Archives for June 2025

Writing for learning

24 June 2025 by Clark Leave a Comment

We write for lots of reasons. It’s all about communication, but with different purposes, there should be different writing. Just for books, the language in a thriller should be different than for thoughtful stories. Writing for ads is different than writing for science. And, writing for learning is different than writing for other purposes. What am I talking about?

What research tells us, as Ruth Clark lets us know, is that we learn better from conversational language. Formal language, such as in an encyclopedia, or a textbook, doesn’t work for elearning or how an instructor talks to an audience. You want to be informal, personal, and more. Yet too often our prose is tedious.

Dialog, in particular, should be authentic to the speaker. I quail when I see characters spouting language straight out of an instructional manual or, worse, a marketing spiel. Good character development goes beyond stereotypes and develops some personality. This should come through in their language. Writing dialog, then, isn’t what most designers have been trained in. Which means that designers shouldn’t write dialog, or at least get external support whether training, even just peer review.

Writing for learning needs to be clear, of course. It also needs to be accurate. And yet, it shouldn’t be onerous to read. If there are barriers to comprehension, you’re putting in unnecessary barriers to your learning outcome. Really, you’re managing cognitive load. Obtuse language impedes processing, and learning is processing-intensive enough!

I’ve talked before about the importance of emotion in learning, for motivation, keeping anxiety under control, building confidence, and more. Writing is one of the most compact forms of media for communicating, and so we want our language to address these issues as well. Conversational language helps reduce anxiety by being familiar, and shows relatedness, part of the Self-Determination Theory of motivation. When folks believe we care about them, they’re more inclined to succumb to our ministrations.

Writing for learning is one of the elements necessary for the appropriate use of media. We should use the right media for the message (with a caveat about the value of novelty), and then we should apply the right media correctly. That is, ensuring we apply the appropriate expertise. We can make changes, such as my common example of Ken Burn’s compelling use of still images in his video documentary of the Civil War, but even then there are accommodations. In short, writing for learning has some particular constraints, and we as designers should be aware of them.

There’s more, of course. What you write in an introduction is different than what’s presented about a model, than the narrative for an example, for the instructions versus the description of the context for retrieval practice, etc. Knowing what the role is, and the appropriate writing, becomes habit with experience, but like all learning, models and feedback help accelerate the path there. You need to know not just what to write, but how and when. Those are my thoughts, what are yours?

In praise of reminders

17 June 2025 by Clark Leave a Comment

I have a statement that I actively recite to people: If I promise to do something, and it doesn’t get into a device, we never had the conversation. I’m not trying to be coy or problematic, there are sound reasons for this. It’s part of distributed cognition, and augmenting ourselves. It’s also part of a bigger picture, but here I am in praise of reminders.

Schedule by clock is relatively new from a historical perspective. We used to use the sun, and that was enough. As we engaged in more abstract and group activities, we needed better coordination. We invented clocks and time as a way to accomplish this. For instance, train schedules.

It’s an artifact of our creation, thus biologically secondary. We have to teach kids to tell time! Yet, we’re now beholden to it (even if we muck about with it, e.g. changing time twice a year, in conflict with research on the best outcomes for us). We created an external system to help us work better. However, it’s not well-aligned with our cognitive architecture, as we don’t naturally have instincts to recognize time.

We work better with external reminders. So, we have bells ringing to signal it’s time to go to another course, or to attend worship. Similar to, but different than other auditory signals (that don’t depend on our spatial attention) such as horns, buzzers, sirens, and the like. They can draw our attention to something that we should attend to. Which is a good thing!

I, for one, became a big fan of the Palm Pilot (I could only justify a III when I left academia, for complicated reasons). Having a personal device that I could add and edit things like reminders on a date/time calendar fundamentally altered my effectiveness. Before, I could miss things if I disappeared into a creative streak on a presentation, paper, diagram, etc. With this, I could be interrupted and be alerted that I had an appointment for something: call, meeting, etc. I automatically attach alerts to all my calendar entries.

Granted, I pushed myself to see just how effective I could make myself. Thus, I actively cultivated my address book, notes, and reminders as well as my calendar (and still do). But this is one area that’s really continued to support my ability to meet commitments. Something I immodestly pride myself for delivering on. I hate to have to apologize for missing a commitment! (I’ll add multiple reminders to critical things!) Which doesn’t mean you shouldn’t, actively avoid all the unnecessary events people would like to add to your calendar, but that’s just self-preservation!

Again, reminders are just one aspect of augmenting ourselves. There are many tools we can use – creating representations, externalizing knowledge, … – but this on in particular as been a big key to improving my ability to deliver. So I am in praise of reminders, as one of the tools we can, and should, use. What helps you?

(And now I’ll tick the box on my weekly reminder to write a blog post!)

Expert in the loop

10 June 2025 by Clark Leave a Comment

A couple of recent occurrences have prodded me to think. (Dangerous, I know!). In this case, generative AI continues to generate ;) hype and concern in close to equal measure. Which means it dominates conversations, including one I had recently with Markus Bernhardt. Then, there was a post by Simon Terry that said something related that doesn’t completely align. So, some thoughts arguing to have an expert in the loop.

First, as a neighbor as well as an AI strategist of renown, I’m grateful Markus and I can regularly converse. (And usually about AI!) His depth and practical experience in guiding organizations complements my long-standing fascination with AI. One item in particular was of note. We were discussing how you need a person to vet what comes out of Generative AI. And it became clear that it can’t just be anybody. It takes someone with expertise in the area to be able to determine if what’s said is true.

That would suggest that the AI is redundant. However, there are limitations to our cognition. As I’ve recounted numerous times, technology does well what we don’t, and vice-versa. So, we use tools. One of the things we do is unconsciously forget aspects of solutions that we could benefit from. Hence, for instance, checklists. In this case, Generative AI can be a thinking partner in that it can spin up a lot of ideas. (Ignoring, for the moment, issues like intellectual property and environmental costs, of course.) They may not be all good, or even accurate, but…they may be things we hadn’t recalled or even thought of. Which would be a nice complement to our thinking. It requires our expertise, but it’s a plausible role.

Now, Simon was talking about how ‘human in the loop’ perpetuates a view of humans as cogs in a machine. And I get it. I, too, worry about having people riding herd on AI. That is, for instance, AI doing the creative work, and humans taking responsibility. That’s broken. But, having AI as a thinking partner, with a human generating ideas with AI, and taking responsibility for the accuracy as well as the creativity, doesn’t seem to be problematic. (And I may be wrong, these are preliminary thoughts!)

Still, I think that just a ‘human in the loop’ could be wrong. Having an expert in the loop, as Markus suggested, may be a more appropriate situation. He pointed out a couple of ways Generative AIs can introduce errors, and it’s a known problem. We have to have a person in the loop, but who? As I recounted recently, are we just training the AI? Still, I can see a case being made that this is the right way to use AI. Not as an agent (acting on its own, *shudder*), but as a partner. Thoughts?

What does ‘evidence-informed’ mean?

3 June 2025 by Clark Leave a Comment

We colloquially tout the Learning Development Accelerator as a society for ‘evidence-based’ practice. Or, more accurately, as ‘evidence-informed’, as Mirjam Neelen & Paul Kirschner advise us in their tome. But, what does ‘evidence-informed’ mean, in practice? Does everything you do have to align with what research tells us? What’s the practical interpretation? So, I have an admission to make.

To start, if you go to the LDA site (I just did), it says: “Explores and encourages research-aligned practices”. That is a noble goal, to be sure. Let’s be clear, however: research doesn’t cover all our particular situations. In fact, it’s unlikely to cover any of our specific situations. Much of the research we use is done on psychology undergraduates, and frequently for education purposes, e.g. K12 or higher ed. Which means it’s indicative of our general cognitive processing, but not our specific situations.

There is research on organizational learning, to be sure. It’s not always pristine laboratory conditions, as it may well be meeting real-world needs. Of course, we do see some A/B-type studies. Still, while legitimate, they’re not likely to be our particular situation. That is, our particular audience, our specific learning objectives, our timeline, our urgency, etc.

So what does one do? We must abstract the underlying principles, and reinstantiate for our circumstances. There are good overall principles, such as the benefit of generative activities and spaced retrieval practice. The nature of these, of course, such as choosing the right activities (Thiagi & Matt have a whole book on this!), and the right parameters for retrieval (we’re asking for that at Elevator9), means that we have to customize. Which means we have to test and tune. We can’t expect to get it right the first time. (Though, we’ll get better over time.)

There will be times, when we’re doing something that’s far enough away that we’re kind of making it up as we go along. (An area I love, as it requires considering all the models I’ve mentally collected over the years.) Then, we may find good examples to use as guidance. Someone’s tried something, and it worked for them. If you look at the LDA Research Checklist, for instance, you’ll see that replicated research is desirable. Well, that’s ideal. We live in the real world, however. BTW, this is a good reason to share what you learn (you may have to anonymize it, for sure): so others benefit.

So, and this is where I make an admission, there will be times where we don’t have adequate guardrails. There are times when we have only some examples, or basically we’re wading into new areas. Then, we are free, with a caveat: we can’t do what’s been shown to be wrong. For instance, learning styles. Or attention-span of a goldfish. Or any of the other myths. My take, and I require this for LDA Press as well, is that we ask for the evidence-base, but we require that submissions not violate what’s known.

So, evidence-based, research-aligned, etc, at least means avoiding what has been shown not to work. It starts from using the best evidence-available to guide design, and then testing (which research also tells us to do!). Why? Because we get better outcomes. We do know that not following research is unlikely to have an impact. Learning design is, at core, a probabilistic game. Increasing the likelihood of a real impact should be what we’re about. Doing so on the basis of research is a faster and more reliable path to having an impact. Ultimately, the answer to the question “what does ‘evidence-informed’ mean?” is better outcomes. Who doesn’t want that?