LADDER - Adaptive Learning Dynamics: A Machine Learning Approach to Personalized Education Through Behavioral Analysis

Stanford TreeHacks Hackathon Project -- Further adapted into a research paper

Author

Raj Kashikar
Email

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Overview

LADDER (Adaptive Learning Dynamics) is an advanced AI-powered Learning Management System (LMS) designed to personalize educational experiences by adapting to individual learning styles through behavioral analysis. Utilizing machine learning models similar to NVIDIA's Merlin for recommendations and Large Language Models (LLMs) like Llama 70b for content tagging, LADDER customizes learning pathways for each student. This project leverages TensorFlow, Next.js, and React.js to create a sophisticated, responsive educational platform.

Key Features

🌟 Advanced Personalization

• ML Models: Utilizes advanced machine learning models to analyze student behaviors and learning patterns.
• LLM Tagging: Employs Llama 70b for sophisticated tagging and analysis of educational content.
• Synthetic Student Profiles: Trains on 100,000 synthetic student profiles, representing diverse learning distributions.

⚡ Cutting-Edge Technology

• TensorFlow Deep Retrieval: Implements TensorFlow for deep learning-based recommendations.
• Modern Tech Stack: Built using Next.js and React.js for a seamless and interactive user experience.

📊 High Performance

• Accuracy: Achieves an initial accuracy of 92% in matching student profiles to effective learning strategies.
• Real-time Adaptation: Continuously adapts to student needs in real-time, optimizing learning pathways.

Installation

Prerequisites

• Node.js
• Python 3.x
• TensorFlow
• Next.js
• React.js

Setup

1. Clone the Repository

   git clone https://github.com/yourusername/ladder.git
   cd ladder

2. Install Dependencies

   npm install
   pip install -r requirements.txt

3. Run the Application

   npm run dev

Usage

Running the LMS

To start the LMS, execute:

npm start

Example Code

Here is an example of how to use the LADDER system for personalized learning recommendations:

from ladder import RecommendationEngine

# Initialize the recommendation engine
engine = RecommendationEngine()

# Get recommendations for a student profile
student_profile = {
    "learning_style": "visual",
    "engagement_level": "high",
    "performance_metrics": {
        "math": 85,
        "science": 90
    }
}

recommendations = engine.get_recommendations(student_profile)
print(recommendations)

Architecture

Data Collection and Synthesis LADDER integrates data synthesis, generating 100,000 synthetic student profiles through probabilistic modeling and machine learning algorithms. Utilizing Gaussian mixture models and Bayesian networks, the system simulates diverse student learning paths for precise personalization.

Neural Network Architecture

The core of LADDER's framework is a sophisticated neural network incorporating convolutional neural networks (CNNs) for pattern recognition and recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) units for sequence prediction. Transfer learning techniques enhance the model’s generalization across diverse learning scenarios.

Algorithmic Learning Style Identification

LADDER employs clustering algorithms like K-means and DBSCAN to categorize learners based on interaction patterns and performance metrics. Reinforcement learning strategies then tailor educational pathways in real-time.

License

This project is licensed under the Apache License, Version 2.0. See LICENSE for more details.