Glossary of Terms
Abstraction (Graph)
A graph operation that hides detail or combines nodes to make larger patterns easier to see.
Example: A dependency review can use abstraction (graph) to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Adaptive Algorithm
A computational procedure that changes recommendations, sequencing, or feedback based on input data.
Example: An adaptive textbook can use adaptive algorithm to choose the next useful concept, resource, or feedback message.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Adaptive Learning
An instructional approach that changes content, sequence, or feedback in response to learner performance and needs.
Example: In an adaptive textbook, adaptive learning can help select the next concept after a learner demonstrates mastery of prerequisites.
See also: Learner Model, Personalized Learning Path.
Adaptive Learning Path
A learning path that updates as evidence about learner mastery changes.
Example: An adaptive textbook can use adaptive learning path to choose the next useful concept, resource, or feedback message.
Adjacency List
A graph representation that stores each node with the nodes directly connected to it, making neighbor lookup and traversal efficient.
Example: A graph script may store node 12 with neighbors 18, 22, and 45 so traversal code can find connected concepts quickly.
Adjacency Matrix
A table representation of a graph in which rows and columns are nodes and cell values indicate whether edges exist.
Example: A validation script can use a matrix cell to show whether concept A has an edge to concept B.
See also: Learning Graph, Node, Edge.
Affective Domain
The domain of learning concerned with attitudes, values, motivation, and emotional engagement.
Example: In chapter design, affective domain can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Agent Autonomy
The degree to which an agent can plan, decide, and act without step-by-step human direction.
Example: An AI-assisted workflow can use agent autonomy to generate artifacts while keeping the result inspectable by humans.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Agent Skill
A reusable instruction package that guides an AI agent through a specialized workflow.
Example: An AI-assisted workflow can use agent skill to generate artifacts while keeping the result inspectable by humans.
Alternate Label
A secondary label, synonym, abbreviation, or variant spelling that points to the same concept as the preferred label.
Alternative Prerequisite
A prerequisite option that can satisfy the same readiness need as another prerequisite without requiring both paths.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Analyzing
The Bloom’s Taxonomy level focused on breaking material into parts and explaining relationships among those parts.
Example: An analyzing objective might ask learners to locate bottleneck concepts and disconnected subgraphs.
See also: Applying, Evaluating, Learning Graph.
Ancestor Concept
A concept that appears upstream of another concept along a directed prerequisite path.
Example: If “Directed Graph” precedes “Learning Graph” through a dependency path, it is an ancestor concept of “Learning Graph.”
Andragogy
The theory and practice of adult learning, emphasizing relevance, autonomy, experience, and immediate application.
Example: A professional course can use andragogy by emphasizing practical graph-building tasks over abstract lecture alone.
Applying
The Bloom’s Taxonomy level focused on using a concept, method, or procedure in a concrete situation.
Example: An applying objective might ask learners to run a validation script on a dependency CSV.
Assessment
A method for collecting evidence about a learner’s knowledge, skill, performance, or progress.
Example: After a quiz, assessment can help decide whether the learner needs review, practice, or a new challenge.
See also: Formative Feedback, Quiz.
Associative Relation
A named connection type that explains how two concepts, categories, or records are associated in the learning model.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Atomic Concept
A concept that is small enough to define, teach, assess, and connect without needing further decomposition.
Example: “Edge Width” is more atomic than “Vis.js Styling” because it can be explained and configured on its own.
Automated Quality Validation
The use of scripts or tools to check generated content or graph data against defined quality rules.
Example: Before publishing, automated quality validation can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Automated Validator
A script or tool that checks graph data, metadata, or generated content against defined validation rules.
Example: Before publishing, automated validator can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Backward Design
A curriculum design approach that starts with desired outcomes, then determines evidence and learning activities.
Example: A chapter designer can use backward design to align explanations, practice, and feedback with how professionals learn.
Balanced Taxonomy
A taxonomy whose categories contain enough concepts to be useful without concentrating too many concepts in one group.
Example: A taxonomy review can use balanced taxonomy to decide whether concepts are grouped clearly and evenly.
Barnes-Hut Solver
A physics solver that approximates many-body repulsion to lay out large network graphs efficiently.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Behaviorism
A learning theory that emphasizes observable behavior shaped by reinforcement, practice, and feedback.
Example: A quiz with immediate corrective feedback uses behaviorist ideas to reinforce accurate recall.
Betweenness Centrality
A graph measure indicating how often a node lies on shortest paths between other nodes.
Example: A dependency review can use betweenness centrality to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Bloom's Taxonomy
A hierarchy of cognitive processes used to classify learning objectives from recall through creation.
Book Build Workflow
The sequence of steps used to generate, validate, assemble, and publish an intelligent textbook.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Book Metrics
Aggregate measures that summarize the size and structure of a textbook.
See also: Intelligent Textbook, MkDocs, GitHub Pages.
Bottleneck Concept
A concept that many later concepts depend on, making it especially important for sequencing and remediation.
Example: When generating a chapter sequence, bottleneck concept can show whether a proposed prerequisite edge is necessary or merely convenient.
Breadth-First Search
A graph traversal method that visits all neighboring nodes before moving to nodes farther away.
Example: A dependency review can use breadth-first search to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Case Study
A concrete example used to examine how concepts apply in a realistic situation.
Example: In the textbook site, case study can connect graph data, Markdown pages, and interactive learning assets.
See also: Intelligent Textbook, MkDocs, GitHub Pages.
Category Assignment
The decision or data value that places a concept into a taxonomy category.
Example: A taxonomy review can use category assignment to decide whether concepts are grouped clearly and evenly.
Category Balance
The degree to which taxonomy categories contain reasonably even and useful numbers of concepts.
Category Boundary
The rule or distinction that determines whether a concept belongs inside or outside a taxonomy category.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Category Coverage
The extent to which a taxonomy category accounts for the concepts it is expected to organize.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Category Overlap
A condition in which two taxonomy categories cover the same concepts or meanings too broadly.
Example: A taxonomy review can use category overlap to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Central Gravity
A layout parameter that pulls nodes toward the center of the visualization.
Example: In a vis.js network, central gravity can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Chapter Metrics
Measures that summarize the size, assets, assessments, and structure of individual chapters.
Example: In the textbook site, chapter metrics can connect graph data, Markdown pages, and interactive learning assets.
Chatbot
A conversational software interface that accepts user messages and returns generated or retrieved responses.
Example: An AI-assisted workflow can use chatbot to generate artifacts while keeping the result inspectable by humans.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Chunking (Cognitive Load)
A strategy for grouping information into meaningful units to reduce working memory demand.
Example: In chapter design, chunking (cognitive load) can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Classification Scheme
A structured method for assigning concepts or resources to named categories.
Example: A taxonomy review can use classification scheme to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Claude Code
An AI coding assistant environment that can inspect files, run commands, edit repositories, and follow local skills.
Example: An AI-assisted workflow can use claude code to generate artifacts while keeping the result inspectable by humans.
Click Event Handling
Code that responds when a user clicks a node, edge, button, or graph area.
Example: In a vis.js network, click event handling can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Clickable Node
A node configured to respond to user selection by opening, focusing, or displaying related information.
Cluster (Graph)
A set of graph nodes that are grouped because they share dense connections, a common category, or a useful visual boundary.
Example: A dependency review can use cluster (graph) to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Cluster Expand/Collapse
An interaction that switches between a compact cluster view and its individual member nodes.
Example: A vis.js activity can use cluster expand/collapse to make graph structure easier to inspect or manipulate.
Clustering (Graph)
A graph operation that groups nodes by connection pattern, shared attribute, or conceptual similarity.
Example: A dependency review can use clustering (graph) to explain how one concept connects to another.
Co-Prerequisite
A concept that should be learned alongside another concept because the two support each other at the same stage.
Cognitive Domain
The part of Bloom’s Taxonomy concerned with mental skills such as remembering, understanding, applying, analyzing, evaluating, and creating.
Example: In chapter design, cognitive domain can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Cognitive Load Theory
A learning theory that explains how working memory limits affect instructional design and learning efficiency.
Example: In chapter design, cognitive load theory can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Cognitivism
A learning theory that emphasizes mental processes such as memory, attention, schema formation, and problem solving.
Example: A chapter designer can use cognitivism to align explanations, practice, and feedback with how professionals learn.
Color Configuration
A visual or interaction feature used to render, inspect, style, or navigate learning graphs in a browser-based graph interface.
Example: In a vis.js network, color configuration can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Competency Model
A structured representation of the skills, knowledge, and performance expectations learners must demonstrate.
See also: Instructional Design, Learning Objective, Mastery Learning.
Competency-Based Education
An educational model in which progress is based on demonstrated mastery of defined competencies.
Example: In chapter design, competency-based education can guide whether learners should recall, practice, analyze, or create with a concept.
Composite Concept
A concept made from several smaller concepts that may need separate definition, sequencing, or assessment.
Example: “Intelligent Textbook” is composite because it combines learning graphs, content generation, learner models, and publishing.
See also: Learning Graph, Node, Edge.
Composite Node
A node that represents a bundle, cluster, or higher-level grouping of multiple nodes.
Example: A dependency review can use composite node to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Concept
A named unit of knowledge that can be defined, classified, taught, assessed, and related to other units in a learning graph.
Example: In a learning-graphs course, “Concept Dependency” can be a node, and an edge can show that it depends on “Directed Graph.”
See also: Learning Graph, Node, Edge.
Concept Centrality
The importance of a concept within a graph based on its connections, paths, or dependency role.
Example: A dependency review can use concept centrality to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Concept Definition
A concise statement that explains what a concept means and distinguishes it from related concepts.
Example: A concept registry can use concept definition to keep labels, descriptions, and identifiers consistent across tools.
Concept Dependency
A prerequisite relationship in which understanding one concept supports or enables understanding another concept.
Example: When generating a chapter sequence, concept dependency can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Prerequisite Relationship, Dependency Analysis.
Concept Granularity
The level of detail at which a concept is represented, from broad topic to narrowly assessable unit.
Example: A dependency review can use concept granularity to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Concept Graph
A graph-based representation that uses nodes for concepts and edges for relationships among those concepts.
Example: A dependency review can use concept graph to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Concept Label
The human-readable name assigned to a concept so people and software can identify it consistently.
Example: In a concept record, concept label can help another tool interpret the same concept label without guessing its meaning.
See also: Metadata Field, Controlled Vocabulary.
Concept List Generation
The workflow step that extracts or proposes a complete set of concept labels from a course description.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Concept List Review
The human review step that checks concept labels for coverage, duplication, granularity, and clarity.
Example: A dependency review can use concept list review to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Concept Provenance
A metadata concept used to name, describe, identify, govern, or exchange learning-graph concepts in a consistent form.
Example: In a concept record, concept provenance can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Concept Similarity
A measure of how closely two concepts relate in meaning, prerequisites, taxonomy, or usage.
Example: A dependency review can use concept similarity to explain how one concept connects to another.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Concept Taxonomy
A classification structure that groups concepts into meaningful categories for organization, navigation, and analysis.
Example: A taxonomy review can use concept taxonomy to decide whether concepts are grouped clearly and evenly.
Concept-to-Content Viewer
An interface that shows how graph concepts map to generated chapters, sections, activities, or assets.
Example: A dependency review can use concept-to-content viewer to explain how one concept connects to another.
Concept-to-Taxonomy Mapping
The assignment of each concept to one or more taxonomy categories.
Example: A taxonomy review can use concept-to-taxonomy mapping to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Connected Component
A group of graph nodes that are connected to one another but separated from nodes outside the group.
Example: A dependency review can use connected component to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Connectivism
A learning theory that emphasizes learning through networks, connections, and access to distributed knowledge.
Example: A chapter designer can use connectivism to align explanations, practice, and feedback with how professionals learn.
Constructive Alignment
An instructional design principle that aligns outcomes, activities, and assessments around the same intended learning.
Example: A chapter designer can use constructive alignment to align explanations, practice, and feedback with how professionals learn.
Constructivism
A learning theory in which learners build understanding by connecting new experiences to prior knowledge.
Example: A chapter designer can use constructivism to align explanations, practice, and feedback with how professionals learn.
Content Generation Pipeline
A workflow that converts source descriptions, graphs, prompts, and templates into learning content.
Example: A dependency review can use content generation pipeline to explain how one concept connects to another.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Content Graph
A graph that represents learning resources, sections, activities, or media and the relationships among those content items.
Example: A dependency review can use content graph to explain how one concept connects to another.
Content Recommendation Engine
A system that selects learning resources based on learner needs, concept relationships, and available content metadata.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Content Sequencing Algorithm
A procedure that selects or orders learning content using goals, prerequisites, learner state, or constraints.
See also: Instructional Design, Learning Objective, Mastery Learning.
Content Similarity
A measure of how closely learning resources resemble one another in topic, format, level, or purpose.
Example: A dependency review can use content similarity to explain how one concept connects to another.
Controlled Vocabulary
An approved set of terms that reduces ambiguity by specifying which labels should be used for a domain.
Example: In a concept record, controlled vocabulary can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field.
Course Description Scoring
The evaluation of a course description for completeness, clarity, audience definition, topics, and learning outcomes.
Course Evaluation
The systematic review of a course’s effectiveness, alignment, usability, and learning impact.
Example: An instructor can use course evaluation to gather evidence about whether a learner is ready for the next concept.
Creating
The Bloom’s Taxonomy level focused on producing a new artifact, plan, model, or solution.
Critical Path
The longest or most constraining prerequisite path that determines how quickly a learner can reach a goal concept.
Example: When generating a chapter sequence, critical path can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
CSV
A delimited text format that stores tabular data as rows and columns separated by commas.
See also: JSON, CSV Header, Data Pipeline.
CSV Header
A data-format or transformation concept used to store, validate, exchange, or publish learning-graph information.
Example: In the build pipeline, csv header can help transform a CSV dependency file into a validated JSON graph.
CSV to JSON Conversion
A transformation that changes tabular CSV records into structured JSON objects or arrays.
Example: In the build pipeline, csv to json conversion can help transform a CSV dependency file into a validated JSON graph.
See also: JSON, CSV, Data Pipeline.
Cubic Bezier Curve
A smooth curve defined by endpoints and control points, often used to draw curved graph edges.
Example: In a vis.js network, cubic bezier curve can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Curriculum Alignment
The consistency among learning objectives, instruction, assessment, standards, and learner needs.
Example: In chapter design, curriculum alignment can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Curriculum Design
The planning of learning goals, content, sequence, assessments, and instructional experiences for a course or program.
See also: Instructional Design, Learning Objective, Mastery Learning.
Custom Canvas Shape
A node shape drawn with custom JavaScript on an HTML canvas instead of using a built-in shape.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Custom Learning Plan
A plan that selects concepts, resources, activities, and assessments for a particular learner or audience.
See also: Instructional Design, Learning Objective, Mastery Learning.
Cycle Detection
The process of finding directed loops that would prevent a learning graph from forming a valid prerequisite order.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Damping
A physics parameter that reduces movement over time so a graph layout can stabilize.
Example: In a vis.js network, damping can make a dense learning graph easier to inspect, filter, or navigate.
Data Pipeline
A sequence of data-processing steps that moves learning-graph information from source files to validated outputs.
Example: A build script can use data pipeline to move graph information between authoring, validation, and publishing steps.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Data Transformation
A change in data format, structure, or representation that makes information usable by another tool or workflow.
Data Validation on Import
A check performed when data is loaded to reject missing fields, invalid references, or malformed values.
Example: In a vis.js network, data validation on import can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
DataSet
A vis.js collection object that stores, updates, and supplies node or edge records to a network visualization.
Example: In a vis.js network, dataset can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Degree Distribution
The pattern showing how many nodes have each number of graph connections.
Dependency Analysis
The process of examining prerequisite relationships to identify learning order, bottlenecks, gaps, cycles, or unnecessary edges.
Example: When generating a chapter sequence, dependency analysis can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship.
Dependency Assertion
A claim that one concept depends on another and should be represented by a directed relationship.
Example: When generating a chapter sequence, dependency assertion can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Dependency Confidence
A rating or judgment indicating how trustworthy a proposed dependency relationship is.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Dependency CSV Generation
The workflow step that creates a tabular file of directed prerequisite relationships among concepts.
Example: A build script can use dependency csv generation to move graph information between authoring, validation, and publishing steps.
Dependency Depth
The number of prerequisite levels between a concept and the foundational concepts beneath it.
Example: When generating a chapter sequence, dependency depth can show whether a proposed prerequisite edge is necessary or merely convenient.
Dependency Gap
A missing prerequisite relationship or concept that leaves a learning path logically incomplete.
Example: When generating a chapter sequence, dependency gap can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Dependency Graph
A directed graph that represents which items must be understood, completed, or available before other items can be used.
Example: When generating a chapter sequence, dependency graph can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Dependency Inference
The process of proposing prerequisite relationships from text, examples, graph structure, or model output.
Example: When generating a chapter sequence, dependency inference can show whether a proposed prerequisite edge is necessary or merely convenient.
Dependency Plausibility
The degree to which a proposed prerequisite relationship appears instructionally and logically reasonable.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Dependency Rationale
An explanation of why one concept should be treated as a prerequisite for another.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Dependency Review
A human or automated check of prerequisite relationships for correctness, necessity, and clarity.
Example: When generating a chapter sequence, dependency review can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Depth-First Search
A graph traversal method that follows one path as far as possible before backtracking.
Example: A dependency review can use depth-first search to explain how one concept connects to another.
Descendant Concept
A concept that appears downstream of another concept along a directed prerequisite path.
Example: If “GraphRAG” builds on “Retrieval-Augmented Generation,” it is a descendant concept in that path.
See also: Learning Graph, Node, Edge.
Desirable Difficulty
A challenge that makes learning effortful enough to improve retention without overwhelming the learner.
Diagnostic Assessment
An assessment used to identify current knowledge, misconceptions, or gaps before instruction begins.
Example: After a quiz, diagnostic assessment can help decide whether the learner needs review, practice, or a new challenge.
See also: Assessment, Formative Feedback, Quiz.
Differentiated Instruction
An instructional approach that adjusts content, process, product, or support for learner differences.
Example: In chapter design, differentiated instruction can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Direct Dependency
A prerequisite relationship represented by a single edge between two concepts.
Example: When generating a chapter sequence, direct dependency can show whether a proposed prerequisite edge is necessary or merely convenient.
Directed Acyclic Graph (DAG)
A directed graph with no cycles, so its nodes can be ordered without returning to an earlier node through directed edges.
See also: Learning Graph, Node, Edge.
Directed Edge
An edge with a source and target, showing that a relationship has a specific direction.
Directed Graph
A graph whose edges have direction, showing that a relationship flows from one node to another rather than operating symmetrically.
See also: Learning Graph, Node, Edge.
Disconnected Subgraph
A set of connected nodes that has no path to the main graph component.
See also: Learning Graph, Node, Edge.
Domain Attribute (Clustering)
A data field used to cluster nodes according to subject area, taxonomy, or functional domain.
Example: In chapter design, domain attribute (clustering) can guide whether learners should recall, practice, analyze, or create with a concept.
Double-Click Event
A user interaction event triggered by two rapid clicks on the same graph element or area.
Example: In a vis.js network, double-click event can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Dublin Core Description
The Dublin Core metadata element that summarizes what a resource or concept record is about.
Example: In a concept record, dublin core description can help another tool interpret the same concept label without guessing its meaning.
Dublin Core Metadata
A widely used set of metadata elements for describing resources with fields such as title, creator, subject, and description.
Example: In a concept record, dublin core metadata can help another tool interpret the same concept label without guessing its meaning.
Dublin Core Title
The Dublin Core metadata element that gives the official name of a resource or concept record.
Example: In a concept record, dublin core title can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Duplicate Concept Detection
A validation process that identifies duplicate concept conditions so graph data can be corrected before it drives instruction.
Example: Before publishing, duplicate concept detection can flag a cycle, missing identifier, or disconnected concept that needs human review.
Edge
A connection between two graph nodes that represents a relationship such as prerequisite, membership, similarity, or navigation.
Example: In a learning-graphs course, “Concept Dependency” can be a node, and an edge can show that it depends on “Directed Graph.”
See also: Learning Graph, Node.
Edge Arrows
Arrowheads on an edge that show relationship direction.
Example: A vis.js activity can use edge arrows to make graph structure easier to inspect or manipulate.
Edge Color
A visual property that controls the color used to draw an edge.
Example: In a vis.js network, edge color can make a dense learning graph easier to inspect, filter, or navigate.
Edge Dashes
A visual property that renders an edge as a dashed rather than solid line.
Example: A vis.js activity can use edge dashes to make graph structure easier to inspect or manipulate.
Edge Direction
The orientation of an edge from source to target that determines how a relationship should be interpreted.
Example: A dependency review can use edge direction to explain how one concept connects to another.
Edge Font
A visual property that controls the typeface, size, and color of edge labels.
Example: In a vis.js network, edge font can make a dense learning graph easier to inspect, filter, or navigate.
Edge Label
Text displayed on an edge to explain the relationship it represents.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Edge Property Editor
An interface for changing the endpoints, label, direction, or style of an edge.
Edge Provenance
Information describing where an edge came from, who created it, or what evidence supports it.
Example: In a concept record, edge provenance can help another tool interpret the same concept label without guessing its meaning.
Edge Record
A structured data row or object that stores the fields needed to represent a edge in the learning graph dataset.
Edge Smoothing
A visual option that curves edges to reduce overlap or improve readability.
Example: In a vis.js network, edge smoothing can make a dense learning graph easier to inspect, filter, or navigate.
Edge Tooltip
Text or HTML shown when a user hovers over or focuses an edge.
Example: In a vis.js network, edge tooltip can make a dense learning graph easier to inspect, filter, or navigate.
Edge Width
A visual property that controls the thickness of an edge line.
Example: A vis.js activity can use edge width to make graph structure easier to inspect or manipulate.
Emergent Knowledge Structure
An organization of understanding that develops from learner activity, concept connections, and context rather than from a fixed outline.
Evaluating
The Bloom’s Taxonomy level focused on judging quality, validity, or suitability using criteria.
See also: Analyzing, Creating, Learning Graph.
Exclusive Category
A taxonomy category rule requiring a concept to belong to only one category within a classification scheme.
Example: A taxonomy review can use exclusive category to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
False Prerequisite
A dependency claim that treats a concept as necessary even though it is not required for the target concept.
Example: When generating a chapter sequence, false prerequisite can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Feedback Analysis
The examination of feedback data to identify patterns, needs, and instructional improvements.
Example: After a quiz, feedback analysis can help decide whether the learner needs review, practice, or a new challenge.
See also: Assessment, Formative Feedback, Quiz.
Feedback Log
A record of feedback events, learner responses, decisions, and changes over time.
Example: After a quiz, feedback log can help decide whether the learner needs review, practice, or a new challenge.
Feedback Loop
A cycle in which output from a process becomes input for improving later actions.
See also: Assessment, Formative Feedback, Quiz.
Fixed Node Position
A node setting that locks one or both coordinates so the node does not move during layout.
Example: A vis.js activity can use fixed node position to make graph structure easier to inspect or manipulate.
Focus Node Navigation
An interaction that centers, highlights, or zooms to a selected node.
Example: A vis.js activity can use focus node navigation to make graph structure easier to inspect or manipulate.
Force-Directed Graph
A graph visualization that positions nodes by simulating forces among nodes and edges.
Example: A vis.js activity can use force-directed graph to make graph structure easier to inspect or manipulate.
Force-Directed Graph Layout
A layout method that uses simulated attraction and repulsion to place graph nodes.
Example: In a vis.js network, force-directed graph layout can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
ForceAtlas2 Layout
A force-directed layout algorithm often used for network exploration and community structure visualization.
Formative Feedback
Feedback provided during learning to help learners improve before final evaluation.
Foundational Concept
A basic concept that many later concepts rely on and that should be established early in a learning path.
Example: A dependency review can use foundational concept to explain how one concept connects to another.
Frequently Asked Question
A common learner question paired with a concise answer that reduces repeated confusion.
Example: After a quiz, frequently asked question can help decide whether the learner needs review, practice, or a new challenge.
See also: Assessment, Formative Feedback, Quiz.
Full Coverage Check
A validation check that confirms every required concept appears in the graph or generated content.
Example: Before publishing, full coverage check can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Generative AI
Artificial intelligence that produces new text, code, images, data structures, or other artifacts from prompts and context.
Example: An AI-assisted workflow can use generative ai to generate artifacts while keeping the result inspectable by humans.
See also: Intelligent Agent, Prompt Engineering.
Git
A version control system that records changes to files and supports collaboration over time.
GitHub Pages
A hosting service that publishes static websites from GitHub repositories.
See also: Intelligent Textbook, MkDocs.
Goal Concept
A target concept that instruction is designed to help a learner understand or apply.
Example: A dependency review can use goal concept to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Graph Cluster
A group of nodes treated as a single visual or analytical unit in a graph.
Example: A dependency review can use graph cluster to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Graph Completeness
The extent to which a graph includes all concepts and relationships needed for its intended learning purpose.
Example: Before publishing, graph completeness can flag a cycle, missing identifier, or disconnected concept that needs human review.
Graph Connectivity
The extent to which nodes in a graph are reachable through paths rather than separated into isolated pieces.
Example: A dependency review can use graph connectivity to explain how one concept connects to another.
Graph Density
The ratio of existing edges to the number of possible edges in a graph.
See also: Learning Graph, Node, Edge.
Graph Diameter
The longest shortest path between any two reachable nodes in a graph.
See also: Learning Graph, Node, Edge.
Graph Editor
An interface for creating, modifying, importing, exporting, and validating graph nodes and edges.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Graph Layout Algorithm
A procedure for calculating node positions in a graph visualization.
See also: Vis.js, Interactive Visualization.
Graph Path
A sequence of nodes connected by edges from a starting node to an ending node.
Example: A dependency review can use graph path to explain how one concept connects to another.
Graph Quality Metric
A measurable indicator used to evaluate graph structure, completeness, consistency, or instructional usefulness.
Example: A graph quality check can use graph quality metric to decide whether generated graph data needs revision.
Graph Quality Score
A quality-control concept used to detect structural, semantic, or pedagogical issues in learning-graph artifacts.
Graph Representation
A data model or visual form that expresses entities as nodes and relationships as edges.
Example: A dependency review can use graph representation to explain how one concept connects to another.
Graph Schema
A formal specification of the fields, records, and constraints allowed in graph data.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Graph Traversal
The process of visiting graph nodes and edges according to a systematic method.
Example: A dependency review can use graph traversal to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
GraphRAG
A retrieval-augmented generation approach that uses graph structure to organize, retrieve, or reason over context.
Example: An AI-assisted workflow can use graphrag to generate artifacts while keeping the result inspectable by humans.
Group Record
A structured data row or object that stores the fields needed to represent a group in the learning graph dataset.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Group Styling
A set of visual rules applied to all nodes or edges within a named group.
Example: A vis.js activity can use group styling to make graph structure easier to inspect or manipulate.
Group Visibility Checkbox
A checkbox that lets users show or hide nodes or edges belonging to a graph group.
Example: In a vis.js network, group visibility checkbox can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
groups.json
A JSON file that defines visual group names, colors, and styling rules for graph nodes.
Example: A vis.js activity can use groups.json to make graph structure easier to inspect or manipulate.
Growth Mindset
The belief that ability can improve through effort, strategy, feedback, and persistence.
Example: A difficult graph-debugging activity can frame mistakes as evidence for revision rather than failure.
Hierarchical Layout
A graph layout that arranges nodes into levels based on direction, rank, or dependency order.
Example: In a vis.js network, hierarchical layout can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Hierarchical Relation
A named connection type that explains how two concepts, categories, or records are associated in the learning model.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Hierarchy
An ordered structure in which broader, parent-level items organize narrower or child-level items beneath them.
Example: A dependency review can use hierarchy to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Homonym Disambiguation
A metadata concept used to name, describe, identify, govern, or exchange learning-graph concepts in a consistent form.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Hop Count
The number of edges traversed between two nodes in a graph path.
Example: A dependency review can use hop count to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Hover State
A temporary visual state triggered when a pointer is positioned over an interactive element.
Example: In a vis.js network, hover state can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Human Dependency Review
A human evaluation of dependency relationships to catch errors that automated checks may miss.
Example: When generating a chapter sequence, human dependency review can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Human-in-the-Loop Review
A workflow checkpoint where a person evaluates, corrects, or approves generated artifacts.
Example: An AI-assisted workflow can use human-in-the-loop review to generate artifacts while keeping the result inspectable by humans.
Hybrid Graph
A graph that combines multiple relationship types, data sources, or organizational structures in one connected model.
Example: A dependency review can use hybrid graph to explain how one concept connects to another.
Hyper-Customized Lesson Plan
A lesson plan generated for a specific learner or group using detailed evidence about goals, mastery, and constraints.
Example: An adaptive textbook can use hyper-customized lesson plan to choose the next useful concept, resource, or feedback message.
Hyper-Personalization
Fine-grained adaptation of instruction using learner data, context, goals, and graph structure.
Example: An adaptive textbook can use hyper-personalization to choose the next useful concept, resource, or feedback message.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Immediate Goal
A near-term learning target that can be reached after a small number of prerequisite concepts.
Example: A dependency review can use immediate goal to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Incoming Edge
An edge that points into a node from another node.
See also: Outgoing Edge, Directed Edge, Learning Graph.
Indegree
The number of incoming edges connected to a node.
Example: A dependency review can use indegree to explain how one concept connects to another.
See also: Out-Degree, Node Degree, Learning Graph.
Indirect Dependency
A prerequisite relationship that exists through one or more intermediate concepts rather than a direct edge.
Example: When generating a chapter sequence, indirect dependency can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Direct Dependency, Transitive Dependency, Concept Dependency.
Instructional Design
The systematic design of learning experiences, activities, assessments, and materials to achieve defined outcomes.
Example: In chapter design, instructional design can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Learning Objective, Mastery Learning.
Intelligent Agent
A software system that uses goals, context, tools, and feedback to decide and perform actions.
Example: An AI-assisted workflow can use intelligent agent to generate artifacts while keeping the result inspectable by humans.
Intelligent Textbook
A textbook that uses structured data, learner evidence, and software agents to adapt content or learning paths.
Example: In the textbook site, intelligent textbook can connect graph data, Markdown pages, and interactive learning assets.
See also: MkDocs, GitHub Pages.
Interactive Simulation
A model that lets learners change inputs and observe how outputs or system behavior changes.
Interactive Visualization
A visual display that responds to user input such as clicking, filtering, dragging, or hovering.
Example: In a vis.js network, interactive visualization can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Graph Layout Algorithm.
Interleaved Practice
A practice strategy that mixes related problem types or topics instead of grouping identical practice together.
Example: In chapter design, interleaved practice can guide whether learners should recall, practice, analyze, or create with a concept.
Intermediate Goal
A mid-level learning target that bridges foundational ideas and final course outcomes.
Example: A dependency review can use intermediate goal to explain how one concept connects to another.
Invalid Dependency ID
An identifier in a dependency record that does not match a valid source or target concept.
Example: Before publishing, invalid dependency id can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
ISO/IEC 11179
An international metadata registry standard used to create precise, concise, distinct, and non-circular definitions for data concepts.
Example: A concept registry can use iso/iec 11179 to keep labels, descriptions, and identifiers consistent across tools.
Iterative Graph Regeneration
A repeated process of revising graph data, rerunning validation, and improving structure or coverage.
Example: A dependency review can use iterative graph regeneration to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Iterative Workflow
A repeated cycle of generation, review, revision, and validation.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
JavaScript
A programming language used to create interactive behavior in web pages and browser-based learning tools.
Example: In the textbook site, javascript can connect graph data, Markdown pages, and interactive learning assets.
See also: Intelligent Textbook, MkDocs, GitHub Pages.
Journey Map
A representation of the steps, decisions, and touchpoints a learner experiences while moving through a course or system.
Example: A dependency review can use journey map to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
JSON
A text format for structured data using objects, arrays, strings, numbers, booleans, and null values.
Example: In the build pipeline, json can help transform a CSV dependency file into a validated JSON graph.
JSON Data Format
A structured text format used to represent graph data as objects, arrays, names, and values.
Example: In the build pipeline, json data format can help transform a CSV dependency file into a validated JSON graph.
See also: JSON, CSV, Data Pipeline.
JSON Graph Export
A workflow that saves graph nodes, edges, groups, and settings to a JSON file.
Example: In a vis.js network, json graph export can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
JSON Graph Import
A workflow that loads graph nodes, edges, groups, and settings from a JSON file.
Example: In a vis.js network, json graph import can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
JSON Schema
A machine-readable specification that defines the required structure and value constraints for JSON data.
Example: In a concept record, json schema can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
JSON Serialization
A data-format or transformation concept used to store, validate, exchange, or publish learning-graph information.
Example: In the build pipeline, json serialization can help transform a CSV dependency file into a validated JSON graph.
See also: JSON, CSV, Data Pipeline.
Just-in-Time Learning
Learning delivered at the moment a learner needs it to solve a task or make a decision.
Example: In chapter design, just-in-time learning can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Knowledge Component
A discrete unit of knowledge or skill that can be taught, practiced, and assessed.
Example: An adaptive textbook can use knowledge component to choose the next useful concept, resource, or feedback message.
Knowledge Graph
A structured network of entities and relationships that makes domain knowledge searchable, connected, and machine-readable.
Example: A dependency review can use knowledge graph to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Knowledge Space Theory
A mathematical theory that models the possible knowledge states a learner can occupy based on prerequisite structure.
Example: In chapter design, knowledge space theory can guide whether learners should recall, practice, analyze, or create with a concept.
Knowledge State
A representation of which concepts or skills a learner is believed to know at a given time.
Example: An adaptive textbook can use knowledge state to choose the next useful concept, resource, or feedback message.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Label Ambiguity
A condition in which a concept label can reasonably refer to more than one meaning.
Example: In a concept record, label ambiguity can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Label Normalization
The process of standardizing labels so equivalent terms use consistent spelling, capitalization, and formatting.
Example: In a concept record, label normalization can help another tool interpret the same concept label without guessing its meaning.
Layout Direction
A setting that determines whether a hierarchical layout flows left-to-right, right-to-left, top-to-bottom, or bottom-to-top.
Example: In a vis.js network, layout direction can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Learned Concept
A concept for which a learner has demonstrated sufficient understanding or mastery.
See also: Learning Graph, Node, Edge.
Learner Model
A data representation of a learner’s goals, prior knowledge, mastery, preferences, and progress.
See also: Adaptive Learning, Personalized Learning Path.
Learning Agent
An intelligent agent that uses learner data and educational goals to recommend, generate, or adapt instruction.
Example: A dependency review can use learning agent to explain how one concept connects to another.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Learning Evidence
Data or observations used to infer learner understanding, progress, or mastery.
Example: After a quiz, learning evidence can help decide whether the learner needs review, practice, or a new challenge.
See also: Assessment, Formative Feedback, Quiz.
Learning Goal
A statement of what a learner should know, do, or produce after instruction.
Example: In chapter design, learning goal can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Learning Graph
A directed graph of concepts that represents prerequisite relationships and supports sequencing, personalization, and validation of learning content.
Example: In a learning-graphs course, “Concept Dependency” can be a node, and an edge can show that it depends on “Directed Graph.”
Learning Graph Validation
The process of checking a learning graph for structural, semantic, and pedagogical correctness.
Example: Before publishing, learning graph validation can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Graph Quality Metric, Quality Gate.
Learning Management System (LMS)
A platform used to deliver courses, track learner progress, manage enrollments, and host learning resources.
Example: In chapter design, learning management system (lms) can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Learning Objective
A specific statement of what a learner should be able to do after instruction.
See also: Instructional Design, Mastery Learning.
Learning Path
An ordered or adaptive route through concepts, resources, and assessments.
Example: An adaptive textbook can use learning path to choose the next useful concept, resource, or feedback message.
Learning Path Constraint
A rule that limits which concepts or resources can appear next in a learning sequence.
Learning Path Recommendation
A suggested next sequence of concepts or resources based on learner state and graph structure.
Example: An adaptive textbook can use learning path recommendation to choose the next useful concept, resource, or feedback message.
Learning Progression
A planned sequence of increasingly sophisticated understandings or performances in a domain.
Example: In chapter design, learning progression can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Learning Sequence
An ordered progression of concepts, activities, or resources selected for instruction.
Example: In chapter design, learning sequence can guide whether learners should recall, practice, analyze, or create with a concept.
Learning Touchpoint
A moment when a learner interacts with content, feedback, assessment, instructor support, or a learning system.
Example: In chapter design, learning touchpoint can guide whether learners should recall, practice, analyze, or create with a concept.
Learning Trajectory
A hypothesized path of increasingly advanced understanding that learners may follow through a domain.
Example: In chapter design, learning trajectory can guide whether learners should recall, practice, analyze, or create with a concept.
Legend Display
A visual guide that explains colors, shapes, groups, or symbols used in a graph.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
License Metadata
Metadata that states the permissions, restrictions, or reuse terms associated with a resource.
Example: In a concept record, license metadata can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
LLM-Generated Dependency Graph
A dependency graph whose nodes or edges are produced by a large language model from prompts and source material.
Example: An AI-assisted workflow can use llm-generated dependency graph to generate artifacts while keeping the result inspectable by humans.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
LMS Integration
The connection between textbook content and a learning management system for delivery, tracking, or reporting.
Example: In the textbook site, lms integration can connect graph data, Markdown pages, and interactive learning assets.
Machine-Readable Metadata
Metadata formatted so software can parse, validate, search, or exchange it without manual interpretation.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Markdown
A lightweight markup format for writing structured text that can be converted to HTML.
See also: JSON, CSV, Data Pipeline.
Mastery Learning
An instructional approach in which learners continue practice and feedback until they demonstrate a defined level of competence.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Mastery Prerequisite
A prerequisite that requires demonstrated mastery before a learner should proceed to a dependent concept.
Example: When generating a chapter sequence, mastery prerequisite can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Mastery State
A recorded estimate of whether a learner has met the required level of understanding for a concept.
Example: In an adaptive textbook, mastery state can help select the next concept after a learner demonstrates mastery of prerequisites.
Mastery Threshold
The criterion used to decide whether evidence is strong enough to count as mastery.
Example: Before publishing, mastery threshold can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Maximum Path Length
The greatest number of edges in the longest relevant path through a graph.
Example: A dependency review can use maximum path length to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Metacognition
Awareness and regulation of one’s own thinking, learning strategies, and understanding.
See also: Instructional Design, Learning Objective, Mastery Learning.
Metadata Field
A named element in a metadata record that stores one kind of descriptive or administrative information.
Example: In a concept record, metadata field can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Controlled Vocabulary.
Metadata Record
A structured collection of metadata fields describing one concept, resource, edge, or graph artifact.
Example: In a concept record, metadata record can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Metadata Validation
The process of checking metadata fields for completeness, format, allowed values, and consistency.
Example: In a concept record, metadata validation can help another tool interpret the same concept label without guessing its meaning.
MicroSim
A small interactive simulation designed to help learners explore a concept through manipulation and feedback.
Example: In the textbook site, microsim can connect graph data, Markdown pages, and interactive learning assets.
See also: Intelligent Textbook, MkDocs, GitHub Pages.
Miscellaneous Category
A fallback taxonomy group used for concepts that do not fit existing categories.
Example: A dependency review can use miscellaneous category to explain how one concept connects to another.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Missing Dependency
A prerequisite relationship that should exist but is absent from the graph data.
Example: Before publishing, missing dependency can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
MkDocs
A static site generator that builds documentation websites from Markdown files and configuration data.
See also: Intelligent Textbook, GitHub Pages.
Multi-Agent Collaboration
A workflow in which multiple agents coordinate specialized tasks to complete a larger objective.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Multiple-Choice Question
An assessment item that asks learners to choose one or more answers from provided options.
Example: After a quiz, multiple-choice question can help decide whether the learner needs review, practice, or a new challenge.
Named Concept
A concept with an explicit label that can be referenced, defined, classified, and linked in a learning graph.
Example: A dependency review can use named concept to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Naming Convention
A metadata concept used to name, describe, identify, govern, or exchange learning-graph concepts in a consistent form.
Necessary Prerequisite
A prerequisite concept that must be understood before the target concept can be learned effectively.
Example: When generating a chapter sequence, necessary prerequisite can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Network Container
The HTML element that holds a rendered vis.js network visualization.
Example: In a vis.js network, network container can make a dense learning graph easier to inspect, filter, or navigate.
Network Initialization
The setup step that creates a vis.js network from a container, node data, edge data, and options.
Example: In a vis.js network, network initialization can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Nine Events of Instruction
Gagné’s instructional sequence for gaining attention, presenting content, guiding practice, and supporting transfer.
Example: In chapter design, nine events of instruction can guide whether learners should recall, practice, analyze, or create with a concept.
Node
A data object in a graph that represents one concept, resource, learner state, or other entity being connected.
Node Background Color
A visual property that sets the fill color inside a node.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Border Color
A visual property that controls the color of a node’s outline.
Example: In a vis.js network, node border color can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Border Width
A visual property that controls the thickness of a node’s outline.
Example: A vis.js activity can use node border width to make graph structure easier to inspect or manipulate.
Node Degree
The number of edges connected to a node, usually counted as incoming, outgoing, or total degree.
Example: A dependency review can use node degree to explain how one concept connects to another.
Node Enricher Script
A script that adds missing metadata, styling, or derived fields to node records.
Example: A dependency review can use node enricher script to explain how one concept connects to another.
Node Font Color
A visual property that controls the color of text displayed on or near a node.
Example: In a vis.js network, node font color can make a dense learning graph easier to inspect, filter, or navigate.
Node Group
A graph attribute that assigns nodes to a shared visual or semantic group.
See also: Learning Graph, Node, Edge.
Node Icon
A symbol used inside or instead of a node to communicate category, function, or state.
Example: In a vis.js network, node icon can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Image
An image used as the visual representation of a node.
Example: In a vis.js network, node image can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Inspector
An interface panel that displays or edits the properties of a selected node.
Example: A vis.js activity can use node inspector to make graph structure easier to inspect or manipulate.
Node Opacity
A visual property that controls how transparent or solid a node appears.
Example: In a vis.js network, node opacity can make a dense learning graph easier to inspect, filter, or navigate.
Node Pinning
The act of fixing a node’s position in an interactive graph layout.
Example: In a vis.js network, node pinning can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Property Editor
An interface for changing the label, group, shape, position, or metadata of a node.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node Record
A structured data entry that stores a graph node’s identifier, label, group, and related properties.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Node Shadow
A visual effect that draws a shadow behind a node to create emphasis or depth.
Node Shape
A visual property that determines the geometric form used to draw a node.
Node Size
A visual property that controls how large a node appears in a graph.
Example: A vis.js activity can use node size to make graph structure easier to inspect or manipulate.
Node Tooltip
Text or HTML shown when a user hovers over or focuses a node.
Example: In a vis.js network, node tooltip can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Node URL Property
A node data field that stores a link to open when the node is activated.
Example: A dependency review can use node url property to explain how one concept connects to another.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Non-Linear Learning Path
A learning route that allows multiple valid sequences through concepts instead of one predetermined order.
Example: A dependency review can use non-linear learning path to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Ontology
A formal model of a domain that defines classes, properties, and relationships among concepts or entities.
Example: A taxonomy review can use ontology to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category.
Ontology Class
A formal category in an ontology used to group entities that share defining characteristics.
Example: A taxonomy review can use ontology class to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Ontology Property
A named relationship or attribute in an ontology that connects classes, entities, or values.
Example: A taxonomy review can use ontology property to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Orphaned Node
A node with no useful incoming or outgoing connections, making it isolated from the learning graph.
Orphaned Node Detection
A validation check that finds nodes without meaningful graph connections.
Example: Before publishing, orphaned node detection can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Out-Degree
The number of outgoing edges connected from a node.
Example: A dependency review can use out-degree to explain how one concept connects to another.
See also: Indegree, Node Degree, Learning Graph.
Outgoing Edge
An edge that leaves a node and points to another node.
See also: Incoming Edge, Directed Edge, Learning Graph.
Overconstraint
A graph condition in which too many prerequisites restrict learning paths more than instructionally necessary.
Example: Requiring “Ontology Property” before every taxonomy lesson may overconstrain a learner who only needs basic category assignment.
p5.js
A JavaScript library for creative coding, commonly used to build browser-based educational simulations.
Example: In the textbook site, p5.js can connect graph data, Markdown pages, and interactive learning assets.
Parallel Learning Paths
Multiple valid routes through concepts that can be pursued independently or in different orders.
See also: Learning Graph, Node, Edge.
Partial Order
An ordering relation where some items must precede others while unrelated items may appear in multiple orders.
Path Length
The number of edges in a graph path.
Example: A dependency review can use path length to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Pedagogical Coherence
The degree to which concepts, dependencies, objectives, assessments, and explanations fit together instructionally.
Personalized Feedback Loop
A cycle in which learner evidence is used to provide feedback and update future instruction.
See also: Assessment, Formative Feedback, Quiz.
Personalized Learning Path
A learning path adjusted to a learner’s goals, prior knowledge, progress, or preferences.
Example: An adaptive textbook can use personalized learning path to choose the next useful concept, resource, or feedback message.
Personalized Sequencing
The selection of concept order for a learner based on goals, prerequisites, mastery, and constraints.
Example: Two learners can receive different next concepts if one has mastered metadata standards and the other still needs label normalization.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Physics Simulation
A layout simulation that applies forces to nodes and edges to find stable graph positions.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Pipe-Delimited Field
A text field that stores multiple values separated by pipe characters.
Example: In a concept record, pipe-delimited field can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Pipeline Session Log
A record of generation steps, decisions, files, validation results, and human review points.
Example: A dependency review can use pipeline session log to explain how one concept connects to another.
Polyhierarchy
A classification structure in which a concept can have more than one broader parent category.
Example: A taxonomy review can use polyhierarchy to decide whether concepts are grouped clearly and evenly.
Preferred Label
The primary label chosen as the standard name for a concept within a controlled vocabulary or glossary.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Prerequisite Chain
A sequence of prerequisite relationships that connects foundational concepts to later or more complex concepts.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Prerequisite Concept
A concept that provides necessary background for learning a dependent concept.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Prerequisite Frontier
The set of concepts that are currently available to learn because their prerequisites have been satisfied.
Example: After a learner masters “Node” and “Edge,” “Directed Graph” may appear on the prerequisite frontier.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Prerequisite Relationship
A directional relationship indicating that one concept should normally be learned before another concept.
Prerequisite-Based Sequencing
A sequencing approach that orders instruction according to prerequisite relationships among concepts.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Prior Knowledge Activation
An instructional strategy that helps learners connect new material to knowledge they already possess.
See also: Instructional Design, Learning Objective, Mastery Learning.
Prompt Engineering
The practice of designing prompts, examples, constraints, and context to guide generative AI output.
Example: An AI-assisted workflow can use prompt engineering to generate artifacts while keeping the result inspectable by humans.
Psychomotor Domain
The domain of learning concerned with physical skills, coordination, and action-based performance.
Example: In chapter design, psychomotor domain can guide whether learners should recall, practice, analyze, or create with a concept.
Quality Gate
A required validation checkpoint that content or data must pass before moving to the next workflow step.
Example: A graph quality check can use quality gate to decide whether generated graph data needs revision.
Quality Metrics Report
A document that summarizes validation results, graph statistics, warnings, and improvement recommendations.
Quiz
A short assessment used to check understanding, practice recall, or provide feedback.
Example: After a quiz, quiz can help decide whether the learner needs review, practice, or a new challenge.
See also: Assessment, Formative Feedback.
Quiz Distractor
An incorrect answer option designed to reveal misconceptions or partial understanding.
See also: Assessment, Formative Feedback, Quiz.
Reachability
The property that one node can be reached from another by following graph edges.
Example: A dependency review can use reachability to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Readiness Estimation
The process of estimating whether a learner is prepared to begin a concept or activity.
Ready-to-Learn Concept
A concept whose prerequisites are already satisfied for a learner, making it appropriate as the next learning target.
Example: A dependency review can use ready-to-learn concept to explain how one concept connects to another.
Real-World Application
A practical use of a concept, method, or system outside a simplified classroom example.
Example: In the textbook site, real-world application can connect graph data, Markdown pages, and interactive learning assets.
See also: Intelligent Textbook, MkDocs, GitHub Pages.
Recommendation Engine
A system that ranks or selects items for a user based on data, rules, similarity, or predicted usefulness.
Recommended Prerequisite
A prerequisite that is helpful for learning a concept but not strictly required.
Redundant Dependency
A dependency edge that repeats an indirect relationship already implied by another path.
Example: When generating a chapter sequence, redundant dependency can show whether a proposed prerequisite edge is necessary or merely convenient.
See also: Concept Dependency, Prerequisite Relationship, Dependency Analysis.
Referential Integrity
A data quality property requiring every reference or identifier to point to an existing valid record.
Example: Before publishing, referential integrity can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Remembering
The Bloom’s Taxonomy level focused on recognizing, recalling, or retrieving information from memory.
Example: A remembering objective might ask learners to list the parts of a concept dependency graph.
See also: Understanding, Bloom's Taxonomy, Learning Graph.
Reproducible Generation
A generation process that can be rerun with the same inputs and produce consistent, reviewable outputs.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Repulsion Solver
A layout solver that pushes nodes away from one another to reduce overlap.
Example: In a vis.js network, repulsion solver can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Retrieval Practice
A learning strategy that strengthens memory by requiring learners to recall information from memory.
Example: In chapter design, retrieval practice can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Retrieval-Augmented Generation
A generation pattern that retrieves relevant source material before producing an answer or artifact.
Example: An AI-assisted workflow can use retrieval-augmented generation to generate artifacts while keeping the result inspectable by humans.
See also: Generative AI, Intelligent Agent, Prompt Engineering.
Rhizombic Learning
A non-linear learning model in which learners explore many connected paths rather than following one fixed sequence.
Example: In chapter design, rhizombic learning can guide whether learners should recall, practice, analyze, or create with a concept.
Root Concept
A concept with no prerequisites inside the graph or subgraph being analyzed.
See also: Terminal Concept, Source Node, Learning Graph.
Scaffolding
Temporary instructional support that helps learners perform tasks they could not complete independently yet.
Example: A chapter designer can use scaffolding to align explanations, practice, and feedback with how professionals learn.
Schema Conformance
The degree to which data follows the fields, types, and constraints defined by a schema.
Example: In a concept record, schema conformance can help another tool interpret the same concept label without guessing its meaning.
Schema Theory
A theory describing how people organize knowledge into mental structures that guide understanding and recall.
Schema Validation
The process of checking data against a formal schema.
Example: In a concept record, schema validation can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Search Feature (Graph)
An interaction feature that finds graph nodes or edges matching a user query.
Self-Dependency Prevention
A validation rule that blocks a concept from depending on itself.
Example: When generating a chapter sequence, self-dependency prevention can show whether a proposed prerequisite edge is necessary or merely convenient.
Self-Directed Exploration
A learning approach in which learners choose paths, questions, or resources based on their goals and interests.
Example: A dependency review can use self-directed exploration to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Self-Regulated Learning
A learner’s ability to plan, monitor, evaluate, and adjust their own learning process.
Semantic Relationship
A meaning-based connection between concepts, such as broader, narrower, related, prerequisite, or equivalent.
Semantic Type
A category that describes what kind of thing a concept represents in a formal model.
Similarity Metric
A calculation used to estimate how alike two objects are along chosen features.
Example: A dependency review can use similarity metric to explain how one concept connects to another.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Sink Node
A node with incoming edges but no outgoing edges in the direction being analyzed.
Example: A dependency review can use sink node to explain how one concept connects to another.
Skill Gap
The difference between what a learner currently knows and what the learner must know to reach a target concept or competency.
Skill Invocation
The act of requesting or triggering a named agent skill for a specific task.
SKOS
A W3C vocabulary for representing concept schemes, labels, broader terms, narrower terms, and related terms in machine-readable form.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
SKOS Alternate Label
A SKOS property used for synonyms, abbreviations, or variant labels for the same concept.
Example: In a concept record, skos alternate label can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
SKOS Broader Relation
A SKOS relationship indicating that one concept is more general than another.
Example: In a concept record, skos broader relation can help another tool interpret the same concept label without guessing its meaning.
SKOS Narrower Relation
A SKOS relationship indicating that one concept is more specific than another.
Example: In a concept record, skos narrower relation can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
SKOS Preferred Label
A SKOS property used for the primary human-readable label of a concept.
Example: In a concept record, skos preferred label can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
SKOS Related Relation
A SKOS relationship indicating association without a broader-or-narrower hierarchy.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Source Node
The starting node of a directed edge or directed path.
Example: A dependency review can use source node to explain how one concept connects to another.
See also: Sink Node, Root Concept, Learning Graph.
Spaced Repetition
A practice strategy that schedules review over increasing intervals to improve long-term retention.
Example: A chapter designer can use spaced repetition to align explanations, practice, and feedback with how professionals learn.
Spiral Curriculum
A curriculum design pattern that revisits important ideas at increasing levels of depth and complexity.
Example: In chapter design, spiral curriculum can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Spring Length Parameter
A physics parameter that controls the preferred distance between connected nodes.
Stabilization
The process by which a physics simulation settles into a stable graph layout.
Strong Connectivity
A directed-graph property in which every node can reach every other node by following edge directions.
Example: A dependency review can use strong connectivity to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Structural Check
A validation check that examines the graph format, identifiers, edge references, and allowed relationship patterns.
Example: Before publishing, structural check can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Student Similarity
A measure of how closely learners resemble one another in goals, mastery evidence, behavior, or needs.
Example: An adaptive textbook can use student similarity to choose the next useful concept, resource, or feedback message.
See also: Adaptive Learning, Learner Model, Personalized Learning Path.
Summative Assessment
An assessment used to judge learning achievement after instruction or at a major checkpoint.
Example: After a quiz, summative assessment can help decide whether the learner needs review, practice, or a new challenge.
Synonym Management
A metadata concept used to name, describe, identify, govern, or exchange learning-graph concepts in a consistent form.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Taxonomy Breadth
The number and spread of categories at a given level of a taxonomy.
Example: A taxonomy review can use taxonomy breadth to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Taxonomy Category
A named group within a taxonomy used to organize concepts with related purpose or meaning.
Example: A taxonomy review can use taxonomy category to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Ontology.
Taxonomy Classifier
A rule, model, or workflow that assigns concepts to taxonomy categories.
Example: A taxonomy review can use taxonomy classifier to decide whether concepts are grouped clearly and evenly.
Taxonomy Depth
The number of hierarchical levels between the broadest and narrowest categories in a taxonomy.
Example: A taxonomy review can use taxonomy depth to decide whether concepts are grouped clearly and evenly.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Taxonomy Distribution Report
A report that shows how concepts are spread across taxonomy categories and flags imbalance or gaps.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Taxonomy Drift
A gradual mismatch between taxonomy categories and the concepts they are meant to organize.
Example: A taxonomy review can use taxonomy drift to decide whether concepts are grouped clearly and evenly.
Taxonomy Generation Step
The workflow step that assigns concepts to a balanced set of taxonomy categories.
Example: A dependency review can use taxonomy generation step to explain how one concept connects to another.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Taxonomy Legend
A visual key that maps taxonomy categories to colors, labels, or symbols in a graph display.
Taxonomy Name Mapping
A mapping that connects taxonomy identifiers to human-readable category names.
Example: A taxonomy review can use taxonomy name mapping to decide whether concepts are grouped clearly and evenly.
Taxonomy Validation
The process of checking taxonomy categories, assignments, balance, and coverage for consistency and usefulness.
Example: A taxonomy review can use taxonomy validation to decide whether concepts are grouped clearly and evenly.
TaxonomyID
A stable identifier that links a concept record to its assigned taxonomy category.
Example: A dependency review can use taxonomyid to explain how one concept connects to another.
See also: Concept Taxonomy, Taxonomy Category, Ontology.
Terminal Concept
A concept at the end of a learning path with no dependent concepts inside the analyzed graph.
See also: Root Concept, Goal Concept, Learning Graph.
Textbook Index
An alphabetical list of important terms and locations that helps readers find where concepts are discussed in a textbook.
Example: In the textbook site, textbook index can connect graph data, Markdown pages, and interactive learning assets.
Third-Party Certification
An external review or credential showing that a learning graph, course, or resource meets defined quality standards.
See also: Learning Graph, Node, Edge.
Tool Use (Agent)
An agent capability that allows software tools, files, APIs, or external systems to be called during work.
Topological Sort
An ordering of directed graph nodes in which each node appears after its prerequisites.
Example: A dependency review can use topological sort to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Transitive Closure
The complete set of direct and indirect reachability relationships implied by a graph.
Transitive Dependency
An indirect prerequisite relationship that exists because one concept depends on another through one or more intermediate concepts.
Transitive Reduction
A simplified graph that removes redundant edges while preserving reachability.
See also: Learning Graph, Node, Edge.
True/False Question
An assessment item that asks learners to judge whether a statement is correct or incorrect.
See also: Assessment, Formative Feedback, Quiz.
Type-Ahead Filtering
A search interaction that narrows choices as a user types characters.
Ultimate Goal
The highest-level learning target that represents the intended end state of a course, chapter, or learning path.
Example: A dependency review can use ultimate goal to explain how one concept connects to another.
See also: Learning Graph, Node, Edge.
Underconstraint
A graph condition in which too few prerequisites allow learning paths that skip necessary preparation.
Example: Allowing “GraphRAG” before “Retrieval-Augmented Generation” may underconstrain the learning path.
See also: Overconstraint, Dependency Gap, Concept Dependency.
Understanding
The Bloom’s Taxonomy level focused on explaining, interpreting, summarizing, or classifying meaning.
Example: An understanding objective might ask learners to explain why a directed graph can represent prerequisites.
See also: Remembering, Applying, Learning Graph.
Unique Concept Identifier
A stable value that distinguishes one concept from every other concept in a graph or registry.
Example: In a concept record, unique concept identifier can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Universal Design for Learning
A design framework that provides multiple ways for learners to engage, understand, and express learning.
Example: In chapter design, universal design for learning can guide whether learners should recall, practice, analyze, or create with a concept.
See also: Instructional Design, Learning Objective, Mastery Learning.
Validation Error
A validation result that indicates data violates a required rule and should be corrected.
Example: Before publishing, validation error can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Validation Warning, Automated Validator, Learning Graph Validation.
Validation Threshold
A minimum score, count, or condition that must be met for an artifact to pass validation.
Example: Before publishing, validation threshold can flag a cycle, missing identifier, or disconnected concept that needs human review.
See also: Learning Graph Validation, Graph Quality Metric, Quality Gate.
Validation Warning
A validation result that signals a possible issue without necessarily blocking progress.
Example: Before publishing, validation warning can flag a cycle, missing identifier, or disconnected concept that needs human review.
Version Metadata
Metadata that records the version, revision, or generation state of an artifact.
Example: In a concept record, version metadata can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Vis-Network Library
The vis.js module focused on drawing and interacting with node-link network diagrams.
Example: In a vis.js network, vis-network library can make a dense learning graph easier to inspect, filter, or navigate.
See also: Vis.js, Interactive Visualization, Graph Layout Algorithm.
Vis.js
A JavaScript visualization library used to render interactive network graphs in a web browser.
Vocabulary Governance
The policies and review practices used to maintain consistent terms, labels, and definitions.
Example: In a concept record, vocabulary governance can help another tool interpret the same concept label without guessing its meaning.
See also: Concept Label, Metadata Field, Controlled Vocabulary.
Vygotsky's Social Theory
A theory emphasizing that learning develops through social interaction, language, culture, and guided support.
Example: A chapter designer can use vygotsky's social theory to align explanations, practice, and feedback with how professionals learn.
Weak Connectivity
A directed-graph property in which nodes are connected if edge direction is ignored.
Working Memory Constraint
A limit on how much new information a learner can actively process at one time.
See also: Instructional Design, Learning Objective, Mastery Learning.
YAML
A human-readable data serialization format commonly used for configuration files.
Zone of Proximal Development
The range of tasks a learner can complete with guidance but not yet independently.
Example: A chapter designer can use zone of proximal development to align explanations, practice, and feedback with how professionals learn.