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Clark Quinn’s Learnings about Learning

Distributed Cognition

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In my last post, I talked about situated cognition.  A second, and related, cognitive revelation is that thinking is distributed between our heads and the world. That is, the model that it all occurs between the ears doesn’t recognize that we incorporate external representations are part of our processing. Hutchins, in his Cognition in the Wild, documented a variety of ways that our thinking is an artefact of our tools and our models.

So, for example, navigation typically involves maps as well as thinking. Business reasoning is typically accompanied by tools like a spreadsheet. We use diagrams, tables, graphs, charts, and more to help us understand situations better. And we are unlikely to be able to do things like long division without paper and pencil or a calculator. This means that putting everything in the head isn’t necessary. And this is just what we  should be doing!  Designing for the right distribution of tasks between world and mind(s) is the optimal solution.

We know that it’s difficult to get things in the head (how hard is it to learn, say, to drive), and therefore undesirable anyway.  It’s about designing solutions that put into the world what can be in the world, and then putting into the head what has to be in the head. This includes performance support in a variety of ways. It also should address what we consider to be worth training.

When we want to optimize performance, we should recognize that we need a bigger picture. We need to consider the person & tools, or people & tools, as a whole entity when it comes to achieving the end goal.  This is also true for learning. Our reflective representations are part of our thinking process. So, too, our collaborative representations.

We are better thinkers and learners when we consciously consider tools, and their availability in the ecosystem. In fact, our ecosystem is the tools and people we have ‘to hand’, accessible in or from the workflow. And elsewhere, in our times for reflection, and discussion. So, have you optimized your, and your organization’s thinking and learning toolset?

Situated Cognition

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In a recent article, I wrote about three types of cognition that are changing how we think about how we think (how meta!).  All are interesting, but they also have implications for understanding for supporting us in doing things.  I think it’s important to understand these cognitions, and their implications. First, I want to talk about situated cognition.

The psychological models of thinking really started with the behavioral models. The core argument was that we couldn’t look ‘inside the box’, and had to study inputs and outputs. Cognitive psychology was a rebellion from this perspective. The new frameworks started showing that we could posit quite a bit about what went on ‘in the box’. We got concepts like sensory, working, and long-term memory, and processes like attention, rehearsal, encoding, and retrieval. With most of our learning prescriptions. However, both were about the ‘the box’.

However, the observed behavior didn’t match the formal logical reasoning that underpinned the model. We needed new explanations. The computational model fell apart. And, despite rigorous attempts to create logical models that described human behavior, they were awkward at best. The shift came when Rumelhart & McClelland, in their PDP book, described what became known as neural networks. Associated with this was a new model of cognition.

What gets activated in the brain is not a reliably pure representation, and is strongly affected by the context. Thinking is ‘situated’ in the context it arises in. If our thinking is the emergent behavior of patterns across neurons, and those patterns are the result of both internal and external stimuli, then we’re very strongly influenced by what’s happening ‘in the moment’.  And that means that we can be captured (and fooled) by elements that may not even be consciously processed.

What this means in practice is that it’s harder than we think to get reliable performance across a range of conditions.  That we should ensure that patterns are generated across ‘noise’ so that they’re reliable in the face of the appropriate triggers, despite any accompanying contextual patterns. and recognize that decisions can be biased, and design scaffolding to prevent in appropriate outcomes. Developing mental models that provide reasoning abilities about causes and outcomes are useful here. This flexibility is advantageous (and why machine learning struggles outside it’s range of training), but we want to tap into it in helpful ways.

Our approaches should reflect what’s known, and therefore we need to keep up.  Situated cognition is a perspective that’s relevant to more effectively supporting individual and organizational performance and learning. So, what is your thinking about this?

Designing with science

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How should we design? It’s all well and good to spout principles, but putting them into practice is another thing. While we always would like to follow learning science, there’re not always all the answers we need. I was thinking about this with a project I’m working on, and it occurred to me that there might be some confusion. So I thought I’d share how I like to think and go about it, and see what you think.

So, first of all, you should go with the science. There are good principles around in a variety of forms.  Some good guidance comes in books such as:

  • eLearning & the Science of Instruction (Clark & Mayer)
  • Design for How People Learn (Dirksen)
  • the Make it Learnable series (Shank)
  • and less directly but no less applicably, Michael Allen’s Guide to eLearning

There’s also ATD’s Science of Learning topic (with some good and some less good stuff).  And the 3 Star Learning site. Both of these, of course, aren’t as comprehensive as a book.   And, of course, you can also go right to the pure journals, like Instructional Science, and Learning Sciences, and the like, if you are fluent in academese.  For that matter, I’ve a video course that is about Deeper Instructional Design, e.g. a design approach with learning science ‘baked in’.

But what I was thinking of what happens when they don’t address the specific concern you are wondering about. The second approach I recommend is theory. In particular, Cognitive Apprenticeship (my favorite model; Collins & Brown), or other theories like Elaboration Theory (Reigeluth), Pebble in a Pond (Merrill), or 4 Component ID (Van Merriënboer). Or, arguably more modern, something from Jonassen on problem-based learning or other more social constructivist approaches.  They’re based on empirical data, but pulled together, and you can often make inferences in between the principles.  While the next step is arguably better, in the real world you want a scrutable approach but one that gets you moving forward the fastest.

Finally, you test. If science and theory can’t provide the answer, you either wing it, but it’s better if you set up an experiment. Ideally, with your sample population.  So, for instance, you don’t know whether to place the learner’s role in the simulation game as a consultant to many orgs or as a role in one org with many situations. There’re tradeoffs: in the former it’s easier to provide multiple contexts for practice, but the latter may be more closely aligned with job performance.   You can test it, and see what learners think about the experience. Of course, it may be that in the process of just designing both that you have some insight. And that’s ok.

And, if you’re a reflective practitioner (and we should be), you might share your findings.  What did you learn?  Learning science advances to the extent that we continue to explore and test.  Speaking of which, how does this approach match with what you do?

Organizational Psychology?

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I read an article calling for organizational psychology and the things these folks do for companies.  And, interestingly, many of the tasks seem like things that I’ve been calling for L&D to do. So now I have to ask what’s the relationship between these two areas?

My background is psychology, specifically the cognitive kind (ok more cog sci than just psych, but still).  And so I’ve been pushing the idea of doing a cognitive analysis of organizations, and incorporating new understandings of cognition in how we run our companies, and more. The point being that we need to align how our organizations operate with how our brains do.

In a sense, then, I’m arguing for a psychological approach to organizations. This includes best principles across the board: working together, learning alone, etc. Yet, I’m typically talking to and about Learning & Development (even when I argue it needs a revolution).  Am I missing the forest for the trees?

Now, it’s clear that the formal role of organizational psychology is bigger. It’s about hiring, and incentives, and occupational stress and a number of other things that I normally don’t consider.  And, it doesn’t seem to be much about technology, the approaches to innovation seem limited, and some of the things it investigates seem more like outcomes.  Yet it also includes training & workforce development, culture, and more.

I also have to say that it’s history seems to be in behavioral psychology. It appears (on the surface, mind you) to be a bit mired in thinking linearly, not networked. Of course, I’m probably biased here, and this is true for L&D too!  There’re probably pockets of modernity as well.

So is L&D a subset? I really don’t know.  I’d like to hear what you have to say on it.  Perhaps my arguments really are (cognitive) organizational psychology.  In another sense, I’m not sure it’s important. It’s not so much where you come from as what you are about, and the methods you use.  Still, this is a question I’d like to hear thoughts on. Is there a definitive answer?

Why L&D Should Lead

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So, I’ve seen a bright future for L&D. It’s possible, and desirable.  But is it defensible?  I want to suggest that it is.  L&D should be the business unit with the best understanding of our brains (except, perhaps, in a neurology company, e.g. medical, or a cognitive company, e.g. AI).  And I’ve argued that’s a key role. So, if we grasp that nettle and lead the change, we could and should be leading the way to a brighter new future for organizational success.

Look, cognitive science is somewhat complex. In fact, the human brain is arguably the most complex thing in the known universe!  However, we have a good understanding of cognition for the purposes of guiding learning and performance in the workplace. Or, as I like to say, understanding how we think, work, and learn.  Moreover, we really can’t (and shouldn’t) be doing our jobs unless we have that knowledge. (I have a workshop that can help. ;)

Now, it’s also becoming a cliche that the organizations that learn fastest will be the ones that thrive (not just survive, or not!). We must learn, individually and together. And knowing how to have people work and play well together, representing, reflecting, collaborating, and more should be L&D’s role. We should be the ones who know the most and best about how to do those things in consonance with how our cognitive architecture works.

And, to be clear, there are lots of practices in organizations that are contrary to the best learning. Fear, lack of time for reflection, micro-management, old-school brainstorming, the list goes on. Without knowledge, we may firmly be convinced we’re doing it right, and instead undermining the best outcomes!  (One way to tell if it’s safe to share in your org: put in a social network. If no one participates…)  On the flip side, there are lots of practices that science tells us work. Details around formal learning, creating spaces for informal learning, practices for short-term and long-term innovation, etc.

We have an uphill battle gaining the credibility we need, but I say start now, and start small. Instill the practices within L&D, take ownership of the necessary skills and knowledge, make it work, document it, and then use that success as a stepping stone to spread the word.

Then, if we are doing that facilitation of learning, you should be able to see that we are enabling the most important work in the organization!  We can be the key to org success, going forward. L&D should lead the change. That’s the vision I see, at least.  Does this sound good and make sense to you?

 

The ITA Jay Cross Memorial Award for 2018: Mark Britz

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In honor of the colleague, mentor, and friend that brought us together, every year the Internet Time Alliance presents the Jay Cross Memorial Award. The award is for an individual who represents the spirit of continuing informal learning for the workplace. This year, Mark Britz is the deserving recipient.

Jay was a fierce champion of social and informal learning. He saw that most of how we learn to do what we do comes from interacting with others.  As a response to his untimely passing, the remaining members of the ITA decided to honor his memory with an award.  Jane Hart, Harold Jarche, Charles Jennings, & myself each year collectively decide an individual who we think best reflects Jay’s vision. And we announce the recipient on the 5th of July, Jay’s birthday.

Mark has resonated and amplified the message of ongoing learning since we first crossed paths. He has interacted with the ITA members regularly via tweets, blogs, and in person when possible.  And we’ve appreciated his engagement with the ideas and his contributions to our thinking.

I got to know Mark’s thinking a bit better when he wrote a case study based upon his work at Systems Made Simple for the Revolution book (Jay wrote the foreword).  And he’s continued to blog about workplace learning at The Simple Shift with short but insightful posts. Currently part of the team running events for the eLearning Guild, Mark manages to consistently touts views that illuminate thinking about the new workplace.

The situation he cites in that case study is exemplary of this type of thinking. Charged with starting a corporate ‘university’ in an organization that was composed of many experts, he knew that ‘courses’ weren’t going to be a viable approach. Instead, he championed and built a social network that pulled these experts together to share voices. The core L&D role was one of facilitating communication and collaboration, rather than presenting information.

For his continuing work promoting communication, collaboration, and continual learning, we recognize Mark’s efforts with the 2018 Internet Time Alliance Jay Cross Memorial Award.

 

 

Microlearning Malarkey

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Someone pointed me to a microlearning post, wondering if I agreed with their somewhat skeptical take on the article. And I did agree with the skepticism.  Further, it referenced another site with worse implications. And I think it’s instructive to take these apart.  They are emblematic of the type of thing we see too often, and it’s worth digging in. We need to stop this sort of malarkey. (And I don’t mean microlearning as a whole, that’s another issue; it’s articles like this one that I’m complaining about.)

The article starts out defining microlearning as small bite-sized chunks. Specifically: “learning that has been designed from the bottom up to be consumed in shorter modules.” Well, yes, that’s one of the definitions.  To be clear, that’s the ‘spaced learning’ definition of microlearning. Why not just call it ‘spaced learning’?  

It goes on to say “each chunk lasts no more than five-then minutes.” (I think they mean 10). Why? Because attention. Um, er, no.  I like JD Dillon‘s explanation: it needs to be as long as it needs to be, and no longer.

That attention explanation?  It went right to the ‘span of a goldfish’. Sorry, that’s debunked (for instance, here ;).  That data wasn’t from Microsoft, it came from a secondary service who got it from a study on web pages. Which could be due to faster pages, greater experience, other explanations. But not a change in our attention (evolution doesn’t happen that fast and attention is too complex for such a simple assessment). In short, the original study has been misinterpreted. So, no, this isn’t a good basis for anything having to do with learning. (And I challenge you to find a study determining the actual attention span of a goldfish.)

But wait, there’s more!  There’s an example using the ‘youtube’ explanation of microlearning. OK, but that’s the ‘performance support’ definition of microlearning, not the ‘spaced learning’ one. They’re two different things!  Again, we should be clear about which one we’re talking about, and then be clear about the constraints that make it valid. Here? Not happening. 

The article goes on to cite a bunch of facts from the Journal of Applied Psychology. That’s a legitimate source. But they’re not pulling all the stats from that, they’re citing a secondary site (see above) and it’s full of, er, malarkey.  Let’s see…

That secondary site is pulling together statistics in ways that are thoroughly dubious. It starts citing the journal for one piece of data, that’s a reasonable effect (17% improvement for chunking). But then it goes awry.  For one, it claims playing to learner preferences is a good idea, but the evidence is that learners don’t have good insight into their own learning. There’s a claim of 50% engagement improvement, but that’s a mismanipulation of the data where 50% of people would like smaller courses. That doesn’t mean you’ll get 50% improvement. They also make a different claim about appropriate length than the one above – 3-7 minutes – but their argument is unsound too. It sounds quantitative, but it’s misleading. They throw in the millennial myth, too, just for good measure.

Back to the original article, it cites a figure not on the secondary site, but listed in the same bullet list: “One minute of video content was found to be equal to about 1.8 million written words”.  WHAT? That’s just ridiculous.  1.8 MILLION?!?!?  Found by who?  Of course, there’s no reference. And the mistakes go on. The other two bullet points aren’t from that secondary site either, and also don’t have cites.  The reference, however could mislead you to believe that the rest of the statistics were also from the journal!

Overall, I’m grateful to the correspondent who pointed me to the article. It’s hype like both of these that mislead our field, undermine our credibility, and waste our resources. And it makes it hard for those trying to sell legitimate services within the boundaries of science.  It’s important to call this sort of manipulation out.  Let’s stop the malarkey, and get smart about what we’re doing and why. 

A good publisher

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I shot this short little video because, well, I have to say that my experience with ATD has been excellent. They’ve done what I’ve needed: listening when they should, arguing with me when they should, responding to my questions, and executing on their responsibilities professionally. They’ve gone above and beyond, and I’m pleased to have them as my publisher on my most recent tome.

If you’re going to complain about the bad things (or, at least, make fun of them :), I reckon you should highlight the good ones too.  They showed their capabilities while serving as co-publisher on my last book, and now they’ve demonstrated the whole deal. Thanks, team!

A solid education platform

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In the past couple of days, I’ve come across two different initiatives to improve education. And certainly our education system can stand improvement. However, each one had the same major flaw, and leaves open an opportunity for improvement not to occur. Over a number of engagements I’ve developed the basis of what I think is a necessary foundation for a viable education platform. It’s time to toss it out and see what you all think.

So, one initiative had a proposal of 10 different areas they wanted people to contribute in. This included AI, and personalization, and ‘out of class’ credit, and more. Which is all good, make no mistake! However, nowhere was there the option of ‘a deeper pedagogy’. And that’s a problem. It’s all too easy to chase after the latest shiny object. It makes us feel like we’re both doing something constructive and keeping up with developments. (Not to mention how much fun it is to play with the latest things.) However, gilding bad design is still bad design! We need to make sure the foundation is strong before we go further.

The other initiative has three ways to contribute: lifelong learning, a marketplace, and emerging technology. And, again, the big gap is talking about the pedagogy to begin with.  With a marketplace, you might get some Darwinian selection process, but why not put it out there from the get-go? Otherwise, it’s just cool tinkering around a broken core.

Three partsSo here’s where I pitch my 3 part story. Note that curriculum is broken too (I’m channeling Roger Schank: ‘Only two things wrong with education, what we teach and how we teach it’), and yet I’m not addressing that. Well, only a second layer of curriculum (see below ;). I think the choice of the first level curriculum is a big issue, but that changes depending on level, goals, etc. Here I’m talking about a platform for delivering the necessary elements of a supportable approach:

  1. The first element is a killer learning experience. What do I mean here? I mean an application-based learning approach. Even for so-called theory classes (e.g. typical higher ed), you do something with this. And the experience is based upon minimal content, appropriate challenge, intrinsic motivation, and more. My claim: this is doable, even when you want to auto-mark as much as possible. Of course, there are still people in the loop.
  2. Which leads to the second element, we as the provider are a partner in your success. It’s not ‘sink or swim’, but instead we’re tracking your progress, intervening when it looks like you’re struggling, and accessible at your time and place. We’re also providing the necessary resources to succeed. And we’re not interested in a curve, we’re competency-based and want everyone to get where they can be. We’re also making sure you’re getting what you need.
  3. And that’s the third element, we develop you. That is, we’re not just developing your knowledge of the field, we’re also developing key success skills. That means we’re giving you chances to practice those skills as well, and tracking them and developing them as well. This includes things like communication, collaboration, design research, and more. So-called 21C skills.

I suggest that with such an approach, and the right curriculum, you’re providing a full suite of what education should be about. And, I suggest, we can do this now, affordably. Technology is part of the picture, learning science is part of the picture, and the commitment to do the right thing is part of the picture. Also, I think this is viable at all levels. K12, higher ed, and workplace.

And, I’ll suggest, anything less really isn’t defensible. We have the know-how, we have the tools, all we need is the will. Yet, despite some notable steps in the right direction, we’re really not there. It’s time to put a stake in the ground. Who’s up for it?

Quip: Learning & Development

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I’ve used this quip quite a bit, as it’s essentially the rationale for the Revolution book.  And I want to make clear what I’m saying, and then qualify it.  It’s about the state of Learning & Development, and sums up one perspective fairly succinctly:

L&D isn’t doing near what it could and should be, and what it is doing it’s doing badly. Other than that, it’s fine.

It’s meant to be a little flip and ‘in your face’, but it’s because I think there’s such potential for L&D!  This is my way of characterizing the situation that might spark some reflection, and even action.

L&D is, largely, about courses.  And unfortunately, too often they’re about content-dump, and an experience that will rank highly on a smile sheet. Which is historically understandable, but scientifically bereft. Compliance aside (and here’s to a competency shift, away from ‘1 hour / year’ or whatever other time-based basis we might find), our courses should be focused on applying knowledge to meaningful tasks, and meaningful feedback. Sufficient, varied, spaced, and deliberate practice! Of course this isn’t everyone’s L&D, but it certainly appears to be all too present.

The second thing is that L&D could be so much broader!  If we’re really worry about organizational performance and continuing improvement (why I suggest L&D should shift to P&D, performance and development), we should do more. Performance support, for instance, should be under the purview of L&D. Otherwise it gets left to chance or those who don’t have the necessary background.

And, then there’s coaching. Recognize that learning takes time, and that we need to continue development beyond the classroom. Thus, coaching’s critical to continued improvement. Again, L&D has a role to play here: developing coaching skills, providing guidance, and tracking.

Then we go beyond formal learning: optimizing the ongoing learning in individuals, teams, and communities. This is organizational learning! There’re processes for individual improvement like PKM, team processes like brainstorming, and community interactions. Leaving these to chance is a mistake, as we can’t assume these skills.

And the outcomes of helping the organization get better beyond the course are big. Not just individual learning, but the organization is learning faster. And that’s a necessity for success, going forward. In short, there’s a lot L&D could be doing that would help the organization that it’s missing now.

Now, complaining as my statement does isn’t necessarily useful, unless it’s constructive, and the point is that we have very comprehensive and specific things we know about doing better. By this quip I don’t mean to criticize; I want to inspire action and improvement. So here’s to revolutionizing L&D. I hope you’ll join us!