If we begin focusing on correlation only don’t we risk empowering those who understand and work with these patterns of correlation and disempowering everyone else.

I was just thinking of television when it was first developed. Everyone thought that TV would revolutionize education. In a matter of a few decades commercial interests were able to manipulate the content and medium to an ends that served their bottom line first.

Amazon, of course, doesn’t ever ask you to explicitly state your preferences for genres of book. Netflix doesn’t ask for explicit information about your taste in movies. And Google doesn’t need semantic analysis do determine which page is better than another.

I think we should collect both; raw data and peoples’ explicit preferences. And people are probably doing so. After all it’s more data to collect, just a different “type”, so to speak.

You’re mental model of what you want is an important information, whether it’s correct or not.

“Petabytes allow us to say: ‘Correlation is enough.’ We can stop looking for models.”

It depends entirely on what we want to use the model for. ‘The model’ is used for a vast number of purposes, all of which have different and valid reasons. I think one has to define the use of the model as well as the model itself, to have a valid reason for appraising or criticising it.

In considering the learner, for instance, the cognitive apprenticeship theory allows us to be aware that though some learned experts may not need the model any more, the learner is still coming to grips with it. Even a poor model can be used appropriately when it comes to pedagogy, the definitive being that it works so that the next learning step can be approached.

So I wonder at your generic statement about models. Even considered in context, I suspect that by eschewing the model out of hand, we could be losing the plot or part of it.

Catchya later
from Middle-earth

Nevertheless, when my husband accidentally used Amazon’s “one click” purchasing when I was the one logged in, Amazon suddenly saw me as a person who not only reads literature about culture and eduction, but also one who buys parts for power tools. I was barraged for months with recommendations for power drills, saws, and carpentry equipment, but also included were interesting book titles, so in a way, they can’t lose, though I did feel somewhat mis-identified as a customer.

Some questions remain for me:

Are educational goals as clear-cut as sales and marketing goals?
Does data-driven education imply endless testing for students?
Does data-drive education mean that we are more concerned with the quanitative result of the group rather than the depth of the learning experience for an individual?

Your closing question is intriguing. Learners probably don’t care, initially, about why a particular educational experience is effective or enjoyable, just that it is. But, at some point, is it important (for both the learner and the designer) to understand why a particular pedagogy, tool, experience, etc. is working?

I don’t know for sure, but intentionality seems to have some value here, particularly if we want learners to become self-directed and develop the ability to drive their own learning in meaningful ways.

I would argue for a balanced approach: more emphasis on using data to drive good design, but without abandoning theory and intentionality. I love Google, but I don’t think the “we don’t know why. . .and we don’t care” principle has perfect application to educational design.