Archives for March 2025

Knowledge or skills?

25 March 2025 by Clark Leave a Comment

Ok, I’ve been wrong before, and it appears I am again. I rail against pure knowledge, and felt Ed Hirsch was making that argument. Yet, Paul Kirschner and co-conspirators have him writing the intro to their latest work, The Case for Knowledge. In it, they make the case for the necessity of knowledge as a necessary precursor for critical thinking skills. And, Paul’s been on the side of Sweller in arguing against critical thinking skills. Yet, there’s also recently been shown that you can make valuable headway with teaching skills. How do we reconcile all this? Is it knowledge or skills that matters?

So, I took Ed Hirsch’s book Cultural Literacy, well, literally. That is, I heard him arguing that folks needed a common basis of facts. And, of course, I agree. I do think we need to all understand what 1492 means. But, to me, it was more. It doesn’t do anything to know that and not know in what context that makes sense: that it was the first western European path opened up to the lands of the Americas. Yes, it’d been done before, and yes, the resulting rapaciousness wasn’t beneficial, but it was the first opening of that particular corridor.

What I thought I saw (and consequently must have been wrong about), was that Hirsch stopped at the knowledge. Because Kirschner and co-authors of the recent work make an eloquent case for the need for knowledge. They’ve argued that critical thinking skills are specifically domain-dependent. That is, you need the knowledge of the domain to know how to adequately use that knowledge to make determinations.

Now, I’ve had mixed thoughts about this. For one, I do think we need these skills. Further, I have also believed that to teach them, you can’t do it without specific domains. On the other hand, I improved analogical reasoning skills (across problems) in my Ph.D. thesis, and succeeded. (At least, in the moment, I wasn’t shooting for persistent improvement.) Further, Micki Chi found self-explanation was a useful approach for understanding examples, and Kate Bielaczyc successfully tutored folks on those skills. More recently, I came across a paper from Bernacki, et al, that improved disadvantaged learners success by teaching learning to learn strategies. How do we reconcile this?

Of course, it’s knowledge and skills. I’d heard it said before, and am inclined to agree, that you get more impact with domain-specific skills. But, good approaches across domains should at least have some impact. I know Valerie Shute and Jeffrey Bonar wrote tutors that focused on experimentation skills across domains: geometric optics, economics, and electrical circuits. Of course, I don’t know whether they yielded impacts! Yet with the results mentioned, it seems like there’s measurable benefit to learning to learn skills.

What is clear, however, is that teaching to pass tests isn’t leading to the ability to think critically. I also recently read that teachers have to teach to the test and haven’t time to teach critical thinking skill. Certainly, from an organizational perspective, you can’t count on your employees knowing how to learn on their own. You might be in a situation where you can hire for such skills, but that’s not going to be all orgs. Further, I’ve argued before with the late Jay Cross that it might be the best investment to train same. Look, the answer to knowledge or skills is yes! You can’t do just one, yet there seems to be too much focus on the former, and not the latter. Don’t trust to folks having the thinking and learning skills you need, develop them. Please!

Applied learning science

18 March 2025 by Clark Leave a Comment

One of my favorite things to do is to help people apply the cognitive and learning sciences (under realistic constraints). That can be to their practices, processes, or products, via consulting, workshops, writing, and more. One thing I’ve done over the past few years is doing this for a particular entity. I was found via a workshop, and ended up coming on as an advisor. They’re now about ready to go live, and it’s time for me to tell you what they’re doing, why, and how. So here’s an application of applied learning science.

It starts with a problem, as many good solutions do. The issue is that, in L&D, too often they’re delivering live sessions to address a particular situation. Whether someone’s said “we need a course on this”, or there’s been a deep analysis, at some point they’ve pulled people together. It could be a day, several days in a row, or even spaced out every other week, every month, what have you. And, we know, that by and large, this isn’t going to lead to change!

Research on learning tells us, quite strongly, that to achieve a persistent new ability to ‘do’, we need to strengthen the learning over time. New information gets forgotten after only a day or two, according to the forgetting curve! So, we need to reactivate the learning. That can be reconceptualization, recontextualization, or reapplication. It can also be reflection, and even planning, and evaluation.

However, it’s been tough to do this reactivation. It typically requires finagling, and faces objections; not just the learners, but also the stakeholders! Such interventions need to be small but effective. That’s what this solution does. Other approaches have been tried, and some other solutions do exist, but this one has a couple of advantages. For one, a clear focus. It’s not doing other things, except reactivating learning.

Ok, one other thing, it’s also collecting data. Too often, there’s no way to know if it learning’s effective. Even if there’s intent, it’s hard to get approval. So, this solution not only reactivates learning as mentioned, it tracks the responses. In practical ways.

What’s been my role? That’s the other thing; we’re applying this in ways that reflect what learning science tells us. Ok, we have to make some inferences, that we’re testing, but we’re starting from good principles. So, I’m advising on the spacing of the learning and the content of the reactivation. We call those prompts, that ask learners to respond. These prompts then gather into small chunks called LIFTs (Learning Interventions Fueling Transformation). (Everyone’s gotta have an acronym, after all, and this plays along with the company name, Elevator 9 ;). The sequence of LIFTs makes a learning journey.

What’s important is how many we need, and how frequently we deliver them. It’s dependent on some factors, so we’re asking about those too: frequency of application, complexity, importance, and prior experience. Hopefully, in clear and useful ways. They’re actively looking for companies that are keen to help us refine this, too (in return for the usual considerations ;).

The end result is a product that easily supplements your live events. Your learners get reactivations, and you get data. Importantly, you get better outcomes from your interventions. This capability is possible, the goal is just to make it easy to do. Moreover, with a solution that not only embodies but shares the underlying learning science, improving you as it does your learners. Win-Win! I generally don’t tout solutions, but this one has actively put learning science (tempered by reality, to be sure) at the forefront. Applied learning science, and technology, the way it ought to be done. It’s been an honor to work with them!

Idealism and reality

11 March 2025 by Clark Leave a Comment

Of late, I’ve been thinking a lot about idealism versus reality (for a lot of reasons). I’ve been a staunch advocate for better learning science in practice (an idealistic stance). And, we’re running a conference because we’ve gotten feedback that folks wonder what that means in practice (reality). My own situation is a case in point as well. So, I’m doing some reflecting on idealism and reality.

To start, I’m a principled kind of person. I try to follow the best recommendations from what we know about how we think, work, and learn. Perhaps I err too much on that side, as I’ve avoided things like commissions and paid endorsements. That’s because I want my recommendations to come from real value, not my personal benefit. Which should be better in the long term, but I’m also aware I’m not a great biz dev type. For perhaps the same reasons. (And, it appears in retrospect, that when I do sell myself, I do so far too cheaply!)

Despite myself, I’ve managed to be involved in some things I care about. People have come to me, and I’ve managed to support the family for the past almost quarter century(!). Yet, there’ve been good times, and lean. (In the latter, currently.) Yet I haven’t been one to jump on bandwagons, for instance the latest hype around Generative AI. You might think a voice of caution would be appropriate, but the evidence appears to be to the contrary. C’est la vie. I’m not intending to change my stance, just being aware and honest with myself (and, consequently, you).

Beyond my own issues, I see that our field still faces challenges. Perhaps from our origins – taking good performers and trying to turn them into trainers without sufficient preparation – we end up trying to meet unrealistic expectations. “Do it once, and it’s good enough!” Cheaply and quickly, of course. If we measured, we might know otherwise, but that’s still too rare. Everyone has faith that we’re ‘sufficient’.

Yet, as an idealist, I see what we could offer, if we could manage to turn things around. I am an optimist (despite any appearances as a curmudgeon to the contrary ;). We could be impacting organizational success with aptly targeted interventions. Moreover, we could be the ones guiding organizations to new insights that find opportunity from increasing change. And so I keep fighting for the principled view. AND, practical steps to get there. I keep hoping (idealistically) that there are those who want to steadily move to a better organizational position where they’re both doing well what they know they need to, and efficiently exploring the new opportunities to adapt to the changing environment.

And, frankly, that’s the opportunity I’m looking to offer. Of course, in reflecting on the realities, I recognize that people also need to find ways to do better within the existing constraints, and steadily (stealthily?) move those constraints to a better place (reality). Having done so for pretty much all my many moons of a career, I do have practical steps around that. That, too, is what’s on offer. There are ways to balance idealism and reality. Stay tuned (or tap in!).

Analogy and models

4 March 2025 by Clark Leave a Comment

I’ve gone on a bit about the value of mental models in instruction (and performance). (I guess this cements my position as a representationalist!) My interest isn’t surprising, given my background. But someone recently pointed out to me an aspect that I hadn’t really commented on. And, I should! So here’s some thoughts on analogy and models.

The initial callout was me talking about models, and communicating them. In particular, I’ve mentioned a number of times the value of diagrams. Yet, someone else pointed out that another useful mechanism is analogy. And this rocked me, because of course! Yet, I’ve neglected this mention.

As context, I’ve been a fan of mental models for thinking since I got the gift of a book on them from my work colleagues as I headed off to grad school. Moreover, I did my PhD thesis on analogy! I broke down analogical processing in a unique way, and looked at performance. finding some processes could be improved. Then I tried training on a subset, and achieved some impact.

Analogy is, by the way, a useful way to communicate models. What’s important in models are the conceptual causal relationships. If there’s another, more familiar model with the same structure, you can use it. For instance, the flow of electricity in wired can be analogized to the flow of water in pipes. Another, flawed, model is saying that the orbit of electrons around a nucleus is like the orbit of planets around a sun.

So, why have I been blind to the use of analogies? Perhaps because I’m so familiar with them that I just assume others see the possibilities? Or maybe I’ve just got a huge blind spot! Still, it’s a big miss on my part.

When you want learners to ‘get’ models (and I think we do), you can present them as diagrams. You can have people embody them through things like Gray’s gamestorming. And, of course, you can use analogies. We have to be careful; empirically, most folks aren’t good at generating them, they focus too much on surface features. Yet, what’s necessary is sharing what cognitive scientists call ‘deep structure’, the important relationships that guide outcomes. People are good at using given analogies, but don’t always recognize them as useful unless prompted.

If, and it’s not a given, we have a familiar structure that happens to share the relationships of the model we’re trying to communicate, we can make an analogy! Though, there are nuances here too. For instance, Rand Spiro found that, when developing an understanding of muscle operation, a progression of analogies was needed to develop the final understanding!

Still, we shouldn’t ignore the possibilities of analogy. Some have argued that we fundamentally understand the world by bringing in prior models to explain. Which isn’t hard to countenance in a ‘predictive coding’ view of the world, that we’re actively trying to explain observations. Wrong models are typically an explanation for misconceptions, using the wrong model in new ways. We have to diagnose and remediate those understandings, because folks don’t tend to replace their models, they patch them. Giving good models a priori, via analogy or otherwise, is a good remedy.

Analogy is a feature of our cognitive architecture and formal representations. It’s a useful way to communicate how the world works, when possible. Like with all things, of course, the nuances matter, but analogy and models are tools we have to facilitate understanding, if indeed we understand them. So let’s, eh?