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Clark Quinn’s Learnings about Learning

Shiny objects and real impact

9 January 2015 by Clark 2 Comments

Yesterday I went off about how learning design should be done right and it’s not easy.  In a conversation two days ago, I was talking to a group that was  supporting several initiatives in adaptive learning, and I wondered if this was a good idea.

Adaptive learning is  desirable.  If learners come from different initial abilities, learn at different rates, and have different availability, the learning should adapt.  It should skip things you already know, work at your pace, and provide extra practice if the learning experience is extended.  (And, BTW, I’m  not talking learning styles).  And this is worthwhile,  if the content you are starting  with is good.  And even then, is it really necessary. To explain, here’s an analogy:

I have heard it said that the innovations for the latest drugs should be, in many cases, unnecessary. The extra costs (and profits for the drug companies) wouldn’t be necessary. The claim is that the new drugs aren’t any more effective than the existing treatments  if they were used properly.  The point being that people don’t take the drugs as prescribed (being irregular,  missing, not continuing past the point they feel better, etc), and if they did the new drugs wouldn’t be as good.  (As a side note, it would appear that focusing on improving patient drug taking protocols would be a sound strategy, such as using a mobile app.)  This isn’t true in all cases, but even in some it makes a point.

The analogy here is that using all the fancy capabilities: tarted up templates for simple questions, 3D virtual worlds, even adaptive learning, might not be needed if we did better learning design!  Now, that’s not to say we couldn’t add value with using the right technology at the right points, but as I’ve quipped in the past: if you get the design right, there are  lots of ways to implement it.  And, as a corollary, if you don’t get the design right, it doesn’t matter how you implement it.

We do need to work on improving our learning design, first, rather than worrying about the latest shiny objects. Don’t get me wrong, I  love  the shiny objects, but that’s with the assumption that we’re getting the basics right.  That was my assumption ’til I hit the real world and found out what’s happening. So let’s please get the basics right, and then worry about leveraging the technology on  top of a strong foundation.

Maybe it is rocket science!

8 January 2015 by Clark 11 Comments

As I’ve been working with the Foundation over the past 6 months I’ve had the occasion to review a wide variety of elearning, more specifically in the vocational and education space, but my experience mirrors that from the corporate space: most of it isn’t very good.  I realize that’s a harsh pronouncement, but I fear that it’s all too true; most of the elearning I see will have very little impact.  And I’m becoming ever more convinced that what I’ve quipped  in the past is true:

Quality design is hard to distinguish from well-produced but under-designed content.

And here’s the thing: I’m beginning to think that this is not just a problem with the vendors, tools, etc., but that it’s more fundamental.  Let me elaborate.

There’s a continual problem of bad elearning, and yet I hear people lauding certain examples, awards are granted, tools are touted, and processes promoted.  Yet what I see really isn’t that good. Sure, there are exceptions, but that’s the problem, they’re exceptions!  And while I (and others, including the instigators of the Serious eLearning Manifesto) try to raise the bar, it seems to be an uphill fight.

Good learning design is rigorous. There’re some significant effort just getting the right objectives, e.g. finding the  right  SME, working with them and not taking what they say verbatim, etc.  Then working to establish the right model and communicating it, making meaningful practice, using media correctly.  At the same time, successfully fending off the forces of fable (learning styles, generations, etc).

So, when it comes to the standard  tradeoff    –  fast, cheap, or good, pick two – we’re ignoring ‘good’.  And  I think a fundamental problem is  that everyone ‘knows’  what learning is, and they’re not being astute consumers.  If it looks good, presents content, has some interaction, and some assessment, it’s learning, right?  NOT!  But stakeholders don’t know, we don’t worry enough about quality in our metrics (quantity per time is not a quality metric), and we don’t invest enough in learning.

I’m reminded of a thesis that says medicos reengineered their status in society consciously.  They went from being thought of ‘quacks’ and ‘sawbones’ to an almost reverential status today by a process of making the process of becoming a doctor quite rigorous.  I’m tempted to suggest that we need to do the same thing.

Good learning design is complex.  People don’t have predictable properties as does concrete.  Understanding the necessary distinctions to do the right things is complex.  Executing the processes to successfully design, refine, and deliver a learning experience that leads to an outcome is a complicated engineering endeavor.  Maybe we do have to treat it like rocket science.

Creating learning should be considered a highly valuable outcome: you are helping people achieve their goals.  But if you really aren’t, you’re perpetrating malpractice!  I’m getting stroppy, I realize, but it’s only because I care and I’m concerned.  We have  got to raise our game, and I’m seriously concerned with the perception of our work, our own knowledge, and our associated processes.

If you agree, (and if you don’t, please do let me know in the comments),  here’s my very serious question because I’m running out of ideas: how do we get awareness of the nuances of good learning design out there?

 

Quinn-Thalheimer: Tools, ADDIE, and Limitations on Design

23 December 2014 by Clark 2 Comments

A few months back, the esteemed Dr. Will Thalheimer encouraged me to join him in a blog dialog, and we posted the first one on who L&D had responsibility to.  And while we took the content seriously, I can’t say our approach was similarly.  We decided to continue, and here’s the second in the series, this time trying to look at what might be hindering the opportunity for design to get better.  And again, a serious convo leavened with a somewhat demented touch:

Clark:

Will, we‘ve suffered Fear and Loathing on the Exhibition Floor at the state of the elearning industry before, but I think it‘s worth looking at some causes and maybe even some remedies.  What is the root cause of our suffering?  I‘ll suggest it‘s not massive consumption of heinous chemicals, but instead think that we might want to look to our tools and methods.

For instance, rapid elearning tools make it easy to take PPTs and PDFs, add a quiz, and toss the resulting knowledge test and dump over to the LMS to lead to no impact on the organization.  Oh, the horror!  On the other hand, processes like ADDIE make it easy to take a waterfall approach to elearning, mistakenly trusting that ‘if you include the elements, it is good‘ without understanding the nuances of what makes the elements work.  Where do you see the devil in the details?

Will:

Clark my friend, you ask tough questions! This one gives me Panic, creeping up my spine like the first rising vibes of an acid frenzy. First, just to be precise—because that‘s what us research pedants do—if this fear and loathing stayed in Vegas, it might be okay, but as we‘ve commiserated before, it‘s also in Orlando, San Francisco, Chicago, Boston, San Antonio, Alexandria, and Saratoga Springs. What are the causes of our debauchery? I once made a list—all the leverage points that prompt us to do what we do in the workplace learning-and-performance field.

First, before I harp on the points of darkness, let me twist my head 360 and defend ADDIE. To me, ADDIE is just a project-management tool. It‘s an empty baseball dugout. We can add high-schoolers, Poughkeepsie State freshman, or the 2014 Red Sox and we‘d create terrible results. Alternatively, we could add World-Series champions to the dugout and create something beautiful and effective. Yes, we often use ADDIE stupidly, as a linear checklist, without truly doing good E-valuation, without really insisting on effectiveness, but this recklessness, I don‘t think, is hardwired into the ADDIE framework—except maybe the linear, non-iterative connotation that only a minor-leaguer would value. I‘m open to being wrong—iterate me!

Clark:

Your defense of ADDIE is admirable, but is the fact that it‘s misused perhaps reason enough to dismiss it? If your tool makes it easy to lead you astray, like the alluring temptation of a forgetful haze, is it perhaps better to toss it in a bowl and torch it rather than fight it? Wouldn‘t the Successive Approximation Method be a better formulation to guide design?

Certainly the user experience field, which parallels ours in many ways and leads in some, has moved to iterative approaches specifically to help align efforts to demonstrably successful approaches. Similarly, I get ‘the fear‘ and worry about our tools. Like the demon rum, the temptations to do what is easy with certain tools may serve as a barrier to a more effective application of the inherent capability. While you can do good things with bad tools (and vice versa), perhaps it‘s the garden path we too easily tread and end up on the rocks. Not that I have a clear idea (and no, it‘s not the ether) of how tools would be configured to more closely support meaningful processing and application, but it‘s arguably a collection worth assembling. Like the bats that have suddenly appeared…

Will:

I‘m in complete agreement that we need to avoid models that send the wrong messages. One thing most people don‘t understand about human behavior is that we humans are almost all reactive—only proactive in bits and spurts. For this discussion, this has meaning because many of our models, many of our tools, and many of our traditions generate cues that trigger the wrong thinking and the wrong actions in us workplace learning-and-performance professionals. Let‘s get ADDIE out of the way so we can talk about these other treacherous triggers. I will stipulate that ADDIE does tend to send the message that instructional design should take a linear, non-iterative approach. But what‘s more salient about ADDIE than linearity and non-iteration is that we ought to engage in Analysis, Design, Development, Implementation, and Evaluation. Those aren‘t bad messages to send. It‘s worth an empirical test to determine whether ADDIE, if well taught, would automatically trigger linear non-iteration. It just might. Yet, even if it did, would the cost of this poor messaging overshadow the benefit of the beneficial ADDIE triggers? It‘s a good debate. And I commend those folks—like our comrade Michael Allen—for pointing out the potential for danger with ADDIE. Clark, I‘ll let you expound on rapid authoring tools, but I‘m sure we‘re in agreement there. They seem to push us to think wrongly about instructional design.

Clark:

I spent a lot of time looking at design methods across different areas – software engineering, architecture, industrial design, graphic design, the list goes on – as a way to look for the best in design (just as I‘ve looked across engagement disciplines, learning approaches, and more; I can be kinda, er, obsessive).   I found that some folks have 3 step models, some 4, some 5. There‘s nothing magic about ADDIE as ‘the‘ five steps (though having *a* structure is of course a good idea).  I also looked at interface design, which has arguably the most alignment with what elearning design is about, and they‘ve avoided some serious side effects by focusing on models that put the important elements up front, so they talk about participatory design, and situated design, and iterative design as the focus, not the content of the steps. They have steps, but the focus is on an evaluative design process. I‘d argue that‘s your empirical design (that or the fumes are getting to me).  So I think the way you present the model does influence the implementation. If advertising has moved from fear motivation to aspirational motivation (c.f. Sach‘s Winning the Story Wars), so too might we want to focus on the inspirations.

Will:

Yes, let‘s get back to tools. Here‘s a pet peeve of mine. None of our authoring tools—as far as I can tell—prompt instructional designers to utilize the spacing effect or subscription learning. Indeed, most of them encourage—through subconscious triggering—a learning-as-an-event mindset.

For our readers who haven‘t heard of the spacing effect, it is one of the most robust findings in the learning research. It shows that repetitions that are spaced more widely in time support learners in remembering. Subscription learning is the idea that we can provide learners with learning events of very short duration (less than 5 or 10 minutes), and thread those events over time, preferably utilizing the spacing effect.

Do you see the same thing with these tools—that they push us to see learning as a longer-then-necessary bong hit, when tiny puffs might work better?

Clark:

Now we’re into some good stuff!  Yes, absolutely; our tools have largely focused on the event model, and made it easy to do simple assessments.  Not simple good assessments, just simple ones. It’s as if they think designers don’t know what they need.  And, as our colleague Cammy Bean’s book The Accidental Instructional Designer’s success shows, they may be right.  Yet I’d rather have a power tool that’s incrementally explorable, but scaffolds good learning than one that ceilings out just when we’re getting to somewhere interesting. Where are the templates for spaced learning, as you aptly point out?  Where are the tools to make two-step assessments (first tell us which is right, then why it’s right, as Tom Reeves has pointed us to)?  Where are more branching scenario tools?  They tend to hover at the top end of some tools, unused. I guess what I’m saying is that the tools aren’t helping us lift our game, and while we shouldn’t blame the tools, tools that pointed the right way would help.  And we need it (and a drink!).

Will:

Should we blame the toolmakers then? Or how about blaming ourselves as thought leaders? Perhaps we‘ve failed to persuade! Now we‘re on to fear and self-loathing…Help me Clark! Or, here‘s another idea. How about you and I raise $5 million in venture capital and we‘ll build our own tool? Seriously, it‘s a sad sign about the state of the workplace learning market that no one has filled the need. Says to me that (1) either the vast cadre of professionals don‘t really understand the value, or (2) the capitalists who might fund such a venture don‘t think the vast cadre really understand the value, (3) or the vast cadre are so unsuccessful in persuading their own stakeholders that truth about effectiveness doesn‘t really matter. When we get our tool built, how about we call it Vastcadre? Help me Clark! Kent you help me Clark? Please get this discussion back on track…What else have you seen that keeps us ineffective?

Clark:

Gotta hand it to Michael Allen, putting his money where his mouth is, and building ZebraZapps.  Whether that‘s the answer is a topic for another day.  Or night.  Or…  so what else keeps us ineffective?  I‘ll suggest that we‘re focusing on the wrong things.  In addition to our design processes, and our tools, we‘re not measuring the right things. If we‘re focused on how much it costs per bum in seat per hour, we‘re missing the point. We should be measuring the  impact  of our learning.  It‘s about whether we‘re decreasing sales times, increasing sales success, solving problems faster, raising customer satisfaction.  If we look at what we‘re trying to impact, then we‘re going to check to see if our approaches are working, and we‘ll get to more effective methods.  We‘ve got to cut through the haze and smoke (open up what window, sucker, and let some air into this room), and start focusing with heightened awareness on moving some needles.

So there you have it.  Should we continue our wayward ways?

Why L&D?

17 December 2014 by Clark 3 Comments

One of the concerns I hear is whether L&D still has a role.  The litany is  that  they’re so far out of touch with their organization, and science, that it’s probably  better to let them die an unnatural death than to try to save them. The prevailing attitude of this extreme view is that the Enterprise Social Network is the natural successor to the LMS, and it’s going to come from operations or IT rather than L&D.  And, given that I’m on record suggesting that we revolutionize L&D rather than ignoring it, it makes sense to justify why.  And while I’ve had other arguments, a really good argument comes from my thesis advisor, Don Norman.

Don’s on a new mission, something he calls DesignX, which is scaling up design processes to deal with “complex socio-technological systems”.   And he recently wrote an article about  why  DesignX that put out a good case why L&D as well.  Before I get there, however, I want to point out two other facets  of his argument.

The first is that often design has to go  beyond science. That is, while you use science when you can, when you can’t you use theory inferences,  intuition, and more to fill in the gaps, which you hope  you’ll find out later (based upon later science, or your own data) was the right choice.  I’ve often had to do this in my designs, where, for instance, I think research hasn’t gone quite far enough in understanding engagement.  I’m not in a research position as of now, so I can’t do the research myself, but I continue to look at what can be useful.  And this is true of moving L&D forward. While we have some good directions and examples, we’re still ahead of documented research.  He points out that system science and service thinking are science based, but suggests design needs to come in beyond those approaches.   To the extent L&D can, it should draw from science, but also theory and keep moving forward regardless.

His other important point is, to me, that he is talking about systems.  He points out that design  as a craft  works well on simple areas, but where he wants to scale design is to the level of systemic solutions.  A noble goal, and here too I think this is an approach  L&D needs to consider as well.  We have to go beyond point solutions – training, job aids, etc – to performance ecosystems, and this won’t come without a different mindset.

Perhaps the most interesting one, the one that triggered this post, however, was a point on why designers are needed.  His point is that others have focuses on efficiency and effectiveness, but he  argued that  designers have empathy for the users as well.  And I think this is really important.  As I used to say the budding software engineers I was teaching interface design to: “don’t trust your intuition, you don’t think like normal people”.  And similarly, the reason I want L&D in the equation is that they (should) be the ones who really understand how we think, work, and learn, and consequently they should be the ones facilitating performance and development. It takes an empathy with users to facilitate them through change, to help them deal with fears and anxieties dealing with new systems, to understand what a good learning culture is and help foster it.

Who else would you want to be guiding an organization in achieving effectiveness in a humane way?   So Don’s provided, to me, a good point on why we might still want L&D (well, P&D really ;)  in the organization. Well, as long as they also addressing the bigger picture and not just pushing info dump and knowledge test.  Does this make sense to you?

#itashare #revolutionizelnd

Challenges in engaging learning

16 December 2014 by Clark 2 Comments

I’ve been working on moving a team to deeper learning design.  The goal is to practice what I preach, and make sure that the learning design is competency-aligned, activity-based, and model-driven.  Yet, doing it in a pragmatic way.

And this hasn’t been without it’s challenges.  I  presented to the team my vision, we worked out a process, and started coaching the team during development.  In retrospect, this wasn’t proactive enough.  There were a few other hiccups.

We’re currently engaged in a much tighter cycle of development and revision, and now feel we’re getting close to the level of effectiveness  and  engagement we need.  Whether a) it’s really better, and b) whether we can replicate it yet scale it as well is an open question.

At core are a few elements. For one, a rabid focus on what learners are  doing is key.  What do they need to be able to do, and what contexts do they need to do it in?

The competency-alignment focus is on the key tasks that they have to do in the workplace, and making sure we’re preparing them across pre-class, in-class, and post-class activities to develop that ability.  A key focus is having them make the decision in the learning experience that they’ll have to make afterward.

I’m also pushing very hard on making sure that there are models behind the decisions.  I’m trying hard to avoid arbitrary categorizations, and find the principles that drove those categorizations.

Note that all this is  not easy.  Getting the models is hard when the resources  provided don’t include that information.  Avoiding presenting just knowledge and definitions is hard work.  The tools we use make certain interactions easy, and other ones not so easy.  We have to map meaningful decisions into what the tools support.  We end up making  tradeoffs, as do we all.  It’s good, but not as good as it could be.  We’ll get better, but we do want to run in a practical fashion as well.

There are more elements to weave in: layering on some general biz skills is embryonic.  Our use of examples needs to get more systematic.  As does our alignment of learning goal to practice activity.    And we’re struggling to have a slightly less didactic and earnest tone;  I haven’t worked hard enough on pushing a bit of humor in, tho’ we are ramping up some exaggeration.  There’s only so much you can focus on at one time.

We’ll be running some student tests next week before presenting to the founder.  Feeling mildly confident that we’ve gotten a decent take on quality learning design with suitable production value, but there is the barrier that the nuances of learning design are  subtle. Fingers crossed.

I still believe that, with practice, this becomes habit and easier.  We’ll see.

Getting Models

4 December 2014 by Clark 2 Comments

In trying to shift from a traditional elearning approach to a more enlightened one, a deeper one, you are really talking about viewing things differently, which is non-trivial. And then, even if you know you want to do better, you still need some associated skills. Take, for example, models.

I’ve argued before that models are a better basis for action, for making better decisions.  Arbitrary knowledge is hard to recollect, and consequently brittle.  We need a coherent foundation upon which to base foundations, and arbitrary information doesn’t help.  If I see a ‘click to learn more’, for instance, I have good clue  that someone’s presenting arbitrary information.  However, as I concluded in the models article, “It‘s not always there, nor even easily inferable.”  Which is a problem that I’ve been wrestling with.  So here’re my interim thoughts.

Others have counseled that not just any Subject Matter Expert (SME) will do.  They may be able to teach material with their stories and experience, and they can certainly do the work, but they may not have a conscious model that’s available to guide novices.  So I’ve head that you have to find one capable. If you don’t, and you don’t have good source material, you’re going to have to do the work yourself.  You might be able to find one in a helpful place like Wikipedia (and please join us  in donating to help keep it going, would you please?), but otherwise you’re going to have to do the hard yards.

Say  you’re wrestling with a list of things, like attacks on networks, or impacts on blood pressure.  There is a laundry list of them, and there may seem to be no central order.  So what do you do?  Well, in these cases where I don’t have one, I make one.

For instance, in attacks on networks, it seems that the inherent structure of the network provides an overarching framework for vulnerabilities.  Networks can be attacked digitally through  password cracking or software vulnerabilities.  The data streams could also be hacked either physically connecting to wires or intercepting wireless signals.  Socially, you can trick people into doing wrong things too.  Similarly with blood pressure, the nature of the system tells us that constricted or less flexible vessels (e.g. from aging) will increase blood pressure. Decreased volume in the system will decrease, etc.

The point is, I’m using the inherent structure to provide a framework that wasn’t given. Is it more than the minimum?  Yes.  But I’ll argue that if you want the information to be available when necessary, or rather that learners will be able to make the right decisions, this is the most valuable thing you can do. And it might take less effort overall, as you can teach the model and support making good inferences more efficiently than teaching all the use cases.

And is this a sufficient approach?  I can’t say that; I haven’t spent enough time on other content. So at this point treat it like a heuristic.  However, it gives you something you can at least take to a SME and have them critique and improve it (which is easier than trying to extract a model whole-cloth ;).

Now there might also be the case that there just isn’t an organizing principle (I’m willing to concede that, for now…). Then, you may  need simply to ask your learners  to do some meaningful processing on the material.  Look, if you’re presenting it, then you’re expecting them to remember it. Presenting arbitrary information isn’t going to do that. If they need to remember it, have them process it.  Otherwise, why present it at all?

Now, this is only necessary when you’re trying to do formal learning; it might be that you don’t have to get it in folks heads and can put it in the world. Do it if you can.   But I believe that what will make a bigger difference for learners, for  performers, will be the ability to make better decisions. And, in our increasingly turbulent times that will come from models, not rote information.  So please, if you’re doing formal learning, do it right, and get the models you need. Beg, borrow, steal, or make, but get them.  Please?

Transformative Experiences

25 November 2014 by Clark 1 Comment

I’ve had the pleasure last  week of keynoting  Charles Sturt University’s annual Education conference.  They’re in the process of rethinking what their learning experience should be, and I talked about the changes we’re trying to make at the Wadhwani Foundation.

I was reminded of previous conversations about learning experience design and the transformative experience.   And I have argued in the past that what would make an optimal value proposition (yes, I used that phrase) in a learning market would be to offer a transformative learning experience.  Note that this is not just about the formal learning experience, but has two additional components.

Now, it does start with a killer learning experience.  That is, activity-based,  competency-driven, model-guided, with lean and compelling content.  Learners need role-plays and simulations to be immersed in practice, and scaffolded with reflection to develop their flexible ability to apply these abilities going forward.  But wait, there’s more!

As a complement, there needs to be a focus on developing the learner as well as their skills. That is, layering on the 21st Century skills: the ability to communicate, lead, problem-solve, analyze, learn, and more.  These need to be included and developed  across the learning experience.  So learners not only get the skills they need to succeed now, but to adapt as things change.

The third element is to be a partner in their success.  That is, don’t give them a chance to sink or swim on the basis of the content, but to look for ways in which learners might be struggling with other issues, and work hard to ensure they succeed.

I reckon that anyone capable of developing and delivering on this model provides a model that others can only emulate, not improve upon.  We’re working on the first two initially at the Foundation, and hopefully we’ll get to the latter soon.  But I reckon it’d be great if this were the model all were aspiring to.  Here’s hoping!

 

 

Learning Problem-solving

11 November 2014 by Clark Leave a Comment

While I loved his presentation, his advocacy for science, and his style, I had a problem with one thing Neil deGrasse Tyson said during his talk. Now, he’s working on getting deeper into learning, but this wasn’t off the cuff, this was his presentation (and he says he doesn’t say things publicly until he’s ready). So while it may be that he skipped the details, I can’t. (He’s an astrophysicist, I’m the cognitive engineer ;)

His statement, as I recall and mapped,  said that math wires brains to solve problems. And yes,  with two caveats.  There’s an old canard that they used to teach Latin because it taught you how to think, and it actually didn’t work that way. The ability to learn Latin taught you Latin, but not how to think or learn, unless something else happened.   Having Latin isn’t a bad thing, but it’s not obviously a part of a modern curriculum.

Similarly, doing math problems isn’t necessarily going to teach you how to do more general problem-solving.  Particularly doing the type of abstract math problems that are the basis of No Child Left Untested, er Behind.  What you’ll learn is how to do abstract math problems, which isn’t part of most job descriptions these days.  Now, if you want to learn to solve meaningful math problems, you have to be given meaningful math problems, as the late David Jonassen told us.  And the feedback has to include the problem-solving process, not just the math!

Moreover, if you want to generalize to other problem-solving, like science or engineering, you need explicit scaffolding to reflect on the process and the generality across domains.  So you  need  some problem-solving in other domains to abstract and generalize across.  Otherwise, you’ll get good at solving real world math problems, which is necessary but not sufficient.  I remember my child’s 2nd grade teacher who was talking about the process they emphasized for writing  –  draft, get feedback, review, refine – and I pointed out that was good for other domains as well: math, drawing, etc.  I saw the light go on.  And that’s the point, generalizing is valuable  in learning, and facilitating that generalization is valuable in teaching.

I laud the efforts to help folks understand why math and science are important, but you can’t let people go away thinking that doing abstract math problems is a valuable activity.  Let’s get the details right, and really accelerate our outcomes.

#DevLearn 14 Reflections

5 November 2014 by Clark 1 Comment

This past week I was at the always great DevLearn conference, the biggest and arguably best yet.  There were some hiccups in my attendance, as  several blocks of time were taken up with various commitments both work and personal, so for instance I didn’t really get a chance to peruse the expo at all.  Yet I attended keynotes and sessions, as well as presenting, and hobnobbed with folks both familiar and new.

The keynotes were arguably even better than before, and a high bar had already been set.

Neil deGrasse Tyson was eloquent and passionate about the need for science and the lack of match between school and life.    I had a quibble about his statement that doing math teaches problem-solving, as it takes the right type of problems (and Common Core is a step in the right direction)  and  it takes explicit scaffolding.  Still, his message was powerful and well-communicated. He also made an unexpected connection between Women’s Liberation and the decline of school quality that I hadn’t considered.

Beau Lotto also spoke, linking how our past experience alters our perception to necessary changes in learning.  While I was familiar with the beginning point of perception (a fundamental part of cognitive science, my doctoral field), he took it in very interesting and useful direction in an engaging and inspiring way.  His take-home message: teach not how to see but how to look, was succinct and apt.

Finally, Belinda Parmar took on the challenge of women in technology, and documented how  small changes can  make a big difference. Given the madness of #gamergate, the discussion was a useful reminder of inequity in many fields and for many.  She left lots of time to have a meaningful discussion about the issues, a nice touch.

Owing to the commitments both personal and speaking, I didn’t get to see many sessions. I had the usual situation of  good ones, and a not-so-good one (though I admit my criteria is kind of high).  I like that the Guild balances known speakers and topics with taking some chances on both.  I also note that most of the known speakers are those folks I respect that continue to think ahead and bring new perspectives, even if in a track representing their work.  As a consequence, the overall quality is always very high.

And the associated events continue to improve.  The DemoFest was almost too big this year, so many examples that it’s hard to start looking at them as you want to be fair and see all but it’s just too monumental. Of course, the Guild had a guide that grouped them, so you could drill down into the ones you wanted to see.  The expo reception was a success as well, and the various snack breaks suited the opportunity to mingle.  I kept missing the ice cream, but perhaps that’s for the best.

I was pleased to have the biggest turnout yet for a workshop, and take the interest in elearning strategy as an indicator that the revolution is taking hold.  The attendees were faced with the breadth of things to consider across advanced ID, performance support, eCommunity, backend integration, decoupled delivery, and then were led through the process of identifying elements and steps in the strategy.  The informal feedback was that, while daunted by the scope, they were excited by the potential and recognizing the need to begin.  The fact that the Guild is holding the Learning Ecosystem conference and their release of a new and quite good white paper by Marc Rosenberg and Steve Foreman are further evidence that awareness is growing.   Marc and Steve carve up the world a little differently than I do, but we say similar things about what’s important.

I am also pleased that  Mobile  interest continues to grow, as evidenced by the large audience at our mobile panel, where I was joined by other mLearnCon advisory board members Robert Gadd, Sarah Gilbert, and Chad Udell.  They provide nicely differing  viewpoints, with Sarah representing the irreverent designer, Robert the pragmatic systems perspective, and Chad the advanced technology view, to complement my more  conceptual approach.  We largely agree, but represent different ways of communicating and thinking about the topic. (Sarah and I will be joined by Nick Floro for ATD’s mLearnNow event in New Orleans next week).

I also talked about trying to change the pedagogy of elearning in the Wadhwani Foundation, the approach we’re taking and the challenges we face.  The goal I’m involved in is job skilling, and consequently there’s a real need and a real opportunity.  What I’m fighting for is to make meaningful practice as a way to achieve real outcomes.  We have some positive steps and some missteps, but I think we have the chance  to have a real impact. It’s a work in progress, and fingers crossed.

So what did I learn?  The good news is that the audience is getting smarter, wanting more depth in their approaches and breadth in what they address. The bad news appears to be that the view of ‘information dump & knowledge test = learning’ is still all too prevalent. We’re making progress, but too slowly (ok, so perhaps patience isn’t my strong suit ;).  If you haven’t, please do check out the Serious eLearning Manifesto to get some guidance about what I’m talking about (with my colleagues Michael Allen, Julie Dirksen, and Will Thalheimer).  And now there’s an app for that!

If you want to get your mind around the forefront of learning technology, at least in the organizational space, DevLearn is the place to be.

 

Cognitive prostheses

28 October 2014 by Clark 2 Comments

While our cognitive architecture has incredible capabilities (how else could we come up with advances such as Mystery Science Theater 3000?), it also has limitations. The same adaptive capabilities that let us cope with information overload in both familiar and new ways also lead to some systematic flaws. And it led me to think about the ways in which we support these limitations, as they have implications for designing solutions for our organizations.

The first limit is at the sensory level. Our mind actually processes pretty much all the visual and auditory sensory data that arrives, but it disappears pretty quickly (within milliseconds) except for what we attend to. Basically, your brain fills in the rest (which leaves open the opportunity to make mistakes). What do we do? We’ve created tools that allow us to capture things accurately: cameras and microphones with audio recording. This allows us to capture the context exactly, not as our memory reconstructs it.

A second limitation is our ‘working’ memory. We can’t hold too much in mind at one time. We ‘chunk’ information together as we learn it, and can then hold more total information at one time. Also, the format of working memory largely is ‘verbal’. Consequently, using tools like diagramming, outlines, or mindmaps add structure to our knowledge and support our ability to work on it.

Another limitation to our working memory is that it doesn’t support complex calculations, with many intermediate steps. Consequently we need ways to deal with this. External representations (as above), such as recording intermediate steps, works, but we can also build tools that offload that process, such as calculators. Wizards, or interactive dialog tools, are another form of a calculator.

Processing information in short term memory can lead to it being retained in long term memory. Here the storage is almost unlimited in time and scope, but it is hard to get in there, and isn’t remembered exactly, but instead by meaning. Consequently, models are a better learning strategy than rote learning. But external sources like the ability to look up or search for information is far better than trying to get it in the head.

Similarly, external support for when we do have to do things by rote is a good idea. So, support for process is useful and the reason why checklists have been a ubiquitous and useful way to get more accurate execution.

In execution, we have a few flaws too. We’re heavily biased to solve new problems in the ways we’ve solved previous problems (even if that’s not the best approach. We’re also likely to use tools in familiar ways and miss new ways to use tools to solve problems. There are ways to prompt lateral thinking at appropriate times, and we can both make access to such support available, and even trigger same if we’ve contextual clues.

We’re also biased to prematurely converge on an answer (intuition) rather than seek to challenge our findings. Access to data and support for capturing and invoking alternative ways of thinking are more likely to prevent such mistakes.

Overall, our use of more formal logical thinking fatigues quickly. Scaffolding help like the above decreases the likelihood of a mistake and increases the likelihood of an optimal outcome.

When you look at performance gaps, you should look to such approaches first, and look to putting information in the head last. This more closely aligns our support efforts with how our brains really think, work, and learn. This isn’t a complete list, I’m sure, but it’s a useful beginning.

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