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Clark Quinn’s Learnings about Learning

Reflecting socially

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About ten years ago, now, Jay Cross and I met and with some other colleagues, started what we called the Meta-Learning Lab. We’ve maintained our interest in meta-learning across our involvement now with the Internet Time Alliance, and a component we identified as one of the most valuable activities you can do is reflection.

We don’t mean just navel-gazing, of course, but instead we mean systematically stepping back and reviewing ongoing activity with a view towards looking for improvement. It’s baked into things like Watts Humphrey’s Personal Software Process, and without that level of rigor, it still has benefits.  Even more so if it’s shared.

So, blogging is one way of sharing your thoughts and getting feedback (as I do here).  The social processing  that happens when sharing is not just for formal learning, but for personal, self-directed learning as well.  Creating a representation of your understanding is valuable in and of itself, to make your thinking concrete, but sharing and getting feedback is even more powerful.

This isn’t just for individuals, of course, but also for teams.  If teams share their collective thinking (blogs again, or perhaps wikis), they can get feedback not just from each other but also from non-team individuals.  This improves the thinking.

And we can start using richer media than just text.  We can capture our understanding with images, audio or video, e.g. conducting interviews (you think differently creating a response to a deep question synchronously than asynchronously).  You can go out and create a video of something that communicates what you think.  You can even film a performance by the individual or team and bring it back for discussion. What a couple of high-tech firms have done, having outstanding performers talk about or perform on video, and adding their own reflections (‘directors notes’ versions), is really powerful for learners too.

Mobile gives us the capability to be more flexible in our communication capture and sharing, which decouples our thinking from the desktop.  We also may be able to review interactions in a social media system, messages and such, to reflect on our communication patterns and improve. And facilitating all this is, to me, one of the opportunities for the learning professional as we start a) expanding our responsibility for all performance, not just ‘training’, and b) start investing our efforts in proportion to the workplace impact (c.f. 70:20:10).

So, I encourage you to start reflecting personally, of course, but consider also reflecting socially, with your colleagues, teammates, and more.  Learning out loud is a key to moving forward faster and more effectively.

Contextualized Learning

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Recently, a colleague videotaped me responding to some questions about how mobile could change learning.  I find I riff a bit in such situations, and one of the ideas I had then is something I wanted to explore a little more. It had to do with context.

What we do, in so many of our formal learning designs, is create artificial contexts.  In face-to-face learning, we’ll do role-plays, and in online learning we’ll create simulations or games.  Now, this makes sense; you want to do practice away from real performance if the consequences are costly. Yet other times, e.g. after the learning experience, they end up performing (and, too often, before they’ve received sufficient practice because of time and money constraints as well as just bad habits).  A further opportunity is that out there in the real world, there may be some contexts that the learner comes across that may be relevant, and we could extend the learning experience.

Context-aware systems give us a chance to do something more here.  If you’re performing a task that’s related to some formal learning, your system could  be equipped to notice, and bring in some appropriate content.  This was the promise of electronic performance support systems, and we can now start doing it not just in custom-designed environments, but we can connect context clues to associated content with semantic rules.  So, if you’re in a coaching meeting, the system could prepare you beforehand, provide support during, and some reflective evaluation afterward.  Say, a checklist.

Similarly, we can notice the context of the learner and even if it’s not a performance situation, if there’s a meaningful connection (I didn’t want to use semantic again :), the system could provide some mention of the linkage, which reactivates and contextualizes the learning, making it more likely to be retained and transferred.

Mobile, of course, decouples this capability from the desktop, and increases the likelihood that the connection opportunities are capitalized on, and even the performance support model can be brought to bear.  The two necessary components are the context-awareness (done via GPS, calendar) and semantic linkages (done with tagging).  This is no longer rocket science, just a product of decent task analysis and content engineering.

I reckon it’s time that we can, and should, lift our game a little to start looking at more sophisticated support technologies. If improving performance matters…and it should.

Cognitive Task Analysis

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While I argue strongly for stepping away more frequently from formally structured learning, not least because we overuse it, there are times when it is crucial.  As naysayers of informal learning like to point out, you wouldn’t want your pilot or heart surgeon to have picked up the task by reading a book. When performance is critical, you really want to understand what the important elements are, whether to train them or  provide support.

A technique for doing that is Cognitive Task Analysis (CTA).  This is not a shortcut, it’s deep in terms of the knowledge elicitation techniques, the analytical task, and the representation of results.  Based in decades of cognitive research, integrating work on mental models, expertise, and more, it provides a mechanism to try to unearth the tacit understanding experts hold. Because experts compile away their knowledge to the point that they no longer have access to it, it is hard to get at this knowledge, and it takes a rigorous process.

While useful for system design, CTA is also valuable for designing performance support, and training.  The deep elicitation process can derive what the task really is, and what should be in the learner’s head and what support can and should be available.  When I talk about the performance ecosystem, particularly for complex tasks, you want just this sort of support to determine what should be distributed across formal learning and performance support.

One of the problems with CTA is that there have been a number of different approaches, and they tend to be buried in academic papers or proprietary processes. The good news is that there’s now a book about CTA, Working Minds, by Beth Crandall, Gary Klein, & Robert Hoffman, academics and practitioners.  It boils down the divergence into a fairly reasonable set of steps, with techniques that can be used at each stage.  The bad news is, of course, that it still is a daunting read, with considerable depth.

If you’ve got performances that absolutely have to be right, you’ll want to do the analysis ala CTA, and use it to decide what really needs to be in training, checklists, etc.  This goes deeper than HPT even, tho’ I think it’s as weak when it comes to the benefits of social learning, but I reckon it’s for expert *performance*, not innovation. That’s another layer.  Still, a valuable tool in the quiver of supporting performance.

Please at least understand what CTA is, and know when you need it. You may not need to be an expert in it, but you should at least be aware.

Ownership versus ubiquity

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The notion that soon everything will be in the cloud, and we’ll just use an interface surface near us is not new.  The notion is that the technology will recognize you and present your environment, ready for you to accomplish your goals.  This is a nice idea, and I can see it working, but it’s not trivial.

Contrast this to the element that Judy Brown talks about as important component of mobile learning.  For her, mobile devices have to be something you’re familiar with and have with you all the time.  And that, to me, is the sticking point.

With an interface surface you come upon, would you necessarily recognize the different ways the interface would manifest?  You don’t want a big touchscreen (despite Minority Report  imaginings) for very complex work, because the research shows your arms fatigue too quickly. So you might have a keyboard on some devices.  And the variety could be high.  And, yes, it’s your interface, but with all the different possible form-factors, could you make it comprehensible?  And you’re still at the mercy of availability of surfaces (kinda like waiting in line for computers to check email at conferences has been).

Now, I can see having a mobile device and  then using an accessible interface that recognizes you by the device proximity, so you’re not stuck. And I can imagine that it would be possible to make a scalable interface (just not necessarily easy).  I do wonder, however, about some surfaces being so designed for aesthetics that the usability is compromised (c.f. The Design of Everyday Things).

And, particularly for my notion of slow learning  (which I need to augment with ubiquity and personalization – quick, I need a new phrase! :), the ability for a device to be with you may be required to do the teachable moment  thing.  That is, having a context-sensitive device right that at the appropriate place and time may be needed to really develop us in the ways we deserve.

So I don’t take that vision of ubiquitous computing surfaces at face value, I think that there are some reasons why mobile devices may still make sense.  Which isn’t to say there’s not a way, but I’m still holding out for something with  me.

Layering learning

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Electronic Performance Support Systems are a fabulous concept, as pioneered by Gloria Gery back in the early 90’s.  The notion is that as you use a system, and have entries or decisions to make, there are tools available that can provide guidance: proactively, intelligently, and context-appropriate.  Now, as I heard the complaint at the time, this would really be just good interface design, but the fact is that many times you have to retrofit assistance on top of a bad design for sad but understandable reasons.

The original were around desktop tasks, but the concept could easily be decoupled from the workplace via mobile devices.  One of my favorite examples is the GPS system: the device knows where you are, and where you want to go (because you told it), and it gives you step by step guidance, even recalculating if you make a change.  Everything from simple checklists to full adaptive help is possible, and I’ve led the design of such systems.

One of the ideas implicit in Gery’s vision, however, that I really don’t  see, is the possibility of having the system not only assist you in performing, but also help you learn. She talked about the idea in her book on the subject, though without elaborating how that would happen, but her examples didn’t really show it and I haven’t seen it in practice in the years since.  Yet the possibility is there.

I reckon it wouldn’t really take much. There is (or should be) a model guiding the decisions about what makes the right step, but that’s often hidden (in our learning as well).  Making that model visible, and showing how it guides the support and recommendations that are made, could be made available as a ‘veneer’ over the system. It wouldn’t have  to be visible, it could just be available at a click or as a preference for those who might want it.

Part of my vision of how to act in the world is to ‘learn out loud’. Well, I think our tools and products could be more explicit about the thinking that went into them, as well.  Many years ago, in HyperCard, you could just use buttons and field, but you could open them up and get deeper into them, going from fixed links to coded responses.  I have thought that a program or operating system could work similarly, having an initial appearance but capable of being explored and customized.  We do this in the real world, choosing how much about something we want to learn (and I still want everyone  who uses a word processor to learn about styles!) about something. Some things we pay someone else to do, other things we want to do ourselves. We learn about some parts of a program, and don’t know about others (it used to be joked that no one knows everything about Unix, I feel the same way about Microsoft Word).

We don’t do enough performance support as it is, but hopefully as we look into it, we consider the possible benefits of supporting the performance with some of the underlying thinking, and generating more comprehension with the associated benefits that brings. It’s good to reflect on learning, and seeing how thinking shapes performance both improves us and can improve our performance as well.

Goin’ Mobile

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This is a copy of an article I’ve written for a Wiley newsletter to promote my mlearning book.  

The indicators are clear: the world is going mobile.   Mobile subscriptions in the developed world are flattening out, not from lack of interest, but from saturation.   People are accessing the internet more from mobile devices than desktops, and some people  only  access the internet via mobile!   And when a small company like Google says that they‘re designing for mobile first and the desktop is an afterthought, it is safe to say mobile is on the move.

And the opportunities are huge.   Through the centuries, we have continually extended our physical capabilities with tools: we‘ve developed more capable clothing to let us go to further extremes, more powerful transportation that can let us travel thousands of miles in a matter of hours, tools that can let us work on the scale of mountains or of molecules.   We‘ve also used tools to augment our brains: books to serve as external memory, calculators to support our computational capability, phones to allow us to communicate at distances.   Digital technology has proven to be the ultimate cognitive augment, doing exactly the things that our brains don‘t do well, so together we‘re truly formidable problem-solvers. And now, we have that capability wherever and whenever we need it.   Which has important implications by itself, but there is more potential, too.

Don‘t be mislead by the label, mLearning is about so much more than courses on a phone.   In fact, that‘s almost contra-indicated.   What mobile learning really is about is  augmenting  formal learning, and augmenting performance regardless.   The old ‘event‘ model of learning really doesn‘t work very well, as our brains only can handle so much at a time. With mobile, however, we can extend that learning over time. And over space: we can turn the entire world into part of our learning environment, or to think about it another way, we can spread our learning environment over the world.   Beyond learning, we can bring specific support to wherever we are: accessing information to make our shopping more effective, our understanding deeper, our interactions richer. We can access information, support our decisions, and share our experiences.

But there‘s also something unique to mobile, beyond a pocketable desktop. As the devices get more capable, they begin to  know  where we are, even which way we‘re facing, and they can start adding unique information specific to our current context, location-aware.   We‘re just beginning to explore the possibilities, and you really do have to think differently to take advantage, but the potential is exciting.   Are you mobilizing?

Checklist Manifesto

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On the advice of Judy Brown, I picked up The Checklist Manifesto, and I have to say it’s a must-read.  This is a short, well-written, and mind-changing book.  Frankly, it ranks up there with Don Norman‘s Design of Everyday Things, and that’s saying a lot.

Atul Gawande is a medical doctor who’s also an eloquent writer.  In the course of his work he’s become interested in reducing errors, and has looked deeply into how to minimize them.  And he’s had the opportunity to put into practice and test his ideas, refining them until they work. This book documents his explorations, developing a thesis that he recognizes has applicability far beyond medicine.  And that’s important for us, if we care about improving outcomes both professional, personal, and societal.

He breaks up flaws in execution into those where we don’t have knowledge, and those where we make errors despite having the requisite knowledge.  And he explores eloquently how likely the latter are in the real world.  Demonstrably smart  and knowledgeable people, acting in complex situations, regularly make mistakes. Those who have heard me speak about how our minds work know that there’s some randomness built into our system. Frankly, we’re not really good at doing rote tasks.  He doesn’t go into the cognitive architecture, but rather documents it via stories and explanations of complexity.  And he develops a particular approach that is striking in it’s simplicity and powerful in it’s effects.

Not surprisingly, given the title, the solution are checklists.  He has two types, ones that help us execute those rote steps that are critical to success, and another that helps connect us at critical times.  He categorizes, in a way I find reminiscent of Van Merriënboer’s elegant task analysis in terms of the knowledge you need and the complex problems you apply it to, the benefits of both remembering those crucial but empirically overlooked steps and of having people build a quick rapport and share the critical information at critical times.  He illustrates with flight and large-scale architecture examples as well as medical,situations where performance literally is life-or-death.  The clear implication is that if it saves lives there, it can save dollars or more anywhere.

And, refreshingly, he admits you’re not going to get it right the first time, and you need to trial, iterate, and refine again. He recognizes that it must be quick, easy to use, and tuned for the context of use.  This is no quick fix, but it ends up providing small easy changes that actually save time as well as reduce error.  It’s really about performance support, and it’s not complex, and it can work.  It’s also a natural match to mobile delivery, which I’m sure is one of the reasons Judy pointed it out.

This short, eloquent book holds the power to make significant improvements in many fields.  I strongly recommend it.

Travels, travails, & thought

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In case you’ve been wondering about my relative paucity of posting, let me confess that I’ve been on the road almost non-stop for months.  Starting with the Australasian Talent Management conference in Sydney, through the Innovations in eLearning Symposium in DC, mLearnCon in San Jose, a visit to Saudi Arabia on behalf of a client, a long-delayed and deserved European tour with the family, a trip back to Australia on behalf of another client, speaking at the Distance Teaching & Learning Conference in Wisconsin, the Southwest Learning Summit in Dallas, and then a family trip to San Diego, I haven’t slept under my own roof for more than 2-3 consecutive nights (with one exception) since mid-May.

I’m not bragging or  looking for sympathy, I’m just explaining (also mentioning that  this is not usual for me). While it’s good, it’s also exhausting, and has really hampered my ability to do other than the associated work. Finally, however, I’m at home for several weeks before beginning another batch: Chicago for Learning 3.0 (an eLearning strategy workshop), Laguna Niguel for  the CLO Symposium (with Jay and Jane from the Internet Time Alliance), both in October, and then DevLearn in Lost Wages (running a mobile strategy workshop), DC for a local ASTD Chapter event (mobile talk & social workshop), and Toronto for CSTD’s annual conference in November.  At least I’m staying on the same continent this time, so the jet lag won’t be quite so bad!  And more time at home in-between.

On the way, I lost my new leather iPad cover when I left the iPad on the plane (got the iPad back, sans the cover ?!?!? *cough* United Airlines*cough*), had the usual debilitating experience with too-expensive data (ATT’s new rates notwithstanding), and didn’t always make the best luggage choices. On the other hand, I was able to resurrect a favorite rolling briefcase, got in a few surfs (so nice to find I can still do it!), generally exercised, ate very and mostly reasonably well, met some great people and had great conversations, and felt like I really was adding value by giving talks, running workshops, doing consulting, etc.  And the time with family was fabulous.

I also had time for some reflections, which I hope to populate here (wish I’d thought to capture them as they occurred, sigh).  One of the first ones is that mobile is more than just a technology, it’s also a lever. Under the mobile banner, we can discuss the whole performance ecosystem: formal learning, performance support, social learning, games, etc.  And  we get additional opportunities with context-sensitive learning. When Google is designing for mobile first, and tablets and smart-phones are on the rise, there’s an opportunity to shake things up.   It’s like the conclusion Barbara Means made on the report from SRI on the difference to face to face and online learning: “the observed  advantage of online learning  is a product of  redesigning the learning experience,  not of the medium per se”.  I think that’s what’s on tap with mobile, and I think we should be looking to pursue those opportunities.

Digital Helplessness(?)

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Recently, I’ve been hearing quite a bit of concern over the possibility that reliance on digital, and increasingly mobile, technology may make us stupider.  And I don’t think this is just easy to dismiss.  In a sense, it could be a case of learned helplessness, where folks find themselves helpless  because after using the tools, folks might not have the information they need when they don’t have the tools.

Recently announced research    shows that folks change what they remember when enabled with search engines: they don’t remember the data, but instead how to find it.  Which could be a problem if they needed to know the data and are not digitally enabled in some context.

As has also been conveyed to me as a concern is whether folks might not engage in learning about their environs (e.g. when traveling), and in other ways miss out on opportunities to learn when dependent on digital devices.  Certainly, as I’ve mentioned before, I’ve been concerned  about how disabled I feel when dissociated from my digital support (my external brain).  Yet is there a concern?

My take is that it might be a concern if people are doing it unconsciously.  I think you could miss out (as m’lady points out when I am reading instead of staring out the window every moment as we take the train through another country :) on some opportunities to learn.

On the other hand, if you are choosing consciously what you want to remember, and what you want to leave to the device, then I think you’re making a choice about how you allocate your resources (a ‘good thing’).  We do this in many ways in our lives already, for instance how much we choose to learn about cooking, and more directly related, how much to learn about how to do formatting in a word processing program.

Yes, I’ve been frustrated without my support when traveling, but that’s chosen (which does not undermine my dismay at the lack of ability to access digital data overseas).  I guess I’m arguing for chosen helplessness :).  So, what are you choosing to learn and what to devolve to resources?

A jot of design

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Ordinarily, I don’t even look at vendor products when offered free trials. I like to remain unbiased, and not give free advice.  I retain the right to look at what interests me, not what might be commercially expedient (a perverted legacy of my academic tenure, no doubt :).

However, two things interested me about this particular offer. First, it was an iPad app supporting design. Given that I’m very much about improving design, *and* quite into mobile, this was of interest. Second, I mistakenly thought it came from Michael Allen’s company Allen Interactions, and he’s not only been an early advocate of engaging design, but also he’s a supremely nice guy to complement his smarts. It turns out, of course, that I jumped too fast to a conclusion, and it’s really from Allen Communications.  Oh well.  I’m talking about DesignJot, btw.

Now, I’m not going to give a formal review, because instead I want to use this as an opportunity to reflect on supporting design.  Though you’ll likely get some idea of what it does and how.

Briefly, this app takes Allen Communications analysis and design process, using the acronym ANSWER, and provides support for using it.  You initiate a new project and then get support for design by having questions and even subtopics and questions under that rubric that you fill out for analysis. That information then populates some initial parts of the design support, which then guides you to define strategies and sub-components.  There are note-taking and sketching tools too.

The notion of supporting the design process is not new, certainly it was key in the toolset used by one of the major content developers in the past, and such performance support is a good idea.  Scaffolding process is an obvious outcome of how our brains work (systematic creativity is not an oxymoron), so the question becomes one of what process you are using as your guide. Without any guidance about ANSWER, I did a spot-check for one of my heuristics and it wasn’t in there. Overall, there seem to be some good and odd things.  Using someone else’s particular process may not be your cup of tea, and while you can add your own questions, youcan’t, as far as I could tell, add to the template.

There are some hiccups, e.g. I was surprised that some of the information isn’t carried forward, and some of the interface is a bit counterintuitive (e.g. home button sort of to the right but close to the middle). On the other hand, there are handy tips for many if not all of the steps.

The choice of making it an iPad app is interesting and understandable.  It certainly makes it easy to carry around as you talk to SMEs, etc., and that makes it reason enough.  The output functions are interesting, however, seeing it produces a ‘project’ file which I *think* only works with another instance of the iPad project (e.g. sharing), or PDFs.  Which isn’t bad, as it’s not clear what else you might use, but I might prefer a more manipulable format like an Excel or HTML output that I might post-process.

I think the idea of creating performance support tools on mobile platforms makes a lot of sense.  Whether you want to trust to their choice of questions and structure is another question.  Overall, it’s an interesting business move, an interesting mobile move, and an interesting chance to reflect on the design process.