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Clark Quinn’s Learnings about Learning

On ‘cognitive presence’

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I recently opined that while I thought the ideas in an article by Dr. Page Chen (COO of Elevator 9, the startup I advise, and very smart) were brilliant, I had reservations about the term ‘cognitive presence’. And maybe I should explain why. So let me do some riffing on cognitive presence.

First, it’s a well-regarded concept. In typical formulations, it’s part of a three-element model of learning under the concept of ‘community of inquiry‘, in conjunction with social presence and teaching presence. Further, it has a well-known definition, with specific phases that are part of my concern about going all the way from learning to application. Specifically, it includes triggering of the idea, exploration of the concepts, integration of the ideas, and resolution into a coherent whole. It is a bit academic, in that it doesn’t necessarily imply ‘doing’, but that’s obviously where it’s being taken.

One issue is that it’s well-known in education. That is, it’s known there, but it’s not necessarily cognizant to other audiences. In particular, I wouldn’t expect organization executives to know it. Frankly, I’m not sure all L&D folks know it! Thus, it’s a concept that’s accurate, but maybe not understood when talking to customers (or investors).

So why my concern? Because the label feels a bit ‘inert’. Presence means, to me, “I’m present”. I’m here. Which doesn’t mean I’m engaged, applying, and closing the loop. It’s absolutely the right idea, it’s just not a good label. That is, for those who don’t about the concept, this label isn’t going to inspire them. And, to be fair, I’m totally willing to be wrong on this. It’s just that I fear that the term, without explanation, isn’t going to lead to the insights about what we’re doing that we want.

Thus, I’m leery of the term, and somewhat keen to find another. Of course, I don’t have an immediate alternative to offer. What would be good? It’s about capturing progress along the trajectory from knowledge to application. Is that the right term, application trajectory? Or…? I’m not (yet? ;) at the stage of having answers, but felt like I should explain my thinking (learning out loud, after all!). What’re your thoughts?

The benefits of learning ‘out loud’ (also known as making a mistake)

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I just released a blog post this morn. And, as soon as I saw it in the wild I had a brutal realization. What occurred to me, of course, is that I’m going about it totally wrong! Or, at least, totally contrary to what I advocate. Yes, I had activities, but I focused on the content. Not a bad thing, maybe, as a step, but the first step? Maybe not. So I’m experiencing the benefits of learning ‘out loud’, via making a mistake.

To start, I’ve been designing a workshop. And, it’s taking me a while, as I want it to be thorough, and good. For instance, I deliberately have it spread out, so to ensure spaced learning. I have reflection in it. Further, I’ve made ‘ToDo’s to go back and add in some processing activities. I largely have application activities already put in. That’s what I talked about. In particular, ensuring good coverage. However, once it was posted (this morn as I write, two days ago as you read), I realized something.

The design I was talking about was, sadly, very different from what I’ve advocated for in the past. And, still believe in! That is, starting with what I want learners to be able to do, and working backwards. Focus first on activity, then on the necessary content. Which is very similar to Cathy Moore’s Map It, but with a bit more focus on the rationale, and feedback (my take, YMMV).

What that means is what I should do is create the activities first. Then, I figure out the minimal content. From there, I can dig into processing activities ala Richter (via Thiagi). Along the way, I can review the activities, match it against the content I’ve developed, and see if need more or different. Yes, it’s an iterative process. But, to be true to my beliefs, and science, and learners, and…I have to design the activities first. So, I’ll keep my existing deck, and start afresh on one (or, at least, some representation). It’s better to take that time. And, yes, it’s not like the real world pressures most have to work through, but then I’ve the luxury to try to do the right thing, not the expedient things.

Let’s be clear: the workshop I designed would be really good, possibly great. I think reflecting and focusing on the activities, however, has a high probability of making a definitively great workshop. Those are my reflections, what are. yours?

There may be a followup post about the revised process, but no promises right now.

Designing a workshop

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I’ve written about writing before, but I don’t think I’ve talked about designing a workshop. Yet, that’s what I’m doing right now, and so I’m also reflecting. Which, of course, is very ‘meta’, because reflection is one of the topics of the workshop! Still, I’m pondering what’s different about designing a workshop versus writing, whether book or article or… So here’re some thoughts.

First, as is the case with writing a book (at least for me) is the process of creating the structure. I outline what I’m doing (even both outlined it and separately created a mindmap, to see what overlaps and gaps I found). For instance, I initially saw this as five separate events, spaced out. When I got into it, putting meat onto the bones as the saying goes, I realized that I wanted to combine three and four. So now there are four separate bits.

Then, as is the case with writing, too, I realize I’m alternating between top-down and bottom-up. Which is demonstrably what designers do, but…it’s interesting to break the myth of the waterfall design. I generate low-level elements, which makes me rethink and restructure bits, which means I need to go down into the details some more.

It’s also the case that I can disappear into design for too long (fortunately, the ribs came out ok). I personally go in bursts. I will get going and time will fly by, and then I hit a wall and just have to stop and take a break. It also is mentally taxing, and I find my thoughts become difficult until I take some time off.

And, I recognize it will take several passes. Already I’ve made ‘todo’s for two specific post-hoc passes. First, I’m getting the ideas and the structure right. Then, I want to go back and make sure I’ve put in all the good ideas, hints/tips/tricks, and all that ensures there’s real value in what I’m doing. Finally, I want to make a third pass that ensures I’m helping the audience process the content successfully. That is, getting them to rephrase the ideas into their own words (a form of reflection).

The latter of which is, specifically, what’s at least partly unique to a workshop versus a book. Yes, I’m making it interactive in the first pass by asking an opening question for each section to get folks creating their own thoughts before I present mine, so as to activate and also to generate any improvements that can come from them. While I think I have plenty of relevant background, I’ve not enough hubris to think that there won’t be ideas that are better than mine. I learn when I run workshops, too!

One of the important things here is to develop real new abilities, not just to present ideas. Learning science tells us we need repeated practice, and that’s definitely got to be part of what this workshop does. So, not only do I want some scaffolded practice in the session, but there’s also ‘homework’ where attendees apply the current idea to their own work and share the learnings therefrom in between sessions. (Research emerging from our work at Elevator 9 suggests that when this is done – and it’s hard to do – the results are better.)

The goal is a workshop that delivers real learning, and real value. I don’t want to just make money selling ideas and good feelings. That’s too frequently what we see (*cough* leadership development *cough*), but that’s not what will lead to long term benefit. And I do care about that (perhaps too much! ;). So, there are some thoughts on designing a workshop. What haven’t I made clear? What am I missing?

(And no thanks to Apple for losing my templates and making me create them anew. Ahem.)

Schemas versus Mental Models

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For reasons (that elude me, except ongoing curiosity), I’ve been looking at ‘content’. That, of course, has meant models, and examples, and feedback. However…my co-director at the LDA*, Matt Richter, chides me that schemas are a goal of learning. And, of course, I was steeped in schemas as a grad student in cog psych. On the other hand, I’ve pushed for mental models as the complement to examples (and the basis for feedback). So how do I reconcile schemas versus mental models?

I got a boost on my recent explorations via this post from Paul Kirschner distinguishing between the two. His characterization of schemas is as fixed knowledge about the world, while models are systems reflecting the dynamics. He also talks about how misconceptions can come from either, and they’re separate, suggesting they’re distinctive.

My searches, however, led me to this other piece by David Didau that argues that they’re not functionally dissimilar. They’re conceptually distinct, but functionally you need a combination. Which makes sense, because there’re interrelations.

The reconciliation I’m finding is that we need models that explain how the world works. As I’ve maintained, you have to predict the way the world reacts to this perturbation versus that one, and then decide which outcome is better. However, you can’t really do that until you know what the elements are, and the relationships between them.

The argument then becomes how you teach. And I reckon you kind of build them iteratively, actually, because you need some to explain some, and then some more to explain some more. Which isn’t all that unfamiliar. I use ‘builds’ to help people see how a model is built, by exposing the diagram bit by bit.

That is, ultimately, that it’s not about schemas versus mental models, it’s about schemas and mental models. Which is a good resolution. Of course,

* Our society for evidence-informed L&D.

KMN

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So, I’m compiling a hopefully comprehensive list of learning to learn strategies. As part of that, I searched on meta-learning strategies. And, in two of the top listed sites, I found, indeed, articles on meta-cognitive strategies. And, a myth. More specifically, one that continues to bedevil our industry (and, relatedly, education). I mean, we’re a quarter way into the new century! I sadly feel like KMN (Kill Me Now).

So, the first site had an article that asked “What meta-learning techniques improve skill acquisition?” After first saying why and what, both good things, the very first recommendation is “understand and adapt to your learning style”. What? I mean, this is such a debunked theme that it’s almost ludicrous. I did comment, but yikes.

So, then going through my other tabs opened from the search results (a meta-learning strategy, btw ;), I find another list. This site has an article listing Examples of Metacognitive Strategies. And, it was only item 5 that was “awareness of learning styles”. Still, here’s a myth pretending to be a valid strategy. Here I couldn’t comment, but I did send a politely worded recommendation to pay attention to the research. (And got a response saying they’d moved on from that position. Ok, but then why did I find it when I did the search?)

In both cases, pointing to an approach that has been deeply investigated and resoundingly dismissed undermines anything and everything else they have to say. Why should I trust what you tell me when I know part of it is wrong? There was other good, and bad, advice in the articles, but sorry, you’ve lost my ability to think you know what you’re talking about.

Yes, I get that the idea of learning styles feels right. Anyone who’s taught recognizes that people learn differently. But…what people think is good, and what actually works, has essentially zero correlation. Yes, you should have different representations. This is to increase access, not to address learning styles, however. And deliberately designing for styles is clearly a waste of money. For instance, creating three different versions of the content for say, visual, auditory, and kinesthetic learners, wouldn’t be justified.

A step back provides this lesson: know what you say before you say it, and don’t say wrong things unless you want to lose credibility. Which is kind of a meta-comment, but that’s a separate issue ;). I don’t really want you to KMN, but…sometimes it feels like we are moving backwards. Can we return to using what’s demonstrably known as a basis? Sure, science gets upgrades, and so sometimes is wrong (flat earth, anyone?) when it moves to a better explanation, but it’s still better than a basis of dogma.

Meta-learning-to-learn

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So, my attention has swung back to learning to learn. So that requires me to think what about meta-learning is worth paying attention to, promulgating, etc. Which is kind of meta-learning-to-learn, no? So, why, and what?

To begin with, as I was explaining to my LDA co-director, Matt Richter, I’ve been a fan of learning to learn for literally decades. As a grad student, I served as a teaching assistant for a class on critical thinking (using Halpern’s Thought and Knowledge). My own PhD thesis was on teaching thinking skills! I joined up with the late Jay Cross to form the Meta-Learning Lab (MLL), promoting these views, after finding it was a shared interest amongst those of us in the East Bay who knew Bill Daul.

So, it makes sense that this theme, connected strongly to my life-long pursuit of learning through technology, would resurface. I think the MLL was ahead of it’s time, but I’m seeing more resurgence of interest. I think that’s for several reasons, for one orgs are realizing that they need to become more agile, and learning skills are key. Also, generative artificial intelligence is raising questions about what value we add, and I’ll go out on a limb and state that to learn is human. Yes, machines learn, but…they really don’t have an understanding, as I’ve pointed to Harnad’s Symbol Grounding Problem before. Arguably, we do increase understanding through learning.

It’s also clear that we’re not optimal. Some argue that there aren’t ‘learning to learn’ skills, yet point out that certain ways of doing things (across domains, mind you), are more and less efficient. You can be better or worse at reading, experimenting, even interacting with others. Sorry, but to me, that’s learning to learn.

Also, I see more and more folks recognizing that these skills are desirable, and undeveloped. So, if we wanted to turn that around, and develop them, what’s needed? That’s where I’m at, compiling a list of skills, and trying to generate a unified model. Yes, there are components – Jarche’s PKM, cognitivce science’s plan-monitor-evaluate, etc – but I reckon it’s best if they’re integrated. That includes breaking down so-called 21C skills into actual component.

Still underway, and I welcome pointers to comprehensive models. I confess that I quibble if I think they’re insufficient (there are lots of models that have necessary, but not sufficient, coverage). I don’t mind reviewing (and stealing :) any elements I think I’ve missed, but I also won’t tout anything that’s incomplete. That includes, BTW, culture, and importantly, how L&D facilitates this. That’s where I am going, of course; we need to find days to build these skills into what we do. For instance, one of our LDA members talked about making pedagogy clear when you deliver a learning experience. That’s exactly right. So, what else? What’s the comprehensive list? Stay tuned.

 

The Three R’s

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I was thinking through the process of bridging, via prompts, and stumbled upon an a framing. It’s pretty simple, so too then is this post. I”m talking about the three R’s, in this case reactivation, reflection, and results. Still, this isn’t a bad way to think about extending the process of learning.

So, as context, bridging is about going from the course to actual impact on the organization. In the LDA, we talk about the chain, from information, through knowledge, performance, schemas, etc, to impact. Generative activities to reprocess the information, and retreival practice to draw it out and apply it. From my perspective, we start with models and examples and practice. That’s to start; ideally we’d go from simulated practice to real application with feedback, and ultimately to assess how it’s going.  But our resources are limited (and sometimes our imaginations), and so we stop short of developing a full solution. What can we do to bridge that gap?

I was thinking about that, and reflecting on the necessary elements. That’s how I came to the three R’s, and I thought I’d least lay them out here. So let’s go through each of the elements.

First, we have reactivation. As I’ve noted previously, this includes elaborated models (reconceptualization), where we build from simple to full models, or present other ways of thinking about the topic. Then there’re new examples (recontextualization), where we expand the space of transfer and complexity. Finally, there are new practice situations (reapplication), where we add in complexity (increasing challenge) and of course new contexts for transfer. That’s to cement our understanding in safe ways.

Then, we should reflect. That carries multiple meanings, and really we’re taking here about reflect ing on application. This includes your planning, what happens when you face reality, adjustments, barriers, and more. Ideally, we’d get feedback from others, but at minimum we can provide model answers. This extends from the formal to the real, helping learners maintain focus, apply, and provide opportunities to improve.

Finally, we should look at results. What are we seeing? It could be self-report, but that’s still data. Others evaluations of impact are still subjective, but triangulating is better. Of course, the ideal would be real data, but that can be difficult. Still, it’s data, and it can be indicative.

Thus, we’re moving from classroom to actual performance. I’ve talked about this before, but I’m still trying to reflect on my own model, and improve it.  The three r’s is one way I’ve thought to frame it, but it might not be as useful as I hope. Still, I do think it’s important enough to keep working on it. I’m keen to hear what you’re thinking and doing, too! Do let me know.

Partnering

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Too often, I’m prone to think about just doing things. For instance, I advocate for L&D to be responsible for performance support, innovation, etc. Yet, there may be groups already doing this, whether ad hoc or organizationally mandated. And…one thing is for sure, you don’t want to reinvent the proverbial wheel. If someone’s doing something, work with them, not on your own. That is, partnering. And, of course, the term is well-known (e.g. listening to Dawn Snyder from our LDE conference). But, what does it mean?

It turns out, in thinking through doing things like performance support well, it’s not just the design of the resources, but it’s also their availability. Many years ago, i talked about the elements needed for content, what Brent Schlenker talked about: the 5-ables. What matters here is that someone may already be responsible for this! It might be in the web team, or the knowledge management team. Sure, if no one’s doing it, by all means take it on. But if someone is, don’t tread on their turf.

Of course, that doesn’t mean you can’t offer to help. If you know more about design of such resources (e.g. visual design), offer that assistance. If you’re concerned about curriculum and coverage, and they’re not, that’s another area to contribute in. Maybe they don’t have good governance, creating things and letting them languish un-updated and maybe expired. It could be that they’re not using technology well, or making it available by role instead of silo. There’re lots of aspects to get this right, and if you can supply any missing bits, all to the good.

The same goes for community and innovation. There could be folks working on either or both. Are they supporting informal learning? Is the culture aligned? Do they promote good practices? All these are areas that could be a contribution from L&D. Not that you have to own it; if you can add value to create a better overall solution, that’s great!

All told, it’s about seeing what skills are already extant, and what’s missing. And offering that in a way that’s not threatening, but seen as adding value. You don’t want to take over things that people want to own, but you do want to make sure they’re doing it well, and in conjunction with an overall org-wide learning strategy. Becoming a learning organization is no longer a nice-to have; the ability to be agile, to adapt, is going to increasingly be the only sustainable differentiator. As a consequence, orgs need to ensure that they’re aligning the elements. You don’t have to drive it (unless it doesn’t exist), but you want to align and improve.

That’s a valuable contribution, regardless of locus. Improving ideas and people has to be in the interest of the org, and of course of L&D. Further, assisting is less-resource intensive than owning. It helps things work better and reduces the needs on your resources, so you can devote efforts elsewhere. Even work to develop the ability so you can release it.  I reckon it’s ‘partner where you can, drive where you must’, but make sure the org’s getting better over time. Ultimately, that’ll be because you care, and that’s what you care about. Right?

The right tech for the job?

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Ok, so this blog is for my musings, and this is very much a musing. However, a couple recent things have prompted some thoughts. The issue is Large Language Models (LLMs). As I’ve said, I have no problem with the tech inherently. It’s really optimized for language roles (as the name implies). What is concerning to me is the hype, and so the use. And, it’s led me to wonder if it is, or what is, the right tech for the job.

So, up until LLMs, when you wanted something done, you built the appropriate tech. You put together specifications, and development teams built it. (And then they asked UI to fix the problems, as Don Norman talked about in The Invisible Computer. Probably then handed off to training folks to address the problems from the bad design outside the UI.). It took time, and money. And, if you didn’t use something like Watts Humphrey’s Personal & Team Software Processes, you likely took too long, and had too many errors.

You could use AI for more decision tasks. So, either symbolic if well-defined, or based upon training data and machine learning if ill-defined in principle. You, of course, still have to live with or address the biases in the rules or databases. And, of course, the brittleness at the edges of the decision space. Still, all told, we had approaches. Then, the world changed.

So, for one, my colleague Kevin Wheeler (a deep expert in the talent world), talked about how the tasks of the talent function are in flux. What he cited was that many of the rote tasks were being made redundant. Which is good, I opined, if we’re removing tasks that people aren’t good at (I’ve said before that we should be doing pattern-matching and meaning-making, and leaving rote to the machines). However, there’s the problem of developing the expertise. So, for instance, as Etienne Wenger and Jean Lave talk about in communities of practice, moving from peripheral tasks to central as you understand the domain, But you need those peripheral tasks!

Plus, we’re seeing people being laid off. Meta just announced another 8000 being laid off, for efficiencies, and there have been announcements from many of the big orgs (and small ones are making similar decisions). Cutting through the smoke, what we sees is that folks face increasing expectations to use AI to do things faster, and the expectations are increasing (without, mind you, an increase in rewards for same). But, increasingly we’re not working together as we used to.

What also showed up in a LinkedIn conversation is the expectation that we can ask LLMs to do the things that we used to do by writing software. And yes, such systems make mistakes, but so do humans, right? Yes, and…we can assign responsibilities with humans, and they’ll be corruptible and more, but we have compliance to deal with that. As Markus Bernhardt is pointing out, we’re not doing a good job on that with our systems. We, too often, haven’t worried about the necessary guardrails, and security, and responsibility, and governance, and….

What is concerning me, as folks like Mark Britz talk about, is that we’re losing the human connection. We’re losing:

  • the upward path for new folks
  • the continual development of our own capability
  • the necessary checks and balances that keep our systems secure

Really, we’re turning away from doing things together to maximize outcomes, and instead are working to be more expedient. In short, we’re trading off effectiveness to achieve efficiency.  And ignoring ethics along the way.

In short, I fear we’re using the ease of doing things with LLMs to avoid the hard work of doing things the right way. Which I resist. I am seeing quite the backlash amongst the folks forced to use AI. Which includes the pressures by execs to use it, and the eagerness of those to promote it who stand to gain (whether vendors or consultants). We see research results showing that folks are thinking less, execs have unrealistic beliefs about how productive it makes people, and it’s pretty simple to recognize that the costs can’t stay as they are. The illusions from smoke and mirrors aren’t a good basis on which to plan.

Don’t get me wrong, I do see the benefits of LLMs. I just see them in context of the bigger picture of people, tech, and the broader picture of AI. And I could be wrong, it’s true. It’s just that there’re some reasons to believe that decades of immersion in the relevant fields have some basis for questioning whether we’re using the right tech for the job. I welcome your thoughts!

Thoughts on meta-coaching!

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So, my last post, I talked about coaching, mostly domain-specific coaching. As I said then, I am thinking about ‘bridging’, crossing the chasm from instruction to application. It’s a chasm because we tend to not do anything post-instruction. Yet, we also know that what’s taught can dissipate quickly if we don’t reinforce. One of the debates has been around domain-specific and domain-independent coaching, and that prompts some further thoughts.

So, what I’ve heard is that coaching is, or at least can be, about guiding the learner to improve. The whole business model of a colleague’s coaching organization was to not match coaches to learning goals, but to deliberately steer away from any domain knowledge. Instead, the learner was asked what barriers were being faced, what could be done, where more knowledge could be found, making plans to take action, and monitoring the plan. Which is all good, to be clear. However, one things strikes me.

What that coaching is doing is supporting the learner in learning. And that’s not a bad thing, but what strikes me is that this is something learners could learn! We (can) do this in other areas. For instance, we can tell learners how to take better notes. Shute & Bonar built a tutor talking about good experimentation strategies. Kate Bielaczyc took Micki Chi’s results on self-explanation and found improvement through training. My own PhD thesis found improvement by instruction on analogical reasoning.

The sad fact is that we don’t teach learners how to be good at learning. Whether K12, higher ed, pr organizations, it’s just not there. I remember my child’s teacher talking about her approach – draft, feedback, revise – and me mentioning that it’d apply to other classes than writing: math, science, … She was amazed (and that’s what worried me)!  Some folks say we can’t teach meta-learning except on a domain, but my take is that we could teach meta-learning across domains. And should.

So, I wonder about coaching where the coach is helping you not only do better, but also learn to do better on your own. Now, that’s not necessarily a good business model (if you wean people off your own services), but it’s the right thing to do. We should be doing it K12 and higher ed, so that it’s already there for people to be better able to improve. In lieu, we could (and should?) do it ourselves. As my late colleague Jay Cross used to opine, it could be the best investment an org could make. Let me extend that to a society. Imagine if everyone was an effective learner! Can we make it happen?