Glossary Generator

Glossary Generator

Generate a comprehensive glossary of terms from a learning graph's concept list with ISO 11179-compliant definitions.

Purpose

This skill automates glossary creation for intelligent textbooks by converting concept labels from a learning graph into properly formatted glossary definitions. Each definition follows ISO 11179 metadata registry standards: precise, concise, distinct, non-circular, and free of business rules. The skill ensures consistency across terminology, validates cross-references, and produces alphabetically ordered entries with relevant examples.

Following a short definition you may provide a discussion of why the term is important in the textbook and an example of how the term is used.

When to Use This Skill

Use this skill after the Learning Graph skill has completed and the concept list has been finalized. All markdown content in the /docs area can also be scanned looking for words or phases that might not be clear to the average high-school student.

The glossary relies on having a complete, reviewed list of concepts from the learning graph's concept enumeration phase. Specifically, trigger this skill when:

• A concept list file exists (typically docs/learning-graph/02-concept-list-v1.md)
• The concept list has been reviewed and approved
• The course description exists with clear learning outcomes
• Ready to create or update the textbook's glossary

Workflow

Step 1: Validate Input Quality

Before generating definitions, assess the quality of the concept list:

1. Read the concept list file (typically docs/learning-graph/02-concept-list-v1.md) 2. Check for duplicate concept labels (target: 100% unique) 3. Verify Title Case formatting (target: 95%+ compliance) 4. Validate length constraints (target: 98% under 32 characters) 5. Assess concept clarity (no ambiguous terms)

Calculate a quality score (1-100 scale):

• 90-100: All concepts unique, properly formatted, appropriate length
• 70-89: Most concepts meet standards, minor formatting issues
• 50-69: Some duplicate concepts or formatting inconsistencies
• Below 50: Significant issues requiring manual review

User Dialog Triggers:

• If score < 70: Ask "The concept list has quality issues. Would you like to review and clean it before generating the glossary?"
• If duplicates found: Ask "Found [N] duplicate concepts. Should I remove duplicates automatically or would you like to review?"
• If formatting issues: Ask "Found [N] concepts with formatting issues. Auto-fix?"

Step 2: Read Course Context

Read the course description file (docs/course-description.md) and any other markdonw files in /docs/**/*.md to understand:

• Target audience (for appropriate example complexity)
• Course objectives (for terminology alignment)
• Prerequisites (for background knowledge assumptions)
• Learning outcomes (for context on concept usage)

Step 3: Generate Definitions

For each concept in the list, create a definition that follows ISO 11179 standards:

Precision (25 points): Accurately capture the concept's meaning

• Define the concept specifically in the context of the course
• Use terminology appropriate for the target audience
• Ensure the definition matches how the concept is used in the course

Conciseness (25 points): Keep definitions brief (target: 20-50 words)

• Avoid unnecessary words or explanations
• Get to the core meaning quickly
• Use clear, direct language

Distinctiveness (25 points): Make each definition unique and distinguishable

• Avoid copying definitions from other sources
• Ensure no two definitions are too similar
• Highlight what makes this concept different from related concepts

Non-circularity (25 points): Avoid circular dependencies

• Do not reference undefined terms in definitions
• Do not create circular chains (A depends on B, B depends on A)
• Use simpler, more fundamental terms in definitions

Example Format:

For a concept "Learning Graph":

#### Learning Graph

A directed graph of concepts that reflects the order that concepts should be learned to master a new concept.

Learning graphs are the foundational data structure use for intelligent textbooks.  They are used to guide
intelligent agents and recommend learning paths for students.

**Example:** In a programming course, the learning graph shows that "Variables" must be understood before "Functions," which must be understood before "Recursion."

Step 4: Add Examples (60-80% of terms)

For most concepts (target: 60-80%), include a relevant example:

• Start with "Example:" (no newline after colon)
• Provide a concrete illustration from the course domain
• Keep examples brief (1-2 sentences)
• Ensure examples clarify the concept without adding confusion

Step 5: Add Cross-References

Where appropriate, reference related terms:

• Use "See also:" for related concepts
• Use "Contrast with:" for opposing concepts
• Ensure all cross-referenced terms exist in the glossary
• Keep cross-references to 1-3 per term

Step 6: Create Glossary File

Generate docs/glossary.md with the following structure:

# Glossary of Terms

#### [Term 1]

[Definition]

**Example:** [Example if applicable]

#### [Term 2]

[Definition]

**Example:** [Example if applicable]

[Continue alphabetically...]

Important formatting rules:

• Sort all terms alphabetically (case-insensitive)
• Use level-4 headers (####) for term names
• Place definition in body text (no special formatting)
• Use "Example:" for examples (bold, with colon)
• Maintain consistent spacing between entries

Step 7: Generate Quality Report

Create docs/learning-graph/glossary-quality-report.md with:

ISO 11179 Compliance Metrics:

For each definition, score on 4 criteria (25 points each):

1. Precision: Does it accurately capture the meaning? 2. Conciseness: Is it brief (20-50 words)? 3. Distinctiveness: Is it unique and distinguishable? 4. Non-circularity: No circular dependencies?

Overall Quality Metrics:

• Average definition length: [X] words
• Definitions meeting all 4 criteria: [X]%
• Circular definitions found: [X]
• Example coverage: [X]%
• Cross-references: [X] total, [X] broken

Readability:

• Flesch-Kincaid grade level: [X]
• Appropriate for target audience: Yes/No

Recommendations:

• List any definitions scoring < 70/100
• Identify circular dependencies to fix
• Suggest concepts needing examples
• Note any broken cross-references

Step 8: Validate Output

Perform final validation:

1. Verify alphabetical ordering (100% compliance required) 2. Check all cross-references point to existing terms 3. Ensure all concepts from input list are included 4. Validate markdown syntax renders correctly 5. Confirm no circular definitions exist

Success Criteria:

• Overall quality score > 85/100
• Zero circular definitions
• 100% alphabetical ordering
• All terms from concept list included
• Markdown renders correctly in mkdocs

Step 9: Update Navigation (Optional)

If mkdocs.yml does not already include the glossary:

1. Read mkdocs.yml 2. Check if "Glossary: glossary.md" exists in nav section 3. If missing, add it in an appropriate location 4. Preserve existing navigation structure

Step 10: Generate Cross-Reference Index (Optional)

Create docs/learning-graph/glossary-cross-ref.json for semantic search:

{
  "terms": [
    {
      "term": "Learning Graph",
      "related_terms": ["Concept Dependency", "Directed Acyclic Graph"],
      "contrasts_with": ["Linear Curriculum"],
      "category": "Educational Technology"
    }
  ]
}

This JSON file enables future features like:

• Semantic search across glossary
• Concept relationship visualization
• Automated suggestion of related terms

Quality Scoring Reference

Use this rubric to score each definition (1-100 scale):

85-100: Excellent

• Meets all 4 ISO 11179 criteria (20+ pts each)
• Appropriate length (20-50 words)
• Includes relevant example
• Clear, unambiguous language
• No circular dependencies

70-84: Good

• Meets 3-4 ISO criteria
• Acceptable length (15-60 words)
• May lack example
• Generally clear
• No serious issues

55-69: Adequate

• Meets 2-3 ISO criteria
• Length issues (too short or too long)
• Missing example where helpful
• Some ambiguity
• Minor circular references

Below 55: Needs Revision

• Fails multiple ISO criteria
• Serious length issues
• Confusing or circular
• Missing context
• Requires complete rewrite

Common Pitfalls to Avoid

Circular Definitions:

• Bad: "A Learning Graph is a graph that shows learning."
• Good: "A directed graph of concepts that reflects the order concepts should be learned."

Too Vague:

• Bad: "A thing used in education."
• Good: "A directed graph of concepts that reflects prerequisite relationships."

Too Long:

• Bad: "A learning graph is a specialized type of directed acyclic graph structure commonly used in educational technology and instructional design contexts to represent the hierarchical and sequential relationships between different conceptual elements that students need to master in order to achieve specific learning outcomes."
• Good: "A directed graph of concepts that reflects the order concepts should be learned to master a new concept."

Business Rules:

• Bad: "Students must complete prerequisites before advancing to dependent concepts."
• Good: "A directed graph showing prerequisite relationships between concepts."

Undefined Terms:

• Bad: "Uses a DAG structure" (if DAG not in glossary)
• Good: "Uses a directed acyclic graph structure"

Output Files Summary

Required:

1. docs/glossary.md - Complete glossary in alphabetical order with ISO 11179-compliant definitions

Recommended:

2. docs/learning-graph/glossary-quality-report.md - Quality assessment and recommendations

Optional:

3. docs/learning-graph/glossary-cross-ref.json - JSON mapping for semantic search 4. Updates to mkdocs.yml navigation if glossary link missing

Example Session

User: "Generate a glossary from my concept list"

Claude (using this skill):

1. Reads docs/learning-graph/02-concept-list-v1.md 2. Validates quality (checks for duplicates, formatting) 3. Reads docs/course-description.md for context 4. Generates ISO 11179-compliant definitions 5. Adds examples to 70% of terms 6. Sorts alphabetically 7. Creates docs/glossary.md 8. Generates quality report 9. Reports: "Created glossary with 187 terms. Overall quality score: 89/100. Added examples to 71% of terms. No circular definitions found."

Installing the Glossary Generator Skill

Quick Start

To use this skill with Claude:

Option 1: Claude.ai Web Interface

1. Go to claude.ai 2. Start a new conversation 3. Upload or reference this skill directory 4. Ask Claude to use the glossary-generator skill

Option 2: Claude Code CLI

If using Claude Code CLI with skills support:

# Copy skill to Claude's skills directory
cp -r /skills/glossary-generator ~/.claude/skills/

# Or use it directly from this location
claude --skill /skills/glossary-generator "Generate a glossary"

Option 3: Direct Reference

Simply tell Claude:

"Please use the glossary-generator skill located at /skills/glossary-generator to create a glossary from my concept list"

Verification

To verify the skill is properly structured:

cd /skills/glossary-generator
ls -la

You should see:

SKILL.md                    # Main skill instructions (required)
README.md                   # Documentation
INSTALL.md                  # This file
references/                 # Reference materials
  └── iso-11179-standards.md

Testing the Skill

Test with Example Data

1. Ensure you have a concept list file:

• Location: docs/learning-graph/02-concept-list-v1.md
• Format: Numbered list of concepts

2. Ensure you have a course description:

• Location: docs/course-description.md
• Contains: Audience, objectives, prerequisites

3. Ask Claude:

   Use the glossary-generator skill to create a glossary from
   docs/learning-graph/02-concept-list-v1.md

4. Expected outputs:

• docs/glossary.md (main glossary file)
• docs/learning-graph/glossary-quality-report.md (quality metrics)

Validate Output

Check that:

• [ ] All concepts from input list appear in glossary
• [ ] Terms are alphabetically sorted
• [ ] Definitions follow ISO 11179 standards
• [ ] 60-80% of terms have examples
• [ ] Quality score > 85/100
• [ ] No circular definitions
• [ ] Markdown renders correctly

Skill Structure Explained

glossary-generator/
│
├── SKILL.md                 # Core instructions for Claude
│   ├── YAML frontmatter     # name, description, license
│   └── Markdown content     # Step-by-step workflow
│
├── README.md                # Human-readable documentation
├── INSTALL.md              # This installation guide
│
└── references/             # Loaded as needed by Claude
    └── iso-11179-standards.md  # Detailed ISO standards

How Claude Uses This Skill

1. Skill Detection: Claude reads YAML frontmatter to know when to use skill 2. Workflow Execution: Claude follows SKILL.md instructions step-by-step 3. Reference Lookup: Claude loads iso-11179-standards.md when needed 4. Quality Assurance: Claude uses scoring rubrics to validate output

Requirements

Input Files Required

• Concept list (markdown file with numbered list)
• Course description (markdown file)

Input Files Optional

• Existing glossary (for updates)
• Previous quality reports (for comparison)

Output Directory Structure

Skill expects this directory structure:

docs/
├── glossary.md                  # Created by skill
├── course-description.md        # Required input
└── learning-graph/
    ├── 02-concept-list-v1.md   # Required input
    └── glossary-quality-report.md  # Created by skill

Troubleshooting

Skill Not Triggering

Problem: Claude doesn't recognize the skill

Solutions:

• Verify SKILL.md has proper YAML frontmatter
• Check that name and description are descriptive
• Explicitly tell Claude to use the skill by name

Missing Reference Files

Problem: Claude can't find ISO standards reference

Solutions:

• Verify references/iso-11179-standards.md exists
• Check file permissions (should be readable)
• Path should be relative to SKILL.md

Output Files Not Created

Problem: Glossary or quality report not generated

Solutions:

• Verify output directories exist (docs/, docs/learning-graph/)
• Check write permissions
• Ensure input files are valid (concept list, course description)

Quality Score Too Low

Problem: Glossary quality score < 85

Solutions:

• Review input concept list for quality issues
• Ensure concepts are clear, unique, properly formatted
• Check that course description provides good context
• Review quality report recommendations

Advanced Configuration

Customizing Output Locations

Tell Claude explicitly where to put outputs:

Use the glossary-generator skill to create a glossary.
Place the glossary at docs/reference/glossary.md
Place the quality report at reports/glossary-quality.md

Adjusting Quality Thresholds

Tell Claude to use different thresholds:

Use the glossary-generator skill with these requirements:
- Minimum quality score: 90/100
- Example coverage: 80% (instead of default 60%)
- Maximum definition length: 40 words (instead of 50)

Focusing on Specific Concepts

Generate glossary for subset of concepts:

Use the glossary-generator skill to create definitions
only for concepts in the "Educational Technology" category

Next Steps

After installing the skill:

1. Test it: Generate a glossary from your concept list 2. Review output: Check quality report and scores 3. Iterate: Refine based on recommendations 4. Integrate: Use glossary in your textbook content

Related Documentation

• Full Specification: /docs/skills/glossary-generator.md
• Skill Index: /docs/skills/index.md
• ISO Standards Reference: references/iso-11179-standards.md
• Example Workflow: See README.md

Support

If you encounter issues:

1. Check this installation guide 2. Review SKILL.md for workflow details 3. Consult ISO standards reference 4. Check quality report for specific guidance

Version

• Skill Version: 1.0
• Last Updated: 2025-01-31
• Compatibility: Claude Code, Claude.ai

Glossary Generator Skill

Automatically generate comprehensive glossaries for intelligent textbooks with ISO 11179-compliant definitions.

Overview

This skill converts concept lists from learning graphs into properly formatted glossary entries. Each definition follows ISO 11179 metadata registry standards ensuring precision, conciseness, distinctiveness, and non-circularity.

Installation

To use this skill with Claude Code or Claude.ai:

1. Install the skill by providing the path to this directory 2. The skill will be available for Claude to use when generating glossaries

Usage

Trigger Phrases:

• "Generate a glossary from my concept list"
• "Create a glossary for the textbook"
• "Build a glossary from the learning graph concepts"

Prerequisites:

• Learning graph concept list exists (e.g., docs/learning-graph/02-concept-list-v1.md)
• Course description file exists (docs/course-description.md)
• Concepts have been reviewed and approved

Typical Workflow:

1. User asks Claude to generate a glossary 2. Skill validates the concept list quality 3. Skill reads course context 4. Skill generates ISO 11179-compliant definitions 5. Skill creates docs/glossary.md with alphabetically sorted entries 6. Skill generates quality report

Output Files

Required

• `docs/glossary.md` - Complete glossary with ISO-compliant definitions
• Alphabetically sorted terms
• Level-4 headers for each term
• Examples for 60-80% of terms
• Cross-references to related terms

Recommended

• `docs/learning-graph/glossary-quality-report.md` - Quality assessment
• ISO 11179 compliance scores per definition
• Overall quality metrics
• Recommendations for improvement
• Circular dependency detection

Optional

• `docs/learning-graph/glossary-cross-ref.json` - Semantic search data
• JSON mapping of term relationships
• Category assignments
• Related/contrasting terms

Quality Standards

ISO 11179 Criteria (100 point scale)

Each definition scored on four 25-point criteria:

1. Precision (25 pts): Accurately captures concept meaning 2. Conciseness (25 pts): Brief definitions (20-50 words target) 3. Distinctiveness (25 pts): Unique and distinguishable from related concepts 4. Non-circularity (25 pts): No circular dependencies or self-references

Success Thresholds

• 85-100: Excellent, publication ready
• 70-84: Good, minor refinements beneficial
• 55-69: Adequate, improvements needed
• Below 55: Requires revision

Overall Glossary Metrics

• Zero circular definitions (required)
• 100% alphabetical ordering (required)
• All concepts from input list included (required)
• 60-80% of terms include examples (target)
• Markdown renders correctly in mkdocs (required)

Skill Contents

glossary-generator/
├── SKILL.md                              # Main skill instructions
├── README.md                             # This file
└── references/
    └── iso-11179-standards.md            # Detailed ISO 11179 guidance

Example Input

Concept List (docs/learning-graph/02-concept-list-v1.md):

1. Learning Graph
2. Concept Dependency
3. Scaffolding
4. Bloom's Taxonomy
...

Example Output

Glossary (docs/glossary.md):

# Glossary of Terms

#### Bloom's Taxonomy

A hierarchical classification of educational learning objectives consisting of six cognitive levels: Remember, Understand, Apply, Analyze, Evaluate, and Create.

**Example:** When designing quiz questions, use Bloom's Taxonomy to ensure a balanced distribution across cognitive levels rather than focusing only on memorization.

#### Concept Dependency

A prerequisite relationship between two concepts where one must be understood before the other can be learned effectively.

**Example:** In a programming course, "Variables" has a concept dependency relationship with "Functions" because understanding variables is required before learning about function parameters.

...

Quality Report Example

Quality Report (docs/learning-graph/glossary-quality-report.md):

# Glossary Quality Report

Generated: 2025-01-31

## Overall Statistics

- **Total Terms:** 187
- **Overall Quality Score:** 89/100
- **Terms Meeting All Criteria:** 172 (92%)
- **Circular Definitions:** 0
- **Example Coverage:** 71%

## ISO 11179 Compliance

- **Precision:** 91% (avg 22.8/25 pts)
- **Conciseness:** 88% (avg 22.0/25 pts)
- **Distinctiveness:** 94% (avg 23.5/25 pts)
- **Non-Circularity:** 100% (avg 25.0/25 pts)

## Recommendations

### High Priority (3 terms)

- **API:** Definition too vague, needs more precision
- **Framework:** Too long (78 words), needs condensing
- **Module:** Similar to "Component", needs distinction

...

References

ISO 11179 Standards

The skill includes detailed guidance on ISO/IEC 11179 metadata registry standards in references/iso-11179-standards.md. This reference covers:

• The four core criteria with examples
• Common patterns by concept type
• Quality checklists
• Scoring rubrics
• Definition templates
• Common pitfalls to avoid

Claude will reference this document as needed when generating definitions.

Best Practices

For Users

1. Review concept list first - Ensure concepts are finalized before generating glossary 2. Provide course context - Complete course description helps with appropriate examples 3. Review quality report - Use recommendations to improve lower-scoring definitions 4. Iterate as needed - Refine definitions based on quality scores

For Definitions

1. Target 20-50 words - Sweet spot for most concepts 2. Include examples - Especially for abstract concepts 3. Use cross-references - Link related terms (but ensure they exist) 4. Match audience level - Use appropriate complexity for target audience 5. Avoid jargon - Unless necessary and defined elsewhere

Troubleshooting

"Quality score is low (<70)"

Cause: Concept list has duplicates, formatting issues, or unclear concepts

Solution:

• Review concept list for duplicates
• Fix Title Case formatting
• Ensure concepts are under 32 characters
• Clarify ambiguous concept labels

"Circular definitions detected"

Cause: Definitions reference each other or undefined terms

Solution:

• Review quality report for specific circular chains
• Use simpler, more fundamental terms
• Ensure all referenced terms are defined

"Missing examples"

Cause: Not enough examples generated (below 60% target)

Solution:

• Re-run with emphasis on examples for abstract concepts
• Manually add examples to complex or important concepts
• Ensure course description provides good context

Version History

• v1.0 (2025-01-31) - Initial release
• ISO 11179 compliance
• Quality scoring and reporting
• Cross-reference generation
• Example coverage

License

MIT License - See LICENSE file for details

Support

For issues, questions, or improvements:

1. Review the detailed specification in /docs/skills/glossary-generator.md 2. Check the ISO 11179 reference guide 3. Examine quality reports for specific guidance 4. Iterate based on feedback

Related Skills

• Learning Graph - Generates the concept list used as input
• Chapter Content Generator - Uses glossary for consistent terminology
• FAQ Generator - References glossary for technical questions
• Concept Validator - Validates glossary coverage of all concepts

ISO 11179 Metadata Registry Standards for Definitions

This reference provides detailed guidance on creating definitions that comply with ISO/IEC 11179 metadata registry standards.

The Four Core Criteria

1. Precision

A definition is precise when it accurately and specifically captures the concept's meaning without ambiguity.

Guidelines:

• State exactly what the concept is, not what it does or how it's used
• Include the essential characteristics that distinguish it from other concepts
• Use terminology appropriate for the target audience
• Avoid generic or vague language

Examples:

❌ Not Precise: "Learning Graph - A useful tool for education"

✓ Precise: "Learning Graph - A directed graph of concepts that reflects the order concepts should be learned to master a new concept"

❌ Not Precise: "API - A way to connect programs"

✓ Precise: "API - A set of protocols and tools that defines how software components should interact"

2. Conciseness

A definition is concise when it conveys the meaning efficiently without unnecessary words.

Guidelines:

• Target 20-50 words for most definitions
• Remove filler words and redundant phrases
• Use active voice when possible
• Get to the core meaning quickly
• Avoid examples in the definition itself (add them separately)

Examples:

❌ Not Concise: "A learning graph is a type of specialized graph structure that is commonly used in educational contexts and learning management systems to represent the various relationships and dependencies that exist between different concepts that students need to learn"

✓ Concise: "A directed graph of concepts that reflects the order concepts should be learned to master a new concept"

❌ Not Concise: "Scaffolding is an instructional method where educators and teachers provide support and assistance to learners and students at the beginning stages of the learning process, and then gradually remove this support as the learners gain more confidence"

✓ Concise: "An instructional strategy where support is provided to learners at the beginning and gradually removed as they gain confidence and competence"

3. Distinctiveness

A definition is distinct when it is unique and clearly differentiates the concept from related concepts.

Guidelines:

• Highlight what makes this concept different from similar concepts
• Avoid copying definitions from other sources verbatim
• Ensure no two definitions in your glossary are too similar
• Include distinguishing characteristics

Examples:

Two related but distinct concepts:

✓ Learning Graph: "A directed graph of concepts that reflects the order concepts should be learned to master a new concept"

✓ Concept Dependency: "A prerequisite relationship between two concepts where one must be understood before the other can be learned effectively"

(Note: These are distinct - one is the overall structure, the other is a relationship within that structure)

❌ Not Distinct: Having both "Learning Graph" and "Knowledge Graph" with nearly identical definitions

4. Non-Circularity

A definition is non-circular when it does not reference the term being defined or create circular chains of dependencies.

Guidelines:

• Do not use the term itself in the definition
• Do not reference terms that themselves reference this term
• Use simpler, more fundamental terms in definitions
• Ensure all referenced terms are either defined elsewhere or commonly understood

Examples:

❌ Circular: "Learning Graph - A graph that represents learning"

✓ Non-Circular: "Learning Graph - A directed graph of concepts that reflects the order concepts should be learned"

❌ Circular Chain:

• "Prerequisite - A concept that must be learned before a dependent concept"
• "Dependent Concept - A concept that requires prerequisites"

✓ Non-Circular:

• "Prerequisite - A concept that must be understood before another concept can be learned effectively"
• "Dependent Concept - A concept that requires prior knowledge of other concepts"

Additional ISO 11179 Principle

5. Unencumbered with Business Rules

Definitions should describe what something is, not how it should be used or what rules apply to it.

Guidelines:

• Avoid prescriptive language (must, should, required)
• Describe the concept, not policies or procedures
• Separate the definition from usage rules
• Focus on the concept's nature, not its governance

Examples:

❌ Contains Business Rules: "Prerequisite - A concept that students must complete before advancing to the next level"

✓ Unencumbered: "Prerequisite - A concept that must be understood before another concept can be learned effectively"

❌ Contains Business Rules: "MicroSim - An interactive simulation that must include user controls and visual feedback"

✓ Unencumbered: "MicroSim - A small-scale, embedded interactive simulation designed to illustrate a concept within educational content"

Definition Template

Use this template as a starting point:

A [fundamental category/type] that [essential characteristic] [distinguishing feature].

Examples:

• "A directed graph that reflects concept prerequisites for learning"
• "An instructional strategy where support is gradually reduced as competence increases"
• "A small-scale simulation that illustrates a concept within educational content"

Quality Checklist

Before finalizing a definition, verify:

• [ ] Precise: Accurately captures the concept's specific meaning?
• [ ] Concise: Uses 20-50 words (or fewer if appropriate)?
• [ ] Distinct: Clearly different from related concepts?
• [ ] Non-Circular: Doesn't reference itself or create circular chains?
• [ ] Unencumbered: Free of business rules and prescriptive requirements?
• [ ] Clear: Understandable by the target audience?
• [ ] Grammatically Correct: Proper sentence structure?
• [ ] Terminology: Uses terms defined elsewhere or commonly understood?

Common Patterns by Concept Type

Objects/Entities

Pattern: "A [type] that [essential characteristic]"

Example: "A directed graph that represents concept dependencies"

Processes/Actions

Pattern: "The process of [action] to [purpose]"

Example: "The process of gradually reducing instructional support as learners gain competence"

Properties/Attributes

Pattern: "The [quality/characteristic] of [entity] with respect to [dimension]"

Example: "The degree to which instructional content builds upon prior knowledge"

Relationships

Pattern: "The relationship between [entity A] and [entity B] where [characteristic]"

Example: "The relationship between two concepts where one must be understood before the other"

Word Count Guidelines

• Minimum: 10 words (most concepts need at least this much)
• Target: 20-50 words (sweet spot for most definitions)
• Maximum: 75 words (beyond this, consider breaking into multiple sentences or adding detail as an example)
• Simple Concepts: 10-25 words may suffice
• Complex Concepts: 30-50 words may be necessary
• Very Complex: 50-75 words, but consider if an example would be better

Readability Targets

• Grade Level: Match the target audience
• High School: Grade 9-10 (Flesch-Kincaid)
• Undergraduate: Grade 12-14
• Graduate: Grade 14-16
• Professional: Grade 14-18
• Sentence Structure: Prefer simple, direct sentences
• Jargon: Use only when necessary for precision and when terms are defined elsewhere
• Active Voice: Preferred when natural

Example Workflow

1. Draft: Write initial definition without worrying about criteria 2. Review Precision: Does it accurately capture the meaning? 3. Review Conciseness: Can any words be removed? 4. Review Distinctiveness: How is it different from related concepts? 5. Review Non-Circularity: Any circular references? 6. Review Business Rules: Any prescriptive language? 7. Revise: Make necessary changes 8. Validate: Check against full checklist 9. Add Example: If concept is abstract or could benefit from illustration

Scoring Rubric

Each criterion worth 25 points:

20-25 points: Fully meets criterion, no issues 15-19 points: Mostly meets criterion, minor issues 10-14 points: Partially meets criterion, notable issues 5-9 points: Minimally meets criterion, major issues 0-4 points: Fails to meet criterion

Overall Quality Score:

• 85-100: Excellent, ready for publication
• 70-84: Good, minor refinements beneficial
• 55-69: Adequate, notable improvements needed
• 40-54: Poor, requires substantial revision
• Below 40: Unacceptable, complete rewrite needed