There’s an excellent article over on Wired right now with interesting implications for our field. The End of Theory reads in part:
“All models are wrong, but some are useful.” So proclaimed statistician George Box 30 years ago, and he was right. But what choice did we have? Only models, from cosmological equations to theories of human behavior, seemed to be able to consistently, if imperfectly, explain the world around us. Until now. Today companies like Google, which have grown up in an era of massively abundant data, don’t have to settle for wrong models. Indeed, they don’t have to settle for models at all…
This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves…
Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise…
There is now a better way. Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.
Let’s temporarily assume Chris is right, for the sake of argument. Could it be that educational research is finally on the brink of making an inch of forward progress? Do mediating educational technologies provide us with the opportunities to capture enough data that we could eventually do this “new kind of research?” Could access to this kind of data finally be the killer app for high technology in education?
Elsewhere in the article, Chris says,
Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required.
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. Is there a time coming when access to a sufficient quantity of educational activity and performance data will finally stomp out the petri dish of poorly informed opinion that is the vast majority of educational research? Would you care if I couldn’t classify your learning style or aptitude a la Cronbach and Snow or your intelligence type a la Gardner if I could consistently give you educational experiences that you found enjoyable and effective? I suspect not.