Developing and Evaluating Equitable Student Assignment Policies

Public school systems in the United States are racially and economically segregated. Districts have developed student assignment policies aimed at integrating schools, but research shows that student assignment policies can themselves exacerbate segregation. Research is necessary to understand the degree to which student assignment policies can reduce segregation and equalize educational opportunity. Tools from optimization and human-centered design are ideally suited to tackle the computationally difficulty problem of operationalizing a student assignment policy and evaluating whether it promotes integration and better academic outcomes. With this award, Lo will use computational approaches to identify a multi-school zone policy that allows for significant parental choice, use community feedback to refine the student assignment policy, and evaluate whether the student assignment policy reduces segregation and academic inequality. Lo will develop expertise on policy measurement and evaluation through mentorship from Sean Reardon, Professor of Poverty and Inequality in Education at Stanford University. Micheal Bernstein, Associate Professor of Computer Science at Stanford University, will provide mentorship in human-centered participatory design.