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Global Micro-Credential Schema Mapping Report: A Vital Step Towards Interoperability and Mobility

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The CTDL Advisory Group guided a project to map micro-credential schemas using the Data Ecosystem Schema Mapper (DESM) tool. This global project aimed to develop an understanding of how different micro-credential data standards and regional and local documents can be aligned to improve interoperability. Micro-credential schemas define key information such as name, description, credits earned, cost, competencies, digital issuance, and more. These elements are essential for individuals and organizations to assess the value of micro-credentials and take actions such as hiring, credit transfer towards other credentials, and other important use cases. By identifying baseline micro-credential element definitions and crosswalking them, the project sought to facilitate better data integration and recognition of micro-credentials across various regions and organizations.

This report outlines the project’s benefits, methodology, and key reflections, offering insights into the importance of schema harmonization in the evolving landscape of micro-credentials. The report covers the benefits of a micro-credential crosswalk, the Data Ecosystem Schema Mapping Tool, the mapping process, and reflections on the mappings. These insights lay the groundwork to further this valuable work, providing benefits such as aiding policymakers, supporting credit recognition for international admissions, and fostering global mobility.

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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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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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