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Chapters

This textbook is organized into 27 chapters covering 400 concepts. Chapters 1-19 form the core course; Chapters 20-27 are an advanced-topics extension covering graph theory, metadata, dependency modeling, taxonomy design, validation, data-pipeline internals, and learner modeling in greater depth.

Chapter Overview

  1. Foundations of Concept Graphs - Introduces the vocabulary of graph-based knowledge representation -- concepts, nodes, edges, and the directed acyclic graph (DAG) structure that makes a learning graph different from a simple table of contents.
  2. Concept Labeling and Metadata Standards - Covers how individual concepts are named, defined, and standardized so a learning graph stays machine-readable and consistent across a large book.
  3. Concept Dependencies and Prerequisites - Explains how prerequisite relationships are modeled between concepts, including foundational concepts, goal concepts, and the different types of learning goals (immediate, intermediate, ultimate).
  4. Concept Taxonomies and Ontologies - Covers how concepts are grouped into balanced, non-overlapping categories using taxonomies and ontologies.
  5. Learning Graph Quality, Validation, and File Formats - Combines the quality-assurance side of learning graphs (cycle detection, orphaned-node detection, connectivity analysis, degree metrics) with the file formats -- JSON, CSV, YAML, and Markdown -- used to store, validate, and exchange them.
  6. Bloom's Taxonomy and Learning Objectives - Introduces the six cognitive-domain levels of the 2001 revised Bloom's Taxonomy -- remembering, understanding, applying, analyzing, evaluating, and creating -- along with the affective and psychomotor domains.
  7. Learning Theories and Instructional Design - Surveys the major learning theories -- constructivism, cognitivism, behaviorism, connectivism, and andragogy -- and how they inform instructional design practices like scaffolding, curriculum design, and mastery learning.
  8. Cognitive Load and Knowledge Space Theory - Covers cognitive load theory (working memory constraints, schema theory, chunking) and knowledge space theory, along with the sequencing frameworks -- spiral curriculum, backward design, constructive alignment, topological sort -- that connect learning-science theory back to the graph structures from earlier chapters..
  9. Mastery, Metacognition, and Instructional Sequencing - Covers competency-based education, spaced repetition, retrieval practice, and other evidence-based sequencing strategies, along with metacognition, self-regulated learning, and growth mindset.
  10. Assessment, Feedback, and Quizzes - Covers how assessments and feedback loops close the loop between what a learning graph says a student should know and what they actually demonstrate.
  11. Vis.js Fundamentals and Node Styling - Introduces vis.js and the vis-network library as the primary tool for visualizing concept dependency graphs, covering DataSet, network initialization, and the full range of node visual properties (shape, color, size, icons, images, shadows)..
  12. Edge Styling and Visual Properties - Covers how prerequisite relationships are rendered visually -- edge labels, color, width, dashes, smoothing, and arrows -- so dependency direction and relationship type stay legible in a rendered graph..
  13. Physics Simulation and Graph Layout - Covers the physics solvers (Barnes-Hut, ForceAtlas2, repulsion, central gravity, damping) and layout algorithms (force-directed, hierarchical) that determine how a large graph arranges itself on screen, plus fixed node positions and stabilization..
  14. Interactive Navigation in Vis.js - Covers the interaction patterns that let a learner explore a rendered graph -- tooltips, hover states, click and double-click events, a node inspector, and type-ahead search with focus navigation..
  15. Graph Clustering and Editing Tools - Covers grouping related nodes into composite clusters that can expand and collapse, plus the interactive graph editor used to create and modify a learning graph directly, including JSON import/export and data validation on import..
  16. Personalization and Adaptive Learning Paths - Covers how a learning graph becomes the backbone for adaptive and hyper-personalized learning paths, including recommendation engines driven by concept, student, and content similarity metrics.
  17. Intelligent Agents and Generative AI - Covers how generative AI and intelligent agents use a learning graph -- prompt engineering, tool use, multi-agent collaboration, retrieval-augmented generation, and GraphRAG -- to enumerate concepts, infer dependencies, and act as learning agents..
  18. Intelligent Textbooks, MicroSims, and Deployment - Covers how a validated learning graph drives the generation of an intelligent textbook itself -- MicroSims, interactive visualizations and simulations, the book-build workflow, and publishing through MkDocs, Git, and GitHub Pages, closing with case studies and real-world applications..
  19. Using a Skill to Generate a Learning Graph - Walks through the actual agent-skill workflow (as implemented in the learning-graph-generator Claude Code skill) for turning a course description into a validated learning graph -- scoring the course description, generating and reviewing a concept list, producing a dependency CSV, running automated quality validation, assigning taxonomy, and iterating with human-in-the-loop review before moving on to chapter design.
  20. Graph Theory II: Paths, Traversal, and Connectivity - Extends Chapter 1's graph vocabulary with directed-edge orientation, paths, reachability, adjacency representations, and the traversal algorithms (depth-first and breadth-first search) that walk a learning graph systematically.
  21. Graph Metrics and Path Analysis - Covers the quantitative metrics -- centrality, betweenness, diameter, density, degree distribution -- and structural concepts -- ancestors, descendants, critical paths, transitive closure -- used to analyze a learning graph's shape and identify bottleneck concepts.
  22. Concept Metadata and Semantic Labeling Standards - Extends Chapter 2's labeling conventions into full SKOS and Dublin Core metadata fields, concept granularity, and the governance practices that keep thousands of concept labels unambiguous and machine-readable.
  23. Advanced Dependency Modeling - Goes beyond a simple prerequisite edge to cover dependency strength, rationale, confidence, review workflows, and the more flexible prerequisite patterns -- co-prerequisites, alternative prerequisites, parallel paths -- real learning graphs need.
  24. Taxonomy Design Deep Dive - Extends Chapter 4's taxonomy basics into classification-scheme design, category-balance metrics, polyhierarchy, and the ontology vocabulary (classes, properties, semantic types) that gives a taxonomy formal rigor.
  25. Graph Validation and Quality Assurance - Extends Chapter 5's validation checks into a full automated-validator toolkit -- schema validation, referential integrity, duplicate detection, and a composite graph quality score -- for catching structural and pedagogical problems before they compound.
  26. Data Pipeline Internals - Opens up the CSV and JSON file formats from Chapter 5 to show the actual record structures, header conventions, and configuration files a generation pipeline reads and writes at each step.
  27. Learner Modeling and Advanced Assessment - Extends Chapter 16's personalization concepts with a formal learner model -- mastery state, knowledge components, readiness estimation -- and the assessment types that measure it.

How to Use This Textbook

Chapters are ordered so that every concept appears after all of its prerequisites -- work through them in sequence for the smoothest path. Readers with prior background in a given area (graph theory, learning science, or vis.js) can jump ahead, but should skim the chapter's concept list first to check for any unfamiliar prerequisite terms. Chapters 20-27 form an advanced-topics extension: each assumes the corresponding core chapter (noted in its Prerequisites section) but not the other extension chapters, so they can be read in any order once their single core prerequisite is complete.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.