As I’ve mentioned, I’m working with a startup looking at extending training through small LIFTs. The problem is that most training is ‘event’ based, where learning is in a concentrated time. Which is fine for performing right after. However, much of what we train for are things that may or may not happen soon. What we want is to go from the knowledge after the event to actually performing in new ways after the event, possibly a long time. We need retention from the learning to the situation, and transfer to all appropriate (and no inappropriate) situations. Thus, we need to think differently. And, as I suggested, we’re looking at supporting people not just with formal learning, but beyond, to developing their ability over time. We really want to be transforming from knowledge to performance. So, what’s that look like?

As usual, when I’m supposed to be sleeping is one of the times I end up noodling things over. And, so it was some nights ago. I was thinking about (as I’m wont to do) the cognitive roles that we need. I talk about practice, and models, and examples, and more recently, generative activities. But that’s formal learning, and we have a good evidence base for that. But what about going forward? What sorts of activities make sense?

Here I’m going out of my comfort zone. Yes, I’ve been doing some reading about coaching, particularly domain-independent vs domain-specific coaching. Now, here I don’t necessarily know what the research says specifically, but I do see the convergence of a variety of different models. So, I can make inferences. And post them here to get corrected!

As you might expect, I made a diagram to help me understand. So, I reckon there’s an early, mid, and late stage of development of capability. Formal learning should really be about getting you ready to apply.

That is the early phase which includes reflection (really, a generative activity), which can be personal (ala scripts) or conceptual (schemas). Also, reactivation. That is, seeing different ways of looking at it (new models), more examples in context, and of course more practice. (Retrieval practice, of course, where you’re applying the knowledge.)

Then, in mid-phase, your learners are applying, but to real situations, not simulations. Their initial plan on how to apply the knowledge might be part of the end of the early stage, but then it’s time to apply. Which could (should?) lead to revisions of the plan, and on reflecting on any barriers. Those barriers could be internal (their own understanding or hangups), or external (lack of resources, situations, tools, etc). The former are grounds for discussion, the latter for action on the part of the org!

Then, at the late stage, learners should be looking at the impact. They can reflect on the impact on them, which could also be a mid-phase action, but ultimately you want to see if they’re having an impact overall. Then, of course, you could want to survey about the learning experience itself. While it’s all data, the org impact is useful data to evaluate what’s going on and how it’s going, and the survey can help you continue to improve either this or your next initiative.

Those’re my initial thoughts on transforming from knowledge to performance. There’s some overlap, no doubt, e.g. you could continue sending reapplications if there aren’t frequent opportunities in the real world. Likewise, your learners should be assessing impact in the need to revise a plan. Still, this seems to make sense in the first instance, at least to me. (Addressing the ‘when’, how much and what spacing, is what I’ll be talking about at DevLearn. ;) Now, it’s over to you. What have I got wrong, am missing, …?

As in the last post, I’ve been judging the iSpring Course Contest (over, of course). And, having finished, one other thing I’ve noticed is a clear distinction between ‘knowing’ and ‘doing’. We’re seeing lots of interest in skills, yet the courses are, with one exception, really assuming that if you know about it, you’ll do it right. Which isn’t a safe assumption! Are you trying to develop knowledge or ability? I’ll suggest you want the latter. And, can do it!

So, in 9 of the 10 cases, the questions are essentially about knowing. Some of them better than others, e.g. some seem to follow Patti Shank’s advice about how to write better multiple choice questions. That is, for instance, reasonably balanced prose describing the alternatives, and only 3 options. Not all follow it, of course.

The problem is that knowing about something isn’t the same as knowing how to do it. So, for instance, knowing that you should calibrate after changing the reagent isn’t the same as remembering to do it. We’ve all probably experienced this ourselves. They pretty much all had quizzes, as required, but most were just testing if you recalled the elements of the course. Not good enough!

What the one course did that I laud was that the final quiz was basically you applying the knowledge in a situation. You weren’t asked what this situation was, but instead chose how to respond. They were linked, each continuing the story, so it was really a linear scenario. Which I realize can be just a series of mini-scenarios! Still, you dragged your response from a list of responses. They weren’t all that challenging to choose between, as the alternatives were pretty clearly wrong, but for good reasons, reflecting the common mistakes. This is the way!

I think some designers were aspiring to this, as they did put the learner into a situation. However, they then asked learners to classify the answer, rather than actually make a decision about action to take, e.g. a mini-scenario. There is an art to doing this well (hence my workshop in two days)! Putting people into a context to choose their actions like they’ll have to do in the real world is the important practice. Of course, mentored live performance is better. Or simulations (tuned to games, of course ;). Even branching scenarios. But mini-scenarios are easily doable within your existing practice.

The question of knowledge or ability is easily answered. In how many cases will the ability to recite knowledge versus make decisions be the defining success factor for your organization? I’ll suggest that making better decisions will be the differentiator your organization needs. The ability to write better mini-scenarios seems to me to be the best investment you can make to have your interventions actually achieve an impact. And if you’re not doing that, why bother?

I get it, when you’ve a hammer, the whole world looks like a nail. Moreover, there’s money on the table, and it’d be a shame not to grab onto it. Still, there’s also integrity. And, frankly, I fear that we’re going down the wrong path. So I’ll rail again, by asking “where’s quality?”

So, a colleague recently provided a link to a report by a well-known analyst. In the report, they call for an AI revolution for L&D. And, yes, I do believe L&D needs a revolution, I wrote a whole book about it. However, I fear that the direction under advisement is focusing on the wrong thing. So here’s what the initial post summarized about the article:

* Despite significant investment, many companies are utilizing outdated learning models that do not deliver substantial business impact.

* Learning needs to be dynamic, personalized, and focused on enablement.

* Chief Learning Officers (CLOs) should re-establish themselves as leaders within the enterprise, focusing not just on learning but on employee enablement.

* Artificial intelligence (AI) offers the potential to speed up content creation, lower costs, and improve operational efficiency, which allows Learning and Development (L&D) to adopt a wider and more strategic role.

Do you see anything wrong with this? I actually agree  with the first point, and probably the third. However, I think we can make a strong case that the second is not the primary issue. And very clearly the fourth point identifies what’s wrong in the second, at least before the last phrase.

So, first, when we invoke learning, we should be very careful to do it right. There are claims that up to 90% of our investment in training is going to waste. However, it’s not because our learning designs aren’t ‘dynamic, personalized, and focused on enablement’, it’s because our learning isn’t designed according to what research says works. Now, our learning needs change as our abilities improve. We start knowing what we need and why. There’re also times when performance support can be more effective than courses. Courses can still be valid, if they’re done well.

That’s the point I continue to make: I maintain that we’ll save more money and have more impact if we focus on good learning design before we invest in fancy technology. That includes AI. We want meaningful practice (which I suggest is still a role for designers, as AI doesn’t understand context), not information dump. Knowledge <> ability to perform. What we need is practice of doing. At least for novices. But beyond that, only effective self-learners will be truly able to leverage information on their own to learn. Even social learning gets better when we understand learning.

So, learning needs to be evidence-informed, first. Then, and only then, can it be dynamic, personalized, etc. Even knowing when and how to use AI as performance support counts (a more valid role, tho’ there needs to be scrutiny of the advice somehow, as AIs can give bad advice). Sure, CLO’s do need to be leaders in the enterprise, but that comes from understanding cognition and learning, and then using those to better enable innovation as well as optimizing performance. Enablement’s fine as a premise, but it’s got to come from understanding. For instance, you can’t get employees contributing just because you put in AI, you need to create a learning culture. (Putting AI into a Miranda organization isn’t going to magically fix the problem.)

Let me be clear: my argument is not Gen AI bad vs Gen AI good. No, it’s learning science involved versus not. I am fine if we start using AI, Gen or otherwise,, but after we’ve made sure we’re doing the right things first. Let me pose a hypothetical: for $30K, would you rather have 3 courses versus 10? What if those 3 courses were designed to actually have an impact, versus 10 that are pretty and full of information, but won’t move a single meaningful needle the organization? Sure, I’ve made up the numbers, but the reality is that we’re talking about achieving real outcomes versus making folks feel good; I’ll suggest “it’s pretty and people like it” is no substitute for improving the outcome.

This makes the last line above more problematic: we don’t need to speed up content creation. Content dump <> learning. Lowering costs and improving efficiency is all good, but after you’ve ensured adequate effectiveness. And no one seems to be talking about that. That’s why I’m asking “where’s quality?” It’s not being discussed, because AI is the next shiny object: “there’s plenty of money to be made”. Anyone else sensing a bubble? And that’s without even considering IP ethics, environmental impact, security, and VC funding. The business model is still up in the air. Hence, my question. Your thoughts?

As an aside, there’s a quote in the paper that illustrates their lack of deep understanding: “As our attention spans shorten”. Ahem. While there’s a credible argument made by Gloria Marks, I still suggest it’s not a change in our cognitive architecture, but instead availability and familiarity. We can still disappear for hours into a novel, movie, or game. It’s a fallacious basis for an argument. 

Truth in advertising: I was tempted to title this “WTAH”, but…I decided that might be too incendiary ;). Hence, “Where’s quality?” Still, you can imagine my mood while reading and then writing this.