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…

Sources of longitudinal achievement data are increasing thanks partially to the expansion of available interim assessments. These tests are often used to monitor the progress of students, classrooms, and schools within and across school years. Yet, few statistical models equipped to approximate the distinctly seasonal patterns in the data exist,…

When the 2017 National Assessment of Educational Progress was released earlier this year, most states learned that their average scores in grades 4 and 8 reading and mathematics remained relatively flat since 2015. More troubling though, students performing at the lowest 10th and 25th percentiles saw a decline in their scores. To confront this…