We know that students, employers, job seekers, and others need comprehensive information to better understand and make decisions about credentials. To accompany Credential Engine’s minimum data policy, we created benchmark models with input from our Advisory Groups, registry participants, and other stakeholders that demonstrate the essential data that users need. These benchmark models are recommendations to credential issuers about the type of information that employers, students, application developers, state agencies, and others will find valuable. We encourage publishers to include as much of this information as is relevant and available.

For this category—and including the subcategories of learning opportunities and assessments—the required information that is included in the minimum data policy is shown along with the benchmark. The benchmark models are inclusive of the “required if available” and “recommended” data in the minimum data policy as well as some additional terms. The models are organized by credential type to accommodate the differences in both structure and critical data points for each type (degree, license, certificate, badge, etc.). Though not all credentials will fit neatly into these boxes, these benchmark models should serve as a guideline for the amount of information that can help transform the credentialing marketplace by bringing transparency and common understanding to credentials.

pdf_Bench Mark Model_Badges and Apprenticeships_190128

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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.

Fact Sheets

Developer Agreement

Organizations that utilize our APIs are allowed a 6-month testing period, after which a developer agreement is required. Download this resource to preview the agreement.

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.

Fact Sheets

Developer Agreement

Organizations that utilize our APIs are allowed a 6-month testing period, after which a developer agreement is required. Download this resource to preview the agreement.

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