Learnlets

Locus of intelligence

6 May 2025 by Clark 1 Comment

I’m not a curmudgeon, or even anti-AI (artificial intelligence). To the contrary! Yet, I find myself in a bit of a rebellion in this ‘generative‘ AI era. And I’m wondering why. The hype, of course, bugs me. But it occurs to me that a core problem may reside in where we put the locus of intelligence. Let me try to make it clear.

In the early days of the computer (even before my time!), the commands were to load memory into registers, conduct boolean operations on them, and to display the results. The commands to do so were at the machine level. We went a level above with a translation of that machine instructions into somewhat more comprehensible terms, assembly language. As we went along, we went more and more to putting the onus on the machine. This was because we had more processor cycles, better software etc. We’re largely to the point where we can stipulate what we want, and the machine will code it!

There are limits. When Apple released the Newton, they tried to put the onus on the machine to read human writing. In short, it didn’t work. Palm’s Pilots succeeded because Jeff Hawkins went for Graffiti as the language, which shared the responsibility between person and processor. Nowadays we can do speech and text recognition, but there are still limitations. Yes, we have made advances in technology, but some of it’s done by distributing to non-local machines, and there are still instances where it fails.

I think of this when I think of prompt engineering. We’ve trained LLMs with vast quantities of information. But, to get it out, you have to ask in the right way! Which seems like a case of having us adapt to the system instead of vice versa. You have to give them heaps more context than a person would need, and they still can hallucinate.

I’m reminded of a fictional exchange I recently read (of course I can’t find it now), where the AI user is being advised to know the domain before asking the AI. When the user queries why they would need the AI if they know the domain, they’re told they’re training the AI!

As people investigate AI usage, one of the results is that your initial intelligence indicates how much use you’ll get out of this version of AI. If you’re already a critical thinker, it’s a good augment. If you’re not, it doesn’t help (and may hinder).

Sure, I have problems with the business models (much not being accounted for: environmental cost, IP licensing, security, VC boosting). But I’m more worried about people depending too much on these systems without truly understanding what the limitations are. The responsible folks I know advocating for AI always suggest having a person in the loop. Which is problematic if you’re giving such systems agency; it’ll be too late if they do something wrong!

I think experimenting is fine. I think it’s also still too early to place a bet on a long-term relationship with any provider. I’m seeing more and more AI tools, e.g. content recommenders, simulation avatars, and the like. Like with the LMS, when anyone who could program a database would build one, I’m seeing everyone wanting to get in on the goldrush. I fear that many will end up losing their shirts. Which is, I suppose, the way of the world.

I continue to be a big fan of augmenting ourselves with technology. I still think we need to consider AI a tool, not a partner. It’s nowhere near being our intellectual equal. It may know more, but it still has limitations overall. I want to develop, and celebrate our intelligence. I laud our partnership with technologies that augment what we do well with what we don’t. It’s why mobile became so big, why AI has already been beneficial, and why generative AI will find its place. It’s just that we can’t allow the hype to blind us to the real locus of intelligence: us.

Before ‘skilling’?

29 April 2025 by Clark Leave a Comment

I’m not immune to the current trends, I just tend to want to cut through the hype. Sometimes, there’s some ‘there’ there (to paraphrase Gertrude Stein). And, of course, most of the time it’s old wine in new bottles. Right now, the trend is ‘skilling’. Which, to me, raises the question: what, then, were you doing before ‘skilling’?

As context: I’d (mostly) gone the academic route. That is, doctorate, post-doc, academic position. For family reasons, I wanted to come back to the US, and took up a corporate position (a whole bunch of stories there, but not now…). I was excited, actually. Here, from science, we knew heaps! Yet we weren’t applying it in schools and higher ed, for pragmatic reasons like history and budget. However, industry would be different. They had real needs, and real money to address them. To quote my eldest, this was “going to be great”.

So, you might imagine my dismay as I came to realize that organizations weren’t spending money on learning. Somehow, learning was a cost. L&D took orders for courses, and expected that for a few thousand dollars, you could take PPTs and PDFs and make a course. That’s what the authoring systems portrayed, and workshops focused on. Still, at that time I wasn’t ready to look to see the bigger picture.

Regardless, I started lobbying for more learning science in what we do. I’ve railed against tools that don’t have different feedback for every wrong answer, pushed design tweaks to incorporate learning science into our processes, and written and spoken and worked to develop the necessary understanding. I co-authored the Serious eLearning Manifesto. I’ve supported and now co-direct the Learning Development Accelerator. I’ve written books!

Thus, hearing the recent call for ‘upskilling’ (with a variety of names and areas of focus) seems to me to be a bit silly. Of course we need to think about organizational directions and capabilities. We should be looking at directions and needs, and making decisions like ‘build or buy’, and when the answer’s ‘build’, we should do it right. We should look at performance consulting up front to make the right determinations, and then see if we’ve created an environment (social and technological) that means folks can get what the need. That could be from courses, job aids, learning resources, coaching/mentoring, the network internal or external, what have you.

Which begs my main question, what were you doing before hand? I know that ‘competency’ assessments were the thing many years ago, but those exercises were costly, time-consuming, and from what I understand, were out of date by the time they were done. Idiosyncratic approaches would be a tactical approach, but not strategic. Maybe strategy is something we’re late to? To be fair, I see more books about it emerging, from folks like Nigel Paine and forthcoming from Lori Niles-Hoffman, and of course I wrote about it about a decade ago.

We’ve got to get more systemic, where we haven’t been. We need to liaise with the relevant entities in our org, like talent, and knowledge management, and org development, etc. We need to work with IT to create an ecosystem, not just rely on one platform to meet all needs (metaphor: the Swiss Army knife is great on the go, but at home I have a richer suite of separate tools). I think it’s time we recognize what was wrong before skilling, and take what’s right and expand it. Let’s be a learning organization, master that, and take it forward. That’s what I think, what think you?

Intelligent Tutoring via Models

22 April 2025 by Clark Leave a Comment

Today I read that Anthropic has released Claude for Education (thanks, David ;). And, it triggered some thinking. So, I thought I’d share. I haven’t fully worked out my thoughts, so this is preliminary. Still, here’re some triggered reflections on Intelligent Tutoring via models.

So, as I’ve mentioned, I’ve been an AI groupie. Which includes tracking the AI and education field, since that’s the natural intersection of my interests. Way back when, Stellan Ohlsson abstracted the core elements of an intelligent tutoring system (ITS), which include a student (learner) model, a domain (expert on the content) model, and an instruction (tutoring) model. So, a student with a problem takes an action, and then we see what an expert in the domain would do. From that basis, the pedagogy determines what to do next. They’ve been built, work in research, and even been successfully employed in the real world (see Carnegie Learning).

Now, I’ve largely been pessimistic about the generative AI field, for several reasons. These include that it’s:

evolutionary, not revolutionary (more and more powerful processors using slight advances on algorithms yields a quantum bump)
predicated on theft and damage (IP and environmental issues)
likely will lead to ill use (laying off folks to reduce costs for shareholder returns)
based upon biz models boosted by VC funds and as yet still volatile (e.g. don’t pick your long term partners yet)

Yet, I’ve been upbeat for AI overall, so it’s mostly the hype and the unresolved issues that are bugging me. So, seeing the features touted for this new system made me think of a potential way in which we might get the desired output. Which is how I (and we) should evolve.

As background, several decades back I was leading a team developing an adaptive learning system. The problem with ITS is that the content model is hard to build; they had to capture how experts reasoned in the field, and then model it through symbolic rules. In this instance I had the team focus on the tutoring model instead, and used a content model based upon learning objects with the relationships between them capturing the knowledge. Thus, you had to be careful in the content development. (This was an approach we got running. A commercial company subsequently brought it to market successfully a decade after our project. Of course, our project was burned to the ground by greed and ego.)

So, what I realized is that, with the right constraints, you could perhaps do an intelligent tutoring system. So, first, the learner model might be primed by a pre-test, but is built by learner actions. The content model could come from training on textbooks. You could do either a symbolic processing of the prose (a task AI can do), or a machine learning (e.g. LLM) version by training. Then, the tutoring model could be symbolic, capturing the best of our rules, or trained on a (procured, not stolen) database of interventions (something Kaplan was doing, for instance). (In our system, we wrote rules, but had parameters that could be tuned by machine learning over time to get better.)

My thought was that, in short, we can start having cross-domain tutoring. We can have a good learning model, and use the auto-categorization of content. Now, this does beg the problem of knowledge versus skills, which I still worry about. (And, continue to look at.) Still, it appears that the particular solution is looking at this opportunity. I’ll be keen to see how it goes; maybe we can have learning support. If we blend this and a coaching engine…maybe the dream I articulated a long time ago might come to fruition.

Why science?

15 April 2025 by Clark 1 Comment

I’ve written in praise of the cognitive and learning sciences. I, however, need to take a step back. It’s becoming increasingly clear to me, sadly, that there are attacks on science itself. Yet, I have a strong belief that it matters. So let me briefly address the question of why science.

As background, I have been steeped in science. It was one of my favorite topics in school, and in college. My PhD is in the underpinnings of how we think. Though it’s been a long while since I was an active scientific researcher, I still apply what’s known. Moreover, I continue to track developments, so I can continue to do so.

As a result, I’ve been a fan of the work of scientists in the cognitive and learning fields. I’ve not only had training in the methods, but I also continue to explore more broadly the methods and the applications. I also love the translators who take that research written in the original academese and turn it into practical advice. Heck, I’m co-director of a society about evidence-based practices.

There has been some ‘confusion’ about the scientific process. “How can you trust it if it admits it’s been wrong?” Er, that’s what it’s about, continually creating explanations about the world. When we know more, we may need to change our explanations. We went from the sun circling the earth to the other way around, and we no longer (should) think the world is flat. If you don’t believe in the findings, how (and why) are you reading this? Technologies developed from scientific endeavor.

To be fair, science has been used for ill as well as good. That’s about people’s ethics, not the outcomes. We have to be mindful of how we apply what we learn. That’s up to our values and morals, which science actually has a lot to say as well. For instance, I’ve made the case that research tells us we do better when we’re inclusive. That’s science telling us what values lead to the best outcomes. When we work with what we know about how we think, work, and learn, we improve the outcomes.

The evidence says that science is better than any alternative. When we apply evidence-based practices, we get the best results. That’s a win. When we turn our backs on it, we lose. Lives can be negatively impacted or lost. That’s not a win. And for our orgs, ignoring science in marketing, operations, sales, etc doesn’t make sense. So, too, for learning and ‘human resources’ in general. And, that’s true for society and government as well. So let’s make sure we’re making decisions in ways that align with science. It may seem more expedient in the short-term to do otherwise, but the long-term results argue for us doing the right thing. When there’re conflicts between beliefs and the evidence, things go better when we adapt beliefs and go with the evidence. “Why science” is because it works better.

Small changes with big impact

8 April 2025 by Clark 4 Comments

In the reality stakes, I recognize that people aren’t likely to throw their whole approach out. Instead, they make the small changes with big impact. Then, of course, they should use success to leverage the opportunity to do more. You can bring in a full evaluation of everything you do by the latest fad, but those tend to be expensive and out of date by the time they’re done. Wherever you are, there’s room for improvement. How do you get there? By understanding how we think, work, and learn.

So, one of the things I’ve done, repeatedly across clients, is look at what they’re doing (including outputs and process). I have tended to do this in a lightweight approach, because I know most folks are sensitive to costs, and want to get the biggest bang for the buck. I’ve done so for content, for design practices, for market opportunities, and more.

To do so means I go through materials, whether products, processes, or plans, to understand the experience and look for ways to improve it. Then, we prioritize those potential opportunities. I then bring my independent observations together for a discussion on what’s useful and necessary. Of course, we always find things that don’t meet those criteria. My concluding reports typically state the goals, the current context, the applicable principles, and recommendations. I’m also happy to work with folks to see how it works out and what tweaks may be of use. Which isn’t every engagement, but it’s not infrequent.

One of the robust outcomes, for what it’s worth, is that folks get insights they (and I) didn’t expect! That may be because I’ve been an interdisciplinary mongrel, with interests in many things, or possibly because the cognitive foundations provide a basis to address most anything. Regardless, I’ve found opportunities to improve in pretty much all situations. These are at every level from how to implement a field to collect information to an assessment of the viability of a go-to-market strategy.

In short, looking at things from the perspective of how our brains work provides insights into ways in which we’ve violated that alignment. Further, it’s a reliable phenomena that pretty much everything we do has opportunities to improve. Sure, not all such moves will be worth the effort, or may conflict with what folks have learned to live with. Still, there’s a pretty-much guaranteed to be valuable changes that can be made. At least, that’s been my experience, and my clients.

What I’m really doing is a cognitive/learning audit. Basically, it’s about going through the cognitive processing cycle repeatedly through an experience. That experience can be the learner’s, the designer’s, purchaser’s, or more. Usually, all of the above! However, what you want to do is to minimize the barriers, and maximize the value. What’re the users goals, what’s perceived, what’s considered, what’s processed, and what happens next.

There are benefits to having been actively investigating our minds for a number of decades now. I know the principles, I know how to apply them, and I also work in the real world. Also, perhaps against my own self-interest, I look to find ways to do it as easily and inexpensively as possible. I know organizations have limitations. Still, pretty much everyone benefits when you look for small changes with big impact. How about you?

Why the EIP Conference

1 April 2025 by Clark Leave a Comment

On my walk today, I was pondering the Evidence-informed Practitioner (EIP) conference (rapidly approaching, hence the top-of-mind positioning). And, I was looked at it a different way. Not completely, but enough. So, I thought I’d share those thoughts with you, as a possible answer to “why the EIP conference?”

To start, the conference was created to fill the gap articulated at our Learning Science conference. To wit, “this is all well and good, but how do we do it in practice?” Which, as I’ve opined, is a fair question. And we resolved to answer that.

I started with pondering, while perambulating, about the faculty. We’ve assembled folks who’ve been there, done that, know the underpinnings, and are articulate at sharing. Sure, we could ask people to submit proposals, but instead we went out and searched for the folks we thought would do this best.

My cogitations went further. What would be the best way for folks to get the answers they need? And, of course, the best is mentored live practice…like most learning would be. And, like most learning, that’s not necessarily practical to organize nor affordable. So, what’s the next best thing?

You could do uni courses in it all. You could read books about it all. Or, you could have a focused design. That is, first you have the best folks available create presentations about it. Then, have discussion forums available to answer the questions that arise. With the presenters participating. Finally, you have live sessions at accessible times to consolidate the content and discussions. Again, with the presenters hosting.

That last is what we’ve actually done. That’s what my reflection told me; this is pretty much the best way to get practical advice you can put into practice right away, and refine it. At least, the best value. From the time the videos are available ’til the live sessions, you have a chance to put what’s relevant to you into practice – that is, try it out – and have experts around to share what you’ve learned and answer the emergent questions!

Let’s be clear. Most confs have presentations and time to talk to the presenters, but not the time between presentations and scheduled discussion to try things out. Here, between my co-director Matt Richter and myself, we created a pedagogy that works.

Further, I got to choose the curriculum, starting with what most folks do (design courses), and then branch out from there: first, the barriers, then forward to analysis, and back to evaluation. Then we go broader, talking about extending learning via motivation and coaching, resources for continuing to learn, technology, and move to not learning via performance support. Finally we on to org-spanning issues including innovation and culture.

This is the right stuff to know, and an almost ideal way to learn it, in a practical format. It’s all asynchronous so you can do it at your own schedule, except for the live sessions, and for each they’re each offered at two different times to increase the likelihood that you can attend the ones you want to. Of course, they’re all taped as well.

But wait, there’s more! (Always wanted to say that. ;) If you order now, using the code EIP10CQ, you get 10% off! That makes a great deal become exceptional! Ok, so I’m laying it on a bit thick, but we really did try to make this the gala event of the season, and a valuable learning experience. So, I hope to see you there. Anyways, that’s my answer to why the EIP conference.

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?