Cathy Moore has a lovely post with a slideshow that talks about using action mapping to design better elearning, and it’s a really nice approach.   While I don’t know from Action Mapping (tm?), I do know that the approach taken avoids the typical mistakes and focuses on the same thing I advocate: what do people need to be able to do?

The presentation rightly points out the problems with knowledge dump, and instead focuses on the business goal first, and then asks you to map out what the learner would need to be able to do to achieve that business goal.   That’s the point I was making in my ‘objectives‘ post of the Broken ID series.

Cathy nicely elaborates on that point, going directly to practice that has them doing the task, as close as possible to the real task.   Finally, she has you bring in the minimum information needed to allow them to do the task.   This is really a great ‘least assistance‘ approach!

Now, it’s not talking about examples or models (though those could fit under the minimum information principle, above), nor introducing the topic, so I’d want to ensure that the learners are engaged into the learning experience up-front, and provide a model to guide their performance in the task.   What this does, however, is give you a framework and set of steps that really focuses on the important elements and avoiding the typical approach that is knowledge-full and value-light.   Recommended.

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This is one in a series of thoughts on some broken areas of ID that I‘m posting for Mondays.   I intend to provide insight into many ways much of instructional design fails, and some pointers to avoid the problems. The point is not to say ‘bad designer‘, but instead to point out how to do good design.

Really, the key to learning is the practice. Learners have to apply knowledge, in the form of skills, to really internalize and ‘own‘ the learning.   Knowledge recitation, in the absence of application, leads to what cognitive science calls ‘inert knowledge‘, that‘s able to be recited back, but isn‘t activated in appropriate contexts.

What we see, unfortunately, is too much of knowledge test, and not meaningful application. We see meaningless questions seeing if people can recite back memorized facts, and no application of those facts to solve problems.   We see alternatives to the right answer that are so obviously wrong that we can pass the test without learning anything!   And we see feedback that‘s not specific to the deficit.   In short, we waste our and the learner‘s time.
What we want is appropriate challenge, contextualized performance, meaningful tasks, appropriate feedback, and more.

First, we should have picked meaningful objectives that indicate what they can do, in what context, to what level, and now we design the practice to determine whether they can do it. Of course, we may need to have some intermediate tasks to develop their skills at an appropriate pace, providing scaffolding to simplify the task until it‘s mastered.

We can scaffold in a variety of ways. We can provide tasks with simplified data first, that don‘t get complicated with other factors.   We can provide problems with parts worked, so learners can accomplish the component skills separately and then combine. We can provide support tools such as checklists or flowcharts to assist, and gradually remove them until the learner is capable.

We do need to balance the level of challenge, so that the task gets difficult at the right rate for the learner: too easy, and the learner is bored; too hard and the learner is frustrated.   Don‘t make it too easy!   If it matters, ensure they know it (and if it doesn‘t, why are you bothering?).

The trick is not only the inherent nature of the task, but many times is a factor of the alternatives to the right answer.   Learners don‘t make random mistakes (generally), they make patterned mistakes that represent inappropriate models that they perceive as appropriate.   We should choose alternatives to the right answer or choice that represent these misconceptions.

Consequently, we need to provide specific feedback for that particular misconception.   That‘s why any quiz tool that only has one response for all the wrong answers should be tossed out; it‘s worthless.

We need to ensure that the setting for the task is of interest to the learner.   The contexts we choose should setup problems that the learner viscerally understands are important problems, and ones that they are interested in.
We also need, as mentioned with examples, that the contexts seen across both examples and practice determine the space of transfer, so that still needs to be kept in mind.

The elements listed here are the elements that make effective practice, but also those that make engaging experiences (hence, the book).   That is, games.   While the best practice is individually mentored real performance, that doesn‘t scale well, and the consequences can be costly.   The next best practice, I argue, is simulated performance, tuned into a game (not turned, tuned).   While model-driven simulations are ideal for a variety of reasons (essentially infinite replay, novelty, adaptive challenge), it can be simplified to branching or linear scenarios.   If nothing else, just write better multiple choice questions!

Note that, here, practice encompasses formative and summative assessment. In either case, the learner‘s performing, it‘s just whether you evaluate and record that performance to determine what the learner is capable of.   I reckon assessment should always be formative, helping the learner understand what they know. And summative assessment, in my mind, has to be tied back to the learning objectives , seeing if they can now do what they need to be able to do that‘s difference.

If you make meaningful challenging, contextualized performance, you make effective practice.   And that‘s key to behavior change, and learning.   So practice making perfect practice, because practice makes perfect.

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This is one in a series of thoughts on some broken areas of ID that I‘m posting for Mondays.   I intend to provide insight into many ways much of instructional design fails, and some pointers to avoid the problems. The point is not to say ‘bad designer‘, but instead to point out how to do good design.

I see several reliable problems with examples, and they aren‘t even the deepest problems. They tend to be mixed in with the concept, instead of separate, if they exist at all.   Then, when they do exist, too often they‘re cookie-cutter examples, that don‘t delve into the necessary elements that make examples successful, let alone are intrinsically interesting, yet we know what these elements are!

Conceptually, examples are applications of the concept in a context.   That is, we have a problem in a particular setting, and we want to use the model as a guide to solving the problem. Note that the choice of examples is important. The broader the transfer space, that is, the more general the skills, the more you want examples that differ in many respects.   Learners generalize the concept from the examples, and the extent to which they‘ll generalize to all appropriate situations depends on the breadth of contexts they‘ve seen (across both examples and practice).   You need to ensure that the contexts the learner sees are as broadly disparate as possible.

Note that we should also be choosing problems and contexts that are of interest to the audience.   Going beyond just the cognitive role, we should be trying to tap into the motivational and engagement factors.   Factor that into the example design as well!

Now, we know that examples have to show the steps that were taken.   They have to have specific steps from beginning to end.   And, I add, those steps have to refer back to the concept that guides the presentation.   You can‘t just say “first you do this, then you do this”, etc, you have to say “first, using the model, you do this, and then the model says to do that”.   You need to show the steps, and the intermediate work products.   Annotating them is really important.

And that annotation is not just the steps, but also the underlying thought processes.   The problem is, experts don‘t even have access to their thought processes anymore!   Yet, their thinking really works along lines like “well, I could‘ve done A, but because of X, and thought B was a better approach, and then I could do C, but because of Y I tried D”, etc.   The point being, there‘s a lot of contextual clues that they evaluate that aren‘t even conscious, yet these clues are really important for learners. (BTW, this is one of the many reasons I recommend comics in elearning, thought bubbles are great for cognitive annotation.)

Another valuable component is showing mistakes and backtracking. This is a hard one to get your mind around, and yet it‘s powerful both cognitively and emotionally.   First, experts model the behavior perfectly, and when learners try, they make mistakes, and may turn off emotionally (“I‘m having trouble, and it looks so easy, I must not be good at this”).   In reality, experts make mistakes all the time, and learners need to know that. It keeps you from losing them altogether!

Cognitively it‘s valuable, too.   When experts show backtracking and repair, they‘re modeling the meta-skills that are part of the expertise.   Unpacking that self-monitoring helps learners internalize the ‘check your answer‘ component that‘s part of expert performance.   This takes more work on the part of the designer, like we had with the concept, but if the content is important (otherwise, why are you building a course), it‘s worth doing right.

Finally, I believe it‘s important to convey the example as a story.   Our brains are wired to comprehend stories, and a good narrative has better uptake.   Having a protagonist documenting the context and problem, and then solving it with the model to achieve meaningful outcomes, is more interesting, and consequently more memorable.   We can use a variety of media to tell stories, from prose, through audio (think mobile and podcasts) and narrated slideshow, animation, or video.   Comics are another channel.   Stories also are useful for conveying the underlying thought processes, via thought bubbles or reflective narration (“What was I thinking?…”).

So, please do good examples.   Be exemplary!

While I agree vehemently with most of a post by Lars Hyland, he said one thing I slightly disagree with, and I want to elaborate on it.   He was disagreeing with   “buying rapid development tools to bash out ill formed ‘e-learning’ to an audience that will not only be unimpressed but also none the wiser – or more productive”, a point I want to nuance.   I agree with not using rapid elearning to create courses for novices, but there is a role for bashing out courses for another audience, the practitioner.   And there’s something deeper here to tease out.

I want to bring up John Carroll’s minimalist instruction, and highly recommend it to you. He focused on a) meaningful tasks, b) active learning quickly, c) including error recogition & recovery, and d) making learning activities self-contained (a lot like games, actually).   In The Nurnberg Funnel, he documented how this design led to 25 cards, 1 per learning goal, that beat a 94 page traditionally designed manual hands-down in outcomes.

Another way to think about it is something Jim Spohrer mentioned to me once. Now, Jim’s been an Apple Fellow, and is leading research at IBM’s Almaden Research Center.   He really cares and likes to help people, but he’s very busy.   So he adopted a ‘least assistance’ principle, where he would ask himself what’s the least he can do to get this person going, because there was more to do and more people to help than he was able to keep up with.   And I think it is a useful way to think about supporting learning.

This sounds a lot like performance support, and that’s definitely a mind-set we need to adopt. When Harold Jarche and Jay Cross talk about the death of the training department, they’re talking about not focusing on courses, and instead taking a broader, performance perspective.   Obviously, we want to talk about portals of resources, but we also need to recognize that there are formal learning situations that don’t require the full formality.

We develop full courses to incorporate motivation, practice, all the things non-self-directed learners need.   But there are times when we need to provide new information and skills to self-directed learners.   When we’re talking to practitioners who are good at their job, know what they’re doing and why, and know that they need to know this information and how they’ll apply it, we can strip away a lot of the window dressing. We can just provide support to a SME so that their talk presents the relevant bits   in a streamlined and effective way, and let them loose.     That, to me, is the role of rapid elearning.

It’s not for novices, but it’s effective, and more efficient.   In this economic climate, we don’t have the luxury of full development of courses for every need.   Moreover, in any climate, we shouldn’t give people what they don’t need, instead we need to focus on what the ‘least assistance’ we can give them is.

In many cases, the least assistance we can give is self-help, which is why I believe social learning tools are one of the best investments that can be made.   The answer may well be ‘out there’, and rather than for learning designers to try to track it down and capture it, the learner can send out the need   and there’s a good chance an answer will come back!   There’s a lot to making such an environment work; it’s not the case that ‘if you build it, they will learn’, but it’s still going to fill a sweet spot in the performance ecosystem that may not be being hit as of now.

Don’t look for everything you can do in one situation, unless you’re flush with too much time and resources (in which case, watch out!), instead look for the least you can do that will get the job done so you can do more for everybody. It’s likely that’s more to their taste, anyway. And that’s enough from me on that!