Hey, it’s Doug, and this I believe is microcast number 108.

I want to talk about skills taxonomies, skills ontologies, and learning pathways in general.

So we live in an ever-changing world.

People talk about how the pace of change is accelerating.

And if that’s true, then the future that our young people are going to inhabit is going to look not very much like ours.

I know, for example, that the world that I inhabit now, like working from home, recording this microcast, et cetera, is very different to the job that my dad did.

And the opportunities I’ve got are very different to the ones that he had.

And the opportunities my son and my daughter have are going to be very different from mine.

Therefore, I find it very strange that we say the world’s going to be changing beyond all recognition while still thinking that doing these really hard and fast skills taxonomies is a good idea.

So the problem that we’ve got, the problem that we’re trying to solve, I think, is that we need to help people get into a good job, like put them in the right place in the economy, which is going to be good for society and also good for them.

It’s going to make best use of their talents and their skills and their interests.

And it’s going to be of good use to the organizations that they serve.

Having said that, how are you supposed to define a skill?

A skill isn’t something that actually exists in the world.

So when I see people talking about skills ontologies as if that’s something that actually exists, a skill is something that’s a made up collections of abilities.

What I mean by that is that someone could look at, I don’t know, a good example of this might be someone solving a Rubik’s cube.

My good friend Brian Mothers once hired somebody based on the fact that they could solve a Rubik’s cube in under a minute.

Why did he do that?

Well, he says it’s because he saw a determination there to solve problems, a determination to get better at something, and also kind of an algorithmic thinking, which would be good for the role of a programmer.

Someone else might just see that being able to solve a Rubik’s cube in under a minute is a waste of time when you could be doing something else.

So it all depends on the eye of the beholder.

And that’s why when we’re looking at things like open badges and digital credentials, one of the things you have to bear in mind is the person who’s going to be viewing that credential at the end of it.

Another example, which I’ve given many times, is that I’ve got a doctorate from the University of Durham.

University of Durham is a world top 100 university.

Lots of people, especially in the US, have never heard of it.

And when I say Durham, they think I mean Durham, North Carolina, and therefore I mean like Duke University.

So the eye of the beholder is really important, both in terms of certification and badging and qualifications, but also in terms of skills.

Who is packaging up that skill and saying that it’s a worthwhile thing to do?

That packaging up of the skill is something that an awarding body can do, someone who’s in a power relationship can do, but also can be done in a peer way, which is why open recognition is so important.

And also one of the things which is part of open recognition is that it’s up to communities and territories to define what a particular skill is and what they are going to decide to recognize.

So when I see calls for we need to lock down what a micro-credential is, we need more skills taxonomies and skills ontologies, I’m not sure that we do.

I feel like the world of the CV and the resume where you have to fit yourself into title boxes, that shouldn’t have to be something that we have to do in the future, especially because if we’re going to use things like AI for hiring, maybe that can translate between different frameworks and maybe that can translate the natural language that people use to describe themselves into your particular way of defining what a skill is in your organization.

Why should somebody else have to know what that is before they’ve started to work for you?

So instead of spending long, long time over-rotating on skills taxonomies and learning pathways that you didn’t go through yourself, how about we try and get people to be able to say what they’re good at in their own words, and maybe let these AI tools that we’re creating do the translation for us.