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Educator preparation, professional development, performance and evaluation
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.
Black and Latinx students are under-represented in Advanced Placement (AP) and Dual Enrollment (DE), and implicit bias of educators has been discussed as one potential contributing factor. In this study, I test whether implicit and explicit racial bias are related to AP and DE participation and racial/ethnic gaps in participation, controlling for various observable contextual factors. I find a small relationship between implicit racial bias and disparate AP participation for Black students relative to White students, and suggestive evidence of a relationship between explicit racial bias and disparate DE participation for Black students relative to White students. Further, more explicitly-biased communities tend to have lower AP participation rates overall. Implications for school leaders regarding implicit bias training and other ways to address systemic inequities in access are discussed.
Strengthening teacher supply is a key policy objective for K–12 public education, but understanding of the early teacher pipeline remains limited. We leverage the universe of applications to a large public university in Texas from 2009–2020 to examine the pipeline into teacher education and employment as a K–12 public school teacher. A unique feature of Texas's centralized higher education application is it solicits potential interest in teacher certification. We document sharply declining interest in teaching over the period. Further, we show that nonwhite, male, and high-achieving students are substantially underrepresented in teacher education. Particularly for race/ethnicity, these disparities are only partially explained by differences in interest at application.
Many teacher education researchers have expressed concerns with the lack of rigorous impact evaluations of teacher preparation practices. I summarize these various concerns as they relate to issues of internal validity, external validity, and measurement. I then assess the prevalence of these issues by reviewing 166 impact evaluations of teacher preparation practices published in peer-reviewed journals between 2002-2019. Although I find that very few studies address issues of internal validity, external validity and measurement, I highlight some innovative approaches and present a checklist of considerations to assist future researchers in designing more rigorous impact evaluations.
We explore the dynamics of competitive search in the K-12 public education sector. Using data from Boston Public Schools, we document how teacher labor supply varies substantially by position types, schools, and the timing of job postings. We find that early-posted positions are more likely to be filled and end up securing new hires that are better-qualified, more-effective, and more likely to remain at a school. In contrast, the number of applicants to a position is largely unassociated with hire quality, suggesting that schools may struggle to identify and select the best candidates even when there is a large pool of qualified applicants. Our findings point to substantial unrealized potential for improving teacher hiring.
Despite growing evidence that classroom interventions in science, technology, engineering, and mathematics (STEM) can increase student achievement, there is little evidence regarding how these interventions affect teachers themselves and whether these changes predict student learning. We present results from a meta-analysis of 37 experimental studies of preK-12 STEM professional learning and curricular interventions, seeking to understand how STEM classroom interventions affect teacher knowledge and classroom instruction, and how these impacts relate to intervention impacts on student achievement. Compared with control group teachers, teachers who participated in STEM classroom interventions experienced improvements in content and pedagogical content knowledge and classroom instruction, with a pooled average impact estimate of +0.56 standard deviations. Programs with larger impacts on teacher practice yielded larger effects on student achievement, on average. Findings highlight the positive effects of STEM instructional interventions on teachers, and shed light on potential teacher-level mechanisms via which these programs influence student learning.
Student surveys are widely used to evaluate university teaching and increasingly adopted at the K-12 level, although there remains considerable debate about what they measure. Much disagreement focuses on the well-documented correlation between student grades and their evaluations of instructors. Using individual-level data from 19,000 evaluations of 700 course sections at a flagship public university, we leverage both within-course and within-student variation to rule out popular explanations for this correlation. Specifically, we show that the relationship cannot be explained by instructional quality, workload, grading stringency, or student sorting into courses. Instead, student grade satisfaction -- regardless of the underlying cause of the grades -- appears to be an important driver of course evaluations. We also present results from a randomized intervention with potential to reduce the magnitude of the association by reminding students to focus on relevant teaching and learning considerations and by increasing the salience of the stakes attached to evaluations for instructor careers. However, these prove ineffective in muting the relationship between grades and student scores.
To study beliefs about ability and STEM major choice, I conduct a field experiment where I provide students with information that they are above average in their top fields of study. I find that STEM students are more likely to switch out of their major and that non-STEM students fail to switch into STEM at the same rates as other fields. I also find that learning you are above average in your top field of study increases STEM major choice by almost a third, as STEM students appear more like to persist and non-STEM students increase their switching into STEM fields.
Using administrative data from D.C. Public Schools, I use exogenous variation in the presence and intensity of teacher monitoring to show it significantly improves student test scores and reduces suspensions. Uniquely, my setting allows me to separately identify the effect of pre-evaluation monitoring from post-evaluation feedback. Monitoring's effect is strongest among teachers with a large incentive to increase student test scores. As tests approach, unmonitored teachers sacrifice higher-level learning, classroom management, and student engagement, even though these pedagogical tasks are among the most effective. One possible explanation is teachers ``teach to the test'' as a risk mitigation strategy, even if it is less effective on average. This is supported by showing teaching to the test has a smaller effect on student test score variance than other teaching approaches. These results illustrate the importance of monitoring in contexts where teachers have the strongest incentive to deviate from pedagogically sound practices.
As states and districts expand their goals for equitable mathematics instruction to focus on cultural responsiveness and rigor, it is critical to understand how teachers integrate multiple teaching approaches. Drawing on survey data from a larger study of professional learning, we use mixture modeling to identify seven unique ways that middle school mathematics teachers integrate ambitious, traditional, and culturally responsive (CR) mathematics instruction. The resulting typology is driven almost exclusively by variation in CR teaching. About half of teachers reported rarely engaging in CR teaching. Teachers who emphasized CR teaching tended to be teachers of color and have high CR teaching self-efficacy. Findings suggest that tailoring teacher development to how teachers blend multiple approaches may best support equitable mathematics instruction.