Archives for March 2009

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This is one in a series of thoughts on some broken areas of ID that I’m posting for Mondays.   The intention is 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 better design.

When it comes to closing the elearning experience, not surprisingly too often we drop the ball here, too.   Our endings tend to be too abrupt, and merely rehash what has been learned, and, if we’re lucky, point out further directions. Not that we don’t want to let them know what they‘ve learned, and indicate that if they want to go deeper, they should go here, and they’re now prepared to learn about this thing over there.   But there’s so much more!

First of all, if we’re viewing this as an experience, developing motivation and addressing the emotional components, and we should be, then we should close off the experience emotionally as well.   We should acknowledge the effort they’ve put in, and celebrate the fact that they‘ve learned the ability to do something new (and it should be do something new, if you‘ve got your objectives right).

Ideally, we’d personalize this, and say something like”you did really good on A, but your B was a little weak, try a bit of C to build that up” or whatever.   We don’t always have the ability to track performance at this more granular level, nor the ability to make the learning content adapt in that way, but it’s conceptually feasible and you should be thinking about how you might accomplish that.

Also, in the introduction, we drilled down from the larger context in the world (right?), and we should similarly drill back up.   Let‘s reconnect the learner with the broader context, and reactivate and associate the learning experience by letting them know how what they now can do plays a role in the world.   It‘s not just “you learned X”, but “you learned X, which means Y”.

Finally, let me add a valuable lesson I learned.   I was working on some content for speaking to the media, and the SMEs (hello, Jane & Susan!) had a nice statement format that worked really well (with a memorable acronym: the SEX statement – Statement, Examples, eXplanation – I’ve never forgotten it :).   However, they realized that the opportunities to apply it might be few and far-between, so they encouraged ways to practice it.   They suggested using it with co-workers, bosses, even your kids!

The important point was the effort they put in to help you keep it active until you needed it, and that‘s too often an element we forget.   We can and should stream out reactivations at a rate that is appropriate for how soon and how often we’ll apply the skills, but our decision about how to support the learner’s retention should be conscious and related to their task and practice opportunities.

Note that this can and should be all done in a minimum amount of words.   It doesn’t take much, a sentence or two at most, unless it‘s been a big elearning experience, but it is appropriate.

So, in summary, make sure you wrap up the learning experience with the same care that you began it. Make it an experience to be remembered!

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.