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Clark Quinn’s Learnings about Learning

About my books

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My booksSo, I’ve written about writing books, what makes a good book, and updated on mine (now a bit out of date). I thought it was maybe time to lay out their gestation and raison d’être. (I was also interviewed for a podcast, vidcast really, recently on the four newest, which brought back memories.) So here’re some brief thoughts on my books.

My first book, Engaging Learning came from the fact that a) I’d designed and developed a lot of learning games, and b) had been an academic and reflected and written on the principles and process. Thus, it made sense to write it. Plus, a) I was an independent and it seemed like a good idea, and b) the publisher wanted one (the time was right). In it, I laid out some principles for learning, engagement, and the intersection. Then I laid out a systematic process, and closed with some thoughts on the future. Like all my books, I tried to focus on the cognitive principles and not the technology (which was then and continues to change rapidly). It went out of print, but I got the rights back and have rereleased it (with a new cover) for cheap on Amazon.

I wanted to write what became my fourth book as the next screed. However, my publisher wanted a book on mobile (market timing). Basically, they said I could do the next one if I did this first. I had been involved in mlearning courtesy of Judy Brown and David Metcalfe, but I thought they should write it. Judy declined, and David reminded me that he had written one. Still I and my publisher thought there was room for a different perspective, and I wrote Designing mLearning. I recognized that the way we use mobile doesn’t mesh well with ‘courses on a phone’, and instead framed several categories of how we could use them. I reckon those categories are still relevant as ways to think about technology!  Again, republished by me.

Before I could get to the next book, I was asked by one of their other brands if I could write a mobile book for higher education. The original promise was that it’d be just a rewrite of the previous, and we allocated a month. Hah! I did deliver a manuscript, but asked them not to publish it. We agreed to try again, and The Mobile Academy was the result. It looks at different ways mobile can augment university actions, with supporting the classroom as only one facet. This too was out of print but I’ve republished.

Finally, I could write the book I thought the industry needed, Revolutionize Learning & Development. Inspired by Marc Rosenberg’s Beyond eLearning and Jay Cross’s Informal Learning, this book synthesizes a performance and technology-enabled push for an ecosystem perspective. It may have been ahead of its time, but it’s still in print. More importantly, I believe it’s still relevant and even more pressing! Other books have complemented the message, but I still think it’s worth a read. Ok, so I’m biased, but I still hear good feedback ;). My editor suggested ATD as a co-publisher, and I was impressed with their work on marketing (long story).

Based upon the successes of those books (I like to believe), and an obvious need in our field, ATD asked for a book on the myths that plague our industry. Here I thought Will Thalheimer, having started the Debunkers Club, would be a better choice. He, however, declined, thinking it probably wasn’t a good business decision (which is likely true; not much call for keynotes or consulting on myths). So, I researched and wrote Millennials, Goldfish & Other Training Misconceptions. In it, I talked about 16 myths (disproved beliefs), 5 superstitions (things folks won’t admit to but emerge anyways) and 16 misconceptions (love/hate things). For each, I tried to lay out the appeal and the reality. I suggest what to do instead, for the bad practices. For the misconceptions, I try to identify when they make sense.  In all cases I didn’t put down exhaustive references, but instead the most indicative. ATD did a great job with the book design, having an artist take my intro comic ideas for each and illustrating them, and making a memorable cover. (They even submitted it to a design competition, where it came close to winning!)

After the success of that tome, ATD came back and wanted a book on learning science. They’d previously asked me to edit the definitive tome, and while it was appealing, I didn’t want to herd cats. Despite their assurances, I declined. This, however, could be my own simple digest, so I agreed. Thus, Learning Science for Instructional Designers emerged. There are other books with different approaches that are good, but I do think I’ve managed to make salient the critical points from learning science that impact our designs. Frankly, I think it goes beyond instructional designers (really, parents, teachers, relatives, mentors and coaches, even yourself are designing instruction), but they convinced me to stick with the title.

Now, I view Learning Experience Design as the elegant integration of learning science with engagement. My learning science book, along with others, does a good job of laying out the first part. But I felt that, other than game design books (including mine!), there wasn’t enough on the engagement side. So, I wanted a complement to that last book (though it can augment others). I wrote Make It Meaningful as that complement. In it, I resurrected the framework from my first book, but use it to go across learning design. (Really, games are just good practice, but there are other elements). I also updated my thinking since then, talking about both the initial hook and maintaining engagement through to the end. I present both principles and practical tips, and talk about the impact on your standard learning elements. In an addition I think is important, I also talk about how to take your usual design process, and incorporate the necessary steps to create experiences, not just instruction. I do want you to create transformational experiences!

So, that’s where I’m at. You can see my recommended readings here (which likely needs an update.) Some times people ask “what’s your next book”, and my true answer at this point is “I don’t know.”  Suggestions? Something that I’m qualified to write about, that there’s not already enough out about, and it’s a pressing need? I welcome your thoughts!

An outside perspective

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Hand holding lensSomeone reached out to me for a case study on addressing a workplace problem. I was willing, but there’s a small problem; I’ve never had to address a workplace learning problem. At least, in the way most people expect. Instead, I provide an outside perspective. What’s that mean?

So, first of all, I don’t come from an instructional design (ID) background. I did get some exposure to educational approaches when I designed my own undergraduate degree in Computer-Based Education. Yet, there weren’t any ID courses where I was a student. As a graduate student, I took psychology courses on learning. I also read Reigeluth’s survey of ID design approaches. Further, I got a chance to interview the gracious and wise David Merrill. But, again, no formal ID courses were on tap.

On the flip side, I was in a vibrant program that was developing a cognitive science degree, and read everything on learning I could find: behavioral, cognitive, social, neural, even machine learning! I was in my post-doc as they were forming the learning science approach, too, and I was at a relevant institution. Still, no ID. So, I do have deep learning roots, just not ID.

Then, after the post-doc, I taught. That is, practiced learning design, and continued reading and talking ID, and attending relevant conferences. Just not a formal ID course. Then I joined a small startup to design an adaptive learning platform, and then started consulting, but never a workplace learning role inwardly faced.

What that means is that I bring an ‘outside’ perspective to L&D. Which, I think, isn’t a bad thing. I’ve helped firms meet realistic goals in innovative ways, courtesy of not having my thinking pre-constrained. I’ve been able to interpret learning science in practical terms, and infer what ID says (also, I’ve read it and reflected in context on it). So, I’ve talked L&D design, and ID improvements, but from the view of an outsider.

Many times outsiders can bring new perspectives. And, they can be ignorant of all the contextual details. Thus, it’s really important to ask and establish those constraints, and then to be sensitive to the ones that they didn’t mention. (One of the benefits of the court jester was to reframe things in ways that showed the humor in the hidden assumptions.) Still, I’m not apologizing. I think the background I’ve acquired is useful to people who need to meet real goals, and have a decent track record in doing so. I welcome your thoughts on whether an outside perspective is of benefit.

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.

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?