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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.

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).]

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.]

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.

Modern archaeological methods increasingly utilize 3D virtual representations of objects, computationally intensive analyses, high resolution scanning, large datasets, and machine learning. With higher resolution scans, challenges surrounding computational power, memory, and file storage quickly arise. Processing and analyzing high resolution scans often requires memory-intensive workflows, which are infeasible for most computers and increasingly necessitate the use of super-computers or innovative methods for processing on standard computers. Here we introduce a novel protocol for en-masse micro-CT scanning of small objects with a {\em mostly-automated} processing workflow that functions in memory-limited settings. We scanned 1,112 animal bone fragments using just 10 micro-CT scans, which were post-processed into individual PLY files. Notably, our methods can be applied to any object (with discernible density from the packaging material) making this method applicable to a variety of inquiries and fields including paleontology, geology, electrical engineering, and materials science. Further, our methods may immediately be adopted by scanning institutes to pool customer orders together and offer more affordable scanning. The work presented herein is part of a larger program facilitated by the international and multi-disciplinary research consortium known as Anthropological and Mathematical Analysis of Archaeological and Zooarchaeological Evidence (AMAAZE). AMAAZE unites experts in anthropology, mathematics, and computer science to develop new methods for mass-scale virtual archaeological research. Overall, our new scanning method and processing workflows lay the groundwork and set the standard for future mass-scale, high resolution scanning studies., Comment: 36 pages, 12 figures, 2 tables. Source code available at https://github.com/oneil571/AMAAZE-MCT-Processing

Command injection and replay attacks are key threats in Cyber Physical Systems (CPS). We develop a novel actuator fingerprinting technique named Time Constant. Time Constant captures the transient dynamics of an actuator and physical process. The transient behavior is device-specific. We combine process and device transient characteristics to develop a copy-resistant actuator fingerprint that resists command injection and replay attacks in the face of insider adversaries. We validated the proposed scheme on data from a real water treatment testbed, as well as through real-time attack detection in the live plant. Our results show that we can uniquely distinguish between process states and actuators based on their Time Constant.

We study the categories $\mathrm{Perv}_{\mathrm{thin}}$ and $\mathrm{Perv}_{\mathrm{thick}}$ of Iwahori-equivariant perverse sheaves on the thin and thick affine flag varieties associated to a split reductive group $G$. An earlier work of the first author describes $\mathrm{Perv}_{\mathrm{thin}}$ in terms of bimodules over the so-called non-commutative Springer resolution. We partly extend this result to $\mathrm{Perv}_{\mathrm{thick}}$, providing a similar description for its anti-spherical quotient. The long intertwining functor realizes $\mathrm{Perv}_{\mathrm{thick}}$ as the Ringel dual of $\mathrm{Perv}_{\mathrm{thin}}$, we point out that it shares some exactness properties with the similar functor acting on perverse sheaves on the finite-dimensional flag variety. We use this result to resolve a conjecture of Arkhipov and the first author, proving that the image in the Iwahori-Whittaker category of any convolution-exact perverse sheaf on the affine flag variety is tilting., Comment: 21 pages

Motivated by the challenge of moment recovery in hydrodynamic approximation in kinetic theory, we propose a data-driven approach for the hydrodynamic models. Inspired by continuous data assimilation, our method introduces a relaxation-based nudging system coupled with a novel discretization technique. This approach facilitates the simultaneous recovery of both the force term and a high-resolution solution from sparsely observed data. To address potential numerical artifacts, we use kernel regression to fit the observed data. We also analyze the convergence of the proposed nudging system under both full and partial data scenarios. When applied to moment systems, the source term involves the derivative of higher-order moments, our approach serves as a crucial step for data preparation in machine-learning based moment closure models. Multiple numerical experiments demonstrate the effectiveness of our algorithm, and we discuss its potential extension to high-dimensional systems.

Astrophysical simulations are computation, memory, and thus energy intensive, thereby requiring new hardware advances for progress. Stony Brook University recently expanded its computing cluster "SeaWulf" with an addition of 94 new nodes featuring Intel Sapphire Rapids Xeon Max series CPUs. We present a performance and power efficiency study of this hardware performed with FLASH: a multi-scale, multi-physics, adaptive mesh-based software instrument. We extend this study to compare performance to that of Stony Brook's Ookami testbed which features ARM-based A64FX-700 processors, and SeaWulf's AMD EPYC Milan and Intel Skylake nodes. Our application is a stellar explosion known as a thermonuclear (Type Ia) supernova and for this 3D problem, FLASH includes operators for hydrodynamics, gravity, and nuclear burning, in addition to routines for the material equation of state. We perform a strong-scaling study with a 220 GB problem size to explore both single- and multi-node performance. Our study explores the performance of different MPI mappings and the distribution of processors across nodes. From these tests, we determined the optimal configuration to balance runtime and energy consumption for our application., Comment: Accepted to PEARC '24 (Practice and Experience in Advanced Research Computing)

The observation of high-$T_c$ superconductivity (HTSC) in concomitant with pressure-induced orthorhombic-tetragonal structural transition in the bilayer La$_{3}$Ni$_2$O$_7$ has sparked hopes of achieving HTSC by stabilizing the tetragonal phase at ambient pressure. To mimic the effect of external physical pressures, the application of chemical pressure via replacing La$^3$$^+$ with smaller rare-earth R$^3$$^+$ has been considered as a potential route. Here we clarify the distinct effects of chemical and physical pressures on the structural transition of bilayer nickelates through a combined experimental and theoretical investigation. Contrary to general expectations, we find that substitutions of smaller R$^3$$^+$ for La$^3$$^+$ in La$_{3-x}$R$_x$Ni$_2$O$_{7-\delta}$, despite of an overall lattice contraction, produce stronger orthorhombic structural distortions and thus require higher pressures to induce the structural transition. We established a quantitative relationship between the critical pressure $P_c$ for structural transition and the average size of $A$-site cations. A linear extrapolation of $P_c$ versus <$r_A$> yields a putative critical value of <$r_A$>$_c$ ~ 1.23 angstrom for $P_c$ ~ 1 bar. The negative correlation between $P_c$ and <$r_A$> indicates that it is unlikely to reduce $P_c$ to ambient by replacing La$^3$$^+$ with smaller R$^3$$^+$ ions. Instead, partial substitution of La$^3$$^+$ with larger cations such as alkaline-earth Sr$^2$$^+$ or Ba$^2$$^+$ might be a feasible approach. Our results provide valuable guidelines in the quest of ambient-pressure HTSC in bilayer nickelates., Comment: 17 pages and 5 figures

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.

Polycrystalline samples of RE2O2CO3 (RE = Pr, Nd, Gd, Tb, Dy, Ho, Er, and Yb) with a unique rare-earth bilayer of triangular lattice were synthesized and studied by DC and AC magnetic susceptibility. Data reveals various magnetic ground states including (i) a nonmagnetic ground state for the Pr sample; (ii) long range magnetic ordering for the Nd, Gd, Tb, Dy, Ho, and Er samples. Besides the Gd sample, they exhibit field-induced spin state transitions. More interestingly, the series spin state transitions in the Nd and Dy samples could be attributed to the field-induced up-up-down (UUD) spin structure. Neutron powder diffraction (NPD) measurements of the Er sample suggest a spiral spin structure below its TN; and (iii) a short-range ordering for the Yb sample. The disrupted inter-layer interaction due to the shift of Yb3+ ions within the bilayer prevents long range magnetic ordering down to 30 mK and makes it another Yb-related triangular lattice antiferromagnet that has the potential to realize a quantum spin liquid state.

In this paper we prove discrete to continuum convergence rates for Poisson Learning, a graph-based semi-supervised learning algorithm that is based on solving the graph Poisson equation with a source term consisting of a linear combination of Dirac deltas located at labeled points and carrying label information. The corresponding continuum equation is a Poisson equation with measure data in a Euclidean domain $\Omega \subset \mathbb{R}^d$. The singular nature of these equations is challenging and requires an approach with several distinct parts: (1) We prove quantitative error estimates when convolving the measure data of a Poisson equation with (approximately) radial function supported on balls. (2) We use quantitative variational techniques to prove discrete to continuum convergence rates on random geometric graphs with bandwidth $\varepsilon>0$ for bounded source terms. (3) We show how to regularize the graph Poisson equation via mollification with the graph heat kernel, and we study fine asymptotics of the heat kernel on random geometric graphs. Combining these three pillars we obtain $L^1$ convergence rates that scale, up to logarithmic factors, like $O(\varepsilon^{\frac{1}{d+2}})$ for general data distributions, and $O(\varepsilon^{\frac{2-\sigma}{d+4}})$ for uniformly distributed data, where $\sigma>0$. These rates are valid with high probability if $\varepsilon\gg\left({\log n}/{n}\right)^q$ where $n$ denotes the number of vertices of the graph and $q \approx \frac{1}{3d}$.

The Ruddlesden-Popper (R-P) bilayer nickelate, La3Ni2O7, was recently found to show signatures of high-temperature superconductivity (HTSC) at pressures above 14 GPa. Subsequent investigations achieved zero resistance in single- and poly-crystalline samples under hydrostatic pressure conditions. Yet, obvious diamagnetic signals, the other hallmark of superconductors, are still lacking owing to the filamentary nature with low superconducting volume fraction. The presence of a novel "1313" polymorph and competing R-P phases obscured proper identification of the phase for HTSC. Thus, achieving bulk HTSC and identifying the phase at play are the most prominent tasks at present. Here, we address these issues in the praseodymium (Pr)-doped La2PrNi2O7 polycrystalline samples. We find that the substitutions of Pr for La effectively inhibits the intergrowth of different R-P phases, resulting in nearly pure bilayer structure. For La2PrNi2O7, pressure-induced orthorhombic-to-tetragonal structural transition takes place at Pc ~ 11 GPa, above which HTSC emerges gradually upon further compression. The superconducting transition temperatures at 18-20 GPa reach Tconset = 82.5 K and Tczero = 60 K, which are the highest values among known nickelate superconductors. More importantly, bulk HTSC was testified by detecting clear diamagnetic signals below ~75 K corresponding to an estimated superconducting volume fraction ~ 57(5)% at 20 GPa. Our results not only resolve the existing controversies but also illuminate directions for exploring bulk HTSC in the bilayer nickelates.

Cleavable metallic antiferromagnets may be of use for low-dissipation spintronic devices; however, few are currently known. Here we present orthorhombic MnRhBi3 as one such compound and present a thorough study of its physical properties. Exfoliation is demonstrated experimentally, and the cleavage energy and electronic structure are examined by density functional theory calculations. It is concluded that MnRhBi3 is a van der Waals layered material that cleaves easily between neighboring Bi layers, and that the Bi atoms have lone pairs extending into the van der Waals gaps. A series of four phase transitions are observed below room temperature, and neutron diffraction shows that at least two of the transitions involve the formation of antiferromagnetic order. Anomalous thermal expansion points to a crystallographic phase transition and/or strong magnetoelastic coupling. This work reveals a complex phase evolution in MnRhBi3 and establishes this cleavable antiferromagnetic metal as an interesting material for studying the interplay of structure, magnetism, and transport in the bulk and ultrathin limits as well as the role of lone pair electrons in interface chemistry and proximity effects in van der Waals heterostructures.

Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian setting when full posterior inference is desired. As an alternative, we propose a novel statistical modeling framework for this data setting, namely a mixture of directed graphical models (MDGMs). The components of the mixture, directed graphical models, can be represented by directed acyclic graphs (DAGs) and are computationally quick to evaluate. The DAGs representing the mixture components are selected to correspond to an undirected graphical representation of an assumed spatial contiguity/dependence structure of the areal units, which underlies the specification of traditional modeling approaches for discrete spatial processes such as Markov random fields (MRFs). We introduce the concept of compatibility to show how an undirected graph can be used as a template for the structural dependencies between areal units to create sets of DAGs which, as a collection, preserve the structural dependencies represented in the template undirected graph. We then introduce three classes of compatible DAGs and corresponding algorithms for fitting MDGMs based on these classes. In addition, we compare MDGMs to MRFs and a popular Bayesian MRF model approximation used in high-dimensional settings in a series of simulations and an analysis of ecometrics data collected as part of the Adolescent Health and Development in Context Study.

This work investigates the online machine learning problem of prediction with expert advice in an adversarial setting through numerical analysis of, and experiments with, a related partial differential equation. The problem is a repeated two-person game involving decision-making at each step informed by $n$ experts in an adversarial environment. The continuum limit of this game over a large number of steps is a degenerate elliptic equation whose solution encodes the optimal strategies for both players. We develop numerical methods for approximating the solution of this equation in relatively high dimensions ($n\leq 10$) by exploiting symmetries in the equation and the solution to drastically reduce the size of the computational domain. Based on our numerical results we make a number of conjectures about the optimality of various adversarial strategies, in particular about the non-optimality of the COMB strategy.

Magnetic skyrmion crystals are traditionally associated with non-centrosymmetric crystal structures; however, it has been demonstrated that skyrmion crystals can be stabilized by competing interactions in centrosymmetric crystals. To understand and optimize the physical responses associated with topologically-nontrivial skyrmion textures, it is important to quantify their magnetic interactions by comparing theoretical predictions with spectroscopic data. Here, we present neutron diffraction and spectroscopy data on the centrosymmetric skyrmion material GdRu$_2$Si$_2$, and show that the key spectroscopic features can be explained by the magnetic interactions calculated using density-functional theory calculations. We further show that the recently-proposed 2-$\mathbf{q}$ "topological spin stripe" structure yields better agreement with our data than a 1-$\mathbf{q}$ helical structure, and identify how the magnetic structure evolves with temperature., Comment: 6 pages, 3 figures

Bigraphs are a versatile modelling formalism that allows easy expression of placement and connectivity relations in a graphical format. System evolution is user defined as a set of rewrite rules. This paper presents a practical, yet detailed guide to developing, executing, and reasoning about bigraph models, including recent extensions such as parameterised, instantaneous, prioritised and conditional rules, and probabilistic and stochastic rewriting., Comment: 34 pages

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.]

We study the impact of a bonus policy implemented by Hawai'i Public Schools starting in fall 2020 that raised the salaries of all special education teachers in the state by $10,000. We estimate that the introduction of this policy reduced the proportion of vacant special education teaching positions by 32%, or 1.2 percentage points, and the proportion of special education positions that were vacant or filled by an unlicensed teacher by 35%, or 4.0 percentage points. The bonus policy did not have significant impacts on special education teacher retention; instead, the impacts of the policy were driven almost entirely by an increase in the number of general education teachers in the state who moved into open special education teaching positions. The effects of the bonus policy were also largest in historically hard-to-staff schools in which all teachers also received "tiered school" bonuses of up to $8,000. Hawai'i therefore represents a unique but instructive case of how strategic financial incentives can help address special education teacher shortages.

Long-range and anisotropic dipolar interactions profoundly modify the dynamics of particles hopping in a periodic lattice potential. Here, we report the realization of a generalized t-J model with dipolar interactions using a system of ultracold fermionic molecules with spin encoded in the two lowest rotational states. We systematically explore the role of dipolar Ising and spin-exchange couplings and the effect of motion on spin dynamics. The model parameters can be controlled independently, with dipolar couplings tuned by electric fields and motion regulated by optical lattices. Using Ramsey spectroscopy, we observed interaction-driven contrast decay that depends strongly both on the strength of the anisotropy between Ising and spin-exchange couplings and on motion. These observations are supported by theory models established in different motional regimes that provide intuitive pictures of the underlying physics. This study paves the way for future exploration of kinetic spin dynamics and quantum magnetism with highly tunable molecular platforms in regimes challenging for existing numerical and analytical methods, and it could shed light on the complex behaviors observed in real materials.

Polar molecules confined in an optical lattice are a versatile platform to explore spin-motion dynamics based on strong, long-range dipolar interactions. The precise tunability of Ising and spin-exchange interactions with both microwave and dc electric fields makes the molecular system particularly suitable for engineering complex many-body dynamics. Here, we used Floquet engineering to realize interesting quantum many-body systems of polar molecules. Using a spin encoded in the two lowest rotational states of ultracold KRb molecules, we mutually validated XXZ spin models tuned by a Floquet microwave pulse sequence against those tuned by a dc electric field through observations of Ramsey contrast dynamics, setting the stage for the realization of Hamiltonians inaccessible with static fields. In particular, we observed two-axis twisting mean-field dynamics, generated by a Floquet-engineered XYZ model using itinerant molecules in 2D layers. In the future, Floquet-engineered Hamiltonians could generate entangled states for molecule-based precision measurement or could take advantage of the rich molecular structure for quantum simulation of multi-level systems., Comment: 20 pages, 4 figures + 4 extended data figures

Two-dimensional (2D) layered materials, whether in bulk form or reduced to just a single layer, have potential applications in spintronics and capacity for advanced quantum phenomena. A prerequisite for harnessing these opportunities lies in gaining a comprehensive understanding of the spin behavior in 2D materials. The low dimensionality motivates an understanding of the spin correlations over a wide length scale, from local to long range order. In this context, we focus on the magnetism in bulk \MPSe ~and \MPS, 2D layered van der Waals antiferromagnetic semiconductors. These materials have similar honeycomb Mn layers and magnetic ordering temperatures, but distinct spin orientations and exchange interactions. We utilize neutron scattering to gain deeper insights into the local magnetic structures and spin correlations in the paramagnetic and ordered phases by systematically investigating a MnPS$_{x}$Se$_{3-x}$ ($x$ = 0, 1, 1.5, 2, 3) series of powder samples using total neutron scattering measurements. By employing magnetic pair distribution function (mPDF) analysis, we unraveled the short-range magnetic correlations in these materials and explored how the non-magnetic anion S/Se mixing impacts the magnetic correlations. The results reveal that the magnetism can be gradually tuned through alteration of the non-magnetic S/Se content, which tunes the atomic structure. The change in magnetic structure is also accompanied by a control of the magnetic correlation length within the 2D honeycomb layers. Complimentary inelastic neutron scattering measurements allowed a quantification of the change in the magnetic exchange interactions for the series and further highlighted the gradual evolution of spin interactions in the series MnPS$_{x}$Se$_{3-x}$.

Noncentrosymmetric triangular magnets offer a unique platform for realizing strong quantum fluctuations. However, designing these quantum materials remains an open challenge attributable to a knowledge gap in the tunability of competing exchange interactions at the atomic level. Here, we create a new noncentrosymmetric triangular S = 3/2 magnet CaMnTeO$_6$ based on careful chemical and physical considerations. The model material displays competing magnetic interactions and features nonlinear optical responses with the capability of generating coherent photons. The incommensurate magnetic ground state of CaMnTeO$_6$ with an unusually large spin rotation angle of 127 deg.(1) indicates that the anisotropic interlayer exchange is strong and competing with the isotropic interlayer Heisenberg interaction. The moment of 1.39(1) $\mu$B, extracted from low-temperature heat capacity and neutron diffraction measurements, is only 46% of the expected value of the static moment 3 $\mu$B. This reduction indicates the presence of strong quantum fluctuations in the half-integer spin S = 3/2 CaMnTeO$_6$ magnet, which is rare. By comparing the spin-polarized band structure, chemical bonding, and physical properties of AMnTeO$_6$ (A = Ca, Sr, Pb), we demonstrate how quantum-chemical interpretation can illuminate insights into the fundamentals of magnetic exchange interactions, providing a powerful tool for modulating spin dynamics with atomically precise control.

The S = 1/2 triangular lattice antiferromagnet (TLAF) is a paradigmatic example of frustrated quantum magnetism. An ongoing challenge involves understanding the influence of exchange anisotropy on the collective behavior within such systems. Using inelastic neutron scattering (INS) and advanced calculation techniques, we have studied the low and high-temperature spin dynamics of Ba2La2CoTe2O12 (BLCTO): a Co2+-based Jeff = 1/2 TLAF that exhibits 120{\deg} order below TN = 3.26 K. We determined the spin Hamiltonian by fitting the energy-resolved paramagnetic excitations measured at T > TN, revealing exceptionally strong easy-plane XXZ anisotropy. Below TN, the excitation spectrum exhibits a high energy continuum having a larger spectral weight than the single-magnon modes, suggesting a scenario characterized by a spinon confinement length that markedly exceeds the lattice spacing. We conjecture that this phenomenon arises from the proximity to a quantum melting point, even under strong easy-plane XXZ anisotropy. Finally, we highlight characteristic flat features in the excitation spectrum, which are connected to higher-order van Hove singularities in the magnon dispersion directly induced by easy-plane XXZ anisotropy. Our results provide a rare experimental insight into the nature of highly anisotropic S = 1/2 TLAFs between the Heisenberg and XY limits., Comment: 5 figures, Supplementary Information can be found on the journal's website

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.

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.

We use over 25 years of longitudinal data from Washington state to provide a descriptive portrait of the paraeducator workforce in the state. Paraeducators are more racially and ethnically diverse than special education teachers, particularly in the last decade, and tend to be less experienced. They also have full-time salaries that are about half of the average special education teacher salary. Paraeducator-to-student ratios have decreased over time in the state, but they are higher in schools serving more students of color. Finally, and perhaps most importantly, paraeducator attrition rates from the state's public school workforce have increased dramatically over time; for example, the paraeducator attrition rate after the 2021-22 school year (23%) was over twice as high as the attrition rate after the 2008-09 school year (8%). These findings have implications for how policymakers and school leaders should approach decision-making related to the paraeducator workforce, as well as how researchers might approach further research with this group of educators.

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 examine the potential to expand and diversify the production of university STEM degrees by shifting the margin of initial enrollment between community colleges and 4-year universities. Our analysis is based on statewide administrative microdata from the Missouri Department of Higher Education and Workforce Development covering enrollees in all public postsecondary institutions statewide. We find that the potential for shifting the enrollment margin to expand degree production in STEM fields is modest, even at an upper bound, because most community college students are not academically prepared for bachelor's degree programs in STEM fields. We also find that shifting the enrollment margin is unlikely to improve racial/ethnic diversity among university STEM degree recipients. This is because community college students at the enrollment margin are less diverse than their peers who enter universities directly.

This study examines the extent to which students obtain postsecondary credentials in the career technical education (CTE) fields of focus they choose in high school. Using school fixed effects models, we find that focusing on a particular CTE field in high school is associated with an increased probability of enrolling and obtaining a postsecondary credential in that field. The secondary-postsecondary relationship varies across focus areas, and it is strongest in health (increase of 12.5 percentage points), which is disproportionately chosen by females. Across all fields of focus, however, most students enroll and obtain a postsecondary credential in fields that are different from what they focused on in high school.

Reading has been at the forefront of early-grade educational interventions, but addressing the educational needs of students in math early on is also critical given that early gaps in math skills widen further over the course of schooling. In this study, we examine the effects of Kentucky's Math Achievement Fund -- a unique state-level program that combines targeted interventions, peer-coaching, and close collaboration among teachers to improve math achievement in grades K-3 -- on student outcomes and the costs associated with this policy. We find significant positive effects of the program not only on math achievement, but also on test scores in reading and non-test outcomes including student attendance and disciplinary incidents. The benefits exist across racial/ethnic groups and students from different socioeconomic statuses, and they are slightly higher for racial minorities. These findings, along with the cost estimate of the program, suggest that this program could provide a cost-effective blueprint to address the educational needs of students in math in early grades.