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Education resources matter when they are allocated and used effectively. Yet, the upstream decisions school boards make about district budgets and resource allocation are understudied. In this descriptive study, we analyze data from 400 publicly available video recordings of financial deliberations in school board budget meetings between spring 2022 and spring 2023. Half of the video recordings are from school boards that received education finance training from the Edunomics Lab at Georgetown University. We find school boards discussed student outcomes in only 15-25% of board meetings focused on financial deliberations. Only about 11% of the variation in financial deliberations can be explained by district characteristics, student achievement, and community characteristics. We find no differences in the discussion of student outcomes for districts with and without the Edunomics training. However, descriptive evidence suggests a positive relationship between the Edunomics training and some summary measures of financial deliberations: the overall level of engagement in budgetary discussions; the likelihood per-unit cost and internal barriers (such as decision-making structure) were mentioned; and the likelihood that the budget was linked to outcomes. These findings underscore the variation in school board deliberations and suggest the potential value of training school board members to influence those deliberations.

Professional references are widely used in hiring decisions, yet their effectiveness remains largely understudied. This study analyzes structured ratings collected from the professional references of teacher applicants and conduct an experiment to see whether the ratings influence hiring managers' assessments of applicants and hiring decisions. There is little evidence that providing reference ratings to hiring managers influences their evaluations of candidates or hiring choices in productive ways. However, the analysis suggests that reference ratings are predictive of future job performance independent of other applicant information available to hiring managers. The result is a paradox: reference ratings offer potentially low-cost, high-value information, but hiring managers do not appear to make productive use of them.

We use student-level data on elementary special education identification from Washington state to explore student identification rates in the months immediately after the onset of the COVID-19 pandemic and over 2 subsequent years. Special education identification rates dropped dramatically in March 2020 through the end of the 2019-20 school year and remained below historical norms through 2020-21 before returning to pre-pandemic levels early in 2021-22. The magnitude of these effects implies that over 8,000 fewer elementary students were identified for special education services during the pandemic in Washington than would have been expected based on prior trends.

Most states responded to the onset of the pandemic by temporarily granting teachers Emergency licenses. These licenses allowed teachers to work in classrooms without passing the typical licensure exams. Since then, several states have extended their use of Emergency licenses, raising questions about how these policies impact the composition of the teacher workforce and student outcomes. In this paper, we examine the result of these policies using data on multiple cohorts of Emergency licensed teachers (ELTs) who taught in Massachusetts between 2021 and 2023. We find that ELTs were slightly more likely to remain in the same school and in the teaching workforce than teachers from other entry routes. However, ELTs' students scored significantly lower on standardized tests in math and science than other students in the same school and same year. Our findings are at odds with earlier, more positive assessments of Emergency licensure in Massachusetts. Our updated results appear to be driven by more recent cohorts of ELTs, rather than the teachers who received Emergency licenses at the start of the pandemic. Overall, this study suggests policymakers should be cautious when drawing sweeping conclusions about the impacts of teacher licensure based solely on the earliest cohort of teachers who obtained pandemic-era Emergency licenses.

The Elementary and Secondary School Emergency Relief Fund (ESSER) was the largest onetime federal investment in K-12 schools in history, funneling almost $200 billion to states and school districts. We use novel data from Washington State to investigate the extent to which ESSER funding causally influenced spending on school personnel. We argue one cannot infer this directly from ESSER claims data because of the fungibility of school budgets. Thus, we rely on a more direct signal of district hiring decisions: public education job postings scraped from district hiring websites. To address endogeneity concerns, our preferred approach employs an instrumental variables strategy that exploits a formula mechanism used to determine Title I funding for 2020-21 (and thus ESSER allocations in 2022) based on the number of Title I formula-eligible children. We find strong, arguably causal, evidence that public school hiring increased in response to the availability of ESSER funding. Specifically, we estimate that each $1,000 in ESSER allocations caused districts to seek to hire $206 in additional staff, disproportionately teachers. These estimates suggest that roughly 12,000 new staff (including 5,100 teachers) were hired in Washington because of ESSER. In the absence of new funding, school staffing budgets will likely need to contract substantially following the sunset of ESSER.

Departmentalized instruction, in which teachers specialize in one or more core subjects and instruct multiple groups of students in a day, has become increasingly prominent in elementary schools. Using 8 years of data from Massachusetts and a difference-in-differences design, we estimate the effects of departmentalization on student achievement. We find that departmentalization has positive effects in English language arts (ELA) and science and mixed evidence of positive effects in math. These positive effects are not driven by teacher productivity improvements: Consistent with prior findings on teacher specialization, teachers are less effective when specializing in math and no more effective in ELA than when teaching self-contained classrooms. Rather, consistent with the theoretical underpinnings for specialization, departmentalized schools tend to assign teachers to their stronger subjects.

Who ends up in the teacher workforce is greatly influenced by who is admitted into teacher education programs (TEPs). To better understand how the preferences of teacher education faculty might shape admissions of STEM teacher candidates, we surveyed faculty who teach content or methods courses to STEM teacher candidates across five universities. Faculty reported that they most value information collected from individual interviews with applicants and data on the number of STEM courses taken in college and their performance in these courses, and least value data on university admissions tests, high school GPA, and teacher licensure test scores. When we investigate faculty members' revealed preferences through a conjoint analysis, we find that faculty most value applicants who have worked with students from diverse backgrounds and applicants from a marginalized racial or ethnic community, and least value whether they received high grades in math and/or science courses. Finally, we find significant variation in these perceptions across respondents in different faculty roles, who teach different courses, and from different institutions: for example, Arts and Sciences faculty tend to value TEP applicants' performance in college STEM courses relatively more than STEM education faculty, while STEM education faculty tend to value applicants' race and ethnicity relatively more than Arts and Sciences faculty.

Graduates of special education teacher education programs can teach in a range of special education settings, raising the potential that their training can occur in very different settings than where they find their first jobs. We follow 263 completers of Moderate Disabilities programs in Massachusetts from their field placements to their early-career teaching positions and study the characteristics of their field placements and the degree to which these are aligned with their early-career teaching positions. We also assess the degree to which alignment is associated with early-career teacher turnover. We found that many of these teachers student-taught in an inclusive setting but were hired into a self-contained special education setting and vice versa, and teachers who experienced this misalignment were more likely to leave the workforce early in their careers. Teachers who student taught with a supervising practitioner without a special education license were also more likely to leave early. Findings suggest that teachers training to educate students with learning disabilities should student teach in a setting that is aligned with where they are likely to be hired, and with a supervising practitioner who is trained in special education.

It is now well established that the COVID-19 pandemic had a devastating and unequal impact on student achievement. Test score declines were disproportionately large for historically marginalized students, exacerbating preexisting achievement gaps and threatening educational and economic inequality. In this paper, we use longitudinal student-level NWEA MAP Growth test data to estimate differences in test score declines for students at different points on the prepandemic test distribution. We also test the extent to which students' schools and districts accounted for these differences in declines. We find significant differences in learning loss by baseline achievement, with lower-achieving student's scores dropping 0.100 SD more in math and 0.113 SD more in reading than higher-achieving students' scores. We additionally show that the school a student attended accounts for about three-quarters of this widening gap in math achievement and about one-third in reading. The findings suggest school and district-level policies may have mattered more for learning loss than individual students' experiences within schools and districts. Such nuanced information regarding the variation in the pandemic's impacts on students is critical for policymakers and practitioners designing targeted academic interventions and for tracking disparities in academic recovery. [Additional funding for this report was provided by Kenneth C. Griffin.]

Correctional education is a prevalent form of rehabilitation programming for prisoners in the United States. There is limited evidence, however, about the labor market returns to credentials received while incarcerated. Using incarceration, educational, and labor market data in Washington State, we study the labor market returns to GEDs and short-term vocational certificates earned in prison. We identify the returns to credentials by a difference-indifferences design that compares changes in earnings and employment for incarcerated persons who earn a credential to those who enroll in a program but fail to complete a GED or certificate. We estimate that GEDs increase post-incarceration earnings by about $450 per quarter and that vocational certificates increase earnings by about $250 per quarter. Degree completers have higher hourly wages, are more likely to be employed, and work more hours following release. For vocational programs, earnings increases are driven by certificates in construction and manufacturing. [The research presented presented in this report uses confidential data from the Education Research and Data Center (ERDC) located within the Washington Office of Financial Management (OFM).]

We analyze the methodological approach and validity of interpretation of using national-level time-series regression analyses relating epidemic outcomes to policies that estimate many models involving permutations of analytic choices (i.e., a "multiverse" approach). Specifically, we evaluate the possible biases and pitfalls of interpretation of a multiverse approach to the context of government responses to epidemics using the example of COVID-19 and a recently published peer-reviewed paper by Bendavid and Patel (2024) along with the subsequent commentary that the two authors published discussing and interpreting the implications of their work. While we identify multiple potential errors and sources of biases in how the specific analyses were undertaken that are also relevant for other studies employing similar approaches, our most important finding involves constructing a counterexample showing that causal model specification-agnostic multiverse analyses can be incorrectly used to suggest that no consistent effect can be discovered in data especially in cases where most specifications estimated with the data are far from causally valid. Finally, we suggest an alternative approach involving hypothesis-drive specifications that explicitly account for infectiousness across jurisdictions in the analysis as well as the interconnected ways that policies and behavioral responses may evolve within and across these jurisdictions.

Individual-level state-transition microsimulations (iSTMs) have proliferated for economic evaluations in place of cohort state transition models (cSTMs). Probabilistic economic evaluations quantify decision uncertainty and value of information (VOI). Prior studies show that iSTMs provide unbiased estimates of expected incremental net monetary benefits (EINMB), but statistical properties of their estimates of decision uncertainty and VOI are uncharacterized. We compare such iSTMs-produced estimates to corresponding cSTMs. For a 2-alternative decision and normally distributed incremental costs and benefits, we derive analytical expressions for the probability of being cost-effective and the expected value of perfect information (EVPI) for cSTMs and iSTMs, accounting for correlations in incremental outcomes at the population and individual levels. Numerical simulations illustrate our findings and explore relaxation of normality assumptions or having >2 decision alternatives. iSTM estimates of decision uncertainty and VOI are biased but asymptotically consistent (i.e., bias->0 as number of microsimulated individuals->infinity). Decision uncertainty depends on one tail of the INMB distribution (e.g., P(INMB<=0)) which depends on estimated variance (larger with iSTMs given first-order noise). While iSTMs overestimate EVPI, their direction of bias for the probability of being cost-effective is ambiguous. Bias is larger when uncertainties in incremental costs and effects are negatively correlated. While more samples at the population uncertainty level are interchangeable with more microsimulations for estimating EINMB, minimizing iSTM bias in estimating decision uncertainty and VOI depends on sufficient microsimulations. Analysts should account for this when allocating their computational budgets and, at minimum, characterize such bias in their reported results.

Retirement plans can create strong financial incentives that have important labor market implications, and many states have adopted alternative plan designs that significantly change these incentives. The authors use longitudinal data to investigate the impact of Washington State's 1996 introduction of a hybrid retirement plan on late-career attrition. The unique setup of Washington's plans allows them to provide empirical evidence on the influence of financial incentives created by statutory retirement eligibility thresholds. Findings show that despite facing very different financial incentives, teachers enrolled in the hybrid and traditional plans respond similarly to reaching a key retirement eligibility threshold. The authors hypothesize that teachers are anchoring to the eligibility thresholds, muting the influence of the financial incentives. They also provide evidence that, in the presence of bright-line eligibility thresholds that can anchor workers' separation behavior, commonly used structural models may overpredict workers' responsiveness to the financial incentives embedded in retirement plans. [This paper will be published in "ILR Review."]

There is widespread speculation (e.g., Johnson, 2021; Klinger et al., 2022; Mathews, 2022; Walker, 2021) and some evidence (e.g., Sanchez & Moore, 2022, Sanchez, 2023) that grading standards have changed over the course of the pandemic, making higher grades relatively easier to achieve and less reflective of objective measures of learning. It is possible--even likely--that shifting grading standards give parents, guardians, and students a confusing or inaccurate picture of what students know and can do, especially considering pandemic- related learning losses (Dorn et al., 2021; Goldhaber et al., 2023; Kuhfeld et al., 2022). Despite considerable theoretical work about grading standards and some evidence of changes in grading over the pandemic, there is limited research on the extent to which eased grading standards continued post-pandemic, as teachers and students returned to normal schooling. In this research brief, researchers used administrative data on student grades from Washington state to assess whether grading standards have returned to pre-pandemic levels.

Scanning probe techniques are popular, non-destructive ways to visualize the real space structure of Van der Waals moir\'es. The high lateral spatial resolution provided by these techniques enables extracting the moir\'e lattice vectors from a scanning probe image. We have found that the extracted values, while precise, are not necessarily accurate. Scan-to-scan variations in the behavior of the piezos which drive the scanning probe, and thermally-driven slow relative drift between probe and sample, produce systematic errors in the extraction of lattice vectors. In this Letter, we identify the errors and provide a protocol to correct for them. Applying this protocol to an ensemble of ten successive scans of near-magic-angle twisted bilayer graphene, we are able to reduce our errors in extracting lattice vectors to less than 1%. This translates to extracting twist angles with a statistical uncertainty less than 0.001{\deg} and uniaxial heterostrain with uncertainty on the order of 0.002%., Comment: 20 pages including supplementary material and 3 main figures

The electronic properties of moir\'e heterostructures depend sensitively on the relative orientation between layers of the stack. For example, near-magic-angle twisted bilayer graphene (TBG) commonly shows superconductivity, yet a TBG sample with one of the graphene layers rotationally aligned to a hexagonal Boron Nitride (hBN) cladding layer provided the first experimental observation of orbital ferromagnetism. To create samples with aligned graphene/hBN, researchers often align edges of exfoliated flakes that appear straight in optical micrographs. However, graphene or hBN can cleave along either zig-zag or armchair lattice directions, introducing a 30 degree ambiguity in the relative orientation of two flakes. By characterizing the crystal lattice orientation of exfoliated flakes prior to stacking using Raman and second-harmonic generation for graphene and hBN, respectively, we unambiguously align monolayer graphene to hBN at a near-0 degree, not 30 degree, relative twist angle. We confirm this alignment by torsional force microscopy (TFM) of the graphene/hBN moir\'e on an open-face stack, and then by cryogenic transport measurements, after full encapsulation with a second, non-aligned hBN layer. This work demonstrates a key step toward systematically exploring the effects of the relative twist angle between dissimilar materials within moir\'e heterostructures., Comment: 40 pages, 15 figures

Biological systems, particularly the brain, are frequently analyzed as networks, conveying mechanistic insights into their function and pathophysiology. This is the first study of a functional network of cardiac tissue. We use calcium imaging to obtain two functional networks in a subsidiary but essential pacemaker of the heart, the atrioventricular node (AVN). The AVN is a small cellular structure with dual functions: a) to delay the pacemaker signal passing from the sinoatrial node (SAN) to the ventricles, and b) to serve as a back-up pacemaker should the primary SAN pacemaker fail. Failure of the AVN can lead to syncope and death. We found that the shortest path lengths and clustering coefficients of the AVN are remarkably similar to those of the brain. The network is ``small-world," thus optimized for energy use vs transmission efficiency. We further study the network properties of AVN tissue with knock-out of the sodium-calcium exchange transporter. In this case, the average shortest path-lengths remained nearly unchanged showing network resilience, while the clustering coefficient was somewhat reduced, similar to schizophrenia in brain networks. When we removed the global action potential using principal component analysis (PCA) in wild-type model, the network lost its ``small-world" characteristics with less information-passing efficiency due to longer shortest path lengths but more robust signal propagation resulting from higher clustering. These two wild-type networks (with and without global action potential) may correspond to fast and slow conduction pathways. Laslty, a one-parameter non-linear preferential attachment model is a good fit to all three AVN networks., Comment: 14 pages, 8 figures at the end of the manuscript, two videos and three datasets in the Source folder

Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.

To make up for pandemic-related learning losses, many U.S. public school districts have increased enrollment in their summer school programs. We assess summer school as a strategy for COVID-19 learning recovery by tracking the academic progress of students who attended summer school in 2022 across eight districts serving 400,000 students. Based on students' spring to fall progress, we find a positive impact for summer school on math test achievement (0.03 standard deviation, SD), but not on reading tests. These effects are predominantly driven by students in upper elementary grades. To put the results into perspective, if we assume that these districts have losses similar to those present at the end of the 2022-23 school year (i.e., approximately -0.2 SD), we estimate summer programming closed approximately 2% to 3% of the districts' total learning losses in math, but none in reading. [This research received funding from Kenneth C. Griffin.]

Nationally, more than 75% of individuals who are credentialed to teach are prepared in traditional college- or university-based teacher education programs (TEPs). But the college and employment pathways that prospective teachers take to TEP enrollment and completion have not been comprehensively examined. A better understanding of how credentialed individuals find their way into TEPs helps us understand the sources of new teacher supply early in the prospective teacher pipeline. With that in mind, we analyze pathways into and through TEPs using historical postsecondary and unemployment insurance data from Washington state. We find that the pathways are quite varied with around 40% of bachelor's-level TEP completers spending at least some time in community colleges and less than 40% enrolling and finishing at the same university directly after high school. Pathways to master's TEP completion are even more varied, with almost half of the completers having prior employment experience. For researchers, this varied landscape raises important questions about the relationship between pathways, candidate persistence, and eventual job performance. For policymakers, the results suggest that efforts to recruit the next generation of teachers need to look beyond the pool of students already enrolled at a 4-year university to include students at 2-year colleges or in the labor force who might be interested in entering a TEP.

This study investigated the underexplored topic of teacher preparation program admissions processes by interviewing faculty and analyzing program documents. We investigated how 31 K-12 "mathematics and science" teacher preparation programs (MSTPPs) and faculty attend to diversity, equity, inclusion, and social and racial justice (DEIJ). Specific foci included applicant recruitment and selection, components of applications (e.g., forms, essays, interviews), and how applicants' DEIJ-related information and orientations factor into admissions. We found that all MSTPPs participating in the study collected information related to DEIJ (e.g., applicants' ethnoracial backgrounds, citizenship), and all interviewed faculty expressed an interest in increasing the diversity of applicants and admitted students. Faculty expressed preferences for applicants who evidenced positive DEIJ orientations, such as recognizing social and ethnoracial injustices, but at the same time, differences were evident in how MSTPPs and faculty attended to DEIJ. Considerations, implication, and dilemmas for teacher preparation programs and faculty are discussed.

The onset of the pandemic in spring 2020 substantially disrupted routes into teaching and offered a unique opportunity to study this process with different requirements for initial entry into the classroom. We examine the impacts of the Temporary Certificate of Eligibility (Temporary CE), which allowed teacher candidates in New Jersey to enter the workforce before completing assessment and performance requirements. Relative to the novice teacher workforce before the pandemic, Temporary CE teachers were substantially more diverse without any significant effects on teacher performance or student test scores. However, Temporary CE holders were less likely to remain in the same school or in the New Jersey teaching workforce between 2020-21 and 2021-22. Although Temporary CE holders disproportionately entered through alternate routes into teaching, these patterns hold for both traditional- and alternate-route entrants.

This qualitative study examines the information collected about applicants to mathematics or science teacher preparation programs (MSTPPs) and how university faculty perceive and value this information in admissions decisions. Based on document review and interviews with MSTPP faculty and admissions directors, we found that broad measures of mathematics and science content background (e.g., achievement test scores, past mathematics and science courses taken) were used more frequently than information on applicants' specific mathematics and science content knowledge and dispositions. In many cases, application components (such as interviews and personal essay statements) were perceived by faculty to be conducive to surfacing applicants' content knowledge and dispositions; however, they were not constructed or employed in a way that afforded the obtainment of this information. We highlight salient examples of MSTPPs' collection and use of information related to mathematics and science and discuss implications for TPP admissions processes.

Prior research has connected characteristics of cooperating teachers who supervise student teaching to performance measures of the teacher candidates they host, suggesting more effective teachers may also be better mentors. The specific measures of cooperating teacher effectiveness considered in this prior literature (value added and performance evaluations), however, are infrequently observable to individuals responsible for student teaching placements. In this paper, we consider a more easily observed proxy for mentor effectiveness: National Board (NB) Certification. We find that NB teachers are considerably more likely to host candidates than other teachers, candidates supervised by NB teachers are slightly more likely to be hired within three years, and these candidates have slightly lower value added in English language arts than their peers, all else being equal. We find no significant relationship between cooperating teacher NB certification and candidates' later attrition and value added in math. We conclude that individuals and policies seeking to leverage student teaching placements to improve student and teacher outcomes may need to focus on less easily observable proxies of cooperating teacher quality than NB certification status.

Given math's importance, states are adopting a range of K12 policies to improve math outcomes. As these and other policies unfold, policymakers need to monitor intended outcomes and ultimately gauge impact. They need answers to basic questions: Are more students taking and passing algebra and advanced math? Are performance gaps changing between student groups or schools? What are the characteristics of our math teachers? Are they changing? How are math teachers with different characteristics distributed across schools and students? Policymakers also need answers to more complex questions: Did a given policy have an effect? If so, why, under what conditions, and for whom? Were there any unintended consequences? With these kinds of questions in mind, this CALDER brief singles out the value of longitudinal, individual-level state administrative data by reviewing questions that can--and can't--be answered by the more limited data made publicly available by states. To illustrate, we focus on three states that are attempting to improve math outcomes and that enroll large numbers of students from historically marginalized student groups: California, Florida, and Texas.

Over the past two decades, graphene has been intensively studied because of its remarkable mechanical, optical, and electronic properties. Initial studies were enabled by manual ``Scotch Tape'' exfoliation; nearly two decades later, this method is still widely used to obtain chemically-pristine flakes of graphene and other 2D van der Waals materials. Unfortunately, the yield of large, pristine flakes with uniform thickness is inconsistent. Thus, significant time and effort are required to exfoliate and locate flakes suitable for fabricating multilayer van der Waals heterostructures. Here, we describe a relatively affordable tabletop device (the ``eXfoliator'') that can reproducibly control key parameters and largely automate the exfoliation process. In a typical exfoliation run, the eXfoliator produces 3 or more large ($\ge400\ \mu$m$^2$) high-quality graphene monolayer flakes, allowing new users to produce such flakes at a rate comparable to manual exfoliation by an experienced user. We use an automated mapping system and a computer vision algorithm to locate candidate flakes. Our results provide a starting point for future research efforts to more precisely identify which parameters matter for the success of exfoliation, and to optimize them., Comment: 11 pages, 10 figures, 12 tables

New properties can arise at van der Waals (vdW) interfaces hosting a moir\'e pattern generated by interlayer twist and strain. However, achieving precise control of interlayer twist/strain remains an ongoing challenge in vdW heterostructure assembly, and even subtle variation in these structural parameters can create significant changes in the moir\'e period and emergent properties. Characterizing the rate of interlayer twist/strain relaxation during thermal annealing is critical to establish a thermal budget for vdW heterostructure construction and may provide a route to improve the homogeneity of the interface or to control its final state. Here, we characterize the spatial and temporal dependence of interfacial twist and strain relaxation in marginally-twisted hBN/hBN interfaces heated under conditions relevant to vdW heterostructure assembly and typical sample annealing. We find that the ferroelectric hBN/hBN moir\'e relaxes minimally during annealing in air at typical assembly temperatures of 170{\deg}C. However, at 400{\deg}C, twist angle relaxes significantly, accompanied by a decrease in spatial uniformity. Uniaxial heterostrain initially increases and then decreases over time, becoming increasingly non-uniform in direction. Structural irregularities such as step edges, contamination bubbles, or contact with the underlying substrate result in local inhomogeneity in the rate of relaxation.

Two-dimensional (2D) semiconducting transition-metal dichalcogenides (TMDCs) are an exciting platform for new excitonic physics and next-generation electronics, creating a strong demand to understand their growth, doping, and heterostructures. Despite significant progress in solid-source (SS-) and metal-organic chemical vapor deposition (MOCVD), further optimization is necessary to grow highly crystalline 2D TMDCs with controlled doping. Here, we report a hybrid MOCVD growth method that combines liquid-phase metal precursor deposition and vapor-phase organo-chalcogen delivery to leverage the advantages of both MOCVD and SS-CVD. Using our hybrid approach, we demonstrate WS$_2$ growth with tunable morphologies - from separated single-crystal domains to continuous monolayer films - on a variety of substrates, including sapphire, SiO$_2$, and Au. These WS$_2$ films exhibit narrow neutral exciton photoluminescence linewidths down to 33 meV and room-temperature mobility up to 34 - 36 cm$^2$V$^-$$^1$s$^-$$^1$). Through simple modifications to the liquid precursor composition, we demonstrate the growth of V-doped WS$_2$, MoxW$_1$$_-$$_x$S$_2$ alloys, and in-plane WS$_2$-MoS$_2$ heterostructures. This work presents an efficient approach for addressing a variety of TMDC synthesis needs on a laboratory scale.

This paper examines how different measures of teacher quality are related to students' long-run educational trajectories. We estimate teachers' "test-based" and "nontest" value-added (the latter based on contributions to student absences, suspensions, grade progression, and grades) and assess how these predict various student postsecondary outcomes. We find that both types of value-added have positive effects on student outcomes. Test-based teacher quality measures have more explanatory power for outcomes relevant for students at the top of the achievement distribution, such as attending a more selective college, while nontest measures have more explanatory power for whether students enroll in college at all.

Anecdotal evidence points to the importance of school principals, but the limited existing research has neither provided consistent results nor indicated any set of essential characteristics of effective principals. This paper exploits extensive student-level panel data across six states to investigate both variations in principal performance and the relationship between effectiveness and key certification factors. While principal effectiveness varies widely across states, there is little indication that regulation of the background and training of principals yields consistently effective performance. Having prior teaching or management experience is not related to our estimates of principal value-added.

We use administrative panel data from seven states covering nearly 3 million students to document and explore variation in "academic mobility," a term we use to describe the extent to which students' ranks in the distribution of academic performance change during their public schooling careers. On average, we show that student ranks are highly persistent during elementary and secondary education--that is, academic mobility is limited in U.S. schools as a whole. Still, there is non-negligible variation in the degree of upward mobility across some student subgroups as well as individual school districts. On average, districts that exhibit the greatest upward academic mobility serve more socioeconomically advantaged populations and have higher value-added to student achievement.

Several decades of research using school administrative data show that teacher quality is inequitably distributed across schools. But these estimates may understate teacher-related inequities if they do not account for how teacher vacancies or late hires are distributed across schools. We investigate these hiring issues using data on a direct proxy of school hiring needs: teacher job postings collected from public school district websites. These data allow us to document how, over the course of the school year, hiring needs vary across districts, schools, and subject areas. We find that schools serving more students of color have greater hiring needs throughout the hiring cycle. We also find that hiring needs for special education and STEM positions are consistently higher than hiring needs for elementary positions. Schools with growing enrollments, as well as schools and subjects with higher prior attrition rates, also tend to have more job postings. Postings for schools in towns and rural areas tend to stay open longer than for schools in suburban and urban areas. Finally, we validate that job postings, which can be obtained quickly and inexpensively, are a good indicator of school and district needs in that they closely line up with eventual teacher hires.

Three years into the COVID-19 pandemic era, concerns about teacher turnover and teacher shortages remain at the top of the education agenda. But contrary to media reports about a "wave of resignations and retirements" (e.g., Heller, 2021), early evidence from state databases showed a more nuanced picture: teacher attrition was actually down during the pandemic's first year (teachers leaving after the 2019-2020 school year) before it increased somewhat in the next year (e.g., Bacher-Hicks et al., 2021, 2022; Bastian & Fuller, 2023; Camp et al., 2022; CERRA, 2022; Goldhaber & Theobald, 2022a,b). In this policy brief, we follow-up and expand on our earlier analyses of teacher mobility and attrition in Washington state with an additional year of data from the 2022-23 school year. We draw on a longitudinal database of school staffing in Washington, the S-275, which now provides 39 years of annual data on teachers and other school employees in the state.

We use publicly available, longitudinal data from Washington state to study the extent to which three interrelated processes--teacher attrition from the state teaching workforce, teacher mobility between teaching positions, and teacher hiring for open positions--contribute to "teacher quality gaps" (TQGs) between students of color and other students in K-12 public schools. Specifically, we develop and implement an agent-based model simulation of decisions about attrition, mobility, and hiring to assess the extent to which each process contributes to observed TQGs. We find that eliminating inequities in teacher mobility and hiring across different schools would close TQGs within 5 years, while just eliminating inequities in teacher hiring would close gaps within 10 years. On the other hand, eliminating inequities in teacher attrition without addressing mobility and hiring does little to close gaps.

To make up for pandemic-related learning losses, many U.S. public school districts have increased enrollment in their summer school programs. We assess summer school as a strategy for COVID-19 learning recovery by tracking the academic progress of students who attended summer school in 2022 across eight districts serving 400,000 students. Based on students' spring to fall progress, we find a positive impact for summer school on math test achievement (0.03 standard deviation, SD), but not on reading tests. These effects are predominantly driven by students in upper elementary grades. To put the results into perspective, if we assume that these districts have losses similar to those present at the end of the 2022-23 school year (i.e., approximately -0.2 SD), we estimate summer programming closed approximately 2% to 3% of the districts' total learning losses in math, but none in reading.

We use data on high school students and teachers from Washington state to connect the observable characteristics of career and technical education (CTE) teachers to various non-test outcomes (absences, disciplinary incidents, grade point average, grade progression, and on-time graduation) of students with and without disabilities in their classrooms. We find that students participating in CTE tend to have better non-test outcomes when they are assigned to a CTE teacher from the state's Business and Industry pathway--designed for CTE teachers with 3 years of industry experience but no formal teacher preparation--relative to being assigned to a traditionally prepared CTE teacher. These results can inform efforts in Washington and across the country to develop and support similar alternative routes to CTE teacher licensure.