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Clark Quinn’s Learnings about Learning

Engaged and/or Effective

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Quadrant diagram of effectiveness by engagement: neither is an info dump, engaged is a trivial pursuit, effective is boring work, unless it's also engaging in which case it's hard fun.I’ve regularly talked about how learning can, and should, be ‘hard fun’. Yet, I haven’t really talked about each, effectiveness and engagement, independently. Of course, there’s a quadrant map that separately talks about engaged and/or effective. Let me remedy the lack!

The lack of either engagement or effectiveness is relatively rare, thankfully. You do see it, when under-skilled and under-resourced folks are making a course. For instance giving SMEs authoring tools or dumping a bunch of PPTs and PDFs on an inexperienced instructional designer. Or, when folks won’t spend enough to even get production values, let alone actual effectiveness. What you get is information dump (because experts don’t have access to what they actually do, research tells us), but not with professional polish. It’s ‘content’ without distinction. More importantly, if there is practice, it’s on knowledge retrieval rather than knowledge application. Which leads to what in cognitive science is called ‘inert knowledge’. It may be remembered, but it won’t be used when relevant.

We also see a lot of ‘tarted up’ information dump. Here, there are good production values. It looks nice, because it’s well-produced. However, it’s still information (usually with a quiz). Here, folks know a bit about visual design, and use tools and templates that make it look good. They may even have experienced designers on staff, but…time and cost expectations keep folks from doing the right thing. It could also be a lack of understanding of the importance of challenging contextual practice. That’s all too common, too! It’s still a trivial pursuit.

Quite simply, learning needs to be effective. If it’s not, we’re wasting money. Now, that’s been shown to be the case in many ways. Over the years, we’ve heard estimates from 10-15% of our training efforts are working. Which means we’re wasting 85-90% of our investment. Yet we know what leads to good learning (e.g. the Serious eLearning Manifesto). Learning science gives us good guidelines, but we still see too much information dump. Yet, if it’s not engaging, learners aren’t likely to commit appropriately, and we’re not optimizing the outcomes. It just seems like work.

When we understand the necessary alignment between engagement and effectiveness, however, we truly can deliver ‘hard fun’. That alignment is what my research and design efforts yielded. It was also the core of my book on serious game design and my most recent tome on making learning meaningful. (The latter is really a complement to my learning science book, and an attempt to bring both together to do learning experience design.)

It’s not necessarily easy to generate ‘hard fun’, nor is it the cheapest option. However, it gets easier with practice (like most things), and it’s the most cost-effective option. That is, if you truly want results. But if you don’t, why are you bothering? There are requirements, like making sure you have a real learning need, but that should be true, regardless.  You shouldn’t be asking about engaged and/or effective, you should be shooting for both. Right?

Daymond John #LHRCon Keynote Mindmap

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Daymond John opened the second day of the Learning Guild’s Learning & Human Resources Technology conference. He spoke on being an entrepreneur.

In an entertaining presentation, he wove his life story with some take-home lessons. (I missed the last one, but my neighbor caught it.)

Support retention and transfer

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In a discussion we were having with David Ganulin on marketing, my colleague Matt Richter ended up talking about how many ‘team building’ activities don’t work. The typical model is an event where folks get together off-campus and face challenges together. They have to work together to overcome the challenges. Yet, Matt’s claim was that the empirical evidence was that the results didn’t transfer back to the workplace. What does it take? How can we support transfer to achieve persistent results?

The classic model is the ‘ropes course’. Folks have to work together to get everyone safely across some challenge. By working together to achieve success, you should build team cohesion and respect the different capabilities of your colleagues. Yet, investigations suggest that what’s learned doesn’t carry back to the workplace. People who got along, when they get back to the workplace, can be surprised and disappointed that the same conflicts exist.

What’s happening, of course, is context-specificity. The resulting benefits worked in the context of the team-building, but it’s not the same context as work. Just like the ‘brain training’ exercises didn’t transfer to other tasks, so to any learning is likely to dissipate quickly and still not transfer to another context. What do we need to do, then, to generate retention over time and support transfer to the workplace as well?

For one, we need more than one practice. I just read the results of interesting research suggesting two stages of memory. The first stage says initial memories can last briefly, but for sustained retention, you need a second stage of retrieval practice. Yes, we should know that, but too often we don’t practice it! (Which also suggests that a test at the end of a learning event may not be a good indicator!) Also, I’ll suggest, if we want appropriate transfer, we have to engineer it.

How do you engineer transfer? I’ll posit two steps. For one, you need experience across several different contexts. So, do task A together, then B which is widely different, then C, which is different again. You could do a task that requires different physical attributes (tall, small, strong, heavy), and then one that requires different creative approaches (art, music, prose). Along the way, you reinforce a particular team approach that works across contexts. You facilitate reflection, as well, on what’s common.

Matt went further, suggesting that then you need to take that facilitation back to the workplace, and I’ll agree that it’d be ideal. If you then brought the models back to the workplace and facilitated their application to situations at work, you could extend the internalization and appropriate re-contextualization of the learning.

One-shot events are unlikely to generate the sustained transfer you need, at least not without specific design and support. If you’re not trying to achieve retention (over time after the event until needed) and transfer (to all appropriate and no inappropriate) situations, why bother? If you do want retention and transfer (and you should), design for it. Specifically engineer to support retention and transfer. Use spaced repetition with increased challenge to achieve the former. Use contextual variance and reflection facilitation to support the latter. When you do, you’ll have outcomes worth the investment.

Conti #LHRCon Keynote Mindmap

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Maurice Conti opened the Learning Guild’s Learning Solutions & Human Resources Technology conference (April ’24 Orlando). He proceeded to talk about the implications of artificial intelligence (AI) for Human Resources (HR).

He started with the history of AI, and proceeded to focus on Generative AI. His core message was that you needed three things: your toolset, your skillset, and your mindset. He evangelized for people be optimistic, not stressed.

More on coaching

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Recently, the LDA had a debate about coaching, following on the podcast interview. The wise Emma Weber represented the pro argument, while the LDA’s own Matt Richter was con. (Note that these are false divides, we explore the topic for the sake of unpacking issues.) Superb moderating from Kat Koppett was a bonus!  As the discussion went, it uncovered more on coaching, without yielding any finality (for reasons we’ll explore).

So, one of the problems emerged immediately, getting into definitions. Matt pushed a bit on the ‘like sport’ notion, where coaching has lots of specific knowledge, while Emma was more on the domain-independent side of coaching. What emerged was that different people have different definitions. Some folks (like me) put coaching further on the domain-dependent side, with mentoring being the more abstract. However, it’s clear others view coaching as the more advanced and deeper side.

This divide isn’t new, but it does provide some barriers, not least to research! As that issue came up, Kat pointed us to a study that began by saying “However, the coaching research suggests a large variety of processes and outcomes, lacking clarity on the primary psychological dimensions most impacted.” Their meta-analysis suggested that “executive coaching is a powerful instrument for organizations to support positive change and personal development.” Which is a good thing, for sure. Their definition does seem to err more on the general side, which is interesting. And, to my own understanding, an important lesson.

One issue that stuck with me was thinking through the range of development. After the formal learning experience, I think there’re times when folks need to be observed, and provided some feedback as they perform. It became clear that the domain-independent model wants the learner to recognize for themselves when they’re not doing well and need to ask for assistance. Yet, a crucial inflection point is making that transition, and I believe that folks aren’t there right away. Similarly, we may not have the resources to add in all the complexities to a particular model for this task initially. So, we expect coaches (read: supervisors and managers) to help develop understanding. Maybe that’s not coaching, by definition, but it’s a task.

I’ll agree at some point you can start guiding folks to their own improvements, but I suspect that only comes when some base level of understanding is reached. We should be clear about this type of interaction as well as the one advocated for coaching! Similarly, we need clarity on labelling! We didn’t end up coming to any finality on that, sadly.

An issue I hadn’t thought about, but became important in the discussion is the issue of appropriate coaching. Clearly, some approaches to coaching don’t work . Knowing when you can expect the coachee to be capable of domain-independent coaching would be one important criteria. Knowing how to ask questions appropriately is another. My concern here is that there are a fair few models about coaching, and with the terminological and empirical barriers, how do you determine the best methods? If we’re to be evidence-based, how can we be?

I can’t say we came to any conclusions, but I do feel we unpacked more of the issues, and did give ourselves some guidance as to what to do when, even if we don’t have agreed upon names for it all yet. Coaching is important, of both types. The data from that study shows coaching can help. We know also that extending the learning experience through feedback on performance helps. We just need to figure out how best to combine them so we know more about coaching. Those are my thoughts , at least, I look forward to yours.

Being proactive?

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On recent edition of the Learning Development Accelerator‘s Think Like A… series, I interviewed Kevin Wheeler. He represented, in our discussion, the role of talent in the organization. Now, I’ve been talking the organizational perspective for a while. Despite that, amongst the pearls of wisdom he dropped was one that really resonated. It had to do with the forces that are gathering, and his suggestion was that L&D should start being proactive.

He was actually talking about talent and L&D in conjunction. One of his points is that we’re two sides of the same coin. There’s a decision about ‘build vs buy’ when meeting the needs of the organization. In this case, L&D is the build while talent is the ‘buy’. His metaphor about a ‘supply chain’ for thinking about talent is apt; his point is to be looking to the sources of talent.

However, what struck me was his perspective that both haven’t been proactive enough. He sees talent & learning being too reactive to needs, instead of looking ahead and making plans. For instance, what skills are necessary to cope with the emergence of generative AI? What do you need? Do you have the foundations in the org or will you need new capabilities that are available? He envisions an executive role that encompasses both L&D and talent to be responsible for ensuring that the org is forward looking in skills and meeting them.

This aligns nicely with the current focus on ‘upskilling’, as everyone’s going nuts trying to figure out what skills, and how to develop or acquire them, at scale. Thinking ahead might not anticipate every revolution, but it’s clear that the foundational technology base has mutated, and that these new capabilities are likely to stick around. The revolution may be over (guesses on that?), but there’s certain to be evolution, likely rapid! How do you cope?

I think there’s strong evidence that L&D has been too reactive – order-taking – and that there are several ways we can be more strategic. That includes being proactive, as well as having a richer suite of solutions instead of courses über alles. It’s also about taking ownership of innovation by practicing it internally, as well. Listening to Kevin was a great opportunity to think about the bigger picture of what we do.

BTW, with the clear caveat that I’m a co-director, we really are trying to make what appears in the LDA be of value. There’re no vendors, it’s all evidence-based principles and practices for L&D. We invite you to check us out. 

Impactful decisions

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I’ve been talking about impact in a variety of ways, and have also posited that decisions are key. I really haven’t put them together, so perhaps it’s time ;). So here’re some thoughts on impactful decisions.

To start with, I’ve suggested that what will make a difference to orgs, going forward (particularly in this age of genAI), is the ability to make better decisions. That is, either ones we’re not making right now, or new ones we need to be able to make.  When we’re moving away from us doing knowledge tasks (e.g. remembering arbitrary bits of information), our value is going to be in pattern-matching and meaning-making. When faced with a customer’s problems, we’ll  need to match it to a solution. We need to look at a market, and discern new products and approaches. As new technologies emerge, we’ll have to discern the possibilities. What makes us special is the ability to apply frameworks or models to situations despite the varying contexts. That’s making decisions.

To do this, there are several steps. What are the situations and decisions that need to be made? We should automate rote decisions. So then we’ll be dealing with recognizing situations, determining models, using them to make predictions of consequences, and choose the right one. We need to figure out what those situations are, the barriers to success, and figuring out what can be in the world, and what needs to be in the head. Or, for that matter, what we can solve in another way!

We also need to determine how we’ll know when we’ve succeeded. That is, what’s the observable measure that says we’re doing it right. It frequently can be triggered by a gap in performance. It’s more than “our sales aren’t up to scratch”, but specifics: time to close? success rate? Similarly for errors, or customer service ratings, etc. It needs to be tangible and concrete.  Or it can be a new performance we need. However, we need some way to know what the level is now and what it should be, so we can work to address it.

I note that it may feel ephemeral: “we need more innovation”, or “we need greater collaboration”, or… Still, these can be broken down. Are people feeling safe? Are they sharing progress? Is constructive feedback being shared? Are they collaborating? There are metrics we can see around these components, and they may not be exhaustive, but they’re indicative.

Then, we need to design to develop those capabilities. We should be designing the complements to our brain, and then developing our learning interventions. Doing it right is important! That means using models (see above) and examples (models in context), and then appropriate practice, with all the nuances: context, challenge, spacing, variation, feedback…  So, first the analysis, then the design. Then…

The final component is evaluation. We first need to see if people are able to make these decisions appropriately, then whether they’re doing so, and whether that’s leading to the needed change. We need to be measuring to see if we’re getting things right after our intervention, it’s translating to the workplace, and leading to the necessary change.

When we put these together, in alignment, we get measurable improvement. That’s what we want, making impactful decisions. Don’t trust to chance, do it by design!

Misplaced organizational focus?

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Conjunctions are interesting learning opportunities. When two things provide different facets, particularly on something you’ve been thinking about, it’s serendipitous. In this case, two widely different readings triggered some reflections asking whether perhaps we’ve a misplaced organizational focus.

So, I’ve been a bit concerned about the rabid interest in generative AI. Not that I think it’s inherently bad, despite its flaws. Instead, my concern is the uses it’s put to. If you think about the classic engineering proposition – cheap, fast, or good; pick 2 – you know you can apply AI to any of the areas. Always, however, it seems that the focus is on cheap and fast. Which concerns me. There’s substantial evidence that our L&D efforts aren’t having an impact. Thus, doing bad faster and cheaper is still bad!

Part of this, it seemed to me, to stem from a rabid focus on short-term returns. I read The Japan That Can Say No many moons ago, and became convinced that a purely financial focus isn’t in the long-term interests of organizations. Now, there’re reinforcement!

First, in Australian news was a report about how a famous economist was rethinking the role of economics. While I didn’t agree with all of it, one aspect that resonated was captured in these bits:

“…we have largely stopped thinking about ethics and about what constitutes human well-being. We are technocrats who focus on efficiency…We often equate well-being with money or consumption, missing much of what matters to people.”

The juxtaposition happened with this quote aggregated by Learnnovators and posted to LinkedIn:

” The early signals of what A.I. can do should compel us to think differently about ourselves as a species. …Those skills are ones we all possess and can improve, yet they have never been properly valued in our economy or prioritized in our education and training…”
– Aneesh Raman, VP, Workforce Expert at LinkedIn & Maria Flynn, President & CEO of Jobs for the Future (JFF)

The overlap, to me, has to do with the undervaluing of what humans bring to the economic table. Efficiency isn’t the only good. Pushing L&D to do ‘box ticking’ learning design faster and cheaper isn’t consonant with recognizing what gives our work meaning. Besides undervaluing what learning design could and should be, it’s disrespectful to the learners and the organization.

I think that what’s driving organizations should be how they contribute to society as a whole. The means to that end is creating an internal environment conducive to supporting people, individually and collectively, to contribute their best in ways that respect what we offer. There are things technology can do that, frankly, we as people shouldn’t. Similarly, there are things we can do that we shouldn’t abrogate. To paraphrase the meme, I don’t want people doing menial tasks leaving the creativity to machines.

A holistic synergy, each doing what they do best to augment the other, alone and together, is optimal. Our economics should support that as well, and to the extent our structures don’t, it may be time to rethink them. Otherwise, it’s a misplaced organizational focus. Thoughts?

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.