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Learning Graph

Introduction

Welcome to the learning graph for Modeling Healthcare Data with Graphs. This section contains comprehensive documentation of the learning graph that underpins this intelligent textbook.

A learning graph is a directed acyclic graph (DAG) that maps out the conceptual dependencies and relationships between topics in a course. It serves as the foundation for personalized learning pathways and adaptive content delivery.

What is a Learning Graph?

A learning graph is:

  • A conceptual roadmap: Shows the relationships between 200 core concepts in healthcare graph modeling
  • A dependency graph: Identifies prerequisite knowledge needed before learning each concept
  • A learning pathway guide: Enables students to navigate the course based on their current knowledge and goals
  • A pedagogical tool: Helps instructors understand the structure and flow of the curriculum

Learning Graph Components

1. Course Description Assessment

View the assessment →

Comprehensive quality analysis of the course description that scored 100/100, confirming readiness for learning graph generation with sufficient breadth and depth to support 200+ concepts.

2. Concept Enumeration

View the 200 concepts →

Complete list of 200 concepts organized into 13 categories covering graph theory, healthcare domain knowledge, analytics, AI/ML, security, and practical applications.

3. Dependency Graph (CSV)

Download learning-graph.csv

The core dependency graph in CSV format containing:

  • 200 concepts with unique IDs
  • 299 dependency relationships
  • Taxonomy classifications
  • DAG structure (no circular dependencies)

4. Learning Graph (JSON)

Download learning-graph.json

Complete learning graph in vis-network.js JSON format including:

  • Metadata (title, description, creator, license)
  • Groups (13 taxonomy categories with color coding)
  • Nodes (200 concepts)
  • Edges (299 directed dependency relationships)

5. Concept Taxonomy

View taxonomy definitions →

13 taxonomic categories organizing concepts into logical groupings:

  • Foundation Concepts (FOUND)
  • Graph Technologies (GTECH)
  • Healthcare Domain (HCARE)
  • Patient Data (PAT)
  • Provider Operations (PROV)
  • Payer & Insurance (PAYER)
  • Financial & Business (FIN)
  • Fraud & Compliance (FRAUD)
  • Graph Analytics (ANAL)
  • AI & Machine Learning (AI)
  • Security & Privacy (SEC)
  • Data Governance (GOV)
  • Capstone & Career (CAP)

6. Quality Metrics

View quality analysis →

Comprehensive quality validation report with a score of 90/100 (Excellent):

  • ✓ Valid DAG structure (no cycles)
  • ✓ No self-dependencies
  • 4 foundational concepts (2.0%)
  • Average 1.50 dependencies per concept
  • Maximum dependency chain length: 11

7. Taxonomy Distribution

View distribution report →

Analysis of concept distribution across categories:

  • Well-balanced distribution
  • Largest category: Patient Data & Provider Operations (25 concepts each, 12.5%)
  • Smallest category: Capstone & Career (5 concepts, 2.5%)
  • All categories within acceptable ranges

Learning Graph Statistics

  • Total Concepts: 200
  • Total Dependencies: 299
  • Taxonomy Categories: 13
  • Foundational Concepts: 4
  • Average Dependencies per Concept: 1.50
  • Maximum Dependency Chain: 11 levels
  • Quality Score: 90/100 (Excellent)

Key Features

Multi-Perspective Learning

The learning graph incorporates three critical healthcare perspectives:

  1. Patient Perspective: Clinical data, diagnoses, treatments, care plans
  2. Provider Perspective: Hospitals, clinics, schedules, referrals, performance
  3. Payer Perspective: Claims, policies, coverage, reimbursement

Comprehensive Coverage

Topics span from foundational concepts to advanced applications:

  • Graph database fundamentals
  • Healthcare domain knowledge
  • Query languages (Cypher, GQL, GSQL)
  • Clinical analytics and decision support
  • Fraud detection and compliance
  • AI/ML integration and LLMs
  • Security (HIPAA, RBAC)
  • Data governance and explainability

Pedagogically Sound Structure

The learning graph follows established educational principles:

  • Clear prerequisite relationships
  • Progressive complexity
  • Multiple learning pathways
  • Foundation → Application → Synthesis progression
  • Capstone projects for knowledge integration

Using the Learning Graph

For Students

  • Use the concept list to understand the full scope of the course
  • Follow dependency paths to identify prerequisite knowledge
  • Track your progress through the 200 concepts
  • Navigate personalized learning pathways based on your goals

For Instructors

  • Understand the logical structure of the course content
  • Identify critical foundational concepts
  • Design lesson plans that respect dependency relationships
  • Create assessments aligned with the concept taxonomy

For Developers

  • Import the learning-graph.json into visualization tools
  • Build adaptive learning systems using the dependency data
  • Create personalized recommendations based on concept relationships
  • Integrate with learning management systems

Next Steps

After exploring the learning graph, you can:

  1. Install the Learning Graph Viewer: Interactive visualization tool for exploring concept relationships
  2. Generate Chapter Structure: Use the learning graph to create optimal chapter organization
  3. Create Glossary: Generate definitions for all 200 concepts
  4. Build Quizzes: Develop assessments aligned with concept taxonomy

Technical Details

  • Format: Learning Graph JSON v1.0
  • Schema: learning-graph-schema.json
  • License: CC BY-NC-SA 4.0 DEED
  • Version: 1.0
  • Generated: November 6, 2025

For questions or feedback about the learning graph, please refer to the course description or contact the course creator.