Refine Your Search
CTDL Linked Open Data Explainer
We sometimes get questions about how CTDL, 1EdTech’s CASE specification, and the Competency and Skills System (CaSS) relate. The short answer is that CTDL is a family of open schemas designed to support Linked Open data that connects resources such as credentials and jobs to competencies; CASE is a specification designed for system-to-system exchange of competency frameworks, and CaSS is an open-source software for competency management that we utilize as part of the Credential Registry publishing system. The information below further describes these technologies and their relationships.
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.
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.
Credential Engine and Western Governors University (WGU) Blog Series
This three-part blog series dives into the partnership between Western Governors University (WGU) and Credential Engine. In addition, how WGU became involved in the work of credential transparency.
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.
Credential Transparency State Partnerships Overview
State and regional partners are working with Credential Engine to use the Credential Transparency Description Language (CTDL) and publish data to the Credential Registry. Credential Engine’s technologies support numerous statewide priorities. This resource provides an overview of our current state and regional partnerships.
Simplifying Credential Data Management using Credential Transparency Identifiers (CTIDs)
Credential Transparency Identifiers (CTIDs) bring the benefits of unique identifiers to credentialing ecosystems. CTIDs allow credentials and their associated information to be distinguished, thoroughly described, and widely recognized through effective data management practices.