07 - Startup Iteration Cycle [Massive Teaching]

From Week 1 Lecture Videos of "Teaching goes massive: new skills required"
by Paul-Olivier Dehaye
See
https://etherpad.mozilla.org/pr8ZtLXODg
and
http://www.elearnspace.org/blog/2014/07/09/congrats-to-paul-olivier-dehaye-massiveteaching/

So, how fast will these studies trickle to
improvements on

the MOOC format, or even improvements in
education in general?

Well, pretty fast, I would say.

To explain this, I'll need to describe how
startups work.

Here's the startup iteration cycle.

We see that we have a product, such as the
Coursera portal for Coursera.

It can be used to measure all kinds of
data.

That could be via A/B testing, for
instance,

as I just explained, or maybe using other

metrics, enrollment in a course or the
click-through

on emails that have been sent to the
students.

Armed with all this data, the startup

engineers can learn about areas that would
need

improvement, and they can generate new
ideas and things to do and build that in.

The whole cycle is not groundbreaking,
sure.

I'm not claiming it is.

The difference is that this cycle is not
bothered

by the usual very slow methodology of
education research.

Sure, this data can be spun off into
papers, and that's what education

researchers have to do, are currently
doing, because that's what their job is.

But, by the time those studies are

published, the engineers will have already
gone

through several iterations of this cycle,
and

the newly published results will already
be obsolete.

They'll, they will already have improved
the platform.

The big, big, difference with five years
ago is that now education is the next area

where software engineers can make an
impact, and

hundreds are working on making it more
efficient.

This is completely new, and you can expect
it to show effects very, very fast.

Just as for many other industries that you
know of,

such as the book industry, the travel
industry, et cetera.