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Open, Interoperable Data for Actionable Credential Ecosystems

Creating an effective, efficient, and fair marketplace for credentials, qualifications, and skills requires collaboration among various stakeholders, including employers, educational providers, quality assurance organizations, assessment bodies, funders, and guidance platforms.
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Since 2016, Credential Engine has led the development and evolution of the open-source Credential Transparency Description Language (CTDL). This language encompasses over 1000 terms for rich descriptions of credentials, competencies, skills, jobs, occupations, providers, quality assurance entities and processes, assessments, pathways, learning opportunities, outcomes, and more. CTDL is a continuously evolving framework that supports global linked open data for credentials. Credential Engine offers unparalleled technical, programmatic, application, and policy expertise. This resource provides deeper insight into the CTDL and Credential Registry, and how they can power and improve learning and work data ecosystems.

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SOLID Data: What it Means

For the U.S. to meet the growing demand for skills and credential data, it is essential that this information be structured, open, linked, interoperable, and durable (SOLID). Credential Engine ensures this by advancing CTDL, the only comprehensive open standard for describing and linking credentials, learning, and work ecosystems, as the foundation for this work.

Fact Sheets

Recognition of Prior Learning: Helping People Move Forward

Recognition of prior learning (RPL) is the process of providing formal acknowledgment and credit for knowledge, skills, and abilities people have gained through work experience, military service, self-study, volunteering, and/or previous education. This includes credit for prior learning (CPL), transfer credit between institutions, and validation of non-traditional learning experiences. RPL empowers people to move forward and build on what they already know rather than starting over, accelerating pathways to credentials and careers.

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Other Resources

SOLID Data: What it Means

For the U.S. to meet the growing demand for skills and credential data, it is essential that this information be structured, open, linked, interoperable, and durable (SOLID). Credential Engine ensures this by advancing CTDL, the only comprehensive open standard for describing and linking credentials, learning, and work ecosystems, as the foundation for this work.

Fact Sheets

Recognition of Prior Learning: Helping People Move Forward

Recognition of prior learning (RPL) is the process of providing formal acknowledgment and credit for knowledge, skills, and abilities people have gained through work experience, military service, self-study, volunteering, and/or previous education. This includes credit for prior learning (CPL), transfer credit between institutions, and validation of non-traditional learning experiences. RPL empowers people to move forward and build on what they already know rather than starting over, accelerating pathways to credentials and careers.

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Our team of experts is ready to help you embark your credential transparency journey. Whether you have questions about our technologies, services, or don’t know how to get started, we’re here to assist.

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