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Clark Quinn’s Learnings about Learning

Processing

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I’ve been thinking a lot about processing in learning of late; what processing matters, when, and why. I thought I’d share my thinking with you and see what you think.  This is  my processing!  :)

We know processing is useful. You can consider Craik & Lockhart’s Levels of Processing model, or look to the importance of retrieval practice as highlighted in Brown, Roediger, and McDaniel’s Make it Stick. The point is that retrieving information from memory and doing things with it increases the likelihood of learning. One of the questions is  “what sort of retrieval (or processing)?”

I’ve always advocated for  applying the information, doing something with it.  But there are actually a variety of useful things we can do:

  • representing information (a form of reflection) whether rewriting, or mindmapping, or…
  • connecting to other known information, personal or professional
  • considering how it would be applied in practice
  • applying it in practice, real or simulated

Of course, we want there to be scrutiny and feedback for the learning to be optimized, etc.

Now, this is in the individual instance, but I’m also looking at the sequence of processing. What would be a series of activities that would develop understanding. So, for instance, for a problem-solving practice like trouble-shooting a process, what might you do? You might have  (say, after a model of the process, and examples) a sequence of :

  • critique someone else’s performance
  • try a simple example of performing
  • try a more complex example (perhaps in a group)
  • …(more examples of performing)
  • try a very complex (read: typical) example

We could throw in related tasks as well either during or as a summary:

  • create a checklist to follow
  • draw a flow diagram
  • create a representation

On a more categorical task, say determining whether a situation qualifies as this or not (with shades of grey in between), we would have a similar structure, but with different types of tasks (again, after initial content such as definition and examples):

  • review a case where it clearly is (white)
  • review a case where it clearly isn’t (black)
  • group review a case of grey (but not too bad)
  • group review a case of grey (more shady)
  • …

Again, we could have interim or summary tasks:

  • summarize the constraints
  • document a proposed process
  • make a plan for how to do it in the future
  • …

What I’ve explicitly added here is when and why to go ‘social‘.  There are benefits for the same, but should they all be social?  I’ll argue that there’s some initial prep that’s individual, to get everyone on the same page. Since all are different, it helps if this is individual. Then there’s often value in doing it socially, for the reasons in the linked post.  Then, I reckon there’s value in doing  something independently, to consolidate the learning. And, of course, to determine what capability the individual has acquired.

The point I want to make is that the processing  flow, the progression from activity to activity, matters. We want to introduce, diverge, and then converge.  We do need to elaborate across contexts to support transfer, and of course increase complexity until they’ve developed the ability to deal with the typical difficulty of cases.

I’m thinking that, too often, we forget the consolidation phase.  And we’re often doing processing that’s somewhat like what we need them to do, but ultimately tangential. There are multiple constraints here to be acknowledged, cognitive such as depth and breadth as well as pragmatic such as cost and time, but we want to find the right intersection.

And my practical question is: where does this fall apart? Are their situations where this doesn’t make sense?  I realize there are other types of outcomes that I haven’t represented (I’m being indicative, not exhaustive ;), but is this a useful way to think about it?

 

Labels, models, and drives

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In my post last week on engagement, I presented the alignment model from my  Engaging Learning  book on designing learning experiences. And as I thought about the post, I pondered several related things about labels, models, and drives. I thought I’d wrestle with them ‘out loud’ here, and troll (in the old sense) to see what you think.

Some folks have branded a model and lived on that for their career. And, in a number of cases, that’s not bad: they’re useful models and their applicability hasn’t diminished. And while, for instance, I think that alignment model is as useful as most models I’ve seen, I didn’t see any reason to tie my legacy to it, because the principles I like to comprehend and then apply to create solutions aren’t limited to just engagement. Though I wonder if people would find it easier to put the model in practice if it had a label.  The Quinn Engagement model or somesuch?

I’ve also created models around mobile, and about performance ecosystems, and more. I can’t say that they’re all original (e.g. the 4Cs of mobile), though I think they have utility. And some have labels (again, the 4Cs, Least Assistance Principle…) Then the misconceptions book is very useful, but the coverage there isn’t really mine, either. It’s just a useful compendium. I expect to keep creating models. But it’d led to another thought…

I’ve seen people driven to build companies. They just keep doing it, even if they’ve built one and sold it, they’re always on it; they’re serial entrepreneurs. I, for instance, have no desire to do that. There are elements to that that aren’t me.    Other folks are driven to do research: they have a knack for designing experiments that tease out the questions that drive them to find answers. And I’ve been good at that, but it’s not what makes my heart beat faster. I do  like action research, which is about doing with theory, and reflecting back. (I also like helping others become able to do this.)

What I’m about is understanding and applying cognitive science (in the broad sense) to help people do important things in ways that are enabled by new technologies.  Models that explain disparate domains are a hobby. I like finding ways to apply them to solve new problems in ways that are insightful but also pragmatic.   If I create models along the way (and I do), that’s a bonus. Maybe I should try to create a model about applying models or somesuch. But really, I like what I do.

The question I had though, is whether anyone’s categorized ‘drives’.  Some folks are clearly driven by money, some by physical challenges. Is there a characterization?  Not that there needs to be, but the above chain of thought led me to be curious. Is there a typology of drives? And, of course, I’m skeptical if there is one (or more), owing to the problems with, for instance, personality types and learning styles :D. Still, welcome any pointers.

Engagement

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In a meeting today, I was asked “how do you define engagement”, and I found it an intriguing question. I don’t know that I have a definition so much as steps to enhance it. Still, it made me think.

What engagement is not, let’s be clear, is tarting content up. It’s not just flashy visuals, stereotypes, and cute prose.  Those things add aesthetics (or, done poorly, undermine same), but that’s not where to go.

Flow stateInstead, I’m looking for an experience that has certain characteristics. One way of looking at it is through the ‘flow’ phenomenon, with cognitive immersion at a level that finds the sweet spot between frustration and boring.  Similarly, for learning, it’s the Zone of Proximal Development, between what you can do with one hand tied behind your back, and what you can’t do no matter how much support you get.  And it’s both.

You there by exploiting the alignment between the elements of practice and engaging experiences. So just as the above diagram can represent either Czikszentmihalyi or Vygotsky, there’s the alignment I highlighted in Engaging Learning  between the elements in greater elaboration. It’s goal, context, challenge, meaningfulness, and more all aligned to create that subjective feeling. And in case you say “you’re extending engagement to learning”, I will note that Koster, in his book A Theory of Fun, explicitly tied what makes games work  is that it’s about learning. So, yeah, that’s the type of engagement I’m interested in, regardless.

One of the simple ways I like to characterize it (and it’s not original with me), is ‘hard fun’.  I think, if nothing else, that’s a great heuristic. It may be like the famous quote about pornography: “you know it when you see it”. Or maybe you can coin a concise definition. And you can attempt to quantify it through objective criteria like galvanic skin response or adrenalin levels. However, I’m perfectly happy to use subjective criteria. If people say they found it challenging but fun, I’m happy. If they say it’s the best way they can see to learn it, my job is done.

I don’t really yet have a good way to define engagement in a concise specification. Do you have a definition of engagement you like?  I’d welcome hearing it!

 

 

Another Day Another Myth-Ridden Hype Piece

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Some days, it feels like I’m playing whack-a-mole. I got an email blast from an org (need to unsubscribe) that included a link that just reeked of being a myth-ridden piece of hype.  So I clicked, and sure enough!  And, as part of my commitment to showing my thinking, I’m taking it down. I reckon it’s important to take these myths apart, to show the type of thinking we should avoid if not actively attack.  Let me know if you don’t think this is helpful.

The article starts by talking about millennials. That’s a problem right away, as millennials is an arbitrary grouping by birthdate, and therefore is inherently discriminatory. The boundaries are blurry, and most of the differences can be attributed to age, not generation. And that’s a continuum, not a group. As the data shows.  Millennials is a myth.

Ok, so they go on to say: “Changing the approach from adapting to Millennials to leveraging Millennials is the key…”  Ouch!  Maybe it’s just me, but while I like to leverage assets, I think saying that about people seems a bit rude.  Look, people are people!  You work with them, develop them, etc. Leverage them?  That sounds like you’re using them (in the derogatory sense).

They go on to talk about Learning Organizations, which I’m obviously a fan of.  And so the ability to continue to learn is important.  No argument. But why would that be specific to ‘millennials’?  Er…

Here’s another winner: “They natively understand the imperative of change and their clockspeed is already set for the accelerated learning this requires.”  This smacks of the ‘digital native’ myth.  Young people’s wetware isn’t any different than anyone else’s. They may be more comfortable with the technology, but making assumptions such as this undermines the fact that any one individual may not fit the group mean. And it’s demonstrable that their information skills aren’t any better because of their age.

We move on to 3 ways to leverage millennials:

  1. Create Cross-pollination through greater teamwork.  Yeah, this is a good strategy.  FOR EVERYONE. Why attribute it just to millennials?  Making diverse teams is just good strategy, period. Including diversity by age? Sure. By generation?  Hype. You see this  also with the ‘use games for learning’ argument for millennials. No, they’re just better learning designs! (Ok, with the caveat: if done well.)
  2. Establish a Feedback-Driven Culture to Learn and Grow Together. That’s a fabulous idea; we’re finding that moving to a coaching culture with meaningful assignments and quick feedback (not the quarterly or yearly) is valuable. We can correct course earlier, and people feel more enagaged. Again,  for everyone.
  3. Embrace a Trial-and-Error Approach to Learning to Drive Innovation. Ok, now here I think it’s going off the rails. I’m a fan of experimentation, but trial and error can be smart or random. Only one of those two makes sense. And, to be fair, they do argue for good experimentation in terms of rigor in capturing data and sharing lessons learned. It’s valuable, but again, why is this unique to millennials? It’s just a good practice for innovation.

They let us know there are 3 more ways they’ll share in their next post.  You can imagine my anticipation.  Hey, we can read  two  posts with myths, instead of just one.  Happy days!

Yes, do the right things (please), but  for the right reasons. You could be generous and suggest that they’re using millennials as a stealth tactic to sneak in messages about modern workplace learning.  I’m not, as they seem to suggest doing this largely with millennials. This sounds like hype written by a marketing person. And so, while I advocate the policies, I eschew the motivation, and therefore advise you to find better sources for your innovation practices. Let me know if this is helpful (or not ;).

Why Myths Matter

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I’ve called out a number of myths (and superstitions, and misconceptions) in my latest tome, and I’m grateful people appear to be interested.  I take this as a sign that folks are beginning to really pay attention to things like good learning design. And that’s important. It’s also  important not to minimize the problems myths can create. I do that in my presentations, but I want to go a bit deeper.  We need to care about why myths matter to limit our mistakes!

It’s easy to think something like “they’re wrong, but surely they’re harmless”.  What can a few misguided intentions matter?  Can it hurt if people are helped to understand if people are different?  Won’t it draw attention to important things like caring for our learners?  Isn’t it good if people are more open-minded?

Would that this were true. However, let me spin it another way: does it matter if we invest in things that don’t have an impact?  Yes, for two reasons.  One, we’re wasting time and money. We will pay for workshops and spend time ensuring our designs have coverage for things that aren’t really worthwhile. And that’s both profligate and unprofessional.  Worse, we’re also not investing in things that might actually matter.  Like, say,  Serious eLearning. That is, research-derived principles about what  actually works. Which is what we should be getting dizzy about.

But there are worse consequences. For one, we could be undermining our own design efforts. Some of these myths may have us do things that undermine the effectiveness of our work. If we work too hard to accommodate non-existent ‘styles’, for instance, we might use media inappropriately. More problematic, we could be limiting our learners. Many of the myths want to categorize folks: styles, gender, left/right brain, age, etc.  And, it’s true, being aware of how diversity strengthens is important. But too often people go beyond; they’ll say “you’re an XYZ”, and people will self-categorize and consequently self-limit.  We could cause people not to tap into their own richness.

That’s still not the worst thing. One thing that most such instruments explicitly eschew is being used as a filter: hire/fire, or job role. And yet it’s being done. In many ways!  This means that you might be limiting your organization’s diversity. You might also be discriminatory in a totally unjustifiable way!

Myths are not just wasteful, they’re harmful. And that matters.  Please join me in campaigning for legitimate science in our profession. And let’s chase out the snake oil.  Please.

Where’s Clark? Fall 2018/Spring 2019 Events Schedule

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Here’re the events where I’ll be through the last quarter of this year, and into the next. Of course, you can always find out what’s up at the Quinnovation News page… But this is a more likely place for you to start unless you’re looking to talk to me about work.  I hope to see you, virtually or in person, at one of these!

The week of October 22-26, Clark will be speaking (the same week!) at DevLearn on measurement and eLearning science, and at AECT on meta-learning architecture. (Yeah, both in one week…long story.)

On Litmos’ Live Virtual Summit on 7-8 November, Clark will talk Learning Experience. Stay tuned!

Clark will be a guest on Relate’s eLearnChat on 15 Nov.

2019

On the 9th of January, Clark will present The Myths that Plague Us as a webinar for HRDQ-U.

Clark will be presenting in the Modern Workplace Learning track at the LearnTec conference in Karlsruhe, Germany that runs 29-31 January.

Feb 25-27, Clark will serve as host of the Strategy Track at Training Magazine’s annual conference, opening with an overview and closing with a strategy-development session.

Clark will speak to the Charlotte Chapter of ISPI on the Performance Ecosystem on March 14.

At the eLearning Guild’s Learning Solutions conference March 25-28, Clark will be presenting a Learning Experience Design workshop, where we’ll go deep on integrating learning science and engagement.

If you’re at one of these events, please do introduce yourself and say hello (I’m not aloof, I’m just shy; er, ok, at least ’til we get to know one another :).

ONE level of exaggeration

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I’ve argued before that we should be thinking about exaggeration in our learning design. And I’ve noticed that it’s a dramatic trick in popular media. But you can easily think of ways it can go wrong. So what would be appropriate exaggeration?

When I look at movies and other story-telling media (comics), the exaggeration  usually is one level.  You know, it’s like real life but some aspect is taken beyond what’s typical. So, more extreme events happen: the whacky neighbor is  maniacal, or the money problems are  potentially fatal, or the unlikely events on a trip are just more extreme.  And this works; real life is mundane, but you go too far and it treads past the line of believability. So there’s a fine line there.

Now, when we’re actually performing, whether with customers or developing a solution, it matters. It’s our  job after all, and people are counting on us.  There’s plenty of stress, because there are probably not enough time, and too much work, and…

However, in the learning situation, you’re just mimicking the real world. It’s hard to mimic the stress that comes from real life. So, I’m arguing, we should be bringing in the extra pressure through the story. Exaggerate!  You’re not just helping a customer, you’re helping the foreign ambassador’s daughter, and international relations are at stake!  Or the person you’re sweet on (or the father of said person) is watching!  This is the chance to have fun and be creative!

Now, you can’t exaggerate everything. You could add extraneous cognitive load in terms of processing if you make it too complex in the details. And you definitely don’t want to change the inherent decisions in the task and decrease the relevance of the learning. To me, it’s about increasing the meaning of the decisions, without affecting their nature. Which may require a bit of interpretation, but I think it’s manageable.

At core, I don’t think I’m exaggerating when I say exaggeration is one of your tools to enhance engagement  and effectiveness. The closer we bring the learning situation to the performance situation, the higher the transfer. And if we increase the meaningfulness of the learning context to match the performance context, even if the details are more dissimilar, I think it’s an effective tradeoff. What do  you think?

Wise technology?

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At a recent event, they were talking about AI (artificial intelligence) and DI (decision intelligence). And, of course, I didn’t know what the latter was so it was of interest. The description mentioned visualizations, so I was prepared to ask about the limits, but the talk ended up being more about decisions (a topic I  am interested in) and values. Which was an intriguing twist. And this, not surprisingly led me back to wisdom.

The initial discussion talked about using technology to assist decisions (c.f. AI), but I didn’t really comprehend the discussion around decision intelligence. A presentation on DA, decision analysis, however, piqued my interest. In it, a guy who’d done his PhD thesis on decision making talked about how when you evaluate the outputs of decisions, to determine whether the outcome was good, you needed values.

Now this to me ties very closely back to the Sternberg model of wisdom. There, you evaluate both short- and long-term implications, not just for you and those close to you but more broadly, and with an  explicit  consideration of values.

A conversation after the event formally concluded cleared up the DI issue. It apparently is not training up one big machine learning network to make a decision, but instead having the disparate components of the decision modeled separately and linking them together conceptually. In short, DI is about knowing what makes a good decision and using it. That is, being very clear on the decision making framework to optimize the likelihood that the outcome is right.

And, of course, you analyze the decision afterward to evaluate the outcomes. You do the best you can with DI, and then determine whether it was right with DA. Ok, I can go with that.

What intrigues me, of course, is how we might use technology here.  We can provide guidelines about good decisions, provide support through the process, etc. And, if we we want to move from smart to  wise decisions, we bring in values explicitly, as well as long-term and broad impacts. (There was an interesting diagram where the short term result was good but the long term wasn’t, it was the ‘lobster claw’.)

What would be the outcome of wiser decisions?  I reckon in the long term, we’d do better for all of us. Transparency helps, seeing the values, but we’d like to see the rationale too. I’ll suggest we can, and should, be building in support for making wiser decisions. Does that sound wise to you?

Example Diagram

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No, not a diagram that’s an example, a diagram about examples!  I created this because I needed a diagram to represent examples. I’ve written about them, and I have diagrams for other components of learning like models. However, I wanted to capture some important points about examples. So here we go.

Example elements

The idea here is that an example should be a story, with narrative flow. You start with a problem, and flow through the process to the outcome.

One of the important elements along the way is showing the steps  and the  underlying thinking. Experts may be saying “you do this, then this” but what they’re not articulating is important to. It’s more like “I could’ve done this  or this, but because of this…” and that needs to be heard.

Even better if a mistake was made, caught, and remedied. Showing that, and how, you monitor performance as you go is important for learners to see. That’s not illustrated here, because it  is optional.

What is captured here is that there is (or should be) a conceptual model guiding your performance, and that should be explicitly referenced in the thinking. It should show how the model was instantiated because of the context, and how it led to the outcome.

These, I argue, are important points about examples that are reflected in the work of Schoenfeld as captured in Cognitive Apprenticeship (by Collins & Brown). Making thinking visible is an important component of learning whether classroom or workplace. So, have I shown  my thinking?

Post popularity?

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My colleague, Will Thalheimer, asked what posts were most popular (if you blog, you can participate too).  For complicated reasons, I don’t have Google Analytics running.  However, I found I have a WordPress plugin called Page Views. It helpfully can list my posts by number of guest views.  I was surprised by the winner (and less so by the runner up). So it makes me wonder what leads to post popularity.

The winner was a post titled  New Curricula?  In it, I quote a message from a discussion that called for meta-cognitive and leadership skills, and briefly made the case to support the idea.  I certainly don’t think it was one of my most eloquent calls for this. Though, of course, I do believe in it.  So why?  I have to admit I’m inclined to believe that folks, searching on the term, came to this post rather than it was so important on it’s own merits.

Which isn’t the case with the post that had the second most views.  This one, titled  Stop creating, selling, and buying garbage!, was a rant about our industry. And this one, I believe, was popular because it could be viewed as controversial, or at least, a strong opinion.  I was trying to explain why we have so much bad elearning (c.f. the  Serious eLearning Manifesto), and talking about various stakeholders and their hand in perpetuating the sorry state of affairs.

Interestingly, I won an award last year for my post on AR (yes, I was on the committee, but we didn’t review our own).  And, I was somewhat flummoxed on that one too. Not that there weren’t good thoughts in it, but it was pretty simple in the mechanism: I (digitally) drew on some photos!  Yet clearly that made something concrete that folks had wondered about.

Of course, I think there’s also some luck or fate in it as well. Certainly, the posts I think are most interesting aren’t the ones others perceive.  But then, I’m biased. And perhaps some are used in a class so you get a number of people pointed to it or something. I really have no way to know.  I note that the posts here at Learnlets are more unformed thoughts, and my attempts at more definitive thoughts appear at the Litmos blog and now at my Quinnsights columns at Learning Solutions.

I’ll be interested in Will’s results (regardless of whether my data makes it in, because without analytics I couldn’t answer some of his questions).  And, of course, I welcome any thoughts you have about what makes a post popular (beyond SEO :), and/or what you’d  like to read!