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Clark Quinn’s Learnings about Learning

What you learn not as important as how you learn!

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I’m going a bit out on a limb here, with a somewhat heretical statement: what you learn is more important than how you learn!  (You could say pedagogy supersedes curricula, but that’s just being pedantic. ;)  And I’m pushing the boundaries of the concept a bit, but I think it’s worth floating as an idea. It’s meta-learning, of course, learning how to learn!   The important point is to focus on what’s being developed.  And I mean this at two levels.

This was triggered by seeing two separate announcements of new learning opportunities.  Both are focused on current skills, so both are focusing on advanced curricula, things that are modern. While the pedagogy of one isn’t obvious (though claimed to be very practical), the other clearly touts the ways in which the learning happens. And it’s good.

So the pedagogy is very hands on. In fact, it’s an activity-based curricula (in my terms), in that you progress by completing assignments very closely tied to what you’ll do on the job. There are content resources available (e.g. expert videos) and instructor feedback, all set in a story.  And this is better than a content-based curricula, so this pedagogy is really very apt for preparing people to do jobs.  In fact, they are currently applying it across three different roles that they have determined  are necessary.

But if you listen to the longer version  (video) of my activity-based learning curricula story, you’ll see I carry the pedagogy forward. I talk about handing over responsibility to the learner, gradually, to take responsibility for the activities, content, product, and reflection.  This is important for learners to start becoming self-improving learners.  The point is to develop their ability to do  meta-learning.

To do so, by the way, requires that you make your pedagogy visible  for the choices that you made, and why.  Learners, to adopt their own pedagogy, need to see  a pedagogy. If you narrate your pedagogy, that is document your alternatives and rationales of choices, they can actually understand more about the learning process itself.

And this, to me, is the essence of the claim. If you start a learning process about  something, and then hand off responsibility for the learning, while making clear the choices that led there, learners become self-learners. The courses that are designed in the above two cases  will, of necessity, change. And graduates from those courses might be out of date before long,  unless they’ve learned  how to stay current. Unless they’ve learned meta-learning.  That can be added in, and it may be implicit, but I’ll suggest that learning to learn is a more valuable long-term outcome than the immediate employability.

So that’s my claim: in the long term, the learner (and society) will be better off if the learner can learn to self-improve.  It’s not an immediate claim or benefit, but it can be wrapped around something that  is of immediate benefit.  It’s the ‘secret sauce’ that organizations could be adding in, whether internally or in their offerings. What surprises me is how seldom I see this approach taken, or even discussed.

Adaptive or just good design?

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A few posts ago, I wrote  about how we might be rushing too fast into cognitive computing. Not that there’s anything wrong with augmenting us, but I was wondering if we’d be better off focusing on developing our non-cognitive systems first. And, of course, it occurred to me after a conversation that there might be another example of this ‘tech fix before smart fix’ problem: adaptive learning over good  design. Is this a tech solution to the wrong problem?

So, I’m a fan of adaptive learning. Heck, I led a team that developed an adaptive learning system back circa 2000 (ahead of the times, as always ;). And I know  that there are good things we can do with adaptive learning.  Some are still relatively impractical (e.g. intelligent tutoring systems), some make sense (e.g. adapting on learner performance),  and, of course, some are pretty silly (e.g. learning styles).  Still, done well, adaptive learning could and should be a benefit.  (And serious games are  adaptive!)

But I would posit that before you charge off for adaptive learning, you make sure you’re doing  good learning design first.  Adaptation on top of good learning design is likely to add some extra benefits, but adaptation on old learning design just doesn’t make sense.  And, I’ll suggest, good learning design is cheaper, and likely to have a bigger impact.

So, for instance, adapting a large knowledge-based course will still leave it as a problematic solution in search of a problem.  Creating a better course focusing on critical  skills with meaningful practice is going to have a bigger impact on the bottom line than adapting the course for  learner progress. It’s not a bad thing, but it’s a secondary concern, I reckon.

Games are a special case of adapting on the basis of learner performance. When well done, they’re the next best thing to live practice.  Yet, at core, they too need good design.

There are a lot of adaptive solutions emerging, with a strong push to optimize your outcomes.  Some of them have a pretty good basis, too. But your goal is to achieve business impact, and what will hit that first, and best, is starting with good design.  Adaptive isn’t a panacea, it’s a fillip to  design, not a replacement.    Master the rules of good design, then come back and add in those extra elements, whether adaptivity and/or game mechanics.   Please?

Technology or preparation?

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In listening to a recent presentation on the trends affecting the workplace and HR, there was mention  about how organizations were using more cognitive technology, AI, etc. and this was changing jobs. There were two additional notes.  First, these efforts aren’t (largely) leading to job losses, as these folks were being reskilled. Second, HR wasn’t involved in 65% of this.  That’s a concern. But one of the things I wondered was whether all the new, smart technology really would help as much as was intended  or needed.

So here’s some context (I may have heard this in conjunction with an early experiment in using mobile devices to support drug trials).  Pharmaceutical companies are continually trying new drugs. One claim is  that if people would follow their medicine regimens, many of these new drugs wouldn’t be necessary.  That is, the drugs are often times trying to require fewer doses with simpler instructions to make up for inappropriate use.

Likewise,  the origin of performance support.  The question is where does the locus of responsibility belong. Interface design people were upset about performance support systems, arguing (correctly) that performance support was being used to make up for bad system design in the first place.  In fact, Don Norman’s book  The Invisible Computer was about how interface design wasn’t being brought in early enough.  The point being that properly designed interfaces would incorporate support for our cognitive limitations inherently, not externally.

So,  many of the things we’re doing are  driven by bad implementation. And that’s what I started wondering: are we using smart technology to enhance an optimized workforce, or to make up for a lack of adequate preparation?  We could be putting in technology to make up for what we’ve been unsuccessful at doing through training and elearning (because we’re not doing that well).

To put it another way, would we get better returns applying what’s known about how we think, work, and learn than bringing in technology? Would adequate preparation be a more effective approach than throwing technology at the problem, at least in some of the cases?   There are strong reasons to use technology to do things we struggle at doing well, and in particular to augment us.  But perhaps a better investment, at least in some cases, would be to  appropriately distribute tasks between the things our brains do well and what technology does better.

Let me be clear; there are technologies that will do things more reliably than humans, and do things humans would prefer not to. I’m all for the latter, at least ;). And we should optimize both technology and people.    I’m a fan of technology to augment us  in ways we want to be augmented.   So my point is more to consider are we doing enough to prepare people and support them working together.  Your thoughts?

Microdesign

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There’s been a lot of talk about microlearning of late – definitions, calls for clarity, value propositions, etc – and I have to say that I’m afraid some of it (not what I’ve linked to) is a wee bit facile. Or, at least, conceptually unclear.  And I think that’s a problem. This came up again in a recent conversation, and I had a further thought (which of course I have to blog about ;).  It’s about how to do microdesign, that is,  how  to  design micro learning. And it’s not trivial.

VirusSo one of the common views of micro learning is that it’s just in time. That is, if you need to know how to do something, you look it up.  And that’s just fine (as I’ve recently ranted). But it’s not  learning. (In short: it’ll help you in the moment, but unless  you design it to support learning, it’s performance support instead).  You can call it Just In Time support, or microsupport,  but properly, it’s not micro learning.

The other notion is a learning that’s distributed over time. And that’s good.  But this takes a bit more thought. Think about it. If we want to systematically develop somebody over time, it’s not just a steady stream of ‘stuff’.  Ideally, it’s designed to optimally get there, minimizing the time taken on the part of the learner, and yet yield reliable improvements.  And  this is complex.

In principle, it should be a steady  development, that reactivates and extends learners capabilities in systematic ways. So, you still need your design steps, but you have to think about granularity, forgetting, reactivation, and development in a more fine-grained way.  What’s the minimum  launch?  Can you do ought but make sure there’s an initial intro, concept, example, and a first practice?  Then, how much do we need to reactivate versus how much do we have to expand the capability in each iteration? How much is enough?  As Will Thalheimer says in his spaced learning report,  the amount and duration of spacing depends on the complexity of the task and the frequency with which it’s performed.

When do you provide more practice, versus another example, versus a different model?  What’s the appropriate gap in complexity?  We’ll likely have to make our best guesses and tune, but we have to think consciously about it.  Just chunking up an existing course into smaller bits isn’t taking the decay of memory over time and the gradual expansion of capability. We have to design an experience!

Microlearning is the right thing to do, given our cognitive architecture. Only so much ‘strengthening’ of the links  can happen in any one day, so to develop a full new capability will take time. And that means small bits over time makes sense. But choosing the right bits, the right frequency, the right duration,  and the right ramp up in complexity, is non-trivial.  So let’s laud the movement, but not delude ourselves either that performance support  or a stream of content is learning. Learning, that is systematically changing the reliable behavior of the most complex thing in the known universe, is inherently complex.  We should take it seriously, and we can.

Learning Design Insights

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I attended a recent Meetup of the Bay Area Learning Design & Technology, and it led to some insights. As context, this is a group that meets in the evening maybe once or every other month or so.  It’s composed of students or new graduates as well as experienced-practitioners. The topic was Themes from a  Hat  (topics are polled and then separate discussions are held). I was tapped to host the Learning Design conversation (there were three others: LMS, Measurement, and Social Learning), and that meant that a subset of the group sat in on the discussion. We had four separate discussions for each group, so everyone had a chance to discuss every topic (except us topic hosts ;).

I’d chosen to start with 3 or four questions to prompt discussion:

  • What is good learning design?
  • Are you doing good learning design?
  • What are the barriers to good learning design?
  • What can we do to improve learning design?

In each case, we never got beyond the first question!  However, in the course of the discussions, we ended up talking quite a bit about the others.  I confess that I’m a just a wee bit opinionated and a stickler for conceptual clarity, so I probably spoke  too much about important distinctions.  Yet  there were also some valuable insights from the group.

First, it was a great group: enthusiastic, with a wide range of experience and backgrounds.  Folks had come into the field from different areas, everything from neuroscience to rabbinical practice!  And there were new students still in a Master’s program, job seekers, and those who were active in work.  Everyone contributed.  While it meant missing #lrnchat, it was worthwhile  to have a different experience.  And everyone was kind enough to understood when I had to have my knee up as rehab (thanks!).

The responses to the first question were very interesting: what is good learning design?  While most everyone talked about features of the experience, we also were talking  both the outcome  and the process.  There even emerged a discussion about what learning was.  I offered  the traditional (behaviorist) description: a change in behavior in the same context, e.g. responding in a different (and presumably better) way.  I also mentioned my usual: learning is action and reflection; instruction is  designed  action and  guided reflection.

One element that appeared in all four groups was ‘engaging’.  Exactly that word. (Only once did I feel compelled to mention that Engaging Learning  was the title of my first book! ;)  There were other terms that encompassed it, including ‘experience’, ‘stimulating’, and ‘motivating’.  I was pleased to see the recognition of the  value! To define it, discussion several times ranged across  things like challenging practice and making it meaningful to learners.

Another element that reoccurred was ‘memorable’. It seemed  what was meant was ‘retention’ (over time until needed) rather than the learning experience was worth recalling. This did bring up a discussion of what led to retention and a discussion of spaced learning.  That is, the fact that our brains can only strengthen associations so much in one day before sleep is needed. Slow learning.  Reactivation.

That same discussion came up with another repeated term: micro learning.  There appeared to be  little  differentiation  between different interpretations of that term, so I made distinctions (as one does ;).  People too often use the term micro learning to mean looking something up just when needed  (such as a video about how to do something).  And that’s  valuable.  Yet  it can  lead to successful performance in the moment without any  learning (e.g. forgotten shortly thereafter). Which is fine, but it’s not learning! Microlearning  might be some very small thing that can be learned right in the moment, but I reckon those are rare. What I really think micro learning could and should be is for spaced learning.  I think that to do that successfully is a non-trivial exercise, by the way.

We covered other topics about design, too.  In at least one group we talked about SME limitations and how to work with them.  We also talked about the  benefits of collaboration, and knowing your audience. And engaging the audience, making the learning meaningful to them  and the organization. Minimalism came up in several different ways as well, not wasting the learner’s time.

One question had arisen in discussion with colleagues, and I took the opportunity in a couple groups to ask about their design practices. The question was how frequent was the process of giving a course demand to a designer and having them work alone from go to whoa.  It varied, but it seemed like there was some of that, there was also a fair bit of both collaboration at least at certain points, and some iterative testing. This was heartening to hear!  Doing  performance consulting and meaningful measurement, however, did appear somewhat challenging.

Overall, there’s an opportunity for some deeper science behind elearning, yet  I was very heartened by the enthusiasm and that the design processes weren’t as ‘solitary waterfall’ as I feared. So, who’s up for a deeper learning science workshop?  ;)

 

Another model for support

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I was thinking about today’s post, wherein I was talking about a couple of packages that  might help organizations move forward. I was reflecting back on some previous posts about engagement models, and was reminded of a more recent one. And I realized this has played out in a couple of ways. And these approaches did provide away to    develop the organization’s abilities to develop better learning.  So this is another model for support for developing at least the learning side of the equation.

consulting talesIn a couple of instances , I’ve worked with organizations on a specific project, but in a particular way.  For each,  my role was to lead the design. In one case, it was for a series of elearning modules. My role was to develop the initial template that the rest of the content fit.  Note that this isn’t a template for tarting it up, but instead a template about what the necessary elements and details around them were to ensure that the elements (e.g. intro, concept, practice, etc) both fit together and reflected the best learning science.  In a more recent instance, it was on a specific focus, but there were several modules that used a similar structure.

What happens, importantly, is that by working collaboratively, we learn together.  Each of these organizations was in the business of developing content, but they were looking to raise their game. So, for instance, through leading the Workplace of the Future project but sharing the thinking behind it, by working out loud in that sense, it’s possible to develop a shared understanding.  And in the latter case, though they’d read the Deeper eLearning series, they got a lot more out of working it through with me.  (And, I’ll suggest, more than also reading the subsequent blog posts I wrote about the project.)

In each case, we created an overall template for the learning, and then detailed what the elements for the template were, and the critical components. When we applied it, usually with me doing it first, and then handing off. It’s really a Cognitive Apprenticeship approach.

So, it’s a slightly more involved approach, with a much more variable scope, but in conjunction with other approaches I’ve mentioned like critiquing content or design processes, it’s one way to get a jump on deeper learning science.  Just trying to think of models that can support improvement, and that’s what I’m trying to push.

 

Support for moving forward

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I have to admit I’ve been a bit surprised to see that movements towards improving elearning and learning strategy  haven’t had more impact. On the learning design side,  e.g. the Serious eLearning Manifesto  and our Future of Work  project, it still seems there’s a focus on content presentation.  And similarly with  learning strategy, so despite  the Revolution, it doesn’t appear that there’s any big move in L&D to take a bigger perspective.  And my question is: “why not?”

So  I’ve been trying to think what might be the barriers to move forward.  What could keep folks from at least taking initial steps?  Maybe folks  are making moves, but I haven’t seen much indication.  So naturally I wondered what sort of support could be needed to  move  forward.

Perhaps it seems too overwhelming?  In the manifesto we did say we don’t expect people taking it all on at once, but we know folks sometimes have trouble breaking it down. Similarly, there’re a lot of components to the full performance ecosystem.  One possibility is that folks don’t know where to start.  I wrote sometime shortly after the manifesto’s release that the best place to start was with practice. And I’ve similarly argued that perhaps the best revolution catalyst is measurement. But maybe that’s too general?

So I wondered if perhaps some specific support would assist.  And so I’ve put together a package for each that’s an initial assessment to identify what’s working, what’s not, and from which to give some initial recommendations.  And I’ve tried to price them so that they’re not too dear, too hard to get approval for, but provide maximum value for minimal investment. Both are based upon the structure of previous successful engagements. (The learning strategy one is a little more because it’s a wee bit more complex. ;)  Both are also based upon frameworks I’ve developed for  each:

elearning design is based upon deeper elearning and the leverage points in the design process

elearning strategy is based upon the performance ecosystem model and the implications for developing and delivering solutions.

In each I’m spending time beforehand reviewing materials, and then just two days on site to have some very targeted interviews and meetings.  The process involves  talking to representative stakeholders and then working with a core team to work through the possibilities and prioritize them. It also includes an overview of the frameworks for each as a basis for a shared understanding.

The goal  is to use an intensive investigation to identify what’s the current status, and the specific leverage points for immediate improvement and longer-term shifts. The output is a recommendation document that documents what’s working and where there are opportunities for improvement and what the likely benefits and costs are.

This isn’t available directly from the Quinnovation site: I’m starting here to talk to those who’ve been tracking the arguments. Maybe that’s the wrong starting point, but I’ve got to start  somewhere. I welcome feedback on what else you might expect or want or what would help.

If  you’d like to check out the two packages and start moving forward, have a look here and feel free to followup through the contact link.  You’ve got to have the 3 Rs: responsibility, resources, and resolve.  If I can help, glad to hear it.  If not, but there’s something else, let me know.  But I really do want to help move this industry forward, and I’ll continue to try to find ways to make that happen.  I invite you to join me!

Tackling the tough stuff

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tacklingI was reflecting a wee bit on my books (and writings in general), and realized that there’s somewhat of a gap when I talk about games, and mobile, and more.  And it’s not unconscious, but instead principled, even if it arises somewhat implicitly. So I thought I’d talk briefly about  why I tend to focus on the design, and not the practical implementations. Briefly, I think the places we fall short are not in executing, but in conceptualizing. And so I focus on tackling what I think is the tough stuff. I think we need to address the things that are more complex. My claim is that if we understand them, we have a better chance of achieving our goals and delivering the necessary outcomes.

I have stated before that I think we can implement most anything we can conceive, the problem is that our conceptions are limited.  So, I talk about design based on  knowing how we think, work, and learn. I think we need these foundations if we’re truly going to realign what we do to actually work.  Frankly, I think we’re working under some misapprehensions (read: myths) that are limiting our ability to succeed.

When I talk about thinking, the myth is that it’s all in our head and logically principled.  It turns out that, instead, our thinking is very biased by circumstance and pre-existing beliefs, and we avoid effortful work.  We trust our instincts in far more circumstances than we should!  Similarly, we distribute our thinking across the world: our tools and representations assist us, and yet we don’t focus enough effort on ensuing that those are effectively designed.  There’s a real possibility for a valuable shift here.

My focus in working is  to recognize that it’s not as individual as our business processes would assume. The ‘individual innovator’ myth is busted, and the empirical results are that we get better outputs when we work together. Certainly for innovation and creative work. Yet we isolate our work, assigning individual resources.  Similarly, people work best when given meaningful goals, but instead we micromanage too often. Again, there are big opportunities to improve our outcomes by reviewing our approaches.

And on learning, I’ve railed time and again about what’s not working, and been joined by colleagues in opposition.  We learn through designed action and guided reflection, not information dump and knowledge test.  Yet that’s not what we see. And again I suggest only small changes are needed to have a substantial  impact.

So, in my books, I don’t talk about so much about how to build a game, or the ways to implement mobile learning, or social learning tools.  These will change. What you want to get your mind around is about  our minds. Then you can design solutions that can be implemented in any  number of ways.  I may not be successful at communicating the solutions, but in general when I speak, run workshops, or yes write, people seem to convey that I’ve had some effect on helping them get a handle on these new approaches.  In addition, figuring out how to apply them is why I’m here.

What I’ve been able to do, successfully across years and organizations, is help align processes, products, services, and more with how our brains work.  And then work within the available resources to create solutions that  reflect those insights in innovative and yet practical solutions.  It takes time to develop the type of thinking I want organizations to adopt, but it’s doable, and I’ve worked with a number of organizations to do just that. Taking the time to address the tough stuff is a bit of an effort. I think it’s an investment in success.  It’s doable, so the only real open question is whether you’re ready to make a shift in thinking,  that leads to a shift in doing, that leads to a better impact for your organization.  And only you can answer that.

Diagramming

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So yesterday I talked about the value of diagrams, but I thought I’d add a bit about the process of actually  creating  diagrams. Naturally,  I created a diagram about it.

Diagram of diagram designI created this diagram for a session  I ran on diagramming a number of years ago.  In that session I talked about our cognitive architecture, why we need models, how diagrams work, properties and design issues, and more. At the end, I proposed a potential process for it.  It captures an ideal picture of how diagramming could work.

So, you need to know the elements of the model you want to relate, identify the relationships, and any dimensions that characterize differences between the elements. Then you have to choose how you’re going to represent them: shape, color, weight, font, and more.

With your elements, you can then place them, connect them, and add the visual coding.  Then, of course, you tune.

This is an ideal process, but in reality it’s much more flexible, at least when I’m creating a model as a way of understanding.  I typically iterate, creating placeholder elements, and moving them around until I think I have the right ones.  Then I go about connecting them to make sure I have the relationships right. Then I work on adding dimensions, and colors, and aligning them, and grouping them, and… Except that I might add some elements, then group, or connect, then add more, and…  it’s a very iterative  process.  It’s a creative process that involves lots of experimentation, revision, and more.

Sometimes, I even use a diagram and then realize it’s not working and revise it. So, for instance, I blogged about a  representation of social process.  I got some feedback that it wasn’t very clear.  So, I made a second stab at it, and I think it worked better. Certainly, I continue to use it without complaints.

And I’m the first to admit that my diagrams may not look as good as the ones that professional graphic designers could create, but they’re good enough (and OmniGraffle does a good job of making it easy for me to make them up to a standard I think is at least acceptable and useful; it’s probably overkill but I’ve stuck with it for years now).  And that’s the point.  If they help you think better, it’s good enough. If it helps you communicate effectively with someone else, even better.  Diagrams are cognitive tools, offloading conceptual complexity to graphic relationships and visual processing.  And with the complex problems we increasingly face, I reckon the more tools the better.

 

Diagram!

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One of the things that I feel is a really useful tool in my ongoing learning, in my ‘making sense of the world’ is diagramming.  I find diagrams to be  a really powerful way to understand not just elements, but relationships.  And yet it doesn’t feel like diagramming gets enough respect.  So I want to make a  case for the diagram.

Language is good. Our brains have evolved to use it. But it has trouble communicating complex relationships.  For an example, once I wrote this:

They found that while subjects would rate the analogies, from best to worst, as literally similar, true analogy, mere appearance, and false analogy, their recall for stories, from best to worst, was literally similar, mere appearance, true analogy, and false analogy.

Try discerning the important difference!  My PhD advisor kindly pointed out that actually parsing this was hard, and recommended a diagram instead. Here’s a rendition of what resulted:

structure task outcomes diagram

In this case it’s much easier to see how the two differed.  (If you want to find out what’s important  in the diagram, I’m happy to talk about analogical reasoning for as long as you can stand it! ;)

The point I’m making is that there are times when diagrams are very useful for communicating.  And, if you’ve followed this blog for a fair amount of time, you’ve seen I use diagrams a lot. I use them to think ‘out loud’, and I think it’s important.  As Larkin & Simon argued in their Cognitive Science article, Why a Diagram is (Sometimes) Worth Ten  Thousand Words,  diagrams let us map conceptual relationships to spatial ones. And so if I want to understand the conceptual relationships, I start laying out spatially, and adjust until they make sense to me.

And my concern is that we aren’t using this powerful visual tool enough.  Sketchnotes are really nice ways to capture presentations, and depending on the skill of the noter, they may communicate it all, or help recall if you’ve seen it. Similarly, my mindmaps of keynotes capture the flow of the discussion and the relationships (at least as I parsed it), but may only make sense if you heard the talk.

But representing things with diagrams is not only a personal thinking tool, it can be a powerful way to communicate concepts, and that’s an important component of a good learning experience design, providing a conceptual model to guide performance.

So I’m surprised we don’t talk about diagrams more. It may seem hard (certainly trying to create an infographic is harder than it seems, from my experience ;), but there’s some systematicity to it. There are principles, and types of diagrams, and more to explore.  And tools that make it easier (though even Powerpoint or Keynote can be used to make diagrams).  Diagrams aren’t the only visuals that help (c.f. graphs and tables), but they’re an important tool in your thinking toolbox.  I encourage you, as part of your meta-learning toolkit, to play around and get your mind around diagrams. Your thinking, and your learning design, can be better as a consequence.