Political Science 101 emphasizes how gerrymandering, or strategic electoral redistricting, is used to create unfair voting situations. But for Ben Kraft, an instructor for the MIT Educational Studies Program, it becomes even more complex from a data perspective.
“While I obviously want students to come out of the class with a basic understanding of the problems of gerrymandering, I also want them to go beyond that,” Kraft writes on MIT ESP’s Tumblr. “I want them to end up with a deeper feel for the complexity of the issue, for the philosophical questions about the nature of democracy that underlie redistricting, and for how the various goals of the process can conflict.”
Kraft uses data points to represent voters, and begins with a relatively easy assignment: Divide one “map” into five districts however you please. Each subsequent district-creating exercise adds more context, like parsing out the data so students can create equipopulous districts; assigning political affiliation to the individual points and optimizing for a certain party; noting incumbency and creating districts to favor the incumbent; and delineating race to make the districts equitable.
At the end of the hypothetical exercise, Kraft then discusses current events and Supreme Court cases that pertain to redistricting. What his methodology represents is how that data visualization and hands-on activities can make a history lessons more engaging and meaningful.
“There are still a lot of things I want to improve about this class,” Kraft writes. “I’ve had fun teaching it, and I think students end up with at least some notion that gerrymandering, and redistricting more generally, is about more than just politicians being good or evil in an obvious way.”