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Program and policy effects
This study provides the first causal analysis of the impact of expanding Computer Science (CS) education in U.S. K-12 schools on students’ choice of college major and early career outcomes. Utilizing rich longitudinal data from Maryland, we exploit variation from the staggered rollout of CS course offerings across high schools. Our findings suggest that taking a CS course increases students’ likelihood of declaring a CS major by 10 percentage points and receiving a CS BA degree by 5 percentage points. Additionally, access to CS coursework raises students’ likelihood of being employed and early career earnings. Notably, students who are female, low socioeconomic status, or Black experience larger benefits in terms of CS degree attainment and earnings. However, the lower take-up rates of these groups in CS courses highlight a pressing need for targeted efforts to enhance their participation as policymakers continue to expand CS curricula in K-12 education.
An increasing body of robust evidence concludes that corequisite remediation in math and English is a cost-effective alternative to traditional developmental education, offering improved immediate course progression and potentially better persistence and completion. This is the first study to disentangle the impacts of the two main elements of the corequisite model: accelerated college course placement and concurrent academic support. Utilizing a fuzzy regression discontinuity design and variation in Texas colleges' implementation of math corequisites, the study shows that college-level math course placement without additional support increases passing rates by 22 percentage points. This effect rises to 36 percentage points with concurrent developmental support. These findings bolster a growing consensus around the benefits of accelerated developmental education and suggest that a corequisite approach may have significant advantages over removing developmental education requirements entirely.
This study evaluates the unintended consequences of the 2012 suspension ban in New York City. I find that the ban induced a substitution towards classification for students at high risk for suspension—Black students, male students, and those in schools with a high reliance on suspension. I find that disabilities that carry greater stigma and experience greater exclusion from the general education classroom drive the increases in classification. This substitution may benefit students if they are now receiving needed services. Simultaneously, ban-induced classifications may simply serve as a partial substitute for suspension.
This concurrent mixed methods study descriptively explores teacher residency programs (TRPs) across the nation. We examine program and participant survey data from the National Center for Teacher Residencies (NCTR) to identify important TRP structures for resident support. Latent class analysis of program-level data reveals three types of TRPs (locally-funded low tuition, multi-funded multifaceted, and federally-funded post-residency support), while regression models indicate significant relationships between individual program structures and participant (residents, graduates, mentors, and principals) perceptions. Qualitative analyses of multiple open response items across participants details four salient TRP structures: providing extended clinical experience, localizing individual support, offering programmatic training, and teaching practical professional knowledge. Findings inform policymakers on TRP investment, practitioners about program design, and researchers for continued large-scale evidence.
Education leaders must identify valid metrics to predict student long-term success. We exploit a unique dataset containing data on cognitive skills, self-regulation, behavior, course performance, and test scores for 8th-grade students. We link these data to data on students' high school outcomes, college enrollment, persistence, and on-time degree completion. Cognitive tests and survey-based self-regulation measures predict high school and college outcomes. However, these relationships become small and lose statistical significance when we control for test scores and a behavioral index. For leaders hoping to identify the best on-track indicators for college completion, the information collected in student longitudinal data systems better predicts both short- and long-run educational outcomes than these survey-based measures of self-regulation and cognitive skills.
A growing body of research shows that students benefit when they demographically match their teachers. However, little is known about how matching affects social-emotional development. We use student-fixed effects to exploit changes over time in the proportion of teachers within a school grade who demographically match a student to estimate matching's effect on social-emotional measures, test scores, and behavioral outcomes. We find improvements for students in grit and interpersonal self-management when matched to teachers of their race and gender. Black female students drive these effects. We also find that matching reduces absences, especially for Black students. Our findings add to the emerging teacher diversity literature by showing its benefits for Black and female students during a critical stage of development.
We leverage log data from an educational app and two-way text message records from over 3,500 students during the summers of 2019 and 2020, along with in-depth interviews in Spanish and English, to identify patterns of family engagement with educational technology. Based on the type and timing of technology use, we identify several distinct profiles of engagement, which we group into two categories: Independent Users who engage with technology-based educational software independently, and Interaction-Supported Users who use two-way communications to support their engagement. We also find that as the demands of families from schools increased during the COVID-19 pandemic, Spanish-speaking families were significantly more likely than English-speaking families to engage with educational technology across all categories of families, particularly as Interaction-Supported Users.
Field supervisors are central to clinical teaching, but little is known about how their feedback informs preservice teachers (PSTs) development. This sequential mixed methods study examines over 3,000 supervisor observation evaluations. We qualitatively code supervisor written feedback, which indicates 2 broad pedagogical categories and 9 separate skills. We then quantize these feedback codes to identify the variation in the presence of these codes across PST characteristics, and then use several modeling techniques to indicate that specific feedback codes are negatively associated with evaluation score. Managing student attention was most detrimental to scores in early observations whereas instructional feedback (e.g., lesson delivery) was prioritized later in clinical teaching. Findings inform teacher preparation policy on understanding PST development and improving supervisory feedback.
This study examines the experience of demotion from a principalship to an assistant principalship and how race and gender can differentially impact career trajectories. Using administrative state dataset of 10,946 observations at the principal level, we used probit regression to determine the overall probability of demotion and Kaplan Meier survival analysis to estimate the differences in probability over time. Our analysis describes not only who experiences demotions, but includes the characteristics of the sending and receiving schools. Survival analysis illustrates how small differences over time in demotion by race resulted in statistically significant systemic differences. We also find that experience matters: for every additional year of experience in the principal role, the probability of experiencing demotion decreases by 0.34%.
This paper presents the results from a randomized controlled trial of Chapter One, an early elementary reading tutoring program that embeds part-time tutors into the classroom to provide short bursts of 1:1 instruction. Eligible kindergarten students were randomly assigned to receive supplementary tutoring during the 2021-22 school year (N=818). The study occurred in a large Southeastern district serving predominantly Black and Hispanic students. Students assigned to the program were over two times more likely to reach the program’s target reading level by the end of kindergarten (70% vs. 32%). The results were largely homogenous across student populations and extended to district-administered assessments. These findings provide promising evidence of an affordable and sustainable approach for delivering personalized reading tutoring at scale.