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Chapters

This textbook is organized into 26 chapters covering 462 concepts.

Chapter Overview

  1. What Is a MicroSim - Defines a MicroSim, explains its role in an intelligent textbook, and introduces the web and AI foundations that the rest of the book builds on.
  2. Anatomy of a MicroSim - Takes a MicroSim apart into its files, regions and metadata so students can read, modify and package any MicroSim they meet.
  3. Learning Objectives and Bloom's Taxonomy - Shows how to state measurable learning objectives, classify them with Bloom's 2001 Taxonomy, and apply cognitive load theory and the instructional design checkpoint before writing any code.
  4. Choosing a MicroSim Type - Presents the catalog of MicroSim types and the routing rubric that maps a learning objective to the type and library best suited to it.
  5. Generating MicroSims with AI Skills - Explains how AI skills and agents generate MicroSims from prompts and specifications, and how to refine, review and debug the results.
  6. p5.js MicroSims - Teaches the p5.js sketch structure, drawing, animation, physics and controls that power the most flexible MicroSim type, including showcase-quality animation.
  7. Charts, Plots and Tables - Covers Chart.js, Plotly and HTML tables for showing quantitative data, comparisons and mathematical functions in interactive form.
  8. Diagrams, Networks and Systems - Covers Mermaid diagrams, vis-network graphs, Venn diagrams and causal-loop diagrams for showing processes, relationships and feedback systems.
  9. Timelines and Maps - Covers vis-timeline and Leaflet for presenting events over time and data over geography.
  10. Image Overlays and Comparison Posters - Explains how to turn an AI-generated image into an interactive MicroSim with callout labels, hover zones, and grid-overlay comparison posters with Explore and Quiz modes.
  11. Verified Posters and Specialized MicroSim Types - Covers fact-verified posters, in which every numeric claim is checked against a cited source, and the specialized types of runnable labs, sorting quizzes, flash cards and builders.
  12. Width-Responsive Design and Iframe Heights - Explains how MicroSims adapt to any container width and how iframe heights are set, synchronized and tested so nothing is clipped or hidden.
  13. Quality Assurance and Automated Layout Review - Covers the quality score, Playwright tests, screenshots and vision-based layout review that catch layout and quality defects before readers do.
  14. Batch Generation from Specifications - Shows how to generate many MicroSims from chapter specifications with status tracking, resumable pipelines and parallel workers.
  15. Metadata, Search and Reuse - Covers the metadata that describes each MicroSim, the search index built from it, and the reuse-before-build practice that keeps a library of MicroSims from duplicating itself.
  16. xAPI Statements and Evidence - Introduces xAPI statements, the three MicroSim verbs, the producer contract, and the evidence classes that separate meaningful interaction from noise.
  17. Instrumenting MicroSims with the xAPI Runtime - Shows how to add xAPI reporting to a MicroSim using the shared runtime, its handles and library adapters, and how to map interactions to concepts.
  18. Mastery Prediction and Knowledge Tracing - Explains how an evidence stream becomes a prediction of concept mastery, using Bayesian knowledge tracing and its guess, slip and learning parameters.
  19. Evaluating the Predictive Fidelity of the xAPI Stream - Teaches how to measure how well the xAPI stream predicts mastery, using held-out assessments, calibration, discrimination and honest reporting of limits.
  20. The Full LRS: Architecture and Ingestion - Describes the full Learning Record Store built for scale: multi-tenancy, pseudonymous identity, the graph data model and the ingestion pipeline.
  21. The Full LRS: Dashboards, Operations and Compliance - Covers what the full LRS delivers and costs: teacher, author and admin dashboards, experiments, capacity and cost models, deployment, failure modes and compliance.
  22. LRS-Lite: Compact Summaries and Browser Storage - Introduces LRS-Lite, the serverless strategy in which each MicroSim summarizes its own session and each student's data lives in a small browser database.
  23. LRS-Lite: Sync, Dashboards and Choosing Full or Lite - Covers multi-device sync and backup for LRS-Lite, browser-side dashboards, and how to choose between the full LRS and LRS-Lite and migrate between them.
  24. Pedagogy, Accessibility and Ethics - Covers the pedagogical, accessibility, equity and ethical foundations for MicroSims that collect learner data, including Universal Design for Learning, privacy and bias in prediction.
  25. Capstone: Building an Instrumented MicroSim Portfolio - Guides students through building an instrumented MicroSim portfolio and reporting how well its event stream predicts mastery of its target concepts.
  26. The Future of MicroSims - Looks ahead: near-term work over the next year, and the long-term prospect of AI generating MicroSims that are fun to use and better able to predict whether a student has mastered a concept.

How to Use This Textbook

The chapters follow the life of a MicroSim: understand it (1-3), choose and generate it (4-5), build it by type (6-11), make it responsive and check its quality (12-13), produce it at scale and describe it (14-15), instrument it and predict mastery from its events (16-19), store and analyze the events with a full LRS or LRS-Lite (20-23), and finish with pedagogy, a capstone and the future (24-26). Dependencies are respected: every concept is introduced in a chapter at or after the chapters that introduce its prerequisites.

Note: Each chapter includes a list of concepts covered. Complete the prerequisite chapters before moving to advanced ones.