As usual, I’m writing in Word. I’d like to use other platforms (Pages? Scrivener? Vellum?), but there are a couple of extenuating circumstances. For one, I’ve been using Word since I wrote my PhD thesis on the Mac II I bought for the purpose. I think that was Word 2.0, circa late 80’s. In other words, I’ve been using Word a long time! Then, the most important thing besides ‘styles‘ (formatting, not learning) is the ability to outline. Word has industrial-strength outlining, and, to use an over-used and over-emphatic point, I live and die by outlines.

I outline my plan before I start writing, pretty much always. Not for blog posts like this, but for anything of any real length beyond such a post. Anything with intermediate headings is almost guaranteed to be outlined. I tend to prefer well-structured narratives (at least for non-fiction?). It likely will change, of course. When my very first book was written, it pretty much followed the structure. Ever since then…  My second book had me rearranging the structure as I typed. My most recent book got restructured after every time I shared it with my initial readers, until suddenly it gelled.

In this case, and not unlike most cases, I move things around as I go. This should be a section all its own. That is superfluous to need. This other goes better here than where I originally put it. And so on. I do take a pass through to reconcile any gaps or transitions, though I try to remedy those as I go.  The goal is to do a coherent treatment of whatever the topic is.

I throw resources in as I go. That is, if I find myself referring to a concept, I put a reminder in a References or Resources section at the end to grab a reference later. I have a separate (ever-growing) file of references for that purpose. Though I may not always include the reference in the document (currently I’m trying to keep the prose lean), but I want folks to have a resource at least.

I also jump around, a bit. Mostly I proceed from ‘go to whoa’, but occasionally I realize something I want to include, and put a note at the appropriate place. That sometimes ends up being prose, until I realize I need to go back to where I was ;). I hope that it leads to a coherent flow. Of course, as above, I do reread sections, and I try to give a final read before I pass on to whatever next step is coming. Typically, that means sending to someone to see if I’m on track or off the rails.

I also am pondering that I may retrofit with diagrams. Sometimes I’ve put them in as I go. At other times, I go back and fill them in. I do love me a good diagram, for the reasons Larkin & Simon articulated (Connie Malamed is doing a good job on visuals over at LinkedIn this month). Sometimes I edit the ones I have as I recognize improvements, sometimes I create new ones, sometimes I throw existing ones in. It’s when I think they’ll help, but I can think of several I probably should make.

The above holds true for pretty much all writing I do beyond these posts. This is for me, first, after all! Otherwise, I solicit feedback (which I don’t always get; I think folks trust me too much, at least for shorter things). I’m sure others work different. Still, these are my thoughts on writing, again. I welcome your reflections!

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First, why me? A couple of reasons. For one, I’m independent. You (should) know that you’ll get an unbiased (expert) opinion. Second, this product was something quite closely related to things I do know about, that is, learning experiences that are educationally sound. Third, the asker was not only a well-known proponent of quality learning, but knew I was also a fan of the work. So, while I’m not an analyst, few same would’ve really understood the product’s value proposition, and I do know the tools market at a useful level. I knew there was nothing else on the market like it, and the things that were closest I also knew (from my authoring simulation games work, as in my first book, and the research reports for the Learning Guild).

The product itself allowed you to author deep learning experiences. That is, where you immerse yourself in authentic tasks, with expert support and feedback. Learning tasks that align with performance tasks are the best practice environments, and in this case were augmented with resources available at the point of need. The main problem was that they required an understanding of deep learning to be able to successfully author. In many cases, the company ended up doing the design despite offering workshops about the underlying principles. Similarly, the industrial-strength branching simulation tools I knew then struggled to survive.

And that was my reason, then, to suggest that the market wasn’t ready. I didn’t think enough corporate trainers, let alone the managers and funding decision-makers, would get the value proposition. There still are many who are ‘accidental’ instructional designers, and more so then. The question, then, is whether such a tool could now succeed. And I’m more positive now.

I think we are seeing greater interest in learning science. The big societies have put it on their roadmaps, and our own little LDA learning science conference was well received. Similarly, we’re seeing more books on learning science (including my own), and more attention to same.  I think more folks are looking for tools that make it easy to do the right thing. Yes, we’re also confronting the AI hype, but I think after the backlash we’ll start thinking again about good, not just cheap and fast. I not only hope, but I think there’s evidence we are getting smarter and more focused on quality. Fingers crossed!

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So, first, it’s the rise in attempts to defraud you. That can be scams, phishing, or more. As I was creating this post, this was a repost on Bluesky:

Robocalls are seeing a massive increase lately. Keep in mind that efforts to stop caller-ID spoofing have largely had no real effect, because callers now use “throw away” numbers that verify correctly and then are abandoned after days or even hours. In fact, if you get an “unknown caller” on your phone, it’s likely NOT a spam call, because spammers can now so easily not bother spoofing or blocking their numbers, they just keep switching to different “legit” numbers that spam blocks usually don’t detect.

Email phishing is on the rise, and much of it now is bypassing SPF and DKIM checks (that Google and other large mailers started requiring for bulk mailings) due to techniques such as DKIM replay and a range of other methods. Fake PayPal invoices are flooding the Net, and they often are passing those checks meant to block them. It’s reported that many of these are coming from Microsoft’s Outlook, with forged PayPal email addresses. Easiest way to detect these is to look at the phone number they want you to call if you have a question — and if it’s not the legit PayPal customer service number you know it’s not really from PayPal. Getting you to call the scammers on the phone is the basis of the entire scheme.

It’s all getting worse, not better. – From Lauren Weinstein Lauren.vortex.com

Another one are Google Calendar announcements, and recently DocuSign frauds. Plus, of course, the continual fake invoices for Macafee, etc. I don’t know about you, but the earlier scam of pretending to be someone on LinkedIn has returned. I’m seeing a renewal of folks saying that I have an interesting profile, or that I’d be a good match for their company’s new initiative. Without knowing anything about me, of course.

Worse, I’m now seeing at least the former showing up in Bluesky (so I’m keeping Mastodon around; quinnovator on both), and even on Academia.edu! I hear about some attempts to crack down on the factories where they house (and exploit) folks to do this. Which, of course, just drives them to smaller and harder to find such activities. The tools are getting more powerful, making it easier.

The one that really gets me is the increasing use of our data to train language models. I was first alerted when a tool (no longer freely available) allowed me to check one of the AI engines. Sure enough, this blog was a (miniscule) percentage of it. In the column on the right, you can see I’m ok with my posts being fodder. Er, only if you aren’t making money, share alike, and provide attribution! Which isn’t the case; I haven’t had contact nor seen remuneration.

This is happening to you, too. As they say, if you’re not paying, you’re the product. If you use Generative AI (e.g. ChatGPT), you’re likely having your prompts tracked, and any materials you upload are fair game. Many of the big tools (e.g. Microsoft) that connect to the internet are also taking your data. Some may make not taking the default, but others aren’t. In short, your data is being used. Sure, it may be a fair exchange, but how do you know?

In short, they’re ripping you off. They’re ripping us off!  And, we can passively accept it, or fight. I do. I report phishing, I block folks on social media, and I tick every box I can find saying you can’t have my data. Do we need more? I like that the EU has put out a statement on privacy rights. Hopefully, we’ll see more such initiatives. The efforts won’t stop; shareholder returns are at stake after all, but I think we can and should stand up for our rights. What say you?

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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?

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One of the good things happened in the first half of the year. I had the pleasure to continue my relationship with the folks at Upside Learning. Amit Garg continued to support learning science through his deeply grounded perspective, which led to a number of good things. One was the continual ideas from Isha Sood for marketing. There were a plethora of steps around publicizing the benefits of learning science. We did webinars, presentations, videos, and more, causing me to think afresh.  Another was working with Vidya Rajagopal to bake learning science into their design practices. She prodded me about the pragmatic constraints and we collaborated on generating new ideas about how to succeed.

Speaking of proselytizing learning science, I was engaged in many activities for the Learning Development Accelerator (LDA). With my co-director Matthew Richter, and the team, we ran a wide variety of activities. While some were members-only, others were publicly available or separate events. For instance, the Learning Science Conference was an opportunity to explore the underlying concepts and research results. We greatly benefitted from the excellent presenters, who we learned much from (as did I in particular!). Stay tuned for the followup!

I’m also grateful for those who participated in a couple of the programs the LDA ran. Both the Think Like A… and the You Oughta Know: Practitioner series drew upon folks who enlarged our perspectives on related fields and doing the work. Likewise with the debates. Of course, the LDA members are also always inquiring about the nuances. The lists are long, but you know who you are; heartfelt thanks!

I also had the chance to continue my involvement with Elevator 9. I learned a lot as the focus moved from a ‘no code’ developed-solution to a focus on developing a serious platform. A benefit was when David Grad’s passion and smart focus was coupled with Page Chen’s learning background and practical experience. It was a pleasure to work with both of them, and we plan to be able to tell you more early in the next year!

Of course, Quinnovation had its own work to do, and I had some really great experiences working with folks on their projects. We looked at the contexts and goals, and figured out steps to proceed along the path. I’m grateful, as I always learn a lot working with folks, and getting the chance to meld my background with their situations and expertise to craft viable solutions. Of course, I welcome hearing if I can assist you in the coming year!

I also did lots of interviews via podcasts, which are enlightening. The many smart hosts ask interesting questions, prompting me to think (and, regularly, rethink). These were coupled with articles for Upside, LDA, and more. I found out that one article back in January for Training Journal was their most read article of that month! Like my blogging here, these are further opportunities that cause me to reprocess my previous thinking.

I’m sure there’re more folks I’m forgetting. Mea culpa, and thanks!

In all, I’ve got a lot to be grateful for. As the research says, I find it boosting my mood as I write. So thanks to the folks above who helped me continue to explore the opportunities and solutions. I’ve much to have gratitude for, and that is the best thing of all. May you, too, have much to be grateful for, and may the holidays and the new year bring you more.

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So, to get a (proper?) Ph.D., you are expected make a unique contribution to understanding. Consider our knowledge as a giant ball, and what a thesis does is push out one tiny bump. The goal is something no one else has done. For instance, for my Ph.D., I broke analogy up into a different set of steps, and measured performance. My specification of steps was unique, but that wasn’t the contribution (in my mind, at least). What I also did was try training to improve those processes (four of the six, for reasons), and it did impact a couple, with good reasons not to have impacted the others. It wasn’t earth-shattering, by any means (I suspect no one cites my thesis!), but it was a contribution. (And, of course, it grounded me in the literature and practices.)

When I think of folks I respect, in many cases it’s because they have made a unique contribution. By the way, I suppose I should be clear: unique isn’t enough, it has to be a positive contribution (which can include ruling out things). It’s like innovation: not just an idea, but a good one!  So, for instance, Will Thalheimer’s been a proponent of evidence-informed practices, but his unique contribution is LTEM. So too with Patti Shank and multiple choice questions, Michael Allen with SAM, Harold Jarche with PKM, etc. I’m kind of thinking right now that Julie Dirksen’s new book is what’s really new!  I am inclined to think that new syntheses are also valuable.

For instance, my own books on myths and learning science are really syntheses, not new ideas. (Maybe my mobile books too?) Reflecting, I think that the three books that I wanted to publish, my first on games, my fourth on L&D strategy, and my most recent on engagement (channeling the core from the first book), are more unique contributions.  Though I will self-servingly and possibly wrongly suggest my way of thinking about contexts, models, and more are innovations. Like Allen’s CCAF (Context – Challenge – Activity – Feedback), perhaps.

Which isn’t to say syntheses that organize things into new and more comprehensible ways isn’t also a contribution. In addition to (immodestly) my afore-mentioned books in that category, I think of folks like Connie Malamed, Christy Tucker, Matthew Richter, Ruth Clark, Jane Bozarth, etc. These folks do a great job of taking received wisdom and collating and organizing it so as to be comprehensible. And I could be providing too short a shrift in some cases.

My stance is that I don’t see enough ‘uniqueness’. Original ideas are few and far between. Which may be expected, but we have to be careful. There are a lot more touted ideas than there are good ones. What really is different? What’s worth paying attention to? It’s not an easy question, and I may be too harsh. There is a role for providing different perspectives on existing things, to increase the likelihood that people hear of it. But those should be new perspectives. I’m not interested in hearing the same ideas from different folks. So, does this make sense, or am I being too harsh?

By the way, I suspect that there are more ideas than we actually hear about. I know people can be hesitant about sharing them for a variety of reasons. If you’ve got an idea, share it with someone! If they get excited, it may well be new and worthwhile. Take a chance, we may all benefit.

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You also hear about the holy trinity of engineering: cheap, fast, or good; pick two. We have real world pressures that want us to do things efficiently. For instance, we have lots of claims that generative AI will allow us to generate more learning faster. Thus, we can do more with less. Which isn’t a bad thing…if what we produce is good enough. If we’re doing good, I’ll suggest, then we can worry about fast and cheap. But doing bad faster and cheaper isn’t a good thing! Which brings us to the second issue.

What is our definition of ‘good’? It appears that, too often, good is if people ‘like’ it. Which isn’t a bad thing, it’s even the first level in the Kirkpatrick-Katzell model: asking what people think of the experience. One small problem: the correlation between what people think of an experience, and it’s actual impact, is .09 (Salas, et al, 2012). That’s zero with a rounding error! What it means is that people’s evaluation of what they think of it, and the actual impact, isn’t correlated at all. It could be highly rated and not be effective, or highly rated and be effective. Etc. At core, you can’t tell by the rating.

What should be ‘good’? The general intent of a learning intervention (or any intervention, really) is to have an impact! If we’re providing learning, it should yield a new ability to ‘do’. There are a multitude of problems here. For one, we don’t evaluate performance, so how would we know if our intervention is having an impact? Have learners acquired new abilities that are persisting in the workplace and leading to the necessary organizational change? Who knows? For another, folks don’t have realistic expectations about what it takes to have an impact. We’ve devolved to a state where if we build it, it must be good. Which isn’t a sound basis for determining outcomes.

There is, of course, a perfectly good reason to evaluate people’s affective experience of the learning. If we’re designing experiences, having it be ‘hard fun’ means we’ve optimized the engagement. This is fine, but only after, we’ve established efficacy. If we’re not having a learning impact in terms of new abilities to perform, what people think about it isn’t of use.

Look, I’d prefer us to be in the situation where perfection to be the enemy of the good! That’d mean we’re actually doing good. Yet, in our industry, too often we don’t have any idea whether we are or not. We’re not measuring ‘good’, so we’re not designing for it. If we measured impact first, then experience, we could get overly focused on perfection. That’d be a good problem to have, I reckon. Right now, however, we’re only focused on fast and cheap. We won’t get ‘good’ until we insist upon it from and for ourselves. So, let’s shall we?

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To be clear, I’m not talking about myths. Already addressed that. But is there something to be taken away? I suggested (and practiced in my book on myths in our industry), that we need to treat people with respect. I suggest that we need to:

• Acknowledge the appeal
• Also address what could be the downsides
• Then, look to the research
• Finally, and importantly, provide an alternative

The open question is whether this also applies to talking learning.

In general, when talking about trying to convince folks about why we need to shift our expectations about learning, I suggest that we need to be prepared with a suite of stories. I recognize that different approaches will work in different circumstances. So, I’ve suggested we should have to hand:

• The theory
• The data/research
• A personal illustrative anecdote
• Solicit and use one of their personal anecdotes
• A case study
• A case study of what competitors are doing

Then, we use the one we think works best with this stakeholder in this situation.

Can we put these together? I think we can, and perhaps should. We can acknowledge the appeal of the current approach. E.g., it’s not costing too much, and we have faith it’s working. We should also reveal the potential flaws if we don’t remedy the situation: we’re not actually moving any particular needle. Then we can examine the situation: here we draw upon one of the second list about approaches. Finally, we offer an alternative: that if we do good learning design, we can actually influence the organization in positive ways!

This, I suggest, is how we might approach convincing stakeholders. And, let me strongly urge, we need to! Currently there are far too many who believe that learning is the outcome of an event. That is, if we send people off to a training event, they’ll come back with new skills. Yet, learning science (and data, when we bother) tells us this isn’t what happens. People may like it, but there’s no persistent change. Instead, learning requires a plan and a journey that develops learners over time. We know how to do good learning design, we just have to do it. Further, we have to have the resources and understanding to do so. We can work on the former, but we should work on the latter, too.

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In our recent learning science conference, one topic that came up was about contexts. That is, I suggest the contexts we see across examples and practice define the space of transfer. We know that contextual performance is better than abstract (c.f. Bransford’s work at Vanderbilt with the Cognitive Technology Group). The natural question is how to choose contexts. The answer, I suggest, is ad hoc: choose the minimal set of contexts that spans the space of transfer. What we’re talking about is looking for a set chosen across contexts that support the best learning.

So, in talks I’ve used the diagram to say that if you choose the set of contexts represented by the ‘o’s, you’ll be unlikely to transfer to A, whereas if you choose the ‘x’s, you’re much more likely. Let me make that concrete: let’s talk negotiation (something we’re all likely to experience). If all your contexts are about vendors (e.g. ‘o’s,) you may not apply the principles to negotiating with a customer, A. If, however, you have contexts negotiating with vendors, customers, maybe even employers (‘x’s), you’re more likely to transfer to other situations. (Though your employer might not like it! ;)

The point that was asked was how to choose the set. You can be algorithmic about it. If you could measure all dimensions of transfer, and ensure you’re progressing from simple to complex along those, you’d be doing the scientific best. It might lead you to choose too many, however. It may be that you can choose a suite based upon a more heuristic approach to coverage. Here I mean picking ones that provide some substantive coverage based upon expertise (say, from your SME or supervisors of performance). I suspect that you’ll have to make your best first guess and then test to see if you’re getting appropriate transfer, regardless.

It’s important to ensure that the set is minimal. You don’t want too many contexts to make the experience onerous. So pick a set that spans the space, but also is slim. The right set will illuminating the ways in which things can vary without being too large. Another criteria is to have interesting contexts. You are, I’ll suggest, free to exaggerate them a little to make them interesting if they’re not inherently so.

You may also need some times when the context says not to use the focus here. What I mean is that while it could seem appropriate to extend whatever’s being learned to this situation, you shouldn’t. Some ideas support over-generalization, and you’ll need to help people learn where those limits are.

Note that the contexts are those across both examples and practice. So, learners will see some contexts in examples, then others in practice. It may be (if it’s complex, or infrequent, or costly) that you need to have lots of practice, and this isn’t a worry. Still, making sure you’re covering the right swatch across contexts will support achieving the impact in all appropriate situations.

I’m less aware of research on the spread of contexts for transfer (PhD topic, anyone?), and welcome pointers. Still, cognitive theory suggests that this all makes sense. It does to me, how about you?

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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. ;)

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