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SOLID Data: What it Means
For the U.S. to meet the growing demand for skills and credential data, it is essential that this information be structured, open, linked, interoperable, and durable (SOLID). Credential Engine ensures this by advancing CTDL, the only comprehensive open standard for describing and linking credentials, learning, and work ecosystems, as the foundation for this work.
Recognition of Prior Learning: Helping People Move Forward
Recognition of prior learning (RPL) is the process of providing formal acknowledgment and credit for knowledge, skills, and abilities people have gained through work experience, military service, self-study, volunteering, and/or previous education. This includes credit for prior learning (CPL), transfer credit between institutions, and validation of non-traditional learning experiences. RPL empowers people to move forward and build on what they already know rather than starting over, accelerating pathways to credentials and careers.
Open Data Principles & Framework for the Education-to-Employment Ecosystem
Data benefits all stakeholders in education-to-employment (E2E) ecosystems. Among those data, learners need access to education and employment outcomes data to make informed decisions about their education and career pathways during and after high school. Similarly, institutions, employers, and policymakers rely on this information to improve and align education programs for career success.
Unlocking the Future of Credential Transparency: Blog Series
At Credential Engine, we're committed to making the credential and skill landscape clearer, more connected, and more useful for everyone, from learners and workers to educators and employers. In this three-part blog series dives into how we're doing just that.
Exploring Qualifications Frameworks at APEC’s SOM3 in Peru and Beyond
Learn about Credential Engine's attendance at the 2024 Asia-Pacific Economic Cooperation (APEC) Third Senior Officials Meeting (SOM3).
