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Clark Quinn’s Learnings about Learning

What sorts of activities?

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When we do learning, we must be active. That is, it’s not enough to receive information. (Unless we’re actively practicing and attending presentations are reflection.) We must do! Then the question becomes one of doing ‘what’? I’m seeing too many of the wrong sorts of things in play, so it’s worth asking: what sorts of activities should we be doing?

Cognitively, we need to perceive information to get it into working memory. From there, to get into long-term memory – and be useful – we need to elaborate and practice retrieval. Elaboration is the process whereby we strengthen connections between the new material and the familiar. This increases the likelihood of activation in context. Then, we need to practice retrieving the knowledge for use. This strengthens our ability to retrieve and apply as we need.

One thing to note is that research shows that we don’t need to retrieve the fact-based knowledge before practicing retrieval to actually use. Our goal for organizational learning is to use information to make meaningful decisions. Better fact-recall isn’t likely to be what will help your organization thrive. Instead, what matters is acquiring the new skills that will define the ability to adapt.

For elaboration, what we increasingly hear is about ‘generative‘ learning activities. These are when you’re taking new information, and processing it more deeply. It can involve rephrasing, visualizing, and of course connecting it to your prior experience. These activities help strengthen the information into long-term memory.

An associated task it to practice using the information. That is, putting learners into situations where they need to use the new information to make decisions that they couldn’t before. The ideal situation, of course, is mentored live practice, but…there are limitations. Individual mentoring isn’t always cost-effective. Also, live practice may have consequences for wrong answers. In many cases, we use simulations. These can be programmed, or branching scenarios. Even mini-scenarios (e.g. better written multiple-choice questions) are a good option.

What we don’t need are fact-check questions. As above, there’s no real benefit. They may make us feel good, but they aren’t inclined to make us better at using the information. There are lots of bad practices around this. We can just use knowledge questions, thinking we’re helping learning (and not). Worse, I’ve seen many cases where they’re asking for arbitrary bits of information that aren’t highlighted. Also, too often we’re presenting way too much information than people can remember at one time (or at all).

So, if we’re to design effective learning, what sorts of activities is an important question. We don’t need fact-checks. We do benefit from processing, and retrieval. That’s worth practicing and performing. Review your work and look at what you’re having learners do. If it’s not elaboration and retrieval, you’re wasting learners’ time and your efforts. Why do that?

 

Why the EIP Conference

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On my walk today, I was pondering the Evidence-informed Practitioner (EIP) conference (rapidly approaching, hence the top-of-mind positioning). And, I was looked at it a different way. Not completely, but enough. So, I thought I’d share those thoughts with you, as a possible answer to “why the EIP conference?”

To start, the conference was created to fill the gap articulated at our Learning Science conference. To wit, “this is all well and good, but how do we do it in practice?” Which, as I’ve opined, is a fair question. And we resolved to answer that. 

I started with pondering, while perambulating, about the faculty. We’ve assembled folks who’ve been there, done that, know the underpinnings, and are articulate at sharing. Sure, we could ask people to submit proposals, but instead we went out and searched for the folks we thought would do this best. 

My cogitations went further. What would be the best way for folks to get the answers they need? And, of course, the best is mentored live practice…like most learning would be. And, like most learning, that’s not necessarily practical to organize nor affordable. So, what’s the next best thing?

You could do uni courses in it all. You could read books about it all. Or, you could have a focused design. That is, first you have the best folks available create presentations about it. Then, have discussion forums available to answer the questions that arise. With the presenters participating. Finally, you have live sessions at accessible times to consolidate the content and discussions. Again, with the presenters hosting. 

That last is what we’ve actually done. That’s what my reflection told me; this is pretty much the best way to get practical advice you can put into practice right away, and refine it. At least, the best value. From the time the videos are available ’til the live sessions, you have a chance to put what’s relevant to you into practice – that is, try it out – and have experts around to share what you’ve learned and answer the emergent questions!  

Let’s be clear. Most confs have presentations and time to talk to the presenters, but not the time between presentations and scheduled discussion to try things out. Here, between my co-director Matt Richter and myself, we created a pedagogy that works. 

Further, I got to choose the curriculum, starting with what most folks do (design courses), and then branch out from there: first, the barriers, then forward to analysis, and back to evaluation. Then we go broader, talking about extending learning via motivation and coaching, resources for continuing to learn, technology, and move to not learning via performance support. Finally we on to org-spanning issues including innovation and culture. 

This is the right stuff to know, and an almost ideal way to learn it, in a practical format. It’s all asynchronous so you can do it at your own schedule, except for the live sessions, and for each they’re each offered at two different times to increase the likelihood that you can attend the ones you want to. Of course, they’re all taped as well. 

But wait, there’s more! (Always wanted to say that. ;) If you order now, using the code EIP10CQ, you get 10% off! That makes a great deal become exceptional! Ok, so I’m laying it on a bit thick, but we really did try to make this the gala event of the season, and a valuable learning experience. So, I hope to see you there. Anyways, that’s my answer to why the EIP conference.

Knowledge or skills?

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Ok, I’ve been wrong before, and it appears I am again. I rail against pure knowledge, and felt Ed Hirsch was making that argument. Yet, Paul Kirschner and co-conspirators have him writing the intro to their latest work, The Case for Knowledge. In it, they make the case for the necessity of knowledge as a necessary precursor for critical thinking skills. And, Paul’s been on the side of Sweller in arguing against critical thinking skills. Yet, there’s also recently been shown that you can make valuable headway with teaching skills. How do we reconcile all this? Is it knowledge or skills that matters?

So, I took Ed Hirsch’s book Cultural Literacy, well, literally. That is, I heard him arguing that folks needed a common basis of facts. And, of course, I agree. I do think we need to all understand what 1492 means. But, to me, it was more. It doesn’t do anything to know that and not know in what context that makes sense: that it was the first western European path opened up to the lands of the Americas. Yes, it’d been done before, and yes, the resulting rapaciousness wasn’t beneficial, but it was the first opening of that particular corridor.

What I thought I saw (and consequently must have been wrong about), was that Hirsch stopped at the knowledge. Because Kirschner and co-authors of the recent work make an eloquent case for the need for knowledge. They’ve argued that critical thinking skills are specifically domain-dependent. That is, you need the knowledge of the domain to know how to adequately use that knowledge to make determinations.

Now, I’ve had mixed thoughts about this. For one, I do think we need these skills. Further, I have also believed that to teach them, you can’t do it without specific domains. On the other hand, I improved analogical reasoning skills (across problems) in my Ph.D. thesis, and succeeded. (At least, in the moment, I wasn’t shooting for persistent improvement.) Further, Micki Chi found self-explanation was a useful approach for understanding examples, and Kate Bielaczyc successfully tutored folks on those skills. More recently, I came across a paper from Bernacki, et al, that improved disadvantaged learners success by teaching learning to learn strategies. How do we reconcile this?

Of course, it’s knowledge and skills. I’d heard it said before, and am inclined to agree, that you get more impact with domain-specific skills. But, good approaches across domains should at least have some impact. I know Valerie Shute and Jeffrey Bonar wrote tutors that focused on experimentation skills across domains: geometric optics, economics, and electrical circuits. Of course, I don’t know whether they yielded impacts! Yet with the results mentioned, it seems like there’s measurable benefit to learning to learn skills.

What is clear, however, is that teaching to pass tests isn’t leading to the ability to think critically. I also recently read that teachers have to teach to the test and haven’t time to teach critical thinking skill. Certainly, from an organizational perspective, you can’t count on your employees knowing how to learn on their own. You might be in a situation where you can hire for such skills, but that’s not going to be all orgs. Further, I’ve argued before with the late Jay Cross that it might be the best investment to train same. Look, the answer to knowledge or skills is yes! You can’t do just one, yet there seems to be too much focus on the former, and not the latter. Don’t trust to folks having the thinking and learning skills you need, develop them. Please!

Idealism and reality

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Of late, I’ve been thinking a lot about idealism versus reality (for a lot of reasons). I’ve been a staunch advocate for better learning science in practice (an idealistic stance). And, we’re running a conference because we’ve gotten feedback that folks wonder what that means in practice (reality). My own situation is a case in point as well. So, I’m doing some reflecting on idealism and reality.

To start, I’m a principled kind of person. I try to follow the best recommendations from what we know about how we think, work, and learn. Perhaps I err too much on that side, as I’ve avoided things like commissions and paid endorsements.  That’s because I want my recommendations to come from real value, not my personal benefit. Which should be better in the long term, but I’m also aware I’m not a great biz dev type. For perhaps the same reasons. (And, it appears in retrospect, that when I do sell myself, I do so far too cheaply!)

Despite myself, I’ve managed to be involved in some things I care about. People have come to me, and I’ve managed to support the family for the past almost quarter century(!). Yet, there’ve been good times, and lean. (In the latter, currently.) Yet I haven’t been one to jump on bandwagons, for instance the latest hype around Generative AI. You might think a voice of caution would be appropriate, but the evidence appears to be to the contrary. C’est la vie. I’m not intending to change my stance, just being aware and honest with myself (and, consequently, you).

Beyond my own issues, I see that our field still faces challenges. Perhaps from our origins – taking good performers and trying to turn them into trainers without sufficient preparation – we end up trying to meet unrealistic expectations. “Do it once, and it’s good enough!” Cheaply and quickly, of course. If we measured, we might know otherwise, but that’s still too rare. Everyone has faith that we’re ‘sufficient’.

Yet, as an idealist, I see what we could offer, if we could manage to turn things around. I am an optimist (despite any appearances as a curmudgeon to the contrary ;). We could be impacting organizational success with aptly targeted interventions. Moreover, we could be the ones guiding organizations to new insights that find opportunity from increasing change. And so I keep fighting for the principled view. AND, practical steps to get there. I keep hoping (idealistically) that there are those who want to steadily move to a better organizational position where they’re both doing well what they know they need to, and efficiently exploring the new opportunities to adapt to the changing environment.

And, frankly, that’s the opportunity I’m looking to offer. Of course, in reflecting on the realities, I recognize that people also need to find ways to do better within the existing constraints, and steadily (stealthily?)  move those constraints to a better place (reality). Having done so for pretty much all my many moons of a career, I do have practical steps around that. That, too, is what’s on offer. There are ways to balance idealism and reality. Stay tuned (or tap in!).

Analogy and models

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I’ve gone on a bit about the value of mental models in instruction (and performance). (I guess this cements my position as a representationalist!) My interest isn’t surprising, given my background. But someone recently pointed out to me an aspect that I hadn’t really commented on. And, I should! So here’s some thoughts on analogy and models.

The initial callout was me talking about models, and communicating them. In particular, I’ve mentioned a number of times the value of diagrams. Yet, someone else pointed out that another useful mechanism is analogy. And this rocked me, because of course! Yet, I’ve neglected this mention.

As context, I’ve been a fan of mental models for thinking since I got the gift of a book on them from my work colleagues as I headed off to grad school. Moreover, I did my PhD thesis on analogy! I broke down analogical processing in a unique way, and looked at performance. finding some processes could be improved. Then I tried training on a subset, and achieved some impact.

Analogy is, by the way, a useful way to communicate models. What’s important in models are the conceptual causal relationships. If there’s another, more familiar model with the same structure, you can use it. For instance, the flow of electricity in wired can be analogized to the flow of water in pipes. Another, flawed, model is saying that the orbit of electrons around a nucleus is like the orbit of planets around a sun.

So, why have I been blind to the use of analogies? Perhaps because I’m so familiar with them that I just assume others see the possibilities? Or maybe I’ve just got a huge blind spot!  Still, it’s a big miss on my part.

When you want learners to ‘get’ models (and I think we do), you can present them as diagrams. You can have people embody them through things like Gray’s gamestorming.  And, of course, you can use analogies. We have to be careful; empirically, most folks aren’t good at generating them, they focus too much on surface features. Yet, what’s necessary is sharing what cognitive scientists call ‘deep structure’, the important relationships that guide outcomes. People are good at using given analogies, but don’t always recognize them as useful unless prompted.

If, and it’s not a given, we have a familiar structure that happens to share the relationships of the model we’re trying to communicate, we can make an analogy! Though, there are nuances here too. For instance, Rand Spiro found that, when developing an understanding of muscle operation, a progression of analogies was needed to develop the final understanding!

Still, we shouldn’t ignore the possibilities of analogy. Some have argued that we fundamentally understand the world by bringing in prior models to explain. Which isn’t hard to countenance in a ‘predictive coding’ view of the world, that we’re actively trying to explain observations. Wrong models are typically an explanation for misconceptions, using  the wrong model in new ways. We have to diagnose and remediate those understandings, because folks don’t tend to replace their models, they patch them. Giving good models a priori, via analogy or otherwise, is a good remedy.

Analogy is a feature of our cognitive architecture and formal representations. It’s a useful way to communicate how the world works, when possible. Like with all things, of course, the nuances matter, but analogy and models are tools we have to facilitate understanding, if indeed we understand them. So let’s, eh?

Contextual Leadership

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I’m not a leadership guru by any means. In fact, having read Pfeffer’s Leadership BS, I’m more of a cynic. However, I have been learning a bit from my LDA co-director Matt Richter (as well as CEO of E9, and leadership coach, David Grad). Matt’s a fan of Keith Grint, UK Historian, who talks about how you need to make decisions differently in different situations. His approach reminds me of another, so here I’m looking at contextual leadership.

Grint talks about three situations:

  • Tame: where things are known, and you just manage
  • Wicked: where things are fluid, and you need to lead a team to address
  • Critical: where things are urgent, and you need to make a decision

The point being that a leader needs to address each objective appropriately to the type of circumstance you’re facing. Makes sense. We know these different situations arise.

What this reminds me of is Dave Snowden’s Cynefin framework (he’s very clear not to call it a model). Again, I’m not au fait with the nuances, but I’ve been a fan of the big picture. The main thing, to me, are the different situations he posits. That includes:

  • Clear means we have known solutions
  • Complicated likewise, but requires certain expertise for success
  • Complex systems, which require systematic exploration
  • Chaotic, and here you just have to do something 

As I understand it, the goal is to move things from chaotic and complex to complicated or clear. (There’s a fifth area in the framework, confusion, but again I’m focusing on the big picture versus nuances.)

So, let’s do a mapping. Here, I posit, tame equates to clear and complicated, wicked is complex, and critical is chaotic. Clearly, there’s a time element in critical that doesn’t necessarily apply in the Cynefin model. Still, despite some differences, one similarity emerges.

The important thing in both models is you can’t use the same approach to all problems. You have to recognize the type of situation, and use the appropriate approach. If it’s critical, you need to get expert advice and make a choice. If it’s not, but it’s new or uncertain, you assign (and lead) a team to investigate. This, to me, is really innovation.

The tame/clear, to me, is something that can and likely should be automated. People shouldn’t be doing rote things, that’s for machines. Increasingly, I’m seeing that we’re now getting computers to do much of the ‘complicated’ too, rightly or wrongly. We can do it right, of course, but there are times when the human pattern-matching is superior, and we always need oversight.

The interesting areas are the complex and chaotic. Those are areas where I reckon there continue to be roles for people. Perhaps that where we should be focusing our efforts. Not everyone needs to be a leader every time, but it’s quite likely that most everyone’s potentially going to be pulled into the decision-making in a wicked or complex situation. How we manage those will be critical, and that’s about managing process to obtain the best out of the group. That’s something I’ve been looking at for a long time (there’s a reason my company is called Quinnovation ;). Particularly the aspects that lead to the most effective outcomes.

So, we can automate the banal, manage the process right in innovating, and be decisive when things are time-critical. Further, we can select and/or develop people to be able to do this. This is what leadership should be, as well as, of course, creating the culture that the group will exist in. Getting the decision-making bit right, though, builds some of the trust that is necessary to accomplish that last bit. Those are my musings, what are yours?

 

 

 

Our (post) cognitive nature?

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A regular commenter (by email) has taken me to task about my recent post on cognitive science. Which is fair, I’m open to criticism; I can always learn more! Yet, I feel that the complaint isn’t actually fair. So I raise the debate here about our (post) cognitive nature. I welcome feedback!

So, the gist of the discussion is whether I’m positing a reductionist and mechanistic account of cognition. I argue, basically, that we are ‘meat’. That is, that our cognition is grounded in our physiology, and that there’s nothing ephemeral about our cognition. There is no ineffable element to our existence. To be clear, my correspondent isn’t claiming a metaphysical element either, it’s more nuanced than that.

What I am missing, supposedly, is the situated nature of our cognition. We are very much a product of our action, is the claim. Which I don’t dispute, except that I will maintain we have to have some impact on our cognitive architecture. Channeling Paul Kirschner, learning is a change in long-term memory, which implies the existence of the latter. For instance, I argued strongly against a view that all that we store from events is the emotional outcome. If that were the case, we’d have nothing to recreate the experience, yet we can recount at least some of the specifics.  More emotional content means more recall, typically.

The accusation is that I’m being too computational, in that even if I go sub-symbolic, I’m still leveraging a computational model of the world. Whereas I believe that our thinking isn’t formal logical (as I’ve stated, repeatedly). Instead, we build inaccurate and incomplete models of the world (having shifted from formal mental models to a more predictive coding view of the world).  Further, those models are instantiated in consciousness in conjunction with the current context, which means they’re not the same each time.

Which is where I get pilloried. Since we haven’t (yet) explained consciousness, there must be something more than the physical elements. At least as I understand it, and it’s not clear I do. Yet, to me, this sort of attitude seems to suggest that it’s beyond comprehension, and maybe even matter. Which I can’t countenance.

So, that’s where the discussion is currently. Am I still cognitivist, or am I post-cognitivist? I’m oversimplifying, because it’s been the subject of a number of exchanges, without resolution as yet. This may trigger more discussion ;). No worries, discussion and even debate is how we learn!

The garden path

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Two recent times, I’ve seen glorious stories of how things could be. And, to be fair, I’ve been guilty myself; I have pursued and purveyed rosy stories. Yet, as I recognize more of the world’s challenges – randomness, illogic, bias, money, and more – I begin to question myself. What is it about the garden path?

The usual story is something along the lines of ‘first this happens, and it leads to this, …, and then this wonderful thing happens.’ The transitions sound plausible, they could happen!  The causal story continues from good outcome to next good outcome, until we get the inevitable results. And, if we’re not careful, we might miss the problem.

There’s also the chance that the transitions won’t happen. Brian Klaas’ Fluke is one story that illustrates the role chance plays. Randomly, things don’t go as planned. Julie Dirksen’s Talk to the Elephant, talks about the ways our systems and people themselves go awry. There are many things that stand in the way of  things working out the way you expect or even intend. As has been said, never predict anything, particularly the future.  I once heard an analysis that says that the trends you observe do tend to continue, but something unexpected always flips them from where you thought things would go.

Another issue are the underlying assumptions. Often, they’re more unlikely than they seem. Will Y happen because X happened (e.g. will this person get the job offer because they rode up in the elevator with the hiring manager)?  Do you even accept the premise of the assumption? Just because someone tells you that the sky is green, are you going to believe them when your own experience may differ.

There are benign situations, and then some that are not.  When I have told such stories, I (sadly) believed them. I have been an idealist (and in many ways still am), so I inferred a world where things worked as planned. (I have learned better, for instance watching a promising enterprise be undermined by ego and greed.) Then there’re the more insidious ones, when someone’s telling a story to convince you to do something that is less likely than is portrayed. In either case, either the innocently naive or the venally misleading, are prevailing upon the gullible. And, of course, I’ve been victim on the receiving end as well.

What’s my point (he asks himself)? I guess that it’s to be wary of such stories. Don’t tread along the rosy trail portrayed without some assessment of the probabilities. Ask yourself if the final outcome is as plausible as the starting point would suggest? There’s lots of room for distraction as you trod the garden path. Be aware of claims that all will follow the same path!

What and why cognitive science?

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Image of the brainI was on LinkedIn, and noted this list of influences in a profile: “complex systems, cybernetics, anthropology, sociology, neuroscience, (evolutionary) biology, information technology and human performance.” And, to me, that’s a redundancy. Why?

A while ago, I said “Departments of cognitive science tend to include psychologists, linguists, sociologists, anthropologists, philosophers, and, yes, neuroscientists. ” I missed artificial intelligence and computer science more generally. Really, it’s about everything that has to do with human thought, alone, or in aggregate. In a ‘post-cognitive’ era, we also recognize that thinking is not just in the head, but external. And it’s not just the formal reasoning, or lack thereof, but it’s personality (affect), and motivation (conation).

Cognitive science emerged as a way to bring different folks together who were thinking about thinking. Thus, that list above is, to me, all about cognitive science! And I get why folks might want to claim that they’re being integrative, but I’m saying “been there, done that”. Not me personally, to be clear, but rather that there’s a field doing precisely that. (Though I have pursued investigations across all of the above in my febrile pursuit of all things about applied cognitive science.)

Why should we care? Because we need to understand what’s been empirically shown about our thinking. If we want to develop solutions – individual, organizational, and societal –  to the pressing problems we face, we ought to do so in ways that are most aligned with how our brains work. To do otherwise is to invite inefficiencies, biases, and other maladaptive practices.

Part of being evidence-informed, in my mind, is doing things in ways that align with us. And there is lots of room for improvement. Which is why I love learning & development, these are the people who’ve got the most background, and opportunity, to work on these fronts. Yes, we need to liase with user experience, and organizational development, and more, but we are (or should be) the ones who know most about learning, which in many ways is the key to thinking (about thinking).

So, I’ve argued before that maybe we need a Chief Cognitive Officer (or equivalent). That’s not Human Resources, by the way (which seems to be a misnomer along the lines of Human Capital). Instead, it’s aligning work to be most effective across all the org elements. Maybe now more than ever before! At least, that’s where my thinking keeps ending up. Yours?

Writing, again

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So, I’m writing, again. Not a book (at least not initially ;), but something. I’m not sure exactly how it’ll manifest, but it’s emerged. Rather than share what I’m writing (too early), I’m reflecting a bit on the process.

As usual, I’m writing in Word. I’d like to use other platforms (Pages? Scrivener? Vellum?), but there are a couple of extenuating circumstances. For one, I’ve been using Word since I wrote my PhD thesis on the Mac II I bought for the purpose. I think that was Word 2.0, circa late 80’s. In other words, I’ve been using Word a long time! Then, the most important thing besides ‘styles‘ (formatting, not learning) is the ability to outline. Word has industrial-strength outlining, and, to use an over-used and over-emphatic point, I live and die by outlines.

I outline my plan before I start writing, pretty much always. Not for blog posts like this, but for anything of any real length beyond such a post. Anything with intermediate headings is almost guaranteed to be outlined. I tend to prefer well-structured narratives (at least for non-fiction?). It likely will change, of course. When my very first book was written, it pretty much followed the structure. Ever since then…  My second book had me rearranging the structure as I typed. My most recent book got restructured after every time I shared it with my initial readers, until suddenly it gelled.

In this case, and not unlike most cases, I move things around as I go. This should be a section all its own. That is superfluous to need. This other goes better here than where I originally put it. And so on. I do take a pass through to reconcile any gaps or transitions, though I try to remedy those as I go.  The goal is to do a coherent treatment of whatever the topic is.

I throw resources in as I go. That is, if I find myself referring to a concept, I put a reminder in a References or Resources section at the end to grab a reference later. I have a separate (ever-growing) file of references for that purpose. Though I may not always include the reference in the document (currently I’m trying to keep the prose lean), but I want folks to have a resource at least.

I also jump around, a bit. Mostly I proceed from ‘go to whoa’, but occasionally I realize something I want to include, and put a note at the appropriate place. That sometimes ends up being prose, until I realize I need to go back to where I was ;). I hope that it leads to a coherent flow. Of course, as above, I do reread sections, and I try to give a final read before I pass on to whatever next step is coming. Typically, that means sending to someone to see if I’m on track or off the rails.

I also am pondering that I may retrofit with diagrams. Sometimes I’ve put them in as I go. At other times, I go back and fill them in. I do love me a good diagram, for the reasons Larkin & Simon articulated (Connie Malamed is doing a good job on visuals over at LinkedIn this month). Sometimes I edit the ones I have as I recognize improvements, sometimes I create new ones, sometimes I throw existing ones in. It’s when I think they’ll help, but I can think of several I probably should make.

The above holds true for pretty much all writing I do beyond these posts. This is for me, first, after all! Otherwise, I solicit feedback (which I don’t always get; I think folks trust me too much, at least for shorter things). I’m sure others work different. Still, these are my thoughts on writing, again. I welcome your reflections!