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Clark Quinn’s Learnings about Learning

Complex thinking

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An interesting article I came across brings up an interesting issue: how do we do complex thinking?  Are some people just better at it?  The short answer appears to be ‘no’.  Instead, a couple of tools play a role, and I think it’s an interesting excursion.

The article says that our brains are limited in thinking about complex situations. Yet, experts can do this.  How? The article cites metaphors as the key, grounding our thinking in models that we’ve developed from our experiences. They draw upon George Lakoff’s work on metaphor (a core aspect of my grad school experience) to explain how our understanding advances.  At core, there’s a fundamental requirement that our knowledge builds upon previous knowledge, which ultimately is grounded in our physical activities.

My PhD thesis topic was thinking with analogy, which shares much with this model. The point being that we use familiar frameworks to make inferences in new areas. We map the familiar to the points in the new that match, and then we extrapolate from the familiar to explain things in the new. And using familiar models as explanatory frameworks are essentially the same process as metaphors. Metaphors tend to be more literal, with a shared point, while analogies go further, and share  structure. The latter is, I’ll suggest, more useful.

Note that the frameworks are built of conceptually-related causal relationships, e.g. models. Thus, when we want to communicate models, we can detail them, but using metaphors or analogies are short-cuts.  When we want someone to be able to understand, particularly to be able to use the reference as a tool to support  doing, we can use them to facilitate comprehension. We want to leverage, as much as possible, pre-existing knowledge.  And people aren’t necessarily great at coming up with analogies (research shows), but they’re good at using them.

Another short-cut that the article cites is diagrams.  Here, we’re making visible the relationships, supporting the understanding. Equations can get specific, but conceptual understanding is facilitated by seeing the connections.

The important outcome is that we all have our cognitive limitations to overcome, but we’ve also developed powerful tools to support these limitations. To the extent we understand how these tools support learning, we can use them to help achieve the outcomes we need.  We  can do complex thinking, with the right tools. Are you facilitating success by leveraging these tools?

Old and new school

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As I mentioned in yesterday’s post, I was asked for my responses to questions about trends.  What emerged in the resulting article, however, was pretty much contrary to what I said. I wasn’t misquoted, as I was used to set the stage, but what followed wasn’t what I said. What I saw was what I consider somewhat superficial evaluation, and I’d like to point to new school thinking instead.

So the article went from my claim about an ecosystem approach to touting three particular trends. And yet, these trends aren’t really new and aren’t really right!  They were touting mobile, gamification, and the ‘realities. And while there’s nothing wrong with any of them, I had said that I didn’t think that they’re the leading trends.

So, first, mobile is pretty much old news. Mobile first?  Er, it‘s only been 8 years or so (!) since Google declared that! What‘s cool about  mobile, still, is sensors and context-awareness, which they don‘t touch on.  And, in a repeated approach, they veered from the topic to quote a colleague. And my colleague was spot on, but it wasn’t in the least about mobile!  They ended this section talking about gamification and AR/VR, yet somehow implied that this was all about mobile. That would be “no”.

Then they talked about users wanting to be active.  Yay!  But, er, again they segued off-topic, taking personalization before going to microlearning and back to gamification and game-based learning(?).  Wait, what?  Microlearning is an ill-defined concept, and conflating it with game-based learning is just silly.  And games are real, but it‘s still hard to do them (particularly do them right, instead of tarted up drill-and-kill).  Of course, they didn‘t really stay on topic.

Finally, the realities. Here they stayed on topic, but really missed the opportunity. While AR and VR have real value, they talked about 360 photography and videography, which is about consumption, not interaction. And, that‘s not where the future is.

To go back to the initial premise – the three big trends – I think they got it wrong.  AI and data are now far more of a driver than mobile. Yes, AR/VR, but interaction, not just ‘immersion‘.  And probably the third driver is the ecosystem perspective, with systems integration and SaaS.

So, I have to say that the article was underwhelming in insight, confused in story, and wrong on topic. It’s like they just picked a quote and then went anywhere they wanted.   It’s old school thinking, and we’re beyond that. Again, my intention is not to continue to unpack wrong thinking (I’m assuming that’s not what you’re mostly here for, but let me know), but since this quoted me, I felt obliged.  It’s past time for new school thinking in L&D, because focusing on content is, like,  so last century.

Trends in L&D

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I agreed to be interviewed for an article, and was sent questions. And I wrote what I thought were cogent answers.  I even dobbed in a couple of colleagues to also be interviewed. However, the resulting article isn’t what I expected at all. Now, I don’t  intend to make all my posts critiques of what’s being said, but sometimes I guess I just can’t help myself!  So first, here’re my original answers.  In my next post, I’ll document the article’s claims, and my rejoinders about what I think are the driving trends in L&D.

The original questions and responses:

How has our thinking evolved on using technology to assist in learning and development?

Thinking around technology for Learning & Development has shifted from delivering ‘courses‘ to looking at the entire learning and performance ecosystem where technology can not only help us perform in the moment but also develop us over time. This adds performance support, resources and portals, and communication and collaboration tools to support learning alone and together from formal through to informal learning. We‘re recognizing that to move forward, organizations that can learn fastest are the ones most likely to not just survive but thrive. However, this goes beyond the tools and the people to the structures, values, and culture that underpin practices.

Do you think the current systems in use for L&D are adequate? If not, why so?

The legacy of the training mentality is keeping us mired in the past. I think that adding portal and social media capabilities to systems with a ‘course‘ DNA isn‘t the path forward. Instead, we should be looking to integrate capabilities from the best instances in every area. We want flexibility to switch tools if we find better solutions to specific needs, not one overworked legacy system. An LMS (learning management system; misnamed because you don‘t manage learning) may well still be of use to manage courses and signups, but it‘s the wrong foundation for the more agile future we need. Supporting curation and creation, and negotiating shared understandings are the learning that‘re going to be most valuable, and that requires not just different tools, but a different mindset. It‘s time to shift from delivery to facilitation.

What technology-assisted learning tools do you think hold the most potential?

Collaborative tools are the most important tools: the ability to collectively generate and manipulate representations that document how our thinking evolves are important. Such tools that support simultaneous and asynchronous work and communication will be key to the ongoing learnings that will propel organizations forward. New tools like VR can lead to deeper formal learnings, and AR will help both as performance support and annotating the world, but collaborative immersion and annotation fit into that first category. When we‘re developing an understanding together, we‘re creating the richest outcome. There are nuances in doing that right, and that‘s part of L&D‘s role too, but it‘s about tapping into the power of people. Technology that facilitates learning together is what will have the biggest impact.

What do you think is next for learning tech? Is there a huge shift coming?

I think the biggest thing coming for learning tech isn‘t the tech. The ICICLE initiative from IEEE that is defining ‘learning engineering‘ is a big move to start getting smarter about integrating the two components: learning science and technology design and development. Too often learning science is ignored (c.f. ‘rapid elearning‘) or the technical sophistication is missing (e.g. tracking done only at the ‘course‘ level). I think that once we get our minds around the importance of the integration, we‘ll be far better positioned to tap into the advancements we‘re seeing. While I think the hype about Artificial Intelligence is overblown, ultimately I believe that we‘ll have more powerful tools to automate what doesn‘t require the sophisticated capabilities of our brains, freeing us up to do the important work. And that work will be collaborating to generate new understandings. I do think there‘ll be a big shift, but it‘ll be coming along slowly. I hope this shift happens, but I think it‘s evolutionary, as change is hard.

Ok, so that’s what I said about the trends in L&D. What you will see is that what they presented is somewhat contrary to what I said here!

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.