I've talked about one very impressive study by John Hattie.
He collected data for years where each of his data points
was itself the result of another study that took a long time.
That's a lot of work.
I want to explain to you how MOOCs offer the
opportunity to do all these studies much more quickly and efficiently.
The first thing to know is every single click on the major platforms is tracked.
If you were to pause the video now, they would know.
If you are watching this accelerated, they know it as well.
They also know how often a student posts on the
forum or how many time they try to post questions.
All these days I analysed, some of the smartest brain
in the world, actually try to make sense out of it.
The question, the big question is, what works best for teaching online?
And maybe even what works best for teaching?
A video of a talking head, beamer slides with
an insert of the instructor, or maybe without the instructor.
Some guy's paper cutouts.
Who knows?
This data is analysed, and it's not a sample size of n equals 20.
But, it's more like n equals two million.
I'm tried to give you a flavor of what is already possible.
Let's start with a concrete situation.
Let's say you have an ad for a cool project to change the world.
You want people to help you.
So you write a flyer that say, what to change the world.
Come and join me at this day and place.
You know that that's not enough to attract people.
So you want to had, to add a hook before.
You don't want, you don't know for sure what to put in.
You think maybe, the line, want cookies, or, want pizza, might attract people.
But what will grab people's attention and attract people to your purchase/?
You don't know, And, in the real world, you wont know.
Unless you try both in parallel, and
see afterwards which one is most suc-, successful.
How do you do that?
Well, by interviewing the people who show up.
This takes time ,and again, it doesn't scale well.
Let's say now your Google, you'll literally
make millions of dollars of revenue per hour.
Most of it through people clicking on ads in the sidebar.
One of your employees thinks that a
particular shade of green will definitely help
highlight the ads, but not too much so that it doesn't bother the users.
You as Google want to try it, because you know there is a lot of money there.
But you are afraid of changing something that works pretty well already.
So what you can do is to pick a random 10% of the users
of a random small country, say, Belgium, and show to them that new green shape.
And then you can compare those users to
other users, where the experiment was not performed.
Maybe you can also pick another country.
Say, the Netherlands, and do the same experiment with another color, say orange.
Or you can try changing the size of the ads, or all at
the same time, fitting each user into
their own highly individualized blend of experiments.
As long as you can extract meaningful data, why wouldn't you do it?
Well that's what Google does.
Every time you go on Google hundreds of experiments are actually performed on you.
The page you get for a particular search, is slightly different
from the page offered to anyone else for the same search.
And they try to optimize that page for you.
This is called A/B testing, and it's used extensively to optimize the web.
I know it sounds silly to talk about this
for education but I don't think it really is silly.
Many blackboards are dyed green because some tests were performed a long
time ago and people figure out this was better for the eyes.
You can see the similarity with Google's experiment there.
Many more studies have been done since
in education to try and improve other factors.
My be more meaningful than just a blackboard color.
But this is slow and requires lots of manual work.
Again, similar studies can be done in a MOOC space, but
actually much more is possible, much faster, and much more efficiently.
For instance, you can alter the material
presented to the students in very subtle ways.
And see what has an effect and what does not.
So let's say you split your class in
two, and measured how effective it actually is to
start a class with a little preview of the problem that you want to solve at the end.
This could be effortless by the teacher, or someone doing educational research.
The technology to perform these studies exists, and is well understood.
Because it was used extensively in other industries
before, and is now being applied to education.
Some conferences are starting on these topics and releasing
lots of insight into what works and what does not.
It's hard to argue when someone comes with a study conducted on millions of students.
You ask for a follow up and six months later, they come back with that follow up.
This is tremendously accelerating for education, and very exciting.