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Learning Graph

Open Learning Graph Viewer Fullscreen

Genetics: Analysis, Genomics, and Modern Inference

The learning graph is the foundational data structure for this intelligent textbook. It maps 450 concepts across 14 taxonomy categories with 617 dependency edges, creating a directed acyclic graph (DAG) that guides personalized learning pathways.

What is a Learning Graph?

A learning graph is a concept dependency network where:

  • Nodes represent individual concepts students need to learn
  • Edges represent prerequisite relationships between concepts
  • Groups organize concepts into color-coded taxonomy categories
  • Foundational concepts (no prerequisites) serve as entry points

The graph supports multiple learning pathways — students can explore different routes through the material based on their goals and prior knowledge.

Graph Statistics

Metric Value
Total Concepts 450
Taxonomy Categories 14
Dependency Edges 617
Foundational Concepts 11
Maximum Chain Length 10
Connected Components 1

Taxonomy Categories

Category TaxonomyID Color Concepts
Foundation Concepts FOUND LightCoral 22
Probabilistic Reasoning PROB PeachPuff 10
Pedigree and Inheritance PED LightPink 40
Genome Structure GSTR Thistle 28
Genetic Variation GVAR Plum 38
Mapping and Linkage MAP PowderBlue 46
Quantitative Genetics QUANT LightYellow 32
Population Genetics POP PaleGreen 21
Gene Regulation REG Aquamarine 45
Experimental Methods EXP LightSteelBlue 39
Genomics and Bioinformatics BIOINFO Honeydew 40
Human and Clinical Genetics CLIN MistyRose 45
Ethics and Society ETHICS Lavender 24
Frontier Topics FRONT PaleTurquoise 20

Reports

Data Files

  • learning-graph.csv — Concept dependency data with taxonomy assignments
  • learning-graph.json — Complete graph in vis-network.js JSON format
  • metadata.json — Dublin Core metadata for the learning graph
  • taxonomy-names.json — Taxonomy ID to human-readable name mapping
  • color-config.json — Color assignments for taxonomy visualization