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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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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.

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

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.

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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