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Using Longitudinal Student Mobility to Identify At-Risk Students
AERA Open ( IF 3.5 ) Pub Date : 2022-01-20 , DOI: 10.1177/23328584211071090
Dan Goldhaber 1, 2 , Cory Koedel 3 , Umut Özek 2 , Eric Parsons 3
Affiliation  

We use administrative data from three states to document the relationships between geographic mobility and student outcomes during K–12 schooling. We focus specifically on nonstructural mobility events—which we define as school changes that do not occur as the result of normal transitions between schools—and on longitudinal measures that capture these events cumulatively for students. We show that the number of nonstructural moves experienced by a student is a powerful indicator of low-test performance and graduation rates. Longitudinal information on student mobility is unlikely to be readily available to local practitioners—that is, individual districts, schools, or teachers. However, due to recent investments in longitudinal data systems in most states, this information can be made available at low cost by state education agencies.



中文翻译:

使用纵向学生流动性来识别有风险的学生

我们使用来自三个州的行政数据来记录 K-12 学校期间地理流动性与学生成绩之间的关系。我们特别关注非结构性流动事件——我们将其定义为学校之间的正常过渡不会发生的学校变化——以及为学生累积捕捉这些事件的纵向测量。我们表明,学生经历的非结构性移动的数量是低测试表现和毕业率的有力指标。当地从业者(即个别学区、学校或教师)不太可能轻易获得有关学生流动性的纵向信息。然而,由于最近在大多数州对纵向数据系统的投资,这些信息可以由州教育机构以低成本提供。

更新日期:2022-01-21
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