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Clark Quinn’s Learnings about Learning

Wisdom into practice

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Many moons ago, I wrote about a personal quest. Recognizing that what I was doing is making people smarter ‘in the moment’, I looked for a stretch goal. That was making people wiser over time. The question, how is that working? Have I been putting wisdom into practice?

First, let me state for the record that I’m not claiming to be a wise person. It’s an aspiration, not a status ;). Moreover, I know my flaws all too well. Instead, I’m talking about what might be wise behavior, and how I might be helping.

As the broader picture, I’m talking about working ‘wiser‘. I’ll suggest it’s about better aligning with how our brains think, work, and learn. It’s also about being explicit about what values we’re invoking in making our decisions.

I do espouse this, but I realize that, in practice, I’m doing something else. I’m working to help us, the L&D community, be wiser. That includes being wise in our decisions, and in our designs as well. This manifests in several ways.

For one, it is about helping educate about learning science, and how it should be the foundation for what we do. We should create designs that reflect about what’s known to support effective learning. We also need design processes that deliver on this. This goes beyond just learning design, of course, and it should go into our performance support (e.g. job aid) design, task design, and informal learning as well.

In a broader sense, it’s also about values. What do we care about? We should, first, care about creating an environment where people can not just survive, but thrive. It’s also about being scientific. That includes measuring what we’re doing. Going further, it’s also about being strategic, such as recognizing the opportunity to move beyond optimal execution, and look to facilitating formal and informal learning.

I think making people wiser over time is an interesting challenge to think about how we might stretch what we do. However, I’m thinking that helping us all be wiser in how we approach our tasks and roles is really what I’m here to do, pragmatically. I guess that’s how I’m putting wisdom into practice. What more could and should I, or we, be doing?

 

Learning Science Bandwagon?

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I’m not alone in carrying the banner for learning science. Others are talking about evidence-based practices, making it stick, and more. This, I maintain, is a good thing. Is there too much of good thing? Is there a problem with a learning science bandwagon?

First, let’s be clear. There are some initiatives that do strike me as redundant or worse. For one, folks have been talking about neuroscience. And I think neuroscience research is quite interesting! What I also believe, buttressed by others, is that there haven’t been any results from neuroscience that are essential for learning science design. All the implications have been previously documented from learning science research at the cognitive or social level. Neuroscience is cool, but its use in learning design tends to be to draw attention (read: marketing), not for any new outcomes.

I feel similarly about the term brain-based. Yes, learning is brain-based. Isn’t it a wee bit redundant to say so? I suppose they’re implying that they’re aligned with how the brain works. Which is a good thing. Still, despite the alliteration, it seems a bit more like hype than being informative. As the saying goes, your mileage may vary.

However, I’m seeing more and more people now talking about learning science. That’s a good thing. Are they jumping on a bandwagon? Maybe, but there’s lots of room. As long as folks are actually digging into what the learning say, and not just paying lip-service, they’re welcome. To be clear, I don’t own the wagon anyway; I’m a practitioner, not a core researcher. So, I really do think it’s great if more and more people start talking, and walking, learning science.

I’ll go further, of course. I think we should be paying attention to what cognitive and learning sciences say about how we think, work, and learn. That is, the applicability of understanding how our minds work goes beyond learning to our overall organizational practices. But, hey, we gotta start somewhere, right? I think it’s good if we’re moving in the right direction. I can quibble that it’s slower than I’d like, but progress is progress.

So, yay, more learning science! C’mon, jump on, the learning science bandwagon; we’ve got space and it appears we’re moving forward. All good. Hope you’ll join us!

Web 3.0 and whither the LMS

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At the recent DevLearn conference, I was part of a Guild Master panel on emerging technologies. It featured notables such as Julie Dirksen, Mark Lassoff, Megan Torrence, Ron Price, Chad Udell, Karl Kapp, and Jane Bozarth, all hosted by Mark Britz. Not surprisingly, I guess, the topic went to the future of the LMS. In a session the next day, Dr. Jen Murphy of QIC talked insightfully (as she does) about the Metaverse, and compared it to Web 3.0. The conjunction of discussion prompted me to reflect on the intersection, considering Web 3.0 and whither the LMS.

To start, I’m not one proposing that the LMS should or will wither. I’ve suggested that courses make sense, particularly for novices. That said, they’re not full development plans. So it’s worth looking, and thinking, deeper. The conversation on the panel suggested the evolution of the LMS, and I think that’s an apt way to think about it.

What prompted this was Dr. Murphy’s comparison of Metaverse to Web 3.0. She argued that Web 3.0 was about user-control of content. That is, it’s about things like P2P, e.g. blockchain, NFT, etc. I’ve had a different view (now over a decade old, admittedly), that we’d moved from producer-generated content, through user-generated content, and the next would be system-generated content. AI can parse content (that people have painstakingly hand-crafted). Then systems can use models and rules to individualize the experience. That’s what web content is doing already.

So, have things changed? The recognition I see is that folks are concerned with identity and rights. Which I applaud, to be clear. The statement is that by having clear documentation, we can reward individual contributions instead of someone owning all the transactions. The latter of which would be part of a ‘system-generated’ web, for sure. Maybe my 3.0 is really 2.5? Or maybe theirs should be 4.0. Not sure I care…

What does matter is what that implies for courses. Obviously, if courses aren’t enough, we need a bigger picture. An associated question is who should own it? I see a development path as having many components. Even courses should be broken up for spacing, and have a follow-on for ongoing feedback whether digitally delivered and/or a coach. There was an LMS that actually allowed you to mix things into your paths: so you could interview someone, or read a book, or…other things besides courses. Made sense.

The other part aligns more closely with the user-controlled vision. I believe (and have stated, not that I can find it) that I think that ultimately, the community should own the path into membership. That is, just as we should determine the path into membership of L&D, a group in sales should determine what the necessary component skills are. They may need facilitation of this, but us ‘owning’ it isn’t right. We should merely be supporting the endeavor.

Again, it doesn’t really matter whether it’s labeled Web 3.0 or not, but I think that having a mechanism to track development, owned by the associated community (or communities) is useful. It’s not really a Learning Management System (you can’t really ‘manage’ learning), but it can include courses, and it is worthwhile. So those are my thoughts on Web 3.0 and whither the LMS, what’re yours?

Kaku #DevLearn Keynote Mindmap

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Michio Kaku opened the second day of DevLearn with a keynote on the future of the mind. He portrayed extrapolations of current research to some speculative ideas of what our future could mean. He talked about research from physics (?!?) on MRI, AI, and more to provide new capabilities.

Bethune #DevLearn Keynote Mindmap

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Kevin Bethune kicked off the 2022 DevLearn conference with a personal story about getting to delivering strategic innovation. Talking about interdisciplinary work that has an impact, he ended up laying out factors in leadership to support innovation. (Apologies, I had to take a brief break, so I missed a small bit. Sorry.)

Feast or Famine

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This week is a wee bit of a hectic one, capping a few of the same. There’s an old saying about feast or famine, and I’m living it. It’s better than the alternative, regardless. A taste:

So, as context, I’ve been doing a few things (as previously noted). In addition to working on a STEM project and advising a startup, co-directing LDA, and continuing Quinnovation work, I’m now also serving as Chief Learning Strategist for Upside Learning. This latter role is exciting for me, as they’re one of many custom elearning solution providers, but the first I’ve seen really committing to learning science.  They want to lift their game; truly refreshing! Also a chance to really practice what I preach, and of course learn what works and what I’m wrong about.  Good folks, too, as I’ve begun working with them.

This all together has manifested in some commitments, including being in the middle of the LDConference. As part of it, I’m running four weeks around learning science, and will be starting 3 weeks on learning technology. In addition, I was asked to open the L&D Conference of People Matters two weeks ago, in India. I also ran a master class the same day. I was able to visit Upside in person before flying back, which was an unexpected bonus. Not completely a surprise, I came back with a raging cough (testing negative and no fever, fortunately).

This week, as a topper, is DevLearn. I like DevLearn, as the Guild runs a good event, and as such it attracts many of my colleagial friends. It’s a chance to hang out with some of my favorite folks! My schedule, of course, is a wee bit frenetic. Monday I run a Make It Meaningful workshop. Tuesday I’m a facilitator for the Learning Leaders forum (on short notice). I’ll have to take a break to run my learning science event for the LDC! Wed and Thurs morn I’ll be spending time in the booth with Upside, culminating in a book giveaway and signing. Thurs afternoon I’m on a Guild Master panel before running my own session on some work I’ve been doing. Back to back busyness…

Finally, Friday, I can attend actually attend sessions before I fly home. After that, it’s just LDC (and LDA) until mid Nov, and then life gets sorta kinda back to normal. I think! It’s definitely feast or famine. 2020 and 2021 were too much famine. I prefer feast. Busy is better than the alternative, though I’m looking forward to catching my breath. Sorry for less reflection than normal, but this is front of mind for now. Next week hopefully we’ll be back to normal here as well ;).

Fewer myths, please

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I had the pleasure of being the opening keynote at the People Matters L&D conference in Mumbai this past week, with a theme of ‘disruption’. In it, I talked about some particular myths and their relation to our understanding of our own brains. Following my presentation, I sat through some other presentations. And heard at least one other myth being used to flog solutions. So, fewer myths, please.

My presentation focused on the evidence that we’re still operating under the assumption that we’re logical reasoners (which I pointed out, isn’t apt). I mentioned annual reviews, bullet points presos, unilateral decisions, and more. I also cited evidence that L&D isn’t doing well, so it is a worry. Pointing to post-cognitive frameworks like predictive coding, situated & distributed cognition, and more, I argued that we need to update our practices. I closed by urging two major disruptions: measurement, and implementing a learning culture in L&D before taking it out to the broader org.

In a subsequent presentation, however, the presenter (from a sponsoring org) was touting how leadership needed to accommodate millennials. I’m sorry, but there’s considerable evidence that ‘generation differences’ are a myth. The boundaries are arbitrary, there’re no significant differences in workplace values, and every effect is attributable to age and experience, not generation. (Wish I could find a link to the ‘eulogy for millennials myth’ two academics wrote.)

Another talk presented a lot of data, but ultimately seemed to be about supporting user preferences. Sorry, but user preferences, particularly for novices, aren’t a good guide. There was also a pitch for an ‘all-singing, all-dancing’ solution. Which could be appealing, if you’re willing to live with the tradeoffs. For instance, locking into whatever features your provider is willing to develop, and living without best-0f-breed for all components.

Yes, it’s marketing hype. However, marketing hype should be based on reality, not myths. I can get promising a bit more than you can deliver, and focusing on features you’re strong on. I can’t see telling people things that aren’t true. My first step in dealing with the post-cognitive brain is to know the cognitive and learning sciences, so you’ll know what’s plausible and what’s not. Not to PhD depth, but to have a working knowledge. That’s the jumping off point to much that’s the necessary disruption, revolution, that L&D needs to have. And fewer myths, please!

Misusing affordances?

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Affordances is a complex term. Originally coined by Gibson, and popularized by Norman, it’s been largely used in terms of designing interfaces. Yet, it’s easy to misinterpret. I may have been guilty myself! In the past, I used it as a way to characterize technologies. Which isn’t really the intent, as it’s about sensory perception and action. So maybe I should explain what I mean, so you don’t think I’m misusing affordances.

To be clear, in interface design, it’s about the affordances you can perceive. If something looks like it can slide (e.g. a scrollbar), it lets you know you might be able to move the target of a related window in a field. Similarly a button affords pushing. One of the complaints about touch screens is that as people work to overload more functions on gestures. There might be affordances you can’t perceive: does a two-fingered swipe do anything differently than a single-finger swipe?

In my case, I’m talking more about what a technology supports. In my analysis of virtual worlds and mobile devices, I was looking to see what their core capabilities are, and so what we might naturally do with them. Similarly with media, what are their core natures?

So, for instance, an LMS’s core affordance is managing courses. Video captures dynamic context.  You might be able to do course management with a spreadsheet and some elbow grease, or you can mimic video with a series of static shots (think: Ken Burns) and narration, but the purpose-designed tool is likely going to be better. There are tradeoffs. You can graft on capabilities to a core, still an LMS won’t naturally serve as a resource repository or social media platform.

It’s an analytical tool, in my mind. You should end up asking: what’s the DNA? For example, you can match the time affordance of different mobile devices to the task. You can determine whether you need a virtual world or VR based upon whether you truly need visual or sensory immersion, action, and social (versus the tradeoffs of cost and cognitive overhead).

With an affordance perspective, you can make inferences about technologies. For instance, LXPs are really (sometimes smart) portals. AI (artificial intelligence)’s best application is IA (intelligence augmentation). AR’s natural niche, like mobile, is performance support. This isn’t to say that each can’t be repurposed in useful ways. AR has the potential to annotate the world. LXPs can be learning guides for those beyond novice stage. AI can serve in particular ways like auto-content parsing (more an automation than an augmentation). Etc.

My intent is that this way of thinking helps us short-circuit that age-old problem that we use new technologies first in ways that mimic old technologies (the old cliche of tv starting out by broadcasting radio shows). It’s a way to generate your own hype curve for technologies: over-enthusiasm leading to overuse, disappointment, and rebirth leveraging the core affordances. Maybe there’s a better word, and I’ve been misusing affordances, but I think the concept is useful. I welcome your thoughts.

Prompted by prep for the advanced seminar on instructional tech for the upcoming Learning & Development Conference.

Myth Persistence

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It’s been more than a decade (and probably several), that folks have been busting myths that permeate our industry. Yet, they persist. The latest evidence was in a recent chat I was in. I didn’t call them out at the time; this was a group I don’t really know, and I didn’t want to make any particular person defensive or look foolish. Sometimes I will, if it’s a deliberate attempt at misleading folks, but here I believe it’s safe to infer that it was just a lack of understanding. I’ll keep calling them out here, though. However, the myth persistence is troubling.

One of the myths was learning preferences. The claim was something like that with personalization we could support people’s preferences for learning. This is, really, the learning styles myth. There’s no evidence that adapting to learners’ preferred or identified styles makes a difference. Learner intuitions about what works is not well correlated with outcomes.. So this wasn’t a sensible statement.

There were several comments on unlearning. There is some controversy on this, some people saying that it’s necessary for organizations if not individuals. I still think it’s a misconception, at least. That is, your learning doesn’t go away and something replaces it, you have to actively practice the new behavior in response to the same context to learn a new way of doing things. It’s people, after all, and that’s how our cognitive architecture works!

Gamification also got a mention. Again, this is more misconception perhaps. That is, it matters how you define it. We had Karl Kapp on the LDA’s You Oughta Know session, talking about gamification (and micro learning). He talks about understanding that it’s more than just points and leaderboards. Yes, it is. However, that term leads people quickly to that mindset, hence my resistance to the term. However, the chat seemed to suggest that gamification, in combination with something else (memory fails), was a panacea. There are no panaceas, and gamification isn’t a part of any major advance. It’s a ‘tuning’ tool, at best.

A final one was really about tech excitement; with all the new tools, we’ll usher in a new era of productivity. Well, no. The transformation really is not digital. That is, if we use tech to augment our existing approaches, we’re liable to be stuck in the same old approaches. Most of which are predicated on broken models of human behavior. The transformation should be humane, reflecting how we really think, work, and learn. Without that, digitization isn’t going to accomplish as much as it could.

So, there’s significant myth persistence. I realize change can be hard and take time. Sometimes that’s frustrating, but we have to be similarly persistent in busting them. I’ll keep doing my part. How about you?

The power of emotion

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Increasingly, we’re seeing that emotion matters. Scientific evidence supports what we intuitively know. Yet, in many cases, our actions don’t support that understanding. At least, in nuance. In particular, our learning designs suffer from trivialized ‘like’ as opposed to useful and effective approaches. We can and should do better to tap into the power of emotion.

Again, I’m using the term ’emotion’ loosely here. While we do care about emotions like joy and grief (though our picture is changing), what we really need to be caring about are non-cognitive elements like motivation, anxiety, and confidence. It’s about designing to appropriately address them: develop motivation, keep a lid on anxiety, and build confidence. Each has it’s elements.

Motivation improves learning outcomes, but requires understanding what makes us interested. We’re driven by a desire to understand the world (c.f. ‘predictive coding‘. Curiosity can assist in developing an interest. Certainly, self-interest plays a role as well, and helping people tune into the positive consequences of a learning experience (or the negative outcomes of not having the requisite understanding) is also useful. Self-Determination Theory (c.f. Deci and Ryan) talks about mastery, autonomy, and relatedness. We can use this to help people connect with others (instructors/peers/experts), give them tasks (autonomy) and support to succeed (mastery).

Anxiety interferes, if it’s too much. While a small amount helps, that’s quickly overwhelming. Given that learning can be intrinsically anxiety-inducing, keeping anxiety to a minimum is important. Making it safe to fail is an important component of this. Psychological safety is an important element in organizational operation, and learning as well.  We can not attach consequences to practice, certainly at first. We can also have the instructor make mistakes as well.

Building confidence is an adjunct here. As people master the skills, at greater and greater levels of challenge (an important component of successful learning experience design), they build confidence. That reduces anxiety, and maintains motivation. We don’t want false confidence, but we can steadily build confidence as we go. Ultimately, we want learners to have sufficient confidence to try out the skills (and succeed) after the learning experience.

There’s lots more that goes into making an experience effective and engaging, but understanding these elements, and how to enact them, is an important component. The power of emotion, properly harnessed, improves learning outcomes (which is what we should be about ;). I’ll be addressing these and more in my workshop Make It Meaningful at the upcoming DevLearn conference in Las Vegas on Oct 24. I’d love to see you there, as we talk about the complement to learning science that combines to achieve those experience goals.