As educational and workforce systems evolve, the challenge of learners and workers carrying their prior knowledge and skills into new opportunities has become increasingly urgent. In response, the American Council on Education (ACE) and Credential Engine have partnered to enhance data transparency, promoting the recognition and integration of prior learning into credentialing programs and career development.

The Importance of Prior Learning Portability

With over a million credentials in the U.S. and a growing mobile workforce, barriers often arise when individuals try to transfer their prior learning across institutions and industries. The absence of standardized guidelines makes it difficult for learners to earn credit, advance their careers, or pursue further education. Prior learning portability efforts address these challenges by empowering individuals to apply the knowledge, skills, and competencies they’ve gained to new opportunities.

In order to help learners find new pathways, it is essential for education and training providers to collaborate and utilize public data to make prior learning transparent. Credential Engine’s linked open data schema, the Credential Transparency Description Language (CTDL), is a powerful tool to help create opportunities for learners that improve their outcomes as well as benefit employers and education providers.

ACE and Credential Engine’s Prior Learning Portability Initiative

ACE and Credential Engine’s collaborative work on prior learning portability began with a shared commitment to credential transparency.  ACE’s National Guide is now published in Credential Engine’s Credential Registry as over 10,000 transfer values represented as CTDL-linked open data. By integrating the National Guide with the Credential Registry, organizations can now publicly signal which credentials they accept as prior learning toward certificate and degree programs.

ACE and Credential Engine further explored the needs of education and training organizations through a Prior Learning Portability pilot. In this pilot, participating training providers invited educational partners to signal acceptance of credit recommended by ACE, demonstrating the practical application and benefits of ACE transfer values published as CTDL. Insights gained from the pilot will inform future opportunities for institutions of higher education to surface credit for prior learning opportunities to potential learners and help learners find colleges and universities well positioned to serve their needs.

Expanding Collaboration for Broader Impact

Building on the pilot’s success, ACE and Credential Engine are expanding their collaboration to drive systemic change across education and workforce ecosystems. This long-term partnership provides national infrastructure that supports lifelong learning by ensuring that prior learning is recognized and integrated into credentialing and career development pathways.

A key component of this collaboration is scaling the use of CTDL, not only within ACE’s National Guide but across a wide range of education and workforce providers. By linking TVs through open data in CTDL, both organizations are simplifying the process for institutions to share information about their credit and transfer policies, creating a more transparent and interoperable system.

This joint effort also aims to unify diverse sources of prior learning—whether from academic institutions, industry certifications, or professional training programs—so they can be easily evaluated and applied toward educational and career advancement. By making credential data machine-readable and interoperable, ACE and Credential Engine are enabling learners, institutions, and employers to fully utilize prior learning.

How to Support Prior Learning Portability

Prior learning portability is essential in validating educational and professional experiences, promoting career progression, and opening pathways for further education. It also fosters inclusivity by making opportunities more accessible to diverse populations. However, for portability to be effective, the surrounding information must be transparent, accessible, and interoperable.

Institutions can support this effort by using CTDL to publicly describe the credit, competencies, and transfer values they accept. By publishing this information as CTDL, institutions ensure their data is not only transparent but also machine-readable and interoperable. This step enables seamless connections between ACE credits and credentialing programs across institutions and industries.

To start publishing your TVs to the Credential Registry and make your data interoperable, review our resources here.

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Tags: Credential Engine, Credential Transparency
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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

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.

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