Our 2019 report about the credential ecosystem and current count of postsecondary and secondary credentials.

Despite increased attention on the need for education and training as a foundation for social and economic security as well as a nationwide expansion in education and training opportunities, the country has never had a good estimate of the number of credentials, much less a strong accounting of their various characteristics and potential returns.

This new report from Credential Engine moves us forward—performing an extensive count and using computational models, when necessary, to estimate that the United States has at least 738,428 unique credentials across 17 separate subcategories. To tackle the credential marketplace we must come together to:

  • Publish Complete and Comprehensive Data on ALL Credentials to the Credential Registry
  • Incorporate Linked Data on Credentials into Tools and AppsLearn more about working with Credential Engine and how you can be part of the solution by reading the report.
Counting US Postsecondary and Secondary Credentials_190925_FINAL

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Tags: Credential Ecosystem, Credential Engine
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.

Fact Sheets

Credential Transparency State Partnerships Overview

State and regional partners are working with Credential Engine to use the Credential Transparency Description Language (CTDL) and publish data to the Credential Registry. Credential Engine’s technologies support numerous statewide priorities. This resource provides an overview of our current state and regional partnerships.

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.

Fact Sheets

Credential Transparency State Partnerships Overview

State and regional partners are working with Credential Engine to use the Credential Transparency Description Language (CTDL) and publish data to the Credential Registry. Credential Engine’s technologies support numerous statewide priorities. This resource provides an overview of our current state and regional partnerships.

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