Toward Fair and Personalized Education Policy: Reducing Racial and State Disparities in Advanced Math Courses

• In Event: Saturday Roundtable Session 9:45 am
  In Roundtable Session: Using Advanced Statistical Methods to Inform Education Policy (Table 32)

Abstract

The current math course-taking plan for high school students across much of the United States is not optimized for every student’s success in advanced mathematics, resulting in disproportionate participation across racial groups and states. In this study, we propose a framework for designing fair, personalized, data-driven education policies based on a recent proposal by Kim and Zubizaretta (2023) by incorporating multilevel fairness considerations within multilevel educational contexts. We evaluate the effectiveness of our approach through extensive simulation studies. Furthermore, we demonstrate its efficacy based on data from the High School Longitudinal Study of 2009, by simultaneously mitigating student-level and state-level disparities in designing optimal math course-taking plans, ultimately aiming to equitably recommend the right math course for the right student.

Authors

• Youmi Suk, Teachers College, Columbia University

• Kwangho Kim, Korea University

• Chan Park, University of Pennsylvania