Concept Taxonomy¶

Concepts in this course are organized into 15 categories. Each category has a short TaxonomyID used in learning-graph.csv and in the graph viewer legend.

Foundations (FOUND)¶

Field-level definitions and the AI/authoring stack that the rest of the course assumes. Learning Sciences itself, its parent disciplines, Bloom's Taxonomy, learning objectives, MkDocs/Markdown, Claude Code, and Agent Skills live here.

Seven Domains Overview (DOMAIN)¶

The top-level framework of the course: the Seven Domains and a named node for each domain (Motivation, Understanding, Retention, Application, Expertise, Measurement, Learning Conditions). Downstream concepts depend on the relevant domain node.

Motivation and Engagement (MOT)¶

Concepts that explain why motivation initiates the attention-encoding-memory chain: Self-Determination Theory (autonomy, competence, relatedness), Flow, Growth Mindset, Self-Efficacy, Attention mechanisms, Curiosity, Interest Development, and the ARCS Model.

Cognitive Architecture (COG)¶

The memory systems (sensory, working, long-term) and their substructures (phonological loop, visuospatial sketchpad, central executive, semantic/episodic/procedural memory, schemas), plus encoding, consolidation, elaboration, chunking, dual coding, multimedia learning, and cognitive load theory (intrinsic, extraneous, germane).

Knowledge Retention (RET)¶

Concepts for making learning durable: retrieval practice, the testing effect, spaced repetition, massed vs distributed vs interleaved practice, the forgetting curve, retrieval cues, recall vs recognition, retrieval/storage strength, desirable difficulty, the Leitner system, and spaced scheduling.

Knowledge Application and Transfer (APP)¶

How knowledge moves from known situations to novel ones: near/far transfer, novel problem solving, unlearning, misconceptions, conceptual change, mental models, analogical reasoning, problem-/case-based learning, worked examples, example-problem pairs, and scenario-based assessment.

Expertise and Mastery (EXP)¶

What changes as learners become experts: deliberate practice, pattern recognition, automaticity, novice/expert differences, expert chunking, the Dreyfus skill model, tacit vs explicit knowledge, mastery learning, and the ten-thousand-hour rule.

Measurement and Feedback (MEAS)¶

Closing the loop: formative/summative assessment, learning analytics, feedback (immediate, delayed, corrective), rubrics, Item Response Theory, diagnostic assessment, metacognition, self-regulated learning, learning dashboards, A/B testing, and quality metrics.

Learning Conditions and Environment (COND)¶

Where learning happens and what supports it: learning environments, scaffolding, Zone of Proximal Development, Social Learning Theory, Communities of Practice, Constructivism, Situated Cognition, Universal Design for Learning, Accessibility, Psychological Safety, Culturally Responsive Teaching, classroom discourse, online and blended learning.

Intelligent Textbook Architecture (ITB)¶

The structural components of an intelligent textbook: learning graphs, concept dependencies, DAG structure, nodes and edges, glossaries, FAQ sections, quiz banks, chapter outlines and content authoring, reference lists, tables of contents, search indexes, site navigation, and print-friendly output.

AI Skills and Tooling (AIST)¶

Claude Agent Skills and the generative-AI tooling used to produce intelligent textbooks: prompt engineering, context windows, token budgeting, the SKILL.md format, skill invocation, and each individual generator skill (Course Description Analyzer, Learning Graph Generator, Book/Chapter Generator, Glossary Generator, FAQ Generator, Quiz Generator, MicroSim Generator, Story Generator, Reference Generator, Book Metrics Generator, Diagram Reports Generator, LinkedIn Announcement Generator, Concept Classifier).

MicroSims (SIM)¶

Interactive educational simulations embedded in chapters: the MicroSim concept and design principles, the libraries (p5.js, Chart.js, Plotly, Mermaid, vis-network, Leaflet, Venn.js), interactive infographics, control-complexity management, iframe embedding, screen-capture automation, and the diagram overlay pattern.

Pedagogical Mascots and Admonitions (MASC)¶

Recurring AI-generated characters and their speech patterns: pedagogical mascot, persona, visual identity, voice guide, mascot admonitions (hint, warning, tip, encouragement, danger, understanding check), admonition CSS styling, and narrative voice consistency.

Graphic Novels and Stories (GN)¶

Short-form narrative content for engagement: graphic novel chapters, the 12-panel story format, panel composition, image prompt engineering, Gemini image generation, historical figure selection, narrative transportation, story arc structure, character design, speech bubbles, caption boxes, historical accuracy checking, and story engagement techniques.

Capstone and Deployment (CAP)¶

Producing and shipping the capstone artifact: the capstone project itself, GitHub Pages deployment, the MkDocs build process, portfolio artifacts, peer review, iterative improvement, accessibility audit basics, publishing workflows, chapter rubric evaluation, and mastery demonstration.

Privacy and Regulation (PRIV)¶

The regulatory and standards surface that attaches once an intelligent textbook moves beyond Level 2 and begins collecting individual student data: the Privacy Inflection Point between Level 2 and Level 3, the major educational-data regulations (FERPA, COPPA, GDPR, CCPA/CPRA), the principle of data minimization, and the open standards (xAPI and the Learning Record Store) that allow student-controlled personalization with portability. This book teaches Level 2 only — this taxonomy exists to make the boundary visible and unmissable, not to teach readers to operate as Level 3+ data controllers.