Publishing linked open data about jobs increases opportunities for people to achieve their learning and career pathway goals spanning education, training, and work.

Using the Credential Transparency Description Language (CTDL) and the Credential Registry offers an open standard, transparent, and data-driven approach to bridging the gap between education and work in data ecosystems. It supports informed decision-making, effective skill matching, and collaborative partnerships for the betterment of learners, employers, and the economy. This one-pager provides key value propositions for improving the connections between learning and work in 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.

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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.

Fact Sheets

The Value of CTDL for AI

The use of AI in relation to credential, skill, and job data is changing the way credential providers, educational institutions, training organizations, employers, government agencies, and others are applying data-driven solutions to skills mapping, credential recommendations, career exploration, and more. The Credential Transparency Description Language (CTDL) and structured data in the Credential Registry improve the accuracy and relevance of these AI applications.

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.

Fact Sheets

The Value of CTDL for AI

The use of AI in relation to credential, skill, and job data is changing the way credential providers, educational institutions, training organizations, employers, government agencies, and others are applying data-driven solutions to skills mapping, credential recommendations, career exploration, and more. The Credential Transparency Description Language (CTDL) and structured data in the Credential Registry improve the accuracy and relevance of these AI applications.

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