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Clark Quinn’s Learnings about Learning

For ‘normals’

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So, I generally advocate for evidence-based practices. And, I realized, I do this with some prejudice. Which isn’t my intent! So, I was reflecting on what affects such decisions, and I realized that perhaps I need a qualification. When I state my prescriptions then, I might have to add “for ‘normals'”.

First, I have to be careful. What do I mean by ‘normal’? I personally believe we’re all on continua on many factors. We may not cross the line to actively qualify as obsessive-compulsive, or attention-deficit, or sensorily-limited. Yet we’re all somewhere on these dimensions. Some of us cross some or more of those lines (if we’re ever even measured; they didn’t have some of these tests when I was growing up). So, for me, ‘normal’ are folks who don’t cross those lines, or cope well enough. Another way to say it is ‘neurotypical’ (thanks, Declan).

What prompted this, amongst other things, is a colleague who insisted that learning styles did matter. In her case, she couldn’t learn unless it was audio, at least at first. Now, the science doesn’t support learning styles. However, if you’re visually-challenged (e.g. legally blind), you really can’t be a visual learner. I had another colleague who insisted she didn’t dream in images, but instead in audio. I do think there are biases to particular media that can be less or more extreme. Of course, I do think you probably can’t learn to ride a bicycle without some kinesthetic elements, just as learning music pretty much requires audio.

Now, Todd Rose, in his book The End of Average, makes the case that no one is average. That is, we all vary. He tells a lovely story about how an airplane cockpit carefully designed to be the exact average actually fit no one! So, making statements about the average may be problematic. While we’ve had it in classrooms, now we also have the ability to work beyond a ‘one-size fits all’ response online. We can adapt based upon the learner.

Still, we need to have a baseline. The more we know about the audience, the better a job we can do. (What they did with cockpits is make them adjustable. Then, some people still won’t fit, at least not without extra accommodation)  That said, we will need to design for the ‘normal’ audience. We should, of course, also do what we can to make the content accessible to all (that covers a wide swath by the way). And, while I assume it’s understood, let me be explicit here that I am talking “for ‘normals'”. We should ensure, however, that we’re accommodating everyone possible.

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!

 

Myths are models

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A recent LinkedIn post talked about how models are good, but myths are bad. Which was a realization for me. I’ve kept myths and models largely separate in my mind, but I realize that’s not the case. Myths are models, just wrong ones. And, I suppose, we need to deal with them as such. (Also, folks hang on to myths and models if they’re tied up with identity, but we should still be able to deal with the logical rationale.)

So, I’m an advocate for mental models. There are a variety of reasons, personal, pragmatic, and principled. Personally, I was gifted a book on mental models by my workmates as I left for graduate school. Pragmatically, they’re useful. On principle, they’re how we reason about the world. Heck, our brains are constantly building them!

The important aspects of models are that they’re predictive (and explanatory). That is, they tell us the outcomes of actions in particular situations. They are models of a small bit of the world, and are used to understand a perturbation of the model. They’re causal, in that they talk about how the world works, and conceptual in that they talk about the elements of the world. They’re incomplete, in that they only need to account for the parts of the world relevant to the particular situation.

Examples include using an analogy of water flowing in pipes for thinking about electric circuits. Or how advertisements use association with valued things or people to induce a positive affect. You can use them to explain what happened, or what will happen. It’s the latter that’s important for the purposes of providing a basis for guiding decisions, and thus their role in learning.  They guide us in deciding how to take actions under different circumstances.

Models can be good or bad. The old ‘planets circling a sun’ model of electrons in orbit around a nucleus of protons and neutrons turned out to be inaccurate as our understanding increased. We then moved to probability clouds as a better model. Many of our mistakes come from using the wrong model, for a variety of reasons. We can mistake the situation, or think a model is accurate and useful.

We should avoid models that aren’t appropriate for the situation. Myths are models that aren’t appropriate for any situation. So, for instance, learning styles, generations, ‘attention span of a goldfish’, and ‘images are processed 60K faster than prose’ are examples of myths. They lead people to make decisions that are erroneous, such as providing different learning prescriptions. They are models, because they do categorize the world and lead to prescriptions about what to do. They’re myths, because their implications will lead to decisions that waste time and money.

As the saying goes, “all models are wrong, but some are useful”. They’re wrong because they’re only part of the world. The good ones give us useful predictions, The bad ones lead us to make bad decisions. The useful ones are to be lauded, shared, and used. Myths, however, should be debunked and avoided. Myths are models, but not all models are good. It’s important that I remember that!

Quality or Quantity?

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Recently, there’s been a lot of excitement about Generative Artificial Intelligence (Generative AI). Which is somewhat justified, in that this technology brings in two major new capabilities. Generative AI is built upon a large knowledge base, and then the ability to generate plausible versions of output. Output can in whatever media: text, visuals, or audio. However, there are two directions we can go. We can use this tool to produce more of the same more efficiently, or do what we’re doing more effectively. The question is what do we want as outcomes: quality or quantity?

There are a lot of pressures to be more efficient. When our competitors are producing X at cost Y, there’s pressure to do it for less cost, or produce more X’s per unit time. Doing more with less drives productivity increases, which shareholders generally think are good. There’re are always pushes for doing things with less cost or time. Which makes sense, under one constraint: that what we’re doing is good enough.

If we’re doing bad things faster, or cheaper, is that good? Should we be increasing our ability to produce planet-threatening outputs? Should we be decreasing the costs on things that are actually bad for us? In general, we tend to write policies to support things that we believe in, and reduce the likelihood of undesirable things occurring (see: tax policy). Thus, it would seem that if things are good, go for efficiency. If things aren’t good, go for quality, right?

So, what’s the state of L&D? I don’t know about you, but after literally decades talking about good design, I still see way too many bad practices: knowledge dump masquerading as learning, tarted up drill-and-kill instead of skill practice, high production values instead of meaningful design, etc. I argue that window-dressing on bad design is still bad design. You can use the latest shiny technology, compelling graphics, stunning video, and all, but still be wasting money because there’s no learning design underneath it.  To put it another way, get the learning design right first, then worry about how technology can advance what you’re doing.

Which isn’t what I’m seeing with Generative AI (as only the latest in the ‘shiny object’ syndrome. We’ve seen it before with AR/VR, mobile, virtual worlds, etc. I am hearing people saying “how can I use this to work faster”,  put out more content per unit time”, etc, instead of “how can we use this to make our learning more impactful”. Right now, we’re not designing to ensure meaningful changes, nor measuring enough of whether our interventions are having an impact. I’ll suggest, our practices aren’t yet worth accelerating, they still need improving! More bad learning faster isn’t my idea of where we should be.

The flaws in the technology provide plenty of fodder for worrying. They don’t know the truth, and will confidently spout nonsense. Generative AIs don’t ‘understand’ anything, let alone learning design. They are also knowledge engines, and can’t create impactful practice that truly embeds the core decisions in compelling and relevant settings. They can aid this, but only with knowledgeable use. There are ways to use such technology, but it comes from starting with the point of actually achieving an outcome besides having met schedule and budget.

I think we need to push much harder for effectiveness in our industry before we push for efficiency.  We can do both, but it takes a deeper understanding of what matters. My answer to the question of quality or quantity is that we have to do quality first, before we address quantity. When we do, we can improve our organizations and their bottom lines. Otherwise, we can be having a negative impact on both. Where do you sit?

The past year

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I note that this is my last post for the year, so I thought I’d summarize a few things. For one, so you can look for anything you’re interested in. Also, so I can recall what I’ve been up to!  So here’s a brief summary of the past year.

Quinnovation

So I’ve been Quinnovation for the past couple of decades, give or take a year. Which has been my vehicle for consulting. I’ve continued to service clients, on a limited basis (owing to some other commitments, see below); I’ve had several ongoing engagements, some that were new this year, at least one which has continued on from previous years.

I don’t mention the organizations and what I’m doing for them, specifically, because that would violate confidentiality (something I care deeply about; my academic background continues to influence my thoughts on integrity). Yet, the topics that emerge can end up fueling blog posts, webinars, conference presentations, and more. While the solutions I provide are specific to their situations, the reflections and revelations are shareable (suitably anonymized).

For the record, I also had a variety of interviews for podcasts and webinars. They’re scattered hither and yon, and also talking about a variety of topics. I can’t even remember them all (mea culpa), but they all seemed to be of interest to the host and audience. More such coming in the new year.

Upside Learning

In the year before this one, I joined up with Upside Learning to serve as their Chief Learning Strategist. This has been a great opportunity to practice what I preach. I’m working with them internally to improve the learning science in their approach, and externally to evangelize and work with clients ready to take it to the next level. Their CEO, Amit Garg, is great to work with, as it’s clear he really cares about learning.

That evangelizing also requires me to be part of the marketing (hi, Isha!). The upside (heh) is getting to talk about important issues, while the downside is occasionally having to use terms like ‘microlearning‘ (though I reserve the right to be subversive about them).  I also am appearing at some events on their behalf. If you’re curious, there are a fair number of articles, ebooks, white papers, videos, and more to be found on their site that I’ve had a hand in. More to come. Check it out!

Learning Development Accelerator

Matt Richter and Will Thalheimer started the Accelerator after their Covid-catalyzed conference was successful. It’s a membership society about the evidence-base for Learning & Development.  I came in when Will took a job and couldn’t meet the demands. While Matt keeps the place running (even more so with the help of Esther), I get to have a hand in the topics we address. It’s small, so far, but the quality is very high (that is, the membership and the speakers for events ;).

The first year I had a series I called You Oughta Know, introducing people with models I thought members should know. This past year it’s been debates on topics (to unpack the underlying thinking). All of the past content is available to members, a growing library. I’ve also been part of the blog, with posts on informal learning (should I choose a new topic for this year?). You can access some of the events even if you’re not a member (typically for a fee), but the blog’s behind the firewall. There are some articles outside the paywall, however. This coming year, we’ll likely keep the debates, and continue to have events. We’ve (read: Matt) also resurrected the podcast, which is free to air. There’ll be more announcements, too.

I’m planning two new series for the coming year. One is YOK: Practitioners. This time it’s people you oughta know because of what they’re doing (people I admire, though I won’t be able to get them all)!  Another that I’m excited about is Think Like A…! This is a series about the related fields we draw upon. As a field, we’re (rightfully) quite acquisitive: we took agile from software engineering, design thinking from UX, etc. We really should be understanding what it means to think like a practitioner in certain fields, to see what we can and should adopt. I’ve already got some people for these endeavors lined up (bwaahaha!). Consider joining if this sounds like something you’d be interested in.

Elevator 9

A last formal role (I have some informal ones too) is as the science advisor to Elevator 9. This is a company founded on the idea of spacing learning out (a worthwhile endeavor). The founder took my learning science class and then asked me to assist. They’re still getting going, though already with clients, but have made some new moves to kick in next year.

In addition to advising them on design behind the scenes, I’ve scripted, and the CEO David Grad has recorded, a series of short videos about learning. While I’ve suggested that they host them on the Elevator 9 site, that hasn’t happened yet (running on the smell of the proverbial oily rag). I think the best way to find them is to search LinkedIn for “Liftology” and then look at all the ‘post’ results. Hopefully, we’ll make that easier early next year (hint hint, nudge nudge).

And that’s more than enough, I reckon. That’s some of what I’ve been up to in the past year. What’s coming? Well, I’ve given away some of it. There’ll be more from all of the above, of course. Stay tuned! I hope you’ve had a great year, and that the next is your best yet. Happy Holidays!

 

Achieving alignment

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I’ve seen, up close and personal, some organizations that demonstrably were lacking alignment. This manifested in various ways. The question then becomes, what do you do to remedy? What leads to achieving alignment?

So, many years ago I spent a summer working on a large engineering floor. The group I was assigned to finally told me to slow down, that I was making them look bad! In another firm we were acquired by, they weren’t happy with sales and fired the team, but then hired the leaders responsible for the broken practice to create a new process. My own previous ISP had a great app, and not only broke their implied promise but lied to me. My current ISP is more human when you can get through to them (and their app is horrid).

What’s common is a lack of alignment across the organization. I’ve eventually come to expect pockets of inefficiency in most organizations (I wonder how any of them make money!). Now, it can be bad management on the part of a particular leader, or miscommunication between units. The main point I see here is the lack of effective communication. It can be just within a team, or upwards to a business unit or community of practice, or between business units.

Look, there are lots of ways to go wrong. Lack of measurement, insufficient resources, culture hiccups, and more. One clear barrier, however, that can solve some of the others, is communication. Even before collaboration, which is better, is communication. We need to be social in appropriate ways.When we have trust and safety, we can towards transparency. When we know what others are doing, we can can work in coordination. We can show our work, we can cooperate, and even collaborate.

Achieving alignment is a useful tool for businesses, but it isn’t automatic. You need to work at it. One of the ways is to work to creating an environment where people are sharing. When you do, the benefits emerge. At least, that’s how I see it. How about you?

BTW, our final LDA debate this year will be tomorrow, December 20, at 1PM ET (10 AM PT), on lying, which is directly tied to transparency! Come for the fun, stay for the learning.

Valuing Diversity

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My lass has us engaging us in an activity. Being in it has sparked a recognition that’s not new, but continues to be important, particularly in the global context! I frequently talk about how diversity is important in getting the best ideas. Moreover, it’s not just ‘tolerating’ it, but valuing diversity. Why?

So the activity is choosing music that matches a theme. Everyone (in this case m’lady, and two offspring), submits four songs to a theme, and then when all are in, you vote. Not on yours, of course! For us, it’s not about who ‘wins’ so much as it’s about exposure to different music.

When we’re evaluating them is when I get a particular reaction. I typically realize “Oh, that reminds me of this other song, and I wish I’d thought of it as a candidate.”  What’s happening is that being exposed to other ideas expands my own thinking. Which is, after all, one of the things that helps us find solutions. Finding more solutions is a step on the path to finding good solutions!

Globally, I’ve heard of a country that is cracking down on diversity, trying to get everyone to adhere to the same world view. This includes diverse languages. Now, to be a country, I agree that there have to be some shared values. However, for the best opportunity for a country to succeed, tapping into the diversity of thoughts provides a greater likelihood of finding the best approaches. You risk stifling innovation to achieve stability, and that’s not a necessary tradeoff.

Diversity can be challenging. It means being able to accept other views, making it safe, and negotiating a shared understanding. On the flip side, that negotiated understanding is likely to be richer than what existed before. In the long term, that challenge leads to better outcomes.  Further, we can work together, when we follow what’s known.

So, if you want to get the best from your unit, whether business, organization, or society, you want to find ways to build diversity. And, then, find ways to use it, productively. We need more than acceptance, or tolerance. We need to be valuing diversity, and when we do, we do better.

One may not be enough

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A recent intersection of talks leads to an interesting issue for L&D. First, we recently talked to Guy Wallace about his recent book, The L&D Pivot Point. Then, we talked to Julie Dirksen about her new book, Talk to the Elephant. The interesting thing is that there’s some overlap between the two ideas that isn’t immediately obvious, but really important. The realization is that when we’re talking about barriers to success, thinking of one may not be enough.

So, Guy’s book is about taking a step above just thinking of course. He’s a proponent of performance improvement consulting, where you analyze the problem before you decree a course as a solution. The important recognition is that there can be multiple barriers to performance, including a lack of skills indicating a course. However, other reasons might be the wrong incentives, a lack of resources, etc. Sometimes a job aid can do better, some times neither that or a course will suffice.

Julie’s book, on the other hand, is a complement to her first book, Design for How People Learn. She recognized that even good design (what her first book did, eloquently) might not help learning stick, and looked at other barriers, such as managers extinguishing the learning. She was more focused on making the learning design succeed.

What she did, however, is provide a rich suite of potential barriers, along with solutions, and suggest that you may need to address more than one. That goes along with, and complements, Guy’s focus.

Just as you design programs that include messaging, training, support, rewards, and more, you should also ensure that you’ve analyzed all the barriers to performance. You might address learning, provide job aids, ensure incentives are aligned, prepare supervisors, and more. Addressing only a particular situation may not be sufficient. You may have several barriers, When it comes to solutions, one may not be enough. This argues (again) for rigorous analysis and a success focus, not just doing what you are comfortable with. In the long term, I reckon this is where we need to go as we move from learning to performance (and development). your thoughts?

Where are we at?

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Signs pointing multiple directions with distances. I was talking with a colleague, and he was opining about where he sees our industry. On the other hand,  had some different, and some similar thoughts. I know there are regular reports on L&D trends, with greater or lesser accuracy. However, he was, and I similarly am looking slightly larger than just “ok, we’re now enthused about generative AI“. Yes, and, what’s that a signal of? What’s the context? Where are we at?

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?

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.