

2. Install Dependencies

Frontend (React/Next.js)

cd frontend
npm install

Backend (Node.js/Express)

cd backend
npm install

3. Set up the Database

• PostgreSQL: Create a database and configure connection settings.
• MongoDB: Set up a MongoDB instance for AI data storage.

4. Run the Application

Frontend (React.js/Next.js)

cd frontend
npm run dev

Backend (Node.js/Express)

cd backend
npm start

The application should now be running at http://localhost:3000 for the frontend and http://localhost:5000 for the backend.

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Usage

1. Registration & Onboarding

• Users create an account by providing basic details and selecting their learning interests and skill levels.
• Based on the input, the platform assigns a default learning path or lets the user choose one.

2. Navigating Learning Paths

• Users can explore structured learning paths, each with modules, submodules, and tasks.
• Paths can range from Full Stack Development to DevOps and Data Science.

3. Daily Challenges

• The system generates daily challenges based on the user's learning path and progress.
• Challenges increase in difficulty to match the user's growing skill level.

4. AI Assessments

• AI evaluates user performance on quizzes and tasks, classifying them as Beginner, Intermediate, or Advanced.
• Feedback is provided to help users improve.

5. Course & Career Recommendations

• After each assessment, the AI recommends supplementary courses or suggests career paths.
• It also tracks soft skills development through activities and tasks.

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User Flow

1. Registration:

   • Users sign up, select their skill level, and choose learning paths.

2. Dashboard:

   • View progress, upcoming challenges, and assessment results.

3. Learning Journey:

   • Explore modules/submodules, complete daily challenges, and improve skills.

4. AI Recommendations:

   • Based on performance, the AI suggests new courses or career opportunities.

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API Documentation

Authentication

• POST /auth/signup – Register a new user.
• POST /auth/login – User login with JWT token.

Learning Path

• GET /paths – Retrieve available learning paths.
• GET /path/{path_id} – Retrieve details of a specific learning path.

Challenges

• GET /challenges/today – Get today's challenge for the user.
• POST /challenges/submit – Submit answers to a challenge.

Assessments

• POST /assessment/submit – Submit quiz/test results for evaluation.
• GET /assessment/result – Retrieve assessment result and feedback.

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Contributing

We welcome contributions to enhance and extend the platform. Here’s how you can contribute:

1. Fork the Repository: Create your own fork of this repository.
2. Clone Your Fork:

   git clone https://github.com/YOUR_USERNAME/personalized-learning-platform.git

3. Create a Branch:

   git checkout -b feature-name

4. Commit Changes:

   git commit -m "Your commit message"

5. Push to Your Fork:

   git push origin feature-name

6. Create a Pull Request: Open a pull request to the main repository.

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License

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

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This README provides a detailed yet easy-to-follow guide for developers, contributors, and users to understand, install, and use your Personalized Learning Platform. Let me know if you'd like to add or modify any sections!