I’ve talked in the past about miniscenarios. By this, I mean rewriting multiple choice questions (MCQs) to actually be situations requiring decisions and choices thereto. I evangelize this, regularly. I’ve also talked about what you need from subject matter experts (SMEs). What I haven’t really done is talk about how how you map information to miniscenarios. So it’s time to remedy that.

So, first, let’s talk about the structure of a mini-scenario. I’ve suggested that it’s an initial context or story, in which a situation precipitates the need for a decision. There’s the right one, and then alternatives. Not random or silly ones, but ones that represent ways in which learners reliably go wrong. There’s also feedback, which is best as story-based consequences first, then actual conceptual feedback.

So what’s the mapping? One of the things we (should) get from SMEs are the contexts in which these decisions come to play.  Thus, the setting for the mini-scenario is one of these contexts. It may be made fantastic in story, but the necessary contextual elements have to exist. (“Pat had been recently promoted to line supervisor…”)

Then, we have the decisions the learners need to be able to make. These often come in the form of performance objectives. This forms the basis for choosing a situation that precipitates the decision, and the decision itself. (“The errors in manufacturing were higher than the production agreement stipulated. Pat:”) Also, at least, the correct answer. (* worked backward through the process.)

The wrong answers come from some other information we need from SMEs: misconceptions. These are the ways that individuals go wrong when performing. I’ve advocated before that you may want different types of SMEs. It may be that supervisors of the performers have more insight here than content experts. Regardless, you want to make these alternatives available as possible responses. You’ll want to address the difficultly of discrimination between alternatives as a way to manipulate the challenge of the task; it should be appropriate to the learners’ level. (*asked team members what they thought the problem was; *exhorted the team to pay more attention to quality).

The feedback starts with the consequences, which you should also get from SMEs. What happens when you get it right? What happens with each wrong answer? These may come from stories about wins and losses that you also want to collect. (“Pat’s team did not like the implicit claim that they weren’t working hard enough.”)

Finally, there’re the models that are the basis for good performance, and consequently also the basis for the feedback. These you should also collect, because you use them to explain why a choice is good or bad. You don’t want to just say right or wrong, learners need to understand the underlying reason to reinforce their understanding. (Which may also mean they also need to see their answer with the feedback, so they remember what they chose.) Importantly, they need specific feedback for each wrong answer, btw, so your implementation tool needs to support that!  (When investigating errors, don’t start with the team. We always look at the process first, as system flaws need to be eliminated first.)

Pretty much everything you need from SMEs plays a role in providing practice. Miniscenarios aren’t necessarily the best practice, but they’re typically available in your authoring environment.  Writing them isn’t necessarily as easy as generating typical recognition questions, but they more closely mimic the actual task, and therefore lead to better transfer. Plus, you’ll get better as you practice. So  know the mapping of information to miniscenarios, practice your miniscenario writing, and put it into play!

This blog is where I “learn out loud”. I try to show my thinking. Yet, I realize I haven’t been doing that as much as I should, or perhaps not as effectively and mindfully as I should  So here’re some reflections on sharing my thinking, or ‘exposing myself’.

Worked examples, as we know from John Sweller, are most effective when they come before practice. Similarly, in Cognitive Apprenticeship, instructors model the desired performance. And I do try to share my thinking. However, I may not have been doing it properly. It needs to unpack the expertise; it can’t be just the knowledge, but what the thinking behind it is. The expertise that experts don’t have access to!

For instance, in my workshops, I often show the outcome of what I (with a team) did, after I have learners do it. Other times, like in the recent L&D conference, I shared my deeper thinking on analyzing technology through affordance, but then didn’t challenge learners to apply that themselves (to be fair, I only had an hour; on the other hand, I didn’t properly prepare).

Increasingly, I think one of the ways to scale my impact is to share my thinking, in context. When people are practitioners, that may be enough, but at some times it may also help to provide ‘challenges’ and provide feedback as well. That, however, doesn’t scale as well. I can only review so many projects…

I guess my biggest take away is to be more conscious about making the underlying thinking clear. I think it’s helpful to learn out loud, but also to ensure that I’m showing the thinking. So, I’ll keep exposing myself…or, at least, my thinking. I’ll try to do it more explicitly and clearly, as well.

I’m not alone in carrying the banner for learning science. Others are talking about evidence-based practices, making it stick, and more. This, I maintain, is a good thing. Is there too much of good thing? Is there a problem with a learning science bandwagon?

First, let’s be clear. There are some initiatives that do strike me as redundant or worse. For one, folks have been talking about neuroscience. And I think neuroscience research is quite interesting! What I also believe, buttressed by others, is that there haven’t been any results from neuroscience that are essential for learning science design. All the implications have been previously documented from learning science research at the cognitive or social level. Neuroscience is cool, but its use in learning design tends to be to draw attention (read: marketing), not for any new outcomes.

I feel similarly about the term brain-based. Yes, learning is brain-based. Isn’t it a wee bit redundant to say so? I suppose they’re implying that they’re aligned with how the brain works. Which is a good thing. Still, despite the alliteration, it seems a bit more like hype than being informative. As the saying goes, your mileage may vary.

However, I’m seeing more and more people now talking about learning science. That’s a good thing. Are they jumping on a bandwagon? Maybe, but there’s lots of room. As long as folks are actually digging into what the learning say, and not just paying lip-service, they’re welcome. To be clear, I don’t own the wagon anyway; I’m a practitioner, not a core researcher. So, I really do think it’s great if more and more people start talking, and walking, learning science.

I’ll go further, of course. I think we should be paying attention to what cognitive and learning sciences say about how we think, work, and learn. That is, the applicability of understanding how our minds work goes beyond learning to our overall organizational practices. But, hey, we gotta start somewhere, right? I think it’s good if we’re moving in the right direction. I can quibble that it’s slower than I’d like, but progress is progress.

So, yay, more learning science! C’mon, jump on, the learning science bandwagon; we’ve got space and it appears we’re moving forward. All good. Hope you’ll join us!