Task Group Archives
Task Groups are convened when substantial CTDL updates are required. They are time-limited, purpose-driven groups that develop and propose enhancements based on real-world needs. Final outputs, including meeting notes, proposals, and GitHub repositories, are preserved here for public access and reuse.Â
Task group final materials include: Approved charter, meeting materials including presentations and relevant examples, use cases, encoded data samples, CTDL domain model, and a CTDL terms proposal. The links below provide access to these materials.Â
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This task group is in progress. Materials will be archived upon completion. See the announcement here.
This focus group developed CTDL terms to provide more specific descriptions of credentials as formally defined by public authorities within national or regional qualification systems. Credential Types reflect how credentials are regulated and recognized in specific contexts and capture distinctions important for transparency, quality assurance, and recognition across systems and borders.
This task group focused on four major areas: describing qualifications frameworks themselves; representing their component progression levels and learning outcome descriptors as linked data; modeling alignments between multiple frameworks; and supporting the alignment of credentials and other resources to qualifications framework progression levels. The group added new CTDL terms and modeling to support global, interoperable transparency of qualifications frameworks and their connections to credentials, learning opportunities, assessments, occupations, and quality assurance.
This group refined the existing CTDL Quantitative Data (QData) schema to improve how education and employment outcomes metrics are modeled. It focused on supporting both simple and complex data sets, representing multidimensional data, clarifying the use of metrics and observations, and enhancing discovery through improved classification of metrics and dimensions.
This group developed CTDL terms to more precisely define and describe various types of certificates issued across industry, vocational, career, technical, secondary, and postsecondary contexts. The updates addressed gaps in the existing model by introducing new terms and refining relationships to assessments, competencies, learning programs, and other credentials. These terms were implemented to improve clarity, alignment, and transparency in how certificates are represented and shared in the Credential Registry and broader credentialing ecosystems.
This group developed CTDL terms to describe rubrics as structured evaluation tools used to assess performance, quality, or achievement against defined criteria and levels. The updates introduced new classes to represent rubrics, criteria, achievement levels, and individual benchmark statements, with support for linking rubrics to credentials, assessments, competencies, jobs, and tasks. These terms were implemented to enable transparent, machine-readable descriptions of how competence is evaluated across educational, workforce, and quality assurance contexts.
This group developed CTDL terms to describe the support services that help individuals access, navigate, and complete credentialing programs, including academic, financial, health, and personal assistance. The updates also support publishing data about eligibility conditions, service locations, and accommodations, along with policies that promote equitable access and outcomes. These terms were implemented to improve transparency around learner supports and to help organizations communicate the services they offer across the education and workforce ecosystems.
This group developed CTDL terms and modeling strategies to represent complex constraints and progression conditions within structured education and career pathways, including logical expressions such as “and/or” combinations, sequencing, credit thresholds, and learning levels. The updates introduced a formal mechanism for expressing constraints that are machine-readable, RDF-conformant, and extensible to real-world data needs—such as eligibility rules for credential attainment or program advancement. These terms were implemented to enhance CTDL’s ability to support dynamic and conditional pathway modeling, improving the accuracy and interoperability of pathway data across systems.
This group developed CTDL terms and publishing workflows to support describing credentials, learning opportunities, and assessments that appear on government and organizational approval lists—such as Eligible Training Provider Lists (ETPL), GI Bill-approved lists, and industry-recognized credentials. The updates support modeling of approval authorities, lifecycle status, list types, jurisdictional relevance, membership periods, and the relationship between primary and secondary sources of credential data. These terms were implemented to streamline how states, agencies, and other stakeholders describe, publish, and manage approval lists using interoperable, linked open data.Â
This group developed CTDL terms and modeling updates to support richer, machine-actionable contextualization of individual competencies—such as skills, knowledge, abilities, and learning outcomes—across education and employment settings. The updates enable the expression of metadata such as the source, purpose, use, and environment in which a competency is relevant, improving how competencies are authored, shared, discovered, and reused. These terms were implemented to strengthen interoperability and transparency across frameworks, organizations, and systems that manage and apply competency and skills data.
This group developed the CTDL Transfer Value Profile and related terms to describe how the value of learning achievements—such as credentials, assessments, and learning opportunities—can be recognized and applied in new educational or employment contexts. The resulting data structures support a wide range of use cases, including transfer credit, credit recommendations, prior learning recognition, and equivalencies across institutions, sectors, and countries. These terms were implemented to enable a shared, machine-readable transfer values that promotes transparency and portability across diverse learning and credentialing systems.
This group developed CTDL classes and properties that link credentials, learning opportunities, assessments, and competencies to employment by describing their relationships to occupations, jobs, work roles, and tasks. The resulting terms allow CTDL to represent specific job functions—beyond broad occupation classifications—and show how educational experiences prepare individuals for real-world employment opportunities. These updates were implemented to support greater transparency about the value of credentials and enable better alignment among learners, credential providers, and employers.
This Task Group focused on expanding the CTDL to include data elements that support transparency around the employment and earnings outcomes of credential holders. The group was divided into two subgroups—one focused on CTDL terms and data modeling, and the other on policy and practice guidance to inform those terms. As a result of this work, new CTDL terms were implemented to represent employment and earnings metrics based on aggregate-level data.
This group developed the foundational CTDL classes and properties needed to describe structured education and career pathways that connect credentials, competencies, learning opportunities, and other CTDL classes as a progression. The work included defining relationships among credentials and resulted in the introduction of the Pathway and Pathway Component classes in CTDL. These terms were implemented to enable machine-readable representations of both organizationally designed and learner-driven pathways.
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