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We used the Stanford education data archive (SEDA) data to examine the heterogeneity among urban school districts in the United States. The SEDA 2.1 includes data sets on students’ mathematics (Math) and English language arts (ELA) achievement from 2008 to 2014 at the district level. Growth mixture modeling was used to uncover the underlying growth trajectories for urban student achievement from the third to the eighth grade. Two and three growth patterns were observed for ELA and Math achievement, respectively, over time. We used the critical theoretical framework QuantCrit to centralize race in the analysis of the data and shared implications for future research.
Ramlackhan et al. (Fri,) studied this question.