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Clark Quinn’s Learnings about Learning

Marathons and Sprints

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(Empty) Lanes on track on a gym field.

Besides Kahnemann’s Fast & Slow book, I’ve also talked about fast and slow innovation. Fast is where you have a specific problem to solve, or product to design, or thing to research, and you do so. Slow is the innovation that happens because you create opportunities for new ideas to flourish: making it safe, keeping the ‘adjacent possible’ open, facilitating creative friction, etc. Similarly, in my writing, I use both marathons and sprints. What do I mean?

So, I tend to have reasonably long time-frames for writing. I now blog once a week, and I tend to queue these up a week or two in advance. My books, of course, when I’m working on them, have deadlines months ahead. Presentations, too, are a form of communication. Overall, I tend to have months between proposals and when I have to deliver them. Occasionally, I’m asked for something on a short time frame, but even that’s several days.

And, in my life, I tend to have time (typically, in the morning) to respond to short term requirements, and also time to nick away at the longer term requirements. I’ve become relatively good at leaving projects open to contribute to them as I can. So, largely, this is the ‘marathon’ life. That is, I take care of details, and then take time to polish off the bigger projects. Which, I acknowledge, is a luxury. The tradeoff is that I haven’t had a secure income for most of the past 2.5 decades ;).

What also happens is that, at some point in my nicking away at a project, it comes together. The picture that’s been gestating finally emerges. Then, I tend to suddenly find myself grinding it out. It could be a chapter, a book, a presentation, or just an article, but ultimately it takes shape. That said, for my most recent tome, an iterative process emerged. I kept sending out the latest version to someone else, and rearranging it based upon their feedback. That is, until I realized that the latest rearrangement felt truly right, and I was done!

This varies, of course. Sometimes I’m asked for something short term, and then I tend to fall back on things I’ve already thought through. This blog, as I’ve mentioned in many ways, forces me to think through things (looking to keep it fed and not repeat myself too much). I don’t mind this, as it still forces me to rearticulate, which often forces me to rethink, which is a good thing! In my reprocessing, I’m not only cementing my understanding, but frequently deepening it!

Overall, however, this cycle of marathons and sprints works. The longer term processing provides the basis for the short-term sprints. As it is, I’m usually as productive as anyone else (possibly more), yet it seems like there’s a lot of time of me just musing. Percolation (fermentation, incubation, pick your metaphor) is a good thing! As a reflection, this strikes me as right. It also strikes me as a prescription: break things up, ensure you have enough time for the big things, and take time to reflect. It works for me! And, I realize, it’s contrary to much of organizational life, which to me says more about organizational life than how you (should) think.

(BTW, in real life, I was always better the longer I had to run; I was usually the slowest person in my phys ed classes in sprints! At least on land…) 

 

Changes at Quinnovation

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Pretty much nothing stays the same, and that includes my situation. One of the activities that has been taking up my time, which I wrote about as recently as 4 June this year, is ending. As a result, there are some interesting outcomes, and some which are still unresolved. So here’s the rundown on some changes at Quinnovation (the vehicle which I consult through, for which Learnlets is the blog).

Amongst the things I’ve been doing is serving as Chief Learning Strategist for Upside Learning. That was a role where they had me evangelizing learning science in L&D, and working with them internally to deliver on it. It was a good situation; their CEO, Amit Garg, really cares about learning science, and the folks I worked with were really stellar. We did videos, blog posts, ebooks, conference presentations, and demos. I did internal and external webinars as well. Even some client work!

However, in an announcement this morning, they have been acquired (so I can now say this out loud), and my relationship with them ends. The boost in funding is a good situation for an organization that can benefit from a boost, and for Amit of course earning returns on his hard work.

What this means is, of course, that Quinnovation has a bit more bandwidth than I did before. I’m still continuing in my role as co-director of the Learning Development Accelerator, and board advisor to Elevator9. And, I have existing and some pending business with clients through Quinnovation. If there’s an org that wants to actively promote (and practice) learning science, I’m happy to hear. Otherwise, if your organization has a need for some guidance around the cognitive and learning sciences for L&D and innovation, let’s talk!  Those are the current changes at Quinnovation (but probably not the last ones ;). Stay curious, my friends!

 

The Damage Done

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There’ve been a recent discussions about misinformation. One question is, what does it hurt? When you consider myths, superstitions, and misconceptions (the breakdown in my book on L&D problems), what can arise? Let’s talk about the damage done.

So, let’s start with myths. These, I claim, are things that have been shown not to have value by empirical research. There are studies that have examined these claims, and found them to not have data to support them. For instance, accommodating learning styles is a waste. Yes, we know people differ in learning, but we don’t have a reliable base. Moreover, people’s choices to work for (or against) their style don’t make a difference in their learning. Some of the instruments are theoretically flawed as well as psychometrically invalid.

What’s the harm? I’ll suggest several ways in which myths harm us. For one, they can cause people to spend resources (money & time)  addressing them that won’t have an impact. It’s a waste! We can also characterize people in ways that limit them; for instance if they think they learn in a particular way, they may avoid a topic or invest effort in an inappropriate way to learn it. Investing in unproven approaches also perpetuates them, propagating the beliefs to others.

Superstitions, as I define them, are beliefs nobody would claim to believe, yet somehow persist in our practices. For instance, few will claim to believe that telling is sufficient to achieve behavior change. Yet, we continue to see information presentation and knowledge test, such as “awareness” training. Why? This is a waste of effort. There aren’t outcomes from these approaches. Typically, they are legacies of expectations from previous decades, yet business practices haven’t been updated. Still, to the extent that we continue these practices, even while decrying them, we’re again wasting time and money. Maybe we tick boxes and make people happy, but we can (and should) do better.

The final category is misconceptions. These are beliefs that some hold, and others decry. They aren’t invalid, but they only make sense in certain circumstances. I suggest that those who defy them don’t have the need, and those who tout them are in the appropriate circumstance. What matters is understanding when they make sense, and then using them, or not, appropriately. If you avoid them when they make sense, you may make your life harder. If you adopt them when they’re not appropriate,  you could make mistakes or waste money.

At the end of the day, the damage done is the cost of wasting money and time. Understanding the choices is critical. To do so best, you can and should understand the underlying cognitive and learning sciences. You should also track the recognized translators of research into practice who can guide you without you having to read the original academese. To be professional in our practice, we need to know and use what’s known, and avoid what’s dubious. Please!

Failing right

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I’ve been reading Amy Edmondson’s Right Kind of Wrong, and I have to say it’s very worthwhile. I’ve been a fan of hers since her book Teaming introduced me to the notion of psychological safety. It’s an element I’ve incorporated into my thinking about innovation and learning. This new book talks about how we have beliefs about making mistakes, and how we can, and should, be failing right.

In this book, she uses examples to vibrantly talk about failure, and how it’s an important part of life. She goes on to talk about different types of failure, and the situations they can occur in, creating a matrix. This allows us to look at when and how to fail. Along the way, she talks about self, situational, and systemic failure.

One of the important takeaways, which echoes a point Donald Norman made in Design of Everyday Things, is that failure may not be our fault! Too often, bad design allows failure, instead of preventing it. Moreover, she makes the point that we have a bad attitude towards failure, not recognizing that it’s not only part of life, but can be valuable!  When we make a mistake, and reflect, we can learn.

Of course, there are simple mistakes. I note that there’s some randomness in our architecture, e.g. To Err is Human. But also, there can be factors we haven’t accounted for, like bad design, or things out of our control. At the most significant level, she talks about complex systems, and how they can react in unpredictable ways. Along the way, what counts as ‘intelligent’ failure is made clear. Some fails are smart, others are not justified.

She also talks about how experiments are necessary to understand new domains. This is, in my mind, about innovation. She also gives prescriptions, at both the personal and org level. Dr. Edmondson talks about the value of persistence, taking ‘good enough’, but also not taking it too personally. She also talks about sharing, as Jane Bozarth would say: Show Your Work. This is for both calling out problems and sharing failure.

Along with a minor quibble about the order in which she presents a couple things, a more prominent miss, to me, is a small shift in focus. She talks about celebrating the ‘pivot’, where you change direction. However,  I’d more specifically celebrate the learning. That is, whether we pivot or not, we say that learning something is good. Of course, I’m biased towards learning, but I’d rather celebrate the learning. Yes, we possibly would do something different, and celebrating action is good, but sharing the learning means others can learn from it too. Maybe I’m being too pedantic.

Still, this is another in her series of books exploring organizational improvement and putting useful tools into our hands. We can, and should expect to not get everything right all the time, and instead should be focusing on failing right. Recommended.

 

Emotions

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Emotion matters. Yes, largely it’s a cultural construct, as Lisa Feldman Barrett tells us. Still, they can help or hinder learning. When designing games or creating meaningful learning, they matter. But they also affect us in our daily activities.

So, my previous post, on misinformation, is personal. I’ve frustration that family members are buying into some of it. I try to maintain a calm demeanor, but it’s challenging. Still, it’s a battle I’ve not yet given up on. Yet, I’m also not immune to the larger effects of emotion.

A curve showing low performance for low and high arousal, but a peak of performance in between.What we know, from the Yerkes-Dodson Curve, is that a little bit of arousal (read: emotion) can help, but too much can hurt. What isn’t clear from my conceptual rendering is what amount is the ‘right’ amount of arousal for optimal performance. I’ll suggest that for learning, it’s pretty low, as learning is stressful (another synonym for arousal). And I do suggest we manipulate emotions (which I admit is shorthand for motivation, anxiety, and confidence, which aren’t the regular definition) to successfully achieve learning outcomes.

However, even general functioning gets difficult when things are stressful. When I look at the design of casinos, for instance, (a way to cope with the too many times I have to go to Vegas for conferences), I note that they deliberately have low information, lights, no clocks, as an information-sparse environment. It is deliberate, so that you’re more focused on the enticements. They want you confused because you’re then more vulnerable to predations.

I fear that there’s a bit of this in our culture. For instance, fear sells: more alarmist headlines lead to more engagement. Which is good for the news business, but perhaps bad for us in several ways. For one, there’s a vested interest in focus on the alarming, not the bigger picture. Similarly, twisting stories to get emotional engagement isn’t unknown. That can be entertaining, but when it’s the information we depend on is manipulated, it’s problematic. Reducing support for education similarly reduces the intelligence people can apply to analysis.

I struggled to focus to find a topic this week, and I realize it’s because of the informational turmoil that’s currently in play. So, I thought I’d write about it (for better or worse ;).  Exaggeration of issues for the sake of clicks and sales, I’ll suggest isn’t a good thing. I’m willing to be wrong, but I worry that we’re over-excited. Our emotions are being played on, for purposes that are not completely benign. That’s a worry. That’s what’s worrying me, what about you?

Misinformation (and the fighting thereof)

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One of the banes of our corporate existence is the existence of myths. (We seem to be immune to conspiracy theories, at least.) I’ve been fighting them in myriad ways, over the years. Approaches include a book, talks, and more. We also need ways to vet new information for veracity. Here are a few steps taken recently for misinformation and the fighting thereof.

First, at the Learning Development Accelerator (LDA), we created a research checklist (warning: members only, but at the free level). This was supposed to be a way to vet claims, starting with the practical, but eventually getting into actually evaluating the research. We don’t necessarily recommend this, by the way. It’s probably better to trust research translators unless you’re really willing to dive into the details. (Translators: folks who’ve demonstrated a reliable ability to both take research and extract the meaningful principles and cut through hype).

Then, Matt Richter, my colleague in the LDA, recommended Alex Edman’s book May Contain Lies. I’ve read it and found it an accessible and thoughtful treatment of analyzing claims and data (recommended). Matt even prompted the LDA to host a ‘meet the author’ with Alex. That’s available to view (may also require free membership).

In it, he reiterated something in the book that I found valuable. He talked about a ‘ladder’ of investigation. Telegraphically, it’s this:

  1. Statement is not fact (the statement must be accurate)
  2. Fact is not data (the fact must be representative)
  3. Data is not evidence (the data must be conclusive)
  4. Evidence is not proof (the evidence must be universal)

What is being said here is that there are several steps to evaluate what folks want to tell (sell) you. If someone just quotes a statement, it’s not necessarily valid unless it’s accurate. Someone could make a claim that’s not actually true (as happens). Then, that statement alone is not data, unless the statement is representative of the general tenor of thought. For instance, a few positive anecdotes aren’t necessarily indicative of everyone’s experience. Then, representative quotes actually have to be sufficient against any other explanations for the same outcome. For instance, finding out that people like something may not be indicative of its actual efficacy. Finally, the evidence has to apply in your situation, not just theirs.

He used some examples, for instance books where they draw inferences from a few successful companies, without determining that other companies with the inferred characteristics also succeed. What’s nice is he has boiled down what can be an overwhelming set of rules into a simple framework. Misinformation isn’t diminishing, it even seems to be increasing. There’s increasing needs to separate out bogus claims for legitimate. We need to be rallying around misinformation and the fighting thereof. Here’re some tools. Good luck!

The easy answer

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In working on something, I’m looking at the likely steps people take. Of course, I’m listing them from easiest to most useful (with the hope that folks understand they should take the latter). However, it’s making me think that, too often, people are looking at the easy answer, not the most accurate one. Because they really don’t know the problem. When does the easy answer make sense? Are we letting ourselves off the hook too much?

So, for instance, in learning we really should do analysis when someone asks for something. “We need a course on X.” “Ok, what tells you that you need this, and how will we know when it’s worked?” In a quick family convo, we established that this sort of un-analytical request is made all the time:

  • “Why isn’t my plant blooming?” (It’s not the season.)
  • “Fix this code.” (The input’s broken, not the code.)
  • …

Yet, people actually don’t do this up-front analysis. Why? It’s harder, it takes more time, it slows things down, it costs more. Besides, we know what the problem is.

DivergeConvergeProblemSolutionExcept, we don’t know what the problem is. Too often, the question or request is making some assumptions about the state of the world that may not be true. It may be the right answer, but it may not. Ensuring that you’ve identified the problem correctly is the first part of the design process, and you should diverge on exploration before you converge on a solution. That’s the double diamond, where you first explore the problem, before you explore a solution.

Perhaps counter-intuitively, this is more efficient. Why? Because you’re not expending resources solving the wrong problem. Are you sure you’ve gotten it right? How do you know when to take the easier path? If you know the answer you need, you’re better equipped to choose the level of solution you need. If you don’t know the question, however, and make assumptions about the root cause, you can go off the rails. And, end up spending effort you didn’t need to.

Look, I live in the real world. I have to take shortcuts (heck, I’m lazy ;). And I do. However, I like to do that when I know the answer, and know that the outcome is good enough to meet the need. I’ll go for the easy answer, if I know it’ll solve the problem well enough. But I can’t if I don’t know the question or problem, and just assume. And we know what happens when we ass-u-me.

Break it down!

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jigsaw puzzle piecesIn our LDA Forum, someone posted a question asking about taking Cathy Moore’s Action Mapping for soft skills, like improving team dynamics. Now, they’re specifically asking about a) people with experience, and b) in the context of not-for-profits, so…I’m not a good candidate to respond. However, what it does raise is a more common problem: how do you train things that are more ephemeral. Like, for instance, leadership, or communication? My short answer is “break it down”. What do I mean? Here’re some thoughts, and I welcome feedback!

Many moons ago, I co-wrote a paper on evaluating social media impacts. There are the usual metrics, like ‘engagement’. That is, are people using the system? Of course, for companies charging for their platform, this could be as infrequent as a person accessing it once a month. More practically, however, it should be a person hitting it at least several times a week, or even several times a day! If you’re communicating, cooperating, and collaborating, you really should be interacting at a fair frequency.

I, on the other hand, argued for more detailed implications. If you’re putting it into a sales team, you should expect not only messages, but more success on sales, shorter sales cycles, etc. So you can get more detailed. These days, you can do even more, and have the system actually tag what the messages are about and count them. You can go deeper.

Which is what I think is the answer here. What skills do you want? For an innovation demo with Upside Learning, I argued we should break it down. That includes how to work out loud, and how to provide feedback, and how to run group meetings. (I’m just reading Alex Edman’s May Contain Lies, and it contains a lot of details about how to consider data and evidence.) We can look for more granular evidence. Even for skills like team dynamics, you should be looking at what makes good dynamics. So, things like making it safe yet accountable, providing feedback on behavior not on the person, valuing diversity, etc. There should be specific skills you want to develop, and assess. These, then, become the skills you design your learning to accomplish. You are, basically, creating a curriculum of the various skills that comprise the aggregated topic.

It may be that you assess a priori, and discover that only some are missing in your teams. That upfront analysis should happen regardless, but is too infrequent. The interlocutor here also mentioned the audience complaining about the time for analysis. Yep, that’s a problem. Reckon you have to sell the whole package: analyzing, designing, and evaluating for impact on performance, not just some improvement. Yet, compared to throwing money away? Seems like targeting intervention efforts should be a logical sell. If only we lived in a rational world, eh?

Still, overall, I think that these broad programs break down into specific skills that can be targeted and developed. And, we should. Let’s not get away with vague intentions, explanations, and consequently no outcomes. Let’s do the work, break it down, and develop actual skills. That, at least, is my take, I welcome hearing yours!

Diving or surfacing?

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Bubbles in water with light behindIn my regular questing, one of the phenomena I continue to explore is design. Investigating, for instance, reveals that, contrary to recommendations, designers approach practice more pragmatically. That’s something I’ve been experiencing both in my work with clients and recent endeavors. So, reflecting, are and should folks be diving or surfacing?

The original issue is how designers design. If you look at recommendations, they typically recommend starting at the top level conceptualization and work down, such as Jesse James Garrett’s Information Architecture approach (PDF of the Elements of User Experience; note that he puts the highest level of conceptualization at the bottom and argues to work up). Empirically, however, designers switch between top-down and bottom-up. What do I do?

Well, it of course depends on the project. Many times (and, ideally), I’m brought in early, to help conceptualize the strategy, leveraging learning science, design, organizational context, and more. I tend to lead the project’s top-level description, creating a ‘blueprint’ of where to go. From there, more pragmatic approaches make sense (e.g. bringing in developers). Then, I’m checking on progress, not doing the implementation. I suppose that’s like an architect. That is, my role is to stay at the top-level.

In other instances, I’m doing more. I frequently collaborate with the team to develop a solution. Or, sometimes, I get concrete to help communicate the vision that the blueprint documents. Which,  in working with an unfamiliar team, isn’t unusual. That ‘telepathy’ comes with getting to know folks ;).

In those other instances, I too will find out that pragmatic constraints influence the overarching conceptualization, and work back up to see how the guidelines need to be adapted to account for the particular instance. Or we need to deconnect from the details to remember what our original objective is. This isn’t a problem! In general, we should expect that ongoing development unearths realities that weren’t visible from above, and vice versa. We may have good general principles, (e.g. from learning science), but then we need to adapt them to our circumstances, which are unlikely to exactly match. In general, we need to abstract the best principles, and then de- and re-contextualize.

I find that while it’s harder work to wrestle with the details (more pay for IDs! ;), it’s very worthwhile. What’s developed is better as a result of testing and refining. In fact, this is a good argument about why we should iterate (and build it into our timelines and budgets). It’s hubris to assume that ‘if we build it, it is good’. So, let’s not assume we can either be diving or surfacing, but instead recognize we should cycle between them. Start at the top and work down, but then regularly check back up too!

Learning Debt?

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In our LDA conversation with David Irons, User Experience (UX) Strategist, for our Think Like A…series, he mentioned a concept I hadn’t really considered. The concept is ‘design debt’, as an extension of the idea of ‘tech debt’. I was familiar with the latter, but hadn’t thought of it from the UX side. Nor, the LXD side! Could we have a learning debt?

So, tech debt is that delta between what good technology design would suggest, and what we do to get products out the door. So, for instance, using an algorithm for sorting that’s quicker with small numbers of entries but doesn’t handle volume. The accrued debt only gets paid back once you go back and redesign. Which, too often, doesn’t happen, and the debt accumulates. The problems can mean it’s difficult to expand capabilities, or keep performance from scaling. I think of how Apple OS updates occasionally don’t really add new features but instead fix the internals. (Hasn’t seemed to happen as much lately?)

Design debt is the UX equivalent. We see expedient shortcuts or gaps in the UX design, for instance.  As Ward Cunningham, an agile proponent, says:

Design debt is all the good design concepts of solutions that you skipped in order to reach short-term goals. It’s all the corners you cut during or after the design stage, the moments when somebody said: “Forget it, let’s do it the simpler way, the users will make do.”

It’s a real thing. You may experience it when entering a phone number into a field, and then hear it’s not in the proper format (though there was no prior information about what the required format is). That’s bad design, and could (and should) be fixed.

This could be true in learning, too. We could we have ‘learning debt’. When we make practice (and I should note for previous and future posts that includes any assessment where learners apply the knowledge we’ve provided) about knowledge instead of application of knowledge, for instance, we’re creating a gap between what they’ve learned to do and what they need to do. That’s a problem. Or when we put in content because someone insists it has to be there rather than a designer deciding it’s necessary for the learning. Which adds to cognitive load and undermines learning!

How often do we go back and improve our courses? If we’re offering workshops or some other instruction, we can adapt. When we create elearning, however, we tend to release it and forget it. When I ask audiences if they have any legacy courses that are out of date and unused but still hanging around their LMS, everyone raised their hands. We may update courses whose info has changed, but how many times do we go back and redo asynchronous courses because we’ve tracked and have evidence that it’s not working sufficiently? Yes, I acknowledge it happens, but not often enough. (*cough* We don’t evaluate our courses sufficiently nor appropriately. *cough*)

Ok, so everyone makes tradeoffs. However, which ones should we make? The evidence suggests erring on the side of better practice and less content. Prototyping and testing is another step that we can take to remove debt up front. With UX, lacks in design early on cost more to fix later. We don’t typically go back and fix, but we can and should. Better yet, test and fix before it goes live. Another way to think about it is that learning debt is money wasted. Build, run, and not learn, or build, test, and refine until learning happens?

There are debts we can sustain, and ones we can’t. And shouldn’t. When our learning doesn’t even happen, that’s not sustainable. Our Minimum Viable Product has to be at least viable. Too often, it’s not. Let’s ensure that viable means achieves an outcome, eh? It might not be optimal improvement, or as minimum in time as possible, but at least it’s achieving an outcome. That’s better than releasing a useless product (despite no one knowing). , even if we get paid (internally or externally). What am I missing?