Glossary of Terms
This glossary contains definitions for the 200 key concepts covered in the Automating Instructional Design course. Each definition follows ISO 11179 metadata registry guidelines: precise, concise, distinct, non-circular, and unencumbered with business rules.
A/B Testing
A research method that compares two versions of a learning experience to determine which performs better based on measurable outcomes.
A/B testing is valuable in instructional design for making data-driven decisions about MicroSim features, layouts, or interaction patterns.
Example: Testing whether learners achieve better outcomes with a slider-based input versus a text field for parameter adjustment.
Ability-Based Feedback
Learner feedback that is tailored to accommodate different skill levels or learning capabilities.
This approach recognizes that learners with varying abilities may need different types of support, scaffolding, or challenge levels.
Example: Providing additional hints for struggling learners while offering extension challenges for advanced learners within the same MicroSim.
Abstract Concepts
Ideas or principles that cannot be directly perceived through the senses and require mental representation to understand.
Abstract concepts often benefit most from interactive simulations that make invisible relationships visible.
Example: Supply and demand, gravity, or probability are abstract concepts that MicroSims can help visualize.
Accessibility Audit
A systematic evaluation of a learning resource to identify barriers that prevent learners with disabilities from fully accessing the content.
Accessibility audits typically check for screen reader compatibility, keyboard navigation, color contrast, and motion sensitivity.
Example: Testing a MicroSim with a screen reader to ensure all interactive elements are properly labeled.
Action Verbs
Words that describe observable, measurable behaviors used in learning objectives to specify what learners will be able to do.
Selecting appropriate action verbs ensures that learning objectives align with the intended cognitive complexity level.
Example: "Calculate," "compare," and "design" are action verbs; "understand" and "appreciate" are too vague for learning objectives.
Age-Based Feedback
Learner feedback that is gathered or interpreted with consideration for the developmental stage of the participant.
Different age groups may express confusion, engagement, or understanding in different ways, requiring adapted feedback collection methods.
Example: Using emoji-based ratings for elementary students versus written reflections for adults.
AI Interpretation
The process by which an artificial intelligence system converts natural language specifications into generated outputs.
Understanding how AI interprets instructions helps instructional designers write specifications that produce desired results.
Example: An AI may interpret "colorful" differently than "using the colors red, blue, and green," leading to different outputs.
AI-Assisted Design
An approach to instructional design that leverages artificial intelligence tools to generate, refine, or evaluate educational content.
AI-assisted design shifts the designer's role from implementation to specification and quality assurance.
Example: Using Claude Code skills to generate interactive simulations from detailed specifications.
Analyze Level
The fourth level of Bloom's Taxonomy involving breaking material into constituent parts and detecting how parts relate to each other and to an overall structure.
Analysis requires learners to go beyond understanding to actively examine and deconstruct information.
Example: A learning objective at this level: "Compare three sorting algorithms in terms of time complexity."
Animation Speed
The rate at which visual changes occur in a dynamic simulation or demonstration.
Animation speed significantly affects cognitive load; speeds that are too fast overwhelm learners while speeds that are too slow lose engagement.
Example: Complex physics simulations often need slower animation speeds than simple demonstrations.
Apply Level
The third level of Bloom's Taxonomy involving carrying out or using a procedure in a given situation.
Application requires learners to use acquired knowledge to solve problems or complete tasks.
Example: A learning objective at this level: "Calculate the mean and standard deviation for a data set."
Assumed Knowledge
The prerequisite knowledge that learners are expected to possess before beginning instruction.
Identifying assumed knowledge helps designers avoid unnecessary explanation while ensuring learners have the foundation needed for new content.
Example: A calculus course assumes knowledge of algebra and trigonometry.
Atomic Concepts
Single, indivisible units of knowledge or skill that can be taught and assessed independently.
Atomic concepts are the building blocks of more complex understanding and serve as the nodes in a learning graph.
Example: "The definition of mean in statistics" is an atomic concept; "perform statistical analysis" is not.
Automated Evaluation
Assessment of learning outcomes or resource quality performed by computer systems without human intervention.
Automated evaluation can provide immediate feedback at scale but may miss nuances that human evaluators catch.
Example: A quiz system that grades multiple-choice questions instantly and provides feedback.
Behavior Constraints
Limits or boundaries placed on the possible interactions within a MicroSim to focus learning on specific concepts.
Well-designed constraints prevent learners from exploring unproductive paths while still allowing meaningful exploration.
Example: Limiting a physics simulation to two dimensions to simplify learning before introducing three-dimensional concepts.
Bloom's Taxonomy
A hierarchical classification system for cognitive complexity levels used to categorize and write learning objectives.
The 2001 revision by Anderson and Krathwohl uses verbs (Remember, Understand, Apply, Analyze, Evaluate, Create) to emphasize active learning.
Example: Using Bloom's Taxonomy to ensure a course includes learning objectives at multiple cognitive levels.
Bug Identification
The process of finding and documenting defects or errors in interactive simulations or software.
Bug identification is part of technical evaluation and ensures MicroSims function correctly before deployment.
Example: Discovering that a slider control produces incorrect values at its boundary positions.
Cause-Effect Display
A visualization that shows how changes in one variable or action lead to changes in another.
Cause-effect displays help learners understand causal relationships that may not be obvious from static descriptions.
Example: A simulation showing how adjusting interest rates affects economic indicators.
Change Log
A documented record of modifications made to a learning resource over time, including the rationale for each change.
Change logs support version control and help teams understand the evolution of educational content.
Example: Recording that a simulation's default parameters were adjusted after user testing revealed confusion.
Change Prioritization
The process of ordering proposed modifications to a learning resource based on impact, effort, and urgency.
Effective change prioritization ensures limited development resources address the most important improvements first.
Example: Prioritizing a fix for a navigation bug over adding an optional advanced feature.
Chart Visualization
A graphical representation of data using bars, lines, areas, or other visual elements to reveal patterns, trends, or comparisons.
Charts are appropriate for learning objectives involving data interpretation, comparison, or trend analysis.
Example: A bar chart comparing population growth across different countries.
Chart.js Library
A JavaScript library for creating responsive, animated charts in web-based applications.
Chart.js is well-suited for MicroSims that need standard chart types with good visual polish and interactivity.
Example: Using Chart.js to create an interactive line chart showing historical temperature data.
Classification Display
A visualization that shows how items are categorized or sorted into groups based on shared characteristics.
Classification displays help learners understand taxonomies, typologies, or categorical relationships.
Example: A Venn diagram showing the overlap between mammals, aquatic animals, and warm-blooded creatures.
Claude Code Skills
Specialized capabilities within Claude Code that enable automated generation of specific types of content, including MicroSims.
Skills provide structured workflows for complex generation tasks, ensuring consistent output quality.
Example: The microsim-generator skill that creates interactive simulations from specifications.
Code Generation
The automated creation of programming code by AI systems based on natural language descriptions or specifications.
Code generation enables non-programmers to create interactive simulations by focusing on what should be built rather than how to build it.
Example: An AI generating JavaScript code for a physics simulation from a plain-language specification.
Cognitive Complexity
The level of mental processing required to complete a learning task, as categorized by frameworks like Bloom's Taxonomy.
Higher cognitive complexity requires more working memory resources and deeper engagement with the material.
Example: Creating a novel solution requires higher cognitive complexity than recognizing a familiar fact.
Cognitive Development
The process by which thinking abilities change and grow over the lifespan, as described by theories such as Piaget's stages.
Understanding cognitive development helps designers create age-appropriate learning experiences.
Example: Designing simpler cause-effect relationships for elementary students than for high school students.
Cognitive Level Match
The alignment between the cognitive complexity of a learning objective and the cognitive demands of its assessment or learning activity.
Mismatches occur when assessments test at a different level than the stated objective.
Example: If an objective states learners will "analyze" but the test only asks them to "recall," there is a mismatch.
Cognitive Load Meter
A visual indicator that estimates and displays the cognitive demands being placed on a learner during an interactive experience.
Cognitive load meters help designers evaluate their choices and help learners self-regulate their learning pace.
Example: A gauge showing that adding more visual elements pushes the interface toward cognitive overload.
Cognitive Load Theory
A framework explaining how the cognitive demands of instruction affect learning, based on the limitations of working memory.
Cognitive load theory distinguishes between intrinsic, extraneous, and germane load, with different design implications for each.
Example: Reducing visual clutter to lower extraneous load and free working memory for learning.
Color Accessibility
Design choices that ensure color is not the only means of conveying information, accommodating users with color vision deficiencies.
Accessible color design often includes patterns, labels, or icons alongside color coding.
Example: Using both color and shape to distinguish data series in a chart.
Color Blindness Design
Design practices that ensure content remains usable and understandable for individuals with various types of color vision deficiency.
Approximately 8% of males and 0.5% of females have some form of color vision deficiency.
Example: Avoiding red-green color combinations as the sole distinguishing feature between elements.
Common Misconceptions
Incorrect beliefs or mental models that are frequently held by learners in a particular domain.
Identifying common misconceptions allows designers to create simulations that specifically address and correct these errors.
Example: The misconception that seasons are caused by Earth's distance from the sun rather than axial tilt.
Completion Criteria
The standards or conditions that define when a learning resource or iteration is considered finished.
Clear completion criteria prevent both premature release and endless refinement.
Example: A MicroSim is complete when it passes technical testing, achieves user testing goals, and meets accessibility standards.
Compound Objectives
Learning objectives that combine multiple atomic concepts or skills into a single statement.
Compound objectives should be decomposed into atomic components for effective instruction and assessment.
Example: "Design and implement a RESTful API with authentication" combines multiple skills into one objective.
Concept Characteristics
The properties or features of a concept that influence how it can best be taught or visualized.
Understanding concept characteristics helps in selecting appropriate visualization paradigms.
Example: Concepts involving change over time may be best suited for timeline or animation visualizations.
Concept Dependencies
The prerequisite relationships between concepts where understanding one concept requires prior understanding of another.
Dependencies form the edges in a learning graph, determining the order in which concepts should be taught.
Example: Understanding "function" depends on first understanding "variable" in programming.
Conceptual Boundary
The limits or edges of where a concept applies, distinguishing it from related or similar concepts.
Exploring conceptual boundaries helps learners understand when and where to apply their knowledge.
Example: Understanding where Newtonian physics stops working and relativistic physics becomes necessary.
Conceptual Change
The process by which learners revise or replace existing mental models with more accurate ones.
Conceptual change is often necessary when learners hold misconceptions that conflict with accurate understanding.
Example: Shifting from believing that heavier objects fall faster to understanding uniform acceleration.
Constraint Simulation
A learning activity that simulates the experience of operating under specific limitations, such as accessibility constraints.
Constraint simulations help designers develop empathy and understanding for diverse learner needs.
Example: Using a MicroSim in grayscale mode to experience how color-blind users perceive the interface.
Content Sharing
The practice of making educational resources available to other educators for reuse, adaptation, or collaboration.
Content sharing increases the return on instructional design investment and promotes best practices.
Example: Publishing MicroSims under Creative Commons licenses for other educators to use.
Contrast Design
The use of differences in color, size, or other visual properties to make important elements stand out from their surroundings.
Adequate contrast is essential for accessibility and reduces extraneous cognitive load.
Example: Ensuring text has at least a 4.5:1 contrast ratio against its background color.
Conversation Prompting
The practice of refining AI-generated outputs through iterative dialogue with the AI system.
Effective conversation prompting treats AI interaction as a collaborative refinement process rather than a single query.
Example: Asking an AI to "make the colors more distinct" after reviewing initial output.
Correlation Display
A visualization showing how two or more variables change in relation to each other.
Correlation displays help learners identify relationships without implying causation.
Example: A scatter plot showing the relationship between study hours and test scores.
Create Level
The sixth and highest level of Bloom's Taxonomy involving putting elements together to form a novel, coherent whole.
Creation requires synthesis of knowledge to produce something new rather than reproducing existing information.
Example: A learning objective at this level: "Design a MicroSim specification for a given learning objective."
Critical Changes
Modifications to a learning resource that are essential for it to function correctly or achieve its learning objectives.
Critical changes take priority over enhancements during the iteration process.
Example: Fixing a bug that causes the simulation to display incorrect data.
Cultural Sensitivity
Awareness of and respect for cultural differences in designing educational content, avoiding assumptions or biases.
Culturally sensitive design uses inclusive examples and avoids content that may alienate learners from different backgrounds.
Example: Using names and scenarios from diverse cultural contexts in example problems.
Data Interpretation
The skill of extracting meaning and insights from data presented in various formats.
Data interpretation is a common learning objective at the Analyze and Evaluate levels of Bloom's Taxonomy.
Example: Reading a population pyramid chart to draw conclusions about demographic trends.
Dependency Mapping
The process of documenting and visualizing the prerequisite relationships between concepts in a curriculum.
Dependency mapping reveals the structure needed for effective learning sequences and identifies bottleneck concepts.
Example: Creating a directed graph showing which concepts must be learned before others.
Design Rationale
The documented reasoning behind design decisions, explaining why particular choices were made.
Recording design rationale helps future designers understand and build upon previous work.
Example: Noting that a particular color scheme was chosen for accessibility rather than aesthetics.
Design Tradeoffs
The competing considerations that require compromises in instructional design, where improving one aspect may diminish another.
Understanding tradeoffs helps designers make conscious choices rather than inadvertently sacrificing important qualities.
Example: Increasing visual simplicity may reduce information completeness.
Design-Test-Refine Cycle
An iterative process of creating a design, evaluating it with users, and making improvements based on findings.
Multiple cycles of design-test-refine lead to progressively better learning experiences.
Example: Testing a MicroSim with learners, gathering feedback, making changes, and testing again.
Diagram Visualization
A visual representation using shapes, lines, and labels to show relationships, processes, or structures.
Diagrams are effective for learning objectives involving understanding of systems, flows, or hierarchies.
Example: A flowchart showing the steps in a decision-making process.
Differentiation Strategy
An approach that adapts instruction to meet the varying needs of different learners within the same experience.
Differentiation can address differences in prior knowledge, learning pace, or preferred modalities.
Example: Providing optional hints that struggling learners can access while others proceed independently.
Distribution Chart
A visualization showing how data values are spread across a range, revealing patterns of frequency or density.
Distribution charts help learners understand concepts like normal distribution, skewness, or outliers.
Example: A histogram showing the distribution of test scores in a class.
Documentation Standard
A consistent format and level of detail for recording information about learning resources to support reuse and maintenance.
Documentation standards ensure that MicroSims can be understood, modified, and maintained by future designers.
Example: A template requiring each MicroSim to include learning objectives, user guide, and technical requirements.
Dynamic Systems
Systems in which elements interact and change over time, often exhibiting emergent behavior.
Dynamic systems are well-suited for simulation-based learning because static representations cannot capture their behavior.
Example: An ecosystem simulation showing predator-prey population dynamics.
Early Childhood Design
Design approaches tailored for learners approximately ages 3-7, emphasizing large touch targets, simple cause-effect relationships, and minimal text.
Early childhood designs must accommodate developing motor skills and pre-reading abilities.
Example: Using drag-and-drop interactions with large, colorful objects rather than typed input.
Edge Case Definition
The specification of how a MicroSim should behave under unusual, extreme, or boundary conditions.
Defining edge cases prevents unexpected behavior that could confuse learners or produce incorrect results.
Example: Specifying what happens when a slider is moved to its minimum or maximum position.
Educational Technology
Tools, systems, and digital resources used to facilitate, enhance, or assess learning.
Educational technology encompasses learning management systems, interactive simulations, assessment tools, and AI assistants.
Example: A MicroSim is an example of educational technology used for concept demonstration.
Educator Collaboration
Working together with other educators to share resources, exchange ideas, and improve instructional practices.
Collaboration multiplies the impact of instructional design efforts and promotes continuous improvement.
Example: A team of teachers contributing to and refining a shared library of MicroSims.
Effectiveness Measure
A quantifiable indicator of how well a learning resource achieves its intended learning objectives.
Effectiveness measures enable evidence-based decisions about instructional design choices.
Example: Comparing pre-test and post-test scores to measure learning gains from a MicroSim.
Elementary Design
Design approaches tailored for learners approximately ages 8-10, featuring guided exploration, scaffolded complexity, and reading support.
Elementary designs can include more text and abstract concepts than early childhood but still need significant scaffolding.
Example: Including optional text-to-speech for written instructions.
Engagement Balance
The appropriate level of interest and motivation generated by a learning experience without creating distraction from learning.
Engagement that focuses attention on learning content is beneficial; engagement through irrelevant features is harmful.
Example: Interactive elements that help learners explore concepts versus decorative animations that distract.
Ethical Research
Research practices that respect participant rights, ensure informed consent, and protect privacy and well-being.
User testing of MicroSims with learners must follow ethical guidelines, especially with children or vulnerable populations.
Example: Obtaining parental consent before testing educational software with minors.
Evaluate Level
The fifth level of Bloom's Taxonomy involving making judgments based on criteria and standards.
Evaluation requires both understanding the subject matter and applying appropriate standards to assess quality or effectiveness.
Example: A learning objective at this level: "Assess whether a MicroSim effectively targets its stated learning objective."
Evaluation Rubric
A structured guide with criteria and standards for assessing the quality of learning resources or learner performance.
Rubrics provide consistency in evaluation and make expectations explicit to both evaluators and creators.
Example: A rubric scoring MicroSims on technical functionality, pedagogical alignment, and accessibility.
Extraneous Load
The cognitive effort wasted on activities that do not contribute to learning, caused by poor instructional design.
Minimizing extraneous load frees working memory resources for actual learning.
Example: Cognitive effort spent searching for related information that is scattered across the screen.
Flowchart
A diagram using standardized shapes and arrows to represent a process, workflow, or algorithm.
Flowcharts help learners understand sequential processes with decision points.
Example: A flowchart showing the steps to troubleshoot a technical problem.
Functionality Testing
The process of verifying that all features of a MicroSim work correctly under normal usage conditions.
Functionality testing catches bugs and errors before learners encounter them.
Example: Testing that all buttons, sliders, and interactive elements respond as expected.
Fundamental Redesign
A major revision of a learning resource that changes its core approach, structure, or implementation.
Fundamental redesign is necessary when incremental improvements cannot address significant problems.
Example: Rebuilding a simulation with a different visualization paradigm after testing shows the original approach was ineffective.
Generation Workflow
The sequence of steps involved in using AI tools to create educational content, from initial prompt to final output.
Understanding the generation workflow helps designers use AI tools efficiently and iteratively.
Example: Writing a specification, generating output, reviewing, providing refinement prompts, and repeating.
Germane Load
The cognitive effort dedicated to learning processes that build schemas and transfer knowledge to long-term memory.
Germane load represents productive mental effort that should be maximized.
Example: Mental effort spent connecting new concepts to prior knowledge or generating explanations.
Graduate Design
Design approaches tailored for graduate-level learners, featuring research applications, theoretical depth, and exploration of limitations.
Graduate designs assume substantial prior knowledge and can present advanced parameter spaces and edge cases.
Example: A simulation allowing exploration of parameter ranges beyond typical undergraduate scenarios.
Guided Exploration
A learning approach that provides structure and direction while allowing learners to make choices and discoveries.
Guided exploration balances learner autonomy with instructional guidance to prevent unproductive wandering.
Example: A simulation with suggested scenarios but freedom to modify parameters.
Hierarchy Display
A visualization showing levels of organization or relationships of subordination within a structure.
Hierarchies are effective for concepts involving classification, organization, or levels of abstraction.
Example: An organizational chart showing reporting relationships in a company.
High School Design
Design approaches tailored for learners approximately ages 14-18, emphasizing real-world applications, data interpretation, and edge cases.
High school designs can include significant complexity and abstract reasoning.
Example: A physics simulation with real-world friction and air resistance considerations.
Human Evaluation
Assessment of learning resources by human reviewers rather than automated systems.
Human evaluation captures nuances, contextual appropriateness, and pedagogical judgment that automated systems may miss.
Example: An expert instructional designer reviewing a MicroSim for pedagogical effectiveness.
Hypothesis Testing
A scientific approach involving making predictions and gathering evidence to support or refute them.
Hypothesis testing is a valuable learning activity that engages higher-order thinking.
Example: A simulation where learners predict an outcome, then run the simulation to test their prediction.
Incremental Improvement
Small, gradual modifications to a learning resource that progressively enhance its quality.
Incremental improvements are appropriate when the fundamental design is sound but refinements are needed.
Example: Adjusting color contrast after accessibility testing identifies issues.
Influence Diagram
A visualization showing how factors or variables affect one another within a system.
Influence diagrams help learners understand complex causal relationships and feedback loops.
Example: A diagram showing how price, demand, supply, and competition influence each other.
Information Density
The amount of information presented within a given visual space.
Optimal information density varies based on learner expertise, content complexity, and display context.
Example: Novice learners typically need lower information density than experts.
Instructional Design
The systematic process of creating educational experiences that make learning efficient, effective, and engaging.
Instructional designers analyze learning needs, design experiences, and evaluate outcomes.
Example: Planning a course that progresses from foundational concepts to advanced applications.
Intelligent Textbook
An interactive digital textbook that adapts to learner needs, tracks progress, and provides personalized learning paths.
Intelligent textbooks often incorporate embedded simulations and assessments.
Example: A textbook that recommends review content when a learner struggles with an assessment.
Intent Preservation
Maintaining the original pedagogical purpose throughout the process of creating or generating learning content.
Intent preservation ensures that AI-generated content serves the stated learning objectives.
Example: Ensuring a specification's learning goals are reflected in the final MicroSim implementation.
Interaction Behavior
How a MicroSim responds to user inputs such as clicks, drags, or parameter changes.
Well-designed interaction behavior provides clear feedback and supports the learning objectives.
Example: A slider that updates a graph in real-time as the user moves it.
Interaction Tracking
Recording and analyzing how learners interact with a MicroSim to understand usage patterns and learning behavior.
Interaction tracking data informs both assessment and iterative improvement.
Example: Logging which parameters learners adjust most frequently in a simulation.
Interactive Simulation
A digital experience that responds to user input and models real or abstract systems dynamically.
Interactive simulations allow learners to explore cause-and-effect relationships through experimentation.
Example: A simulation of planetary orbits where learners can adjust masses and velocities.
Intrinsic Load
The cognitive effort required by the inherent complexity of the learning material itself.
Intrinsic load is determined by element interactivity and learner prior knowledge; it can be managed but not eliminated.
Example: Calculus has higher intrinsic load than basic arithmetic regardless of how it is taught.
Intuitiveness
The quality of a design that allows users to understand how to use it without explicit instruction.
Intuitive designs follow familiar conventions and provide clear affordances.
Example: A play button that learners immediately recognize and know how to use.
Issue Identification
The process of recognizing and documenting problems in AI-generated content that need correction.
Issue identification is a critical skill for quality assurance in AI-assisted design workflows.
Example: Noticing that generated code produces incorrect output for certain input values.
Iteration Management
The practice of organizing and tracking multiple cycles of design refinement efficiently.
Good iteration management prevents duplication of effort and maintains progress toward completion.
Example: Using version control and change logs to track improvements across multiple iterations.
Iterative Refinement
The process of progressively improving a learning resource through repeated cycles of testing and modification.
Iterative refinement accepts that initial designs are rarely optimal and builds in improvement processes.
Example: Testing a MicroSim, gathering feedback, making changes, and testing again until quality goals are met.
Job-Relevant Scenarios
Learning contexts and examples drawn from authentic workplace situations that learners will encounter professionally.
Job-relevant scenarios increase motivation and transfer of learning to real performance.
Example: A corporate training simulation using actual company processes and data.
Keyboard Navigation
The ability to operate all interactive elements using only keyboard inputs without requiring a mouse or touch.
Keyboard navigation is essential for users who cannot use pointing devices and for screen reader users.
Example: Using Tab to move between controls and Enter to activate buttons.
Leaflet Library
An open-source JavaScript library for creating interactive maps in web-based applications.
Leaflet is appropriate for MicroSims involving geographic or spatial learning objectives.
Example: A simulation showing historical migration patterns on an interactive map.
Learner Control
Design features that allow learners to make choices about pacing, sequence, or depth of content.
Learner control accommodates individual differences and supports self-regulated learning.
Example: Speed controls that let learners slow down or speed up animations.
Learner Feedback
Information gathered from learners about their experience with a learning resource.
Feedback informs iterative improvement and validates design decisions.
Example: Surveys, interviews, or observation notes from user testing sessions.
Learning Analytics
The measurement, collection, analysis, and reporting of data about learners and their contexts.
Learning analytics help identify struggling learners, effective content, and opportunities for improvement.
Example: Dashboards showing completion rates and performance patterns across a course.
Learning Objective
A clear, specific statement describing what learners will be able to do after completing an instructional experience.
Effective learning objectives use action verbs and describe observable, measurable behaviors.
Example: "Given a data set, the learner will calculate the standard deviation with 90% accuracy."
Learning Outcome
The actual result of a learning process, representing what learners can demonstrably do after instruction.
Learning outcomes are assessed to determine whether learning objectives were achieved.
Example: Post-test scores showing that learners can perform the targeted skill.
Learning Pathway
A recommended sequence through concepts or modules based on dependencies and learning efficiency.
Learning pathways guide learners through content in an order that respects prerequisite relationships.
Example: A visual map showing the recommended order for completing course modules.
Library Organization
The systematic arrangement and cataloging of learning resources for efficient storage, retrieval, and reuse.
Good library organization enables educators to find and adapt existing resources rather than creating from scratch.
Example: Tagging MicroSims by subject area, grade level, and visualization type.
LMS Integration
The connection of learning resources with a Learning Management System to enable assignment, tracking, and grading.
LMS integration allows MicroSims to be part of a cohesive course experience with centralized data.
Example: Embedding a MicroSim in Canvas or Moodle so completion is automatically recorded.
Long-Term Memory
The memory system with virtually unlimited capacity for storing information over extended periods.
Knowledge in long-term memory is organized in schemas and must be retrieved to be used.
Example: Recalling the multiplication table learned years ago.
Maintenance Planning
The process of anticipating and preparing for ongoing updates, fixes, and improvements to learning resources.
Maintenance planning ensures resources remain functional and current over time.
Example: Scheduling quarterly reviews to check for broken links or outdated content.
Manual Adjustment
Modifications made directly to generated code or content by a human rather than through AI regeneration.
Manual adjustment is appropriate for small fixes that don't warrant a full regeneration cycle.
Example: Changing a color value in generated code rather than asking the AI to regenerate.
Map Visualization
A graphical representation using geographic features to display spatial relationships or location-based data.
Maps are appropriate for learning objectives involving geography, spatial patterns, or location-based analysis.
Example: A choropleth map showing population density by region.
Mathematical Relations
Quantitative relationships between variables expressed through equations, functions, or other mathematical representations.
Understanding mathematical relations is often a learning objective at the undergraduate and graduate levels.
Example: The relationship between force, mass, and acceleration expressed as F = ma.
Measurable Outcomes
Statements specifying what learners will do, how well they will do it, and under what conditions.
Measurable outcomes enable objective assessment of learning achievement.
Example: "Without reference materials, learners will list all 50 U.S. states within 5 minutes."
Mental Effort
The cognitive resources a learner is actively expending during a learning experience.
Mental effort is a limited resource that should be directed toward productive learning activities.
Example: The concentration required to understand a complex diagram.
Mental Model
An internal representation of how something works, used to understand, predict, and reason about systems.
MicroSims can help learners build accurate mental models of abstract or invisible processes.
Example: A learner's understanding of how electricity flows through a circuit.
Mermaid Library
A JavaScript-based diagramming and charting tool that generates diagrams from text-based descriptions.
Mermaid is useful for quickly creating flowcharts, sequence diagrams, and other structured visualizations.
Example: Generating a flowchart from a simple text description of process steps.
MicroSim
A small, focused interactive simulation designed to teach a specific concept or skill through exploration and visualization.
MicroSims bridge the gap between abstract learning objectives and concrete interactive experiences.
Example: An interactive supply-and-demand curve that responds to user-adjusted parameters.
MicroSim Generator
An AI skill that creates interactive simulations from natural language specifications.
The MicroSim generator translates pedagogical intent into working code and visual designs.
Example: Using the microsim-generator skill to create a physics simulation from a written specification.
Middle School Design
Design approaches tailored for learners approximately ages 11-13, introducing abstract concepts, multiple variables, and hypothesis testing.
Middle school designs can include more complexity than elementary but need clear scaffolding.
Example: A simulation allowing manipulation of two variables simultaneously to explore their relationship.
Misconception
An incorrect belief or understanding that a learner holds about a concept or phenomenon.
Misconceptions can be resistant to change and may interfere with learning accurate information.
Example: The belief that summer is hotter because Earth is closer to the sun.
Misconception Correction
Instructional strategies specifically designed to help learners identify and replace incorrect beliefs.
Effective correction often involves making the misconception explicit before presenting accurate information.
Example: A simulation that first reveals learners' predictions, then shows results that contradict common misconceptions.
Misconception Reinforcement
When instruction inadvertently strengthens incorrect beliefs rather than correcting them.
Poorly designed simulations may reinforce misconceptions if they don't directly address common errors.
Example: A simulation that never shows edge cases where intuitive assumptions break down.
Model Comparison
A learning activity involving examining multiple representations or explanations of the same phenomenon.
Comparing models helps learners understand both the strengths and limitations of each representation.
Example: Comparing the wave and particle models of light.
Motion Simulation
An animation showing objects moving through space over time.
Motion simulations are effective for physics concepts, spatial relationships, and process flows.
Example: A simulation of projectile motion showing trajectory paths.
Multilingual Support
Design features that make content accessible to speakers of different languages.
Multilingual support may include translation, localization, or language selection options.
Example: A MicroSim with labels available in English, Spanish, and Mandarin.
Multiple Variables
Learning situations involving manipulation or analysis of more than one changing quantity simultaneously.
Multiple variables introduce complexity appropriate for middle school and beyond.
Example: A simulation where learners adjust both temperature and pressure to observe effects.
Network Graph
A visualization showing entities and their connections or relationships as nodes and edges.
Network graphs are effective for concepts involving relationships, dependencies, or social structures.
Example: A graph showing how characters in a novel are connected to each other.
Nice-to-Have Changes
Improvements to a learning resource that would enhance quality but are not essential for function or objectives.
Nice-to-have changes are lower priority than critical changes and may be deferred or dropped.
Example: Adding a feature that no user has specifically requested.
Objective Alignment
The correspondence between stated learning objectives and the content, activities, or assessments in a resource.
Strong alignment ensures that everything in a MicroSim serves the stated learning goals.
Example: Verifying that every interactive element helps learners achieve the specified objective.
Objective Decomposition
The process of breaking compound learning objectives into their atomic component concepts or skills.
Decomposition reveals prerequisites, appropriate sequencing, and points where assessment can occur.
Example: Breaking "design and implement an API" into separate design and implementation objectives.
Observation Technique
A method for gathering information by watching how learners interact with a learning resource.
Observational data reveals usability issues and learning behaviors that surveys may miss.
Example: Noting where learners pause, show confusion, or request help during testing.
Output Interpretation
The process of reviewing and understanding AI-generated content to evaluate its quality and correctness.
Skilled output interpretation enables designers to identify what needs refinement.
Example: Reviewing generated code to verify it produces the intended behavior.
Output Validation
The process of verifying that AI-generated content meets requirements and functions correctly.
Output validation catches errors before learners encounter them.
Example: Testing a generated simulation with various inputs to confirm correct behavior.
p5.js Animation
Animations created using the p5.js JavaScript library, particularly suited for creative and interactive visual content.
p5.js provides an accessible way to create custom animations and interactive visualizations.
Example: A particle system demonstrating Brownian motion.
Paradigm Selection
The process of choosing an appropriate visualization type based on concept characteristics and learning objectives.
Matching paradigms to concepts improves learning effectiveness.
Example: Choosing a timeline visualization for a history concept rather than a network graph.
Parameter Space
The range of possible values for all adjustable parameters in a simulation or model.
Understanding parameter space helps learners explore boundaries and edge cases.
Example: The full range of possible masses, velocities, and angles in a projectile simulation.
Pedagogical Evaluation
Assessment of whether a learning resource effectively supports its stated learning objectives.
Pedagogical evaluation goes beyond technical function to examine educational effectiveness.
Example: Determining whether a simulation actually helps learners understand the targeted concept.
Peer Feedback
Input and critique provided by fellow learners or colleagues rather than instructors.
Peer feedback provides multiple perspectives and develops evaluative skills in the reviewers.
Example: Learners reviewing each other's MicroSim specifications and suggesting improvements.
Physics Simulation
An interactive visualization that models physical phenomena such as motion, forces, or energy.
Physics simulations make invisible forces and relationships visible through animation and interaction.
Example: A simulation showing gravitational attraction between objects of different masses.
Piaget Stages
Jean Piaget's theory describing four stages of cognitive development from infancy through adolescence.
Piaget's stages inform age-appropriate design decisions for educational content.
Example: Designing concrete, hands-on simulations for learners in the concrete operational stage.
Plotly Library
A JavaScript library for creating interactive, publication-quality graphs and charts.
Plotly offers more sophisticated charting options than simpler libraries, suitable for advanced data visualization.
Example: Creating an interactive 3D scatter plot for multivariate data exploration.
Portfolio Project
A comprehensive culminating project that demonstrates mastery of course concepts through a complete design artifact.
Portfolios allow learners to integrate and apply skills across multiple areas.
Example: Creating a complete MicroSim package including specification, implementation, testing, and documentation.
Prediction Prompt
An instructional technique that asks learners to predict an outcome before revealing it.
Predictions engage learners actively and make misconceptions visible when predictions are incorrect.
Example: "Before clicking Run, predict what will happen when mass is doubled."
Prerequisite Knowledge
Concepts or skills that must be understood before new material can be effectively learned.
Identifying prerequisites helps sequence instruction and diagnose learning difficulties.
Example: Understanding fractions is prerequisite to learning about ratios.
Prior Knowledge Support
Design features that help learners activate or build the background knowledge needed for new content.
Supporting prior knowledge reduces intrinsic load and improves learning outcomes.
Example: Including a brief review module before introducing advanced topics.
Process Timeline
A visualization showing steps or phases of a process arranged in chronological order.
Process timelines help learners understand sequential procedures or historical developments.
Example: A timeline showing the stages of the water cycle.
Productive Failure
A learning approach that allows learners to struggle with challenging problems before receiving instruction.
Productive failure can lead to deeper understanding than immediate correct answers.
Example: Letting learners attempt to solve a problem and discover why their approach doesn't work.
Progressive Disclosure
A design strategy that reveals information gradually rather than all at once.
Progressive disclosure reduces initial cognitive load while allowing access to complexity when needed.
Example: A MicroSim that starts with basic features and unlocks advanced options as learners progress.
Prompt Engineering
The skill of crafting effective instructions and queries for AI systems to produce desired outputs.
Effective prompts are specific, provide context, and anticipate how AI will interpret the request.
Example: Including concrete examples in a prompt to guide AI output style.
Reading Support
Design features that help learners with developing reading skills access text content.
Reading support may include text-to-speech, simplified vocabulary, or visual aids for text.
Example: Icons accompanying menu items so learners don't rely solely on reading.
Real-World Application
The use of authentic, practical contexts to demonstrate how concepts apply outside educational settings.
Real-world applications increase motivation and support transfer of learning.
Example: A simulation using actual economic data rather than simplified hypothetical scenarios.
Reduced Motion
A design accommodation that minimizes or eliminates animation for users who are sensitive to motion.
Operating systems provide reduced motion preferences that well-designed MicroSims should respect.
Example: Providing a static alternative for users who have enabled reduced motion settings.
Refinement Prompt
A follow-up instruction to an AI system that requests specific modifications to previously generated content.
Effective refinement prompts are specific about what should change and why.
Example: "Change the graph colors to be more distinct for colorblind users."
Reflection Journal
A document where learners record thoughts, questions, and insights about their learning process.
Journaling supports metacognition and self-regulated learning.
Example: Documenting design decisions and lessons learned during a portfolio project.
Regeneration Decision
The choice between asking an AI to generate new content versus manually editing existing output.
Regeneration is appropriate for substantial changes; manual editing is faster for small fixes.
Example: Regenerating when the fundamental approach is wrong; editing when only values need adjustment.
Relationship Graph
A visualization showing connections between entities, emphasizing how things are related to each other.
Relationship graphs help learners understand networks, dependencies, and social structures.
Example: A graph showing trade relationships between countries.
Remember Level
The first and foundational level of Bloom's Taxonomy involving retrieving knowledge from long-term memory.
Remembering provides the foundation for higher-order thinking but is not sufficient on its own.
Example: A learning objective at this level: "List the six levels of Bloom's Taxonomy in order."
Research Applications
Uses of concepts in formal research contexts, typically at graduate or professional levels.
Research applications extend beyond standard use cases to cutting-edge exploration.
Example: Using a simulation to test theoretical predictions in a research study.
Responsiveness Testing
Evaluation of how a learning resource adapts to different screen sizes and device types.
Responsive design ensures accessibility across desktops, tablets, and mobile phones.
Example: Testing a MicroSim on various screen sizes to verify layout adjusts appropriately.
Reusability
The quality of a learning resource that enables it to be used in multiple contexts without modification.
High reusability increases return on development investment and promotes consistency.
Example: A well-documented MicroSim that can be embedded in different courses.
Rubric Development
The process of creating structured evaluation guides with explicit criteria and performance levels.
Well-developed rubrics ensure consistent, fair, and transparent assessment.
Example: Creating a rubric for evaluating MicroSim specifications before generation.
Scaffolded Complexity
An approach that gradually increases difficulty or complexity as learners demonstrate mastery.
Scaffolding supports learners through challenging content without overwhelming them.
Example: A simulation that adds new variables only after learners master simpler configurations.
Schema Formation
The process of building organized knowledge structures in long-term memory.
Schemas enable efficient processing by chunking related information together.
Example: Developing a mental framework for understanding different types of graphs.
Scope Creep Prevention
Practices that prevent unplanned expansion of a project beyond its original goals.
Preventing scope creep ensures resources are focused on priority features.
Example: Maintaining a strict list of requirements and deferring "nice-to-have" features.
Screen Reader Support
Design features that enable users of screen reading software to access and navigate content.
Proper screen reader support includes semantic markup, alternative text, and logical reading order.
Example: Adding descriptive alt text to all images and ensuring controls are properly labeled.
Self-Evaluation
The process of learners assessing their own work against specified criteria.
Self-evaluation develops metacognitive skills and promotes self-regulated learning.
Example: Using a rubric to rate your own MicroSim before peer review.
Sequence Display
A visualization showing items arranged in a meaningful order, often temporal or procedural.
Sequence displays help learners understand steps, phases, or progressions.
Example: A timeline showing the order of steps in a laboratory procedure.
Set Visualization
A graphical representation showing collections of items and their membership in categories.
Set visualizations like Venn diagrams help learners understand classification and overlap.
Example: A Venn diagram showing the intersection of mammals and aquatic animals.
Simple Cause-Effect
A straightforward relationship where one action leads to one predictable outcome.
Simple cause-effect relationships are appropriate for early childhood and elementary learners.
Example: "Press the button, the light turns on."
Simulation Readiness
The degree to which a learning objective is appropriate for delivery through interactive simulation.
Not all objectives benefit from simulation; some are better served by other methods.
Example: Objectives involving dynamic processes or exploration are more simulation-ready than factual recall.
Spatial Visualization
A representation showing physical arrangement, location, or geographic relationships.
Spatial visualizations help learners understand concepts involving position, distance, or geographic context.
Example: A map showing the locations of historical events.
Specification Ambiguity
Unclear or imprecise language in a MicroSim specification that could be interpreted multiple ways.
Ambiguity leads to AI generating output that doesn't match designer intent.
Example: "Make it colorful" is ambiguous; "Use blue, red, and yellow with high contrast" is specific.
Specification Document
A detailed written description of what a MicroSim should contain, how it should behave, and what it should teach.
Complete specifications include learning objectives, visual descriptions, interactions, and success criteria.
Example: A multi-page document describing a physics simulation in enough detail for AI generation.
Split Attention Effect
The increased cognitive load that occurs when learners must mentally integrate information from separate sources.
Avoiding split attention by integrating related information improves learning efficiency.
Example: Placing labels directly on diagram elements rather than in a separate legend.
State Machine Diagram
A visualization showing the possible states of a system and the transitions between them.
State diagrams help learners understand systems with discrete conditions and rules for changing between them.
Example: A diagram showing the states of water (solid, liquid, gas) and transition conditions.
Success Criteria
The specific conditions that define whether a learning resource or learning activity has achieved its goals.
Clear success criteria enable objective evaluation of outcomes.
Example: "Learners will correctly identify the equilibrium point in 8 out of 10 trials."
Technical Evaluation
Assessment of whether a learning resource functions correctly from an engineering perspective.
Technical evaluation addresses bugs, performance, compatibility, and responsiveness.
Example: Testing that a simulation runs without errors on target browsers and devices.
Template Library
A collection of reusable patterns or starting points for creating new learning resources.
Templates speed development and promote consistency across resources.
Example: A library of common MicroSim interaction patterns that can be adapted for new content.
Test Interpretation
The process of analyzing results from user testing to draw meaningful conclusions.
Skillful interpretation distinguishes between individual quirks and systematic usability issues.
Example: Recognizing that multiple testers struggling with the same feature indicates a design problem.
Theoretical Foundations
The underlying principles, models, or frameworks that explain phenomena at a conceptual level.
Theoretical understanding is typically emphasized at undergraduate and graduate levels.
Example: Understanding why the laws of thermodynamics constrain possible engine designs.
Think-Aloud Protocol
A research method where participants verbalize their thoughts while completing a task.
Think-aloud protocols reveal cognitive processes and usability issues not visible from behavior alone.
Example: Asking a learner to describe what they're thinking while using a MicroSim.
Time-Efficient Design
Design approaches that respect learners' limited time by focusing on essential content and minimizing inefficiencies.
Time efficiency is particularly important for corporate training where opportunity costs are high.
Example: Removing optional content that doesn't contribute to core learning objectives.
Timeline Visualization
A graphical representation showing events or data points arranged along a temporal axis.
Timelines help learners understand chronology, duration, and temporal relationships.
Example: A timeline showing the sequence of events leading to World War I.
Touch Target Size
The physical dimensions of interactive elements on touch screens, affecting usability and accessibility.
Larger touch targets reduce errors and accommodate users with motor difficulties.
Example: Making buttons at least 44x44 pixels for comfortable touch interaction.
Trend Chart
A visualization showing how values change over time, revealing patterns, cycles, or directions.
Trend charts help learners understand temporal patterns and make predictions.
Example: A line chart showing stock price movements over a year.
UDL Principles
The three principles of Universal Design for Learning: multiple means of engagement, representation, and action/expression.
UDL provides a framework for creating learning experiences accessible to diverse learners.
Example: Offering content in both text and audio formats to support different learning preferences.
Undergraduate Design
Design approaches tailored for undergraduate-level learners, featuring theoretical foundations and mathematical relationships.
Undergraduate designs assume more prior knowledge and can include more complex interactions.
Example: A simulation requiring manipulation of mathematical equations to explore relationships.
Understand Level
The second level of Bloom's Taxonomy involving constructing meaning from instructional messages.
Understanding goes beyond recall to demonstrate comprehension through explanation, classification, or comparison.
Example: A learning objective at this level: "Explain the difference between formative and summative assessment."
Universal Design
An approach to design that creates products and environments usable by all people without need for adaptation.
Universal design benefits everyone, not just those with identified disabilities.
Example: Captioning videos benefits deaf users but also those in noisy environments or learning new languages.
UX Evaluation
Assessment of the user experience aspects of a learning resource, including usability, satisfaction, and engagement.
UX evaluation ensures that learners can use the resource effectively and enjoyably.
Example: Evaluating whether learners can navigate a MicroSim without confusion or frustration.
Venn Diagram
A diagram using overlapping circles to show logical relationships between sets or categories.
Venn diagrams help learners understand inclusion, exclusion, and intersection of categories.
Example: A diagram showing which animals are both mammals and aquatic.
Version Control
The practice of tracking and managing changes to files, code, or content over time.
Version control enables collaboration, rollback of problematic changes, and documentation of evolution.
Example: Using Git to track changes to MicroSim code and specifications.
vis-network Library
A JavaScript library for creating interactive network graphs with nodes and edges.
vis-network is well-suited for MicroSims involving relationship visualization or dependency mapping.
Example: Creating an interactive concept map where learners can explore connections.
vis-timeline Library
A JavaScript library for creating interactive timelines with events and ranges.
vis-timeline is appropriate for MicroSims involving historical sequences or project scheduling.
Example: An interactive timeline of the Civil Rights Movement.
Visual Affordances
Design properties that suggest how an element can be used or interacted with.
Clear affordances reduce the need for explicit instructions and lower extraneous load.
Example: A button that appears raised suggests it can be clicked.
Visual Description
Written explanation of what visual elements should appear and how they should look.
Detailed visual descriptions enable AI to generate designs matching designer intent.
Example: Specifying "a blue circle 50 pixels in diameter centered in the canvas."
Visual Simplicity
A design quality characterized by minimal visual elements, each serving a clear purpose.
Visual simplicity reduces extraneous cognitive load and focuses attention on learning content.
Example: Removing decorative graphics that don't contribute to learning.
Visualization Paradigm
A general category of visual representation defined by its characteristic structure and affordances.
Different paradigms suit different types of concepts and learning objectives.
Example: Network graphs, timelines, and charts are different visualization paradigms.
Vocabulary Level
The complexity of language used in educational content, matched to the target audience.
Appropriate vocabulary reduces unnecessary cognitive load from unknown words.
Example: Using "speed" for elementary learners and "velocity" for high school physics.
Vygotsky Theory
Lev Vygotsky's theory emphasizing social interaction and the zone of proximal development in learning.
Vygotsky's ideas inform scaffolding approaches and peer learning designs.
Example: Providing support that enables learners to accomplish more than they could independently.
Working Memory
The cognitive system for temporarily holding and manipulating information during mental tasks.
Working memory has limited capacity (4-7 items) and serves as the bottleneck in learning.
Example: Holding multiple numbers in mind while performing mental arithmetic.