Search for EdWorkingPapers here by author, title, or keywords.
Nearly one in five U.S. students attends a rural school, yet we know very little about achievement gaps and academic growth in rural schools. This study leverages a unique dataset that includes longitudinal test scores for more than five million 3rd to 8th grade students in approximately 17,000 public schools across the 50 states, including 900,000 students attending 4,727 rural schools. We find rural achievement and growth to be slightly above public schools. But there is considerable heterogeneity by student race/ethnicity. For all grades and subjects, White-Black and White-Hispanic gaps are smaller in rural schools than gaps nationwide, and White-Native American gaps are larger in rural schools than gaps nationwide. Separate analyses by racial/ethnic subgroup show that rural Black, Hispanic, and Native American students are often growing slower than their respective subgroup national average. In contrast, White students are often growing faster than the national average for White students.
Many interventions in education occur in settings where treatments are applied to groups. For example, a reading intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are non-randomly allocated, outcomes across the treated and control groups may differ due to the treatment or due to baseline differences between groups. When this is the case, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed matching methods designed for contexts where treatments are clustered. This form of matching, known as multilevel matching, may be well suited to many education applications where treatments are assigned to schools. In this article, we provide an extensive evaluation of multilevel matching and compare it to multilevel regression modeling. We evaluate multilevel matching methods in two ways. First, we use these matching methods to recover treatment effect estimates from three clustered randomized trials using a within-study comparison design. Second, we conduct a simulation study. We find evidence that generally favors an analytic approach to statistical adjustment that combines multilevel matching with regression adjustment. We conclude with an empirical application.
Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practice for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in Wake County, North Carolina.
English Learners (ELs) lag behind their peers in postsecondary attainment. As the EL population in the U.S. continues to grow, so does concern over their underrepresentation in higher education. Research shows that Early College High Schools have a significant impact on high school and college outcomes for students from low income and racial/ethnic minority backgrounds, but how similar opportunities might extend to ELs remains unknown. We report findings from the first three years of an intervention that offers Early College opportunities in high schools serving large EL populations. Leveraging an exogenous policy change and rich administrative records, we examine the outcomes of pre- and post-program cohorts of ELs (N=15,090) in treated and untreated high schools. We find a large, significant impact on the number of college credits earned in 12th grade but no effect on immediate college attendance after high school. The probability of attending a four-year college significantly decreased.
This study presents a framework that uses academic trajectories in the middle grades for identifying students in need of intervention and providing targeted support. We apply a set of academic college readiness benchmarks to rich longitudinal data for more than 360,000 students in 5900 schools across 49 states and the District of Columbia. In both math and reading, each student was assessed up to six times (fall and spring of 6th, 7th, and 8th grade). We show that student-level and school-level demographic characteristics significantly predict academic trajectories. Compared to White and Asian students, higher proportions of Black and Hispanic student are consistently off-track for college readiness throughout middle school. Among students who started 6th grade on track, being male, Black, Hispanic, and attending schools with a higher percentage of students who are eligible for free or reduced-price lunch are positively associated with falling off track.
While teacher evaluation policies have been central to efforts to enhance teaching quality over the past decade, little is known about how teachers change their instructional practices in response to such policies. To address this question, this paper drew on classroom observation and survey data to examine how early career teachers’ (ECTs’) perceptions of pressure associated with teacher evaluation policies seemed to affect their enactment of ambitious mathematics instruction. As part of our analysis, we also considered the role that mathematical knowledge for teaching (MKT) and school norms regarding teaching mathematics shape the potential influence of teacher evaluation policies on ECTs’ instructional practices. Understanding how the confluence of these factors is associated with teachers’ instruction provides important insights into how to improve teaching quality, which is one of the most important inputs for student learning.
Sixty-seven school finance reforms (SFRs) in 27 states have taken place since 1990; however, there is little empirical evidence on the heterogeneity of SFR effects. We provide a comprehensive description of how individual reforms affected resource allocation to low- and high-income districts within states. We then examine whether characteristics of the SFR, such as the funding formula that was adopted, predict effect size heterogeneity. Taken together, this research aims to provide a rich description of variation in states' responses to SFRs, as well as explanation of this heterogeneity as it relates to contextual factors.
Please check back soon
With 55 million students in the United States out of school due to the COVID-19 pandemic, education systems are scrambling to meet the needs of schools and families, including planning how best to approach instruction in the fall given students may be farther behind than in a typical year. Yet, education leaders have little data on how much learning has been impacted by school closures. While the COVID-19 learning interruptions are unprecedented in modern times, existing research on the impacts of missing school (due to absenteeism, regular summer breaks, and school closures) on learning can nonetheless inform projections of potential learning loss due to the pandemic. In this study, we produce a series of projections of COVID-19-related learning loss and its potential effect on test scores in the 2020-21 school year based on (a) estimates from prior literature and (b) analyses of typical summer learning patterns of five million students. Under these projections, students are likely to return in fall 2020 with approximately 63-68% of the learning gains in reading relative to a typical school year and with 37-50% of the learning gains in math. However, we estimate that losing ground during the COVID-19 school closures would not be universal, with the top third of students potentially making gains in reading. Thus, in preparing for fall 2020, educators will likely need to consider ways to support students who are academically behind and further differentiate instruction.
Researchers commonly interpret effect sizes by applying benchmarks proposed by Cohen over a half century ago. However, effects that are small by Cohen’s standards are large relative to the impacts of most field-based interventions. These benchmarks also fail to consider important differences in study features, program costs, and scalability. In this paper, I present five broad guidelines for interpreting effect sizes that are applicable across the social sciences. I then propose a more structured schema with new empirical benchmarks for interpreting a specific class of studies: causal research on education interventions with standardized achievement outcomes. Together, these tools provide a practical approach for incorporating study features, cost, and scalability into the process of interpreting the policy importance of effect sizes.