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.

Evidence demonstrates that the United States’ current education, training and employment systems create particularly burdensome barriers to success for Black, Hispanic/Latino, Asian American and Pacific Islander and Native American students, returning adults, and students from low-income backgrounds. To achieve its vision and dismantle inequities in education and employment access and outcomes, Credential Engine seeks to advance the universal adoption of transparent, open data to give people clear and consistent information that empowers them to find, evaluate and obtain the skills and credentials—and to navigate pathways across those skills and credentials–that maximize the likelihood of equitable outcomes.

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 in two related areas:

  1. The data about credentials, providers, programs, quality, outcomes, pathways and support services that must be made available as linked, open, transparent data in order to support equity, and to help students, workers, and others make their most informed decisions. These data elements are organized into three tiers:
    1. Tier 1:  No Excuses. These are data points that all providers should be able to publish now.
    2. Tier 2:  Time to Stretch. These are data points that all providers should be actively working on being able to analyze and publish.
    3. Tier 3:  Future Forward. These are data points that point to where the field is headed, and providers should be assessing how to build capacity to analyze and publish them.
  2. Principles for data use that developers of counseling, guidance, navigation and pathways tools and services should follow to ensure that information made open and transparent can bring the greatest value and equity to students and workers.  We also encourage those procuring such tools and systems to incorporate these principles into their RFPs and contracts.

The implementation of these recommendations will help meaningfully advance the collective work to bring about a more equitable education and training marketplace through the use of linked, open, interoperable data, like the Credential Transparency Description Language (CTDL). Credential Engine will provide resources and technical support for the publishing and use of these data, and can work with states, systems and districts, schools, colleges and universities, and others for integration into procurement and other policies.

View additional resources here:

Learn more at credentialengine.org/equity.

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Tags: Credential Engine, Credential Transparency, CTDL, Data
Fact Sheets

Open, Interoperable Data for Actionable Credential Ecosystems

Creating an effective, efficient, and fair marketplace for credentials, qualifications, and skills requires collaboration among various stakeholders, including employers, educational providers, quality assurance organizations, assessment bodies, funders, and guidance platforms.

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

Open, Interoperable Data for Actionable Credential Ecosystems

Creating an effective, efficient, and fair marketplace for credentials, qualifications, and skills requires collaboration among various stakeholders, including employers, educational providers, quality assurance organizations, assessment bodies, funders, and guidance platforms.

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.

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