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Clark Quinn’s Learnings about Learning

Do you feel lucky?

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roulette wheelOne of the things that I feel is undervalued is the role of luck. We hear about how the successful – the winners in business – get that way by virtue of their intelligence and diligence. Yet, if you think about it, lots of folks are smart and work hard. Yet not all succeed. Which made me wonder just how much of success is luck. I asked Siri (I was on a walk) and got the link to an article where they actually researched this. As to the answer, do you feel lucky?

The article starts with a suite of evidence. I know I’m mighty lucky to have been born as a white male in California, had both parents, was able to secure a really good education, and more. The data says that all these things are boons to the likelihood of success. There were also all sorts of weird variations (including middle initials contributes to success?).

Further, the article reports on how two researchers ran some simulations. They had characters with varying degrees of ‘talent’, and then also some good and bad luck. What happened, of course, is that the folks with a combination of luck and ‘talent’, did best. Talent alone didn’t do it, nor luck. In fact, the most talented didn’t succeed the most. “The most successful agents tended to be those who were only slightly above average in talent but with a lot of luck in their lives.”

The research goes further. It’s typical, in academia, that folks who get grants then are more likely to get subsequent grants. Which, it turns out, isn’t the best option. A different simulation by other researchers suggested random was better!  And, arguably, the best policy was giving everyone the same amount!

When we take this back to the real world, what seems to be important is that luck plays a big role in success. Those folks at the very top appear to have been very lucky. Further, their future success isn’t guaranteed (note that currently there’s a prime example of over-valuing previous success). If you’re smart, and dedicated, you’re more likely to do well, but you can also be subject to the slings and arrows of fortune which can similarly contribute.

I think we should be wary of rewarding past success with greater opportunity. We should also be wary of any assessment of how smart someone must be, just because they are successful. There are a lot of factors that contribute to success (for instance, research suggests, that being taller and having a deeper voice, increases the likelihood of doing well in business). They do say luck favors the prepared mind, so do work hard. But you’re also dependent on the vicissitudes of fate. Do you feel lucky?

For ‘normals’

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So, I generally advocate for evidence-based practices. And, I realized, I do this with some prejudice. Which isn’t my intent! So, I was reflecting on what affects such decisions, and I realized that perhaps I need a qualification. When I state my prescriptions then, I might have to add “for ‘normals'”.

First, I have to be careful. What do I mean by ‘normal’? I personally believe we’re all on continua on many factors. We may not cross the line to actively qualify as obsessive-compulsive, or attention-deficit, or sensorily-limited. Yet we’re all somewhere on these dimensions. Some of us cross some or more of those lines (if we’re ever even measured; they didn’t have some of these tests when I was growing up). So, for me, ‘normal’ are folks who don’t cross those lines, or cope well enough. Another way to say it is ‘neurotypical’ (thanks, Declan).

What prompted this, amongst other things, is a colleague who insisted that learning styles did matter. In her case, she couldn’t learn unless it was audio, at least at first. Now, the science doesn’t support learning styles. However, if you’re visually-challenged (e.g. legally blind), you really can’t be a visual learner. I had another colleague who insisted she didn’t dream in images, but instead in audio. I do think there are biases to particular media that can be less or more extreme. Of course, I do think you probably can’t learn to ride a bicycle without some kinesthetic elements, just as learning music pretty much requires audio.

Now, Todd Rose, in his book The End of Average, makes the case that no one is average. That is, we all vary. He tells a lovely story about how an airplane cockpit carefully designed to be the exact average actually fit no one! So, making statements about the average may be problematic. While we’ve had it in classrooms, now we also have the ability to work beyond a ‘one-size fits all’ response online. We can adapt based upon the learner.

Still, we need to have a baseline. The more we know about the audience, the better a job we can do. (What they did with cockpits is make them adjustable. Then, some people still won’t fit, at least not without extra accommodation)  That said, we will need to design for the ‘normal’ audience. We should, of course, also do what we can to make the content accessible to all (that covers a wide swath by the way). And, while I assume it’s understood, let me be explicit here that I am talking “for ‘normals'”. We should ensure, however, that we’re accommodating everyone possible.

Facilitating in the dark

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I recently spoke to the International Association of Facilitators – India, having chosen to focus on transfer. My intent was for them to be thinking about ensuring that the skills they facilitate get applied when useful. My preparation was, apparently, insufficient, leaving me to discover something mid-talk. Which leads me to reflecting on facilitating in the dark.

So, I’m not a trained facilitator (nor designer, nor trainer, nor coach). While I’ve done most of this (with generally good results),I’m guided by the learning science behind whatever. So, in this case, I thought they were facilitating learning by either serving as trainers or coaches. Imagine my surprise when I found out that they largely facilitate without knowing the topic!

In general, to create learning experiences, we need good performance objectives. From there, we design the practice, and then align everything else to succeed on the final practice. We also (should) design the extension of the learning to coaching past any formal instruction, and generally ensuring that the impact isn’t undermined.

How, then, do you get models, examples, and provide feedback on practice if you don’t know the domain? What they said was that they were taking it from the learners themselves. They would get the learners together and facilitate them into helping each other, largely. This included creating an appropriate space.

To me, then, there are some additional things that need to be done. (And I’m not arguing they don’t do this.) You need to get the learners to:

  • articulate the models
  • provide examples
  • ensure that they articulate the underlying thinking
  • think about how to unpack the nuances
  • ensure sufficient coverage of contexts
  • provide feedback on others’ experiences

This is in addition to creating a safe space, opening and closing the experience, etc.

So it caused me to think about when this can happen. I really can’t see this happening for novices. They don’t know the frameworks and don’t have the experience. They need formal instruction. Once learners have had some introduction and practice, however, this sort of facilitation could work. It may be a substitute for a community of practice that might naturally provide this context. You’d just be creating the safe space in the facilitation instead of the community.

The necessary skills to do this well, to be agile enough mentally to balance all these tasks, even with a process, is impressive.  I did ask whether they ended up working in particular verticals, because it does seem like even if you came in facilitating in the dark, you couldn’t help learn while doing the facilitation. There did seem to be some agreement.

Overall, while I prefer people with domain knowledge doing facilitation, I can see this. At least, if the community can’t do it itself. We don’t share enough about learning to learn, and we could. I do think a role for L&D is to spread the abilities to learn, so that more folks can do it more effectively. The late Jay Cross believed this might be the best investment a company could make!

Nonetheless, while facilitating in the dark may not be optimal, it may be useful. And that, of course, is really the litmus test. So it was another learning opportunity for me, and hopefully for them too!

 

Myths are models

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A recent LinkedIn post talked about how models are good, but myths are bad. Which was a realization for me. I’ve kept myths and models largely separate in my mind, but I realize that’s not the case. Myths are models, just wrong ones. And, I suppose, we need to deal with them as such. (Also, folks hang on to myths and models if they’re tied up with identity, but we should still be able to deal with the logical rationale.)

So, I’m an advocate for mental models. There are a variety of reasons, personal, pragmatic, and principled. Personally, I was gifted a book on mental models by my workmates as I left for graduate school. Pragmatically, they’re useful. On principle, they’re how we reason about the world. Heck, our brains are constantly building them!

The important aspects of models are that they’re predictive (and explanatory). That is, they tell us the outcomes of actions in particular situations. They are models of a small bit of the world, and are used to understand a perturbation of the model. They’re causal, in that they talk about how the world works, and conceptual in that they talk about the elements of the world. They’re incomplete, in that they only need to account for the parts of the world relevant to the particular situation.

Examples include using an analogy of water flowing in pipes for thinking about electric circuits. Or how advertisements use association with valued things or people to induce a positive affect. You can use them to explain what happened, or what will happen. It’s the latter that’s important for the purposes of providing a basis for guiding decisions, and thus their role in learning.  They guide us in deciding how to take actions under different circumstances.

Models can be good or bad. The old ‘planets circling a sun’ model of electrons in orbit around a nucleus of protons and neutrons turned out to be inaccurate as our understanding increased. We then moved to probability clouds as a better model. Many of our mistakes come from using the wrong model, for a variety of reasons. We can mistake the situation, or think a model is accurate and useful.

We should avoid models that aren’t appropriate for the situation. Myths are models that aren’t appropriate for any situation. So, for instance, learning styles, generations, ‘attention span of a goldfish’, and ‘images are processed 60K faster than prose’ are examples of myths. They lead people to make decisions that are erroneous, such as providing different learning prescriptions. They are models, because they do categorize the world and lead to prescriptions about what to do. They’re myths, because their implications will lead to decisions that waste time and money.

As the saying goes, “all models are wrong, but some are useful”. They’re wrong because they’re only part of the world. The good ones give us useful predictions, The bad ones lead us to make bad decisions. The useful ones are to be lauded, shared, and used. Myths, however, should be debunked and avoided. Myths are models, but not all models are good. It’s important that I remember that!

Valuing Diversity

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My lass has us engaging us in an activity. Being in it has sparked a recognition that’s not new, but continues to be important, particularly in the global context! I frequently talk about how diversity is important in getting the best ideas. Moreover, it’s not just ‘tolerating’ it, but valuing diversity. Why?

So the activity is choosing music that matches a theme. Everyone (in this case m’lady, and two offspring), submits four songs to a theme, and then when all are in, you vote. Not on yours, of course! For us, it’s not about who ‘wins’ so much as it’s about exposure to different music.

When we’re evaluating them is when I get a particular reaction. I typically realize “Oh, that reminds me of this other song, and I wish I’d thought of it as a candidate.”  What’s happening is that being exposed to other ideas expands my own thinking. Which is, after all, one of the things that helps us find solutions. Finding more solutions is a step on the path to finding good solutions!

Globally, I’ve heard of a country that is cracking down on diversity, trying to get everyone to adhere to the same world view. This includes diverse languages. Now, to be a country, I agree that there have to be some shared values. However, for the best opportunity for a country to succeed, tapping into the diversity of thoughts provides a greater likelihood of finding the best approaches. You risk stifling innovation to achieve stability, and that’s not a necessary tradeoff.

Diversity can be challenging. It means being able to accept other views, making it safe, and negotiating a shared understanding. On the flip side, that negotiated understanding is likely to be richer than what existed before. In the long term, that challenge leads to better outcomes.  Further, we can work together, when we follow what’s known.

So, if you want to get the best from your unit, whether business, organization, or society, you want to find ways to build diversity. And, then, find ways to use it, productively. We need more than acceptance, or tolerance. We need to be valuing diversity, and when we do, we do better.

Lazy thinking?

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Now, I’m the last who should throw stones. I can be quite guilty of lazy thinking, particularly when there’re commercial decisions to be made. (Providers have done a fabulous job of making sure you can’t compare apples to apples, and when there’re so many such situations…) Yet, there’s one place where I struggle with the consequences. That’s in our professional field, and it seems like there’re too many opportunities to yield.

A trigger was a recent conversation where an individual was talking about generations. Grouping folks by when they’re born is problematic at best. For one, the boundaries used seem to vary by who’s doing the categorizing. Not a solid basis. Moreover, the research suggests that there really aren’t meaningful differences. What do exist are explainable by age differences (which isn’t the same thing, for one it’s a continuum, not discrete chunks). Really, it’s a mild form of age discrimination, differentiating people by when they’re born, not who they are or how they behave. (Also problematic is the notion that events affect certain segments of the population, but that’s a longer conversation).  It’s one of the myths in my book on same.

Other examples include learning styles, hemispheres, gender differences, and more. First, they’re categorizations on things that people can’t control. Second, they don’t get backing from data. I just read that medical science has been excluding women from research based upon an assumption about temperature variability that was exposed as being irrelevant!

Sure, it’s much easier if we can reliably group people into segments that mean we have a reliable basis to do different things. Marketers do this with psychographics, for instance. Demographics can also matter. The problem here is that we’re using unreliable metrics. First, there are assumptions that turn out to be flawed. They frequently use self-report, also problematic. Some also have a flawed theoretical foundation.

Yes, it’s hard to keep on top of all of this. Ideally, you’d have time to investigate them all. In practice, there are other things to do. We all need ways to simplify our lives. Plus, vendors are telling you that they, at least, are immune to the complaints (with self-interest at stake).  On the other hand, there are good sources of insight from reliable translators of research. There are also practices we can follow to make it manageable. More help is on the way (at the LDA we’re working on it; stay tuned).

While lazy thinking is understandable, it’s not acceptable, at least not in our professional field. While we may not be sued for malpractice, we certainly should be responsible. So let’s avoid taking the easy path, at least when it matters. In our professional capacity, it matters when we’re designing for our learners. Let’s do so on evidence, not assumptions.

DnD n LnD

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multi-sided diceLast Friday, I joined in on a Dungeons & Dragons (DnD) campaign. This wasn’t just gratuitous fun, however, but was explicitly run to connect to Learning & Development folks (LnD). Organized by the Training, Learning, and Development Community (a competitor to LDA? I have bias. ;), there was both some preliminary guidance, and outcomes. I was privileged to play a role, and while not an official part of the followup (happening this week), I thought I’d share my reflections.

So, first, my DnD history. I played a few times while in college, but… I gave it up when a favorite character of mine was killed by an evil trap (that was really too advanced for our party). I’ve played a lot of RPGs since then, with a lot of similarities to the formal DnD games (tho’ the actual ones are too complex). Recently, with guidance from offspring two, our family is getting back into it (with a prompt from a Shakespeare and DnD skit at the local Renaissance Faire).

Then, I’ve been into games for learning since my first job out of college, programming educational computer games. It also became the catalyst for my ongoing exploration of engagement to accompany my interest in cognition/learning, design, and technology. The intersection of which is where I’ve pretty much stayed (in a variety of roles), since then! (And, led to my first book on how to do same.)

Also, about DnD. It’s a game where you create a character. There are lots of details. For one, your characteristics: strength, dexterity, wisdom, intelligence, and more. Those combine with lots of attributes (such race & role). Then, there’s lots of elaboration: backstory, equipment, and more. This can alter during the game, where your abilities also rise. This adds complexity to support ongoing engagement. (I heard one team has been going for over 40 years!)

Characters created by the players are then set loose in a campaign (a setting, precipitating story, and potential details). A Dungeon Master runs the game, Keegan Long-Wheeler in our case, writing it and managing the details. Outcomes happen probabilistically by rolling dice. Computers can play a role. For one, through apps that handle details like rolling the dice. Then folks create games that reflect pre-written campaigns.

One important thing, to me, is that the players organize and make decisions together. We were a group who didn’t necessarily know each other, and we were playing under time constraints. This meant we didn’t have the dialog and choices that might typically emerge in such playing. Yet, we managed a successful engagement in the hour+ we were playing. And had fun!

I was an early advocate of games for learning. To be clear, not the tarted up drill and kill we were mostly doing, but inspired by adventure games. John Carroll had written about this back in the day, I found out. However, I’d already seen adventure games having the potential to be a basis for learning. Adventure games naturally require exploring. In them, you’re putting clues together to choose actions to overcome obstacles. Which, really, is good learning practice! That is, making decisions in context in games is good practice for making decisions in performance situations. Okay, with the caveat that you should design the game so that decisions have a natural embed.

The complexity of DnD is a bit much, in my mind, for LnD, but…the design!  The underlying principles of designing campaigns bears some relation to designing learning experiences. I believe designing engaging learning may be harder than designing learning or games, but we do have good principles. I do believe learning can, and should, be ‘hard fun‘.  Heck, it’s the topic of my most recent tome! (I believe learning should be the elegant integration of learning science with engagement.)

This has been an opportunity to reflect a bit on the underlying structure of games, and what makes them work. That’s always a happy time for me. So, I’m curious what you see about the links between games and learning!

What does it take to leave?

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I did it, I finally left. I’m not happy about it, but it had to happen. (Actually, it happened some weeks ago.) So, what does it take to leave?

I’m talking about Twitter (oh, yeah, ‘X’ as in what’s been done to it ), by the way. I’d been on there a fair bit. Having tossed my account, I can’t see when my first tweet was, but at least since 2009. How do I know? Because that’s when I was recruited to help start #lrnchat, an early tweetchat that has still been going as recently as this past summer! I became an enthusiast and active participant.

And, let me be clear, it’s sad. I built friendships there with folks with long before I met them in person. And I learned so much, and got so much help. I like to tell the story about when I posted a query about some software, and got a response…from the guy who wrote it! For many years, it was a great resource, both personal and professional!

So, what happened? Make no mistake, it was the takeover by Elon Musk. Twitter went downhill from there, with hiccups but overall steadily. The removal of support, the politics, the stupid approaches to monetization, the bad actors, it all added up. Finally, I couldn’t take it any more. Vote with your feet. (And yes, I’m mindful of Jane Bozarth’s admonition: “worth every cent it cost you”. Yep, it was free, and that was unexpected and perhaps couldn’t be expected to last. However, I tolerated the ads, so there was a biz basis!)

Perhaps it’s like being an ex-smoker, but it riles me to see media still citing X posts in their articles. I want to yell “it’s dead, what you hear are no longer valid opinions”. I get that it’s hard, and lots of folks are still there, but… It had become, and I hear that it continues to be, an increasing swamp of bad information. Not a good source!

So where am I now? There isn’t yet an obvious solution. I’m trying out Mastodon and Bluesky. If you’re there, connect! I find the former to be more intimate. The latter is closer to twitter, but I’m not yet really seeing my ‘tribe’ there. I am posting these to both (I think). I’m finding LinkedIn to be more of an interaction location lately, as well, though it’s also become a bit spammy. #sideeffects? I keep Facebook for personal things, not biz, and I’m not on Instagram. I also won’t go on Threads or TikTok.

So, what does it take to leave? I guess when the focus turns from facilitating enlightening conversation at a reasonable exchange, to monetization and ego. When there’s interference in clean discourse, and opposition to benign facilitation. And, yes, I’m not naive enough to believe in total philanthropy (tho’ it happens), but there are levels that are tolerable and then there’s going to a ridiculous extreme. Wish I had $44B to lose! I know I’m not the only one wishing those who’ve earned riches would focus on libraries and other benevolent activities instead of ego-shots into space, but this is the world we’ve built. Here’s to positive change in alignment with how people really think, work, and learn.

Modeling mental models

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I’ve talked in the past about mental models (and continue to do so), but they seem a difficult concept to grasp. I was discussing them again in preparation for this post by partner Elevator 9. Despite their utility, grounding in fundamental cognition, and value in learning, they continue to be absent or misunderstood in our learning interventions. It’s worth taking some time and modeling mental models in use.

feedback loopTo start with, what are mental models? Wikipedia defines them as “an internal representation of external reality”. Really, it’s an explanation of how a small part of the world works. Wikipedia goes on to add “the mind constructs ‘small-scale models’ of reality that it uses to anticipate events”. They can manifest as equations, or sets of rules, but really, to me, they’re constructed from causal conceptual relationships. That is, there are entities, connected by causality. For example, feedback loops are a mental model. They occur in many situations, and what happens is that something takes the output, and feeds it back to the input, positively or negatively, to influence subsequent actions.  For instance, a thermostat uses the temperature as a way to determine whether to turn on or off a heater or cooling device.

The next question is why mental models. Back to Wikipedia: they “can help shape behaviour and set an approach to solving problems”. To me, they’re explanatory and predictive, in that they can explain why something occurred, and predict the outcome of various actions. It’s that latter that provides the instructional value. We want people to make good decisions. If they have a basis for determining the outcome of different courses of action, they can choose the best one. Providing them with a model for the domain, e.g. interpersonal relations (c.f. situational leadership), gives learners a way to choose optimally.

There’s evidence that our brains will build models, and that they won’t necessarily be good ones. In addition, if we don’t have a good model, we try to patch it rather than replace it. Which isn’t necessarily effective. Thus, the best approach is to provide a good model up front, and demonstrate its use. Then we use examples to demonstrate models in context, showing how the abstract concepts map to real world elements. Further, in general, we provide them before we give practice in most cases.

Instructionally, then, providing models is good support for making decisions. I’ve argued before that making decisions is more likely to be the deciding value for organizations (as opposed to fact recall). Thus, instruction around making better decisions is important. Therefore, instruction using models is going to be valuable! As a relevant aside, in many cases they’re valuably communicated via diagrams or, in the case where dynamics are important for comprehension, via animation. The point is that as they’re conceptual, you save the photos or videos for the examples.

Finally, if models are useful, then they should be part of our instructional toolkit. One small problem is that subject matter experts have compiled their knowledge away, and may not have conscious access to the mental models they use. This makes it difficult to extract them and make them comprehensible to novices. Yet, clearly, they’re useful, so we should be doing that. Knowing what they are and why they’re useful is, hopefully, a motivator for making the effort. And, succeeding!

So, please, spend the effort. We should be modeling mental models to our learners. Find, refine, and present the models via examples. Then develop them through practice, and use them in feedback, explicitly. With models, we have a better basis for learning design, and better chances for successful improvement. Which is what it’s about, right?

Note: I talk about mental models in my book Learning Science for Instructional Designers.

 

Engaging people at work

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Last week, Donald Taylor wrote an interesting post, wondering about ‘learner engagement’. That’s a topic I do talk a wee bit about ;). He closed with a call for feedback. So, while I did comment there, I thought it potentially would benefit from a longer response. I think it’s more general than learner engagement, so I’m talking about engaging people at work. (But it’s still relevant to his thesis without quibbling about that!)

In his post, he talked about three levels: asset, culture, and environment. I’m not sure I quite follow (to me, culture is an environmental level), and I’ve talked about individual, team, and organizational levels. To his point, however, there are steps to take at every level.

He starts at the individual level, talking about designing learning experiences. I agree with his ‘do deeper analysis’ recommendation, but I’d go further. To me, it’s not just if they recognize that content’s valuable, it’s about building, and maintaining, motivation while controlling anxiety (c.f. Make It Meaningful!). I don’t think he’d disagree.

At the next level up, it’s about making sure people are connected. Here, I’d point to Self-Determination Theory (SDT), and ‘relatedness’. I don’t mind Dan Pink’s reinterpretation of that to ‘purpose’, in that I think people need to know how what they’re doing contributes to something bigger, and that something bigger supports society as a whole.

Finally, to me, is culture. You want a ‘learning organization‘, as Don agrees. He says to start with a sympathetic manager, but I think L&D needs to create that culture internally first, then take it to the broader organization (and starting with said manager is a good next step).

I think that latter step solves Don’s final step of breaking down barriers, but he’s a smart guy and I’m willing to believe I’m missing some nuance. I do like his focus on ‘find a measure’ to use. However, ultimately, it should improve a lot of measures around adapting to change: innovation, retention, and success.  That’s my take, I welcome yours!