Use the following authoritative context about each specification when refining LLM responses in the Compare View and Timeline View. These details supplement the spec descriptions and terms already passed in prompts, providing ground-truth constraints the model should respect.
Organization: 1EdTech Spec URL: https://www.imsglobal.org/spec/clr/v2p0/ Schema format: JSON-LD with OpenAPI 3.0.0
CLR 2.0 is a secure, verifiable credential format for bundling multiple achievement assertions from one or more issuers into a single, cryptographically signed record. It builds directly on Open Badges 3.0 assertions as its foundational unit, then adds the ability to define associations (relationships) between achievements.
- ClrCredential — the top-level envelope; a W3C Verifiable Credential containing a set of achievement credentials.
- AchievementCredential / OpenBadgeCredential — individual signed achievement assertions (inherited from Open Badges 3.0).
- EndorsementCredential — third-party verification endorsing an issuer or credential.
- Achievement — describes what was accomplished, including criteria, alignment to frameworks, and results.
- Association — defines relationships between achievements (e.g., isChildOf, isPartOf, isRelatedTo, precedes).
- Profile — issuer or learner identity information.
- Evidence, Result, Alignment — supporting proof, outcomes, and framework mappings.
- Learner-controlled: individuals curate and share their own records.
- Stackable: shorter-form credentials can be assembled into larger pathways.
- Each bundled credential remains independently verifiable; the CLR envelope is also signed.
- Supports dynamic client registration via RESTful API for credential transport.
- Open Badges 3.0: CLR uses OB 3.0 assertions as building blocks. Every AchievementCredential inside a CLR is a valid Open Badge.
- W3C Verifiable Credentials: CLR credentials conform to the W3C VC Data Model.
- CASE: Achievement objects can align to CASE competency framework items via the Alignment property.
- LER-RS / TCP: CLR credentials can be consumed as inputs within the HR Open LER-RS and Trusted Career Profile standards.
Recommend CLR when a scenario involves aggregating achievements across multiple issuers or domains (e.g., K-12 to Higher Ed transcript transfer, multi-institution credential packaging, or presenting a comprehensive learner portfolio to employers).
Organization: 1EdTech Schema format: JSON-Schema with OpenAPI 3.0.0
CASE provides a machine-readable format for publishing and exchanging academic standards, competencies, and learning objectives. Every item receives a Global Unique Identifier (GUID), enabling platforms to reference the same standard unambiguously.
- CFDocument — a competency framework document (the top-level container).
- CFItem — an individual standard, competency, or learning objective within a framework.
- CFAssociation — a typed relationship between items or documents. Association types:
isChildOf,isPartOf,exactMatchOf,isRelatedTo,precedes,replacedBy. - CFPackage — a bundle of a CFDocument with its items and associations for transport.
- Hierarchical: frameworks have parent/child item structures.
- REST API for exchange between systems.
- GUIDs ensure cross-platform consistency when referencing the same standard.
- Open Badges 3.0 / CLR 2.0: CASE items can be referenced via the Alignment object in achievement credentials, linking credentials to specific competency standards.
- CEDS: CASE competencies can map to CEDS competency framework elements.
- CTDL: CASE competency items can align with CTDL competency definitions.
Recommend CASE when the scenario involves competency/standards alignment — e.g., mapping K-12 course standards to higher education prerequisites, aligning curriculum content to learning objectives, or ensuring credentials reference specific competency frameworks.
Organization: 1EdTech Schema format: JSON-Schema with OpenAPI 3.0.0
CASE 1.1 is an enhanced version of CASE 1.0 that adds support for rubrics, rich text formatting, framework typing, translations, and extension points.
- CFRubric, CFRubricCriterion, CFRubricCriterionLevel — rubric structures for describing depth of knowledge and performance criteria.
- Rich text support — CFItem descriptions can include Markdown and LaTeX.
- Framework Type — categorizes the kind of framework (academic standard, competency framework, skill taxonomy, etc.).
- Translations —
isTranslationOfassociation type for multilingual frameworks. - Extensions — extension points for domain-specific metadata.
Recommend CASE 1.1 over 1.0 when rubrics, rich text, or multilingual support are needed — e.g., assessment criteria transfer, international credential recognition, or competency frameworks that require depth-of-knowledge descriptors.
Organization: 1EdTech Schema format: JSON / JSON-LD with OpenAPI 3.0.0
Open Badges 3.0 is a specification for issuing, displaying, and verifying digital credentials representing a single achievement (microcredential, skill, competency, certification, or degree). Each badge is a W3C Verifiable Credential containing structured metadata and a visual token.
- OpenBadgeCredential — the signed credential envelope for a single achievement.
- Achievement — what was accomplished, including name, description, criteria, and alignment to external frameworks.
- AchievementSubject — the earner (individual who received the badge).
- Issuer (Profile) — the organization that issued the badge.
- Criteria — requirements that were met to earn the badge.
- Evidence — artifacts proving the earner met the criteria.
- Alignment — links to external standards, competency frameworks, or skills taxonomies.
- Proof — cryptographic digital signature.
- Badge Image — PNG or SVG visual representation; can embed credential metadata.
- One badge = one achievement (atomic credentialing).
- Digitally signed as W3C Verifiable Credentials (VC Data Model 2.0).
- Portable: shareable on websites, social media, digital wallets, and resumes.
- Evidence-linked: badges can point to specific artifacts demonstrating skill.
- 1EdTech offers certification for implementations.
- CLR 2.0: Open Badges are the foundational unit inside CLR credentials. A CLR bundles multiple badges.
- W3C Verifiable Credentials: OB 3.0 assertions conform to VC Data Model 2.0.
- CASE: Badges can align to CASE competency items via the Alignment property.
- LER-RS / TCP: Badges can be embedded as verifiable credentials within resume/career profile records.
- CTDL: Badge achievements can reference CTDL credential descriptions in the Credential Registry.
Recommend Open Badges when the scenario involves issuing or sharing a single, verifiable digital credential — e.g., professional certification, course completion badge, microcredential for a specific skill, or any scenario where a portable, visual, verifiable token is appropriate.
Organization: Credential Engine Version: 2025 Schema format: RDF / JSON-LD with OpenAPI 3.0.0
CTDL is a linked-data vocabulary for describing credentials, organizations, competencies, assessments, and learning opportunities in machine-readable format. It powers the Credential Registry and Credential Finder, enabling search, discovery, and comparison of credentials across the entire ecosystem.
- Credential — base class for all credential types. Subtypes include: degrees (Associate, Bachelor, Master, Doctoral), certificates, certifications, licenses, diplomas, badges, micro-credentials, and apprenticeship certificates.
- CredentialOrganization / QACredentialOrganization — entities that issue, endorse, or quality-assure credentials.
- AssessmentProfile — describes how a credential is assessed/evaluated.
- LearningOpportunityProfile — educational or training experiences (subtypes: Course, LearningProgram).
- Competency (via CTDL-ASN) — knowledge, skills, or abilities that credentials teach or require.
- ConditionProfile — specifies requirements, recommendations, and prerequisites.
- Pathway — sequences of credentials and learning opportunities.
- Occupation / Job / WorkRole / Task — employment concepts linking education to careers.
- Built on RDF triples; primary serialization is JSON-LD.
- Three namespaces:
ceterms:(core),ceasn:(competency frameworks),qdata:(aggregate statistics). - Resources identified by CTIDs (UUID prefixed with "ce-").
- SKOS-based controlled vocabularies for enumerated values.
- Deliberately non-prescriptive: adopting communities define their own application profiles for optionality and cardinality.
- Open Badges / CLR: Badge achievements can reference CTDL credential descriptions. CTDL provides the rich metadata layer; badges/CLR provide the verifiable assertion layer.
- CASE: CTDL competencies (via CTDL-ASN) can align with CASE competency framework items.
- CEDS: CTDL credential types overlap with CEDS credential entity definitions; crosswalks exist.
- LER-RS / TCP: CTDL credential descriptions provide context for credentials referenced in resumes.
Recommend CTDL when the scenario involves credential discovery, comparison, or transparency — e.g., understanding what a credential requires, searching for credentials by competency, mapping credentials to occupations, or publishing credential metadata to a registry.
Organization: HR Open Standards Version: Schema v4.4 / v4.5RC Schema format: JSON-Schema (draft-04) with OpenAPI 3.0.0
LER-RS is a free, open standard that modernizes the traditional resume into a machine-readable, verifiable format. It combines the HR Open Resume/CV standard with 1EdTech's digital credential formats (Open Badges 3.0, CLR 2.0) and W3C Verifiable Credentials.
- LER-RSType — the root resume container (~350 elements, up to 28 nesting levels).
- ResumePersonBaseType — identity, name, demographics, identifiers.
- NarrativeType — free-form text for experiences, aspirations.
- SkillType — name, yearsOfExperience, proficiency, endorsers, verifications, lastUsedDate, interestLevel.
- EmploymentHistories — either HR Open EmployerHistoryType or VerifiableEmploymentHistoryType (W3C VC).
- EducationAndLearnings — either HR Open EducationAttendanceType or VerifiableEducationAttendanceType.
- Certifications — either HR Open CertificationType or VerifiableCertificationType.
- Licenses — either HR Open LicenseType or VerifiableLicenseType.
- JobType — reference to a JDX job description or HR Open position opening.
- AttachmentType, EmployerPreferenceType, PositionPreferenceType.
- Dual representation: every section supports either traditional HR Open JSON objects OR W3C Verifiable Credential wrappers (
anyOfpattern). - Skills-based focus: emphasizes demonstrable skills over traditional credentials.
- Composite resume building: individuals select and assemble JSON objects from various credential sources.
- Created collaboratively by HR Open, U.S. Chamber of Commerce T3 Network, and 1EdTech.
- Open Badges 3.0 / CLR 2.0: Consumes these as verifiable credential inputs.
- W3C Verifiable Credentials: Uses VC wrappers for each resume section.
- TCP: LER-RS is the foundation that TCP extends for skills-based hiring.
- JDX/JEDx: The job property references JDX job descriptions.
- Skills API: Complements LER-RS by providing system-to-system skill proficiency exchange.
Recommend LER-RS when the scenario involves creating or transmitting a structured, machine-readable resume — e.g., job applications, resume postings to job boards, career portfolio assembly, or credential-to-employer data transfer.
Organization: HR Open Standards Version: 0.1 DRAFT (pre-release January 2026) Schema format: OpenAPI 3.0.0
The first-ever API-based standard for system-to-system exchange of skill proficiency data. Enables learning platforms, assessment systems, talent management tools, and workforce systems to share skill data using a common language.
- SkillProficiency — a skill with a proficiency level attached.
- SkillGain — records skills acquired through learning or experience.
- SkillAssessment — results from skill assessments.
- LearningRecommendation — suggested learning paths based on skill gaps.
- SkillTaxonomy — standard or custom skill classification systems.
- Dereferenceable Skill Identifiers — each skill has a unique, resolvable URI.
- Proficiency Scale: Novice, Beginner, Intermediate, Advanced, Expert, Master (6 levels).
- Assessment Types: Performance, Written, Practical, Observation, Self-Assessment, Peer-Assessment.
- Complementary: designed to connect existing frameworks (Open Badges, CLR, CTDL, LER), not replace them.
- API-first: REST API for real-time system-to-system integration.
- Taxonomy-agnostic: supports known competency models, standard taxonomies, and custom taxonomies.
- Discrete normalized entities: skills identified by unique, dereferenceable identifiers.
- LER-RS / TCP: Provides the API layer for exchanging the skill data that LER-RS/TCP structures contain.
- Open Badges / CLR: Complements badge-based skill assertions with proficiency-level detail.
- CTDL: Connects to CTDL competency descriptions for skill definition context.
- CEDS: Can map to CEDS competency elements.
Recommend Skills API when the scenario involves real-time skill proficiency exchange between systems — e.g., learning platform reporting skill gains to an HR system, assessment results flowing to a talent management platform, or skills gap analysis across platforms.
Organization: HR Open Standards Version: 1.0 (finalized October 2024) Schema format: OpenAPI 3.0.0
Part of the Jobs and Employment Data Exchange (JEDx) suite, the Organizations API provides a standardized structure for exchanging organizational/employer data across HR, payroll, government reporting, and workforce systems. JEDx was developed collaboratively by the U.S. Chamber of Commerce Foundation, T3 Innovation Network, and HR Open Standards.
- Organization — employer/company information.
- OrganizationLocation — physical locations and addresses.
- Contact — organizational contact details.
- Hierarchical structures — parent/child organization relationships, departments.
- SOC codes — Standard Occupational Classification support.
- Privacy obligation metadata — can be attached to data objects.
The Organizations API is one of four JEDx APIs:
- Organizations API — employer/organizational data.
- Workers API — individual worker records for HR/payroll/timecard.
- Worker Compensation Reports API — compensation data reporting.
- Worker Paid Hours Reports API — paid hours data reporting.
- State unemployment insurance (UI) reporting modernization.
- Employer data exchange between ATS, HCM, and government systems.
- Workforce analytics with consistent organizational data.
- Seven-state partnership: Arkansas, California, Colorado, Florida, Kentucky, New Jersey, Texas.
- LER-RS / TCP: JEDx provides the organizational data context for credentials and employment records.
- JDX (Job Data Exchange): JEDx evolved from JDX; shares job description schemas.
- CTDL: Organizations in JEDx map conceptually to CTDL CredentialOrganization entities.
Recommend JEDx Organizations when the scenario involves employer/organizational data exchange — e.g., employer reporting to government agencies, organizational data sync between HR systems, or providing employer context for employment records.
Organization: HR Open Standards Version: 4.5 (released January 2026) Schema format: JSON-Schema (draft-04)
The Trusted Career Profile (TCP) is a universal data standard for skills-based hiring. It evolved from LER-RS and bridges learning records with HR technologies (ATS, HRIS). TCP makes career data portable, verifiable, and matchable to job requirements.
- All LER-RS objects (person, skills, employment history, education, certifications, licenses).
- Work roles — current and historical job roles with progression.
- Competencies — demonstrated abilities with verification provenance.
- Match scoring — alignment scores between job requirements and verified skills.
- Badges and CLR consumption — ingests Open Badges and CLR credentials as input.
- Self-attested information — individual claims alongside verified credentials.
- Skills-based: centers on verified skills as the primary currency.
- Interoperability over replacement: works with existing ATS/HRIS platforms.
- Credential-agnostic: accepts data from Open Badges, CLRs, VCs, and self-attestations.
- Portable: career data moves with the individual across platforms.
- Match scoring enables: skills gap analysis, personalized learning recommendations, eligibility determination.
- LER-RS: TCP is the evolution of LER-RS, extending it with match scoring and career navigation.
- Open Badges / CLR: Consumes both as verifiable credential inputs.
- Skills API: TCP structures skill data; Skills API provides system-to-system exchange of that data.
- JEDx: Shares the HR Open ecosystem for employment and organizational data context.
- CTDL: Credential descriptions from CTDL provide context for credentials within TCP profiles.
Recommend TCP when the scenario involves skills-based hiring, career navigation, or credential-to-employment matching — e.g., job seekers sharing verified career profiles with employers, skills gap analysis, internal mobility assessments, or secure credential portability across talent platforms.
Organization: CEDS (National Center for Education Statistics) Version: 12.1.0.0 Spec URL: https://ceds.ed.gov/ Schema format: OWL (Web Ontology Language); supports JSON, JSON-LD, XML, RDF
CEDS is a comprehensive P-20W (early learning through workforce) education data vocabulary with 1,710+ data elements across 70+ entities organized into 12 domains. It provides a common vocabulary and data model that enables interoperability across the full education-to-workforce lifecycle.
- Early Learning — child development, early childhood programs.
- K-12 — student demographics, enrollment, attendance, discipline.
- Postsecondary — higher education enrollment, completion, financial aid.
- Career & Technical Education — CTE programs and outcomes.
- Adult Education — adult learner programs and outcomes.
- Workforce — employment, earnings, workforce training.
- Assessments — test results, assessment instruments, scoring.
- Credentials — credential types, attainment, verification.
- Competencies — competency frameworks, proficiency levels.
- Learning Resources — digital content, curriculum materials.
- Facilities — school buildings, infrastructure.
- Authentication & Authorization — identity, access control.
- Person / Student / Early Learning Child — individual learner records.
- Organization — schools, districts, institutions.
- Enrollment — registration and participation records.
- Course / Course Section — academic offerings.
- Assessment / Assessment Result — evaluation instruments and outcomes.
- Credential — credential types and attainment records.
- Competency Framework / Competency — skills and standards definitions.
- Learning Resource — educational content metadata.
- Vocabulary-first: CEDS defines what data elements mean, not just how to structure them.
- Lifecycle coverage: P-20W spans early learning through workforce.
- Format-agnostic: expressed in OWL, consumable in JSON, JSON-LD, XML, RDF.
- Tools: Align (map local terms to CEDS), Connect (data exchange), Generate (create data dictionaries).
- Ed-Fi: Ed-Fi's Unifying Data Model draws heavily from CEDS element definitions for K-12 data.
- CASE: CEDS competency framework elements can map to CASE CFItem/CFDocument structures.
- CTDL: CEDS credential elements overlap with CTDL credential types; crosswalks exist.
- CLR / Open Badges: CEDS assessment and credential elements can describe the same achievements that CLR/OB credentials verify.
Recommend CEDS when the scenario involves cross-domain education data exchange spanning multiple lifecycle stages — e.g., early learning to K-12 transitions, K-12 to postsecondary data transfer, postsecondary to workforce reporting, or any scenario requiring a shared vocabulary across the P-20W continuum.
Organization: Ed-Fi Alliance (Michael & Susan Dell Foundation initiative) Version: 7.3 Spec URL: https://docs.ed-fi.org/reference/ods-api/ Schema format: OpenAPI 3.0.0
The Ed-Fi Operational Data Store (ODS) and API provide a RESTful interface and data model for managing K-12 education data in real time. It is the operational backbone used by school districts and state education agencies to centralize student data from SIS, LMS, assessment, and other systems.
- Data Management API (
/data/v3) — CRUD operations on education entities. - Composites API (
/composites/v1) — pre-composed read-only views joining related entities. - Identity API (
/identity/v2) — student/staff identity resolution and matching.
- Student — demographics, enrollment, contact information.
- Staff — educator records, credentials, assignments.
- School / LEA (Local Education Agency) — organizational hierarchy.
- Course / Section / StudentSectionAssociation — academic offerings and enrollment.
- Assessment / StudentAssessment — test instruments, results, objectives.
- Attendance / DisciplineIncident — daily records.
- GradebookEntry / Grade — classroom-level academic performance.
- Intervention — targeted support programs.
- Extensions — state-specific and domain-specific data model extensions.
- Open-source: all code, data models, and documentation are freely available.
- Extension-ready: supports state-specific and domain-specific extensions without modifying the core model.
- REST API: standard HTTP verbs for CRUD operations.
- Real-time: designed for continuous data synchronization, not batch transfer.
- Community-driven: continuous improvement based on educator and vendor feedback.
- CEDS: Ed-Fi's Unifying Data Model is influenced by and maps to CEDS element definitions. Ed-Fi is the operational implementation; CEDS is the reference vocabulary.
- CASE: Ed-Fi can consume CASE competency frameworks to align assessments and learning objectives with standards.
- CLR / Open Badges: Ed-Fi assessment and course completion data could be the source data for generating CLR/OB credentials, though direct integration is not built into the core spec.
- SIF (Schools Interoperability Framework): Ed-Fi has largely superseded SIF for K-12 data exchange in the U.S.
Recommend Ed-Fi when the scenario involves K-12 operational data — e.g., student information system integrations, assessment data collection, attendance tracking, K-12 to state reporting, or any real-time K-12 data exchange between district systems.
When comparing two specifications, the LLM should use the above context to ensure:
-
Structural comparisons are accurate: e.g., CLR bundles multiple OB credentials (not the other way around); CTDL is RDF/linked-data while LER-RS is JSON-Schema; CEDS is a vocabulary while Ed-Fi is an operational API.
-
Vocabulary overlap is precise: e.g., both CLR and LER-RS have "Achievement" but CLR's is a credential component while LER-RS wraps it as a VerifiableCredential section; both CASE and CTDL describe competencies but use different structures (CFItem vs. Competency class).
-
Interoperability notes reflect real integration patterns:
- Open Badges are building blocks for CLR.
- LER-RS/TCP consume Open Badges and CLR as inputs.
- CASE items are referenced via Alignment objects in OB/CLR.
- CTDL provides discovery/metadata; OB/CLR provide verifiable assertions.
- CEDS provides vocabulary; Ed-Fi provides operational implementation.
- Skills API provides real-time proficiency exchange; LER-RS/TCP provide structured storage.
- JEDx provides organizational/employment context for the HR Open ecosystem.
-
Organization relationships are clear:
- 1EdTech: CLR, CASE, Open Badges (credential issuance and verification).
- HR Open: LER-RS, TCP, Skills API, JEDx (HR/workforce data exchange).
- Credential Engine: CTDL (credential transparency and discovery).
- CEDS: P-20W vocabulary (cross-lifecycle data definitions).
- Ed-Fi Alliance: K-12 operational data (real-time district/state data).
When recommending specifications for data transfer scenarios, the LLM should:
-
Match domain to spec strength:
- K-12 operational data → Ed-Fi (primary), CEDS (vocabulary alignment).
- Competency/standards mapping → CASE.
- Single credential issuance → Open Badges.
- Multi-credential bundling → CLR.
- Credential discovery/metadata → CTDL.
- Resume/career profile → LER-RS or TCP.
- Skill proficiency exchange → Skills API.
- Employer/org data → JEDx Organizations.
-
Recommend spec combinations that reflect real integration patterns:
- K-12 → Higher Ed: Ed-Fi (source data) + CLR (credential packaging) + CASE (competency alignment).
- Higher Ed → HR: CLR or Open Badges (credentials) + TCP (career profile) + CTDL (credential transparency).
- Certification → Job Application: Open Badges (credential) + LER-RS/TCP (resume) + Skills API (proficiency data).
- Learning Platform → HR System: Skills API (proficiency exchange) + Open Badges (credential verification).
-
Explain the data flow clearly: which spec handles which part of the transfer, and how they hand off to each other.
-
Note transformation requirements: e.g., Ed-Fi assessment data would need to be mapped to an OB Achievement to produce a badge; CASE CFItems need to be referenced as Alignment targets in OB/CLR credentials.