Search Results for: engag

As part of the continual rethink about what I offer and to who (e.g. training department rethinks to managers, directors, VPs; experience design reviews/refines to learning teams), my thoughts on learning experience design took a leap.   I’ve argued that the skills in Engaging Learning (my book) are the ones that are critical for Pine & Gilmore’s next step beyond their experience economy, the transformative experience economy. But I’ve started to think deeper.

John Seely Brown challenged us at the Learning Irregulars meeting that what fundamentally made a difference was a ‘questing disposition’ found in certain active learning communities.   This manifests as an orientation to experimentation and learning. My curiosity was whether it was capable of being developed, as I’m loath to think that the 10% that learn despite schooling :) is inflexible because I believe that more and better learning has a chance to change our world for the better.

I hadn’t finished the article he subsequently sent me (coming soon), but it drove me back to some early thinking on attitude change.   I recognize that just learning skills aren’t enough, and that a truly transformative experience subjectively needs to result in a changed worldview, a feeling of new perspectives.   This could be a change in attitude, a new competency, or a fundamental change in perspective.

Which brings me back to looking at myth and ritual, something I tried to get my mind around before. I was looking for the Complete Idiot’s Guide to Ritual, and the closest thing I could find is Rapport’s Ritual and Religion in the Making of Humanity, which is almost impenetrably dense (and I’m trained and practiced at reading academic prose!).   However, the takeaway is that ritual is hard to design, most artificial attempts fail miserably.

Others have suggested that transformation is at core about movement, which takes me back to ritual.   Both a search on transformation and a twitter response brought that element to the surface.   The other element that the search found was spirituality (not just religious).   Which is not surprising, but not necessarily useful.

Naturally, I fall back to thinking from the perspective of creating an experience that will yield that transformational aesthetic, but it’s grounded in intuition rather than any explicit guidance. Still, I think there’s something necessary in the perspective that skills alone isn’t enough, and as I said before, as much of our barriers may be attitude or motivation as knowledge and skills.

I’ve skimmed ahead in JSB’s article, and can see I need a followup post, but in the interim, I’d welcome your thoughts on designing truly transformative experiences, not just learning experiences.

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This is the last formal post in a series of thoughts on some broken areas of ID that I’ve been 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.

We’ve been talking about lots of ways instructional design can be wrong, but if that’s the case, the process we’re using must be broken too.   If we’re seeing cookie-cutter instructional design, we must not be starting from the right point, and we must be going about it wrong.

Realize that the difference between really good instructional design, and ordinary or worse, is subtle.   Way too often I’ve had the opportunity to view seemingly well-produced elearning that I’ve been able to dismantle systematically and thoroughly.   The folks were trying to do a good job, and companies had paid good money and thought they got their money’s worth.   But they really hadn’t.

It’d be easy to blame the problems on tight budgets and schedules, but that’s a cop-out.   Good instructional design doesn’t come from big budgets or unlimited timeframes, it comes from knowing what you’re doing.   And it’s not following the processes that are widely promoted and taught.

You know what I’m talking about – the A-word, that five letter epithet – ADDIE.   Analysis, Design, Development, Implementation, and Evaluation.   A good idea, with good steps, but with bad implementation.   Let me take the radical extreme: we’re better off tossing out the whole thing rather than continue to allow the abominations committed under that banner.

OK, now what am I really talking about?   I was given a chance to look at an organization’s documentation of their design process.   It was full of taxonomies, and process, and all the ID elements.   And it led to boring, bloated content.   If you follow all the procedures, without a deep understanding of the underpinnings that make the elements work, and know what can be finessed based upon the audience, and add the emotional elements that instructional design largely leaves out (with the grateful exception of Keller’s ARCS model).

The problem is that more people are doing design than have sufficient background, as Cammy Bean’s survey noted.   Not that you have to have a degree, but you do have to have the learning background to understand the elements behind the processes.   Folks are asked to become elearning designers and yet haven’t really had the appropriate training.

Blind adherence to ADDIE will, I think, lead to more boring elearning than someone creative taking their best instincts about how to get people to learn.   Again, Cathy Moore’s Action Mapping is a pretty good shortcut that I’ll suggest will lead to better outcomes than ADDIE.

Which isn’t to say that following ADDIE when you know what you’re doing, and have a concern for the emotional and aesthetic side (or a team with same), won’t yield a good result, it will.   And, following ADDIE likely will yield something that’s pretty close to effective, but it’s so likely to be undermined by the lack of engagement, that there’s a severe worry.

And, worse, there’s little in their to ensure that the real need is met, asking the designer to go beyond what the SME and client tells you and ensure that the behavior change is really what’s needed.   The Human Performance Improvement model actually does a better job at that, as far as I can tell.

It’s not hard to fix up the problem.   Start by finding out what significant decision-making change will impact the organization or individual, and work backward from there, as the previous posts have indicated. I don’t mean to bash ADDIE, as it’s conceptually sound from a cognitive perspective, it just doesn’t extend far enough pragmatically in terms of focusing on the right thing, and it errs too much on the side of caution instead of focusing on the learner experience.It’s not clear to me that ADDIE will even advocate a job aid, when that’s all that’s needed (and I’m willing to be wrong).

Our goal is to make meaningful change, and that’s what we need to do.   I hope this series will enable you to do more meaningful design.   There may be more posts, but I’ve exhausted my initial thoughts, so we’ll see how it goes.

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!