Archives for January 2024

Do you feel lucky?

30 January 2024 by Clark Leave a Comment

One of the things that I feel is undervalued is the role of luck. We hear about how the successful – the winners in business – get that way by virtue of their intelligence and diligence. Yet, if you think about it, lots of folks are smart and work hard. Yet not all succeed. Which made me wonder just how much of success is luck. I asked Siri (I was on a walk) and got the link to an article where they actually researched this. As to the answer, do you feel lucky?

The article starts with a suite of evidence. I know I’m mighty lucky to have been born as a white male in California, had both parents, was able to secure a really good education, and more. The data says that all these things are boons to the likelihood of success. There were also all sorts of weird variations (including middle initials contributes to success?).

Further, the article reports on how two researchers ran some simulations. They had characters with varying degrees of ‘talent’, and then also some good and bad luck. What happened, of course, is that the folks with a combination of luck and ‘talent’, did best. Talent alone didn’t do it, nor luck. In fact, the most talented didn’t succeed the most. “The most successful agents tended to be those who were only slightly above average in talent but with a lot of luck in their lives.”

The research goes further. It’s typical, in academia, that folks who get grants then are more likely to get subsequent grants. Which, it turns out, isn’t the best option. A different simulation by other researchers suggested random was better! And, arguably, the best policy was giving everyone the same amount!

When we take this back to the real world, what seems to be important is that luck plays a big role in success. Those folks at the very top appear to have been very lucky. Further, their future success isn’t guaranteed (note that currently there’s a prime example of over-valuing previous success). If you’re smart, and dedicated, you’re more likely to do well, but you can also be subject to the slings and arrows of fortune which can similarly contribute.

I think we should be wary of rewarding past success with greater opportunity. We should also be wary of any assessment of how smart someone must be, just because they are successful. There are a lot of factors that contribute to success (for instance, research suggests, that being taller and having a deeper voice, increases the likelihood of doing well in business). They do say luck favors the prepared mind, so do work hard. But you’re also dependent on the vicissitudes of fate. Do you feel lucky?

For ‘normals’

23 January 2024 by Clark 5 Comments

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

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

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

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

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

Facilitating in the dark

16 January 2024 by Clark 1 Comment

I recently spoke to the International Association of Facilitators – India, having chosen to focus on transfer. My intent was for them to be thinking about ensuring that the skills they facilitate get applied when useful. My preparation was, apparently, insufficient, leaving me to discover something mid-talk. Which leads me to reflecting on facilitating in the dark.

So, I’m not a trained facilitator (nor designer, nor trainer, nor coach). While I’ve done most of this (with generally good results),I’m guided by the learning science behind whatever. So, in this case, I thought they were facilitating learning by either serving as trainers or coaches. Imagine my surprise when I found out that they largely facilitate without knowing the topic!

In general, to create learning experiences, we need good performance objectives. From there, we design the practice, and then align everything else to succeed on the final practice. We also (should) design the extension of the learning to coaching past any formal instruction, and generally ensuring that the impact isn’t undermined.

How, then, do you get models, examples, and provide feedback on practice if you don’t know the domain? What they said was that they were taking it from the learners themselves. They would get the learners together and facilitate them into helping each other, largely. This included creating an appropriate space.

To me, then, there are some additional things that need to be done. (And I’m not arguing they don’t do this.) You need to get the learners to:

articulate the models
provide examples
ensure that they articulate the underlying thinking
think about how to unpack the nuances
ensure sufficient coverage of contexts
provide feedback on others’ experiences

This is in addition to creating a safe space, opening and closing the experience, etc.

So it caused me to think about when this can happen. I really can’t see this happening for novices. They don’t know the frameworks and don’t have the experience. They need formal instruction. Once learners have had some introduction and practice, however, this sort of facilitation could work. It may be a substitute for a community of practice that might naturally provide this context. You’d just be creating the safe space in the facilitation instead of the community.

The necessary skills to do this well, to be agile enough mentally to balance all these tasks, even with a process, is impressive. I did ask whether they ended up working in particular verticals, because it does seem like even if you came in facilitating in the dark, you couldn’t help learn while doing the facilitation. There did seem to be some agreement.

Overall, while I prefer people with domain knowledge doing facilitation, I can see this. At least, if the community can’t do it itself. We don’t share enough about learning to learn, and we could. I do think a role for L&D is to spread the abilities to learn, so that more folks can do it more effectively. The late Jay Cross believed this might be the best investment a company could make!

Nonetheless, while facilitating in the dark may not be optimal, it may be useful. And that, of course, is really the litmus test. So it was another learning opportunity for me, and hopefully for them too!

Myths are models

9 January 2024 by Clark 4 Comments

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

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

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

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

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

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

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

Quality or Quantity?

2 January 2024 by Clark 4 Comments

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?