Setting Story

I’ve been thinking about the deep challenge of motivating uninterested learners.  To me, at least part of that is making the learning of intrinsic interest.  And one of those elements is practice, and this is arguably the most important element to making learning work.  So how to do we make practice intrinsically interesting?

One of the challenging but important components of designing meaningful practice is choosing a context in which that practice is situated.  It’s really about finding a story line that makes the action meaningful to both the learner and the learning. It’s creative (and consequently fun), but it’s also not intrinsically obvious (which I’ve learned after trying to teach it in both game design and advanced ID workshops). There are heuristics to be followed (there’s no guaranteed formula except brainstorm, winnow, trial, and refine), however, that can be useful.

While Subject Matter Experts (SMEs) can be the bane of your existence while setting learning goals (they have conscious access to no more than 30% of what they do, so they tend to end up reciting what they know, which they do have access to), they can be very useful when creating stories. There’s a reason why they’ve spent the requisite time to become experts in the field, and that’s an aspect we can tap into. Find out why it’s of interest to them.  In one instance, when asking experts about computer auditing, a colleague found that auditors found it like playing detective, tracking back to find the error.  It’s that sort of insight upon which a good game or practice exercise can hinge.

One of the tricks to work with SMEs is to talk about decisions.  I argue that what is most likely to make a difference to organizations is that people make better decisions, and I also believe that using the language of decisions helps SMEs focus on what they do, not what they know.  Between your performance gap analysis of the situation, and expert insight into what decisions are key, you’re likely to find the key performances you want learners to practice.

You also want to find out all the ways learners go wrong.  Here you may well hear instructors and/or SMEs say “no matter what we do, they always…”. And that’s the things you want to know, because novices don’t tend to make random errors.  Yes, there’s some, owing to our cognitive architecture (it’s adaptive), which is why it’s bad to expect people to do rote things, but it’s a small fraction of mistakes.  Instead, learners make patterned mistakes based upon mistakes in their conceptualizations of the performance, aka misconceptions.  And  you want to trap those because you’ll have a chance to remediate them in the learning context. And they make the challenge more appropriately tuned.

You also need the consequences of both the right choice and the misconceptions. Even if it’s just a multiple choice question, you should show what the real world consequence is before providing the feedback about why it’s wrong. It’s also the key element in scenarios, and building models for serious games.

Then the trick is to ask SMEs about all the different settings in which these decisions embed. Such decisions tend to travel in packs, which is why scenarios are better practice than simple multiple choice, just as scenario-based multiple choice trumps knowledge test.  Regardless, you want to contextualize those decisions, and knowing the different settings that can be used gives you a greater palette to choose from.

Finally, you’ll want to decide how close you want the context to be to the real context.  For certain high-stakes and well-defined tasks, like flying planes or surgery, you’ll want them quite close to the real situation.  In other situations, where there’s more broad applicability and less intrinsic interest (perhaps accounting or project management), you may want a more fantastic setting that facilitates broader transfer.

Exaggeration is a key element. Knowing what to exaggerate and when is not yet a science, but the rule of thumb is leave the core decisions to be based upon the important variables, but the context can be raised to increase the importance.  For example, accounting might not be riveting but your job depends on it.  Raising the importance of the accounting decision in the learning experience will mimic the importance, so you might be accounting for a mob boss who’ll terminate your existence if you don’t terminate the discrepancy in his accounts!  Sometimes exaggeration can serve a pedagogical purpose as well, such as highlighting certain decisions that are rare in real life but really important when they occur. In one instance, we had asthma show up with a 50% frequency instead of the usual ~15%, as the respiratory complications that could occur required specific approaches to address.

Ultimately, you want to choose a setting in which to embed the decisions. Just making it abstract decreases the impact of the learning, and making it about knowledge, not decisions, will render it almost useless, except for those rare bits of knowledge that have to absolutely be in the head.  You want to be making decisions using models, not recalling specific facts. Facts are better off put in the world for reference, except where time is too critical. And that’s more rare than you’d expect.

This may seem like a lot of work, but it’s not that hard, with practice.  And the above is for critical decisions. In many cases, a good designer should be able to look at some content and infer what the decisions involved should be.  It’s a different design approach then transforming knowledge into tests, but it’s critical for learning.  Start working on your practice items first, aligned with meaningful objects, and the rest will flow. That’s my claim, what say you?

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