Urban/Rural Science Achievement Disparities: A Causal Forest Analysis of Low-Performing Students in TIMSS 2023

• In Event: AER Roundtable Session Friday 1:45 pm - Gold 4
  In Roundtable Session: Understanding Science Achievement Using Large-Scale and Longitudinal Data

Abstract

This study aims to investigate urban/rural disparities in science achievement among low-performing students. We applied a machine learning-based causal inference method, causal forest, on 2,049 low-performing eighth grade students in TIMSS 2023 U.S. dataset. The results showed that urban students performed significantly lower than their rural peers. A variable selection process embedded in the causal forest model identified the seven influential factors across student, teacher, and school levels. Using a directed acyclic graph and the estimates of the average treatment effect (ATE), we found that lower student confidence in science (BSBGSCS; ATE= -0.286) and extremely weak urban school discipline (BCBGDAS; ATE= -0.522) were the most critical attributes in causing the observed disparities. The implications of these findings are discussed.

Authors

• Li Zhu, University of Iowa

• Zhenhan Fang, University of Iowa

• Mingyu Huang, University of Iowa