Learning Graph

Welcome to the Learning Graph for Data-Driven Ethics and Systems Change. This learning graph provides a structured roadmap of 250 concepts that students will master throughout the course.

What is a Learning Graph?

A learning graph is a Directed Acyclic Graph (DAG) that represents the relationships between concepts in a course. Each node represents a concept, and edges represent prerequisite dependencies - showing which concepts must be understood before others.

Run the Learning Graph Viewer

Course Overview

This learning graph contains:

• 250 Concepts organized into 13 categories
• 405 Dependencies showing learning pathways
• 5 Foundational Concepts that serve as entry points

Taxonomy Categories

The concepts are organized into the following categories:

Category	Count	Description
Foundation Concepts	25	Core ethics, research methods, and critical thinking
Harm Measurement	30	DALYs, social costs, and impact assessment
Data Gathering	25	Ethical data collection and bias detection
Systems Foundations	26	Feedback loops, stocks and flows
System Archetypes	22	Common patterns like Tragedy of the Commons
Market and Power	17	Market failures and power dynamics
Industry Cases	25	Tobacco, fossil fuels, fast fashion studies
Leverage Points	20	Donella Meadows' intervention framework
Advocacy Strategies	17	Policy design and movement building
Behavioral Economics	10	Nudges and choice architecture
Communication	9	Data visualization and persuasion
Capstone and Reform	6	Reform proposals and project skills
Corporate Responsibility	20	ESG, sustainability, accountability

Learning Pathways

The graph supports multiple learning pathways:

1. Systems Track: Complex Systems → Systems Thinking → System Archetypes → Leverage Points
2. Measurement Track: Ethics → Harm Definition → DALYs → Industry Comparisons
3. Advocacy Track: Behavioral Economics → Policy Design → Movement Building → Reform Proposals

Files in This Section

• Course Description Assessment - Quality analysis of course description
• Concept List - Complete list of 250 concepts
• Concept Taxonomy - Category definitions
• Quality Metrics - Graph validation report
• Taxonomy Distribution - Category distribution analysis

Technical Files

• learning-graph.csv - Concept dependencies in CSV format
• learning-graph.json - Full graph in vis-network.js JSON format
• metadata.json - Course metadata

Next Steps

After reviewing the learning graph:

1. Review the concept list for completeness and accuracy
2. Check dependencies ensure learning pathways make sense
3. Run the book-chapter-generator skill to create chapter structure
4. Generate chapter content using the chapter-content-generator skill