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Clark Quinn’s Learnings about Learning

What I’m up to

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Ok, so it’s been a wee bit too much about me (my books, themes), yet it occurs to me that I should document what I’m doing. (Which I’ve done before, but this is looking forward, too.) Not just for me (though it helps ;), but it’s because I realized my thinking other than books is actually getting spread out in various places. So, here’s what I’m up to…

Mostly, it’s centering around applying the cognitive and learning sciences to the design of solutions. In a variety of ways, of course. I’ve been working with Upside Learning, serving as their Chief Learning Strategist. They want to do more than pay lip service to learning science (which I laud). I’m working with them on evangelism, internal development, and more. I’m also working with Elevator 9, in this case as advisor. They’re a platform solution to complement live events, again doing so in alignment with our brains. I’m also serving as co-director of the Learning Development Accelerator. That’s a society focused on evidence-informed L&D, and we explore what this approach means in practice. In each, I’ve been advancing my own understanding, and sharing the learnings.

So, at LDA, you can find our podcasts, blog posts (some of which are free to air!), and some programs (some likewise). For members, we’re running some internal programs as well. I’ve been pleased to augment my previous program on You Oughta Know with this year’s YOK Practitioner, where I get to interview some really amazing people. Then there’s also the Think Like A…series, where we talk to representatives of adjacent fields we (should) be plagiarizing. Then there are workshops, and we’re always developing more things.

At Elevator 9, while most of the work is behind the scenes, I did author, and David Grad (the CEO) read and taped, a series of ‘liftologies’. These are short videos  talking about the learning science that goes into their offering. When they redo the website, they’ll be easy to find, but right now they’re visible through the E9 LinkedIn page posts.

Upside Learning, on the other hand, has been proactive. They do a podcast with the CEO, Amit Garg (yes, I’ve been on it). They have a blog (and I’ve written some for them). I’ve also done some quick videos on myths. In addition, I’ve written some of their ebooks (topics like impact, microlearning, scenarios). And, of course, some webinars as well. These continue.

All this in conjunction with continuing as Quinnovation! I continue with a few clients, on a limited basis. These, of course, are not public, though the thoughts can percolate out (e.g. in this blog). I’m still doing some events, mostly virtually. For instance, I’ll be talking about the alignment between effective education and engaging events at LXDCon on Tues the 11th (at 7AM PT ). I’ll also be at DevLearn and Learning 2024.

That’s all I can think of at the moment. There’s more in the offing, of course. But for now, that’s what I’m up to. This blog may be (more than) enough, but the other sites prompt different thinking. They’re worth knowing about on their own, too!  If you’re interested, these are places to either become evidence-based, apply it, or get it done. Obviously, it’s something I think is important for our industry. (As is knowing the human information processing loop, which I’ve made freely available.) Whatever you do, however you do it, please do avoid the myths and apply the science.

My Themes

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While there’s a high correspondence between my books and what I believe, it’s not one to one. While there’s overlap, there’s also unique (outsider?) perspectives. So as much for me as you, here’re my themes. It’s about applying what we know about cognition and learning. That also includes the emotional side. Moreover, we also need to apply it to the design process. That is, we, as designers, are applying, but also are subject to, what’s known about how we think, work, and learn. That’s led to a variety of things that are covered here.

It starts with a core focus on learning. Which starts with the core of cognition, the human information processing loop, but goes beyond. I think that core, by the way, is a critical thing that really everyone who designs for people (and that’s everyone) should know. (Made a video freely available to that end.) The phenomena that arise from and augment that architecture play a role here. It covers, by the way, material in two of my books: the learning science one and my myths one. It’s also the basis for my participation in the Serious eLearning Manifesto. It’s about us applying, correctly, what’s known about creating learning experiences that lead to real outcomes. I still think my focus on activity-based learning is an important way to think about creating experiences.

A complement to that is my focus on engagement in learning. Here I’m reflecting what’s been studied about making experiences engaging, across games, theatre and film, fiction, flow, and more. The first manifestation was in my book on designing serious games, but it’s morphed. My latest book is a complement to the learning science side and as a generalization of those early principles on games. I’ll be talking about this at LXDCon.

However, when we talk about performance, the picture broadens. (A topic I’ll be discussing for Upside Learning.) Marc Rosenberg talked about going beyond (e)learning, and Jay Cross wrote about informal learning. I like to think of an ecosystem approach to meeting the full suite of performance needs. This includes not just courses, but also performance support. However, it also goes further, talking about innovation as well. As I like to say, when you’re doing research, design, trouble-shooting, etc, you don’t know the answer when you begin, so it too is learning. I tried to capture this in my book on where L&D should go.

An older theme, about mobile, is in some sense no longer relevant. Mobile (for corporations and universities) has become ubiquitous and is part of the performance ecosystem. In fact, part of the recognition of the ecosystem perspective came from thinking about mobile with the recognition that it’s least about courses on a phone, and about so much more. The frameworks I created then – augmenting learning, performance support, social/informal, and context-specific – however, strike me as still worthwhile to consider. It’s really about the alignment of technology with our minds, which includes interface design and more.

Thus, implicit in the ecosystem perspective is technology. One thing we lag in is being smart about our systems. While web spinners have been using tagged content and rules, we typically still create experience hard coded in their delivery. We thus neglect content engineering, and similarly content management (e.g. the lifecycle). I was on this theme a number of years ago (content and context), but it’s sadly still relevant. I think the advent of generative AI may get folks to start thinking more about discrete content for adaptive delivery, but I’d still use a different approach to implement.

Again, it’s the application of how we think, work, and learn, to the design of solutions. In my case, for performance outcomes for individuals and organizations. Not sure what my next theme will be (or whether there’ll even be one, these are still all too relevant). I’m not sure this is comprehensive, so hopefully this first stab will give me time to think about it more!

About my books

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My booksSo, I’ve written about writing books, what makes a good book, and updated on mine (now a bit out of date). I thought it was maybe time to lay out their gestation and raison d’être. (I was also interviewed for a podcast, vidcast really, recently on the four newest, which brought back memories.) So here’re some brief thoughts on my books.

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!

An outside perspective

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Hand holding lensSomeone reached out to me for a case study on addressing a workplace problem. I was willing, but there’s a small problem; I’ve never had to address a workplace learning problem. At least, in the way most people expect. Instead, I provide an outside perspective. What’s that mean?

So, first of all, I don’t come from an instructional design (ID) background. I did get some exposure to educational approaches when I designed my own undergraduate degree in Computer-Based Education. Yet, there weren’t any ID courses where I was a student. As a graduate student, I took psychology courses on learning. I also read Reigeluth’s survey of ID design approaches. Further, I got a chance to interview the gracious and wise David Merrill. But, again, no formal ID courses were on tap.

On the flip side, I was in a vibrant program that was developing a cognitive science degree, and read everything on learning I could find: behavioral, cognitive, social, neural, even machine learning! I was in my post-doc as they were forming the learning science approach, too, and I was at a relevant institution. Still, no ID. So, I do have deep learning roots, just not ID.

Then, after the post-doc, I taught. That is, practiced learning design, and continued reading and talking ID, and attending relevant conferences. Just not a formal ID course. Then I joined a small startup to design an adaptive learning platform, and then started consulting, but never a workplace learning role inwardly faced.

What that means is that I bring an ‘outside’ perspective to L&D. Which, I think, isn’t a bad thing. I’ve helped firms meet realistic goals in innovative ways, courtesy of not having my thinking pre-constrained. I’ve been able to interpret learning science in practical terms, and infer what ID says (also, I’ve read it and reflected in context on it). So, I’ve talked L&D design, and ID improvements, but from the view of an outsider.

Many times outsiders can bring new perspectives. And, they can be ignorant of all the contextual details. Thus, it’s really important to ask and establish those constraints, and then to be sensitive to the ones that they didn’t mention. (One of the benefits of the court jester was to reframe things in ways that showed the humor in the hidden assumptions.) Still, I’m not apologizing. I think the background I’ve acquired is useful to people who need to meet real goals, and have a decent track record in doing so. I welcome your thoughts on whether an outside perspective is of benefit.

AI as a System

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At the recent Learning Guild‘s Learning & HR Tech Solutions conference, the hot topic was AI. To the point where I was thinking we need a ‘contrarian’ AI event! Not that I’m against AI (I mean, I’ve been an AI groupie for literal decades), I think it’s got well-documented upsides. (And downsides.) Just right now, however, I feel that there’s too much hype, and I’m waiting for the trough of disillusionment to hit, ala the Gartner hype cycle.  In the meantime, though, I really liked what Markus Bernhardt was saying in his session. It’s about viewing AI as a system, though of course I had a pedantic update to it ;).

So, Markus’ point was that we should separate out data from the processing method. Markus presented a simple model to think about AI that I liked. In it, he proposed three pieces that I paraphrase:

  • the information you use as your basis
  • the process you use with the information
  • and the output you achieve

Of course, I had a quibble, and ended up diagramming my own way of thinking about it. Really, it only adds one thing to his model, an input! Why?

So I have the AI system containing the process and data it operates on. I like that separation, because you can use the same process on other data, or vice versa. As the opening keynote speaker, Maurice Conti, pointed out, the AI’s not biased, the data is. Having good data is important. As is having the right process to achieve the results you want (that is, a good match between problem and process; the results are the results ;). Are you generating or discriminating, for instance? Then Markus said you get the output, which could be prose, and image, a decision, …

However, I felt that it’s important to also talk about the input. What you input determines the output. With different queries, for instance, you can get different results. That’s what prompt engineering is all about! Moreover, your output can then be part of the input in an iterative step (particularly if your system retains some history). Thus, thinking about the input separately is, to me, a useful conceptual extension.

It may seem a trivial addition, but I think it helps to think about how to design inputs. Just as we align process with data for task, we need to make sure that the input matches to the process to get the best output. So, maybe I’m overcomplicating thinking about AI as a system. What say you?

Engaged and/or Effective

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Quadrant diagram of effectiveness by engagement: neither is an info dump, engaged is a trivial pursuit, effective is boring work, unless it's also engaging in which case it's hard fun.I’ve regularly talked about how learning can, and should, be ‘hard fun’. Yet, I haven’t really talked about each, effectiveness and engagement, independently. Of course, there’s a quadrant map that separately talks about engaged and/or effective. Let me remedy the lack!

The lack of either engagement or effectiveness is relatively rare, thankfully. You do see it, when under-skilled and under-resourced folks are making a course. For instance giving SMEs authoring tools or dumping a bunch of PPTs and PDFs on an inexperienced instructional designer. Or, when folks won’t spend enough to even get production values, let alone actual effectiveness. What you get is information dump (because experts don’t have access to what they actually do, research tells us), but not with professional polish. It’s ‘content’ without distinction. More importantly, if there is practice, it’s on knowledge retrieval rather than knowledge application. Which leads to what in cognitive science is called ‘inert knowledge’. It may be remembered, but it won’t be used when relevant.

We also see a lot of ‘tarted up’ information dump. Here, there are good production values. It looks nice, because it’s well-produced. However, it’s still information (usually with a quiz). Here, folks know a bit about visual design, and use tools and templates that make it look good. They may even have experienced designers on staff, but…time and cost expectations keep folks from doing the right thing. It could also be a lack of understanding of the importance of challenging contextual practice. That’s all too common, too! It’s still a trivial pursuit.

Quite simply, learning needs to be effective. If it’s not, we’re wasting money. Now, that’s been shown to be the case in many ways. Over the years, we’ve heard estimates from 10-15% of our training efforts are working. Which means we’re wasting 85-90% of our investment. Yet we know what leads to good learning (e.g. the Serious eLearning Manifesto). Learning science gives us good guidelines, but we still see too much information dump. Yet, if it’s not engaging, learners aren’t likely to commit appropriately, and we’re not optimizing the outcomes. It just seems like work.

When we understand the necessary alignment between engagement and effectiveness, however, we truly can deliver ‘hard fun’. That alignment is what my research and design efforts yielded. It was also the core of my book on serious game design and my most recent tome on making learning meaningful. (The latter is really a complement to my learning science book, and an attempt to bring both together to do learning experience design.)

It’s not necessarily easy to generate ‘hard fun’, nor is it the cheapest option. However, it gets easier with practice (like most things), and it’s the most cost-effective option. That is, if you truly want results. But if you don’t, why are you bothering? There are requirements, like making sure you have a real learning need, but that should be true, regardless.  You shouldn’t be asking about engaged and/or effective, you should be shooting for both. Right?

Daymond John #LHRCon Keynote Mindmap

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Daymond John opened the second day of the Learning Guild’s Learning & Human Resources Technology conference. He spoke on being an entrepreneur.

In an entertaining presentation, he wove his life story with some take-home lessons. (I missed the last one, but my neighbor caught it.)

Support retention and transfer

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In a discussion we were having with David Ganulin on marketing, my colleague Matt Richter ended up talking about how many ‘team building’ activities don’t work. The typical model is an event where folks get together off-campus and face challenges together. They have to work together to overcome the challenges. Yet, Matt’s claim was that the empirical evidence was that the results didn’t transfer back to the workplace. What does it take? How can we support transfer to achieve persistent results?

The classic model is the ‘ropes course’. Folks have to work together to get everyone safely across some challenge. By working together to achieve success, you should build team cohesion and respect the different capabilities of your colleagues. Yet, investigations suggest that what’s learned doesn’t carry back to the workplace. People who got along, when they get back to the workplace, can be surprised and disappointed that the same conflicts exist.

What’s happening, of course, is context-specificity. The resulting benefits worked in the context of the team-building, but it’s not the same context as work. Just like the ‘brain training’ exercises didn’t transfer to other tasks, so to any learning is likely to dissipate quickly and still not transfer to another context. What do we need to do, then, to generate retention over time and support transfer to the workplace as well?

For one, we need more than one practice. I just read the results of interesting research suggesting two stages of memory. The first stage says initial memories can last briefly, but for sustained retention, you need a second stage of retrieval practice. Yes, we should know that, but too often we don’t practice it! (Which also suggests that a test at the end of a learning event may not be a good indicator!) Also, I’ll suggest, if we want appropriate transfer, we have to engineer it.

How do you engineer transfer? I’ll posit two steps. For one, you need experience across several different contexts. So, do task A together, then B which is widely different, then C, which is different again. You could do a task that requires different physical attributes (tall, small, strong, heavy), and then one that requires different creative approaches (art, music, prose). Along the way, you reinforce a particular team approach that works across contexts. You facilitate reflection, as well, on what’s common.

Matt went further, suggesting that then you need to take that facilitation back to the workplace, and I’ll agree that it’d be ideal. If you then brought the models back to the workplace and facilitated their application to situations at work, you could extend the internalization and appropriate re-contextualization of the learning.

One-shot events are unlikely to generate the sustained transfer you need, at least not without specific design and support. If you’re not trying to achieve retention (over time after the event until needed) and transfer (to all appropriate and no inappropriate) situations, why bother? If you do want retention and transfer (and you should), design for it. Specifically engineer to support retention and transfer. Use spaced repetition with increased challenge to achieve the former. Use contextual variance and reflection facilitation to support the latter. When you do, you’ll have outcomes worth the investment.

Conti #LHRCon Keynote Mindmap

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Maurice Conti opened the Learning Guild’s Learning Solutions & Human Resources Technology conference (April ’24 Orlando). He proceeded to talk about the implications of artificial intelligence (AI) for Human Resources (HR).

He started with the history of AI, and proceeded to focus on Generative AI. His core message was that you needed three things: your toolset, your skillset, and your mindset. He evangelized for people be optimistic, not stressed.

More on coaching

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Recently, the LDA had a debate about coaching, following on the podcast interview. The wise Emma Weber represented the pro argument, while the LDA’s own Matt Richter was con. (Note that these are false divides, we explore the topic for the sake of unpacking issues.) Superb moderating from Kat Koppett was a bonus!  As the discussion went, it uncovered more on coaching, without yielding any finality (for reasons we’ll explore).

So, one of the problems emerged immediately, getting into definitions. Matt pushed a bit on the ‘like sport’ notion, where coaching has lots of specific knowledge, while Emma was more on the domain-independent side of coaching. What emerged was that different people have different definitions. Some folks (like me) put coaching further on the domain-dependent side, with mentoring being the more abstract. However, it’s clear others view coaching as the more advanced and deeper side.

This divide isn’t new, but it does provide some barriers, not least to research! As that issue came up, Kat pointed us to a study that began by saying “However, the coaching research suggests a large variety of processes and outcomes, lacking clarity on the primary psychological dimensions most impacted.” Their meta-analysis suggested that “executive coaching is a powerful instrument for organizations to support positive change and personal development.” Which is a good thing, for sure. Their definition does seem to err more on the general side, which is interesting. And, to my own understanding, an important lesson.

One issue that stuck with me was thinking through the range of development. After the formal learning experience, I think there’re times when folks need to be observed, and provided some feedback as they perform. It became clear that the domain-independent model wants the learner to recognize for themselves when they’re not doing well and need to ask for assistance. Yet, a crucial inflection point is making that transition, and I believe that folks aren’t there right away. Similarly, we may not have the resources to add in all the complexities to a particular model for this task initially. So, we expect coaches (read: supervisors and managers) to help develop understanding. Maybe that’s not coaching, by definition, but it’s a task.

I’ll agree at some point you can start guiding folks to their own improvements, but I suspect that only comes when some base level of understanding is reached. We should be clear about this type of interaction as well as the one advocated for coaching! Similarly, we need clarity on labelling! We didn’t end up coming to any finality on that, sadly.

An issue I hadn’t thought about, but became important in the discussion is the issue of appropriate coaching. Clearly, some approaches to coaching don’t work . Knowing when you can expect the coachee to be capable of domain-independent coaching would be one important criteria. Knowing how to ask questions appropriately is another. My concern here is that there are a fair few models about coaching, and with the terminological and empirical barriers, how do you determine the best methods? If we’re to be evidence-based, how can we be?

I can’t say we came to any conclusions, but I do feel we unpacked more of the issues, and did give ourselves some guidance as to what to do when, even if we don’t have agreed upon names for it all yet. Coaching is important, of both types. The data from that study shows coaching can help. We know also that extending the learning experience through feedback on performance helps. We just need to figure out how best to combine them so we know more about coaching. Those are my thoughts , at least, I look forward to yours.