Who Stays and Who Leaves? Findings from a Three-Part Study of Teacher Turnover in NYC Middle Schools

This paper synthesizes findings from the Research Alliance's investigation of teacher turnover in New York City's public middle schools. These years are widely recognized as a critical turning point for students, and the NYC Department of Education (DOE) is pursuing a range of middle school improvement initiatives. The stability of the middle school teaching force has the potential to facilitate or complicate these efforts, yet there have been few studies of the rates and patterns of teacher turnover in the City's middle schools.This study provides the most current, comprehensive look at middle school teacher turnover to date. Drawing on a range of data sources -- including DOE human resource records from the last decade, a survey of over 4,000 full-time middle school teachers, and in-depth case studies in four middle schools -- this paper examines how long middle school teachers remain in their schools, how long they intend to stay, and what predicts whether or not they leave. It also explores how various aspects of teachers' work environment may influence these decisions. Among the key findings: Among middle school teachers who entered their school during the last decade, more than half left that school within three years -- significantly higher than the rates seen for elementary and high school teachers. Of the teachers who leave, most exit the NYC public school system altogether, and only about 1 in 10 transition to another grade 6-8 school. The findings point to several strategies that may be useful for increasing middle school teachers' lengths of stay

Peptide mass fingerprinting using field-programmable gate arrays

The reconfigurable computing paradigm, which exploits the flexibility and versatility of field-programmable gate arrays (FPGAs), has emerged as a powerful solution for speeding up time-critical algorithms. This paper describes a reconfigurable computing solution for processing raw mass spectrometric data generated by MALDI-TOF instruments. The hardware-implemented algorithms for denoising, baseline correction, peak identification, and deisotoping, running on a Xilinx Virtex-2 FPGA at 180 MHz, generate a mass fingerprint that is over 100 times faster than an equivalent algorithm written in C, running on a Dual 3-GHz Xeon server. The results obtained using the FPGA implementation are virtually identical to those generated by a commercial software package MassLynx

Consistent parameter identification of partial differential equation models from noisy observations

This paper introduces a new residual-based recursive parameter estimation algorithm for linear partial differential equations. The main idea is to replace unmeasurable noise variables by noise estimates and to compute recursively both the model parameter and noise estimates. It is proven that under some mild assumptions the estimated parameters converge to the true values with probability one. Numerical examples that demonstrate the effectiveness of the proposed approach are also provided

Identification of N-state spatio-temporal dynamical systems using a polynomial model

A multivariable polynomial model is introduced to describe n-state spatio-temporal systems. Based on this model, a new neighbourhood detection and transition rules determination method is proposed. Simulation results illustrate that the new method performs well even when the patterns are corrupted by static and dynamical noise

A cellular automata modelling of dendritic crystal growth based on Moore and von Neumann neighbourhood

An important step in understanding crystal growth patterns involves simulation of the growth processes using mathematical models. In this paper some commonly used models in this area are reviewed, and a new simulation model of dendritic crystal growth based on the Moore and von Neumann neighbourhoods in cellular automata models are introduced. Simulation examples are employed to find ap- propriate parameter configurations to generate dendritic crystal growth patterns. Based on these new modelling results the relationship between tip growth speed and the parameters of the model are investigated

Multiscale modelling and identification of a class of lattice dynamical systems

A new multiscale modelling framework is introduced to describe a class of lattice dynamical systems (LDS), which can be used to model natural systems involving multiphysics and the multi-resolution facets of a single spatio-temporal dynamical system. The emphasis of the paper is on the multi-resolution facets, with respect to the spatial domain, of a single spatio-temporal dynamical system by using a Haar wavelet decomposition technique. A multiscale identification method for such systems is then proposed, which can be considered as a dual of the multigrid method. The proposed identification method involves three steps: the system dynamics at some specific scale of interest are identified using a recursive least-squares algorithm; the residual is then projected onto coarser scales using Haar wavelets and the parameter estimation errors are minimized; and finally a coarse correction procedure is applied to the original scale. An outstanding advantage of the proposed identification method is a saving on the computational costs. Numerical examples are provided to demonstrate the application of the proposed new approach

Multiscale time series modelling with an application to the relativistic electron intensity at the geosynchronous orbit

In this paper, a Bayesian system identification approach to multiscale time series modelling is proposed, where multiscale means that the output of the system is observed at one(coarse) resolution while the input of the system is observed at another (One) resolution. The proposed method identifies linear models at different levels of resolution where the link between the two resolutions is realised via non-overlapping averaging process. This averaged time series at the coarse level of resolution is assumed to be a set of observations from an implied process so that the implied process and the output of the system result in an errors-in-variables ARMAX model at the coarse level of resolution. By using a Bayesian inference and Markov Chain Monte Carlo (MCMC) method, such a modelling framework results in different dynamical models at different levels of resolution at the same time. The new method is also shown to have the ability to combine information across different levels of resolution. An application to the analysis of the relativistic electron intensity at the geosynchronous orbit is used to illustrate the new method