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.