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  • Good morning.

  • So, I’m going to try and wander

  • and if I make it back to the podium it’s because I forgot what I was going to say and my notes are there.

  • Um, so, at, ah CAST,

  • where I work, we pioneered a framework

  • called Universal Design for Learning.

  • And today, I actually want to tell you about the problem

  • that we were trying to solve with that framework.

  • And it’s the myth of the average learner.

  • And I’ll argue that this myth

  • has actually hurt our competative advantage in education

  • because its lead us to a place where we

  • design learning environments

  • that ignore variability and

  • also our greatest asset, our diversity.

  • So, my basic point that I want make today is

  • I think straightforward,

  • that learning is way more variable

  • that a lot of people assume,

  • and that the understanding that variability is critical

  • when we think about designing this next generation of learning environments.

  • So I've been told that, um,

  • one way if you want to get a new perspective on your own field

  • is that you can step back and look somewhere else for analogies.

  • So that's what I want to do. We'll get to

  • issues of STEM and issues of cyber learning but I want to get there

  • with two examples variability that will seem really far removed:

  • shoes and Rubik's Cubes.

  • And we're gonna go with shoes first.

  • Now we were told at the kickoff

  • that there'd be some imagination going on today, so this is my contribution to

  • the imagination.

  • I want for you to imagine just for a moment that

  • we live in a world that believes in a different myth:

  • the myth the average foot.

  • And let's just call it a men's size eight an a half.

  • And I also want you to imagine

  • that you were all asked to be here not because you're smart,

  • but because you might have potential to be great sprinters.

  • So, just a simple question,

  • if you want to think about how you might fare, given your foot size,

  • in a world that was a size eight and a half,

  • it's obvious right?

  • Like the closer you are to that eight and a half,

  • the more likely it is that you would be able to reach your full potential,

  • whatever that is. But if you're size 13,

  • you're in trouble. Actually we'd just say you're bad at running,

  • but that's a different conversation.

  • Um, the good news is, that we don't actually live in a world that believes this myth,

  • because we can see the variability.

  • And because we can see it, we design for it.

  • And when we design for it, we end up

  • being able to recognize talent that would have otherwise

  • gone unnoticed, like Usain Bolt.

  • If you don't know who he is, you will in the London Olympics.

  • Fastest man in the world by far, and

  • he's six-five and wears about a size thirteen.

  • So, the problem is, we DO live in a world,

  • I would argue, where most people believe in some version

  • of the myth of the average learner.

  • Which is a little surprising, if you think about it,

  • that we'll, we can recognize that our feet very

  • but we have a hard time extending that for things like how we learn,

  • and even our brains for that matter.

  • And as someone who spends time thinking about the brain,

  • this one really is kind of funny to me. I mean, your brain has like

  • billions of neurons interacting to form networks

  • that interact with your environment, and are constantly being shaped by your experience.

  • Yet somehow, magically,

  • out of that complexity, we think that our brains end up more or less the same?

  • But the truth is, they're not.

  • I mean, neuroscience is crystal clear on this point.

  • That when it comes to the brain,

  • just like when it comes to learning,

  • variability's the rule, not the exception.

  • Now, that variability

  • is harder to see sometimes, than it it with like,

  • foot size, but it's there

  • and it really matters.

  • So let me give you an example.

  • The Rubik's Cube.

  • This has to be

  • one of the most frustratingly simple tasks

  • ever devised, right?

  • So, most of us have tried it,

  • some of us may have completed it,

  • but very few of us are what I would call an expert at it,

  • which is: can you solve it in under 30 seconds?

  • I know, right? So there are people that can do this,

  • but what's more interesting than that, is that when you look at

  • that elite group it turns out: there are many different strategies

  • that they can use to get them to expertise.

  • For example, if you're good at

  • being able to do pattern recognition really quickly,

  • there is a specific strategy for you, and it's a different strategy

  • that if you're one of those wiz kids that can memorize 50 algorithms in your head.

  • But there's a strategy for you based on that, too.

  • The only strategy you don't wanna use is the one I used as a little kid,

  • which involves peeling stickers off. Right?

  • It turns out, people totally know you cheated, and, uh, it takes way longer than 30 seconds.

  • Um, the trick is here, is that basically if you wanted to be

  • an expert at the Rubik's Cube, there are many strategies you could choose from

  • and the best strategy would depend on your particular variability.

  • Now, I wanna stay with the Rubik's Cube example,

  • and I want to extend this to a different dimension variability, not just strategy.

  • This is T.V. Raman, a colleague of mine.

  • He's a computer programmer Google. Bright guy, and he's been blind since childhood.

  • So we just think for a minute how we might design a learning environment

  • where Raman could demonstrate his potential at a Rubik's Cube.

  • So, that's pretty straightforward. Before we could even think about

  • differences in strategy,

  • we would have to think about the way the cube is represented to begin with.

  • Color is an arbitrary choice, and it's not the only way that we could have

  • represented the cube.

  • So, a simple answer would be just to change the representation,

  • and people have done this. Here's a, a Braille Rubik's cube.

  • and if you're like me the first thing I did, I looked at that and said "wow, that is beautiful."

  • Right? And then I start thinking, "but wait a minute. Is that really good design?

  • Why is it white? Now, only if you can read Braille can you use that cube."

  • If you don't read Braille, you're locked out of it. And now we're stuck

  • building two cubes. So now we start to lose economy of scale.

  • But it didn't have to be that way, right? If we would have thought about variability up front,

  • we could have designed a cube

  • from the beginning with multiple representations

  • and then imagine if we actually, at the same time, in that learning environment,

  • explicitly taught the the different strategies that could get you to expertise.

  • Now we would be somewhere closer to supporting variability.

  • Somewhere closer to Universal Design for Learning.

  • And somewhere where we would be able to recognize talent

  • that would have otherwise been overlooked like TV Raman.

  • Turns out he's a man of many talents, including being an expert at the Rubik's Cube.

  • 24 seconds.

  • It's kind of amazing actually, but the thing is, is that's only possible

  • through good design. And I would argue good design is only possible

  • when we understand variability.

  • So what does this mean for STEM, and cyber learning in a sec?

  • Well, if there's a lot of variability in something as simple

  • as a Rubik's Cube, think how much variability there is

  • in something as complex as inquiry science.

  • And this is, this is important because the increase in variability

  • means but there's a bigger cost to believing in the myth

  • of the average learner, because it means we will design environments

  • that are not really a very good fit for most students.

  • And it means that we have the risk of missing out on a lot of talent

  • that could have added to our competitive advantage.

  • But the thing is, it's even bigger than that. Think about the United States.

  • We are one of the most diverse countries in the history of the world,

  • but that means that the myth hurts worse than it does other countries

  • because it leads us to neglect what are our greatest assets:

  • our diversity and our capacity for innovation.

  • So it makes me think about this: We often hear these reports that say in high schools our

  • performance in STEM is lower on average than it is to some other countries.

  • But I would ask you: what if that's not the right way to think about it?

  • What if the average doesn't actually define us?

  • What if we tried to win in a different way?

  • by being very serious about designing environments that genuinely support

  • the full range of the learners in our classrooms?

  • It's a different approach, and it would be challenging, but I think it would be worth it.

  • So, lastly, when I think about what this means

  • for why we're here, for cyber learning, I think it means we have a choice to make.

  • What do we want cyber learning to be?

  • Do we we want it to be a collection of tools that makes the average better on average?

  • Or do we want it to be an ecosystem of learning opportunities?

  • I vote for the ecosystem but that's going to mean

  • to get there I think we gotta understand variability and

  • we need to understand how to design for it.

  • Thank you.

Good morning.

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託德-羅斯:可變性很重要 (Todd Rose: Variability Matters)

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    Amy.Lin 發佈於 2021 年 01 月 14 日
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