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The COVID-19 pandemic drew new attention to the role of school boards in the U.S. In this paper, we examine school districts' choices of learning modality -- whether and when to offer in-person, virtual, or hybrid instruction -- over the course of the 2020-21 pandemic school year. The analysis takes advantage of granular weekly data on learning mode and COVID-19 cases for Ohio school districts. We show that districts respond on the margin to health risks: all else equal, a marginal increase in new cases reduces the probability that a district offers in-person instruction the next week. Moreover, this negative response is magnified when the district was in-person the prior week and attenuates in magnitude over the school year. These findings are consistent with districts learning from experience about the effect of in-person learning on disease transmission in schools. We also find evidence that districts are influenced by the decisions of their peers.
Student absenteeism is often conceptualized and quantified in a static, uniform manner, providing an incomplete understanding of this important phenomenon. Applying growth curve models to detailed class-attendance data, we document that secondary school students' unexcused absences grow steadily throughout a school year and over grades, while the growth of excused absences remain essentially unchanged. Importantly, students starting the school year with a high number of unexcused absences, Black and Hispanic students, and low-income students accumulate unexcused absences at a significantly faster rate than their counterparts. Lastly, students with higher growth rates in unexcused absences consistently report lower perceptions of all aspects of school culture than their peers. Interventions targeting unexcused absences and/or improving school culture can be crucial to mitigating disengagement.
Given the simultaneous rise in time-to-graduation and college GPA, it may be that students reduce their course load to improve their performance. Yet, evidence to date only shows increased course loads increase GPA. We provide a mathematical model showing many unobservable factors -- beyond student ability -- can generate a positive relationship between course load and GPA unless researchers control student schedules. West Point regularly implements the ideal experiment by randomly modifying student schedules with additional training courses. Using 19 years of administrative data, we provide the first causal evidence that taking more courses reduces GPA and increases course failure rates, sometimes substantially.
Few question the value of teacher-student relationships (TSRs) for educational outcomes. TSRs are positively associated with students’ achievement and engagement, as well as teachers’ well-being. Building and maintaining these crucial classroom relationships, however, is not easy. Drawing on prominent motivation theories in educational psychology, I present the Motivating Teacher-Student Relationships framework for understanding what motivates teachers to build positive TSRs. In particular, I focus on how teachers’ motivational beliefs about TSRs energize, direct, and sustain their efforts to engage in relationship-building behaviors and, thus, lead to positive relationships with their students. To build positive TSRs, teachers must believe it is their role to build TSRs, value TSRs, and believe they can successfully build TSRs (i.e., have relational self-efficacy). These beliefs are shaped by teachers’ sociocultural contexts and can facilitate or undermine the development of these learning relationships. With a greater understanding of how motivational beliefs influence social relationships, the field of education can more effectively develop theoretically grounded interventions to improve TSRs and mitigate inequality.
Nearly all states with public prekindergarten programs use mixed-delivery systems, with classrooms in both public schools and community-based settings. However, experts have long raised concerns about systematic inequities by setting within these public systems. We used data from five large-scale such systems that have taken steps to improve equity by setting (Boston, New York City, Seattle, New Jersey, and West Virginia) to conduct the most comprehensive descriptive study of prekindergarten setting differences to date. Our public school sample included 2,395 children in 383 classrooms in 152 schools, while our community-based sample is comprised of 1,541 children in 201 classrooms in 103 community-based organizations (CBOs). We examined how child and teacher demographic characteristics, structural and process quality features, and child gains differed by setting within each of these systems. We found evidence of sorting of children and teachers by setting within each locality, including of children with higher baseline skills and more educated teachers into public schools. Where there were differences in quality and children’s gains, these tended to favor public schools. The localities with fewer policy differences by setting – NJ and Seattle – showed fewer differences in quality and child gains. Our findings suggest that inequities by setting are common, appear consequential, and deserve more research and policy attention.
The George Floyd Protests of the Summer of 2020 initiated public conversations around the need for antiracist teaching. Yet, over time the discussion evolved into policy debates around the use of Critical Race Theory in civics courses. The rapid transition masked the fact that we know little about Americans' policy preferences. Do Americans support antiracist teaching? What factors best explain support/opposition? How does critical race theory factor in? Using a series of original survey experiments, this study shows that Americans maintain strong support for antiracist teaching, but that support is drastically weakened when curriculum features the term "critical race theory."
There is increasing concern about risky behaviors and poor mental health among school-aged youth. A critical factor in youth well-being is school attendance. This study evaluates how school organization and structure affect health outcomes by examining the impacts of a popular urban high school reform -- “small schools” -- on youth risky behaviors and mental health, using data from New York City. To estimate a causal estimate of attending small versus large high schools, we use a two-sample-instrumental-variable approach with the distance between student residence and school as the instrument for school enrollment. We consider two types of small schools – “old small schools,” which opened prior to a system-wide 2003 reform aimed at increasing educational achievement and “new small schools,” which opened in the wake of that reform. We find that girls enrolled in older small schools are less likely to become pregnant, and boys are less likely to be diagnosed with mental health disorders than their counterparts in large schools. Both girls and boys enrolled in more recently opened small schools, however, are more likely to be diagnosed with violence-associated injuries and (for girls only) with mental health disorders. These disparate results suggest that improving a school’s organization and inputs together is likely more effective in addressing youth risky behaviors than simply reducing school size.
Data science applications are increasingly entwined in students’ educational experiences. One prominent application of data science in education is to predict students’ risk of failing a course in or dropping out from college. There is growing interest among higher education researchers and administrators in whether learning management system (LMS) data, which capture very detailed information on students’ engagement in and performance on course activities, can improve model performance. We systematically evaluate whether incorporating LMS data into course performance prediction models improves model performance. We conduct this analysis within an entire state community college system. Among students with prior academic history in college, administrative data-only models substantially outperform LMS data-only models and are quite accurate at predicting whether students will struggle in a course. Among first-time students, LMS data-only models outperform administrative data-only models. We achieve the highest performance for first-time students with models that include data from both sources. We also show that models achieve similar performance with a small and judiciously selected set of predictors; models trained on system-wide data achieve similar performance as models trained on individual courses.
Improving teacher selection is an important strategy for strengthening the quality of the teacher workforce. As districts adopt commercial teacher screening tools, evidence is needed to understand these tools’ predictive validity. We examine the relationship between Frontline Education’s TeacherFit instrument and newly hired teachers’ outcomes. We find that a one SD increase on an index of TeacherFit scores is associated with a 0.06 SD increase in evaluation scores. However, we also find evidence that teachers with higher TeacherFit scores are more likely to leave their hiring schools the following year. Our results suggest that TeacherFit is not necessarily a substitute for more rigorous screening processes that are conducted by human resources officials, such as those documented in recent studies.
Teachers are among the most important inputs in the education production function. One mechanism by which teachers might affect student learning is through the grading standards they set for their classrooms. However, the effects of grading standards on student outcomes are relatively understudied. Using administrative data that links individual students and teachers in 8th and 9th grade Algebra I classrooms from 2006 to 2016, we examine the effects of teachers’ grading standards on student learning and attendance. High teacher grading standards in Algebra I increase student learning both in Algebra I and in subsequent math classes. The effect on student achievement is positive and similar in size across student characteristics and levels of ability, students’ relative rank within the classroom, and school context. High teacher grading standards also lead to a modest reduction in student absences.