Archives for September 2020

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?