Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
Program and policy effects
In 2016, the GED® introduced college readiness benchmarks designed to identify testers who are academically prepared for credit-bearing college coursework. The benchmarks are promoted as awarding college credits or exempting “college-ready” GED® graduates from remedial coursework. I show descriptive evidence that those identified as college-ready by these benchmarks enroll and persist in college at significantly higher rates than others who pass the GED® exam, but at lower rates than recent graduates with traditional high school diplomas. Regression discontinuity estimates show that crossing a college readiness threshold does not substantially influence testers' college enrollment or persistence during the two years following their first test attempt. Relatedly, I observe little exam retaking by those who fall narrowly short of the minimum college readiness score thresholds. This contrasts strongly with retaking behavior near the lower GED® passing threshold that determines eligibility for a high school equivalency credential. Those who narrowly fail a GED® subject test are over 100 times more likely to retest than those who fall just short of a college readiness benchmark in the same subject. GED® college readiness benchmarks do not currently appear to promote better college outcomes, but in the absence of more detailed test score information they offer a simple heuristic to predict short-run college enrollment and persistence among GED® graduates, particularly for those who identify educational gain as a primary reason for testing. The results highlight the promise and challenges associated with building pathways for non-traditional students to earn credit for prior learning.
The COVID-19 pandemic’s impact on preschool children’s school readiness skills remains understudied. This research investigates Head Start preschool children’s early numeracy, literacy, and executive function outcomes during a pandemic-affected school year. Study children (N = 336 assessed at fall baseline; N = 237-250 assessed in spring depending on outcome; fall baseline sample: mean age = 51 months; 46% Hispanic; 36% Black Non-Hispanic; 52% female) in a network of Head Start centers in four states (Nevada, New Jersey, Pennsylvania, and Wisconsin) experienced low in-person preschool exposure compared to national pre-pandemic norms. Children experienced fall to spring score gains during the pandemic-affected year of 0.05 SD in executive function, 0.27 SD in print knowledge, and 0.45-0.71 SD in early numeracy skills. Descriptively, for two of the three early numeracy domains measured, spring test score outcomes were stronger among children who attended more in-person preschool. We discuss implications for future research and policy.
Using a rich dataset that merges student-level school records with birth records, and leveraging three alternative identification strategies, we explore how increase in access to charter schools in twelve districts in Florida affects students remaining in traditional public schools (TPS). We consistently find that competition stemming from the opening of new charter schools improves reading—but not math—performance and it also decreases absenteeism of students who remain in the TPS. Results are modest in magnitude.
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 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.
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.
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.