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Clark Quinn’s Learnings about Learning

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Do you feel lucky?

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roulette wheelOne of the things that I feel is undervalued is the role of luck. We hear about how the successful – the winners in business – get that way by virtue of their intelligence and diligence. Yet, if you think about it, lots of folks are smart and work hard. Yet not all succeed. Which made me wonder just how much of success is luck. I asked Siri (I was on a walk) and got the link to an article where they actually researched this. As to the answer, do you feel lucky?

The article starts with a suite of evidence. I know I’m mighty lucky to have been born as a white male in California, had both parents, was able to secure a really good education, and more. The data says that all these things are boons to the likelihood of success. There were also all sorts of weird variations (including middle initials contributes to success?).

Further, the article reports on how two researchers ran some simulations. They had characters with varying degrees of ‘talent’, and then also some good and bad luck. What happened, of course, is that the folks with a combination of luck and ‘talent’, did best. Talent alone didn’t do it, nor luck. In fact, the most talented didn’t succeed the most. “The most successful agents tended to be those who were only slightly above average in talent but with a lot of luck in their lives.”

The research goes further. It’s typical, in academia, that folks who get grants then are more likely to get subsequent grants. Which, it turns out, isn’t the best option. A different simulation by other researchers suggested random was better!  And, arguably, the best policy was giving everyone the same amount!

When we take this back to the real world, what seems to be important is that luck plays a big role in success. Those folks at the very top appear to have been very lucky. Further, their future success isn’t guaranteed (note that currently there’s a prime example of over-valuing previous success). If you’re smart, and dedicated, you’re more likely to do well, but you can also be subject to the slings and arrows of fortune which can similarly contribute.

I think we should be wary of rewarding past success with greater opportunity. We should also be wary of any assessment of how smart someone must be, just because they are successful. There are a lot of factors that contribute to success (for instance, research suggests, that being taller and having a deeper voice, increases the likelihood of doing well in business). They do say luck favors the prepared mind, so do work hard. But you’re also dependent on the vicissitudes of fate. Do you feel lucky?

Facilitating in the dark

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I recently spoke to the International Association of Facilitators – India, having chosen to focus on transfer. My intent was for them to be thinking about ensuring that the skills they facilitate get applied when useful. My preparation was, apparently, insufficient, leaving me to discover something mid-talk. Which leads me to reflecting on facilitating in the dark.

So, I’m not a trained facilitator (nor designer, nor trainer, nor coach). While I’ve done most of this (with generally good results),I’m guided by the learning science behind whatever. So, in this case, I thought they were facilitating learning by either serving as trainers or coaches. Imagine my surprise when I found out that they largely facilitate without knowing the topic!

In general, to create learning experiences, we need good performance objectives. From there, we design the practice, and then align everything else to succeed on the final practice. We also (should) design the extension of the learning to coaching past any formal instruction, and generally ensuring that the impact isn’t undermined.

How, then, do you get models, examples, and provide feedback on practice if you don’t know the domain? What they said was that they were taking it from the learners themselves. They would get the learners together and facilitate them into helping each other, largely. This included creating an appropriate space.

To me, then, there are some additional things that need to be done. (And I’m not arguing they don’t do this.) You need to get the learners to:

  • articulate the models
  • provide examples
  • ensure that they articulate the underlying thinking
  • think about how to unpack the nuances
  • ensure sufficient coverage of contexts
  • provide feedback on others’ experiences

This is in addition to creating a safe space, opening and closing the experience, etc.

So it caused me to think about when this can happen. I really can’t see this happening for novices. They don’t know the frameworks and don’t have the experience. They need formal instruction. Once learners have had some introduction and practice, however, this sort of facilitation could work. It may be a substitute for a community of practice that might naturally provide this context. You’d just be creating the safe space in the facilitation instead of the community.

The necessary skills to do this well, to be agile enough mentally to balance all these tasks, even with a process, is impressive.  I did ask whether they ended up working in particular verticals, because it does seem like even if you came in facilitating in the dark, you couldn’t help learn while doing the facilitation. There did seem to be some agreement.

Overall, while I prefer people with domain knowledge doing facilitation, I can see this. At least, if the community can’t do it itself. We don’t share enough about learning to learn, and we could. I do think a role for L&D is to spread the abilities to learn, so that more folks can do it more effectively. The late Jay Cross believed this might be the best investment a company could make!

Nonetheless, while facilitating in the dark may not be optimal, it may be useful. And that, of course, is really the litmus test. So it was another learning opportunity for me, and hopefully for them too!

 

Lazy thinking?

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Now, I’m the last who should throw stones. I can be quite guilty of lazy thinking, particularly when there’re commercial decisions to be made. (Providers have done a fabulous job of making sure you can’t compare apples to apples, and when there’re so many such situations…) Yet, there’s one place where I struggle with the consequences. That’s in our professional field, and it seems like there’re too many opportunities to yield.

A trigger was a recent conversation where an individual was talking about generations. Grouping folks by when they’re born is problematic at best. For one, the boundaries used seem to vary by who’s doing the categorizing. Not a solid basis. Moreover, the research suggests that there really aren’t meaningful differences. What do exist are explainable by age differences (which isn’t the same thing, for one it’s a continuum, not discrete chunks). Really, it’s a mild form of age discrimination, differentiating people by when they’re born, not who they are or how they behave. (Also problematic is the notion that events affect certain segments of the population, but that’s a longer conversation).  It’s one of the myths in my book on same.

Other examples include learning styles, hemispheres, gender differences, and more. First, they’re categorizations on things that people can’t control. Second, they don’t get backing from data. I just read that medical science has been excluding women from research based upon an assumption about temperature variability that was exposed as being irrelevant!

Sure, it’s much easier if we can reliably group people into segments that mean we have a reliable basis to do different things. Marketers do this with psychographics, for instance. Demographics can also matter. The problem here is that we’re using unreliable metrics. First, there are assumptions that turn out to be flawed. They frequently use self-report, also problematic. Some also have a flawed theoretical foundation.

Yes, it’s hard to keep on top of all of this. Ideally, you’d have time to investigate them all. In practice, there are other things to do. We all need ways to simplify our lives. Plus, vendors are telling you that they, at least, are immune to the complaints (with self-interest at stake).  On the other hand, there are good sources of insight from reliable translators of research. There are also practices we can follow to make it manageable. More help is on the way (at the LDA we’re working on it; stay tuned).

While lazy thinking is understandable, it’s not acceptable, at least not in our professional field. While we may not be sued for malpractice, we certainly should be responsible. So let’s avoid taking the easy path, at least when it matters. In our professional capacity, it matters when we’re designing for our learners. Let’s do so on evidence, not assumptions.

DnD n LnD

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multi-sided diceLast Friday, I joined in on a Dungeons & Dragons (DnD) campaign. This wasn’t just gratuitous fun, however, but was explicitly run to connect to Learning & Development folks (LnD). Organized by the Training, Learning, and Development Community (a competitor to LDA? I have bias. ;), there was both some preliminary guidance, and outcomes. I was privileged to play a role, and while not an official part of the followup (happening this week), I thought I’d share my reflections.

So, first, my DnD history. I played a few times while in college, but… I gave it up when a favorite character of mine was killed by an evil trap (that was really too advanced for our party). I’ve played a lot of RPGs since then, with a lot of similarities to the formal DnD games (tho’ the actual ones are too complex). Recently, with guidance from offspring two, our family is getting back into it (with a prompt from a Shakespeare and DnD skit at the local Renaissance Faire).

Then, I’ve been into games for learning since my first job out of college, programming educational computer games. It also became the catalyst for my ongoing exploration of engagement to accompany my interest in cognition/learning, design, and technology. The intersection of which is where I’ve pretty much stayed (in a variety of roles), since then! (And, led to my first book on how to do same.)

Also, about DnD. It’s a game where you create a character. There are lots of details. For one, your characteristics: strength, dexterity, wisdom, intelligence, and more. Those combine with lots of attributes (such race & role). Then, there’s lots of elaboration: backstory, equipment, and more. This can alter during the game, where your abilities also rise. This adds complexity to support ongoing engagement. (I heard one team has been going for over 40 years!)

Characters created by the players are then set loose in a campaign (a setting, precipitating story, and potential details). A Dungeon Master runs the game, Keegan Long-Wheeler in our case, writing it and managing the details. Outcomes happen probabilistically by rolling dice. Computers can play a role. For one, through apps that handle details like rolling the dice. Then folks create games that reflect pre-written campaigns.

One important thing, to me, is that the players organize and make decisions together. We were a group who didn’t necessarily know each other, and we were playing under time constraints. This meant we didn’t have the dialog and choices that might typically emerge in such playing. Yet, we managed a successful engagement in the hour+ we were playing. And had fun!

I was an early advocate of games for learning. To be clear, not the tarted up drill and kill we were mostly doing, but inspired by adventure games. John Carroll had written about this back in the day, I found out. However, I’d already seen adventure games having the potential to be a basis for learning. Adventure games naturally require exploring. In them, you’re putting clues together to choose actions to overcome obstacles. Which, really, is good learning practice! That is, making decisions in context in games is good practice for making decisions in performance situations. Okay, with the caveat that you should design the game so that decisions have a natural embed.

The complexity of DnD is a bit much, in my mind, for LnD, but…the design!  The underlying principles of designing campaigns bears some relation to designing learning experiences. I believe designing engaging learning may be harder than designing learning or games, but we do have good principles. I do believe learning can, and should, be ‘hard fun‘.  Heck, it’s the topic of my most recent tome! (I believe learning should be the elegant integration of learning science with engagement.)

This has been an opportunity to reflect a bit on the underlying structure of games, and what makes them work. That’s always a happy time for me. So, I’m curious what you see about the links between games and learning!

Get the basics right first!

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I’m currently advising several organizations on their approaches to the use of technology to support learning. Moreover, I’ve been doing so for more than two decades, and see a lot more such situations as well. One of the things that I struggle with is seeing folks getting all agog over new technology, yet without getting the design right beforehand.  Thus, let me make a simple suggestion: get the basics right first!

So, we know what leads to good learning. Heck, I’ve written a book summarizing what’s known about it, and I’m not the only one. Despite that fact that humans are complex, and increasingly so are our learning goals, there exist robust principles. We know that we should provide a sufficient quantity of appropriately challenging contextualized practice with aligned feedback, for instance. That is, if we actually want to achieve an outcome.

Yet, too often, we don’t see this. We see, instead, information presentation. Sometimes even with a knowledge test! Yet, such an effort is unlikely to lead to any meaningful change. That is, the investment’s wasted!

Worse, too often we see this being done with fancy new tools. Sure, I get as attracted to shiny new objects as anyone. However, I want to understand their core affordances for learning. Anyone had the dubious pleasure of attending a slide presentation in a virtual world? Or maybe being presented with animated presentations of lots of facts? The new tools may have a short-term effect of novelty, but that’s it. The fundamental aspects of how our brains learn are what’s going to make, or break, a learning investment.

On the other hand, if we start with getting the learning right, first, then there may be additional value coming from the tech. Adaptivity, on top of quality learning design, can accelerate the outcomes.  Immersion, at the right time and place, is better than not. Language models, properly used, can have big impacts. However, it comes from knowing the specific capabilities, and matching them to the need.

While I haven’t done the ‘back of the envelope’ calculation (I’m not a financial whiz), I can state with a fair degree of comfort that you’re better off doing simple learning with good design. Bad design with shiny tech is still bad design! You’ll more likely have an impact putting your investment into learning quality than using fancy tech to deliver dreck. Of course, once you’ve done that, the investment in tech can do a lot more!

I’m not against new tech, heck I’ve written on games, mobile, and more! What I’m against is new tech in lieu of good design. And I’m even more enamored of good tech on top of good design.  So, get the basics right first, then add in the shiny objects. That way you’re going to have a good return on your $$, and that’s a good thing. Right?

PS, speaking of basics, we’ll be running a debate tomorrow (11 Oct) discussing the Learning Experience Design (LXD) label. I’m sure we’ll unpack critical issues. Check it out. 

Engaging people at work

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Last week, Donald Taylor wrote an interesting post, wondering about ‘learner engagement’. That’s a topic I do talk a wee bit about ;). He closed with a call for feedback. So, while I did comment there, I thought it potentially would benefit from a longer response. I think it’s more general than learner engagement, so I’m talking about engaging people at work. (But it’s still relevant to his thesis without quibbling about that!)

In his post, he talked about three levels: asset, culture, and environment. I’m not sure I quite follow (to me, culture is an environmental level), and I’ve talked about individual, team, and organizational levels. To his point, however, there are steps to take at every level.

He starts at the individual level, talking about designing learning experiences. I agree with his ‘do deeper analysis’ recommendation, but I’d go further. To me, it’s not just if they recognize that content’s valuable, it’s about building, and maintaining, motivation while controlling anxiety (c.f. Make It Meaningful!). I don’t think he’d disagree.

At the next level up, it’s about making sure people are connected. Here, I’d point to Self-Determination Theory (SDT), and ‘relatedness’. I don’t mind Dan Pink’s reinterpretation of that to ‘purpose’, in that I think people need to know how what they’re doing contributes to something bigger, and that something bigger supports society as a whole.

Finally, to me, is culture. You want a ‘learning organization‘, as Don agrees. He says to start with a sympathetic manager, but I think L&D needs to create that culture internally first, then take it to the broader organization (and starting with said manager is a good next step).

I think that latter step solves Don’s final step of breaking down barriers, but he’s a smart guy and I’m willing to believe I’m missing some nuance. I do like his focus on ‘find a measure’ to use. However, ultimately, it should improve a lot of measures around adapting to change: innovation, retention, and success.  That’s my take, I welcome yours!

Emotion is the new ID

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Ok, so the title’s a bit over the top, but…I think there’s something here. Everyone is now talking about how AI can take over a bunch of ID roles. Frankly, I agree (and have said so). In thinking about it (on a walk, as usual ;), I realized there’s a reframing, and I think it’s important. Despite being a tad flip, I do think emotion is the new ID.

So, there are things AI can’t do. It doesn’t really understand, basically. It can look at relationships and infer structure from good content! (That is, if there’s bad content, the inferences are also bad). We still need oversight, basically. So, one role will be to check AI output for accuracy. However, that’s something that largely comes from domain expertise. We’ve always needed subject matter experts to review output.

When I say AI doesn’t really understand, I mean more, however. It’s syntactically manipulating to generate semantics, but semantics is still largely cognitive. Yet as humans, we’re affective (personality) and conative (motivation) as well. In short, we’re emotional (not purely rational). Context matters. Meaning matters! We need to address these elements in our learning experiences.

Thus, I posit that it takes humans to write the introduction to learning experiences, to set the ‘hook‘. Similarly, it takes humans to make practice activities (aka assessment) that have an engaging context, appropriate challenge, and naturally embed the task. Essentially, making the practice meaningful. That’s something we, uniquely, can do.

When I wrote my book Make It Meaningful, I was explicitly addressing the fact that much of ID addresses the learning science alone (if even doing that). It was designed as a complement to my learning science book, to provide a complete LXD picture. What I didn’t expect was the advent of the LLM AIs. Yet, serendipitously (it seems to me, with the usual caveat ;), the latest book addresses the most important part of learning that AI can’t do now or  in the foreseeable future.

Look, I strongly believe that we don’t pay enough attention to engagement, and yet we can. (Note: I do not mean the trivial engagement approaches: tarted-up content presentation like ‘click to see more’, fancy production values, etc.) I run workshops online and face-to-face on this because it’s my passionate and informed belief, not because it’s going to make me rich (it won’t). It just so happens that with this advent, I think it’s even more true that emotion is the new ID. Fortunately, I think we do know how to do it. I think it gives us a role going forward; a way to answer the question: but what about AI? We just have to be prepared to respond. Are you?

Grounded in practice

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Many years ago, I was accused of not knowing the realities of learning design. It’s true that I’ve been in many ways a theorist, following what research tells us, and having been an academic. I also have designed solutions, designed design processes, and advised orgs. Still, it’s nice to be grounded in practice, and I’ve had the opportunity of late.

So, as you read this, I’m in India (hopefully ;), working with Upside Learning. I joined them around 6 months ago to serve as their Chief Learning Strategist (on top of my work as Quinnovation, as co-director of the Learning Development Accelerator, and as advisor to Elevator9). They have a willingness to pay serious attention to learning science, which as you might imagine, I found attractive!

It’s been a lot of marketing: writing position papers and such. The good news is it’s also been about practice. For one, I’ve been running workshops for their team (such as the Missing LXD workshop with the LDA coming up in Asia-friendly times this summer). We’ve also created some demos (coming soon to a sales preso near you ;). I’ve also learned a bit about their clients and usual expectations.

It’s the latter that’s inspiring. How do we bake learning science into a practical process that clients can comprehend? We’re working on it. So far, it seems like it’s a mix of awareness, policy, and tools. That is, the design team must understand the principles in practice, there need to be policy adjustments to support the necessary steps, and the tools should support the practice. I’m hoping we have a chance to put some serious work into these in my visit.

Still, it’s already been eye-opening to see the realities organizations face in their L&D roles. It only inspires me more to fight for the changes in L&D that can address this. We have lots to offer orgs, but only if we move out of our comfort zone and start making changes. Here’s to the revolution L&D needs to have!

 

Missing LXD Workshop

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We interrupt your regularly scheduled reading for this commercial announcement:

What is Learning Experience Design (LXD)? Further, why should you care? Finally, (and arguably most important) what does it mean you should do differently? Those, to me, are important questions. My short answer is that LXD is the elegant integration of learning science and engagement. Which, to me, implies some important nuances on top of what’s traditionally done in instructional design (ID). How to address it? There’s actually quite a lot in LXD, but it’s also a lot of overlap with traditional ID practices and processes. I reckon the easiest (and best) way to address it is to talk about the delta. That is, what’s different between the two. So, in my role for Upside Learning, I developed a missing LXD workshop. We ran it internally to good outcomes, and now, you can take it!

I believe that the difference starts with objectives; you can’t make a meaningful experience if you don’t have learners acquiring relevant new skills (not just an information dump). From there, there are nuances on designing individual practice activities, and then aggregated into practices (that is, putting practices together). Moving on, we look at the content elements of models and examples, and then the emotional aspects of learning. The workshop closes by looking at a design process that accommodates these. Recognizing that folks don’t want to throw out their whole process to start anew, it works from a generic model.

In the workshop, I cover each of those topics in a week; so it’s a six week experience. In between, I ask attendees to do some interim processing to both cement their understanding and to change their practices. Each week we’ll cover underlying concepts, see examples of what we’re talking about, actively process the information, and do a major application task.

To make this available more broadly, Upside’s partnered with the Learning Development Accelerator (LDA) to deliver it. Full disclosure: I’m co-director of the LDA, and Chief Learning Strategist for Upside Learning (in addition to my ongoing role for Quinnovation). (So, it’s all about me! :) Seriously, I think this puts together the tools I believe are necessary to lift our industry.

To be clear, since the advance notice timeframe puts this in summer, we’re offering it in Asia time-frames first (tho’ anyone is welcome!):

Australian Eastern Standard Time: July 7, 14, 21, 28, August 4 and 11 from 12h00 to 14h00 each day
Singapore Time: July 7, 14, 21, 28, August 4 and 11 from 10h00 to 12h00 each day
India Standard Time: July 7, 14, 21, 28, August 4 and 11 from 07h30 to 09h30 each day
New York Time: July 6, 13, 20, 27, August 3 and 10 from 22h00 to 24h00 each day

We’re offering it for US$100 to LDA members, and US$350 to non-members (for only $40 more, you get the full LDA offerings as well).

We’re planning to offer the missing LXD workshop again at a later date at East Coast/Europe friendly times (probably at a steeper price, we’ll have worked the bugs out ;). You can find out more at the LDA site. It’s got learning science and engagement bundled up into a coherent whole, for those who’ve already been doing ID and want to lift their game. I hope you’ll find it worth your while.

We now return you to your regularly scheduled reading until next week at the usual time.

Misconceptions?

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Several books ago, I was asked to to talk about myths in our industry. I ended up addressing myths, superstitions, and misconceptions. While the myths persist, the misconceptions propagate, aided by marketing hype. They may not be as damaging, but they also are a money-sink, and contribute to the lack of our industry making progress. How do we address them?

The distinctions I make for the 3 categories are, I think, pretty clear. Myths are beliefs that folks will willingly proclaim, but are contrary to research. This includes learning styles, attention span of a goldfish, millennials/generations, and more (references in this PDF, if you care). Superstitions are beliefs that don’t get explicit support, but manifest in the work we do. For example, that new information will lead to behavior change. We may not even be aware of the problems with these! The last category is misconceptions. They’re nuanced, and there are times when they make sense, and times they don’t.

The problem with the latter category is that folks will eagerly adopt, or avoid, these topics without understanding the nuances. They may miss opportunities to leverage the benefits, or perhaps more worrying, they’ll spend on an incompletely-understood premise. In the book, I covered 16 of them:

70:20:10
Microlearning
Problem-Based Learning
7 – 38 – 55
Kirkpatrick
NeuroX/BrainX
Social Learning
UnLearning
Brainstorming
Gamification
Meta-Learning
Humor in Learning
mLearning
The Experience API
Bloom’s Taxonomy
Learning Management Systems

On reflection, I might move ‘unlearning’ to myths, but I’d certainly add to this list. Concepts like immersive learning, workflow learning, and Learning Experience Platforms (LXPs)  are some that are touted without clarity. As a consequence, people can be spending money without necessarily achieving any real outputs. To be clear, there are real value in these concepts, just not in all conceptions thereof. The labels themselves can be misleading!

In several of my roles, I’m working to address these, but the open question is “how?” How can we illuminate the necessary understanding in ways that penetrate the hype? I truly do not know. I’ve written here and spoken and written elsewhere on previous concepts, to little impact (microlearning continues to be touted without clarity, for instance). At this point, I’m open to suggestions. Perhaps, like with myths, it’s just persistent messaging and ongoing education. However, not being known for my patience (a flaw in my character ;), I’d welcome any other ideas!