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Educator preparation, professional development, performance and evaluation
Advanced course-taking in high school sends an important signal to college admissions officers, helps reduce the cost and time to complete a post-secondary degree, and increases educational attainment and future earnings. However, Black and Hispanic students in the U.S. are underrepresented in Advanced Placement coursework and dual enrollment (i.e. early college). In this paper, we systematically examine the social, demographic, economic, and policy factors that are predictive of racial gaps in AP enrollment and access to DE across the U.S. We find that many of the same factors that predict higher AP access overall also predict higher racial/ethnic gaps in AP, suggesting that policies aimed at increasing AP access need to specifically attend to the inequitable access, rather than simply focusing on increasing access overall. We also find evidence that that might indicate opportunity hoarding by White families contributes to AP gaps – but not DE gaps – suggesting that DE acts as a more equitable avenue for access to college coursework. Our most novel contribution to the literature is our analysis of policies aimed at reducing teacher shortages in high needs areas, in which we find no evidence that the disparities in access to advanced coursework were reduced following implementation of these policies.
A core motivation for the widespread teacher evaluation reforms of the last decade was the belief that these new systems would promote teacher development through high-quality feedback. We examine this theory by studying teachers’ perceptions of evaluation feedback in Boston Public Schools and evaluating the district’s efforts to improve feedback through an administrator training program. Teachers generally reported that evaluators were trustworthy, fair, and accurate, but that they struggled to provide high-quality feedback. We find little evidence the training program improved perceived feedback quality, classroom instruction, teacher self-efficacy, or student achievement. Our results illustrate the challenges of using evaluation systems as engines for professional growth when administrators lack the time and skill necessary to provide frequent, high-quality feedback.
From 2010 onwards, most US states have aligned their education standards by adopting the Common Core State Standards (CCSS) for math and English Language Arts. The CCSS did not target other subjects such as science and social studies. We estimate spillovers of the CCSS on student achievement in non-targeted subjects in models with state and year fixed effects. Using student achievement data from the NAEP, we show that the CCSS had a negative effect on student achievement in non-targeted subjects. This negative effect is largest for underprivileged students, exacerbating racial and socioeconomic student achievement gaps. Using teacher surveys, we show that the CCSS caused a reduction in instructional focus on nontargeted subjects.
Growing evidence shows that a student's growth mindset (the belief that intelligence is malleable) can benefit their academic achievement. However, due to limited information, little is known about how a teachers’ growth mindset affects their students’ academic achievement. In this paper, we study the impact of teacher growth mindset on academic achievement for a nationwide sample of 8th and 10th grade students in Chile in 2017. Using a student fixed effect model that exploits data from two subject teachers for each student, we find that being assigned to a teacher with a growth mindset increases standardized test scores by approximately 0.02 standard deviations, with larger effects on students with high GPAs and particularly on students in low socioeconomic schools.
A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.
Recent interest to promote and support replication efforts assume that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. In direct replication designs, replication failure is evidence of bias or incorrect reporting in individual study estimates, while in conceptual replication designs, replication failure occurs because of effect variation due to differences in treatments, outcomes, settings, and participant characteristics. The paper demonstrates how multiple research designs may be combined in systematic replication studies, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.
Despite calls for more evidence regarding the effectiveness of teacher education practices, causal research in the field remains rare. One reason is that we lack designs and measurement approaches that appropriately meet the challenges of causal inference in the context of teacher education programs. This article provides a framework for how to fill this gap. We first outline the difficulties of doing causal research in teacher education. We then describe a set of replicable practices for developing measures of key teaching outcomes, and propose causal research designs suited to the needs of the field. Finally, we identify community-wide initiatives that are necessary to advance effectiveness research in teacher education at scale.
Although the Janus v. AFCSME (2018) decision fundamentally changed the institutional context for U.S. teachers’ unions by placing all public school teachers in a “Right to Work” (RTW) framework, little research exists to conceptualize the effects of such policies that hinder unionization. To fill this gap, I exploit the different timing across states in the passage of RTW policies in a differences-in-differences framework to identify how exposure to a RTW policy affects students, teachers, and education policymaking. I find that RTW policies lead to declines in teachers’ union power, but contrary to what many union critics have argued, I find that efforts to weaken unions did not result in political opportunities for education reforms nor did they improve student achievement outcomes.
We show that fade out biases value-added estimates at the teacher-level. To do so, we use administrative data from North Carolina and show that teachers' value-added depend on the quality of the teacher that preceded them. Value-added estimators that control for fade out feature no such teacher-level bias. Under a benchmark policy that releases teachers in the bottom five percent of the value-added distribution, fifteen percent of teachers released using traditional techniques are not released once fade out is accounted for. Our results highlight the importance of incorporating dynamic features of education production into the estimation of teacher quality.
Teachers are among the most important school-provided determinants of student success. Effective teachers improve students’ test scores as well as their attendance, behavior, and earnings as adults. However, students do not enjoy equal access to effective teachers. This article reviews some of the key challenges associated with teacher policy confronted by school leaders and education policymakers, and how the tools of applied economics can help address those challenges. The first challenge is that identifying effective teachers is difficult. Economists use value-added models to estimate teacher effectiveness, which works well in certain circumstances, but should be just one piece of a multi-measure strategy for identifying effective teachers. We also discuss how different policies, incentives, school characteristics, and professional-development interventions can increase teacher effectiveness; this is important, as schools face the daunting challenge of hiring effective teachers, helping teachers to improve, and removing ineffective teachers from the classroom. Finally, we discuss the supply and mobility of teachers, including the consequences of teacher absenteeism, the distribution of initial teaching placements, and the characteristics and preferences of those who enter the profession.