Archives for February 2024

I’ve been on the stump for mental models since, well, I left for grad school. My work colleagues, as a going away present, gave me a copy of Gentner & Stevens’ Mental Models. My Ph.D. thesis also incorporated them. (In it, I looked at how people used such models, and then looked at the effects of training interventions. TL;DR: there were some benefits!)  However, I suspect that the idea is a bit complex, and I tend to err on the side of the conceptual (mea culpa!). So, I thought it was time to be getting concrete about mental models.

For background, mental models are simplified explanations about how some portion of the world works. They’re causal conceptual models that connect the relationships and how effects propagate through systems. Done well, they give us a basis to explain and predict outcomes. For performance, they give us a basis to make decisions; we can run them and see what the various outcomes of choices of actions would be. Then we can choose the best one. And they guide decisions in many different circumstances.

For instance, for something procedural, they tell us why we doing things in a certain order. For instance, when I’m cooking breakfast, I want the onions nicely browned. I know not to put them in before the pan heats, because my mental model of heat transference and vegetable properties lets me know that throwing them into a hot pan will brown them. Instead, if I throw them into a cold pan and raise the heat, they’re likely to just get soft. (There’re nuances around this, of course.) And this can stretch to let me know when to throw in various things, and also why to cook the meat, take it out, and then throw in the veggies. If I forget a step, my mental models can fill in and help me regenerate the necessary step.

Similarly for other decisions. In trouble-shooting, for instance, my knowledge of what makes cars work lets me know that if it’s not running, it’s likely one of two sources. Given that petrol engines require an air/fuel mixture ignited by a spark, I can suspect that either the fuel or electrical system is at fault. Then I can take steps to test each. (Ok, at least I could, back in the days of caburetors and distributors. Electronic ignition and fuel injection have thrown off my game!) Knowing the causal properties let me break down the possible contributors. It’s not going to be the brakes!

And so on. In fact, models guide many types of decisions. Good models, based on empirical research, such as cognitive load theory, give us reasons to do things like precede practice with examples. (Bad models, of course, like learning styles, lead us to waste time and money chasing unobtainable benefits.)

Research on mental models tells us several things. For one, our brains are always building models. It also tells us that once we have a model, we’re reluctant to replace it (no “please sir, may I have another model”), and instead, patch it. Thus, if we want to support the optimal performance, we should provide good models, and build their relevance through examples that show them, and addressing them in feedback.

I hope that the two examples here make the use of models a bit more comprehensible. The goal, of course, is that you start paying more attention to them in your learning designs. They’re not necessarily obvious to elicit, but they are there. Do try to find them, represent them explicitly, and refer to them in examples and feedback. Oh, and do avoid the bad ones (you can look to the research translators for guidance). They’re part of what makes us best at performing optimally, and so should be part of your learning solutions. (In other words, build and use your model of mental models! :)

I apparently talk about alignment a lot. There’re good reasons, of course. First, I am referring to two different alignments. One is aligning organizations with the way our brains work. Otherwise, we won’t get the best out of people. The other is aligning our learning experience designs with what we know about how our brains work. Also critically important. In this case, prompted as always by conversations, I realized that I wanted to explore the nuances of aligning for the latter.

So, I’ve talked before about how  we should make sure we have meaningful objectives, and then align practice to that. I’ve also become enlightened about how important examples are as well. But within both of those, there’s more.

It came up in reviewing a design, looking to refine the approach. In it, there were examples, but they weren’t being used quite systematically enough. Then, the practice also wasn’t quite reflecting what people did. In the course of the conversation, I realized that there were nuances that seemed to be missing.

When you’re focusing on performance, you should be looking at what people will need to be doing. Too often, folks can talk about what they want people to know. However, what matters is what people do. Thus, you really need to dig down into that.

Then, you need to be making sure your examples show people doing whatever it is they need to be able to do. Similarly, you need to be asking people to be doing that, as well. For good examples, they should have a narrative flow, and show the underlying thinking. Good practice should require contextualized decision making like they’ll have to actually perform. Not characterizing the situation, but making decisions based upon those situations. So, not saying “is this an X or a Y situation”, but instead “do you choose action A or B”?

Then, of course, there are the actual choices of situation. The first task should be elementary. It may require scaffolding, so the circumstances might be simple, or some of the task is performed, etc. Then, you systematically add complexity in the task, while also broadening the situations seen. You’re simultaneously supporting both the acquisition of skill, and the ability to transfer to appropriate situations.

Then, of course, you want to make the situations appropriately compelling. That may mean choosing the best stories, some exaggeration, and storytelling. For practice, of course, there’s also the feedback: performance-focused, model-based, and minimal.

Look, I’m not saying this is easy. If it was easy, we’d get AI to do it ;). Yet AI doesn’t, and really can’t, understand the nuances of aligning. We can, and do. Yes, it is somewhat rocket science, done properly. We’re talking about systematically creating change in arguably the most complex thing in the known universe, after all. However, we do have good principles and practices. We just need to make sure we know, and use them.

That’s what makes our field so fascinating and important, after all. The creativity involved is also why it’s fun. Then, we’re also achieving important goals, improving people. We owe it to our stakeholders to do it right. (We are the leaders of the future economy, after all!)  That’s my take, what am I missing?

In a conversation with some colleagues, we were discussing how to handle wrapping up learning. In it, I realized something that I’ve always assumed, but don’t know that I’ve articulated. So, let me explain why I say “they nest!”

For context, we were talking about designing elearning. In this case, we have four modules that make up a full course. We’re using an ongoing story in the first three, and then the fourth includes several scenarios for the learner.

The question that prompted my reflection was an ask about how to wrap up the story. My inference was that the question was whether  we needed to wrap the story at the end of the last module. My answer was that we wrapped up the story at the end of module 3, and kick off module 4 stating that we’re moving on. Then we’d wrap up the whole experience at the end of the fourth module.

What I realized was that I haven’t articulated that each learning experience itself has an opening and a closing. So, when smaller learning experiences are embedded in a larger one (which isn’t all that unusual), we need to close off the embedded ones. We’ll also need to open the transition to the next embedded learning experience from the previous one. Then we finally close off the last one. That is, we open and close each learning experience on its own.

In short, these experiences embed, or to put it another way, they nest. Which is aligned with how we think about things anyways: for instance the ways we can talk about wheels, or talk about cars and their wheels. This way of thinking about it makes sense to me, how about you?