In the particular instance, they provided training in particular areas. That is, folks would attend their courses and then, at least theoretically, be able to perform in new ways. Yet, they felt that folks didn’t necessarily sustain allegiance to them nor their offerings.

I asked what else they offered.  From the perspective of a performer, I’m not there to learn! Instead, I’m there to acquire new skills so I can perform better. And, if we take to heart what performance consulting has to say, there’re also resources such as job aids. These lead to success where learning isn’t even necessary. There’s more, too.

We can go further, of course. What about community? If you’re focused on a particular area of performance, would it make sense to be connected to others in the same endeavor? I’ll suggest that it’s likely. As folks develop in ability, they need to start interacting with others.

This organization wasn’t alone, of course. I’ve engaged with a number of organizations over the years that faced the same issue. (Whether they knew it or not.) In fact, I suspect it’s more prevalent that we agree. Particularly in this era of information available online, how do you generate a sustained relationship?

It seems to me that if we’re taking a higher perspective, we’ll realize that courses are just a component of a full development ecosystem. Of course, there are lots of issues involved: finding ways to curate or create all the elements, content management, platform choice and integration, and more. Still, this seems to me to be at least part of the answer. So, what am I missing?

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I’m not going to ask this cold, of course. I’ve thought about it a bit myself, creating an initial framework (click on the image to see it larger). Ironically, considering my stance, it’s based around ADDIE. That’s because I believe the elements are right, just that it’s not a good basis for a design process. However, I do think we may need different tools for the stages of analysis, design, development, implementation, and evaluation, even if don’t invoke them in a waterfall process. I also have categories for overarching strategy, and for specific learning topics. These are spaces in which resources can reside.

There are also several different types of resources I’ve created categories for. One is an overview of the particular spaces I indicate above. Another are for information resources, that drill into a particular approach or more. These can be in any format: text or video typically. Because I’m weird for diagrams, I have them separately, but they’d likely be a type of info resource. Importantly, one is tools. Here I’m thinking performance support tools we use: templates, checklists, decision trees, lookup tables. These are the things I’m a bit focused on.

Of course, this is for evidence-based practices. There are plenty of extant frameworks that are convenient, and cited, but not well-grounded. I am looking for those tools you use to accomplish meaningful solutions to real problems that you trust. I’m looking for the ones you use. The ones that provide support for excellent execution. In addition to the things listed above, how about processes? Frameworks? Models? What enables you to be successful?

Obviously, but importantly, this isn”t done! That is, I put my first best thoughts out there, but I know that there’s much more. More will come to me (already has, I’ve already revised the diagram a couple of times), but I’m hoping more will come from you too. That includes the types of resources, spaces, as well as particular instances.

The goal is to think about the resources we have and use. I welcome you putting in, via comments on the blog or wherever you see this post, and let me know which ones you find to be essential to successful execution. I’d really like to know what L&D resources do we use. Please take a minute or two and weigh in with your top and essential tools. Thanks!

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Google Docs. I write, a lot. And, increasingly, I want others to weigh in. I am cranky that I have to choose a tool instead of just going to one place to collaborate,  and I struggle with the file structure of Drive, but the feature set within Docs is good enough to support collaborative writing. And collaborative work in general is something I strongly advocate for. Collective intelligence, as Nigel Paine refers to it. For myself, however, – articles, books –  I still use…

Microsoft Word. I’m not a big fan of the parent company (they have glommed on to the current plan for subscriptions, which makes financial sense but is a bad customer experience), and it’s not the writing tool that Scrivener is, but I’m so familiar with it (started using circa 1988) and the outlining is industrial strength (a feature I love and need). It’s the start of most of my writing.

Apple Freeform.  I still use Omnigraffle, but I’m keen to support free tools, and this one’s proprietary format isn’t any worse than any others. I could use Google Draw, I suppose, particularly when collaborating, but somehow folks don’t seem to collaborate as much around diagrams. Hmm…

WordPress. This is the tool I use to write these blog posts. It’s a way for me to organize my thinking. Yes, it’s writing too, but it’s for different types of writing (shorter, more ‘in the moment’ thoughts). While the comments here are fewer, they still do come. Announcements get auto-posted to LinkedIn, Mastodon, & Bluesky.

LinkedIn. This is where I get more comments than, these days, I do on my blog. Plus, we use it to write and talk about the Learning Development Accelerator and Elevator 9. I follow some folks, and connect with lots. It remains my primary business networking tool. Feel free to connect with me (if you’re in L&D strategy ;).

Mastodon & Bluesky. Yes, this counts as two, but I use them very similarly. Since the demise of Twitter (eX), I’ve looked for an alternative, and regularly stay with these two. They’re (slightly) different; Mastodon seems a bit more thoughtful, Bluesky is more dynamic, but they’re both ways to stay in touch with what people are thinking, largely outside the L&D space. Still haven’t found all my peeps there, but I’m Quinnovator (of course) on both.

News Apps/Sites. I’m also learning via news apps, again staying up with what’s happening in the larger world. So, I get Yahoo News because one email is there. Also, I check some sites regularly: ABC (Australia, not US), BBC, and Apple News (because it’s on my iPad). I’m counting this as one because otherwise it’d overwhelm my count.

Apple Mail. I subscribe to a few newsletters, mostly on learning science, and some blogs. They come in email (directly or via Feedblitz). This is all part of Harold Jarche’s Personal Knowledge Mastery elements of Seek – Sense – Share, and these are updated regularly but are part of the seek. Some of the writing I do is the sharing. Making sense is the above writing, diagramming, and…

Apple Keynote. Creating presentations for webinars, workshops, speaking engagements such as keynotes, and the like is another way I make sense of the world. So, having a good tool to create them is critical, and Keynote works more the way I think than PowerPoint does.

So that’s it, my 10. It may not work for Jane’s categorization (sorry!), but it captures the way I think about it. Please do share yours, too! (There are more ways than writing a post, so find the one that works for you.)

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So, my premise for the past couple of decades is to decouple what learners see from how it’s delivered. That is, have discreet learning ‘objects’, and then pull them together to create the experience. I’ve argued elsewhere that the right granularity was by learning role: concepts are separate from examples, from practice, etc. (I had team members participating in the standards process.) The adaptive platform was going to use these learning objects to customize the sequence for different learners. This was both within a particular learning objective, and across a map of the entire task hierarchy.

The way the platform was going to operate was typical in intelligent tutoring systems, with a twist. We had a model of the learner, and a model of the pedagogy, but not an explicit model of expertise. Instead, the expertise was intrinsic to the task hierarchy. This was easier to develop, though unlikely to be as effective. Still, it was scalable, and using good learning science behind the programming, it should do a good job.

Moreover, we were going to then have machine learning, over time, improve the model. With enough people using the system, we would be able to collect data to refine the parameters of the teaching model. We could possibly be collecting valuable learning science evidence as well.

One of the barriers was developing content to our specific model. Yet I believed then, and still now, that if you developed it to a standard, it should be interoperable. (We’re glossing over lots of other inside arguments, such as whether smart object or smart system, how to add parameters, etc.) That was decades ago, and our approach was blindsided by politics and greed (long sordid story best regaled privately over libations). While subsequent systems have used a similar approach (*cough* Knewton *cough*), there’s not an open market, nor does SCORM or xAPI specifically provide the necessary standard.

Artificial intelligence (AI) has changed over time. While evolutionary, it appears revolutionary in what we’ve seen recently. Is there anything there for our purposes? I want to suggest no. Tom Reamy, author of Deep Text, argues that hybrids of symbolic and sub-symbolic AI (generative AI is an instance of the latter) have potential, and that’s what we were doing. Systems trained on the internet or other corpuses of images and/or text aren’t going to provide the necessary guidance. If you had a sufficient quantity of data about learning experiences with the characteristics of your own system, you could do it, but if it exists it’s proprietary.

For adaptive learning about tasks (not knowledge; a performance focus means we’re talking about ‘do’, not know), you need to focus on tasks. That isn’t something AI really understands, as it doesn’t really have a way to comprehend context. You can tell it, but it also doesn’t necessarily know learning science either (ChatGPT can still promote learning styles!). And, I don’t think we have enough training data to train a machine learning system to do a good job of adapting learning. I suppose you could use learning science to generate a training set, but why? Why not just embed it in rules, and have the rules work to generate recommendations (part of our algorithm was a way to handle this)? And, as said, once you start running you will eventually have enough data to start tuning the rules.

Look, I can see using generative AI to provide text, or images, but not sequencing, at least not without a rich model. Can AI generate adaptive plans? I’m skeptical. It can do it for knowledge, for sure, generating a semantic tree. However, I don’t yet see how it can decide what application of that knowledge means, systematically. Happy to be wrong, but until I’m presented with a mechanism, I’m sticking to explicit learning rules. So, where am I wrong?

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My first book, Engaging Learning came from the fact that a) I’d designed and developed a lot of learning games, and b) had been an academic and reflected and written on the principles and process. Thus, it made sense to write it. Plus, a) I was an independent and it seemed like a good idea, and b) the publisher wanted one (the time was right). In it, I laid out some principles for learning, engagement, and the intersection. Then I laid out a systematic process, and closed with some thoughts on the future. Like all my books, I tried to focus on the cognitive principles and not the technology (which was then and continues to change rapidly). It went out of print, but I got the rights back and have rereleased it (with a new cover) for cheap on Amazon.

I wanted to write what became my fourth book as the next screed. However, my publisher wanted a book on mobile (market timing). Basically, they said I could do the next one if I did this first. I had been involved in mlearning courtesy of Judy Brown and David Metcalfe, but I thought they should write it. Judy declined, and David reminded me that he had written one. Still I and my publisher thought there was room for a different perspective, and I wrote Designing mLearning. I recognized that the way we use mobile doesn’t mesh well with ‘courses on a phone’, and instead framed several categories of how we could use them. I reckon those categories are still relevant as ways to think about technology!  Again, republished by me.

Before I could get to the next book, I was asked by one of their other brands if I could write a mobile book for higher education. The original promise was that it’d be just a rewrite of the previous, and we allocated a month. Hah! I did deliver a manuscript, but asked them not to publish it. We agreed to try again, and The Mobile Academy was the result. It looks at different ways mobile can augment university actions, with supporting the classroom as only one facet. This too was out of print but I’ve republished.

Finally, I could write the book I thought the industry needed, Revolutionize Learning & Development. Inspired by Marc Rosenberg’s Beyond eLearning and Jay Cross’s Informal Learning, this book synthesizes a performance and technology-enabled push for an ecosystem perspective. It may have been ahead of its time, but it’s still in print. More importantly, I believe it’s still relevant and even more pressing! Other books have complemented the message, but I still think it’s worth a read. Ok, so I’m biased, but I still hear good feedback ;). My editor suggested ATD as a co-publisher, and I was impressed with their work on marketing (long story).

Based upon the successes of those books (I like to believe), and an obvious need in our field, ATD asked for a book on the myths that plague our industry. Here I thought Will Thalheimer, having started the Debunkers Club, would be a better choice. He, however, declined, thinking it probably wasn’t a good business decision (which is likely true; not much call for keynotes or consulting on myths). So, I researched and wrote Millennials, Goldfish & Other Training Misconceptions. In it, I talked about 16 myths (disproved beliefs), 5 superstitions (things folks won’t admit to but emerge anyways) and 16 misconceptions (love/hate things). For each, I tried to lay out the appeal and the reality. I suggest what to do instead, for the bad practices. For the misconceptions, I try to identify when they make sense.  In all cases I didn’t put down exhaustive references, but instead the most indicative. ATD did a great job with the book design, having an artist take my intro comic ideas for each and illustrating them, and making a memorable cover. (They even submitted it to a design competition, where it came close to winning!)

After the success of that tome, ATD came back and wanted a book on learning science. They’d previously asked me to edit the definitive tome, and while it was appealing, I didn’t want to herd cats. Despite their assurances, I declined. This, however, could be my own simple digest, so I agreed. Thus, Learning Science for Instructional Designers emerged. There are other books with different approaches that are good, but I do think I’ve managed to make salient the critical points from learning science that impact our designs. Frankly, I think it goes beyond instructional designers (really, parents, teachers, relatives, mentors and coaches, even yourself are designing instruction), but they convinced me to stick with the title.

Now, I view Learning Experience Design as the elegant integration of learning science with engagement. My learning science book, along with others, does a good job of laying out the first part. But I felt that, other than game design books (including mine!), there wasn’t enough on the engagement side. So, I wanted a complement to that last book (though it can augment others). I wrote Make It Meaningful as that complement. In it, I resurrected the framework from my first book, but use it to go across learning design. (Really, games are just good practice, but there are other elements). I also updated my thinking since then, talking about both the initial hook and maintaining engagement through to the end. I present both principles and practical tips, and talk about the impact on your standard learning elements. In an addition I think is important, I also talk about how to take your usual design process, and incorporate the necessary steps to create experiences, not just instruction. I do want you to create transformational experiences!

So, that’s where I’m at. You can see my recommended readings here (which likely needs an update.) Some times people ask “what’s your next book”, and my true answer at this point is “I don’t know.”  Suggestions? Something that I’m qualified to write about, that there’s not already enough out about, and it’s a pressing need? I welcome your thoughts!

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To start with, I’ve suggested that what will make a difference to orgs, going forward (particularly in this age of genAI), is the ability to make better decisions. That is, either ones we’re not making right now, or new ones we need to be able to make.  When we’re moving away from us doing knowledge tasks (e.g. remembering arbitrary bits of information), our value is going to be in pattern-matching and meaning-making. When faced with a customer’s problems, we’ll  need to match it to a solution. We need to look at a market, and discern new products and approaches. As new technologies emerge, we’ll have to discern the possibilities. What makes us special is the ability to apply frameworks or models to situations despite the varying contexts. That’s making decisions.

To do this, there are several steps. What are the situations and decisions that need to be made? We should automate rote decisions. So then we’ll be dealing with recognizing situations, determining models, using them to make predictions of consequences, and choose the right one. We need to figure out what those situations are, the barriers to success, and figuring out what can be in the world, and what needs to be in the head. Or, for that matter, what we can solve in another way!

We also need to determine how we’ll know when we’ve succeeded. That is, what’s the observable measure that says we’re doing it right. It frequently can be triggered by a gap in performance. It’s more than “our sales aren’t up to scratch”, but specifics: time to close? success rate? Similarly for errors, or customer service ratings, etc. It needs to be tangible and concrete.  Or it can be a new performance we need. However, we need some way to know what the level is now and what it should be, so we can work to address it.

I note that it may feel ephemeral: “we need more innovation”, or “we need greater collaboration”, or… Still, these can be broken down. Are people feeling safe? Are they sharing progress? Is constructive feedback being shared? Are they collaborating? There are metrics we can see around these components, and they may not be exhaustive, but they’re indicative.

Then, we need to design to develop those capabilities. We should be designing the complements to our brain, and then developing our learning interventions. Doing it right is important! That means using models (see above) and examples (models in context), and then appropriate practice, with all the nuances: context, challenge, spacing, variation, feedback…  So, first the analysis, then the design. Then…

The final component is evaluation. We first need to see if people are able to make these decisions appropriately, then whether they’re doing so, and whether that’s leading to the needed change. We need to be measuring to see if we’re getting things right after our intervention, it’s translating to the workplace, and leading to the necessary change.

When we put these together, in alignment, we get measurable improvement. That’s what we want, making impactful decisions. Don’t trust to chance, do it by design!

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As I’ve probably regaled you with before, I was designing and programming educational computer games, and asking questions like “should we use spacebar and return, or number keys to navigate through menus?” (This was a long time ago.) I came across an article that argued for ‘cognitive engineering’, applying what we knew about how we think to the design of systems. Innately I understood that this also applied to the design of learning. I ended up studying with the author of the article, getting a grounding in what was, effectively, ‘applied cognitive science’.

Now, my focus on games has been on them as learning solutions, and that includes scenarios and simulation-driven experiences. But, when looking for solutions, I realize that learning isn’t always the answer. Many times, for instance, we are better off with ‘distributed‘ cognition. That is, putting the answer in the world instead of in our heads. This is broader than learning, and invokes cognitive science. Also, quite frankly, many problems are just based in bad interface designs!  Thus, we can’t stop at learning. We truly are more about performance than learning.

In a sense, we’re engineers; applying learning and cognitive science to the design of solutions, (just as chemical engineering is about applying chemistry). Interestingly, the term learning engineering has another definition. This one talks about using the benefits of engineering approaches, such as data, and technology-at-scale, to design solutions. For instance, making adaptive systems requires integrating content management, artificial intelligence, learning design, and more.

Historically, our initial efforts in technology-facilitated learning did take teams. The technology wasn’t advanced enough, and it took learning designers, software engineers, interface designers and more to generate solutions like Plato, intelligent tutoring systems, and the like.  I’ve argued that Web 1.0 took the integration of the tech, content design, and more, which usually was more than one person could handle. Now, we’ve created powerful tools that allow anyone to create content. Which may be a problem! The teams used to ensure quality. Hopefully, the shift back comes with a focus on process.

We can apply cognitive science to our own design processes. We’ve evolved many tools to support not making reliable mistakes: design processes, tools like checklists, etc. I’ll suggest that moving to tools that make it easy to produce content haven’t been scaffolded with support to do the right thing. (In fact, good design makes it hard to do bad things, but our authoring tools have been almost the opposite!)  There’s some hope that the additional complexity will focus us back on quality instead of being a tool for quantity. I’m not completely optimistic in the short term, but eventually we may find that tools that let us focus on knowledge aren’t the answer.

I’m thinking we will start looking at how we can use tools to help us do good design. You know the old engineering mantra: good, fast, and cheap, pick 2. Well, I am always on about ‘good’. How do we make that an ongoing factor? Can we put in constraints so it’s hard to do bad design? Hmm… An interesting premise that I’ve just now resurrected for myself. (One more reason to blog!) What’re your thoughts?

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When I’m optimistic, I think I see signs of an awakening awareness. There are more books on learning science, for instance. (That may be more publishers and people looking for exposure, but I remain hopeful.)  I see a higher level of interest in ‘evidence-based’. This is all to the good (if true). That is, we could and should be beginning to look at how and why to use technology to facilitate learning appropriately.

On the cynical side, of course, is other evidence. For example, the interest in generative AI seems to be about ways to reduce costs. That’s not really what we should be looking at. We should be freeing up time to focus on the more important things, instead of just being able to produce more ‘content’ with even less investment. The ‘cargo cult’ enthusiasm about: VR, AR, AI, etc still seems to be about chasing the latest shiny object.

As an aside, I’ll still argue that investing in understanding learning and better design will have a better payoff than any tech without that foundation. No matter what the vendors will tell you!  You can have an impact, though of course you risk having a previous lack of impact exposed…

So, his point was that he thought that more and more leaders of L&D are realizing they need that foundation. I’d welcome this (see optimism, above ;).  Similarly, when I argue that if Pine & Gilmore are right (in The Experience Economy) as to what’s the next step, we should be the ones to drive the Transformation Economy (experiences that transform you).  Still,  is this a reliable move in the field? I still see folks who come in from other areas of the biz to lead learning, but don’t understand it. I’ll also cite the phenomena that when folks come into a new role they need to be seen to be doing something. While them getting their mind around learning would be a good step, I fear that too many see it as just management & leadership, not domain knowledge. Which, reliably, doesn’t work. Ahem.

Explaining the present, let alone predicting the future, is challenging. (“Never predict anything, particularly the future!”) Yet, it would help to sort out whether there is (finally) the necessary awakening. In general, I’ll remain optimistic, and continue to push for learning science, evidence, and more. That’s my take. What’s yours? Where are we at?

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Just as background, I took an AI course as an undergrad, to start. Given the focus on thinking and tech (two passions), it’s a natural. I regularly met my friend for lunch after college to chat about what was happening. When I went to grad school, while I was with a different advisor, I was in the same lab as David Rumelhart. That happened to be just at the time he was leading his grad students on the work that precipitated the revolution to neural nets. There was a lot of discussion of different ways to represent thinking. I also got to attend an AI retreat, sponsored by MIT, and met folks like John McCarthy, Ed Feigenbaum, Marvin Minsky, Dan Dennet, and more! Then, as a faculty member in computer science, I had a fair affiliation with the AI group. So, some exposure.

So, first, AI is about using computer technology to model intelligence. Usually, human intelligence, as a cognitive science tool, but occasionally just to do smart things in any means possible. Further, I feel reasonably safe to say that there are two major divisions in AI: symbolic and sub-symbolic. The former dominated AI for several decades, and this is where a system does formal reasoning through rules. Such systems do generate productive results (e.g. chatbots, expert systems), but eventually don’t do a good job of reflecting how people really think. (We’re not formal logical reasoners!)

As a consequence, sub-symbolic approaches emerged, that tried architectures to do smart things in new ways. Neural nets end up showing good results. They find use in a couple of different ways. One is to set them loose on some data, and see what they detect. Such systems can detect patterns we don’t, and that’s proven useful (what’s known as unsupervised learning).

The other is to give them a ‘training set’ (also known as supervised learning), a body of data about inputs and decisions. You provide the inputs, and give feedback on the decisions until they make them in the same way.Then they generalize to decisions that they haven’t had training on. It’s also the basis of what’s now called generative AI, programs that are trained on a large body of prose or images, and can generate plausible outputs of same. Which is what we’re now seeing with ChatGPT, DALL-E, etc. Which has proven quite exciting.

There are issues of concern with each. Symbolic systems work well in well-defined realms, but are brittle at the edges. In supervised learning, the legacy databases unfortunately frequently have biases, and thus the resulting systems also have these biases! (For instance, housing loan data have shown bias.) They also don’t understand what they’re saying. So generative AI systems can happily tout learning styles from the corpus of data they’ve ingested, despite scientific evidence to the contrary.

There are issues in intellectual property, when the data sources don’t receive acknowledgement nor recompense.  (For instance, this blog has been used for training a sold product, yet I haven’t received a scintilla of return.) People may lose jobs if they’re currently doing something that AI can replace. While that’s not bad (that is, don’t have people do boring rote stuff), it needs to be done in a way that doesn’t leave those folks destitute. There should be re-skilling support. There are also climate costs from the massive power requirements of such systems. Finally, such systems are being put to use in bad ways (e.g. fakes). It’s not surprising, but we really should develop the guardrails before these tools reach release.

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I’m talking about Twitter (oh, yeah, ‘X’ as in what’s been done to it ), by the way. I’d been on there a fair bit. Having tossed my account, I can’t see when my first tweet was, but at least since 2009. How do I know? Because that’s when I was recruited to help start #lrnchat, an early tweetchat that has still been going as recently as this past summer! I became an enthusiast and active participant.

And, let me be clear, it’s sad. I built friendships there with folks with long before I met them in person. And I learned so much, and got so much help. I like to tell the story about when I posted a query about some software, and got a response…from the guy who wrote it! For many years, it was a great resource, both personal and professional!

So, what happened? Make no mistake, it was the takeover by Elon Musk. Twitter went downhill from there, with hiccups but overall steadily. The removal of support, the politics, the stupid approaches to monetization, the bad actors, it all added up. Finally, I couldn’t take it any more. Vote with your feet. (And yes, I’m mindful of Jane Bozarth’s admonition: “worth every cent it cost you”. Yep, it was free, and that was unexpected and perhaps couldn’t be expected to last. However, I tolerated the ads, so there was a biz basis!)

Perhaps it’s like being an ex-smoker, but it riles me to see media still citing X posts in their articles. I want to yell “it’s dead, what you hear are no longer valid opinions”. I get that it’s hard, and lots of folks are still there, but… It had become, and I hear that it continues to be, an increasing swamp of bad information. Not a good source!

So where am I now? There isn’t yet an obvious solution. I’m trying out Mastodon and Bluesky. If you’re there, connect! I find the former to be more intimate. The latter is closer to twitter, but I’m not yet really seeing my ‘tribe’ there. I am posting these to both (I think). I’m finding LinkedIn to be more of an interaction location lately, as well, though it’s also become a bit spammy. #sideeffects? I keep Facebook for personal things, not biz, and I’m not on Instagram. I also won’t go on Threads or TikTok.

So, what does it take to leave? I guess when the focus turns from facilitating enlightening conversation at a reasonable exchange, to monetization and ego. When there’s interference in clean discourse, and opposition to benign facilitation. And, yes, I’m not naive enough to believe in total philanthropy (tho’ it happens), but there are levels that are tolerable and then there’s going to a ridiculous extreme. Wish I had $44B to lose! I know I’m not the only one wishing those who’ve earned riches would focus on libraries and other benevolent activities instead of ego-shots into space, but this is the world we’ve built. Here’s to positive change in alignment with how people really think, work, and learn.

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