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K-12 Education
While multiple studies have examined the impact of school turnaround, less is known about reforms under the Every Student Succeeds Act (ESSA). To advance this literature, we examine North Carolina’s Restart (NCR) model. NCR aligns with ESSA by giving school leaders increased flexibility. Also, NCR differs from previous turnaround models by repackaging a traditionally sanction-based approach to instead motivate school leaders with increased autonomy. Using comparative interrupted time series models, we find positive NCR effects in math, but not in English Language Arts or on non-test-based student outcomes. Also, nearly a quarter of the positive NCR effect can be explained by decreased teacher and principal turnover. These results provide evidence to support current shifts toward reform models featuring local autonomy.
This study reports the findings from a year-long randomized evaluation assessing the impact of assigning 62 classrooms in Nigeria to receive either blocked or interleaved math problem sets. Blocked practice sessions focused on a single skill at a time. Interleaved problem sets alternated between different skills within a practice session. On tests of short-term retention, interleaved practice increased test scores by 0.29 standard deviations. In contrast, we find no evidence that interleaving improves average performance on a cumulative assessment measuring retention of material over the academic year. We find some evidence of large impacts on the cumulative assessment at the bottom of the distribution, but these impacts appear to be offset by negative impacts at the top.
While recent studies have demonstrated the potential of automated feedback to enhance teacher instruction in virtual settings, its efficacy in traditional classrooms remains unexplored. In collaboration with TeachFX, we conducted a pre-registered randomized controlled trial involving 523 Utah mathematics and science teachers to assess the impact of automated feedback in K-12 classrooms. This feedback targeted “focusing questions” – questions that probe students’ thinking by pressing for explanations and reflection. Our findings indicate that automated feedback increased teachers’ use of focusing questions by 20%. However, there was no discernible effect on other teaching practices. Qualitative interviews revealed mixed engagement with the automated feedback: some teachers noticed and appreciated the reflective insights from the feedback, while others had no knowledge of it. Teachers also expressed skepticism about the accuracy of feedback, concerns about data security, and/or noted that time constraints prevented their engagement with the feedback. Our findings highlight avenues for future work, including integrating this feedback into existing professional development activities to maximize its effect.
Practice-based teacher education has increasingly been adopted as an alternative to more traditional, conceptually-focused pedagogies, yet the field lacks causal evidence regarding the relative efficacy of these approaches. To address this issue, we randomly assigned 185 college students to one of three experimental conditions reflective of common conceptually-focused and practice-based teacher preparation pedagogies. We find significant and large positive effects of practice-based pedagogies on participants’ skills in eliciting and responding to student thinking as demonstrated through a written assessment and a short teaching episode. Our findings contribute to a developing evidence base that can assist policymakers and teacher educators in designing effective teacher preparation at scale.
This paper studies how school spending impacts student achievement by exploiting the US interstate branching deregulation as state tax revenue shocks. Leveraging school finance data from universal school districts, our difference-in-differences estimation reveals that deregulation leads to an increase in per-pupil total revenue and expenditure. The rise in revenue is primarily attributed to higher state revenues, while the expenditure increase is more prominent in low-income school districts. Using restricted-use student assessments from the Nation’s Report Card, we find that deregulation results in improved student achievement, with no distributional effects evident across students’ ability, race, or free lunch status. We introduce an instrumental variables approach that accounts for dynamic treatment effects and estimate that a one-thousand-dollar increase in per-pupil spending leads to a 0.035 standard deviation improvement in student achievement.
Market-based policies, especially school vouchers, are expanding rapidly and shifting students out of traditional public schools. This essay broadens, deepens, and updates prior critiques of the free market logic in five ways. First, while prior articles have pointed to some of the conditions necessary for efficient market functioning, I provide a more comprehensive list. Second, with an up-to-date literature review, I show that all of these conditions fail to hold to an unusual extent in schooling, relative to other markets. Third, because of these failures, I argue that the strongest critique of the free market approach to schooling comes from the intellectual home of markets—economics. Fourth, I show that the issues leading to inefficiency are the same ones leading to inequity. Fifth, I argue that the analysis points to specific roles for government, which go well beyond those included in new universal school voucher policies, but which are also narrower than the roles of government encompassed in traditional public education. For these reasons, the current direction of policy is off-track and apparently inconsistent with the main criteria on which we evaluate education policy and even with the values that voucher advocates themselves profess.
While it is commonly believed that teachers take more absences than other professionals, few empirical studies have systematically investigated the prevalence of teacher absences in the US. This study documents the level of teacher absences and compares it with other college-educated workers. Using the Monthly Current Population Survey between the 1995 and 2019 school years, we conduct descriptive and regression analysis to estimate the level of teacher absences and the absence gaps between teachers and other college-educated workers. Additional regression analysis using data from the Leave Module of the American Time Use Survey is conducted to explain the gaps in absences between teachers and other observationally similar college-educated workers. The analysis reveals that 7% of teachers are absent at least once weekly, accounting for around 4% of their weekly working time. Compared to observationally similar college-educated workers, teachers take the same, if not less, amount of absences. Further investigation of teachers’ absence behaviour indicates that teachers report fewer demands for absences, have fewer paid leaves, and are more likely to attend work despite needing to be absent. We also find that individuals who prefer fewer absences tend to enter the teaching profession. This study adds to the emerging group of research examining the nature, determinants, and consequences of teacher absences using national-level data. Our findings imply that policymakers may be able to use more support programs to increase teacher attendance.
Noncognitive constructs such as self-e cacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by comparing the predictive validity of two most widely used metrics on noncogntive outcomes|observable academic behaviors (e.g., absenteeism, suspensions) and student self-reported social and emotional learning (SEL) skills|for the likelihood of high school graduation and postsecondary attainment. Our ndings suggest that conditional on student demographics and achievement, academic behaviors are several-fold more predictive than SEL skills for all long-run outcomes, and adding SEL skills to a model with academic behaviors improves the model's predictive power minimally. In addition, academic behaviors are particularly strong predictors for low-achieving students' long-run outcomes. Part-day absenteeism (as a result of class skipping) is the largest driver behind the strong predictive power of academic behaviors. Developing more nuanced behavioral measures in existing administrative data systems might be a fruitful strategy for schools whose intended goal centers on predicting students' educational attainment.
Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary within persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of “item parameter drift” (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time.
We study the progressivity of state funding of school districts under Tennessee’s weighted student funding formula. We propose a simple definition of progressivity based on the difference in exposure to district per-pupil funding between poor and non-poor students. The realized progressivity of district funding in Tennessee is much smaller—only about 17 percent as large—as the formula weights imply directly. The attenuation is driven by the mixing of poor and non-poor students within districts. We further show the components of the Tennessee formula not explicitly tied to student poverty are only modestly progressive. Notably, special education funding is essentially progressivity-neutral for poor students. If we adjust the formula so all factors except individual student poverty receive zero weight and distribute the excess to poor students, we can increase the progressivity of district funding by 124 percent. We interpret this as the opportunity cost of the non-poverty-based funding components, measured in terms of progressivity.