What’s wrong with America’s math curriculum?
I’ve written endlessly on this subject, but for now I want to focus on the claims of Steven Levitt and others in a new version of an earlier Freakonomics episode. Levitt et al. are far from alone in decrying the emphasis on algebra and calculus in high-school math; they make a strong case for putting the main focus on data fluency.
Do listen to that episode, but also read the text at the same link. This is one of those rare times where I recommend—in fact, highly recommend—reading the comments. The contributors make a convincing case that data analysis and visualization are more useful in everyday life than calculus and algebra.“It’s embarrassing,” says Levitt, “that we teach a math curriculum that nobody is using.”
But is it really embarrassing? I will accept the conclusion that we should teach data fluency—including statistics, with tools like spreadsheets—a lot more than we do. Of course most people (not everybody!) use data more than they use calculus. But is that really what determines, what should determine, which topics go in a college-prep high school curriculum? Is that the question we ask in science? in history? in English? Surely math doesn’t exist in isolation. Do we really determine our secondary curricula by what people use in everyday life? Should we?
The current pandemic offers a somewhat orthogonal take on these questions. Yes, the general public badly needs lessons on how to visualize, present, and interpret data and statistics. And don’t forget probability, a related and too often neglected discipline! But the simplistic solution offered in the podcast—drop Algebra II—is not convincing. And, as I suggest in the previous paragraph, why are we asking these question only in math class? (I don’t mean that we should teach data fluency within other subjects. Yes, as Levitt suggests, we should indeed do that. What I mean here is that our Balkanization of academic subjects prevents us from questioning the current curriculum in science, history, and English, about which we can ask almost identical questions.)
For more information, see the website for Data Science 4 Everyone.