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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?

Telling stories

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I’ve talked about examples before, and have long suggested that examples for learning should be in the form of stories. Today I thought a little deeper about  how those stories can be told. And I thought of a particular entailment about video. But let me go through the whole  story.

So, first, an example needs to do several things.  Foremost, examples show how a concept is used in context to guide performance and achieve a solution. To put it another way, examples illustrate how a model is used to make decisions in a specific situation.  It could be situational coaching, a sales process, how to run a brainstorming session, or the basis of a good presentation. Importantly, examples need to show the underlying thinking behind what aspects of the situation made this model relevant, how it was mapped to the situation, how  it stipulated as actions to take, and what the outcomes were. It can also show mistakes, backtracking, and repair.

So how does this map to media?  There are (at least) three good ways to tell such a story. One, obviously, is prose. It’s easy to show the underlying thinking or discussions as “dialog” (internal or otherwise).  You can even illustrate model with a diagram, and convey context with a picture.  And this works.  However, for variety, you could use more visual treatments.

I’ve argued in the past that graphic novel formats also make sense. They strike a balance between conceptual and contextual,  and tell a story nicely. You can use thought bubbles to show the underlying thinking, include diagrams as a separate panel. And you can include necessary context but simplify the unnecessary context.

Finally, there’s video. This can be an animated cartoon (e.g. dynamic graphic novel), a narrated slideshow (think: Ken Burns), or a documentary style.  The question: how do you represent the model? Is it a separate narrated animation, or an overlay on the video (or both)?  I don’t think it matters as long as it’s explicit, and the underlying thinking is shown linking the concept to the context.

The point is that the nuances matter. It’s not just ‘content’; the elements that we provide have specific roles and we need to understand those.

Social Cognition

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In the two preceding posts, I discussed situated and distributed cognition. In this closing post of the series, I want to talk about social cognition. They’re related, and yet each needs explicit consideration. If we don’t know how we think, work, and learn, we can’t optimally support both performance in the moment and continual innovation over time.

The traditional definition of social cognition is how we think about social interactions.  But here I’m emphasizing instead the fact that our thinking isn’t just in our heads or our tools, but also across our partners. That’s partly distributed cognition, but I want to emphasize it.  And this is true for formal and informal learning as well as performing.

There are two ways to think about this. For one, we benefit from formal social interactions as ways to get richer interpretations. It works the same way when we are problem-solving: working together (under constraints) increases the likelihood of the best outcome. As I like to say, the room is smarter than the smartest person in the room  if you manage the process right.  The implications of this are several.

First, we need to make sure we have the right constraints. When we have people working together, it helps if it’s the right people  and the right environment.  We know that diversity helps, as long as there is overlap in values. Similarly, it needs to be psychologically safe to contribute, the environment helps to be open, and there needs to be time for reflection.

There’re also benefits to mentoring and coaching, helping people in the moment. We want to succeed, and we like to be challenged, and we learn when we are, so having scaffolding helps. Developing coaching and mentoring skills is a good investment in the workplace.

There’re also times when we want help, or someone else does and we can help. That is, we need to support serendipitous inquiry. It helps, by the way, to assist people in learning how to ask questions or answer them in useful ways. There also needs to be the channels to accomplish these goals.

Recognize that there are times when the answer can come from the network, not our own efforts. Particularly if things are changing fast, or the situation’s unique or hard to anticipate. In fact, it frees us up to do more if we take advantage of that as often as possible!  It takes nurturing the networks to become a community so that the answer’s likely to be right.

The point being, there are lots of considerations to making the ecosystem sociable as well as effectively distributed and situated.  If you want to optimize the environment, it helps to have the latest understanding of the users of that environment. Hope this makes sense, and in the spirit of social, I welcome your thoughts and comments!

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?

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?

 

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.  

Top 10 Learning Tools 2018

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Every year of late, Jane Hart has polled about people’s top 10 learning tools. From this, she creates a list of the top 100 learning tools. It’s a fun and valuable exercise, so as usual I’ll weigh in with mine (in no particular order).  Looking at last year’s post, I see I’ve switched from Google to DuckDuckGo (privacy issues), and from Skype to Zoom (functionality).  And I mention email over Slack, the former of which I may not always mention but use, versus Slack which has kind of slowed down for me.

  1. DuckDuckGo: a search engine is my first recourse when I have questions. And not liking the tracking, I’ve switched and made DDG my default.
  2. Twitter: drinking from the fire hose that is twitter is one way I see what other people are talking about.  And share what I discover.
  3. Mail: I still use email (yeah, I know).  I talk to people that way, and respond to requests, but also get questions answered and pointers to things.  (I’m bad about using the phone, mea culpa.)
  4. GoodReader: I use GoodReader on my iPad to read longish PDFs, white papers, and things. Also to read and mark up the journal or conference submissions I review. Or for requests for colleagues.
  5. MS Word: writing is one of the first ways I make sense of the world. Articles, columns, books.  And they all get written in Word. I wouldn’t mind disentangling myself, but it’s pretty much a lingua franca  and has industrial-strength outlining, which I rely on for longer writing.
  6. WordPress (e.g. this blog): is the other way I write and make sense of the world. It gets autoposted to LinkedIn and Facebook (at least, notifications), so comments there or here are a way for me to learn.
  7. Keynote is the presentation tool I use, and I create the stories I present at conferences in Keynote.
  8. OmniOutliner: another tool I use to be organized is OmniOutliner. Outlines are great ways for me to think in, or even just related columns. I can’t see how to do the outline function as well in something like a spreadsheet, or I would, because the columns in OO are, well, very expensive to upgrade.
  9. Omnigraffle: another way I make sense is to diagram. And…while I’m a bit grumbly at their support right now, OmniGraffle is still my ‘go to’ tool for this. I don’t need all the power, and it’s expensive, but I haven’t found a replacement yet that works as intuitively.
  10.  Zoom: I’ve pretty much switched to Zoom over Skype, so talking to my ITA colleagues,  or having video chats with folks is most often through Zoom these days.

Please do share your list, too!

Services

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From time to time, it’s worth a reminder that Quinnovation (the firm behind the blog) is available to help you.  Here are the services you can look to from me, in case you want to accelerate your success.

And a wee bit of self-promotion: if expertise comes from years of practice, how about 3+ decades of investigating the breadth and depth of learning & performance, and exploration of technology support?  Why not get assistance from where the thinking originates, not the several-steps away diluted version?

Consulting:

Learning Design: are your design processes yielding the outcomes they should and need to? I have worked with many organizations to generate or tighten learning design processes to reflect learning science (not myths). I recognize that most organizations can’t completely revamp their approaches, so I look to the small changes with the biggest impact. A white paper talks about this.

Performance Ecosystem Strategy: are you leading your organization forward in learning (read: innovation) or are you still taking orders for courses?  Based on the book, I’ve helped a number of organizations understand the full spectrum of possibilities, evaluate their situation, and prioritize short-, medium-, and long-term steps.  Another white paper talks about this.

Games & Mobile: I’ve helped a number of organizations get their minds, strategies, and design processes around mobile and/or games, based upon  those  books.

Workshops

Want to get your team up to speed on learning science, strategy, games, mobile, or more?  I have workshops on each that are interactive, engaging, and effective. Preferably, they’re coupled with followup to extend the learning (applying the learning science), and that can be done in a variety of ways.

Presentations

A number of organizations around the world have booked me to speak to their audiences. They have been about the subjects of my books, or the future of learning technology in general. And have indicated they were quite satisfied with the result ;). If you want a credible, engaging presenter around intelligence augmentation, I’m a candidate.

Writing

In addition to books, I write white papers, blog posts, and articles for others. I could do the same for you.

Coaching

If you’re a learning leader that would like assistance over time addressing your organization’s needs, it would certainly be worth a conversation. I haven’t done this formally, but it seems like a natural extension.

And, of course, there are combinations of these services as well. You can find out more at the official Quinnovation site. Next week we return you to your regularly scheduled blog at this same channel.

Nuances Matter

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I’ve argued before that the differences between well-designed and well-produced learning, and just well-produced learning, are subtle. And, in general, nuances matter. So, in my recent book, the section on misconceptions spent a lot of time unpacking some terms. The goal there was ensuring that the nuances were understood. And a recent event triggered even more reflection on this.

Learnnovators, a company I’ve done a couple of things with (the Deeper eLearning series, and the Workplace of the Future project), interviewed me once quite a while ago. I was impressed then with the depth of their background research and thoughtful questions. And they recently asked to interview me on the book. Of course, I agreed. And again they impressed me with the depths of their questions, and I realized in this case there was something specific going on.

In their questions, they were unpacking what common concerns would be about some of the topics.  The questions dug in to ways in which people might think that the recommendations are contrary to personal experience, and more.  There were very specifically looking for ways in which folks might think to reject the findings.  And that’s important. I believe I had addressed most of them in the book, but it was worth revisiting them.

And that’s the thing that I think is important about this for our practice. We can’t just do the surface treatment. If we just say: “ok we need some content, and then let’s write a knowledge test on it”, we’ve let down our stakeholders.  If we don’t know the cognitive properties of the media we use, don’t sweat the details about feedback on assessment, don’t align the practice to the needed performance, etc., we’re not doing our job!

And I don’t mean you have to get a Ph.D. in learning science, but you really do need to know what you’re doing. Or, at least, have good checklists and quick reference guides to ensure you’re on track. Ideally, you review your processes and tools for alignment to what’s known. And the tools themselves could have support. (Ok, to a limit, I’ve seen this done to the extent of handcuffs on design.)

Nuances matter,  if you care about the outcomes (and if you don’t, why bother? ;).  I’ve been working on both a checklist and on very specific changes that apply to various places in design processes that represent the major ways folks go wrong. These problems are relatively small, and easy to fix, and are designed to yield big improvements. But unless you know what they are, you’re unlikely to have the impact you intend.