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Clark Quinn’s Learnings about Learning

The cognitive basis of LXD

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Image of the brainWhen ATD asked me to write the learning science book, I’d already had the intention of writing a Learning Experience Design (LXD) book. I’d even begun, and the first section on learning was underway, so essentially I was partly done! I’d also realized that it was going to be monumental undertaking. This is because LXD, to me, encompasses three things, all based on cognitive science. To properly address it, I would have to be talking a master’s course, not just a book!. So here I’d like to make the case why I think that there’s a cognitive basis of LXD.

First, look at the three elements of LXD: learning, experience, and design. For experience, you can think: engagement and/or emotion. That is, ensuring that there’re explicit feelings associated, not just occurrences. Each one of those three things, then, has a cognitive underpinning.

As I’ve discussed previously, learning science was an outgrowth of cognitive science. The inter-disciplinary approach to cognition that inspired the formation of cognitive science subsequently led to learning science. Design, too, was a subject of study. I happened to be a grad student at the time that user-centered approaches, subsequently UX, were being explored. This, too, is cognitive; first because design approaches have to reflect aligning with how users brains work. Then, also, because design processes have to accommodate how designers brains work, and don’t!

Then we come to the experience side. It turns out that understanding ‘experience’ is a cognitive exercise as well. Why are we driven by curiosity? How come we remember emotionally-charged events better? What creates positive affect? It’s an interdisciplinary approach as well, integrating research on emotion and events and more. It’s the topic of my just-released book (which includes design as well, to serve as the complement to my learning science book).

I continue to explore all three, from a professional responsibility and personal interest. I admit I nerd out about these things, and am always eager to find out more and discuss it. And  I’ve do have  a bias. My Ph.D. is in Cog Psych, so I do look at world with that filter. But I also see that the perspective provides some useful leverage. My current ideal is to make experiences that are transformative, in that they change people in ways that they want, or need, to change. That’s the goal.

I will continue to maintain that knowing the underpinning architecture, and then the manifestations in the three areas, are important. I believe that knowing the cognitive basis of LXD is an advantage in being able to execute against the requirements in optimal ways. So, am I missing anything?

 

Confidence and Correctness

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Not surprisingly, I am prompted regularly to ponder new things. (Too often, in the wee hours of the morning…) In this case, I realize I haven’t given a lot of thought to the role of confidence  (PDF). It’s a big thing in the system my co-author on an AI and ID paper, Markus Bernhardt, represents, so I realized it’s time to think  about it some more. Here are some thoughts on confidence and correctness.

Confidence by correctnessThe idea is that it matters whether you get it right, or not, and whether you’re confident, or not. That is, they interact (creating the familiar four quadrant model). You can be wrong and unconfident (lo/no), wrong and confident (hi/no),  right and unconfident (lo/yay), and right and confident (hi/yay). Those are arguably importantly different. In particular for what they imply about what sort of intervention makes sense.

I was pondering what this suggests for interventions. I turned it 90 degrees to the left, to put low/no to the left, or beginning spot, and hi/yay to the right, and the other two in-between.  Simplified, my view is that if you’re wrong and not confident, you don’t know it. If you’re wrong and believe you know it, you’re at a potential teachable moment. When you’re right, but not confident, you’re ready for more practice. If you’re right and confident, it may be time to move on.

Which suggests, looking back at my previous exploration of worked examples, that the very first thing to do is to provide worked examples if they’re new. At some point, you give them practice. If they get it right but aren’t confident, you give more practice at roughly the same level. If they’re wrong but confident, you give them feedback (and arguably ramp them backwards). Eventually they’re getting it right  and confident, and at that point you move on (except for some spaced and varied reactivation).

Assessing confidence is an extra step, but there seems to be a valid reason to incorporate it in your learning design. The benefits of being able to more accurately target your interventions, at least in an adaptive system, suggest that the effort is worth it. That’s my initial thinking on confidence and correctness. What’s yours?

My Personal Knowledge Management Approach

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Last week, in our Learning Development Accelerator You Oughta Know session, we had Harold Jarche as a guest. Harold’s known for many things, but in particular his approach to continual learning. Amongst the things he shared was a collection of others’ approaches. I checked and I hadn’t made a contribution! So with no further ado, here’s my personal knowledge management approach.

First, Harold’s Personal Knowledge Management (PKM) model has three components: seek, sense, and share. Seeking is about information coming in, that is, what you’re looking for and the feeds you track. It can be in any conceivable channel, and one of the important things is that it’s  your seeking. Then, you make sense of what comes in, finding ways to comprehend and make use of it. The final step is to share back out the sense you’ve made. It’s a notion of contributing back. Importantly, it’s not that necessarily anybody consumes what you share, but the fact that you’ve prepared it for others is part of the benefit you receive.

Seek

Most seeking is two-fold, and mine’s no exception. First of all there’s the ‘as needed’ searches for specific information. Here I typically use DuckDuckGo as my search engine, and often end up at Wikipedia. With much experience, I trust it.  If there are multiple hits and not a definitive one, I’ll scan the sources as well as the title, and likely open several. Then I review them until I’m happy.

The second part is the feeds. I have a number of blogs I’m subscribed to. There are also the people I follow on Twitter. On LinkedIn, a while ago I actively removed all my follows on my connections, and only retained ones for folks I trust. As I add new people, I similarly make a selection of those I know to trust, and ones who look interesting from a role, domain, location, or other diversity factor.  An important element is to be active in selecting feeds, and even review your selections from time to time.

Sense

Sometimes, I’m looking for a specific answer, and it gets put into my work. Other times, it’s about processing something I’ve come across. It may lead me to diagramming, or writing up something, frequently both (as here). Diagramming is about trying to come to grip with conceptual relationships by mapping them to spatial ones. Writing is about creating a narrative around it.

Another thing I do is apply knowledge, that is put it into action. This can be in a design, or in writing something up. This is different than just writing, for me. That is, I’m not just explaining it, I’m using it in a solution.

Share

To share, I do things like blog, do presentations and workshop, and write books. I also write articles, and sometimes just RT. Harold mentioned, during the session, that sharing should be more than just passing it on, but also adding value. However, I do sometimes just like or share things, thinking spreading it to a different audience is value. If you’re not too prolific in your output, I reckon that the selected shares add value. Of course, in general if I pass things on I do try to make a note, such as when sharing someone else’s blog that I thought particularly valuable.

So that’s my process. It’s evolving, of course. We talked about how our approaches have changed; we’ve both dropped the quantity of posts, for instance. We’re also continually updating our tools, too. I’ve previously noted how comments that used to appear on my blog now appear on LinkedIn.

To be fair, it’s also worth noting that this approach scales. So workgroups and communities can do a similar approach to continually processing. Harold’s done it in orgs, and it factors nicely into social learning as well. One attendee immediately thought about how it could be used in training sessions!

So that’s a rough cut at my PKM process. I invite you to reflect on yours, and share it with Harold as well!

I discuss PKM in both my Revolutionize L&D book, and my Learning Science book.

Experts and Explanations

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blueprint pencil rulerI’ve been going through several different forms of expert documentation. As a consequence, I’ve been experiencing a lot of the problems with that! Experts have trouble articulating their thinking. This requires some extra work on the part of those who work with them, whether instructional designers, technical writers, editors, whoever. There are some reliable problems with experts and explanations that are worth reviewing.

The start of the problem is that the way we acquire expertise is to take our conscious thinking and automatize it, basically. We want to make our thinking so automatic that we no longer have to consciously think about it. So, we basically compile it away. Which creates a problem. For one, what we process into memory may not bear a close resemblance to what we have heard and applied. That is, the semantic language we use to guide our practice and internalize may not be what we store as we automate it.

It’s also the case that we lose access to that compiled away expertise. There’s evidence of this, for one from the results of research by the Cognitive Technology group at the University of Southern California showing experts can’t access about 70% of what they do! Another piece of evidence is the widespread failure of so-called ‘expert systems’ in the 80s, resulting in the AI winter. Whether the locus of the problem is in what actually gets stored, or access to it, the result is that  what we were told to do, and say we do, may not actually be close to what we actually do.

Another problem is that experts also lose touch with what they grappled with as novices. What they take for granted isn’t even on the radar of novices. So it’s difficult to get them to provide good support for acquiring skills or understanding. Their attempts at explanations for reference of instruction fail.

All told, this leads to systematic gaps in content. I’ve been seeing this manifest in explanations that may say what to do, but not why or how. There may be a lack of examples, and the thinking behind the examples I  do see isn’t there.  There’s also a lack of visual support. They’re not including diagrams when it’s conceptual relationships that need understanding. They’re also not including images when context is needed. They shouldn’t necessarily be blamed, because they don’t need the support and can’t even imagine that others do!

It’s clear that experts should not be the ones doing the explanations. They’re experts, and they have valuable input, but there needs to be a process to avoid these problems. We need tech writers, IDs, and others to work with experts to get this right. Too often we see experts being tasked with doing the explanations, and we live with the consequences.

What to do? One step is to let experts know that their expertise is in their domain, but the expertise in extracting that expertise and presenting it lies in others. To do so convincingly, you’ll need the science about why. For another, know techniques to unearth that underlying thinking. Also allow time in your schedule for this to happen. Don’t think the SME can just give you information; you’ll have to process what you get to rearrange it into something useful. You may also need some sticks and carrots.

As I wrestle with the outputs of experts, here’s my plea. There are wonderful ways experts and explanations can work out, but don’t take it for granted. Don’t give experts the job of communicating to anyone but other experts, or to experts on working with experts to get explanations. Fair enough?

Examples before practice

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I’ve been wrong before, and I’ll be wrong again, and that’s ok <warning: link is NSFW>. It’s like with science: if you change your mind, you weren’t lying before, you’ve learned more now. So I’ve been wrong about the emphasis between practice and examples. What I’ve learned is that, in general, practice isn’t the only area of importance, and the benefits of examples before practice.

So, as part of the Learning Development Accelerator‘s YOK (You Oughta Know) series, I got the chance to interview John Sweller. I’ve known John, I’m very honored to say, from my days at UNSW. I was aware of his reputation as a cog sci luminary, but he also turned out to be a really  nice person. He’s the originator of Cognitive Load Theory (CLT), and he was kind enough to agree to talk about it.

As background, he’s tapped into David Geary’s biologically primary and biologically secondary learning. The core idea is that some things we’ve evolved to learn, like speaking. Then there are things we’ve developed intellectually, like reading and writing, that aren’t natural. Instruction is to assist us to acquire the latter.  The latter typically has high ‘element interactivity’, whereby there are complex interrelationships to master. That is, it’s complex.

CLT posits that we have limited cognitive capacity, and overwhelming that capacity interferes with learning. The model talks about two types of load. The first is intrinsic load, that implied by the learning task. The second is extrinsic load, coming from additional factors in the particular situation. The premise is that learning complex things (biologically secondary) has such a high intrinsic load that we really need to focus on managing load so we can gradually acquire the entailed relationships.

There are a number of implications of CLT, but one is about the value of worked examples. An important element is showing the thinking  behind the steps. A second empirical result is that worked examples  are better than practice! At least, initially, for novices. Yet this upends one of my recommendations, which is generally that the most important thing we can do to improve our learning is focus on better practice. I still believe that, but now with the caveat after worked examples.  

Now, he didn’t tell us when that happens, e.g. when you switch from worked examples to practice. However, like the answer to how much spacing needed for the spaced practice effect, I suspect the answer is ‘it depends’. There’s the ‘expertise reversal’ effect that says as you gain experience, the value of worked examples falls and the value of practice raises. That point, I’d suggest, is dependent on the prior knowledge of the learners, the complexity of the material, the scope, and more.

I’m now recommending, particularly for new material, that improving the learning outcomes includes meaningful practice  after  quality worked examples. That’s my new, better, understanding. Make sense?

As an aside, I talked about CLT in my most recent book, on learning science, with a diagram. In it, I only included intrinsic and extrinsic, as those two seemed critical, yet the classic theory also includes  germane intrinsic load. One of the audience members asked him about that, and John opined that he probably needn’t have included germane. Vindication!

Good and bad advice all in one!

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I was asked to go to read an article and weigh in. First, please don’t do this if you don’t know me. However, that’s not the topic here, instead, I want to comment on the article. Realize that if you ask me to read an article, you’re opening yourself up to my opinion, good  or bad. This one’s interesting, because it’s both. Then the question is how do you deal with good and bad advice all in one.

This article is about microlearning. If you’ve been paying attention (and there’s no reason you should be), I’ve gone off on the term before. I think it’s used loosely, and that’s a problem because there are separate meanings, which require separate designs, and not distinguishing them means it’s not clear you know what you’re talking about. (If someone uses the term, I’m liable to ask which they mean! You might do the same.).

This article starts out saying that 3-5 minute videos are  not  microlearning. I have to agree with that. However, the author then goes on to document 15 points that are important about microlearning. I’ll give credit for the admission that there’s no claim that this a necessary and complete set. Then, unfortunately, I also have to remove credit for providing no data to  support the claims!  Thus, we have to evaluate each on it’s own merits.  Sorry, but I kinda prefer some sort of evidence, rather than a ‘self-evident’ fallback.

For instance, there’s a claim for brevity. I’ve liked the admonition (e.g. by JD Dillon) that microlearning should be no longer, and no shorter, than necessary. However, there’s also a claim here that it should be “3 – 10 minutes of attention span”. Why? What determines this? Human attention is complex, and we can disappear into novels, or films, or games, for hours. Yes, “Time for learning is a critical derailer”, but…it’s a factor of how important, complex, and costly if wrong the topic is. There’s no one magic guideline.

The advice continues in this frame: there’re calls for simplicity, minimalism, etc. Most of these are good principles,  when appropriately constrained. However, arbitrary calls for “one concept at a time is the golden rule”  isn’t necessarily right, and isn’t based on anything other than “our brains need time for processing”. Yes, that’s what automation is about, but to build chunks for short term memory, we have to activate things in juxtaposition. Is that one concept? It’s too vague.

However, it could be tolerated if some of the advice didn’t fall prey to fallacious reasoning. So, for instance, the call for gamification leans into “Millennials and Gen Z workforce” claims. This is a myth. Gamification itself is already dubious, and using a bad basis as an assumed foundation exacerbates the problem.  There are other problems as well. For one, automatically assuming social is useful is a mistake. Tying competition into the need to compete is a facile suggestion. Using terms like ‘horde’ and ‘herd’ actually feels demeaning to the value of community. A bald statement like “Numbers speak louder than words!” similarly seems to suggest that marketing trumps matter. I don’t agree.

Overall, this article is a mixed bag. So then the question arises, how do you rate it? What do you do? Obviously, I had to take it apart. The desire for a comment isn’t sufficient to address a complex suite of decent principles mixed up with bad advice and justified (if at all) on false premises. I have to say that this isn’t worth your time. There’s better advice to be had, including on microlearning. In general, I’ll suggest that if there’s good and bad advice all in one, it’s overall bad. Caveat emptor!

Generic Thinking Skills?

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Recently, a colleague asked a few of us about our views on critical thinking skills. This is actually a contentious topic. There are broad claims of the need for them, increasingly, even showing up in job advertisements. On the other hand, researchers and others have weighed in against them, saying that expertise is the only lever. I tend to lump critical thinking skills in with the broader issue of generic thinking skills, so what are the issues?

Upfront, I’ll admit that I like the concept of generic thinking skills. Say, for instance,  learning-to-learn skills. That is, domain independent skills that lead to better approaches. It seems to make sense that, in the absence of specific knowledge, some general approaches are more useful than others. For instance, faced with a new domain, I’d be inclined to expect that systematic experimentation and observation would be better than random trial and error.

On the other hand, prominent psychologists like John Sweller and Paul Kirschner have said that domain-specific skills are the only way to bet. There is significant evidence that expertise matters in successful approaches to problem-solving, and others. While we have some innate skills for domains that are biologically primary, learning in other domains requires expertise.

Is there, then, any evidence for generic skills? Based on Micki Chi’s work on the value of self-explanation, Kate Bielaczyc and others have found that instruction on systematically explaining steps in examples help, across domains. In my own Ph.D. thesis, I trained folks on analogical reasoning skills, and found improvement (for component skills that weren’t a) already ceilinged or b) were perceived to be immutable, across different problem types.

How, then, do we reconcile these conflicting viewpoints? My (self- :) explanation is that it’s a matter of degree, a continuum rather than a dichotomy. The more domain knowledge you possess, the more likely you are to find a good answer. However, what if you’re in a new domain where you don’t have relevant expertise to hand? In that case, I’ll suggest that there are benefits to some approaches over others, and training those general skills is justifiable. That is, general skills are weaker than domain specific skills, but general skills are better than nothing!

We know that there are practices that improve outcomes. For instance, I’ve written about how to, and not to, do brainstorming. Similarly, I believe Harold Jarche’s Seek-Sense-Share model works across domains. Systematic creativity is  not an oxymoron!  That’s the story I’m holding on to about generic thinking skills. What are your thoughts on the topic?

Reflecting (on 2021)

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I don’t think I’ve made a habit of it, but it occurs to me that it might be good to reflect a little on this past year. In particular, I want to revisit the areas I’ve been focusing on. There’ve been some emergent themes, and it’s worth it (to me, at least ;) to think a bit more about them. So here’s what I’m thinking about while reflecting on 2021.

Obviously, the cognitive and learning sciences have been a theme. The publication of my book on  learning science this year was a catalyst, as you might expect. In it, I covered not only the basics, but some of the extended areas. These extended areas include thinking about situated learning and the importance of context, distributed cognition and the use of external representations, and an area new for me, embodied cognition including gesture and motion. Annie Murphy Paul’s  The  Extended Mind covers these nicely.

Another topic is talking about engagement (including four posts on the topic, starting here). Which I view as the complement to the learning science side. I think of learning experience design as the elegant integration of learning science and engagement, and am continually working to create a definitive approach to the latter as I’ve done with the former. (Stay tuned.)

Coping with change is another recurrent theme. As we are facing increasing chaos, the ability of organizations to adapt requires innovation. Which, really, is a form of learning. I argue further that it’s an area L&D  should be engaged in. Agility will be a critical differentiator for organizations, and it’s an opportunity to be more central to organizational success.

I’ve also been on about how the transformation organizations need shouldn’t start with digital. I think this is an increasingly important realization in this era of change. To be successful, organizations need to work in coherence with how their people think, work, and learn. If you get that right, digitization can facilitate outcomes. However, if you digitize some of the old approaches that are holdovers from prior eras, you can limit the effectiveness of the investments.

Reviewing my past year’s posts, there’s a mix of other topics. I’ve continued my usual ‘takedowns’ of myths, shared thoughts on education, and unpacking nuances of learning design. A mixed bag, but then this blog is about my various ideas. So that’s my current reflections.

Take note, there will be some changes to announce come the new year. Until then, please have a safe and happy holiday season, and best wishes for the new year.

 

Time for Reflection

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My dad used to regale me with this tale of his best friend, who told his new employer when he started: “If you see me with my feet up on my desk and it appears I’m sleeping, I’m not. I’m working. I’ll still do the work of 2 other engineers.” And he did!  I love this story, because it brings out an element that we seem to be losing, the value of taking time for reflection.

Now, he may actually have been sleeping, yet that doesn’t concern me; sleeping is a mechanism for processing, too. What concerns me are folks who can’t be seen to be taking time off from ‘the work’. We’re in a mode where we push people to work harder and faster. We say “work smarter” but don’t tell people what that means!

I’ve spent time in a job early in my career reading (relevant) magazines like Byte, with my feet up on the desk. Yet, I immodestly suggest I cranked out work  at least as fast as my colleagues. I found reading, and now searching for answers, to be a valuable use of my time. Why? Because I’m  learning. I reflect on what I do and how to do it better, learning to do new things that I need to meet my current challenges.

Sure, I do the work. However, I also take walks, put my feet up and ponder, and more. I blog, for instance ;). There are other ways I write as well, and experiment, and look to refine my thinking. Also, I look things up, read books, and generally track my field and answers to specific questions.  My work improves as a consequence. Moreover, we  all benefit from taking time to reflect. It’s documented in the work conducted by Garvin, Edmondson, & Gino as one of the elements of a learning organization.

So, I’ll keep promoting, and practicing, taking time for reflection. I hope you can, too. Moreover, I hope you can help get such time recognized as valuable in your organization. We focus too much on the fast, and as they say: “fast, cheap, or good, pick 2”. I’m not sure fast is always the best solution. Certainly for learning. After all, it is about learning…

 

The case for good timekeeping

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It occurs to me that maybe not everyone has the same view of timekeeping that I do. So I thought I’d make the case for good timekeeping. To me, it’s about  coping with change.

To me, it starts with respect. This includes respect for the audience, the speaker, topic, and context. There are times when timekeeping  should be lax, I believe. My first take is that the defining circumstances are when there are no people involved who aren’t already part of it, there’s not a fixed time agenda, speaker times aren’t set, and the outcome is more important than punctuality.. In other words, rare for a public event.

When an event is public, there tend to be some other constraints. Most importantly, there’s liable to be a schedule. People need to know when to arrive. If it’s a more than one event in the schedule, extending beyond 1 hour, and the audience is diverse (e.g. not just one company), time becomes increasingly important.

Why should we care? Back to the starting point, people might be coming in to see someone in particular. If the schedule isn’t adhered to, folks who were counting on a particular time could be disappointed. If there’s a start and end time, and block, speakers further down the agenda could be impacted if someone runs long. Neither is fair.

I have experienced folks who seem to be unaware of time. In my personal experience as a speaker, I was on a joint presentation where the leadoff presenter seemed to forget that there was anyone else on the agenda! He was gracious once it was pointed out, but it was uncomfortable for me to have to break in and remind him. I’ve also seen people unaware that they were running long, and others unable to amend their presentation on the fly when they  did become aware. I’ve literally seen someone have to stop where they are instead of finding a way to wrap up!

Having had early experience  being  a moderator, after being the victim of such sessions, I made a commitment (in line with the event organizer’s intent) to be rigorous. I’ve now no qualms about, after giving fair warning, stopping someone who hasn’t maintained control. To the contrary of a possible position, I think it’s rude  not to! It’s the speaker’s fault, no one else’s. They aren’t being professional and respectful of the audience and the other speakers. That also includes getting out of the way in a timely manner if it’s a scheduled room, leaving time for the next person to set up.

So my guidelines for timekeeping:

  1. Designate someone as the timekeeper; there can’t be a question over who’s job it is.
  2. Warn speakers ahead of time about the rigor and practices. No excuses!
  3. Have a practice for signaling if things are getting close, e.g. signs for # of mins left, colored lights, messages in chat (tho’ some people seem unable to process them; beware), what have you.
  4. As a presenter, if you don’t have a good basis for assessing your likely length (e.g. I’m about a slide a minute, though with some quick builds it can be faster), practice and check your timing! Realize that live it’s likely to go a bit longer than your practice. Trim if necessary.
  5. Also, have enough awareness of your material that you can adapt on the fly. Sometimes other things happen (once the power went out in a presentation), and you have to adjust.
  6. Be firm; interrupt and stop speakers when it’s time.

That’s off the top of my head; I’m sure there are more comprehensive and thorough lists. My point is to be aware, and prepared. As a speaker, I appreciate it. As an audience member as well. That’s my case for good timekeeping.  What do you think?