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Clark Quinn’s Learnings about Learning

Engineering solutions

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Every once in a while, I wonder what I’m doing (ok, not so infrequently ;). And it’s easy to think it’s about applying what’s known about learning to the design of solutions. However, it’s more. It is about applying science results to designing improvements, but, it’s broader than learning, and not just individual. Here are some reflections on engineering solutions.

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

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

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

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

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

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

 

Why DEI?

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At the event I attended a bit ago, one of the discussions was on Diversity, Equity, and Inclusion (DEI). I attended, to hear what was up. There were discussions of how to instigate DEI, but one thing I felt was missing, so of course I chimed in at the end. Actually, I learned something else as well, so that’s worth reciting to. So, why DEI?

There are, of course, lots of good reasons. For one, the privileges I’ve had haven’t been shared. Folks often come from less opportune backgrounds than others have had the advantage of. Moreover, such advantage hasn’t been accounted for before they get to work. Unfortunately, schools and social welfare haven’t adequately addressed this We have racism, and misogyny, and other forms of discrimination to deal with. ‘Us against them’ isn’t a healthy perspective. However, perhaps you wonder, why should organizations be a source of remedy?

My argument it pretty simple, really. Research says that we get better results when we have diversity in looking for solutions. There’s a pretty simple explanation why, too. What we’re doing, when looking for answers (research, design, trouble-shooting) is searching a potential solution space. It’s easy to not explore thoroughly. I’ve talked about brainstorming, for instance, as something we can do badly or well. That’s about process. But there’s more.

Garvin, Edmondson, & Gino wrote about learning organization dimensions, and one of the four aspects of a supportive learning environment was “appreciation of differences”. I want to emphasize it’s not ‘tolerating diversity’, it’s valuing it! In exploring that space of solutions, the more diversity in the group, the more likely we are to cover a big range. (There’re caveats, of course, particularly that all have to share a commitment to finding an answer.) Homogeneity is the enemy here!

Of course, this means equity in treatment, and inclusion. If you’re excluding people, you’re not taking advantage of diversity. If you’re not promoting equity, the injustices perpetuate. The only good way to get people to feel good about diversity if it is equitable and inclusive.

Interestingly, one of the hosts mentioned that there’s separate evidence of value. This was something I hadn’t heard. Apparently, having more diversity in the room makes people more diverse in their thinking. That is, even before getting people to generate ideas, people’s attitudes are more diverse because of the observed variety. I haven’t been able to confirm this, but I have no reason not to believe it, and it’s an interesting (and valuable) result.

Now, as said, there are lots of good reasons. But one that is very pragmatic is that you get better solutions when different viewpoints are incorporated. We should be looking at complementary and varied viewpoints. That involves bringing different people together that have something to offer, and just being different is one! Celebrate that!

So, that’s why DEI in my mind; done right, the outcome is better!  Overall, we fare better when we work in the ways that align with how our brains operate. That’s alone and together. Let’s do the best for us and our organizations.

Domain-independent coaching?

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At an event this past week, I sat in on a discussion of coaching. Asking folks what coaching was, there were lots of responses about ‘establishing rapport’, ‘asking questions’, etc. I admit I was a wee bit curious amongst all this, thinking about specifics. Which prompted some reflections. My question is about whether there can be domain-independent coaching.

To start, I was thinking about how to develop people just after a learning ‘event’ or experience. They’ve been developed to a certain level, and then we’d like to continue their development. To do so, I thought feedback would be useful, and specifically tying the learning to any relevant task, and providing feedback to fine-tune their performance. Specifically, this requires knowing the domain they’re learning about, observing their performance (in some way), and identifying ways in which they went right, or wrong. That, in my mind, requires specific knowledge about how the mental models play out in context. This, for example, is what we see in sports coaching.

As context, I remember talking to a very smart individual who runs a business that does coaching as a service, at scale. To do this, they have to have folks who know coaching, but pragmatically can’t necessarily know the domain. I was curious how this could work, but empirically it does. Coupled with the responses of folks around the table, I had to reconcile my specifics with a more general approach.  How can this work?

Of course, I started thinking about the trajectory of learners. They start as novices in any particular domain, then proceed to become practitioners, and can become experts. As they progress, they need less specifics. If you look at situated leadership as a model, you go from providing direction and support, to eventually removing the (domain-specific) direction, then the support, as they become capable. Thus, coaching can move to asking about how they’re feeling about it, and to apply their own knowledge to the situation. That is, you can start asking about the process and their thoughts rather than focusing on specifics.

Of course, to me, if you apply the domain-independent coaching at the wrong time, you can delay (or extinguish) their development. On the other hand, continuing with micromanaging performance can be similarly restricting. So, I reckon you can shift to domain-independent coaching, after you have developed a minimum viable level of capability.  That’s my reconciliation; what’re your thoughts?

We can be logical

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So, I’ve been on a bit of a crusade saying we’re not formal logical reasoning beings. And, I do think it’s important to emphasize this in the face of some legacy beliefs. On the other hand, I think there’s evidence that we can be logical. So, how do we reconcile this?

The reason I push against a belief that we’re logical is that too often we are designing as if that’s the case. We see it in way too many policies, practices, and the like. Yet, as has been documented, that’s not our default.

On the other hand, we can be effective reasoners. We have created complex mathematics, advanced science, and generally improved our situation. Something is going on. But what?

Well, Kahneman talks about how we, effectively, have two systems, fast and slow. The slow one takes cognitive effort, so we tend to avoid it. The fast one, then, is default. It’s based upon instinct. Which can be good in two situations: one, where our instincts are likely to be right (e.g. dealing with biologically primary information) or where we have expertise. It can also be bad, where we use it inappropriately.

On the other hand, we can use the slow route. It’s hard, but it works.  This is where we reason things out. (We have to be careful, because being hard, we can depend on it inappropriately.) We can use cognitive support, and complementary skills, but we can document the situation, explore alternatives, trial solutions, and reason our way to good decisions.

And we should! Frankly, I’d rather have in office a policy wonk building coalitions of expertise than a solitary ‘profile’ claiming solutions across the board. I want evidence-based approaches, not simplistic and wrong answers to complex problems!

So, we can be formal logical reasoning beings. Under the right circumstances, with the right support. We should automate what we can so we build the necessary expertise, and provide the conditions for good decisions. That can sometimes be fast, and sometimes be slow, but better to be right than to be expedient. Not perfect, of course, but I’m suggesting we err on the side of likelihood.

That’s my view, at any rate. We can be logical, and that’s a matter of design. We should evaluate and optimize situations so we get the best decisions. That recognizes when training is helpful, when performance support can be used, and when we should support good innovation (problem-solving, research, design, etc). So let’s take a healthy informed look at how we make decisions, and increase the likelihood of good ones. That’s my decision, at any rate. What’s yours?

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.

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?