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Clark Quinn’s Learnings about Learning

Archives for August 2025

Is ‘average’ good enough?

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As this is my place to ‘think out loud’, here’s yet another thought that occurred to me: is ‘average’ good enough? And, just what am I talking about? Well, LLMs are, by and large, trained on a vast corpora. Essentially, it’s averaging what is known. It’s creating summaries of what’s out there, based upon what’s out there. (Which, BTW, suggests that it’s going to get worse, as it processes its own summaries! ;) But, should we be looking to the ‘average’?

In certain instances, I think that’s right. If you’re below average in understanding, learning from the average is likely to lift you up. You can move from below average to, well, average. Can you go further? If you’re in well-defined spaces, like mathematics, or even programming, what LLMs know may well be better than average. Not as good as a real expert, but you can raise your game. Er, that is, if you really know how to learn.

Using these systems seems to become a mental crutch, if you don’t actually do the thinking. While above average people seem to be able to use the systems well, those below average don’t seem to learn. IF you used it to provide knowledge, and then put that knowledge into practice, and get feedback (so, for instance, experimenting), you could fine tune your performance (not as eloquently as having someone provide feedback, but perhaps sufficiently). However, this requires knowing how to learn, and the evidence here is also that we don’t do that well.

So, generative AI models give you average answers. Except, not always. They hallucinate (and always will, if this makes sense). For instance, they’ll happily support learning styles, because that’s a zombie idea that’s wrong but won’t die. They can even make stuff up, and don’t know and can’t admit to it. If you call them on it, they’ll go back and try again, and maybe get it right. Still, you really should have an ‘expert’ in the loop. Which may be you, of course.

Look, I get that they can facilitate speed. Though that would just seem to lead your employer to expect more from you. Would that be accompanied by more money? Ok, I’m getting a bit out of my lane here, but I’m not inclined. But is faster better?

Also, ‘average’ worries me. As I’ve written, Todd Rose wrote a book called The End of Average that is truly insightful. Indeed, one of those books that makes you see the world in a different way, and that’s high praise. The point being that average removes the quality. Averaging removes the nuances, the details, as does summarization. Ideally, you should be learning from the best, not the average, if learning is social (as Mark Britz likes to point out).

Sure, it can know the average of top thoughts, but what’s better is having those top thinkers. If they’re disagreeing, that’s better for dialog, but not summarization. In truth, I’d rather learn from a Wikipedia page put together by people than a Gen AI summary, because I don’t think we can trust GenAI summaries as much as socially constructed understanding. And it’s not the same thing.

So, I’ll suggest ‘average’ isn’t nearly good enough in most cases. We want people who know, and can do. I don’t mind if folks find GenAI useful, but I want them to use it as support, not as a solution. Hey, there’s a lot that can be done with regular AI in many instances, and Retrieval Augmented Generation (RAG) systems offer some promise of improvement for GenAI, but still not perfect outcomes. And, still, all the other problems (IP, business models, and…). So, where’ve I gone wrong?

Note, I should be putting references in here, but I’ve read a lot lately and not done a good job of saving the links. Mea culpa. Guess you’ll just have to trust me, or not. 

Training Organization Fails

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I’ve worked with a lot of organizations that train others. I’ve consulted to them, spoken to them, and of course written and spoken for them. (And, of course, others!) And, I’ve seen that they have a reliable problem. Over the years, it occurs to me that these failures stem from a pattern that’s understandable, and also avoidable. So I want to talk about how a training organization fails. (And, realize, that most organizations should be learning organizations, so this is a bigger plea.)

The problem stems from the orgs’ offering. They offer training. Often, certification is linked. And folks need this, for continuing education needs. What folks are increasingly realizing is that much of the learning they’re offering is now findable on the web. For free. Which means that the companies not seeing the repeat business. Even if required, they’re not seeing loyalty. And I think there’s a simple reason why.

My explanation for this is that the orgs are focusing on training, not on performance solutions. People don’t want training for training’s sake, by and large. Sure, they need continuing education in some instances, so they’ll continue (until those requirements change, at least). Folks’ll take courses in the latest bizbuzz, in lieu of any other source, of course.  (That’s currently Generative Artificial Intelligence, generically called AI; before that as an article aptly pointed out it was the metaverse, or crypto, or Web 3.0, …)

What would get people to do more than attend the necessary or trendy courses? The evidence is that folks persist when they find value. If you’re providing real value, they will come. So what does that take? I posit that a full solution would be comprised of three things: skill development, performance support, and community.

Part 1: Actual learning

The first problem, of course, could be their learning design. Too often, organizations are falling prey to the same problems that belabor other organizational learning; bad design. They offer information instead of practice. Sure, they get good reviews, but folks aren’t leaving capable of doing something new. That’s not true of all, of course (recently engaged with an organization with really good learning design), but event-based learning doesn’t work.

What should happen is that the orgs target specific competencies, have mental models, examples, and meaningful practice. I’ve talked a lot about good learning design, and have worked with others on the same (c.f. Serious eLearning Manifesto). Still, it seems to remain a surprise to many organizations.

Further, learning has to extend beyond the ‘event’ model. That is, we need to space out practice with feedback. That’s neglected, though there are solutions now, and soon to be available. (Elevator 9, cough cough. ;) Thus, what we’re talking about is real skill development. That’s something people would care about. While it’s nice to have folks say they like it, it’s better if you actually demonstrate impact.

Part 2: Performance support

Of course, equipping learners with skills isn’t a total solution to need. If you really want to support people succeeding, you need more than just the skills. Folks need tools, too. In fact, your skill development should be built to include the tools. Yet, too often when I ask, such orgs admit that this is an area they don’t address.

There are times when courses don’t make sense. There are cognitive limits to what we can do, and we’ve reliably built ways to support our flaws. This can range from things performed rarely (so courses can’t help), through information that’s too volatile or arbitrary, to things done so frequently that we may forget whether we’ve taken a step. There are many situations in pretty much any endeavor where tools make sense. And providing good ones to complement the training, and in fact using those tools as part of the training, is a great way to provide additional value.

You can even make these tools an additional revenue stream, separate from the courses, or of course as part of them. Still, folks want solutions, not just skill development. It’s not about what you do for them, but about who they become through you (see Kathy Sierra’s Badass!).

Part 3: Community

The final piece of the picture is connecting people with others. There are several reasons to do this. For one, folks can get answers that courses and tools are too coarse to address. For another, they can help one another. There’s a whole literature on communities of practice. Sure, there are societies in most areas of practice, but they’re frequently not fulfilling all these needs (and they’re targets of this strategic analysis too). These orgs can offer courses, conferences, and readings, but do they have tools for people? And are they finding ways for people to connect? It’s about learning together.

I’ve learned the hard way that it takes a certain set of skills to develop and maintain a community. Which doesn’t mean you shouldn’t do it. When it reaches critical mass (that is, becomes self-correcting), the benefits to the members are great. Moreover, the dialog can point to the next offerings; your market’s right there!

There’s more, of course. Each of these areas drills down into considerable depth. Still, it’s worth addressing systematically. If you’re an org offering learning as a business, you need to consider this. Similarly, if you’re an L&D unit in an org, this is a roadmap for you as well. If you’re a startup and want to become a learning organization, this is the core of your strategy, too. It’s the revolution L&D needs ;). Not doing this is a suite of training organization fails.

My claim, and I’m willing to be wrong, is that you have to get all of this right. In this era of self-help available online, what matters is creating a full solution. Anything else and you’ll be a commodity. And that, I suggest, is not where you want to be. Look, this is true for L&D as a whole, but it’s particularly important, I suggest, for training companies that want to not just survive, but thrive in this era of internet capabilities.

Top 10 Learning Tools 2025

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Every year, the inimitable Jane Hart collects what people say are their top 10 tools for learning. The results are always intriguing, for instance, last year AI really jumped up the list. You can vote using this form, or email your list to her via the address on that page. I’ve participated every year I’ve known about it, and do so again. Here’s my list. Realize this is for ‘learning’, not formal education per se. It’s whatever makes sense for you.

Writing

I write, a lot. It’s one way of my making sense of things. So…Microsoft Word remains my goto tool. Less and less so, of course. I’ve been using Google Docs to collaborate with others quite a bit, and am currently using Apple’s Pages for that purpose. Still, I think of Word as my ‘goto’ tool, at least for now. I don’t like Microsoft, and am trying to wean myself away, but I really really need industrial strength outlining, and no one else has measured up.

Apple’s Notes needs a mention. I use it, a lot. Several things are pinned to the top (including my SoMe canned responses, and shopping lists). I also share recipes with family members (on Apple devices), take notes on books and the like, keep a list of ‘to consume’ (books, movies). I also use Notability for biz notes, but it’s not as ubiquitous, and I may just shift everything to notes as there’ve been an increasing number of ‘offers’ to upgrade. Yuck.

And, of course, WordPress for this blog. Here’s where I share preliminary thoughts that end up appearing in articles, presentations, or books. It’s a way to share thinking and get feedback.

Diagramming

I’m still using OmniGraffle. I tried using Google’s Draw, and Apple’s Freeform, but… OmniGraffle’s positives are its user interface. It works the way I want to think about it. Sure, it’s probably changed my thinking to adapt to it too, but from the get go I found using it to be sweet. In fact, as I’ve recounted, I immediately redid some diagrams in it that I’d created in other ways previously just because it was so elegant. The downsides are not only that it’s Mac-only (I work with many other folks), but that it’s not collaborative. Diagramming is one of the ways I make sense of things.

Presentation

Apple’s Keynote remains my preferred presentation tool. I continue to use it to draft presentations. It defaults to my ‘Quinnovation’ theme, tho’ for reasons (working with others, handouts w/o color, builds, etc) I will use a plain white theme. I even have built a deck of diagram builds, so I can paste them into presos but have them to hand rather than having to remake them each time. It’s another way to share.

Connection

Apple Mail, for email, is an absolute necessity. I have to stay in touch with folks, and mail’s critical to coordinate and share.

I use Safari all the time as my browser, tho’ occasionally I have to have Chrome-compatibility, at which time I use Brave; Chrome-compatible but without Google’s intrusiveness. Takes me to Wikipedia, a regular trusted source for looking things up.

Zoom remains my ‘goto’ virtual meeting tool (all my meetings are virtual these days!). I of course use Microsoft’s Teams (but only through the browser now, was able to turf the app), and Google Meet, but only as others request. Of course, connecting with others is critical to learning.

Wow, I’m running out of time and space. Let’s see: Slack is a coordination tool I use a lot with the LDA, and Elevator 9. It’s also a way to share thinking, so it’s a learning tool too.

There’s more, so I guess I’ll use my last slot and aggregate my Social Media tools. That includes LInkedIn, Bluesky, and Mastodon. All three get notification of blog posts, but other than that each has its separate uses. LinkedIn is for biz connections, and reading what others are posting. Bluesky is mostly what Twitter used to be (before it became Xitter), fun, quantity. Mastodon’s more restrained in growth, but the underlying platform is really resistant to political/business corruption.

That’s all I can think of. I welcome hearing your thoughts and seeing the results.

Beyond Design

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When you look at the full design process, I admit to a bias. Using Analysis-Design-Development-Implementation-Evaluation, ADDIE, (though I prefer more iterative models: SAM, LLAMA, …), I focus early. There are two reasons why, but I really should address them.  So let’s talk beyond ‘design’ and why my bias might exist. (It pays to be a bit reflective, or defensive?, from time to time.)

I do believe that it’s important to get the first parts right. I’ve quipped before that if you get the design right, there are lots of ways to implement it. To do that, you need to get the analysis and design right. So I focus there. And, to be sure, there’s enough detail there to suit (or befuddle) most. Also, lots of ways we go wrong, so there’s suitable room for improvement. It’s easy, and useful, to focus there.

Another reason is that implementation, as implied in the quip, can vary. If you have the resources, need, and motivation, you can build simulation-driven experiences, maybe even VR. There are different ways to do this, depending. And those ways change over time. For instance, a reliable tool was Authorware, and then Flash, and now we can build pretty fancy experiences in most authoring tools. It’s a craft thing, not a design thing.

Implementation does matter. How you roll things out is an issue. As Jay Cross & Lance Dublin made clear in Implementing eLearning, you need to treat interventions as organizational change. That includes vision, and incentives, and communication, and support, and… And there’s a lot to be learned there. Julie Dirksen addresses much in her new book Talk to the Elephant about how things might go awry, and how you can avoid the perils.

Finally, there’s evaluation. Here, our colleague Will Thalheimer leads the way, with his Learning Transfer Evaluation Model (LTEM). His book, Performance Focused Learner Surveys comes closest to presenting the whole model. Too often, we basically do what’s been asked, and don’t ask more than smile sheets at best. When, to be professional, we should have metrics that we’re shooting to achieve, and then test and tune until we achieve them.

Of course, there’re also my predilections. I find analysis and design, particularly the latter, to be most intellectually interesting. Perhaps it’s my fascination with cognition, which looks at both the product and process of design. My particular interest is in doing two things: elegantly integrating cognitive and ‘emotional‘ elements, and doing so in the best ways possible that push the boundaries but not the constraints under which we endeavor. I want to change the system in the long term, but I recognize that’s not likely to happen without small changes first.

So, while I do look beyond design, that’s my more common focus. I think it’s the area where we’re liable to get the best traction. Ok, so I do say that measurement is probably our biggest lever for change, but we’ll achieve the biggest impact by making the smallest changes that improve our outcomes the most. Of course, we have to be measuring so that we know the impact!

Overall, we do need the whole picture. I do address it all, but with a bias. There are others who look at the whole process. The aforementioned Julie, for one. Her former boss and one of our great role-models, Michael Allen, for another. Jane Bozarth channels research that goes up and down the chain. And, of course, folks who look at parts. Mirjam Neelen & Paul Kirschner, Connie Malamed, Patti Shank, they all consider the whole, but tend to have areas of focus, with considerable overlap. Then we go beyond, to performance support and social, and look to people like Mark Britz, Marc Rosenberg, Jay Cross, Guy Wallace, Nigel Paine, Harold Jarche, Charles Jennings, and more.

All to the good, we benefit from different perspectives. It’s hard to get your mind around it all, but if you start small, with your area, it’s easy to begin to see connections, and work out a path. Get your design right, but go beyond design as well to get that right (or make sure it’s being done right to not undermine the design ;). So say I, what say you?

The ‘right’ level

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So, I know I’ve talked about this before (not least, here), but it seems to continue to persist. What I’m talking about is the continuing interest in neuroscience for L&D. And, as has been said by others, it’s the wrong level of analysis. What, then, is the ‘right’ level? Here’re my thoughts, and I welcome yours.

This is not to say neuroscience isn’t valuable. It objectively is. We gain insights that bolster some views, and nuance others. That’s important, for sure. We find out about mirror neurons, important for social learning. And, for instance, we can find that dopamine ramps up more for preferred motivators, and orients us in those directions. That’s interesting. It also suggests that we should make sure we’re involving people’s motivation for learning.

However, my point is that we know this already. Cognitive science tells us this. So, for instance, at the neural level, learning is about reinforcing patterns, strengthening connections between neurons at an aggregate level. That’s great. However, how we do that is by triggering patterns in conjunction, to strengthen them. How do we trigger patterns? With words, images, etc. Things that mean something. That’s cognitive!

There’s a level above, too, the social level. Here, we are presented with what others think. Which is useful to understand. But, for learning, we have to translate back to the cognitive level. That is, we need to think about how seeing how others interpreted the same signs, and what that means for ours. Social learning is valuable, but…while we enact it publicly, our understanding of why and how will depend on what we know.

For instance, brainstorming. Without a cognitive understanding, we won’t know how to do it right. We can learn, empirically, that we get better results when we think alone first before converging (and other aspects, like avoiding premature evaluation). Why? When we get to the cognitive analysis, we recognize that if we haven’t generated our own ideas first, others’ ideas can constrain our thinking.

Sure, I’m biased. I was steeped in the cognitive perspective. Yet, when I look at what works and why, I see the meaningful analysis coming from the cognitive level. Likewise, when I see people tout ‘neuro’ and ‘brain-based’, etc, all the results I hear are really cognitive ones. Certainly, ones that cognitive science has already shown benediction for.

So, I keep learning (another recommendation from cognitive science ;). And I have no doubt that we’ll learn things from neuroscience as that field matures. Still, for good prescriptions for learning design, cognitive is the ‘right’ level for analysis. Which means it’s the right level to study and understand. Please, ensure you do understand learning science before you design for others. That’s so you’ll create experiences that honor our learners by providing learning that works: is meaningful and effective. Which is really what we should be about. Those are my thoughts, what are yours?