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Program and policy effects
The educative Teacher Performance Assessment (edTPA) - a performance-based examination for prospective PreK-12 teachers to guarantee teaching readiness - has gained popularity in recent years. This research offers the first causal evidence about the effects of this nationwide initiative on teacher supply and student outcomes of new teachers. We leverage the quasi-experimental setting of different adoption timing by states and analyze multiple data sources containing a national sample of prospective teachers and students of new teachers in the US. We find that the new license requirement reduced the number of graduates from teacher preparation programs by 14%. The negative effect is stronger for non-white prospective teachers at less-selective universities. Contrary to the policy intention, we find evidence that edTPA has adverse effects on student learning.
This paper examines how financial aid reform based on postsecondary institutional performance impacts student choice. Federal and state regulations often reflect concerns about the private, for-profit sector's poor employment outcomes and high loan defaults, despite the sector's possible theoretical advantages. We use student level data to examine how eliminating public subsidies to attend low-performing for-profit institutions impacts students' college enrollment and completion behavior. Beginning in 2011, California tightened eligibility standards for their state aid program, effectively eliminating most for-profit eligibility. Linking data on aid application to administrative payment and postsecondary enrollment records, this paper utilizes a differences-in-differences strategy to investigate students' enrollment and degree completion responses to changes in subsidies. We find that restricting the use of the Cal Grant at for-profit institutions resulted in significant state savings but led to relatively small changes in students' postsecondary trajectories. For older, non-traditional students we find no impact on enrollment or degree completion outcomes. Similarly, for high school graduates, we find that for-profit enrollment remains strong. Unlike the older, non-traditional students, however, there is some evidence of declines in for-profit degree completion and increased enrollment at community colleges among the high school graduates, but these results are fairly small and sensitive to empirical specification. Overall, our results suggest that both traditional and non-traditional students have relatively inelastic preferences for for-profit colleges under aid-restricting policies.
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.
Is public housing bad for children? Critics charge that public housing projects concentrate poverty and create neighborhoods with limited opportunities, including low-quality schools. However, whether the net effect is positive or negative is theoretically ambiguous and likely to depend on the characteristics of the neighborhood and schools compared to origin neighborhoods. In this paper, we draw on detailed individual-level longitudinal data on students moving into New York City public housing and examine their academic outcomes over time. Exploiting plausibly random variation in the precise timing of entry into public housing, we estimate credibly causal effects of public housing using both difference-in-differences and event study designs. We find credibly causal evidence of positive effects of moving into public housing on student test scores, with larger effects over time. Stalled academic performance in the first year of entry may reflect, in part, disruptive effects of residential and school moves. Neighborhood matters: effects are larger for students moving out of low-income neighborhoods or into higher-income neighborhoods, and these students move to schools with higher average test scores and lower shares of economically disadvantaged peers. We also find some evidence of improved attendance outcomes and reduction in incidence of childhood obesity for boys following public housing residency. Our study results refute the popular belief that public housing is bad for kids and probe the circumstances under which public housing may work to improve academic outcomes for low-income students.
School bullying is widespread and has substantial social costs. One in five U.S. high school students report being bullied each school year and these students face greater risks of serious mental health challenges that extend into adulthood. As the COVID-19 pandemic forced most students into online education, many have worried that cyberbullying prevalence would grow dramatically. We use data from Google internet searches to examine changing bullying patterns as COVID-19 disrupted in-person schooling. Pre-pandemic historical patterns show that internet searches provide useful information about actual bullying behavior. Real-time data then shows that searches for school bullying and cyberbullying both dropped about 30-40 percent as schools shifted to remote learning in spring 2020. This drop is sustained through the fall and winter of the 2020-21 school year, though the gradual return to in-person instruction partially returns bullying searches to pre-pandemic levels. These results highlight how in-person interaction is an important mechanism underlying not only in-person school bullying, but also cyberbullying. We discuss how this otherwise damaging shock to students and schools provides insight into the mixed impact of the pandemic on student well-being.
Covid-19-induced school closures generated great interest in tutoring as a strategy to make up for lost learning time. Tutoring is backed by a rigorous body of research, but it is unclear whether it can be delivered effectively remotely. We study the effect of teacher-student phone call interventions in Kenya when schools were closed. Schools (n=105) were randomly assigned for their 3rd, 5th and 6th graders (n=8,319) to receive one of two versions of a 7-week weekly math-focused intervention—5-minute accountability checks or 15-minute mini-tutoring sessions—or to the control group. Although calls increased student perceptions that teachers cared, accountability checks had no effect on math performance up to four months after the intervention and tutoring decreased math achievement among students who returned to their schools after reopening. This was, in part, because the relatively low-achieving students most likely to benefit from calls were least likely to return and take in-person assessments. Tutoring substituted away from more productive uses of time, at least among returning students. Neither intervention affected enrollment. Tutoring remains a valuable tool but to avoid unintended consequences, careful attention should be paid to aligning tutoring interventions with best practices and targeting interventions to those who will benefit most.
Over the last decade, more and more schools have adopted Universal Free Meals (UFM), a program that provides meals free of charge to all students, regardless of household income. Recent research finds UFM increases participation in school meals, improves test scores, and reduces incidences of bad behavior. Additionally, advocates cite stigma reduction as one of UFM’s many benefits, but to date, scholars have yet to provide empirical evidence of this claim. This paper fills the gap in the literature by being the first to examine whether UFM influences student perceptions of school climate. I use individual, student survey responses and school meal participation data from New York City Department of Education (NYCDOE) to investigate whether and to what extent UFM changes participation behavior and student perceptions of their school climate. Using a difference-in-differences design, I exploit students’ staggered exposure to UFM, among those that are ever exposed, to investigate if UFM influences participation and improves student perceptions of bullying, fighting, respect, and safety. I find UFM increases school lunch participation among students that were previously eligible for free meals but rarely participated, suggesting that UFM affects participation patterns beyond simply reducing the price of food. All students, regardless of socioeconomic status, report reductions in perceptions of bullying and fighting within school, as well as improvements in perceptions of safety outside of school. Notably, students ever designated as eligible for free/reduced price meals and those that ate school lunches last year report feeling safer inside the school cafeteria. Thus, not only does UFM improve perceptions associated with stigma for students who directly interact with UFM, but the program also has positive effects for all students regardless of their socioeconomic status.
Online courses provide flexible learning opportunities, but research suggests that students may learn less and persist at lower rates compared to face-to-face settings. However, few research studies have investigated more distal effects of online education. In this study we analyzed six years of institutional data for three cohorts of students in thirteen large majors (N=10,572) at a public research university to examine distal effects of students’ online course participation. Using online course offering as an instrumental variable for online course taking, we find that online course taking of major-required courses leads to higher likelihood of successful four-year graduation and slightly accelerated time-to-degree. These results suggest that offering online course-taking opportunities may help students to more efficiently graduate college.
Barriers to accessing financial aid may keep students from matriculating to college. To test whether FAFSA completion is one of these barriers, I utilize a natural experiment brought about by a Louisiana mandate for seniors to file the FAFSA upon graduation from high school. Exploiting pre-treatment FAFSA completion rates as a treatment intensity in a dosage differences-in-differences specification, I find that a 10 percentage point lower pre-treatment FAFSA completion rate for a school implies a 1 percentage point larger increase in post-mandate college enrollment.
In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers' uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers' uptake at scale is challenging, as existing methods require expensive annotation by experts. We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. We find that although repetition captures a significant part of uptake, pJSD outperforms repetition-based baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation. We apply our uptake measure to three different educational datasets with outcome indicators. Unlike baseline measures, pJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.