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Last week, I expressed my gratitude for folks from this past year. That’s looking back, so it’s time to gaze a touch ahead. With some thoughts on the whole idea! So here’s looking forward to 2025. (Really? 25 years into this new century? Wow!)

First, I’m reminded of the talk I heard once. The speaker, who’d if memory serves had written a book about predicting the future, explained why it was so hard. His point was that, yes, there are trends and trajectories, but he found that there was always that unexpected twist. So you could expect X, but with some unexpected twist. For instance, I don’t think anyone a year ago really expected Generative AI to become such a ‘thing’.

There was also the time that someone went back and looked at some predictions of the coming year, and evaluated them. That didn’t turn out so well, including for me! While I have opinions, they’re just that. They may be grounded in theory and 4+ decades of experience, but they’re still pretty much guesswork, for the reason above.

What I have done, instead, for a number of years now is try to do something different. That is, talk about what I think we should see. (Or to put it another way, what I’d like to see. ;). Which hasn’t changed much, somewhat sadly. I do think we’ve seen a continuing rise of interest in learning science, but it’s been mitigated by the emergence of ways to do cheaper and faster. (A topic I riffed on for the LDA Blog.) When there’s pressure to do work faster, it’s hard to fight for good.

So, doing good design is a continued passion for me. However, in the conversations around the Learning Science conference we ran late this year, something else emerged that I think is worthy of attention. Many folks were looking for ways to do learning science. That is, resolving the practical challenges in implementing the principles. That, I think, is an interesting topic. Moreover, it’s an important one.

I have to be cautious. When I taught interface design, I deliberately pushed for more cognition than programming. My audience was software engineers, so I erred on getting them thinking about thinking. Which, I think, is right. I gave practical assignments and feedback. (I’d do better now.) I think you have to push further, because folks will backslide and you want them as far as you can get them.

On the other hand, you can’t push folks beyond what they can do. You need to have practical answers to the challenges they’ll face in making the change. In the case of user experience, their pushback was internal. Here, I think it’s more external. Designers want to do good design, generally. It’s the situation pragmatics that are the barrier here.

If I want people to pay more attention to learning science, I have to find a way to make it doable in the real world. While I’m finding more nuances, which interests me, I have to think of others. Someone railed that there are too many industry pundits who complain about the bad practices (mea culpa). That is, instead of cheering on folks that they can do better. And I think we need both, but I think it’s also incumbent to talk about what to do, practically.

Fortunately, I have not only principle but experience doing this in the real world. Also, we’ve talked to some folks along the way. And we’ll do more. We need to find that sweet spot (including ‘forgiveness is easier than permission’!) where folks can be doing good while doing well.  So that’s my intention for the year. With, of course, the caveat above! That’s what I’m looking forward to. You?

The good news is, the Learning Science Conference has gone well. The content we (the Learning Development Accelerator, aka LDA) hosted from our stellar faculty was a win. We’ve had lively discussions in the forum. And the face to face sessions were great! The conference continues, as the content will be there (including recordings of the live sessions). The open question is: what next? My short answer is going beyond learning science.

So, the conference was about what’s known in learning science. We had topics about the foundations, limitations, media, myths, informal/social, desirable difficulty, applications, and assessment/evaluation. What, however, comes next? Where do you go from a foundation in learning science?

My answer is to figure out what it means! There are lots of practices in L&D that are grounded in learning science, but go from there to application. My initial list looks like this:

1. Instructional design. Knowing the science is good, but how do you put it into a process?
2. Modalities. When you’re doing formal learning, you can still do it face to face, virtually, online, or blended. What are the tradeoffs, and when does each make sense?
3. Performance consulting. We know there are things where formal learning doesn’t make sense. We want gaps and root causes to determine the right intervention.
4. Performance support. If you determine job aids are the answer, how do you design, develop, and evaluate them? How do they interact with formal learning?
5. Innovation. This could (and should; editorial soapbox) be an area for L&D to contribute. What’s involved?
6. Diversity. While this is tied to innovation, it’s a worthy topic on its own. And I don’t just mean compliance.
7. Technology. There are lots of technologies, what are their learning affordances? XR, AI, the list goes on.
8. Ecosystem. How do you put the approaches together into a coherent solution for performance? If you don’t have an ‘all singing, all dancing’ solution, what’s the alternative?
9. Strategy. There’s a pretty clear vision of where you want to be. Then, there’s where you are now. How do you get from here to there?

I’m not saying this is the curriculum for a followup, I’m saying these are my first thoughts. This is what I think follows beyond learning science. There are obviously other ways we could and should go. These are my ideas, and I don’t assume they’re right. What do you think should be the followon? (Hint: this is likely what next year’s conference will be about. ;)

There’ve been a recent discussions about misinformation. One question is, what does it hurt? When you consider myths, superstitions, and misconceptions (the breakdown in my book on L&D problems), what can arise? Let’s talk about the damage done.

So, let’s start with myths. These, I claim, are things that have been shown not to have value by empirical research. There are studies that have examined these claims, and found them to not have data to support them. For instance, accommodating learning styles is a waste. Yes, we know people differ in learning, but we don’t have a reliable base. Moreover, people’s choices to work for (or against) their style don’t make a difference in their learning. Some of the instruments are theoretically flawed as well as psychometrically invalid.

What’s the harm? I’ll suggest several ways in which myths harm us. For one, they can cause people to spend resources (money & time)  addressing them that won’t have an impact. It’s a waste! We can also characterize people in ways that limit them; for instance if they think they learn in a particular way, they may avoid a topic or invest effort in an inappropriate way to learn it. Investing in unproven approaches also perpetuates them, propagating the beliefs to others.

Superstitions, as I define them, are beliefs nobody would claim to believe, yet somehow persist in our practices. For instance, few will claim to believe that telling is sufficient to achieve behavior change. Yet, we continue to see information presentation and knowledge test, such as “awareness” training. Why? This is a waste of effort. There aren’t outcomes from these approaches. Typically, they are legacies of expectations from previous decades, yet business practices haven’t been updated. Still, to the extent that we continue these practices, even while decrying them, we’re again wasting time and money. Maybe we tick boxes and make people happy, but we can (and should) do better.

The final category is misconceptions. These are beliefs that some hold, and others decry. They aren’t invalid, but they only make sense in certain circumstances. I suggest that those who defy them don’t have the need, and those who tout them are in the appropriate circumstance. What matters is understanding when they make sense, and then using them, or not, appropriately. If you avoid them when they make sense, you may make your life harder. If you adopt them when they’re not appropriate,  you could make mistakes or waste money.

At the end of the day, the damage done is the cost of wasting money and time. Understanding the choices is critical. To do so best, you can and should understand the underlying cognitive and learning sciences. You should also track the recognized translators of research into practice who can guide you without you having to read the original academese. To be professional in our practice, we need to know and use what’s known, and avoid what’s dubious. Please!