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Credential Transparency Description Language Fact Sheet

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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Fact Sheets

Publishing Jobs Data with CTDL: 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.

Fact Sheets

The Value of CTDL for AI

As uses of AI and machine learning are very quickly evolving for applications like skills mapping, learning opportunity recommendations, and career exploration, CTDL provides huge advantages for improved accuracy and relevance in these applications. The CTDL schema and CTDL data in the Credential Registry are highly useful for training and refining AI models because they are structured data that is organized, predefined, and formatted consistently. And the more data that is available in CTDL, the more thoroughly AI tools can analyze patterns in the linked open data and make valuable connections. Credential Engine is working with partners on innovations that combine CTDL as a rich data schema, the huge body of CTDL data that is already in the Credential Registry, and new AI-assisted tools that publish to and consume from the Credential Registry. This resource provides an overview of structured data and the value of CTDL for AI.

Report

Equity Advisory Council Report and Recommendations

Credential Engine’s work is centered around data transparency. Transparent, linked, open data has been identified as a particularly valuable tool for revealing inequities, understanding their root causes, and then informing and driving systemic change in a number of areas, including postsecondary education and training. Credential Engine understands that in a society rife with inequities, a commitment to open data use alone is not sufficient. To support the intentional identification and publishing of key data to aid the field in assessing equitable pathways, transfer, and the recognition of learning, Credential Engine convened a broad coalition of equity-focused thought leaders, called the Equity Advisory Council (EAC). The Council, along with HCM Strategists, and Credential Engine staff worked diligently to create a report of recommendations.

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Fact Sheets

Publishing Jobs Data with CTDL: 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.

Fact Sheets

The Value of CTDL for AI

As uses of AI and machine learning are very quickly evolving for applications like skills mapping, learning opportunity recommendations, and career exploration, CTDL provides huge advantages for improved accuracy and relevance in these applications. The CTDL schema and CTDL data in the Credential Registry are highly useful for training and refining AI models because they are structured data that is organized, predefined, and formatted consistently. And the more data that is available in CTDL, the more thoroughly AI tools can analyze patterns in the linked open data and make valuable connections. Credential Engine is working with partners on innovations that combine CTDL as a rich data schema, the huge body of CTDL data that is already in the Credential Registry, and new AI-assisted tools that publish to and consume from the Credential Registry. This resource provides an overview of structured data and the value of CTDL for AI.

Report

Equity Advisory Council Report and Recommendations

Credential Engine’s work is centered around data transparency. Transparent, linked, open data has been identified as a particularly valuable tool for revealing inequities, understanding their root causes, and then informing and driving systemic change in a number of areas, including postsecondary education and training. Credential Engine understands that in a society rife with inequities, a commitment to open data use alone is not sufficient. To support the intentional identification and publishing of key data to aid the field in assessing equitable pathways, transfer, and the recognition of learning, Credential Engine convened a broad coalition of equity-focused thought leaders, called the Equity Advisory Council (EAC). The Council, along with HCM Strategists, and Credential Engine staff worked diligently to create a report of recommendations.

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