In an earlier post, I made a defense  of cognitive psychology (really, to me, cognitive science, a bigger umbrella). And, previously, the case for learning science. And I’m coming at learning science again, with a personal interest.

Learning science is an interdisciplinary field, including cognitive science, educational psychology, and more. Having emerged relatively late, it’s now finding a solid footing with a unified approach to looking at how we learn, and how to facilitate it.

Most importantly, having this knowledge is critical for those who practice learning. In fact, I’ve railed against learning malpractice, and that’s a legitimate concern. We, should, as professionals, have a solid basis for our decisions. Just as you wouldn’t want your doctor not to know biochemistry and biophysics, and your electrician not to understand voltage and current, you similarly should want your instructional designers to understand how learning proceeds.

Yet, sad to say, it’s not the case that what we see in practice is well-grounded in what learning science tells us. Such that several of us banded together to prescribe what  should be done!

It goes beyond courses, of course. We shouldn’t be using courses when job aids will suffice, as cognitive science tells us. (Our brains are bad at remembering rote, abstract, arbitrary, and voluminous information.) We should be facilitating informal learning as well.

All of this, done right, depends on understanding learning science, again. Seriously, everything that L&D does largely boils down to knowing how our brains work. And the better we know it, the better we can make decisions. This includes avoiding myths, buying platforms and services, designing experiences, facilitating learning, and more.

So what can you do? There are a fair bit of resources out there already. I’ve created a reading list. I’ll have more to announce soon. I can also announce that I’ll be running a learning science (er, effective learning strategies) workshop, through HR.com. It’s a five week session, starting Oct 21. Cog Sci 101, learning artifacts, social/emotional/cultural, I’ve tried to give a good coverage.  I believe, as the first one, it has a ‘pilot’ pricing!  Whether I see you there or not, I hope you do ensure a good basis for your practice.

A recent Donald Clark post generated an extension from Stephen Downes. I respect both of these folks as people and as intellects, but while I largely agreed with one, I had a challenge with the other. So here’s a response, in defense of cognitive psychology. The caveat is that my Ph.D. is  in Cognitive Psychology, so I may be defensive and biased, but I’ll try to present scrutable evidence.

Donald Clark’s post unpacks the controversies that surround efforts to measure the complicated concept of ‘intelligence’.  He starts with the original Binet measure, and talks about how it’s been misused and has underlying problems. He goes through multiple intelligences, and emotional intelligence as well, similarly unpacking the problems and misuses. I’m reminded of Todd Rose’s  End of Average,  which did a nice job of pointing out the problems of trying to compress complex phenomena into single measures.

He goes on to talk about how it may be silly to talk about intelligence. His argument talks about all the different ways computers can do impressive computational tasks, under the rubric  artificial intelligence (AI). While I laud the advances, my focus still remains on IA (intelligence augmentation), that is, using computers in conjunction with our own capability rather than purely on AI.

Stephen Downes responded to Donald’s article with a short piece. In it, he takes up the story of intelligence and argues that education and cognitive psychology have put on layers of ‘cruft’ (“extraneous matter“) on top of the neural underpinnings. And I have a small problem with that. In short, I think that the theories that have arisen have provided useful guidance for designing systems and learnings that wouldn’t have emerged from strictly neural explanations.

Take, for example, cognitive load. John Sweller’s theory posits that there are limits to our mental resources. Thus, having extraneous material can interfere with the ability to process what’s necessary. And it’s led to some important results on things like the importance of worked examples, and making useful diagrams.

We can also look to principles like Bjork’s desirable difficulty. Here, the type of practice matters (as also embodied in Ericsson’s deliberate practice), needing to be at the right level.  This might be more easily derivable from neural net models, but still provided a useful basis for design.

I could go on: the value of mental models, what makes examples work, the value of creating a motivating introduction, and so on. I’d suggest that these aren’t obvious (yet) from neural models. And even if they are, they are likely more comprehensible from a cognitive perspective than a neural. Others have argued eloquently that neural is the wrong level of analysis for designing learning.

I will suggest, in defense of cognitive psychology, that the phenomena observed provide useful frameworks. These frameworks give us hooks for developing learning experiences that are more complicated to derive from neural models. As I’ve said, the human brain is arguably the most complex thing in the known universe.  Eventually, our neural models may well advance enough to provide more granular and accurate models, but right now there’s still a lot unknown.

So I’m not ready to abandon useful guidance, even if some of it is problematic. Separating out what’s useful from what’s been overhyped may be an ongoing need, but throwing it all away seems premature. That’s my take, what’s yours?

I’ve been pondering more about curiosity and ‘making it meaningful’ and how we might work on motivation to make learning truly meaningful. I’v come up with a rough cut. So, here’s a proposal for a heuristic approach to motivation.

As I mentioned, the desired true intrinsic motivation may be a goal too far. When possible, perhaps in a deeply specialized field, I’d go for it. In fact, that’s my first recommendation:

1. If there‘s a surprising answer to a question that‘s directly relevant, use it

I’ve seen folks do this by asking questions that the audience is likely to choose one answer, and it’s counter-intuitively wrong. Here, it has to be directly relevant to the question! For instance, asking in a ‘how to do multiple choice right’ class what they think is the right number of choices (turns out: 3). This is close to true intrinsic motivation, because folks interested in the topic might be surprised about the result, and therefore inquisitive. Surprise is great if you can get it!

However, that’s not assured. My second step is a bit more complex, but still straightforward. Here, I’m shooting for the level below intrinsic motivation, and looking for a recognition that someone does need it. Thus, second step is:

2. If there‘s either of the following –

a. Stats demonstrating meaningful aggregate consequences of solving, or not

b. A vivid consequence of solving, or not

– go with it

That is, if you can find either data, or a very visceral personal response, you use that to help people  get that it’s important. It’s playing on the consequences of having, or not, the knowledge. (Which is something I talk about in my LXD workshops, and in my forthcoming one, stay tuned.)

Again, it  has to be meaningful to the domain. Which brings up my last suggestion:

3. If neither, maybe this isn‘t needed!

Reallly, if you can’t find some reason why this is intrinsically important, why are you doing it? Even for compliance training, there’s a reason. Tap into it! Or you’re likely to be wasting time and money. (Give me counter examples, I invite you!)

I’m not sure what order 2a and 2b should be in. Maybe that depends on the audience (individualist vs collectivist?). Still, this is my first stab a heuristic approach to motivation, and I invite your feedback. Make sense, or off track?

I received, in my email, a solicitation for a webinar titled 5 Tips to Engage Gen Z and Millennial eLearners in 2020 and Beyond.  And, as you might imagine, it tweaked my sensibilities for the worse. My initial reaction is to provide, as a palliative, tips to avoid millennials marketing hype.

The content starts off with this scintillating line: “if you‘re searching for current, new ways to engage people online and keep your business thriving, look to your youngest learners.” What? Why do you want current and new ways to engage people? How about the evidence-based ways instead? Tested and validated ones. And why your youngest learners? Organizations need to be continually learning across all employees. Why not just your  newest employees (regardless of age)?

So, your first tip is to look for phrases like ‘new’ as warnings, and look for “research-based” or “evidence-based” instead. “Science-based” is likely okay, as long as it’s not neuroscience-based (wrong level) or brain-based (which is like saying ‘leg-based walking’ as someone aptly put it.)

Second tip: don’t be ageist. Why focus on their age at all? Deal with people by their knowledge and background. It’s discriminatory, really.

The ad goes on: “To future-proof your learning program, make sure your content is designed with these young professional learners in mind.”   What’s different for these learners? Their cognitive architecture isn’t fundamentally different; evolution doesn’t work that fast. So why would you do something just for them (and discriminate against others) instead of doing what’s right for the topic?

Next tip: avoid any easy and inappropriate categorizations. Don’t try to divide content or experiences in trendy ways instead of meaningful ways.

You should already be leery. But wait, there’s more! “On one hand, they can be distracted, overwhelmed, and impatient. On the other, they are highly collaborative, technically-savvy, and driven by fairness and storytelling.”This is like a horoscope; it fits most everyone, not just young people. We all have distractions and increasingly feel overwhelmed. And our brains are wired for storytelling.   These describe human nature! And that ‘tech savvy’ bit is a clear pointer to the digital native myth. Doh!

They then go on. “With this in mind, how can you effectively engage this digitally dependent group to attract, train, and retain them?” Um, with what attracts, trains, and retains humans in general?  That would be helpful!

Thus, another tip: let’s not make facile attributions that falsely try to portray a meaningful difference. Let’s focus on design that addresses capturing and maintaining attention and motivation, and communicating in clear and compelling ways. And skip mashing up myths, ok?

We’re not  quite done with the pitch: “how to level up your existing learning strategy to meaningfully engage your Millennial and Gen Z learners.” This is just a rehash of the tips above. Meaningfully engage  all your learners!

There’s also this bullet list of attractions:

• What motivates Millennials and Gen Z and how to tailor your learning strategy to keep them engaged
• Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
• A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

This could easily be rewritten as:

• What motivates Millennials and Gen Z learners and how to tailor your learning strategy to keep them engaged
• Ways in which traditional learning programs fail younger learners and how you can prevent these common mistakes
• A step-by-step process for evaluating your instructional content, providing you with an actionable blueprint on transforming your content

And, for all I know, that’s what they’re really doing. That would be actually useful, if they avoid perpetuating the myths about generational differences. But, as you can tell, they’re certainly trying to hit buzzword bingo in drawing you in with trendy and empty concepts. Whether they actually deliver is another issue.

Please, avoid the marketing maelstrom. Follow these tips to avoid millennials marketing hype, and focus on real outcomes. Thanks!

Looking into motivation, particularly for learning, certain elements appear again and again.   So I’ve heard ‘relevance’, ‘meaningfulness‘, consequences, and more. Friston suggests that we learn to minimize surprise. One I’ve heard, and wrestled with, is curiosity. It’s certainly aligned with surprise. So I’ve been curious about curiosity.

Tom Malone, in his Ph.D. thesis, talked about intrinsically motivating instruction, and had curiosity along with fantasy and challenge. Here he was talking about helping learners see that their understanding is incomplete. This is in line with the Free Energy Principle suggesting that we learn to do better at matching our expectations to real outcomes.

Yet, to me, curiosity doesn’t seem enough. Ok, for education, particularly young kids, I see it. You may want to set up some mismatch of expectations to drive them to want to learn something. But I believe we need more.

Matt Richter, in his well-done L&D conference presentation on motivation, discussed self-determination theory. He had a nice diagram (my revision here) that distinguished various forms of motivation. From amotivated, that is, not, there were levels of external motivation and then internal motivation. The ultimate is what he termed intrinsic motivation, but that’s someone wanting it of their own interest. Short of that, of course, you have incentive-driven behavior (gamification), and then what you’re guilted into (technically termed Introjection), to where you see value in it for yourself (e.g. WIIFM).

While intrinsic motivation, passion, sounds good, I think having someone be passionate about something is a goal too far. Instead, I see our goal as helping people realize that they need it, even if not ‘want’ it. That, to me, is where consequences kick in. If we can show them the consequence of having, or not, the skills, and do this for the right audience and skills, we can at least ensure that they’re in the ‘value’ dimension.

So, my take is that while we should value curiosity, we may not be able to ensure it. And we can ensure that, with good analysis and design, we can at least get them to see the value. That’s my current take after being curious about curiosity. I’d like to hear yours!