https://dmccreary.github.io/graph-lms/
A research and documentation project exploring graph-based approaches to Learning Management Systems, focusing on AI-driven hyper-personalized learning experiences and sophisticated content recommendation engines.
We promote the use of emerging technologies to aid human learning, focusing on how to represent knowledge that can be used to create real-time hyper-personalized learning experiences for everyone.
- Graph-Based Data Models: Comprehensive JSON models representing LMS entities and relationships
- Interactive Visualizations: Web-based simulations demonstrating learning graph concepts
- Architecture Documentation: Detailed system design for graph-based LMS implementations
- Concept Frameworks: Learning trajectories, content graphs, and xAPI integration guides
- Development Tools: Python scripts for graph analysis and visualization
- Concept Graphs: Mapping knowledge relationships and prerequisites
- Content Graphs: Structuring educational materials and resources
- Learning Trajectories: Personalized pathways through educational content
- Experience API (xAPI): Standardized learning activity tracking
- Learning Record Store (LRS): Data storage and analytics
- Graph-based Integrated Learning Architecture (ILA): Modern educational system design
- Concepts - Learning graphs, trajectories, and xAPI fundamentals
- Architecture - Core system designs and components
- Data Models - Graph representations with vis.js visualizations
- MicroSims - Interactive demonstrations and templates
- Prompts - AI prompts for educational content generation
- Python 3.x
- Conda (recommended) or pip
# Clone the repository
git clone https://github.com/dmccreary/graph-lms.git
cd graph-lms
# Create and activate conda environment
conda create -n mkdocs python=3
conda activate mkdocs
# Install dependencies
pip install mkdocs "mkdocs-material[imaging]"
# Serve locally at http://localhost:8000
mkdocs serve
# Build static site
mkdocs build
# Deploy to GitHub Pages
mkdocs gh-deploy# Visualize graph data models with NetworkX
cd src/view-data-model
python view-networkx.pygraph-lms/
├── docs/ # Documentation content
│ ├── concepts/ # Learning concepts and frameworks
│ ├── arch/ # System architecture
│ ├── sims/ # Interactive simulations
│ └── view-data-model/ # Data model visualizations
├── src/ # Source code and tools
│ ├── view-data-model/ # Graph data and Python scripts
│ └── tools/ # Utility scripts
├── data-models/ # Core data model definitions
└── mkdocs.yml # Site configuration
- Universal Education - The right to education is a fundamental human right
- Open Access - Promoting free education around the world
- AI-Driven Learning - Leveraging advanced AI and ML technologies
- Graph-Based Knowledge - Using knowledge graphs for personalized recommendations
- Privacy-First - Building advanced AI tutors while maintaining student privacy
- Documentation: MkDocs with Material theme
- Visualizations: vis.js for graph rendering, p5.js for interactive simulations
- Data Analysis: Python with NetworkX and matplotlib
- Deployment: GitHub Pages
This project is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
See LICENSE for full license details.
This project is made possible by the following open source software:
- MkDocs - Static site generator for project documentation
- Material for MkDocs - Beautiful and responsive documentation theme
- Python - Programming language and ecosystem
- NetworkX - Graph analysis and visualization library
- Matplotlib - Plotting and visualization library
- PyMdown Extensions - Markdown extensions for enhanced formatting
- Vis.js - Dynamic network visualization library
- p5.js - Creative coding platform for interactive simulations
- Cairo Graphics - 2D graphics library for social card generation
- Experience API (xAPI) - Learning technology interoperability standard
- Creative Commons - Open licensing framework
We welcome contributions to improve the documentation and add new educational content. Please feel free to submit issues and pull requests.
Created by Dan McCreary - Feel free to reach out on LinkedIn for questions or collaboration opportunities.
Building the future of personalized learning through graphs, LLM and advanced AI technologies