The Credential Transparency Description Language (CTDL) is a scheme (a type of-mini language that people and systems can use to understand each other even if their data comes from different sources) that anyone can use to share information about credentialing data. The CTDL not only provides a common and unified way of describing information in the Credential Registry, it is also an open language that can be used on the web. This powerful feature makes it dramatically easier for students, businesses, researchers, and automated systems to discover, understand, and compare information about credentials from a variety of sources.
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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.

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Connecting Learning and Jobs with CTDL Data: One-Pager

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.

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

Connecting Learning and Jobs with CTDL Data: One-Pager

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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