A new recommended benchmark and publishing template from Credential Engine helps states, institutions, and agencies bring noncredit education data into the credential transparency ecosystem.
Credential Engine has released a new recommended benchmark and publishing template that maps the Noncredit Data Taxonomy 3.0 to the Credential Transparency Description Language (CTDL). The recently released Noncredit Data Taxonomy, developed by the Rutgers Education and Employment Research Center (EERC) and higher education partners through the State Noncredit Data Project, is a nationally recognized framework that organizes noncredit education data into a common structure, covering program design, student outcomes, enrollment, and finance and policy. The new benchmark model connects that framework to the CTDL, providing a practical, structured pathway for states, institutions, and workforce agencies to publish noncredit education data in a format that is consistent, comparable, and interoperable, bringing noncredit data into the broader credential transparency ecosystem as a visible and connected part.
Noncredit education and non-degree credentials are growing fast. Yet, the data surrounding these programs has remained fragmented, inconsistent, and largely invisible to the tools and systems that students, employers, and policymakers rely on to make informed decisions. With an estimated 4.1 million community college students currently enrolled in noncredit offerings, the stakes for getting this data right are significant, and this benchmark model is a concrete step toward closing that gap.
What Is the Noncredit Data Taxonomy?
The Noncredit Data Taxonomy is a product of the State Noncredit Data Project (SNDP), a multi-phase initiative led by the Rutgers EERC in partnership with the University of North Carolina at Charlotte, the University of Michigan, and the University of California–Irvine. Working alongside eight state partners, the project has spent years building a shared language for how noncredit data is collected and understood across diverse state systems.
Version 3.0 of the taxonomy organizes noncredit data into four primary sections:
- Purpose and Design — what a program is, how it’s structured, and how it’s delivered
- Student Outcomes — academic, labor market, and credential outcomes for completers
- Enrollment and Demographics — who is participating in noncredit programs
- Finance and Policy — how programs are funded and governed
This version significantly expands on earlier iterations, adding new data elements around accessibility, student services, provider information, and workforce policy data, including data relevant to Workforce Innovation and Opportunity Act (WIOA) and Workforce Pell programs. In total, the taxonomy comprises more than 90 data elements and operational definitions.
Why Map It to the CTDL?
A taxonomy is only as powerful as the infrastructure that supports it. Having a shared framework for what data to collect is an important first step. Still, for that data to be truly useful across states, systems, and stakeholders, it also needs to be published in a structured, open, linked, interoperable, and durable (SOLID) format that can be discovered, compared, and used at scale.
That’s where the CTDL comes in. The CTDL is SOLID, developed for describing credentials, learning opportunities, and other related data in a way that is both human-readable and machine-actionable. When data is published to the Credential Registry using the CTDL, it becomes interoperable, meaning it can be connected to other data, surfaced in tools and platforms, and understood consistently regardless of the state or institution that produced it.
By mapping the Noncredit Data Taxonomy 3.0 to the CTDL, the benchmark model bridges two important efforts: the field’s growing consensus on what noncredit data matters, and the technical infrastructure needed to make that data visible and usable across the many systems.
What Is the Benchmark Model?
The new State Noncredit Data Taxonomy Benchmark Model is part of Credential Engine’s Recommended Benchmarks models that go beyond the Minimum Data Policy baseline to offer more comprehensive, high-value data publishing guidance. The benchmark provides a complete mapping of the taxonomy’s data elements to the corresponding CTDL classes and properties, giving organizations a clear translation layer between the noncredit data framework and the Credential Registry’s publishing standards.
An accompanying publishing guidance page offers a practical starting point, including a smaller template focused on the most commonly available data elements for organizations ready to begin contributing noncredit data to the Credential Registry today.
Getting Started
The State Noncredit Data Taxonomy Benchmark Model is available on the Credential Engine Benchmarks page. Organizations ready to publish can also access the publishing template and guidance on the Credential Engine Guidance site.
The development of this benchmark model was supported by the Strada Education Foundation.
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About Credential Engine: Credential Engine is a nonprofit organization dedicated to bringing transparency to the credential marketplace. Through the Credential Registry and the Credential Transparency Description Language (CTDL), Credential Engine enables organizations to publish clear, comparable, and machine-readable information about credentials, learning opportunities, and quality assurance. For more information, visit credentialengine.org.
About the Rutgers Education and Employment Research Center: The Rutgers Education and Employment Research Center (EERC) is housed within the School of Management and Labor Relations at Rutgers University. EERC conducts research and evaluation on programs and policies at the intersection of education and employment. For more information, visit smlr.rutgers.edu/eerc.
About the State Noncredit Data Project: The State Noncredit Data Project (SNDP), led by the Rutgers Education and Employment Research Center, has worked with eight state partners across two project phases to document and advance noncredit data collection at the state level. The project’s Taxonomy 2.0, released in February 2025, represents a significant refinement of the original framework based on lessons learned from applying it to real-world state data systems — including both established systems in Iowa, Louisiana, and Virginia, and emerging systems in Maryland, New Jersey, Oregon, South Carolina, and Tennessee. For more information, visit https://sndp.noncreditresearch.org/.