January seems like a good time to review the work that I am doing at the moment. Rather than try all of it in one post, I’ll take it one project at a time. This one is about my work with Credential Engine, a US-based not for profit that aims to provide information about educational and occupational credentials on offer and the things related to them. The aim is to empower learners with the information they need to make decisions about their educational options.

(Note, I think of educational / occupational credential as synonymous with qualification, and tend to use credential as a shorthand for that.)

About the work

I provide consultancy to Credential Engine on their RDF vocabulary, CTDL, the Credential Transparency Description Language. I’ve been associated with CTDL for longer than the Credential Engine has been around: I was on the technical advisory committee for the precursor project, CTI, the Credential Transparency Initiative, seven years ago.

[Aside, fun fact: the first job I had in learning technology was in another CTI, the Computers in Teaching Initiative. Yes this was a factor in my agreeing to serve on the advisory committee.]

The CTDL is key to Credential Engine’s mission to make credentials more transparent by providing more information about how to obtain them, for example who offers them, what competencies (knowledge, skills, attributes) are necessary to earn them, how are these competencies assessed, what opportunities are available to learn them, and what are the likely outcomes (in terms of things such as employability and salary) of possessing the credential. As such CTDL describes a lot more than just the bare details of a credential, it goes far beyond into organizations, competencies, learning opportunities and outcomes data. In fact, by CTDL we actually mean three related vocabularies:

• CTDL, itself which covers the core of credentials, courses, pathways, organizations;
• CTDL-ASN, an extension of the vocabulary for competency frameworks and related entities developed for the Achievement Standards Network;
• QDATA, for quantitative data about outcomes.

As well as the bare vocabulary definitions we also provide a Handbook with sections for each of the vocabularies, covering how the terms are designed to be used to meet various use cases.

About my role

My first contract with Credential Engine was to set up and lead the EOCred W3C Community Group to improve and extend Schema.org’s capability to describe educational and occupational credentials. CTDL was created following the same model as schema.org, and Credential Engine were keen to keep the two languages harmonized. The outcome of that project was the schema.org EducationalOccupationalCredential class and related terms, and some documentation from the working group about the how to address the use cases we identified.

More recently I have been working more closely with the core Credential Registry team on developing CTDL. They have well-established policies and procedures  for updates, which include setting up open working groups to “socialize” major proposals. While I have been working with them we have produced major updates to address Credit Transfer, Education to Work relationships, Educational Pathways, Scheduling Patterns, Approval Lists, as well as many minor tweaks on an almost monthly basis. Coming soon: assessment rubrics.

One of the things that I really appreciate about the Credential Engine work is that it gives me the freedom (or lets me take the liberty) to explore new RDF-related technologies that might benefit CTDL. The best example of this is how I have been able to build some working knowledge of SHACL as part of our thinking on how we express the CTDL data model and applications profiles of it in such a way that data can be validated. This has helped me justify my (otherwise unfunded) contribution to the Dublin Core Tabular Application Profile work. Other examples come from wanting to make sure CTDL is used as widely as possible, include contributing to the W3C Verifiable Credentials for Education community group, PESC’s work on transcripts and ETF training events on linked data.

Best of all, Credential Engine have a great team, it’s a pleasure to work with them.