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The systematic development of horticulture curriculum for Indiana was the focus of this research project which validated a job task list for use in instructional material development. The job title catalog, A Landscape Gardener, was selected from those currently available through the Vocational-Technical Consortium of States (V-TECS) program. A purposive study as outlined in the V-TECS technical reference handbook was undertaken to validate this job title catalog for Indiana. Survey instruments were sent to job incumbent personnel in horticulture businesses and data from twenty returned surveys was tabulated and analyzed. From the selected list of 165, job incumbents selected 109 as those most commonly performed, also indicating tools commonly used and amount of time spent at various tasks. Finally the validated list of tasks contained in the job title catalog were sequenced to facilitate further work in instructional materials development. (Survey instruments and survey data are included in the appendixes.) (JH)

There is a large and growing number of non-degree credential offerings between a high school diploma and a bachelor's degree, as well as degree programs beyond a bachelor's degree. Nevertheless, research on the financial returns to non-degree credentials and degree-granting programs is often narrow and siloed. To fill this gap, we leverage a national sample of individuals across nine MSAs and four industries to examine the relative financial returns to a variety of non-degree credentials and degree programs. Leveraging fixed-effect models, we explore the relationship between completing a credential or degree and earnings premiums. We find that an associate's, bachelor's, master's and doctorate degree follows a similar model of returns in which the number of schooling years is linearly related to proportional earnings premiums. However, students completing sub-baccalaureate certificates, post-baccalaureate certificates, and non-school credentials appear to get larger financial returns for less time. Furthermore, while the returns to both non-degree credentials and degree granting programs generally favored males over females and non-binary persons, this was not the case for race/ethnicity. Although individuals from Asian and White racial/ethnic groups often maintained an advantage in traditional education settings, Black individuals earned greater premiums from non-school credentials than White individuals, which may represent an opportunity to close racial/ethnic gaps in earnings.

The pivotal role of Algebra in the educational trajectories of U.S. students continues to motivate controversial, high-profile policies focused on when students access the course, their classroom peers, and how the course is taught. This random-assignment partnership study examines an innovative district-level reform--the Algebra I Initiative--that placed 9th-grade students with prior math scores well below grade level into Algebra I classes coupled with teacher training instead of a remedial pre-Algebra class. We find that this reform significantly increased grade-11 math achievement (ES = 0.2 SD) without lowering the achievement of classroom peers eligible for conventional Algebra I classes. This initiative also increased attendance, district retention, and overall math credits. These results suggest that higher expectations for the lowest-performing students coupled with aligned teacher supports is a promising model for realizing students' mathematical potential. [The Stanford Sequoia K-12 Research Collaborative provided support for this paper.]

A growing body of research has documented extensive credit loss among transfer students. However, the field lacks theoretically driven and empirically supported frameworks that can guide credit loss research and reforms. We develop and then test a comprehensive framework designed to address this gap using novel administrative credit loss data from Texas. Our results demonstrate how the likelihood of credit loss varies across course characteristics, majors, pretransfer academics, student characteristics, and sending and receiving institutions. Additionally, we are able to disentangle general credit loss from major credit loss and examine how they vary across institutions, majors, and the combination of both. The extensive variation in credit loss among universities in particular underscores the need for future research and reform.

Generative AI, particularly Language Models (LMs), has the potential to transform real-world domains with societal impact, particularly where access to experts is limited. For example, in education, training novice educators with expert guidance is important for effectiveness but expensive, creating significant barriers to improving education quality at scale. This challenge disproportionately hurts students from under-served communities, who stand to gain the most from high-quality education and are most likely to be taught by inexperienced educators. We introduce Tutor CoPilot, a novel Human-AI approach that leverages a model of expert thinking to provide expert-like guidance to tutors as they tutor. This study presents the first randomized controlled trial of a Human-AI system in live tutoring, involving 900 tutors and 1,800 K-12 students from historically under-served communities. Following a preregistered analysis plan, we find that students working on mathematics with tutors randomly assigned to have access to Tutor CoPilot are 4 percentage points (p.p.) more likely to master topics (p<0.01). Notably, students of lower-rated tutors experienced the greatest benefit, improving mastery by 9 p.p. relative to the control group. We find that Tutor CoPilot costs only $20 per-tutor annually, based on the tutors' usage during the study. We analyze 550,000+ messages using classifiers to identify pedagogical strategies, and find that tutors with access to Tutor CoPilot are more likely to use strategies that foster student understanding (e.g., asking guiding questions) and less likely to give away the answer to the student, aligning with high-quality teaching practices. Tutor interviews qualitatively highlight how Tutor CoPilot's guidance helps them to respond to student needs, though tutors flag common issues in Tutor CoPilot, such as generating suggestions that are not grade-level appropriate. Altogether, our study of Tutor CoPilot demonstrates how Human-AI systems can scale expertise in real-world domains, bridge gaps in skills and create a future where high-quality education is accessible to all students. [Additional funding provided by Accelerate.]

Although community colleges have served as a gateway to universities for millions of students--disproportionately so for students from populations historically underrepresented in higher education--prior research has demonstrated that the majority of vertical transfer students lose at least some of their pretransfer credits. However, researchers examining how credit loss relates to subsequent college outcomes have been hindered by data limitations. For this study, we drew from the literature on academic momentum and examined the relationship between credit loss, institutional retention, and postsecondary persistence. Our use of novel administrative data from Texas enabled us to disentangle major credit loss from general credit loss and study the contribution of each credit loss type to posttransfer outcomes. Our analyses show that both forms of credit loss are inversely related to institutional retention, but the relationships between credit loss and postsecondary persistence are far less consistent. We found evidence suggesting that major credit loss is more strongly related to both retention and persistence than general credit loss. We did not find evidence that the relationship between credit loss and posttransfer outcomes is moderated by students' race/ethnicity, economic status, or gender, and we found only limited evidence of moderation by major.

Understanding the factors that influence student outcomes is crucial for both parents and schools when designing effective educational strategies. This paper explores the impact of peer age on both cognitive and non-cognitive outcomes using a randomized sample of middle school students. By analyzing how exogenous variations in peer age affect students' academic performance, self-expectations and confidence, health perceptions, behavioral traits, and social development, we highlight the important role that peer age plays in educational contexts. Our findings reveal that an increase in the average age of classmates results in negative effects on both cognitive and non-cognitive outcomes of a student. We also identify significant heterogeneous effects based on student relative age and gender. We delve into potential mechanisms behind these effects and study inputs from the perspective of student themselves, parents, teachers, and the school within the framework of the education production function. The results suggest that students' persistence in their studies, the quality of friendships, and the school environment they are exposed to are the primary drivers of our main findings. These findings underscore the importance of addressing age disparities within classrooms to enhance students' cognitive and non-cognitive development.

Racial disparities in violence exposure and criminal justice contact are a subject of growing policy and public concern. We conduct a large-scale, randomized controlled trial of a six-month behavioral health intervention combining intensive mentoring and group therapy designed to reduce criminal justice and violence involvement among Black and Latinx youth in Chicago. Over 24 months, youth offered the program experienced an 18 percent reduction in the probability of any arrest and a 23 percent reduction in the probability of a violent-crime arrest. These statistically significant impacts, with smaller magnitudes, continue to persist up to 3 years post randomization. To better understand the behavior change we observe given an arrest is a proxy for criminal behavior, we create a supervised machine learning algorithm from arrest narratives that determines if an arrest was initiated more or less at the discretion of police. We find that the program's impacts are concentrated in arrests where officers have less discretion in initiating contact, while having little impact on more discretionary contact arrests (e.g. a young person exhibiting "suspicious" behavior). This analysis suggests the effects of the program are being driven by a reduction in youth offending behavior rather than by avoiding police contact.

U.S. public schools are engaged in an unprecedented effort to expand tutoring in the wake of the COVID-19 pandemic. Broad-based support for scaling tutoring emerged, in part, because of the large effects on student achievement found in prior meta-analyses. We conduct an expanded meta-analysis of 265 randomized controlled trials and explore how estimates change when we better align our sample with a policy-relevant target of inference: large-scale tutoring programs in the U.S. aiming to improve standardized test performance. Pooled effect sizes from studies with stronger target-equivalence remain meaningful but are only a third to a half as large as those from our full sample. This result is driven by stark declines in pooled effect sizes as program scale increases. We explore four hypotheses for this pattern and document how a bundled package of recommended design features serves to partially inoculate programs from these attenuated effects at scale.

Mastery learning -- the process by which students must demonstrate proficiency with a single topic before moving on -- is well recognized as one of the best ways to learn, yet many teachers struggle or remain unsure about how to implement it into a classroom setting. This study leverages two field experiments to test the efficacy of a program designed to encourage greater mastery learning through technology and proactive continuous teacher support. Focusing on elementary and middle school mathematics, teachers receive weekly coaching in how to use Computer Assisted Learning (CAL) for students to follow a customized roadmap of incremental progress. Results indicate significant intent-to-treat effects on math performance of 0.12-0.22 standard deviations. Further analysis shows that these gains are concentrated among students in classrooms with at least an average of 35 minutes of practice per week. Teachers able to achieve high-dosage practice have a high degree of initial buy-in, a clear implementation strategy for when practice occurs, and a willingness to closely monitor progress and follow-up with struggling students. [The Wilson Sheehan Lab for Economic Opportunities (LEO) at Notre Dame provided additional support for this research.]

The use of four-day school weeks (4dsw) in the United States has expanded rapidly over the past two decades. Previous work examines the impact of 4dsw on student outcomes, but little research to date examines the effect on school employees even though schools in some locales have adopted 4dsw to recruit and retain staff. This paper examines the effect of 4dsw adoption in Oregon, a state with widespread 4dsw use, on teacher and other school staff retention by leveraging a staggered roll-out of the schedule using a difference-in-differences design. We find that adopting a four-day week increased turnover among teachers, but that turnover among non-teaching staff was largely unaffected. The findings suggest that policymakers interested in implementing 4dsw for improved school employee retention should exercise caution and be attentive to the full set of incentives offered to staff.

This study examined the impact of COVID-19 on academic "redshirting" in kindergarten, the practice of holding a child back for a year and enrolling them in kindergarten at age 6, using student-level data on all Delaware kindergarten students from fall 2014 through fall 2022. The rate of redshirting declined by 40% in fall 2020, then increased by 44% (relative to pre-pandemic baseline) in fall 2021, and more for some subgroups of children traditionally less likely to redshirt. Further, redshirting was not restricted to children with summer birthdays, as in previous years, with growth seen across the age distribution. Redshirting had not returned to pre-pandemic baseline by fall 2022. These findings point to changes in the motivations for redshirting kindergarten students since the pandemic.

Paraeducators are among the largest categories of public education employees and are increasingly seen as a pool of potential teachers. However, little is known about paraeducator-to-teacher transitions. Using statewide administrative data, we show that while paraeducators may be more racially/ethnically diverse than the teacher workforce, Black and Hispanic paraeducators are less likely than White paraeducators to transition into teaching. We additionally show that teachers with paraeducator experience are similarly effective to teachers without paraeducator experience. Lastly, we use simulations to show that the potential for the paraeducator-to-teacher pipeline to diversify the teaching profession may be limited unless they are highly targeted. Our results have policy design implications for efforts to expand the paraeducator-to-teacher pipeline or to diversify the teacher workforce.

The U.S. has witnessed a resurgence of labor activism, with teachers at the forefront. We examine how teacher strikes affect compensation, working conditions, and productivity with an original dataset of 772 teacher strikes generating 48 million student days idle between 2007 and 2023. Using an event study framework, we find that, on average, strikes increase compensation by 8% and lower pupil-teacher ratios by 0.5 students, driven by new state revenues. We find little evidence of sizable impacts on student achievement up to five years post-strike, though strikes lasting 10 or more days decrease math achievement in the short-term.

Purpose: Urban school districts often face challenges in filling principal vacancies with effective leaders, especially in high-needs schools. Prospective principals' engagement with the job application process may contribute to these challenges. The goal of this study is to better understand the job search strategies and behaviors of prospective principals and how their approaches might contribute to leadership staffing challenges in high-needs schools. Research Design and Methods: We employ a convergent mixed-methods design that draws on data from two urban school systems. We pair analysis of interviews of 36 principals who have recently navigated the districts' hiring systems with multiple years of applications and other administrative data provided by the two districts. We explored how patterns and themes that emerged from each data source were confirmed or disconfirmed with the other source. Findings: Guided by a job-search model, our analysis uncovers three main findings. First, the typical principal applicant conducted a targeted rather than a wide search, reflecting multiple strategies, preferences, and relational factors. Second, elementary educators showed a strong propensity to apply to the same grade level. Third, leaders applied to schools serving larger proportions of historically marginalized students at similar rates as other schools, reflecting their motivations to work with underserved students. Implications for Research and Practice: Considerations informing prospective principals' job searches are multifaceted. High-needs schools are desirable to many principal candidates. Identifying and strategically recruiting candidates with preferences for working in such schools can be a strategy for districts seeking to overcome challenges in filling principal vacancies.

From October 2021, the government introduced a pilot in 30 local authorities to support 16 to 18-year-old children looked-after (CLA) and care leavers (CLs) in general further education (FE) colleges through the extension of Pupil Premium Plus funding to post-16 (PP+ Post-16). The 6-month pilot was completed between autumn 2021 and spring 20221 , before funding was extended to a further 28 local authorities in autumn 2022 and subsequently extended to all local authorities in England in autumn 2023. The purpose of PP+ Post-16 was also extended in 2023/24 to provide funding to support all CLA and CLs at post-16, rather than focusing on support for CLA and CLs in general FE colleges. This mixed methods evaluation is formative in intention and involves an exploratory study of the use of the funding by virtual schools (VS). It also considers early evidence about progress towards the outcomes in the Theory of Change (ToC), developed during the pilot evaluation and updated at the start of this evaluation through a series of ToC workshops with VSHs. The outcomes are arranged in the ToC under 3 headings, which are outcomes relating to young people, post-16 settings and joint working. This interim report presents findings from year 1 of the evaluation (2023/24) based on a national online survey of VSHs, case study interviews in 6 local authorities involving interviews with a range of stakeholders, and documentary analysis. [This report was produced with support from the Cordis Bright and the Rees Centre, University of Oxford.]

We use data from the applications North Carolina public school districts and charter schools submitted for Elementary and Secondary School Emergency Relief (ESSER) to investigate the sense that educational leaders made of the pandemic as it unfolded. LEAs understood the pandemic as a multifaceted problem. Nearly all applications addressed four problems: (1) public health, (2) academics and learning loss, (3) student and community well-being, and (4) instructional access. However, we document considerable variation in problem emphasis over time, across LEAs, and across organizational sector. The pandemic was not a single organizational problem, but many simultaneous problems posed in varying and shifting combinations. We argue this multi-faceted organizational view should be a starting point for assessments of LEAs' pandemic response.

This article provides a review of prior empirical work exploring whether and to what extent school district racial composition affects the costs associated with providing equal educational opportunity to achieve a common set of outcomes. This prior work mainly involves education cost function modeling, on several specific states and in an earlier version of our national education cost model. Here, we update the national education cost model and apply a series of tests for selecting the optimal cost model and determining a) whether it is necessary to retain measures of racial composition in the model and b) the effect those measures have on the estimated costs to achieve common outcomes. We find that the optimal model includes an interaction term between % enrollment that is black and population density and that for majority Black enrollment urban districts, the predicted costs per pupil are 20 to 50% higher when using models with this measure than when using models with race neutral alternatives. While changes in cost estimates for these districts are large, aggregate national cost increases from including racial composition are 1.3 to 2.7% in most years.

Using data from the National Longitudinal Study of Youth 1997, we examine differences in educational experiences and in social and economic mobility for youths experiencing poverty relative to their more affluent peers. We also explore the extent to which different educational experiences are associated with greater mobility for students experiencing poverty. We find that youths from poverty are less than half as likely as their more affluent peers to earn a living wage, reach the top quartile of income, or attain a high level of economic wellbeing and stability. They also have less educational opportunity in their youth, particularly when it comes to academic experiences. Meanwhile, the educational experiences where there are the largest inequities are also the ones that are most predictive of long-term mobility for students from poverty, suggesting that having the opportunity to do well in school may help young people improve their economic standing and achieve broader levels of wellbeing later in life. At the same time, students experiencing poverty who have exceptional academic outcomes on average still do not manage to exceed the average adult income of the typical student not coming from poverty. Altogether, our findings point to both the importance and inadequacy of academic experiences for breaking the cycle of intergenerational poverty.

Students' postsecondary course-taking is of interest to researchers, yet has been difficult to study at large scale because administrative transcript data are rarely standardized across institutions or state systems. This paper uses machine learning and natural language processing to standardize college transcripts at scale. We demonstrate the approach's utility by showing how the disciplinary orientation of students' courses and majors align and diverge at 18 diverse four-year institutions in the College and Beyond II dataset. Our findings complicate narratives that student participation in the liberal arts is in great decline. Both professional and liberal arts majors enroll in a large amount of liberal arts coursework, and in three of the four core liberal arts disciplines, the share of course-taking in those fields is meaningfully higher than the share of majors in those fields. To advance the study of student postsecondary pathways, we release the classification models for public use. [Additional funding provided by the Michigan Institute of Data Science at the University of Michigan.]

Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings. [Funding for this paper was received from the Global Innovation Fund.]

Whole-school reforms have received widespread attention, but a critical limitation of the current literature is the lack of evidence around whether these extensive and costly interventions improve students' long-term outcomes after they leave reform schools. Leveraging Tennessee's statewide turnaround reforms, we use difference-in-differences models to estimate the effect of attending a turnaround middle school on student outcomes in high school, including test scores, attendance, chronic absenteeism, disciplinary actions, drop out, and high school graduation. We find little evidence to support improved long-run student outcomes -- mostly null effects that are nearly zero in magnitude. Our results contribute to a broad call for educational researchers to examine whether school reforms meaningfully affect student outcomes beyond short-term improvements in test scores.

We examine the efficiency of traditional school districts versus charter schools in providing students with teachers who meet their demographic and education needs. Using panel data from the state of Michigan, we estimate the relationship between enrollment of Black, Hispanic, special education, and English learner students and the presence of Black, Hispanic, Special Education, and ESL teachers, and test whether this relationship differs at charter and traditional district-run schools. Because charter schools typically have less market power in hiring than large districts, we compare charter school employment practices to traditional public schools in districts of comparable size. Our results suggest that charter schools are more likely to employ same race teachers for Black students but not Hispanic students, and districts schools are slightly better at providing ESL and SPED teachers. We conclude that charter autonomy does not necessary generate better student-teacher matches, but Michigan charters may occupy a market niche by serving Black students and staffing Black teachers.

Sometimes a treatment, such as receiving a high school diploma, is assigned to students if their scores on two inputs (e.g., math and English test scores) are above established cutoffs. This forms a multidimensional regression discontinuity design (RDD) to analyze the effect of the educational treatment where there are two running variables instead of one. Present methods for estimating such designs either collapse the two running variables into a single running variable, estimate two separate one-dimensional RDDs, or jointly model the entire response surface. The first two approaches may lose valuable information, while the third approach can be very sensitive to model misspecification. We examine an alternative approach, developed in the context of geographic RDDs, which uses Gaussian processes to flexibly model the response surfaces and estimate the impact of treatment along the full range of students that were on the margin of receiving treatment. We demonstrate theoretically, in simulation, and in an applied example, that this approach has several advantages over current approaches, including over another nonparametric surface response method. In particular, using Gaussian process regression in two-dimensional RDDs shows strong coverage and standard error estimation, and allows for easy examination of treatment effect variation for students with different patterns of running variables and outcomes. As these nonparametric approaches are new in education-specific RDDs, we also provide an R package for users to estimate treatment effects using Gaussian process regression.

The path to becoming a school principal is characterized by a variety of trajectories that reflect the diverse experiences and backgrounds of aspiring leaders. While ideally the road to the principalship would result in a proportional and representative body of principals, research has shown this is rarely the case. To gain a better understanding of where sorting mechanisms may occur along the principal pipeline, this paper longitudinally analyzes the full, start-to-finish career trajectories of over 1.6 million educators in Texas for 30 years. Using social sequence analysis and discrete-time hazard modeling, we find that (1) emergent principals tend to stay in their first teaching position longer than other educators and most often take a direct pathway towards the principalship; (2) proportionally, more principals emerge from elementary, ELA, Social Studies, or STEM fields, while fewer come from Special Education; (3) holding other features constant, male and Black educators are more likely to become a principal while female and Hispanic educators are less likely; and (4) educators are more likely to first become principals when transitioning to a smaller school with more Black and/or Hispanic students. While the pipeline does result in a balanced principal market in some areas, increasing efforts to encourage a more diverse content area representation as well as representation for Hispanic educators in Texas will be particularly important.

Over the past decade, there has been a significant increase in the number of U.S. institutions offering STEM-eligible degree programs in economics. This paper documents the trends in STEM-degree offerings across degree levels and examines the share of foreign students and other characteristics of institutions that offer STEM-eligible programs. Using a difference-in-differences design, this paper finds that departments with a proportion of foreign students above the sample median are 6 and 9 percentage points more likely to offer a STEM-eligible degree program at the bachelor's and master's levels, respectively, after the STEM designation in 2013. Additionally, the Tobit regression results suggest that early adopters of STEM-eligible programs are associated with a higher share of foreign students, private institutions and doctoral and research institutions.

Inequality in college has both structural and psychological causes; these include the presence of self-defeating beliefs about the potential for growth and belonging. Such beliefs can be addressed through large-scale interventions in the college transition (Walton & Cohen, 2011; Walton et al., 2023) but are hard to measure. In our pre-registered study, we provide the strongest evidence to date that the belief that belonging challenges are common and tend to improve with time ("a process-oriented perspective"), the primary target of social-belonging interventions, is critical. We did so by developing and applying computational language measures to 25,000 essays written during a randomized trial of this intervention across 22 broadly representative US colleges and universities (Walton et al., 2023). We compare the hypothesized mediator to one of simple optimism, which includes positive expectations without recognizing that challenges are common. Examining the active control condition, we find that socially disadvantaged students are, indeed, significantly less likely to express a process-oriented perspective spontaneously, and more likely to express simple optimism. This matters: Students who convey a process-oriented perspective, both in control and treatment conditions, are significantly more likely to complete their first year of college full-time enrolled and have higher first-year GPAs, while simple optimism predicts worse academic progress. The social-belonging intervention helped distribute a process-oriented perspective more equitably, though disparities remained. These computational methods enable the scalable and unobtrusive assessment of subtle student beliefs that help or hinder college success. [This research was supported by Stanford's Institute for Human-Centered AI (HAI).]

High-achieving minority students have fewer friends than their majority counterparts. Exploring patterns of friendship formation in the Add Health data, we find strong racial homophily in friendship formations as well as strong achievement homophily within race. However, we find that achievement matters less in cross-racial friendships. As a result, high-achieving Black students lose Black friends as they move away from the mean achievement of their group, but do not gain high-achieving White friends in offsetting fashion. We find that high-achieving Black students have 0.9 fewer friends, mainly attributable to the fact that they are exposed to fewer high-achieving peers within their own race. We find that this could account for as much as 5 to 9 percent of the racial wage gap observed among high achievers.

This study provides the first large-scale quantitative exploration of mathematical language use in upper elementary U.S. classrooms. Our approach employs natural language processing techniques to describe variation in teachers' and students' use of mathematical language in 1,657 fourth and fifth grade lessons in 317 classrooms in four districts over three years. Students' exposure to mathematical language varies substantially across lessons and between teachers. Results suggest that teacher modeling, defined as the density of mathematical terms in teacher talk, does not substantially cause students to uptake mathematical language, but that teachers may encourage student use of mathematical vocabulary by means other than mere modeling or exposure. However, we also find that teachers who use more mathematical language are more effective at raising student test scores. These findings reveal that teachers who use more mathematical vocabulary are more effective math teachers.

The immediate impacts of COVID-19 on K12 schooling are well known. Over nearly 18 months, students' academic performance and mental health deteriorated dramatically. This study aims to identify if and how the pandemic led to longer-term changes in core aspects of schooling. Drawing on semi-structured interviews with 31 teachers and administrators across 12 districts in two states, we find that schools today look quite different in several areas including the availability and use of instructional technology, instructional practice, parent-teacher communication, and the balance between academics and social-emotional well-being. We interpret these findings through the lens of institutional theory, and discuss implications of the changes for practitioners and policymakers.

The teacher workforce in science, technology, engineering, and math (STEM) has been a perpetual weak spot in public schools' teaching rosters. Prior reports show the pipeline of new STEM teachers into the profession is weak while demand for instruction in STEM fields continues to grow. This paper seeks to document whether and how the STEM teacher workforce in high-need settings has been impacted by these pressures. It analyzes successive waves of nationally representative teacher survey data to explore demographics and qualifications among the secondary STEM teacher workforce in high-need settings has fared over time. Results show the STEM teacher workforce in high-need schools is consistently less likely to be experienced, less likely to hold any degree in a STEM field, less likely to hold a master's degree, and less likely to be fully certified than STEM teachers in more advantaged settings. Yet, surprisingly, the observed qualifications gaps across high- versus low-need settings are either stable or slightly narrowing over time. Certain STEM fields--namely, physical sciences and computer science--rely on a less qualified workforce than those in math or biology, with low levels of teacher qualifications observed across both high- and low-need settings. Though even when considering field-specific alignment between teachers' background qualifications and their teaching assignments, the qualifications gap between high- and low-need settings has been slowly shrinking in three of four STEM fields analyzed here. In addition to high-need schools, small schools (based on enrollment size) rely more heavily on underqualified STEM teachers.

This paper reports findings from a nationally representative survey of K-12 teachers in May 2023 that examines the potential long-term impacts of COVID-19 on public schooling. The findings suggest fundamental ways in which school operations, instructional practice and parent-teacher interaction have changed since the pandemic. Some changes seem promising; others suggest caution. While policymakers may not be able to directly influence some of the reported changes in the short run, monitoring the evolution of school practices (and their consequences for children) will position educational leaders to help teachers and students address the consequences of the COVID-19 pandemic going forward.

This study examines the effects of universal public pre-kindergarten for 3-year-olds (Pre-K3) on later public education outcomes, including enrollment, school mobility, special education status, and in-grade retention from kindergarten through second grade. While universal pre-kindergarten programs typically target 4-year-olds, interest in expanding to 3-year-olds is growing. Using the centralized assignment lottery in the District of Columbia as the basis for a quasi-experimental design, we find that Pre-K3 students are more likely to persist in the public system and remain in the same school. These effects are strongest for residents of low-income neighborhoods and communities of color and for students enrolled in dual language programs. Overall, public Pre-K3 appears to stabilize children's early educational experiences, especially those starting furthest from opportunity.

Racial disparities in infant health conditions have persisted for decades. However, there is surprisingly limited evidence regarding the long-term consequences of these disparities. Using novel linked administrative data from Texas and the shift to Medicaid Managed Care (MMC), I show that MMC-driven declines in infant health worsened cognitive and noncognitive outcomes for Black children, while MMC-driven enhancements in infant health improved noncognitive outcomes and educational attainment for Hispanics. Effects concentrate in low-value added districts for either demographic, suggesting that the long run impacts of changes to early life health conditions are more pronounced in less effective schools for one's demographic. [Additional support from the Hellman Fellows Find.]

Dual enrollment (DE) is one of the fastest growing programs that support the high school-to-college transition. Yet, there is limited empirical evidence about its impact on either students' college application choices or admission outcomes. Using a fuzzy regression discontinuity approach on data from two cohorts of ninth-grade students in one anonymous state, we found that taking DE credits increased the likelihood of applying to highly selective in-state four-year institutions. Attempting DE credits also increased the probability of gaining admission to a highly selective in-state four-year college. Heterogeneous analysis further indicates that the gains were extended across Black, Latinx, and white student populations.

Targeted school funding is a potentially valuable policy lever to increase educational equality by race, ethnicity, and income, but it remains unclear how to target funds most effectively. We use a regression discontinuity approach to compare districts that narrowly passed or failed a school funding election. We use close tax elections in 9 states to identify effects of operating funds and close bond elections in 8 states to identify effects of capital funds. Results indicate positive achievement returns to spending, especially for math achievement and for operating funds. We find similar returns to spending by race, ethnicity, and income (not statistically different), but we find significantly larger returns for students in low-resource districts than in high-resource districts, including larger returns for Black, Latinx, and low-income students. Mediation analyses suggest spending on teacher salaries and counselors may be particularly effective mechanisms to increase achievement among Black and low-income students. [Additional funding for this report was provided by the Gilead Foundation Creating Possible Fund and the Population Studies and Training Center at Brown University.]

This study uses a concurrent embedded mixed-methods design to assess the impact of additional funding on student outcomes in a large, urban school district in the Southeastern United States. The district implemented student-based budgeting (SBB), which allocates dollars to schools based on student characteristics using a weighted student funding (WSF) formula and provides flexibility to principals to allocate those dollars under site-based budgeting. Using simulated instruments in a difference-in-differences framework, we estimate the impact of additional funding on student outcomes provided by WSF. Student test scores in math and ELA increased by 0.14 and 0.12 standard deviations, respectively. Our qualitative analysis suggests that the flexibility given to principals was a key mechanism that improved student outcomes.

Specialized high schools are an increasingly popular way to prepare young adults for postsecondary experiences and expand school choice. While much literature ex- amines charter school spillover effects and the effects of specialized schools on the students who attend them, little is known about the spillover effects of specialized high schools on traditional public schools (TPS). Using an event study design, we show that one type of specialized high school, North Carolina's Cooperative Innovative High Schools, initially attracted students who were higher achieving and more likely to be white than TPS students, but these specialized schools became more representative of the district population over time. On average, the opening of specialized schools had a mix of null and positive spillover effects on TPS student achievement. While there is some evidence of negative spillovers from the first schools that opened, the effects become more positive over time.

The United States is facing growing teacher shortages that may disproportionately affecting schools serving high proportions of students of color, low-income students, and those in rural or urban areas. Special education teachers (SETs) are particularly in demand. Each year, nearly half of all vacancies are filled with teachers switching from one school to another, yet little research has addressed the nuances of within-career sorting, especially by subject. Utilizing longitudinal data covering 27 years and over 1.2 million teachers in Texas, this study examines SET switching patterns relative to core subject teachers, utilizing discrete time hazard modeling, fixed-effect regressions, and geographic information system mapping. Results show SETs switch schools at much higher rates, associated with experience, salary, and student demographics, yet generally transfer shorter distances than their peers. These findings highlight differential subject-specific labor market dynamics, suggesting targeted recruitment and retention strategies to address widespread shortages.

Catholic schools have seen more than a 30% decline in enrollment over the past 20 years. While some of the decline in enrollment may have been spurred by secular trends or the Church abuse scandal, the increase in schools of choice, principally public charter schools, may explain at least some of this decline. In this paper we estimate the effect of the opening of charter schools in proximity to Catholic schools across the entire U.S. We find that the opening of a nearby charter school has a negative impact on Catholic school enrollment and increases the likelihood that the school will close. We also find that charter openings induce greater racial isolation. Findings are especially pronounced in K8 schools, rather than high schools.

A digital information explosion has transformed cities' residential and educational markets in ways that are still being uncovered. Although urban stratification scholars have increasingly scrutinized whether emerging digital platforms disrupt or reproduce longstanding segregation patterns, direct links between one theoretically important form of digital information--school quality data--and neighborhood and school segregation are rarely drawn. To clarify these dynamics, we leverage an exogenous digital information shock, in which the Los Angeles Times' website revealed measures of a particularly important school quality proxy--schools' value-added effectiveness--for nearly all elementary schools in the Los Angeles Unified School District. Results suggest that although the information shock had no detectable effects on residential sorting or neighborhood racial segregation, it did exert modest effects on school sorting--particularly for Latino and Asian students--albeit not in ways that materially diminished school racial segregation because the racial compositions of high and low value-added schools were broadly similar both before and after the information shock. We conclude that the urban stratification implications of digital information may be more nuanced than often appreciated, with effects shaped by racial heterogeneity in both constraints and preferences vis-à-vis specific types of information and operating through mechanisms beyond residential segregation.

In the context of an ongoing national conversation about teacher shortages, we build on prior literature on the efficacy of teacher certification pathways by comparing entry and exit patterns based on age at the time of certification. All trainees who complete a state certification process have invested substantial time and resources into entering teaching. Competing employment opportunities and expectations might vary with age. We use both linear regression and discrete-time hazard models to examine employment and subsequent exit of newly certified teacher trainees in Michigan from 2011 to 2023. We find that while mid-career entrants in their 30s and 40s compose a small share of new certificates, they are more likely to enter a teaching position and no more likely to subsequently exit than counterparts who were certified in their early 20s. Mid-career pathways also contribute to teacher diversity by attracting more Black and male teachers who enter and persist.

Despite well-designed curriculum materials, teachers often face challenges in their implementation due to diverse classroom needs. This paper investigates whether Large Language Models (LLMs) can support middle-school math teachers by helping create high-quality curriculum scaffolds, which we define as the adaptations and supplements teachers employ to ensure all students can access and engage with the curriculum. Through Cognitive Task Analysis with expert teachers, we identify a three-stage process for curriculum scaffolding: observation, strategy formulation, and implementation. We incorporate these insights into three LLM approaches to create warmup tasks that activate background knowledge. The best-performing approach, which provides the model with the original curriculum materials and an expert-informed prompt, generates warmups that are rated significantly higher than warmups created by expert teachers in terms of alignment to learning objectives, accessibility to students working below grade level, and teacher preference. This research demonstrates the potential of LLMs to support teachers in creating effective scaffolds and provides a methodology for developing AI-driven educational tools. [This report was funded by Stanford's Center for Human-Centered AI and the Stanford Accelerator for Learning.]

This manuscript explores the argument for lower student-to-school counselor ratios in U.S. public education. Drawing upon a comprehensive historical review and existing research, we establish the integral role of school counselors and the notable benefits of reduced student-to-counselor ratios. Our analysis of national data exposes marked disparities across states and districts, with the most underfunded often serving higher percentages of low-income students and students of color. This situation raises significant ethical concerns, prompting a call for conscientious policy reform and targeted investment. Informed by emerging best practices, we propose recommendations for enhancing counselor staffing and ultimately student outcomes. This ethical argument underscores the need for proactive actions and provides a basis for future research to further delineate the impact of school counselor ratios on educational equity and student success.

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can reason about our safety policies in context when responding to potentially unsafe prompts, through deliberative alignment. This leads to state-of-the-art performance on certain benchmarks for risks such as generating illicit advice, choosing stereotyped responses, and succumbing to known jailbreaks. Training models to incorporate a chain of thought before answering has the potential to unlock substantial benefits, while also increasing potential risks that stem from heightened intelligence. Our results underscore the need for building robust alignment methods, extensively stress-testing their efficacy, and maintaining meticulous risk management protocols. This report outlines the safety work carried out for the OpenAI o1 and OpenAI o1-mini models, including safety evaluations, external red teaming, and Preparedness Framework evaluations.

High-resolution in-beam $\gamma$-ray spectroscopy was used to study excited states of the neutron-deficient nucleus $^{32}$Ar populated in fast-beam induced four- and six-nucleon removal reactions from $^{36,38}$Ca. One new $\gamma$-ray transition and indications for an additional two were found, allowing for a glimpse at the level scheme beyond the known $2^+_1$ state. The nature of the new $1900(4)$-keV transition is discussed in the context of the known energy spectrum of the mirror nucleus $^{32}$Si and shell-model calculations using the FSU and SDPF-M cross-shell effective interactions. Its resulting parent state at $3767(5)$ keV, more than $1.3$ MeV above the proton separation energy, is tentatively assigned to have mixed sd-shell and $2p$-$2h$ character. It might either be the mirror of the $J^{\pi}=2^+_2$ state of $^{32}$Si at $4230.8(8)$ keV, but with a decay branch favoring a transition to the $2^+_1$ over the ground state, or the mirror of the $4983.9(11)$-keV state with quantum numbers $0^+$. The resulting mirror-energy differences of $-473(5)$ and $-1218(5)$ keV are both sizable when compared to systematics; in the latter case it would, in fact, be among the largest reported to date in the entire nuclear chart or suggest the potential existence of an additional, hitherto unidentified, low-lying $0^+$ state of $^{32}$Si., Comment: 8 pages, 3 figures

In-beam $\gamma$-ray spectroscopy was used to study excited states of the neutron-deficient nucleus $^{37}$K populated in fast-beam inelastic-scattering and proton-removal reactions at high-momentum loss. New $\gamma$-ray transitions and $\gamma\gamma$ coincidence relationships were established using the $\gamma$-ray tracking array GRETINA. The extension of the level scheme up to the first $(13/2^-)$ state highlights the potential of this recently demonstrated population pathway for studies of isospin symmetry involving mirror-energy differences. The nature of the newly identified states is discussed in comparison to shell-model calculations with the FSU cross-shell effective interaction. The calculated occupation numbers of individual orbitals are shown to offer a consistent explanation of the measured mirror-energy differences between $^{37}$K and $^{37}$Ar., Comment: 8 pages, 6 figures

Transitioning from quantum computation on physical qubits to quantum computation on encoded, logical qubits can improve the error rate of operations, and will be essential for realizing valuable quantum computational advantages. Using a neutral atom quantum processor with 256 qubits, each an individual Ytterbium atom, we demonstrate the entanglement of 24 logical qubits using the distance-two [[4,2,2]] code, simultaneously detecting errors and correcting for lost qubits. We also implement the Bernstein-Vazirani algorithm with up to 28 logical qubits encoded in the [[4,1,2]] code, showing better-than-physical error rates. We demonstrate fault-tolerant quantum computation in our approach, guided by the proposal of Gottesman (2016), by performing repeated loss correction for both structured and random circuits encoded in the [[4,2,2]] code. Finally, since distance-two codes can correct qubit loss, but not other errors, we show repeated loss and error correction using the distance-three [[9,1,3]] Bacon-Shor code. These results begin to clear a path for achieving scientific quantum advantage with a programmable neutral atom quantum processor., Comment: 17 pages, 16 figures

Arrays of optically trapped neutral atoms are a promising architecture for the realization of quantum computers. In order to run increasingly complex algorithms, it is advantageous to demonstrate high-fidelity and flexible gates between long-lived and highly coherent qubit states. In this work, we demonstrate a universal high-fidelity gate-set with individually controlled and parallel application of single-qubit gates and two-qubit gates operating on the ground-state nuclear spin qubit in arrays of tweezer-trapped $^{171}$Yb atoms. We utilize the long lifetime, flexible control, and high physical fidelity of our system to characterize native gates using single and two-qubit Clifford and symmetric subspace randomized benchmarking circuits with more than 200 CZ gates applied to one or two pairs of atoms. We measure our two-qubit entangling gate fidelity to be 99.72(3)% (99.40(3)%) with (without) post-selection. In addition, we introduce a simple and optimized method for calibration of multi-parameter quantum gates. These results represent important milestones towards executing complex and general quantum computation with neutral atoms.