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Objectives & Theoretical Framework
The United States is a hub for immigrating families, a trait only magnified in today’s interconnected and conflict-ridden world. Once in the U.S., immigrating children and youth join the K-12 education system. Yet we know little about how immigrant students are distributed across school districts, nor do we know what factors predict distribution or what the implications of the relative dispersion or concentration of these students is on linguistic and academic growth (Dentler & Hafner, 1997; Taylor & Sidhu, 2012). The density of the co-ethnic community can bolster resources, opportunities and assets among immigrant families (Portes & Rumbaut, 2006; Portes & Zhou, 1993). At the same time, concentration of immigrant students into segregated schools and neighborhoods can create under-funded “ESL [English as a second language] ghettos” (Valdes, 1997, p. 12) with access to limited institutional and instructional resources (Gándara & Aldana, 2014; Oakes, 2005). In this paper, we grapple with these questions through analysis of two state-wide datasets from new immigrant destination states. Specifically, we ask the following three research questions: 1) How are recently-arrived immigrant (RAI) students distributed across and within school districts? 2) Does distribution operate differently for different subgroups of RAI students or RAI students with different background characteristics? 3) What is the association of RAI distribution and student linguistic and academic outcomes among different subgroups of students?
Methods & Data
In this study, we analyze student-level data from two states. The federal Department of Education sets aside funds for districts experiencing increases in their proportion or number of students who have arrived in the U.S. within three academic years. As such, states increasingly collect data on who among enrolled students count as “immigrant” students for the purposes of federal funding. We take advantage of this, combining multilevel regression with mapping analyses, to examine the distribution of these students across and within school districts. We examine how distribution differs for RAIs with different key characteristics, including students from different linguistic groups, English learner RAIs, refugee RAIs, RAIs with limited or interrupted formal education, RAIs with disabilities, low income RAI students, and other factors. Finally, we link student distribution to analyses of student linguistic and academic growth using hierarchical linear modeling.
Results & Significance
Analyses are ongoing. Preliminary findings suggest that RAI students are concentrated in specific districts and that this distribution is linked to RAI characteristics, specifically income level, refugee status, and interrupted formal education status. With regard to within district concentration, we observe different patterns in different school districts, linked again to RAI characteristics such as age and grade. For example, within district concentration of RAI students is greater at more advanced ages and grades. Analyses of the relationship of concentration with student growth are too nascent to report. These findings have important implications for theory and practice. They suggest that school districts face extremely different circumstances, both with regard to the number of RAI students served, but importantly also the characteristics of those RAI students.