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Teacher shortages and lack of teacher diversity have led to growing efforts nationally to recruit teaching assistants (TAs) to be classroom teachers. Substitute teachers are not typically considered in these efforts. We pair longitudinal administrative data from a mid-sized urban district with survey follow-up to address how TAs and substitute teachers contribute to filling staffing shortages and diversifying the teacher workforce. While substitute teachers are rarely included in formal Grow-Your-Own efforts, they bring racial and ethnic diversity to the district in similar ways to TAs yet face fewer barriers to becoming classroom teachers. As a result, they do so at much higher rates and help prevent vacancies in hard-to-staff classrooms.

We investigate the mechanisms by which a student's age relative to classmates (i.e., relative age) influences risky health behaviors among European adolescents. Using a two-stage least squares approach, we show that relatively young students are more prone to engage in risky behaviors. These results hold after controlling for absolute age, country fixed effects, and birth season effects. In the second part of the paper, we conduct two sets of analyses on possible mechanisms. First, causal mediation analyses reveal that students' perceived academic performance is the primary mediator. Second, additional analyses suggest that perceptions of substance risks and peer usage prevalence may also play a significant role.

Teacher turnover is especially pronounced among teachers of color who play critically important roles in the success of students of color. A growing literature points to racial isolation as one factor that is associated with Black teacher job satisfaction in particular, which in turn could play a role in a teacher's decision to remain in a school. However, little is known about whether having more race-congruent colleagues might be a potential mechanism that leads teachers to stay, and whether that relationship might vary by teacher race. Drawing on statewide administrative data from Pennsylvania, I examine the extent to which colleague racial congruence impacts the likelihood of teacher turnover and what transferring teacher's destination schools imply about their revealed preferences for colleague race congruence and other factors. Using a series a fixed effects models to account for within- and between-school teacher sorting, I find that a 10 percentage point increase in racially congruent colleagues decreases the likelihood of teacher turnover by 6% of a standard deviation, with larger impacts for Black teachers (0.09 SD) than for White teachers (0.03 SD). However, nonlinear models point to diminishing returns to greater race congruence, as the marginal effect of additional race congruence attenuates to close to zero as a teacher crosses the 50% race congruence threshold. Transferring teachers appear to select into schools with greater proportions of racially congruent colleagues, suggesting a revealed preference for colleague demographics--but again only to a point.

In this study, we highlight the differences in classroom-, teacher-, and school-level factors in 8th and 9th grade algebra experiences along socioeconomic and racial/ethnic lines using nationally representative survey data from the American Mathematics Educator Study. Several takeaways emerge from our analysis. First, we show that highest-poverty schools (i.e., schools in the top poverty quartile) are significantly less likely to offer algebra in 8th grade unconditionally (i.e., without needing to meet certain conditions) for all students or to offer algebra at all compared to lowest-poverty schools (i.e., schools in the lowest poverty quartile). Second, we find significant differences in which factors (e.g., parent requests, teacher referrals) are considered when placing students in advanced math courses in 8th and 9th grade that may affect the access of students from disadvantaged backgrounds to these courses or to more advanced pathways. Third, we show significant differences in 8th and 9th grade math teacher qualifications and classroom activities in math courses, with teachers in highest-poverty schools being significantly more likely to have received alternative credentials, less likely to have completed student-teaching during their preparation program, and less likely to have completed their state's licensure requirements for math. 8th and 9th grade math teachers in highest-poverty schools were also more likely to report that they spend more than half of their instruction time addressing math topics below grade level or addressing disciplinary issues. Mostly similar, albeit weaker, patterns emerge when we examine discrepancies along school racial/ethnic composition. Offering 8th grade algebra in high-poverty school settings (or making it available to more or all students) could help close socioeconomic gaps in algebra enrollment in 8th grade and grant more equitable access to advanced math coursework in the long-run. That said, focusing on the provision of 8th grade algebra alone will likely not remedy the opportunity gaps in access to (and completion of) advanced math courses in high school since our findings suggest that highest-poverty high schools are also significantly less likely to offer college credit-bearing math courses. Further, our findings suggest that increasing the provision of algebra in 8th grade may present three challenges: (1) staffing these courses with qualified teachers; (2) providing strong supports for students who struggle with algebra; and, relatedly, (3) making algebra placement decisions that minimize failure and maximize success for the greatest number of students. Taken together, our findings demonstrate systemic inequities across racial/ethnic and socioeconomic lines in terms of access to, and experiences in, 8th and 9th grade math courses.

Reclassification, the process by which English learner (EL) students exit EL classification, often determines ELs' access to mainstream academic coursework. While existing research finds that many students who demonstrate English proficiency do not reclassify, few studies evaluate policies that effectively reclassify eligible students. This study examines the impact of shifting reclassification responsibility from school districts to the state in Michigan. Using a difference-in-regression discontinuities design, we find that state-level responsibility increases reclassification rates by 35 percentage points compared to district-level responsibility. The effects are larger for Spanish speakers, indicating that state procedures may reduce linguistic bias. Our findings contribute to the literature on default policies in K-12 education and provide evidence on policies that promote equity in EL education.

Chronic absenteeism is a critical issue that has been linked to many adverse student outcomes. The current study focuses on improving a key system already in place in many school districts--early warning systems (EWSs)--in order to decrease chronic absenteeism in students' earliest schooling years. Using a demographically diverse population of students followed from PreK to third grade in Boston Public Schools (N=6,698), we demonstrate how and why two modern machine learning algorithms--the Synthetic Minority Oversampling Technique (SMOTE) and Extreme Gradient Boosting (XGBoost)--can improve EWS accuracy in proactively identifying students who are at risk of becoming chronically absent. The best-performing XGBoost model with SMOTE was approximately 52 percentage points more accurate (in terms of recall rate) than the logistic regression model closest to those used in current EWSs in correctly predicting students who would be chronically absent in third grade. Our analyses introduce varying probability thresholds and the incorporation of different years of data, showing the potential of these models to cater to school districts aiming to leverage machine learning predictions while adhering to budgetary or intervention constraints.

Attending college is a significant human capital investment but only about 60% of those who start college will have a completed degree six years later. This makes identifying the skills associated with college success an important policy concern. We surveyed over 1,100 entering college freshmen, majoring in business and engineering at a public university in the US, and combined this information with administrative data to create a comprehensive data set that, in addition to the usual academic performance data, cognitive ability measures, and demographics, also included measures of non-cognitive skills, personality traits, student expectations about college success and performance at graduation. With this information, we analyzed if students' subjective expectations about their future success in college are related to non-cognitive skills and whether they are realistic, compared to student's performance at graduation. We identify students performing below and above objective expectations, both at the end of their freshmen year and at graduation, and study non-cognitive skills related to their objective performance. We find that non-cognitive skills are associated with academic subjective expectations of college success and objective performance in college, even after controlling for cognitive ability. However, many students enter college with unrealistic subjective expectations about their future performance and this could influence their on-time graduation.

Evaluations of Early College, a type of intervention that enables simultaneous enrollment in secondary and post-secondary courses in the United States, consistently find positive effects on educational attainment across racial and socioeconomic groups. Unlike Early College initiatives in other states, Massachusetts launched Early College in Fall 2018, enabling a within-school as well as a whole-school intervention, in which each participating school may enroll some or all of its students in Early College, with guiding principles of equitable access, guided academic pathways, student support, and connection to career. This study uses propensity score matching to evaluate the impact of participating in Massachusetts Early College on students' educational attainment. Positive effects found on college enrollment, with statistically significant positive interactions between the treatment and being socioeconomically disadvantaged, and on college persistence, with statistically significant positive interactions between the treatment and being Latinx, suggest the intervention may help promote equitable access to higher education in Massachusetts. Massachusetts is a local-control state, where public school governance is legally delegated to district and school boards located in the communities they serve, as opposed to the state government, making it more difficult to have state-wide interventions. The flexibility of Massachusetts Early College renders it more easily replicated in local-control states, than the whole school models previously studied.

This article examines the risk of crossover contamination in individual-level randomization, a common concern in experimental research, in the context of a large-enrollment college course. While individual-level randomization is more efficient for assessing program effectiveness, it also increases the potential for control group students to cross over into the treatment group, thus biasing treatment effect estimates. This study provides empirical evidence from a pilot intervention in two sections of a college-level introductory chemistry course, where a course-specific chatbot was introduced. We tested two randomization strategies: simple student-level randomization and laboratory-level randomization. We hypothesized that the greatest risk for crossover would have occurred under the simple individual randomization approach, however, no crossover occurred in either condition. Survey responses and system usage data indicate that this was not due to a lack of interaction among students or disinterest in the chatbot. These findings suggest that student-level randomization, even in an in-person course setting, can proceed with minimal risk of contamination for testing our focal intervention.

Differences in effect sizes between researcher developed (RD) and independently developed (ID) outcome measures are widely documented but poorly understood in education research. We conduct a meta-analysis using item-level outcome data to test potential mechanisms that explain differences in effects by RD or ID outcome type. Our analysis of 45 effect sizes from 30 studies shows that both greater standard deviations of item-specific treatment effects and lower correlations between item-specific effects and item easiness predict larger effect sizes and reduce the observed difference between RD and ID measures from 0.24 SDs to 0.15 SDs. The findings advance our understanding of how item properties predict educational intervention outcomes and underscore the affordances of analyzing item-level data for building theory in education research.

This study examines the impact of statewide Restorative Justice (RJ) policy reforms in Michigan and Texas on student disciplinary outcomes and behavior, in light of increasing concerns over the negative effects of zero-tolerance policies. As schools move away from exclusionary discipline practices, this research focuses on three primary questions: (1) Are these policies effectively implemented statewide? (2) Do they contribute to a reduction in problematic behaviors, such as bullying? (3) Does the distinction in policy implementation--Michigan's prescriptive approach versus Texas's permissive framework--affect outcomes? Utilizing school district-level data and a penalized synthetic control estimator for multiple treated districts, the analysis reveals that Texas has an overall reduction in out-of-school suspensions and bullying incidents, while Michigan shows an increase. However, taking these as a main takeaway would be misleading, as at a fine-grained level, more than half of the Michigan school districts show a reduction in bullying incidents. The results are further discussed, revealing patterns of racial composition in the districts with respect to their success in implementation of reforms. The findings highlight the critical role of implementation fidelity and the importance of local context in assessing the success of RJ initiatives while also filling a critical gap in understanding the multifaceted consequences of RJ practices.

This article examines the impact of within-class age differences on educational outcomes, using students' birth months in Madrid's primary schools as a natural experiment. Employing a regression discontinuity design, we analyze third-grade students to investigate these age-related effects. Additionally, we explore whether early childhood education attendance works as a mitigating factor. Results indicate that relatively older students achieve higher scores in both Language and Mathematics and have lower grade retention rates. However, this gap is attenuated among students who attended childhood education for two years or more. These novel results highlight the importance of early childhood education in reducing natural inequalities that may persist over time.

Prior evidence shows that early childhood education (ECE) can serve as a protective factor that boosts maltreated children's school readiness outcomes. Yet, less is known about ECE's relationship to other developmental domains critical to their wellbeing including their adaptive behaviors and cognitive development. Focusing on a broader range of outcomes allows for a more holistic picture of the ways in which ECE influences maltreated children's developmental wellbeing. This study investigates ECE's relationship to maltreated children's adaptive behaviors (daily living and socialization skills) and cognitive development (attention and memory; perception and concepts) using data on a sample of 1,570 children (Mean age = 11.5 months at baseline) from the National Survey of Child and Adolescent Well-Being II. To estimate ECE's association with children's outcomes, this study uses the quasi-experimental method of propensity score weighting which accounts for observable selection bias between children in ECE versus not in ECE. In the short-term (Mean age = 22 months), ECE leads to lower daily living skills as well as higher perception and concept scores. These effects did not persist as children approached their formal schooling years (Mean age = 42 months). Effects were not detected on either their social skills or attention and memory. These findings demonstrate mixed evidence of ECE's relationship to maltreated children's outcomes and underscores the importance of identifying critical features of ECE that might need to be tailored to the specific needs of maltreated children.

In this bibliometric review of the research landscape on achievement gaps, we analyze temporal trends and geographic distributions, identify key scholars and publications, and uncover the intellectual structure and thematic focus of achievement gap research. By examining 1,607 achievement gap studies between 1937 and 2023, we find that the scholarship has evolved through four distinct stages: pre-1960, 1960-1999, 2000-2010, and post-2010. Author co-citation analysis reveals six major schools of thought that underpin how scholars conceptualize and study achievement gap: Child Development, Economic Analysis, Social Contexts of Schools, Schooling Process, School Discipline, and Psychological Dynamics. Our findings underscore the need for more interconnected, interdisciplinary approaches that integrate various paradigms to address the achievement gap comprehensively. We advocate for future research to move beyond isolated impacts by promoting collaborative efforts among all stakeholders from multiple disciplines.

States are increasingly adopting changes to K-12 funding systems in order to promote and encourage student engagement in secondary-level career and technical education (CTE). Two of the most prevalent reforms include: a) establishing tiered weights for CTE in school funding formulas based on the connection between a program of study and workforce needs and b) incentive grant programs that provide funds based on student attainment of industry-recognized credentials. However, it is unclear whether and how these changes induce higher levels of meaningful and useful CTE engagement. This study evaluates the impact of these policy changes on state funding for CTE and high school level CTE enrollment.

There is limited empirical evidence about educational interventions for students experiencing homelessness, who experience distinct disadvantages compared to their low-income peers. We explore how two school staffing interventions in New York City shaped attendance outcomes of students experiencing homelessness using administrative records from 2013-2022 and a difference-in-differences estimator. We find suggestive evidence that an intervention that placed social workers in schools to serve students experiencing homelessness is associated with a 1.2 percentage point increase in average attendance rates of students in shelter. We discuss this small association relative to program costs and implications for education policies targeting homeless students.

This study addresses the important yet underexplored question of whether the Common Core State Standards in Mathematics, which emphasize critical thinking and problem-solving, as well as the computer-based assessments aligned with the Common Core, have facilitated or hindered learning for students with disabilities. By analyzing administrative data from a large county in California, we track mathematics achievement trends before and after the implementation of the Common Core. Our findings show a significant widening of the achievement disparity in mathematics between students with and without disabilities, suggesting that the Common Core and computerized assessments disproportionately affected students with disabilities.

Healthcare services outside of school impact the likelihood of receiving a school-based special education classification and services. Using Massachusetts administrative data on public school students, this paper employs difference-in-differences to examine the impacts of expanded Medicaid coverage for mental and behavioral healthcare brought by the "Rosie D." lawsuit of 2009. "Rosie D." caused a 0.3 percentage point (15 percent) increase in emotional disorder (ED) identification among low-income grades 9-12 students. After "Rosie D.," students with ED were more likely to be Black or multiracial. Students were also more likely to have experienced suspension or chronic absenteeism before ED identification. Finally, grades K-8 students with ED were educated in less inclusive settings.

The multitude of tasks performed in the labor market requires skills in many dimensions. Traditionally, human capital has been proxied primarily by educational attainment. However, an expanding body of literature highlights the importance of various skill dimensions for success in the labor market. This paper examines the returns to cognitive, personality, and social skills as three important dimensions of basic skills. Recent advances in text analysis of online job postings and professional networking platforms offer novel methods for assessing a wider range of applied skill dimensions and their labor market relevance. A synthesis and integration of the evidence on the relationship between multidimensional skills and earnings, including the matching of skill supply and demand, will enhance our understanding of the role of human capital in the labor market.

This paper examines the effects of Pell Grant eligibility on student outcomes. Using a regression discontinuity (RD) design and a partial identification approach, the study provides bounds on the treatment effects that account for selection bias arising from the loss of grant eligibility. While initial eligibility is determined by financial need alone, students must achieve Satisfactory Academic Progress (SAP) to retain the grant. Students eligible for the maximum grant aid are 26 percentage points less likely to persist in the year they lose grant eligibility than those with less aid. This negative effect on persistence extends to graduation; these students are 8 percentage points less likely to graduate within 4 years. Furthermore, these two groups of students differ in their underlying characteristics, which introduces attrition bias into the estimates. Finally, to address this selection bias, I provide bounds on the effects of Pell Grant on student outcomes. While naive RD estimates find no effect on completion rates, bounding estimates reveal that students eligible for the maximum grant aid are up to 4, 2, and 2 percentage points more likely to graduate from a 4-year institution within 4, 5, and 6 years compared to those with less aid, respectively. Furthermore, these eligible students graduate with a higher GPA than previously estimated. These positive effects are larger than those found in earlier studies.

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 effect of remote learning on student performance has been a frequent topic of research and discussion in the aftermath of the COVID-19 pandemic, yet little is known about the impact of remote instruction on the performance of teachers. This study documents how relative effectiveness of teachers changed when moving from in-person to remote instruction and analyzes the characteristics of teachers associated with greater relative effectiveness during remote instruction. Using matched student/teacher-level data from three large metro-Atlanta school districts, we estimate teacher value-added models to measure the association between teacher characteristics and a teacher's relative contribution to test score growth before and during the period of virtual instruction in the 2020-21 school year. We find evidence of increased variation in overall teacher effectiveness during remote instruction. Results are driven by veteran teachers, who appear relatively more effective in virtual instruction than their less-experienced peers, and by the very best in-person teachers, some of which experience large declines in relative effectiveness when shifting to remote instruction.

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.

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.