Reusable Competency Definitions

Reusable Competency Definitions or RCDs are part of the IEEE Total Learning Architecture and are described in IEEE standard IEEE 1484.20.1.

What are Reusable Competency Definitions?

RCDs describe what you need to know to do a job. For example, an RCD can describe:

Knowledge, Skills, Abilities, and Other behaviors (KSAOs) required in the workplace
A common language for describing competencies across organizations
Assessment and evaluation criteria for measuring proficiency
Alignment between education/training and operational performance

Core Purpose and Benefits

Reusable Competency Definitions provide a standardized, machine-readable format for expressing competencies that can be shared and reused across different educational and training systems. This standardization offers several key benefits:

Interoperability

RCDs enable different systems and organizations to exchange competency information without ambiguity. A competency defined in one learning platform can be understood and utilized by another system, facilitating learner mobility and credential portability.

Consistency

By establishing a common vocabulary and structure, RCDs ensure that competencies are described consistently across contexts, reducing confusion and enabling accurate comparison of learning outcomes.

Efficiency

Organizations can leverage existing competency definitions rather than creating new ones from scratch, saving time and resources while building on established best practices.

Data-Driven Decision Making

Standardized competency data enables sophisticated analytics and reporting, helping organizations track skill gaps, measure training effectiveness, and make informed decisions about learning investments.

IEEE 1484.20.1 Standard

The IEEE 1484.20.1 standard, formally titled "Standard for Learning Technology - Data Model for Reusable Competency Definitions," provides the technical specification for creating and managing RCDs.

Key Components of the Standard

Competency Identifier

Each RCD must have a unique, persistent identifier (typically a URI) that enables unambiguous reference and retrieval. This identifier remains constant even as the competency definition evolves.

Competency Title

A concise, human-readable name for the competency that clearly indicates what it represents (e.g., "Java Programming," "Statistical Analysis," "Customer Service Communication").

Competency Description

A detailed explanation of the competency that specifies what a learner should know or be able to do. Descriptions should be:

Specific: Clearly define the scope and boundaries
Measurable: Include observable behaviors or outcomes
Action-oriented: Use verbs that describe performance
Context-aware: Specify the conditions or situations where the competency applies

Competency Type

Classification of the competency into categories such as:

Knowledge: Facts, concepts, principles, and theories
Skills: Practical abilities and techniques
Abilities: Cognitive or physical capacities
Attitudes: Dispositions and values
Behaviors: Observable actions in specific contexts

Proficiency Levels

RCDs can define multiple levels of mastery, such as:

Novice/Awareness
Basic/Foundational
Intermediate/Proficient
Advanced/Expert
Master/Authority

Each level should have clear criteria and performance indicators.

Structure and Metadata

A complete RCD includes both required and optional metadata elements that provide context and enable discovery:

Required Elements

Identifier: Unique URI for the competency
Title: Short descriptive name
Description: Detailed competency statement

Optional Elements

Subject: Domain or discipline classification
Language: Language code for internationalization
Version: Version identifier for tracking changes
Status: Current lifecycle state (draft, active, deprecated)
Creator: Individual or organization that authored the RCD
Date: Creation or modification timestamps
Related Competencies: Links to prerequisite, related, or advanced competencies

Relationship to Total Learning Architecture

Within the IEEE Total Learning Architecture (TLA), RCDs serve as the foundational building blocks that connect various system components:

Integration Points

Learning Content

RCDs tag learning resources with the competencies they address, enabling: - Competency-based content search and discovery - Automatic learning path generation - Gap analysis between current and target competencies

Assessment Systems

RCDs define what assessments should measure: - Alignment of test items to specific competencies - Proficiency level determination - Evidence collection for competency attainment

Learning Record Store (LRS)

xAPI statements reference RCDs to record: - Competency-based learning activities - Achievement and progression data - Skills inventory and portfolio building

Credentialing Systems

RCDs specify the competencies represented by: - Digital badges and micro-credentials - Certificates and degrees - Professional certifications

Competency Frameworks and Taxonomies

RCDs are often organized into structured frameworks that show relationships and hierarchies:

Framework Types

Occupational Frameworks

Define competencies required for specific jobs or career paths: - Industry-specific skill standards - Professional role definitions - Career progression ladders

Educational Frameworks

Organize academic competencies by: - Subject area and grade level - Degree program requirements - Learning outcome alignment

Organizational Frameworks

Custom competency models for specific organizations: - Corporate competency models - Training program outcomes - Performance management criteria

Competency Relationships

RCDs can be linked to express:

Prerequisites: Foundational competencies required before attempting advanced ones
Related Competencies: Complementary or frequently co-occurring competencies
Aggregated Competencies: Higher-level competencies composed of multiple sub-competencies
Alternative Competencies: Different approaches to achieving similar outcomes

Creating Effective RCDs

Developing high-quality RCDs requires careful attention to several best practices:

Use Action Verbs

Begin competency statements with clear, observable action verbs from Bloom's Taxonomy or similar frameworks:

Knowledge: Define, list, recall, recognize
Comprehension: Explain, describe, summarize, interpret
Application: Apply, demonstrate, implement, use
Analysis: Analyze, compare, contrast, differentiate
Synthesis: Create, design, develop, construct
Evaluation: Evaluate, assess, critique, justify

Be Specific and Measurable

Avoid vague terms like "understand" or "appreciate." Instead, specify observable evidence:

❌ Poor: "Understand database concepts" ✅ Good: "Design normalized relational database schemas for business applications"

Include Context

Specify conditions, tools, or constraints:

"Develop REST APIs using Python and Flask framework, following OpenAPI 3.0 specification, with proper authentication and error handling"

Define Proficiency Criteria

For each level, specify: - Performance expectations - Quality standards - Independence requirements - Complexity of tasks

Maintain Granularity Balance

RCDs should be: - Not too broad: "Software Development" is too general - Not too narrow: "Use semicolons in JavaScript" is too specific - Just right: "Implement error handling in asynchronous JavaScript applications"

RCDs in Graph-Based Learning Systems

In graph database architectures, RCDs become nodes with rich relationship capabilities:

Graph Representation

{
  "id": "urn:uuid:competency-python-data-analysis",
  "type": "Competency",
  "title": "Python Data Analysis",
  "description": "Analyze datasets using Python libraries (pandas, numpy) to extract insights and create visualizations",
  "domain": "Data Science",
  "level": "Intermediate",
  "prerequisites": [
    "urn:uuid:competency-python-basics",
    "urn:uuid:competency-statistics-fundamentals"
  ],
  "enables": [
    "urn:uuid:competency-machine-learning",
    "urn:uuid:competency-data-visualization"
  ],
  "assessedBy": [
    "urn:uuid:assessment-pandas-project",
    "urn:uuid:assessment-data-analysis-quiz"
  ],
  "taughtIn": [
    "urn:uuid:course-data-science-101",
    "urn:uuid:module-python-analytics"
  ]
}

Query Capabilities

Graph databases enable powerful queries:

Learning Path Discovery: Find optimal sequences of competencies from current state to learning goal
Gap Analysis: Identify missing competencies between a learner's profile and a target role
Competency Clustering: Discover commonly co-occurring competencies for bundle recommendations
Impact Analysis: Assess downstream effects of changes to competency definitions

Integration with xAPI

RCDs work seamlessly with the Experience API (xAPI) to create rich, competency-based learning records:

xAPI Statement Examples

Competency Assertion

{
  "actor": {"mbox": "mailto:learner@example.com"},
  "verb": {
    "id": "http://adlnet.gov/expapi/verbs/mastered",
    "display": {"en-US": "mastered"}
  },
  "object": {
    "id": "urn:uuid:competency-python-data-analysis",
    "definition": {
      "type": "http://adlnet.gov/expapi/activities/competency",
      "name": {"en-US": "Python Data Analysis"}
    }
  },
  "result": {
    "success": true,
    "completion": true,
    "score": {"scaled": 0.85}
  },
  "context": {
    "contextActivities": {
      "category": [{
        "id": "http://id.tincanapi.com/recipe/competency-framework/0_1"
      }]
    }
  }
}

Competency Progress

{
  "actor": {"mbox": "mailto:learner@example.com"},
  "verb": {
    "id": "http://adlnet.gov/expapi/verbs/progressed",
    "display": {"en-US": "progressed"}
  },
  "object": {
    "id": "urn:uuid:competency-python-data-analysis",
    "definition": {
      "type": "http://adlnet.gov/expapi/activities/competency"
    }
  },
  "result": {
    "extensions": {
      "http://id.tincanapi.com/extension/proficiency-level": "intermediate"
    }
  }
}

Use Cases and Applications

Corporate Training

Organizations use RCDs to: - Define job role requirements and hiring criteria - Create personalized learning paths based on skill gaps - Track employee skill development over time - Align training programs with business objectives - Support succession planning and talent development

Higher Education

Universities leverage RCDs to: - Design competency-based degree programs - Enable prior learning assessment and credit transfer - Map program outcomes to industry requirements - Provide detailed skill transcripts to graduates - Facilitate program review and accreditation

Professional Certification

Certification bodies use RCDs to: - Define certification requirements clearly - Create valid and reliable assessments - Issue verifiable digital credentials - Maintain certification through continuing education - Enable stackable credentials and learning pathways

Government and Military

Public sector applications include: - Workforce development programs - Job training for transitioning service members - Civil service competency frameworks - Inter-agency mobility and skill portability

Tools and Technologies

Several platforms and standards support RCD creation and management:

Competency Authoring Tools

CASE Network: Collaborative competency framework development
Achievement Standards Network (ASN): Repository of educational standards
Open Badges: Competency-based digital credentialing
IMS Global standards: CASE, Open Badges, Comprehensive Learner Record

Graph Database Platforms

Neo4j: Property graph database for competency networks
Amazon Neptune: Managed graph database service
ArangoDB: Multi-model database with graph capabilities
TigerGraph: Enterprise-scale graph analytics

Learning Platform Integration

Competency-Based Education (CBE) systems: Specialized LMS platforms
Learning Record Store (LRS): xAPI-compliant data stores
Talent Management Systems: HR platforms with competency tracking
Portfolio Systems: Evidence-based competency demonstration

Challenges and Considerations

Governance and Maintenance

Version Control: Managing updates while maintaining backward compatibility
Authority: Determining who can create and modify competencies
Deprecation: Handling obsolete competencies gracefully
Quality Assurance: Ensuring consistency and accuracy across large frameworks

Adoption Barriers

Complexity: RCD standards can seem overwhelming to practitioners
Legacy Systems: Integrating with existing non-standard approaches
Cultural Change: Shifting from course-based to competency-based thinking
Resource Requirements: Initial investment in framework development

Technical Challenges

Identifier Persistence: Ensuring URIs remain resolvable over time
Semantic Precision: Avoiding ambiguity in competency descriptions
Granularity Decisions: Finding the right level of detail
Localization: Translating and culturally adapting competencies

Future Directions

The evolution of RCDs is being shaped by several emerging trends:

Artificial Intelligence

Automated Competency Extraction: Using NLP to derive competencies from job postings and course descriptions
Intelligent Matching: AI-driven recommendation of learning resources to competency gaps
Dynamic Frameworks: Self-organizing competency structures based on learning data
Skill Prediction: Forecasting future competency requirements based on industry trends

Blockchain and Decentralization

Verifiable Credentials: Cryptographically secured competency assertions
Self-Sovereign Identity: Learner-controlled competency portfolios
Distributed Registries: Decentralized competency definition and discovery

Micro-Credentials and Modularity

Granular Credentials: Competency-specific badges and certificates
Stackable Pathways: Modular learning that aggregates to larger credentials
Just-in-Time Learning: Targeted competency development for immediate needs

Labor Market Integration

Skills-Based Hiring: Direct matching of RCD profiles to job requirements
Real-Time Skill Demand: Labor market analytics informing competency frameworks
Career Guidance: Personalized pathways based on competency trajectories

Conclusion

Reusable Competency Definitions represent a fundamental shift toward more precise, portable, and actionable descriptions of learning outcomes. By providing a standardized, machine-readable format for competencies, RCDs enable interoperability across learning systems, support personalized learning pathways, and create transparent connections between education and workforce needs.

As part of the Total Learning Architecture, RCDs serve as the semantic foundation that links learning content, assessments, credentials, and career opportunities. Their integration with graph databases and xAPI creates powerful capabilities for competency tracking, gap analysis, and adaptive learning.

While challenges remain in governance, adoption, and technical implementation, the benefits of competency-based approaches are driving continued investment and innovation. As AI, blockchain, and other emerging technologies mature, RCDs will play an increasingly central role in creating flexible, lifelong learning ecosystems that respond to the dynamic needs of learners and employers alike.

References

IEEE 1484.20.1-2007: Standard for Learning Technology - Data Model for Reusable Competency Definitions
IEEE Total Learning Architecture
Experience API (xAPI)
Learning Record Store (LRS)
IMS Global Competency and Academic Standards Exchange (CASE)
HR Open Standards for Competency Data
O*NET Content Model and Occupational Standards