High-Stakes Testing and Student Achievement: Problems for the No Child Left Behind Act

Under the federal No Child Left Behind Act of 2001 (NCLB), standardized test scores are the indicator used to hold schools and school districts accountable for student achievement. Each state is responsible for constructing an accountability system, attaching consequences -- or stakes -- for student performance. The theory of action implied by this accountability program is that the pressure of high-stakes testing will increase student achievement. But this study finds that pressure created by high-stakes testing has had almost no important influence on student academic performance

High-Stakes Testing and Student Achievement: Does Accountability Pressure Increase Student Learning?

This study examined the relationship between high-stakes testing pressure and student achievement across 25 states. Standardized portfolios were created for each study state. Each portfolio contained a range of documents that told the “story” of accountability implementation and impact in that state. Using the “law of comparative judgments,” over 300 graduate-level education students reviewed one pair of portfolios and made independent evaluations as to which of the two states’ portfolios reflected a greater degree of accountability pressure. Participants’ judgments yielded a matrix that was converted into a single rating system that arranged all 25 states on a continuum of accountability “pressure” from high to low. Using this accountability pressure rating we conducted a series of regression and correlation analyses. We found no relationship between earlier pressure and later cohort achievement for math at the fourth- and eighth-grade levels on the National Assessment of Educational Progress tests. Further, no relationship was found between testing pressure and reading achievement on the National Assessment of Education Progress tests at any grade level or for any ethnic student subgroup. Data do suggest, however, that a case could be made for a causal relationship between high-stakes testing pressure and subsequent achievement on the national assessment tests—but only for fourth grade, non-cohort achievement and for some ethnic subgroups. Implications and directions for future studies are discussed

A study of the deep structure of the energy landscape of glassy polystyrene: the exponential distribution of the energy-barriers revealed by high-field Electron Spin Resonance spectroscopy

The reorientation of one small paramagnetic molecule (spin probe) in glassy polystyrene (PS) is studied by high-field Electron Spin Resonance spectroscopy at two different Larmor frequencies (190 and 285 GHz). The exponential distribution of the energy-barriers for the rotational motion of the spin probe is unambigously evidenced at both 240K and 270K. The same shape for the distribution of the energy-barriers of PS was evidenced by the master curves provided by previous mechanical and light scattering studies. The breadth of the energy-barriers distribution of the spin probe is in the range of the estimates of the breadth of the PS energy-barriers distribution. The evidence that the deep structure of the energy landscape of PS exhibits the exponential shape of the energy-barriers distribution agrees with results from extreme-value statistics and the trap model by Bouchaud and coworkers.Comment: Final version in press as Letter to the Editor on J.Phys.:Condensed Matter. Changes in bol

Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions

This is the pre-print version of the article found in the Monthly Weather Review (http://journals.ametsoc.org/toc/mwre/138/10).Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the relevant fluid equations. Often, limited area models are constructed to interpret intensive datasets collected over a specific region, from a variety of observational platforms. These data are noisy and they typically do not span the domain of interest uniformly in space and time. Traditional numerical procedures cannot easily account for these uncertainties. A hierarchical Bayesian modeling framework is developed for solving boundary value problems in such settings. By allowing the boundary process to be stochastic, and conditioning the interior process on this boundary, one can account for the uncertainties in the boundary process in a reasonable fashion. In the presence of data and all its uncertainties, this idea can be related through Bayes' Theorem to produce distributions of the interior process given the observational data. The method is illustrated with an example of obtaining atmospheric streamfunction fields in the Labrador Sea region, given scatterometer-derived observations of the surface wind field

Spatio-Temporal Hierarchical Bayesian Modeling: Tropical Ocean Surface Winds

This is the author's version of the article found in the Journal of the American Statistical Association. The publisher's version can be found at http://pubs.amstat.org/loi/jasa.Spatio-temporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal variability. The complexity of these processes and large number of observation/prediction locations preclude the use of traditional covariance-based space-time statistical methods. Alternatively, we focus on conditionally-specified (i.e., hierarchical) spatio-temporal models. These methods offer several advantages over traditional approaches. Primarily, physical and dynamical constraints are easily incorporated into the conditional formulation, so that the series of relatively simple, yet physically realistic, conditional models leads to a much more complicated space-time covariance structure than can be specified directly. Furthermore, by making use of the sparse structure inherent in the hierarchical approach, as well as multiresolution (wavelet) bases, the models are computable with very large datasets. This modeling approach was necessitated by a scientifically meaningful problem in the geosciences. Satellite-derived wind estimates have high spatial resolution but are limited in global coverage. In contrast, wind fields provided by the major weather centers provide complete coverage but have low spatial resolution. The goal is to combine these data in a manner that incorporates the space-time dynamics inherent in the surface wind field. This is an essential task to enable meteorological research as no complete high resolution surface wind datasets exist over the world oceans. High resolution datasets of this kind are crucial for improving our understanding of: global air-sea interactions affecting climate, tropical disturbances, and for driving large-scale ocean circulation models.Support for this research was provided for CKW, DN, and LMB by the NCAR Geophysical Statistics Project, sponsored by the National Science Foundation (NSF) under Grant DMS93-12686. Support for RFM and CKW is provided by the NCAR NSCAT Science Working Team cooperative agreement with NASA JPL. NCAR is supported in part by the NSF

Ocean ensemble forecasting. Part I: Ensemble Mediterranean winds from a Bayesian hierarchical model

A Bayesian hierarchical model (BHM) is developed to estimate surface vector wind (SVW) fields and associated uncertainties over the Mediterranean Sea. The BHM–SVW incorporates data-stage inputs from analyses and forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and SVW retrievals from the QuikSCAT data record. The process-model stage of the BHM–SVW is based on a Rayleigh friction equation model for surface winds. Dynamical interpretations of posterior distributions of the BHM–SVW parameters are discussed. Ten realizations from the posterior distribution of the BHM–SVW are used to force the data-assimilation step of an experimental ensemble ocean forecast system for the Mediterranean Sea in order to create a set of ensemble initial conditions. The sequential data-assimilation method of the Mediterranean forecast system (MFS) is adapted to the ensemble implementation. Analyses of sample ensemble initial conditions for a single data-assimilation period in MFS are presented to demonstrate the multivariate impact of the BHM–SVW ensemble generation methodology. Ensemble initial-condition spread is quantified by computing standard deviations of ocean state variable fields over the ten ensemble members. The methodological findings in this article are of two kinds. From the perspective of statistical modelling, the process-model development is more closely related tophysicalbalances than inpreviousworkwithmodels for the SVW.Fromthe ocean forecast perspective, the generation of ocean ensemble initial conditions via BHM is shown to be practical for operational implementation in an ensemble ocean forecast system. Phenomenologically, ensemble spread generated via BHM–SVW occurs on ocean mesoscale time- and space-scales, in close association with strong synoptic-scale wind-forcing events. A companion article describes the impacts of the BHM–SVW ensemble method on the ocean forecast in comparisons with more traditional ensemble methods

Emergency Preparedness Training for Hospital Nursing Staff, New York City, 2012–2016

Purpose Many nurses are trained inadequately in emergency preparedness (EP), preventing them from effectively executing response roles during disasters, such as chemical, biological, radiological, nuclear, and explosive (CBRNE) events. Nurses also indicate lacking confidence in their abilities to perform EP activities. The purpose of this article is to describe the phased development of, and delivery strategies for, a CBRNE curriculum to enhance EP among nursing professionals. The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) and the National Center for Disaster Preparedness at Columbia University's Earth Institute led the initiative. Methods Curriculum development included four phases. In Phases I and II, nursing staff at 20 participating NYC hospitals conducted 7,177 surveys and participated in 20 focus groups to identify training gaps in EP. In Phase III, investigators developed and later refined the CBRNE curriculum based on gaps identified. In Phase IV, 22 nurse educators (representing 7 of the original 20 participating hospitals) completed train‐the‐trainer sessions. Of these nurse educators, three were evaluated on their ability to train other nurses using the curriculum, which investigators finalized. Findings The CBRNE curriculum included six modules, a just‐in‐time training, and an online annual refresher course that addressed EP gaps identified in surveys and focus groups. Among the 11 nurses who were trained by three nurse educators during a pilot training, participant knowledge of CBRNE events and response roles increased from an average of 54% (range 45%–75%) on the pre‐test to 89% (range 80%–90%) on the posttest. Conclusions By participating in nursing CBRNE training, nurses increased their knowledge of and preparedness to respond to disasters. The train‐the‐trainer curriculum is easily adaptable to meet the needs of other healthcare settings. Clinical Relevance The CBRNE curriculum can be used to train nurses to better prepare for and more effectively respond to disasters