You don't have "Computer" skills

Skills are hot. In the workplace learning upskilling, reskilling, and skills-first topics were the hottest last year. Until AI came along. Now, it's all about prompt engineering... Anyway, if you're not in the weeds of skills, let me illustrate how this seemingly straightforward concept is way more complex.

You don't have computer skills! #NoComputerSkillsForTheWicked -- LinkedIn

According to LinkedIn, I don't have computer skills.

Ha! I'm broken like AI chips on the table. This is sad because most applicants who applied to a certain job have computer skills, so they are a better match.

In one sense, it's true. I don't have "computer" skills. But it all depends on the definition and how you measure it.

Here's what I do have:

1. Master's Degree in Computer Science
2. A thesis written in C++ 30 years ago where I built an artificial neural network using backpropagation algorithm (and yes, I decided on a sigmoid function for trigger thresholds)
3. 20+ years of experience leading technology-driven projects in workplace learning
4. Dabbling in languages: C++, PHP, JavaScript, PEARL, Java, Python, R, etc.
5. Ability to do magic by augmenting out-of-the-box learning authoring tools using JavaScript
6. Extensive data literacy expertise from building full programs to facilitating sessions at national conferences
7. Six months Harvardx Data Science certificate with hands-on R

etc.

What I DO NOT have though, is the tag/label in my LinkedIn profile that literally says: Computer.

The definition of a skill is one thing. What you do with the skill is another. How well you do what you do with the skill is another. Old-fashioned ATS may look for exact words mined from a job description. In 2023, at the age of AI, this probably should not be the case.

Skills are also not binary.

They have a scale. Measuring where your skills are on a scale is tricky. Here are some approaches:

1. Not measuring it all. - As funny as it may sound, the last three decades of content-driven LMS courses often functioned on this level.
2. Inference by what you do in the LMS - A notch above is the "what courses you have completed" approach based on your LMS transcript. I just have to say, if the world only knew about me what I do in the LMS, I wouldn't expect much of an opinion about my presence.
3. Self-reporting - This is now getting the actual person involved. You can do this in horrible ways by selecting 1-5 or based on a specific, measurable, and observable set of rubrics. Good start. Pretty subjective.
4. Peer assessment - Now you're talking. This may be still biased but at least you know what your PERCEIVED skills levels are. It's fun to get some reality checks on the difference between your beliefs/intent and perceived value to the team.
5. Manager/Expert assessment - This approach takes time and is expensive but it is an important piece of the puzzle. Especially, if your performance is evaluated by the same person.
6. Reliable, valid, and practical assessments for real - Basically, a well-written assessment that does what it's supposed to be doing. For technical skills, it involves using and applying those skills, not just selecting the culprit from a lineup of questions. As for "soft-skills," another story.. More on that below.
7. Triangulate skills measures - None of the 1-6 will work in itself most likely. You will need to figure out the combination of these elements.

About soft-skills.

20 years ago I had to build empathy training for customer service agents. I swear, we're still building empathy trainings. Back in those days, I made the mistake of treating soft-skills as a standalone entity. For example, people learned about communication, active listening, etc. Today, I know it was a waste of time. Soft-skills should not be taught out of context, but rather in a specific domain related to specific jobs-to-be-done.

What people need is not a well-rounded education about how to communicate but how to apply communication skills when sending an email, for example.

I told you it's more complex than it seems...

Skills have relationships.

They overlap. They influence each other. They interfere. And they also fade. For example, in a call center where the learning curve is extremely important, agents may forget skills when they don't use it for a while, which complicates the prediction model (I just read this study on parametric and non-parametric learner regressions to predict the learning curve change).

And once you get the handle on all this...

There is the other side, which is often a bigger mess:

What skills does one need in a specific org and role? And what level of skills is required to do well?

If you lived through the exciting times of the competency-based era, you know what a mess that was. Also a good money-maker for large-scale HR systems that promised to solve your nine-boxing and competency headaches. Never did.

But, let's be optimistic!

Let's assume you have a well-defined skill set needed for the job AND you managed to build some sort of skills profile for people. Now, you can show the skills gap!!

Imagine as a manager: you can see all your direct reports' skills profile with the gap they need to work on! As an extension, you may even see the gap between their current role and the next move. Lovely!

There's one thing left: how to get from A to B. What is the fastest path for closing the skills gap?

Hint: it's not about taking enough courses.

That's a good topic for another article itself.

Where are you on this complex journey?