Tag Archives: talent pipeline

What am I doing here? 2. Open Competencies Network⤴

from @ Sharing and learning

I am continuing my January review of the projects that I am working on with this post about my work on the Open Competencies Network (OCN). OCN is a part of the T3 Network of Networks, which is an initiative of US Chamber of Commerce Foundation aiming to explore “emerging technologies and standards in the talent marketplace to create more equitable and effective learning and career pathways.” Not surprisingly the Open Competencies Network (OCN) focuses on Competencies, but we understand that term broadly, including any “assertions of academic, professional, occupational, vocational and life goals, outcomes … for example knowledge, skills and abilities, capabilities, habits of mind, or habits of practice” (see the OCN competency explainer for more). I see competencies understood in this way as the link between my interests in learning, education, credentials and the world of employment and other activities. This builds on previous projects around Talent Marketplace Signalling, which I also did for the US Chamber of Commerce Foundation.

About the work

The OCN has two working groups: Advancing Open Competencies (AOC), which deals with outreach, community building, policy and governance issues, and the Technical Advisory Workgroup. My focus is on the latter. We have a couple of major technical projects, the Competency Explorer and the Data Ecosystem Standards Mapping (DESM) Tool, both of which probably deserve their own post at some time, but in brief:

Competency Explorer aims to make competency frameworks readily available to humans and machines by developing a membership trust network of open registries each holding one or more competency frameworks and enabling search and retrieval of those frameworks and their competencies from any registry node in the network.

DESM was developed to support data standards organizations—and the platforms and products that use those standards—in mapping, aligning and harmonizing data standards to promote data interoperability for the talent marketplace (and beyond). The DESM allows for data to move from a system or product using one data standards to another system or product that uses a different data standard.

Both of these projects deal with heterogeneous metadata, working around the theme of interoperability between metadata standards.

About my role

My friend and former colleague Shiela once described our work as “going to meetings and typing things”, which pretty much sums up the OCN work. The purpose is to contribute to the development of the projects, both of which were initiated by Stuart Sutton, whose shoes I am trying to fill in OCN.

For the Competency Explorer I have helped turn community gathered use cases into  features that can implemented to enhance the Explorer, and am currently one of the leads of an agile feature-driven development project with software developers at Learning Tapestry to implement as many of these features as possible and figure out what it would take to implement the others. I’m also working with data providers and Learning Tapestry to develop technical support around providing data for the Competency Explorer.

For DESM I helped develop the internal data schema used to represent the mapping between data standards, and am currently helping to support people who are using the tool to map a variety of standards in a pilot, or closed beta-testing. This has been a fascinating exercise in seeing a project through from a data model on paper, through working with programmers implementing it, to working with people as they try to use the tool developed from it.

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New work with the Credential Engine⤴

from @ Sharing and learning

Credential Engine logoI am delighted to be starting a new consulting project through Cetis LLP with the Credential Engine, helping them make credentials more transparent in order to empower everyone to make more informed decisions about credentials and their value. The problem that the Credential Engine sets out to solve is that there are (at the last count) over 730,000 different credentials on offer in the US alone. [Aside: let me translate ‘credential’ before going any further; in this context we mean what in Europe we call an educational qualification, from school certificates through to degrees, including trade and vocational qualifications and microcredentials.] For many of these credentials it is difficult to know their value in terms of who recognises them, the competences that they certify, and the occupations they are relevant for. This problem is especially acute in the relatively deregulated US, but it is also an issue when we have learner and worker mobility and need to recognise credentials from all over the world.

The Credential Engine sets out to alleviate this problem by making the credentials more transparent through a Credential Registry. The registry holds linked data descriptions of credentials being offered, using the Credential Transparency Description Language, CTDL, which is based largely on schema.org. (Note that neither the registry nor CTDL deals with information relating to whether an individual holds any credential.) These descriptions include links to Competence Frameworks described in the Credential Engine’s profile of the Achievement Standards Network vocabulary, CTDL-ASN. The registry powers a customizable Credential Finder service as well as providing a linked data platform and an API for partners to develop their own services–there are presentations about some example thrid-party apps on the Credential Engine website.

I have been involved with the Credential Engine since the end of 2015, when it was the Credential Transparency Initiative, and have since worked with them to strengthen the links between the CTDL and schema.org by leading a W3C Community Group to add EducationalOccupationalCredentials ot schema.org. I’ve also helped represent them at a UNESCO World Reference Level expert group meeting, helped partners interested in using data from the registry at an appathon in Indianapolis.  I have come to appreciate that there is a great team behind the Credential Engine, and I am really looking forward to continuing to work with them. I hope to post regular updates here on the new work as we progress.

There are strong linkages between this work and the other main project I have on talent marketplace signalling, and with talent pipeline management in general; and also with other areas of interest such as course description  and with work of the rest of Cetis in curriculum analytics and competency data standards. This new project isn’t exclusive so I will continue to work in those areas.  Please get in touch if you would like to know more about partnering with the Credential Engine or are interested in the wider work.

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One year of Talent Marketplace Signaling⤴

from @ Sharing and learning

I chair the Talent Marketplace Signaling W3C Community Group, this progress report is cross-posted from its blog

It is one year since the initial call for participation in the Talent Marketplace Signaling W3C Community Group. That seems like a good excuse to reflect on what we have done so far, where we are, and what’s ahead.

I’m biassed, but I think progress has been good. We have 35 participants in the group, we have had some expansive discussions to outline the scope and aims of the group, the detail of which we filled in with issues and use cases. We also had some illuminating discussions about how we conceptualize the domain we are addressing (see most of August in the mail list archive). Most importantly, I think that we have made good on the aim arising from our initial kick-off meeting to identify issues arising from use cases and fix them individually with discrete enhancements to schema.org. Here’s a list of the fixes we have suggested that have been accepted by schema.org, drawn from the schema.org release log:

Translating those back to our use cases / issues we can now:

Looking forward…

First I want to note that many of those contributions have been accepted into what schema.org calls its pending section, which it defines as “a staging area for work-in-progress terms which have yet to be accepted into the core vocabulary”. While there are caveats about terms in pending being subject to change and that they should be used with caution, their acceptance into the core of the schema.org vocabulary relies on them being shown to be useful. So we have a task remaining of promoting and highlighting the use of these terms and showing how they are used. Importantly, “use” here means not just publishing data, but the existence of services built on that data.

Looking at the remaining issues that we identified from our use cases and examples, it seems that we have come to the end of those that can be picked off individually and dealt with without consequences elsewhere. Several are issues of choice, along the lines of “there’s more than one way to do X, can we clarify which is best?” Best practice is difficult to define and identify, and there will be winners and losers whatever option is picked. The choice will depend on analysis of whatever existing practice currently is as well as trade-offs such simplicity versus expressiveness. Another example where existing practice is important comes with issues that will affect how Google services such as Job Search work. Specifically, Google recommends values for employmentType that don’t seem to match all requirements, and these values are just textual tokens whereas we might want to suggest the more flexible and powerful DefinedTerm. However, we don’t want to recommend practice that conflicts with getting job postings listed properly by Google. While some Google search products leverage schema.org terms, the requirements that they specify for value spaces like the different employmentTypes are not defined in schema.org; and while schema.org development is open, other channels are required to make suggestions that affect Google products. The final category of open issue that I see is where a new corner of our domain needs to be mapped, rather just one or two new terms provided. This is the case for providing information about assessments, and for where we touch on providing information about the skills etc. that a person has.

So, there is more work to be done. I think starting with some further work on examples and best practice is a good idea. This will involve looking at existing usage, and mapping relevant parts of schema.org to other specifications (that latter task is happening in other fora, so probably something to report on here rather than start as a separate task). As ever, more people in the group and engagement from key players is key to success, so we should continue to try to grow the membership of the group.

Thank you all for your attention and contributions over the last year; I’m looking forward to more in the coming months.

Ackowledgement / disclosure

I (Phil Barker) remain grateful to the continued support of the US Chamber of Commerce Foundation, who fund my involvement in this group.

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On Talent Pipeline Management⤴

from @ Sharing and learning

I’m prompted by a #femEdTech tweet to write about some of the work I’m involved in regarding linking education to employment:

This is going to be a tricky topic to write about, if I get it wrong one way or another I will either offend people with whom I enjoy working or seem to be giving the opposite message to the one I intend.

The work in question is on Talent Signalling for the Job Data Exchange, but what I have in mind in particular is some of the wider context for that work, which goes under the banner of Talent Pipeline Management. Now, there is a lot that I don’t like about the rhetoric and metaphors here, I won’t dwell on them, if you’re likely to get it you won’t need it explaining. Once I got passed that, what  impressed me, was the idea brought in from supply chain management, explained to me by Bob Sheets, that if you want to go beyond a low quality commodity-like approach (by analogy cheap components  sourced with price as the only criterion) you needed to “go deep”. That is, you need to build a deep relationship to create understanding–it’s all social constructivism now–between those all involved education, training and learning, those involved in recruitment, and those involved in strategic planing for the local economy.

The approach seems much deeper than I have seen in the UK, for example in industry liaison committees at Universities, because it involves getting all levels & contexts of education provider together to work with industry and business on things like curricula and training opportunities. This is described in detail through the TPM Academy. Again, anyone from an education background will flinch at the industry-focused utilitarian view of education shown in how it is presented, but the underlying idea seems valuable.

So my current thoughts and questions are: how does this look from the learner/worker/job seeker point of view? [Quick note to self: check on whether they are included in the conversations defining curricula.] I think that is key to keeping this work on the right side of education being just about satisfying the need for cheap labour. Secondary question: is my glibly stated opinion that this goes deeper than approaches I’ve seen in the UK just an admission of ignorance? [Answers in the comments please!]

Going forward, my work will continue to look at the data that can be communicated through things like job adverts, course and qualification descriptions, trying to build the underlying infrastructure that allows “faster clearer signals” and stonger linkages between employment and education / training to help build these deeper relationships. I’m also getting involved in how  individuals’ acheivements can be represented semantically, so that will bring in a whole raft of questions about who controls the creation and dissemination of this data.

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Indiana Appathon Credential Data Learn and Build⤴

from @ Sharing and learning

This week I took part in the Credential Engine’s Indiana Appathon in Indianapolis. The Credential Engine is a registry of information about educational and occupational credentials (qualifications, if you prefer; or not, if you don’t) that can be earned, along with further information such as what they are useful for, what competencies a person would need in order to earn one and what opportunities exist to learn those competencies. Indiana is one state that is working with the Credential Engine to ensure that the credentials offered by all the state’s public higher education institutions are represented in the registry. About 70 people gathered in Indianapolis (a roughly equal split between Hoosiers and the rest of the US, plus a couple of Canadians and me) with the stated intentions of Learn and Build: learn about the data the Credential Engine has, how to add more and how to access what is there, and build ideas for apps that use that  data, showing what data was valuable and potentially highlighting gaps.

circles and lines representing entity-relationship domain modelsI was there as a consequence of my project work (supported by the Credential Engine) to represent Educational and Occupational Credentials in schema.org, with the aim of helping  people understand the benefits of putting credentials on to the open web. Cue my chance to reuse here my pictures of how schema.org can act as a cross-domain unifying schema for linked data and how different domains link together from Education to HR.

I’ve been involved in a few events where the idea is to try to get people together to learn/discuss/make, and I know it is really difficult to get the right balance between structure and flexibility. Too much pre-planned activity and delegates don’t get to do what they want, too little and they are left wondering what they should be doing. So I want to emphasize how hugely impressed I was with the event organization and facilitation: Laura Faulkner and colleagues at Credential Engine and Sonya Lopes and team at Learning Tapestry did a great job. Very cleverly they gave the event a headstart with webinars in advance to learn about the aims and technology of the credential engine, and then in Indianapolis we had a series of  activities. On day one these were: cycling through quick, informal presentations in small groups to find out about the available expertise; demos of existing apps that use data from the Credential Engine; small group discussions of personas to generate use cases; generating ideas for apps based on these. On day two we split into some ‘developer’ groups who worked to flesh out some of these ideas (while the ‘publisher’ group did something else, learnt more about publishing data into the Credential Engine, I think,–I wasn’t in that group), before the developer groups presented their ideas to people from the publisher group in a round-robin “speed-dating” session, and then finally to the whole group.

I went wanting to learn more about what data and connections would be of value in a bigger ecosystem around credentials and for the more focussed needs of individual apps. This I did. The one app that I was involved with most surfaced a need for the Credential Engine to be able to provide data about old, possibly discontinued credentials so that this information could be accessed by those wanting more details about a credential held by an individual. I think this is an important thing to learn for a project that has largely focussed on use cases relating to people wanting to develop their careers and look forward to what credentials are currently on offer that are relevant to their aspirations. I (and others I spoke to) also noticed how many of the use cases and apps required information about the competencies entailed in the credentials, quite often in detail that related to the component courses of longer programs of study. This, and other requirements for fine detail about credentials, is of concern to the institutions publishing information into the Credential Engine’s registry. Often do not have all of that information accessible in a centrally managed location, and when they do it is maybe not at the level of detail or in language  suitable for externally-facing applications.

This problem relating to institutions supplying data about courses was somewhat familiar to me. It seems entirely analogous to the experiences of the Jisc XCRI related programs (for example, projects on making the most of course data and later projects on managing course-related data). I would love to say that from those programs we now know how to provide this sort of data at scale and here’s the product that will do it.., but of course it’s not that simple. What we do have are briefings and advice explaining the problem and some of the approaches that have been taken and what were the benefits from these: for example, Ruth Drysdale’s overview Managing and sharing your course information and the more comprehensive guide Managing course information. My understanding from those projects is that they found benefits to institutions from a more coherent approach to their internal management of course data, and I hope that those supplying data to the Credential Engine might be encouraged by this. I also hope that the Credential Engine (or those around it who do funding) might think about how we could create apps and services that help institutions manage their course data better in such a way that benefits their own staff and incidentally provides the data the Credential Engine needs.

Finally, it was great to spend a couple of days in sunny Indianapolis, catching up with old friends, meeting in-person with some colleagues who I have only previously met online and doing some sightseeing. Many thanks to the Credential Engine for their financial assistance in getting me there.

Photo of brick build building next to corporate towers
Aspects of Inidianapolis reminded me of SimCity 2000
White column with statues
Monument in centre of town
plan of a city grid for Inianapolis
The original city design plan (plat)
Photo of indoor market
One of the indoor markets
Fountain and war memorial
The sign said no loitering, but I loitered. That, in the background, is one of the biggest war memorials I have seen


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