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Clark Quinn’s Learnings about Learning

Quinnovation ‘to go’

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The travel schedule is booting up again, and I’ll be hither and yon speaking about this and that for a good part of the coming two months. More specifically:

  • From 2-3 Oct I’ll be running a two day elearning strategy  workshop at Learning 3.0 in Chicago.  If you want to get above the individual tactics and see how the pieces fit together, and work on a plan for you and your org, I hope to see you there.  Then on Tuesday the 4th, I’ll be talking about creating Engaging Learning.
  • Then, on 12 Oct in Laguna Niguel at the CLO Fall Symposium, I’ll be joining with my ITA colleagues Jay Cross and Jane Hart to talk about controversial issues for CLOs.  This will be fun and worthwhile, as we will be aiming at some sacred cows.
  • It’s off to Las Vegas at the beginning of November for DevLearn, where I’ll be running a mobile learning strategy session on the the 1st.  If you want to get beyond just designing a one-off, and look at the broader picture of how to make mobile a part of your solution, it’s the place to be.
  • That’s followed by Learning 2011 in Orlando Nov 6-9, where I’ll be hosting an author session for Designing mLearning.
  • I’m still not done, as I head later that week to DC to speak to the local ASTD chapter with a talk on mobile learning and a social learning workshop.  That latter will talk about both formal and  informal learning, as well as looking at the different tools.
  • And, to cap it off, I’ll be presenting at the Canadian Society for Training & Development’s annual conference in Toronto on Friday the 18th of November, looking forward and more broadly at the role of learning in the organization.

That may seem  like a lot (and it is), but traveling on only one continent will seem easy after this past May-August ;).  I hope to see you at one or more of these learning events!

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.

Meaningful processing

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Sometimes I worry about the myths that are out there about learning.  Ok, to be honest I worry about them a lot. Learning styles, generational differences, digital natives, the list goes on. But one that has personally been surfacing a lot is the type of activity that leads to meaningful learning.  So it’s time for me to lay it out, for the record.

I’ve talked previously about social processing, so I’m going to focus specifically on individual processing.  And, realize, my goals are not the ability to recite rote knowledge, but I’ll even address that. Note, by the way, that there are really two types of knowledge (c.f. Van Merriënboer), the things you need and the complex problems you apply them to.  So, first we’ll start with the knowledge you need, and then the problems you apply them to.

To help folks get knowledge down, memorizing the core facts they’ll draw upon in solving complex problems, the main component necessary is reactivating the knowledge.  You need to match the term with the definition, the model with it’s relationships, etc.  Sheer repetition doesn’t help, even here it’s making choices and getting feedback.

So, for instance, coloring a poster with the associated words doesn’t do the necessary processing, you need to activate the necessary concepts with connections to relevant things.  You need to semantically process the terms again and again.  Elaborating them, putting them in context, applying them to simple problems is necessary.  Flash cards work because they require the association task.  Just exposure doesn’t work, even with testing, it’s discrimination from competing alternatives.

Then we get to the application. And frankly, if you’re not having folks learn things to use  them, why are you bothering? That’s why I like Cathy Moore’s Action Mapping, she works backwards from the task and then only focuses on the knowledge necessary to do  the task.  A good heuristic approach that couples elegantly to a principled foundation.  And, as converging theories suggest, you need to be applying knowledge to support the ability to transfer that skill out of the learning experience.

So, you need to be looking at the knowledge to be learned in a more discriminating fashion than just exposure, and you then need to be applying that knowledge to a suite of tasks to support making it useful. There’s more, such as the necessary spread of tasks to support appropriate decontextualization to support transfer, and sufficient and spacing of practice to support retention, but here I just want to emphasize that rote exposure to knowledge doesn’t mean it will be learned, and that learning facts without applying them doesn’t lead to any meaningful outcome.  So can we start focusing on learning activities that generate meaningful processing?  Please?

 

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.

Working Smarter

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Work smarter, not harder.

Have you heard that?  I did, in my first job out of college; my boss said it, but it wasn’t clear what it meant.  What does ‘work smarter’ mean?  I already thought I was working smarter.  Well, as I’ve learned (in conjunction with my ITA colleagues), it means a number of things that organizations can, and should, do.

So, what is known about when we work smarter? We work smarter under a number of conditions: when we have a clear goal of what we’re supposed to achieve and we recognize it’s importance; when we’re free to experiment, explore, and even fail; when we have colleagues to collaborate with; and  when we have the resources we need available ‘to hand’.  This provides some guidance about what an organization should be doing to optimize the likelihood of success.

We need to be doing meaningful work that we’re excited about.  We need to be connected to a vision, and understand how our role contributes.  There needs to be transparency above and below as well as ahead, so we can see how the parts are working together.

We also need a culture where that transparency is empowering, not threatening. It has to be safe to perform in public, to share our thoughts, and to both provide and receive help to others.  Where, when mistakes are made, the lessons are learned and shared.

We need to see it as important to contribute, and be enabled to communicate to the right people, and be able to work together to get the job done.  We need time to reflect as well, to take time to think about what we’re doing. We should be doing that publicly too. We need to learn out loud and together.

Finally, we need the tools available. We shouldn’t have to take time to go multiple places to get what we need, and use inconsistent interfaces to use them. We should have an environment where we’re focused on our tasks, and can get who and what we need to stay focused.

How to work smarter isn’t a mystery. The mystery is why  more organizations aren’t systematically breaking down the barriers to working smarter.  Are you ready to get going?

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?

Social Cognitive Processing

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In an earlier post, I tried to convey the advantages of social activities in formal learning from the cognitive processing perspective, but my diagram apparently didn’t work for everyone.  I took another shot for a presentation I gave on mobile social at the Guild’s mLearnCon, and I thought I’d raise it here as well.

Cognitive reprocessing via social interactionI’m going through this diagram line by line, from the top.

If you go from just having an idea (first line) to trying to capture it as a product (next), whether a diagram or a screed, to communicate to some hypothetical reader, you find out that you might not have thought it out fully (the first benefit to having a personal journal, e.g. blogging).  And you do some processing to generate that product.

Then, if someone actually reads it, they  do some processing.

If they write a response, they do more processing to crystalize their thoughts.

Then, the author, when reading it, also does some more processing.

If someone else reads it, that person does some processing, and if they write  a comment, well, the process continues.

The author could then write a reply to one or both, and that causes even  more  processing. And so on.

And this is good.  Processing is part of learning, and focused processing is part of good learning design.  So, having learners capture and communicate their thoughts is a valuable learning activity.  It can be personal reflections, e.g. “what does this explain in my past” or “what will I do differently in the future”, or responses to a question.

If other learners are asked to read and constructively  comment (not just “great post”), you can get valuable learning outcomes.

Cognitive processing in group assignmentsThis extends to the social learning situation. Here, you have every learner contribute their initial  thoughts on a group assignment (recommended).

Then, every learner reads the other proposals, and they start to put out their integrated ideas.

As they negotiate a shared understanding as a group response, some great processing is happening.

Ultimately, they create an outcome that’s richer than what they’d create on their own.

If you’ve created the right  amount of ambiguity in the project, you’ll get some great discussions.  The processing benefits here are because the learners will bring somewhat different interpretations and experiences to the project, and that diversity allows a mre robust understanding to emerge.

Consequently, I suggest that social learning adds benefits to the learning experience beyond what individual assignments can achieve. You can mimic some of these effects by staging additional information, but it’s not quite as effective as individual learning (nor near as engaging).

So, does this make sense?  And, hopefully, inspire you to find ways to add social interaction into your learning experiences? It’s not unique to social media, but social media give you a channel to bring these benefits to learning whenever and wherever.