Archives for September 2026

Into action?

22 September 2026 by Clark Leave a Comment

I’m having a look at a book on evaluation that I am finding has a lot of value. A familiar model (not well-referenced, mind you), for one. Not as nuanced as Thalheimer’s Learning Transfer Evaluation Model (LTEM), but comprehensible. And importantly, really good analytics of problems and ways to communicate them. However, there’s one aspect that I’ve seen elsewhere, and it’s bothered me without me being able to articulate why, until now. And I realize it’s about putting learning into action. What do I mean?

So, the book talks about three types of learning outcomes: basically informational, then changing behavior but not impact, and then finally impact. The explicit belief is that organizations have learnings of all three types, and therefore they’ve aligned evaluations to that belief And, as I said, they have (so far, I’m not done ;) good evaluations at each step that identify ways in which the learning can go wrong and remedies. In many ways, reminiscent of Julie Dirksen’s latest and important book Talk to the Elephant. All good, but I have a quibble about that initial breakdown.

To me, you don’t give people information and expect them to do nothing with it. You give it with a reason: whether it’s what HR policies are available, or how you handle commission, or what have you. You want people to use the available HR opportunities if relevant, and likewise you’d like commission to be motivating. Sure, you can see whether people passed a test on the knowledge, but why would you give it to them unless you expect them to do something differently as a result? That, then – doing something differently – is really your goal.

The same with workplace behavior change. You don’t want it to lead to anything meaningful? I doubt that. Why would you ask for things to change if you didn’t think it mattered? If you do, then there’s got to be some reason, some outcome, and therefore some way to measure it. You want people to do things different for a reason! That is, there’s some impact. And that, to me, should be checked.

In both cases, ultimately your motivation is for something different to happen. Which should impact some organizational metric. Otherwise, why did you bother? The point being, I don’t think giving people information, or asking them to do something differently but it’s not meaningful to the org, is done just because. I think it’s because you want some thing to change, you want the audience to put it into action.

I have to ask myself, of course, whether folks might do something and not care to measure the impact. Ok, for Cover Your Assets type of stuff maybe, but that’s learning for legal’s sake, not learning’s sake. Is this just too persnickety? I suggest that it’s not, in that assuming you’ll have a good impact is kind of naive. Don’t you want to ensure that you are?

Look, it’s good at each stage to look for reasons it might not be working, and remedy thereto. But that’s doesn’t mean you can stop after one or another stage. You should have a reason, and you should therefore have an outcome that you’re looking for, and therefore that you can measure. It could just be observation, and it may be subtle and/or long-term, but I’ll suggest that there are measures. I think you do want to put learning in action, and thus have an impact. Yes, I recognize that’s an ideal, but is there really any justifiable reason to stop short? I’m perfectly willing to hear that there aren’t the resources to do more, but that’s pragmatics, not principle. On principle, am I wrong? Seriously? Let me know.

Two things I’m currently on about

15 September 2026 by Clark Leave a Comment

I’ve been talking a fair bit about AI, but that’s not really what I’m interested in right now. So maybe it’s worth talking a bit more about two things I’m currently on about. I’d welcome your feedback!

So, first, I’m on about bridging. For obvious reasons, I’ve been thinking about the gap between training and sustained impact on the business. That includes spacing (warning: PDF), reflection, planning, after-action reviews, coaching, evaluation, and more. I’ve already expressed some thoughts on these, and it’ll of course continue. The main thing is that too many orgs aren’t thinking about it. And they should, because without concrete plans to facilitate the initial learning, it’s lost. Not a good investment!

For one way my thinking on bridging will continue is that we’re beginning to see results from analyzing data being collected by the use of the Elevator 9 platform (caveat, I’ve been involved in the initial design). It appears that following the recommended spacing works. The corollary, of course, is that not following the recommended spacing doesn’t work. That includes not doing the reactivations, and also trying to do them in a rush at the end. Not surprising, but interesting nonetheless.

The other area I’m increasingly thinking is important is meta-learning, or learning to learn (see diagram). I like Harold Jarche’s Personal Knowledge Mastery, simplified, as a framework. Now, I’ve been a fan of meta-learning since at least grad school (took Jean Mandler’s class on it), and probably before. Right now, of course, I think it’s essential to make good decisions about how to use generative AI, if you’re going to. However, I reckon it’s also important in more general terms. It’s the key to innovation, for one, because (as I maintain), it’s a form of learning. (Can’t find where I’ve stated it in my blog, which doesn’t mean I haven’t, because I’m pretty sure I have!) It’s also the key to continual personal development, which means doing better at anything you care about. And you do care about something!

There likely is a third area (besides the general long-term interest in learning, technology, design, engagement, etc that are where my interests and experience lie). It’s just that I haven’t expressly thought about it, like I have the two above. I still think there’s not enough interest in the alignment between engagement and learning, which I explored in both my first book and my next to most recent one. For that matter, there’s not even sufficient understanding of learning science (topic of my sixth book), too many myths (my fifth book), and overall the wrong focus for L&D (my fourth book, if you happen to notice a trend :).

Of course, I’m basically on about helping orgs apply the cognitive and learning sciences to identify, understand, and provide remedies to problems they’re not quite sure whether or why they exist. (Hence the Learning Development Accelerator’s Learning Science Conference.) That’s the bigger picture. But, life’s short and there’s only so much one can focus on at any one time. So, bridging and meta-learning are two things I’m currently on about, while I continue to track what’s happening in all the related fields I care about. Am I missing anything?

A layer above

8 September 2026 by Clark Leave a Comment

I recently wrote a post about a secure, sustainable way to take advantage of artificial intelligence (AI, and all forms). The point was to not leave you vulnerable. And, I don’t think I was wrong, but I was a wee bit too limiting. Elizabeth Dalton (who I’m working with via Elevator 9) pointed me to an article that argues in much more depth about the necessary structure. So, my learning is that there’s a layer above that’s necessary as well.

In the post, I wrote about using small hybrid agents to do things, instead of using one monolithic entity to address all the needs. It was sort of a ‘use the right tool for the job’ article. And I do think it makes sense, for multiple reasons. For one, you control what you release when you control where and how when you host your own solutions. For another, they can be run securely on your or hosted storage. That means without connecting to anyone else. Yes, you do have to develop the systems, but that’s become relatively easy. And, you own what you create. That’s important.

What this article adds is the integrating layer. What they call sovereign AI, and that’s a term I’m going to use more frequently. You need to have the infrastructure that hosts the agents to be something you build and own as well. The article was by an entity that I don’t particularly know, but I trust more because it’s open. And, my inference is that there are other ways to do it, but the important thing is that you do do it.

If my understanding is correct, under such a scheme you can have an agent that’s one of those big ones (from Anthropic or OpenAI or…) in there as well. Just that they’ve containment and constraints, and so given specific access. It doesn’t allow the systems to run rampant, hacking your security for instance.

To me, that integration, that complete ownership, is the only scrutable way to do things. I’m not a developer, so I can’t weigh in more, but the article talks not only the importance, but the steps. A layer above is a necessary extension to the core idea of ‘build your own’ that has big and long-term implications for a scrutable and successful implementation. Why would you want anything else?

Theory and Practice into Practice

1 September 2026 by Clark 1 Comment

One of the things that goes into my work is taking research and theory, and putting it into practice. Which manifests in a number of ways, but I thought I’d share one. Credit goes to the team at Elevator 9, because this is definitely a team effort. That is, others take my initial stabs, theory and practice, and turn it into practice. And the results are inspiring.

So, Elevator 9 is about taking what’s done in training events, and augmenting that to actually yield change that sticks. Importantly, also to document same. To do that, they started with my interpretation of the impact of spacing on learning, to yield an initial set of predictions. We moderated that on the basis of practicality.

They have some initial data, and it actually supports the premises. When folks follow the learning recommendations, they learn. When, for instance, they instead try to address all the interventions in one batch at the end, it doesn’t work. Of course, if learners don’t do it at all, they don’t become performers.

One barrier to all this is that people are busy. When the intervention recommendations come, it may not be at a suitable time. That led the team (e.g. COO Page Chen and Elizabeth Dalton primarily, in this case) to look at research on what leads people to actually choose to engage. Naturally, the recommendations, including from CEO David Grad, were to have a ‘pause’ button that would allow the learner to have the recommendation come later the same day. More importantly, the recommendation was to specify a particular time that would be good.

The premise is that when folks can commit to another time, they’re more likely to actually do it. If they actually specify a time, they’re making an even stronger commitment. That’s all to the good! (And that outcome wouldn’t have come from one person, but it comes from partnering.) This is theory again translating into practice.

It makes sense; research on spaced learning typically is done by college students in psych or education classes, in controlled studies. We’re instead looking at real people in real contexts. The reality we all face is different than the controlled conditions most research is conducted in. To be fair, we want those controlled conditions first, to give us predictions, but then we need to adapt to the current circumstances.

In fact, that’s the reason to pay attention to learning science (*cough* The Learning Science Conference *cough*), to get the first best guess and then refine through testing. If you don’t pay attention to the science, either you’re tuning will take longer, or you’ll be so wrong you abandon the initiative (or fall back on mistaken folklore). Neither’s optimal. So, to take theory and practice into practice, you need to take your interpretations of learning science, and then test and refine. It’s work, but the outcome is better. And, that’s what we’re about after all, right?