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Plotting Inequalities, Building Resistance

By Adam Renner, Bridget Brew, and Crystal Proctor

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Home > Archives > Volume 26 No.2 - Winter 2011/2012
 

Illustration: Michael Duffy

By Adam Renner, Bridget Brew, and Crystal Proctor

Media depictions of San Francisco show idyllic images of fog pouring under the Golden Gate Bridge or happy tourists riding cable cars, but rarely the mostly nonwhite neighborhoods of the east side. San Francisco public schools have a bad track record of mimicking this masquerade, with very low numbers of African American and Latina/o students making it to senior year, and less than a quarter of those who do, graduating with the credits to move on to college. Our high school, the June Jordan School for Equity (JJSE), is located on the east side of the city, and was started by a group of teachers and parents who were disturbed by the high numbers of black and brown youth being underserved and then dropping out. We are an intentionally small school with a focus on social justice.

Our commitment to send students of color to college means that they need a strong math education. As members of the math department, we believe, like Bob Moses, that math literacy in itself is a civil rights issue for students of color. We have seen too many “math haters” end up in remedial classes in college, short-circuiting their career options.

The teachers who helped found our school were mostly from the humanities departments, and it is easier to imagine getting straight to a student’s heart and experiences with a great piece of literature or history told from a non-oppressor perspective than it is to imagine the quadratic formula liberating anyone. Part of our school’s mission is to help our students become agents of social change, so making explicit connections to social issues in math class is something that we try to do, though many math standards do not make this easy. Still, when the lesson involves important math skills, social justice, and something that will grab student attention, there is the potential that class will be exciting instead of mundane.

The Scatter Plot Project

No one took making explicit social justice connections more seriously than Adam Renner, who started as a 9th-grade math teacher at JJSE fall 2010 after many years as a teacher educator at the university level. In one of his first major projects, he had his students use math skills as a way to dig into a deeper understanding of the chasmic divide between rich and poor in our city. He wanted to shed light on the impact of economics and the structures of racism on education, housing, and job opportunities.

Adam began by introducing his students to Zip Skinny (www.zip skinny.com), a user-friendly website for finding and comparing data about local communities. Our students live primarily in three San Francisco ZIP codes: the Excelsior, Visitacion Valley, and Bayview/Hunters Point. Along with mining for data in these ZIP codes, Adam selected four other ZIP codes for comparison: the Mission (an eclectic, centrally located neighborhood), the Presidio (one of San Francisco’s wealthiest neighborhoods), and the Outer Sunset and Outer Richmond (two neighborhoods along San Francisco’s Pacific coast). He asked the students to record in a table the following data: median neighborhood income, percentage of high school completion or higher, percentage of bachelor’s completion and higher, unemployment rate, and percentage of nonwhite residency.

The freshmen had to find these data independently using Zip Skinny. Then, in carefully constructed groups, they had to graph two different sets of data on the same coordinate plane in order to discover the relationship between the sets of data. One example of a scatter plot they created was comparing X = median income vs. Y = high school completion; another was X = college completion vs. Y = percentage of nonwhite residency. In this way, students could see what it means for two circumstances to be related or correlated, but not necessarily by cause and effect. They also saw the difference between a weak correlation (the points are spread out) and a strong correlation (the points are almost in a line), as well as the idea of positive correlation (one circumstance increases with the other) and negative correlation (one circumstance decreases as the other increases). As they were learning the mathematical terms for data analysis, they began to discover that math can describe and order their world.



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