Can We Make Math More Accessible?

Why are 76% of all math PhDs awarded to men?  One major reason, according to Stanford math professor Jo Boaler, is the way math is taught.

At Stanford University, I teach some of the country’s highest achievers. But when they enter fast-paced lecture halls, even those  were successful in high school mathematics start to think they’re not good enough. One of my undergraduates described the panic she felt when trying to keep pace with a professor: “The material felt like it was flying over my head,” she wrote. “It was like I was watching a lecture at 2x or 3x speed and there was no way to pause or replay it.” She described her fear of failure as “crippling.” This student questioned her intelligence and started to rethink whether she belonged in the field of math at all.

Research tells us that lecturers typically speak at between 100 and 125 words a minute, but students can take note of only about 20 words a minute, often leaving them feeling frustrated and defeated.

This style of teaching doesn’t work for lots of people — one college math class was enoug to turn me off from math.  But it hits women and people of color especially hard.

When students struggle in speed-driven math classes, they often believe the problem lies within themselves, not realizing that fast-paced lecturing is a faulty teaching method. The students most likely to internalize the problem are women and students of color.

But there’s no reason math has to be taught the way it currently is.  Recently Boaler ran an interesting math teaching experiment that had impressive results.

In a recent summer camp with 81 middle school students, we taught mathematics through open, creative lessons to demonstrate how mathematics is about thinking deeply, rather than calculating quickly. After 18 lessons, the students improved their mathematics achievement on standardized tests by an average of 50%, the equivalent of 1.6 years of school.

What’s true in math is even more true in data science: if we want more people to use data science, we need to take a hard look at how it’s taught.

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