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Clark Quinn’s Learnings about Learning

Formal Learning is (or should be) Expensive!

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It’s becoming clear to me that we’re making a big mistake in our thinking. We seem to think that formal learning is relatively cost-effective, and may even think that performance support and social are more costly.   Yet we need to realize that formal learning is likely our most costly approach!

To start with, we should be doing sufficient analysis to ensure that the need is indeed a skill shift. If it’s an information problem, it should be solved with a job aid. Courses are more expensive.  And we need to take the time that the skill shift really is needed; it’s not a motivation problem or some other problem. In other words, we need to take the time to identify what business problem this is solving that a course will affect, and the associated metric.  That takes time.

Then we need to design an intervention that will address that skill shift: we need to determine what the change in the workplace behavior needs to be to impact that metric, and then design an objective that reflects that needed behavior change.  This is not trivial: a poorly formed objective about knowledge, not behavior, isn’t going to have an impact on the business.

Then, to do formal learning well, you need appropriate and sufficient practice.   That takes time to design properly, ideally with scenarios or simulation-driven interactions.  And the practice needs to be aligned with the learner; it has to be meaningful to them.  Enough of them. This takes time.

Then we need to create an appropriate model to guide their behavior, and introduce it appropriately. And find meaningful examples that illustrate the concept being applied in context, across sufficient contexts.  This takes time, though no more time (once you determine a course is the answer) than other learning design once you get experienced in this more advanced way of designing.  And it takes development resources.

And, of course, if you’re not doing the above, why are you bothering? It’s not going to hit the mark.  We don’t, frankly, and to the extent we don’t, we undermine the  likelihood  that our interventions will have the desired impact.  The point being that courses should not be our first line of defense!

Rapid elearning is cheap and fast, but it’s not going to have any impact.  Most of what we do doesn’t have any impact. If we want to have impact, we have to do it right, and that’s not a cheap proposition.  We need to worry about measuring more than cost/bum, and worry about hitting the business goal.  Then we can truly determine whether we should go this route, rather than another.  But, seriously, you shouldn’t be throwing formal learning at a problem unless you’re willing to do it right. There are times it  will be the right answer, but right now we’re throwing too much money away.  Let’s stop, and do it right  when  it’s right.  And that will be both expensive  and worth it.

 

Designing Learning is a Probabilistic Exercise

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At the Guild’s recent mLearnCon, I was having a conversation in which I was reminded that designing performance interventions is a probabilistic exercise, and it occurred to me that we’re often not up front enough about it.  And we need to be.

When we start our design process with a performance vision, we have an idea of what ideal performance would be.  However, we make some assumptions about the performer and their ability to have comprehended our learning interventions and any resources we design or are available in the context.  We figure our learning was successful (if we’re doing it right, we’re  not letting them out of our mitts until they’ve demonstrated the ability to reliably perform what we need), and we figure our performance solutions are optimal and useful (and, again, we should test until we know).  But there are other mitigating factors.

In the tragic plane crash in the Tenerife’s – and pilots train as much as anybody – miscommunication (and status) got in the way.  Other factors like distraction, debilitating substances, sleep deprivation and the like can also affect performance.  Moreover, there’s some randomness in our architecture, basically.  We don’t do everything perfectly all the time.

But more importantly, beyond the actual performance, there’s a probability involved in our learning interventions.   Most of our research based results  raise the likelihood of the intervention affecting the outcome.  Starting with meaningful objectives, using model-based concepts, contextualized examples, meaningful practice, all that increases the probability.

This holds true with performance support, coaching/mentoring, and more.  Look, humans don’t have the predictable properties of concrete or steel.  We are much more complex, and consequently variable. That’s why I went from calling it cognitive engineering to cognitive design. And we need to be up front about it.

The best thing to do is use the very best solutions to hand; just as we over-engineer bridges to ensure stability (and, as Henry Petrovski points out in To Engineer is Human, on subsequent projects we’ll relax constraints until ultimately we get failure), we need to over-design our learning.  We’ve gotten slacker and slacker, but if it’s important (and, frankly, why else are we bothering), we need to do the right job.  And tarting up learning with production values isn’t the same thing.  It’s easier, since we just do it instead of having to test and refine, but it’s unlikely to lead to any worthwhile outcomes.

As I’ve argued before, better design doesn’t take longer, but there is a learning curve. Get over the curve, and start increasing the likelihood that your learning will have the impact you intend.

Defining Mobile

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At the recent Guild mLearnCon  mobile learning event, I had a thought that seems to answer a long time debate.  The debate centers on the definition of a mobile device.  The feature/smart phone is obviously a candidate, and tablets seem pretty clearly included too, but the ongoing issue has been whether a laptop counts. And I may have finally discovered a way of looking at it that answers the question.

Eschewing the more abstract and academic definitions, the one that has most resonated with me has been Judy Brown’s.  As I recall it, her characteristics are:

  • small enough to fit in a pocket or purse,
  • you’re familiar with it,
  • instant on,
  • and a battery that will last all day.

And this has been pretty good, because most laptops don’t fit the latter criteria, their batteries didn’t used to be able to go all day.  However, this is a characteristic-based definition, e.g. about inherent properties of the device, and this can change. The new MacBook Airs, for instance, now have a battery that will last all day. And, even if the 13″ is too big, the 11″ or some other might soon fit the criteria. That is, we’re hitting a moving target.

What struck me the other day, however, was looking at it not from inherent properties of the devices, but from usage affordance, i.e. how one uses the device.  Because it struck me: to me, it’s not really a mobile device unless you can use it with two hands, standing up or in motion. More importantly, it has to be a natural usage: holding up a netbook with one hand and hunt-and-peck with one hand doesn’t qualify.  In short, if you can’t use it with two hands while moving, it’s not really mobile.

This strikes me as a way that will inherently allow new devices and new capabilities, yet still clearly distinguish what’s mobile and what’s not.  So, for instance, devices with keyboard that turns around and becomes a tablet?  A tablet’s mobile: hold with one hand, touch with the other.  A two-handed keyboard is not. Will this fall apart?  Probably, as the ultimate mobile test is whether it’s a device that goes with you  everywhere: to the market, to a party, even to the bathroom.  And some may be able to, but which ones really  do? Regularly?  If I had a small enough tablet, or iPod touch, probably, but the phone, yes!  However, in some contexts, e.g. work, a tablet might go with me to all my work contexts, and then it qualifies  if it meets the criteria: of being able to used  naturally,  standing up.

This, to me, seems to provide a better criteria, at least for now.  What say you?

 

Chuck Martin #mLearnCon Keynote Mindmap

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Chuck Martin gave a lively and valuable keynote at #mLearnCon, with stats on mobile growth, and then his key components of what he thinks will be driving mobile.  He illustrated his points with funny and somewhat scary videos of how companies are taking advantage of mobile.

MartinKeynoteMindmap

Christopher Pirie #mlearncon Keynote Mindmap

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Christopher Pirie opened the eLearning Guild’s mLearnCon mobile learning conference with a fair overview of technology for learning.  He talked about the usual trends, and pointed to some interesting game apps for learning.  Kodu, in particular, is an interesting advancement on things like Scratch and StageCast’s Creator.

I was somewhat surprised by his pointer to Bloom as the turning point to modern learning design, as I’d be inclined to point more to Collins & Brown’s Cognitive Apprenticeship. I also think he should take a look at Donald Clark’s criticisms of Mitra’s Hole in the Wall.  Finally, the characterization between the overhead projector as characteristic of 1991 and the Kinect for 2012 is a bit spurious: in 1991 we also had HyperCard, and in 2012 I don’t see the Kinect in many classrooms yet, but his point is apt about the potential for change we have at our fingertips.

Overall, a nice kickoff for the conference.

PirieKeynoteMindmap

What I wish I’d learned in school

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I was asked, somewhat out of the blue, what I wish I’d learned in school, and I thought it an interesting question. I also thought it worth putting out to a slightly broader audience.

So, here’re some off-the-cuff thoughts about what I wish I’d learned in school:

  • meta-cognitive and meta-learning skills (e.g. the SCANS competencies beyond the basic skills)
  • that it is ok to fail, and that persistence and effort is as much a part of learning as achievement
  • that your epistemological beliefs – what you believe learning is – affect your outcomes
  • how to work in groups on projects
  • about design, and the value of feedback and revision
  • how reflection on process is as important as reflection on product

These are just a few thoughts, but it’s a start. And let me ask what you wish you’d learned in school?

Technology Architecture

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A few years ago, I created a diagram to capture a bit about the technology to support learning (Big ‘L’ Learning).  I was revisiting that diagram for some writing I’m doing, and thought it needed updating.  The point is to characterize the relationship between underpinning infrastructure and mechanisms to support availability for formal and informal learning.

TechNToolsHere’s the accompanying description:  As a reference framework, we can think of a hierarchy of levels of tools.   At the bottom is the hardware, running an operating system and connecting to networks.   Above that are applications that deliver core services. We start with the content management systems, from the delivery perspective, which maintains media assets.   Above that we have the aggregation of those assets into content, whether full learning consisting of introductions, concepts, examples, practice items, all the way to the summary, or user-generated content via a variety of tools.   These are served up via delivery channels and managed, whether through webinars, courses, or simulations through a learning management system (LMS) on the formal learning side, or self-managed through social media and portals on the informal learning side. Ultimately, these activities can or will be tracked through standards such as SCORM for formal learning or the new experience API (xAPI) for informal learning.

I add, as a caveat: Note that this is merely indicative, and there are other approaches possible. For instance, this doesn‘t represent authoring tools for aggregating media assets into content. Similarly, individual implementations may not have differing choices, such as not utilizing an independent content management system underpinning the media asset and content development.

So, my question to you is, does this make sense?  Does this diagram capture the technology infrastructure for learning you are familiar with?

Evidence-based Design

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In my last post, I asserted that we need evidence-based design for what we do.  There are a number of sources for same. Of course, you could go do a Master’s or Ph.D. in cognition and learning, but there are shorter paths.

There are several good books out (and I believer that there is at least one more on the way) that summarize the implications of research design. Ruth Clark has been a co-author on a couple, eLearning and the Science of Instruction, and the subsequent Efficiency in Learning.  Julie Dirksen’s Design for How People Learn is another good one. Michael Allen’s work on design is also recommended, e.g. Guide to eLearning.

Will Thalheimer, Ruth, and Julie regularly write and talk about these things in other forums than books.  Go listen to them!  I try as well, though often filtered through games, mobile, or elsewhere.  There’re others, too.

A number of people run workshops on deeper design. I know I have one, and I’m sure others have them as well. Do try to make sure that it covers both cognitive and emotional elements, focusing on meaningful change.

There are gaps: there isn’t all the research we need, or at least not digested.  The role of emotional engagement isn’t as well fleshed out as we’d like, and some of the research is frankly focused on studies too small to give practical guidelines (c.f. the consternation on serious game design that surrounded a recent post).  Where we don’t have research, we have to make inferences from theoretical frameworks, but you should know those too.  It’s better than going on ‘intuition’ or folk science.

Still, there’s no excuse to do un-engaging, over-written, and under-practiced learning.  Better design doesn’t take longer (with the caveat that there’s some initial hiccup ’til we make the change).  We have the knowledge, and the tools aren’t the barrier.  Let’s do better, please!

No Folk Science-Based Design!

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When we have to act in the world, make decisions, there are a lot of bases we use.  Often wrongly.  And we need to call it out and move on.

As I pointed out before, Kahneman tells us how we often make decisions on less than expert reflection, more so when we’re tired, and create stories about why we do it. If we’re not experts, we shouldn’t trust our ‘gut’, but we do.  And we will use received wisdom, rightly or wrongly, to justify our choices.  Yet sometimes the beliefs we have about how things work are wrong.

While there’s a lot of folk science around that’s detrimental to society and more, I want to focus on folk science that undermines our ability to assist people in achieving their goals, supporting learning and performance.  Frankly, there are a lot of persistent myths that are used to justify design decisions that are just wrong.  Dr. Will Thalheimer, for instance, has soundly disabused Dale’s Cone.  Yet the claims continue. There’re more: learning styles, digital natives, I could go on.  They are  not sound bases for learning design!

It goes on: much of what poses under ‘brain-based’ learning, that any interaction is good, that high production values equal deep design, that knowledge dump and test equals learning.  Folks, if you don’t  know, don’t believe it.  You  have  to do better!

Sure, some of it’s compelling.  Yes, learners do differ.  That doesn’t mean a) that there are valid instruments to assess those differences, or more importantly b) that  you should teach them differently.  Use the best learning principles, regardless!   And using the year someone’s born to characterize them really is pretty coarse; it almost seems like discrimination.

Look, intuition is fine in lieu of any better alternative, but when it comes to designing solutions that your organization depends on, doing anything less than science-based design is frankly fraudulent.  It’s time for evidence-based design!

How I Work

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David Kelly posted the following:

Lifehacker has a series called “How I Work. Every Wednesday they feature a new guest and the gadgets, apps, tips, and tricks that keep them going. It‘s a very interesting series that gives you a glimpse into how different people work and solve problems.

After recently seeing  Daniel Pink‘s interview  some colleagues and I thought it would be interesting to answer these questions as well as a fun way to share and get to know each other better.  I invite you to participate as well – I‘ll link other people‘s postings at the bottom of this post.

I decided to join in:

Location

Walnut Creek, CA

Current Gig

Executive Director of Quinnovation and Senior Director of Interaction & Mobile for the Internet Time Alliance.

Current mobile device

iPhone 4 & (original) iPad

Current computer

MacBookPro 13″ (w/ Apple Monitor)

One word that best describes how you work

Interruptedly (and, yes, I made that word up)

What apps/software/tools can’t you live without?

Looking at what’s open or has been recently: Safari, TweetDeck, Mail,  Skype, Reminders, iCal,  Word, Keynote, Notes, OmniGraffle, & OmniOutliner.

I keep up with what’s new with Safari and TweetDeck, maintain communication channels with Mail and Skype, keep myself organized with iCal and Reminders, write with Word and Notes, plan and present with Keynote, and think things through with OmniGraffle and OmniOutliner.

QuinnovationWorldHeadquartersWhat’s your workspace like?

Somewhat compact and crowded.  I moved from a bigger desk to our smallest room to accommodate the changing needs of our kids.  The room also houses a couch that becomes a bed for guests, and some shelves, so there’s not a lot of space. It’s organized for efficiency and effectiveness, not aesthetics.

What’s your best time-saving trick?

To put things into my calendar or my reminder list  now!

What’s your favorite to-list manager?

I struggled after losing Palm Desktop, but finally have settled on Reminders (Apple’s tool), as it synchs across devices seamlessly.

Besides your phone and computer, what gadget can’t you live without?

Definitely my iPad.  It replaces computer on many trips, and serves as a content and interactive device at times when I’m in more leisure than on the go.  If that’s cheating, it’d be a pocket tool kit: usually the Coast micro-tool, or Swiss-Tech Micro-Tech when traveling (no blade). Always need a file, screwdriver, …

What everyday thing are you better at than anyone else?

I’d like to say diagramming, representing models, but I don’t know if that’s ‘everyday’. If not, I’d say taking what ever’s left over in the fridge and making a real meal out of it.

What do you listen to while you work?

Not bloody much.  I can’t listen to most music while working, as the lyrics interfere with my thinking.

Are you more of an introvert or an extrovert?

I’m definitely an introvert, but I’m also a ham (a nice tension, eh?).  So I don’t mind being on stage, but as soon as I’m off I go back to ‘I wonder if someone will talk to me’, and get drained when I’m around too many people.  I work best in small groups.

What’s your sleep routine like?

I work hard to get a regular eight hours,  having read the research. So it’s usually  to bed sometime between 10 and 11, and the house wakes up around 6.  Travel wreaks havoc with that, but caffeine helps.

Fill in the blank. I’d love to see _______ answer these same questions.

Alan Kay or John Seely Brown

What’s the best advice you’ve ever received?

To be myself.