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Clark Quinn’s Learnings about Learning

Small changes with big impact

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In the reality stakes, I recognize that people aren’t likely to throw their whole approach out. Instead, they make the small changes with big impact. Then, of course, they should use success to leverage the opportunity to do more. You can bring in a full evaluation of everything you do by the latest fad, but those tend to be expensive and out of date by the time they’re done.  Wherever you are, there’s room for improvement. How do you get there? By understanding how we think, work, and learn.

So, one of the things I’ve done, repeatedly across clients, is look at what they’re doing (including outputs and process). I have tended to do this in a lightweight approach, because I know most folks are sensitive to costs, and want to get the biggest bang for the buck. I’ve done so for content, for design practices, for market opportunities, and more.

To do so means I go through materials, whether products, processes, or plans, to understand the experience and look for ways to improve it. Then, we prioritize those potential opportunities. I then bring my independent observations together for a discussion on what’s useful and necessary. Of course, we always find things that don’t meet those criteria. My concluding reports typically state the goals, the current context, the applicable principles, and recommendations. I’m also happy to work with folks to see how it works out and what tweaks may be of use. Which isn’t every engagement, but it’s not infrequent.

One of the robust outcomes, for what it’s worth, is that folks get insights they (and I) didn’t expect! That may be because I’ve been an interdisciplinary mongrel, with interests in many things, or possibly because the cognitive foundations provide a basis to address most anything. Regardless, I’ve found opportunities to improve in pretty much all situations. These are at every level from how to implement a field to collect information to an assessment of the viability of a go-to-market strategy.

In short, looking at things from the perspective of how our brains work provides insights into ways in which we’ve violated that alignment. Further, it’s a reliable phenomena that pretty much everything we do has opportunities to improve. Sure, not all such moves will be worth the effort, or may conflict with what folks have learned to live with. Still, there’s a pretty-much guaranteed to be valuable changes that can be made. At least, that’s been my experience, and my clients.

What I’m really doing is a cognitive/learning audit. Basically, it’s about going through the cognitive processing cycle repeatedly through an experience. That experience can be the learner’s, the designer’s, purchaser’s, or more. Usually, all of the above! However, what you want to do is to minimize the barriers, and maximize the value. What’re the users goals, what’s  perceived, what’s considered, what’s processed, and what happens next.

There are benefits to having been actively investigating our minds for a number of decades now. I know the principles, I know how to apply them, and I also work in the real world. Also, perhaps against my own self-interest, I look to find ways to do it as easily and inexpensively as possible. I know organizations have limitations. Still, pretty much everyone benefits when you look for small changes with big impact. How about you?

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!

Applied learning science

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One of my favorite things to do is to help people apply the cognitive and learning sciences (under realistic constraints). That can be to their practices, processes, or products, via consulting, workshops, writing, and more. One thing I’ve done over the past few years is doing this for a particular entity. I was found via a workshop, and ended up coming on as an advisor. They’re now about ready to go live, and it’s time for me to tell you what they’re doing, why, and how. So here’s an application of applied learning science.

It starts with a problem, as many good solutions do. The issue is that, in L&D, too often they’re delivering live sessions to address a particular situation. Whether someone’s said “we need a course on this”, or there’s been a deep analysis, at some point they’ve pulled people together. It could be a day, several days in a row, or even spaced out every other week, every month, what have you. And, we know, that by and large, this isn’t going to lead to change!

Research on learning tells us, quite strongly, that to achieve a persistent new ability to ‘do’, we need to strengthen the learning over time. New information gets forgotten after only a day or two, according to the forgetting curve! So, we need to reactivate the learning. That can be reconceptualization, recontextualization, or reapplication. It can also be reflection, and even planning, and evaluation.

However, it’s been tough to do this reactivation. It typically requires finagling, and faces objections; not just the learners, but also the stakeholders! Such interventions need to be small but effective. That’s what this solution does. Other approaches have been tried, and some other solutions do exist, but this one has a couple of advantages. For one, a clear focus. It’s not doing other things, except reactivating learning.

Ok, one other thing, it’s also collecting data. Too often,  there’s no way to know if it learning’s effective. Even if there’s intent, it’s hard to get approval. So, this solution not only reactivates learning as mentioned, it tracks the responses. In practical ways.

What’s been my role? That’s the other thing; we’re applying this in ways that reflect what learning science tells us. Ok, we have to make some inferences, that we’re testing, but we’re starting from good principles. So, I’m advising on the spacing of the learning and the content of the reactivation. We call those prompts, that ask learners to respond. These prompts then gather into small chunks called LIFTs (Learning Interventions Fueling Transformation). (Everyone’s gotta have an acronym, after all, and this plays along with the company name, Elevator 9 ;).  The sequence of LIFTs makes a learning journey.

What’s important is how many we need, and how frequently we deliver them. It’s dependent on some factors, so we’re asking about those too: frequency of application, complexity, importance, and prior experience. Hopefully, in clear and useful ways.  They’re actively  looking for companies that are keen to help us refine this, too (in return for the usual considerations ;).

The end result is a product that easily supplements your live events. Your learners get reactivations, and you get data. Importantly, you get better outcomes from your interventions. This capability is possible, the goal is just to make it easy to do. Moreover, with a solution that not only embodies but shares the underlying learning science, improving you as it does your learners. Win-Win! I generally don’t tout solutions, but this one has actively put learning science (tempered by reality, to be sure) at the forefront. Applied learning science, and technology, the way it ought to be done. It’s been an honor to work with them!

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?

Evidence-Informed Practitioner conference deal

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a mortar-boarded lightbulb on books, with the words "LDA Conference: L&D, the Evidence-Informed Practitioner, live online and asynchronous sessions April 7 - May 2 ldaccelerator.comSo, this is a wee bit not my normal post, but…I did want to let you know about the Evidence-Informed Practitioner conference we (the Learning Development Accelerator) are running come April. This won’t be my last post on it, of course!  Still, I’ll entice you with some details, and give you a special deal. It’s too good not to let you know about the Evidence-Informed Practitioner conference deal.

So, first, the conference is a follow-on to the Learning Science Conference we held last fall. That was a great conference, but there was one repeated sentiment: “but how do we do this in practice?” A fair question!  And, frankly, a topic that’s gotten my mind going in other ways (stay tuned ;). So, we decided to offer a conference to address it.

First, the conference follows the well-received format we saw for that last event. We have the important topics, with canned presentations beforehand, discussions forums to discuss, and then live sessions. The presentations were great, and the emerging discussions were really insightful!

Then, we have top presenters, and I mean really top. People who’ve been there, done that, and in many cases wrote the book or built the company. Julie Dirksen, Dawn Snyder, Will Thalheimer, Lori Niles-Hoffman, Dave Ferguson, Emma Weber, Maarten Vansteenkiste, and Nigel Paine, along with Nidhi Sachdeva and Kat Koppett. These are folks we look to for insight, and it’s a real pleasure to bring them to you.

I get to offer you 10% off. You can use my code to get 10% off the regular price. The secret password is EIP10CQ. That’s EIP (the conference acronym), 10 (percent), CQ (my initials).

I realize I should’ve mentioned this all before, but it’s not TOO late. Hope to see you there, it’ll be great (as my firstborn used to say)! Look, I don’t usually do such a promotion, but I really am excited to offer Evidence-Informed Practitioner conference deals. Hope to see you there!

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!

Fads and foundations

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Two recent things have prompted some reflection. For one, the LDA had another workshop with Emma Weber, in this case on transfer of learning. At the same time, Dave Snowden, on LinkedIn, was pointing to a post suggesting being wary of the latest management infatuation. How are they related? Well, to me it’s about fads and foundations.

So Emma’s workshop was about how to use coaching to facilitate post-event transfer. Her approach had a domain-independent coaching model. In it, the coaching is applied for roughly 30 minutes over a period of time, with at least a week between. She was looking to drill into what people wanted to accomplish and keep them on track. Also, doing so without being expert in the area of endeavor. In fact, to the contrary. Which I laud, with a caveat. As I’ve opined before, I think that we need domain-specific feedback until learners have a level of capability. They have to be able to know  what they don’t know and acquire it. They also need to critique their own performance. (She believes that the course should get people to that level; I’m a bit more cautious. Should.)

Now, what the post suggested was that the big consulting companies had a pattern of boosting the latest management approach. They then indicate expertise, and get businesses to follow them. The consultants then move on, without checking to see whether the fad has led to any improvement. (A small plug here for using your friendly neighborhood consultant for a reality check before embarking on heavy investment.) This reminds me of Alex Edman’s book May Contain Lies where he demonstrated how many management books took a biased data set and used that to make sweeping generalizations that weren’t justified. Nor checked for continuing success.

The link is that too often, folks will bring in a new executive, even CEO, who isn’t in their business but has had success elsewhere. A reliable situation is that they will have learned some MBA-spiel, like cost-cutting, and successfully applied it in a particular instance. (The ones who aren’t successful we don’t hear about.) Then, their approach doesn’t work in the new situation. Because it’s a new situation! They don’t have the foundational knowledge. Another recent item I saw said how a business had failed with a new CEO, and had to then hire another who knew the business to set it right. (If only I could remember where!)

The underlying message is that the world is contextual (see Brian Klaas’ Fluke). Without the knowledge of how the world works here, we’re liable to apply too-general approaches that aren’t matched to the current situation. When we acquire the contextual knowledge, we can then self-help. Yet, we do better when we know the situation. We need informed analysis and aligned interventions! This is something we can, and should, do.