983 resultados para Eisner, Brian


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Why not the Best Schools? offers a ten-point, ten-year plan for an education revolution that will result in the transformation of Australia’s schools. Expectations have been raised in Australia and comparable countries for an ‘education revolution’ that will secure success for all students in all settings. Such a revolution must ensure the alignment of educational outcomes, the skills required for a strong economy, and the needs of a harmonious society. Why not the Best Schools? goes beyond system characteristics to provide an in-depth account of how transformation occurs in schools. Fifty indicators are provided to help shape strategies for policy makers and practitioners in schools and school systems. Guidelines for leadership and governance ensure a future-focus for those who are determined to ensure that all students will succeed in the twentieth-first century. This book draws on a five-year study culminating in the International Project to Frame the Transformation of Schools conducted in Australia, China, England, Finland, the United States and Wales. The findings are consistent with the McKinsey & Company report on the world’s best performing school systems and those arising from OECD’s PISA.

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"Expectations have been raised in Australia and comparable countries for an 'education revolution' that will secure success for all students in all settings. Such a revolution must ensure the alignment of educational outcomes, the skills required for a strong economy, and the needs of a harmonious society. Why not the Best Schools? offers a ten-point, ten-year plan for an education revolution that will result in the transformation of Australia's schools. Why not the Best Schools? goes beyond system characteristics to provide an in-depth account of how transformation occurs in schools. Fifty indicators are provided to help shape strategies for policy makers and practitioners in schools and school systems. Guidelines for leadership and governance ensure a future-focus for those who are determined to ensure that all students will succeed in the twentieth-first century. This book draws on a five-year study culminating in the International Project to Frame the Transformation of Schools conducted in Australia, China, England, Finland, the United States and Wales. The findings are consistent with the McKinsey & Company report on the world's best performing school systems and those arising from OECD's PISA."--publisher website

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It has been an assumption of most anti-pornography discourse that porn damages women (and children) in a variety of ways. In Porno? Chic!, the author interrogated this assumption by examining the correlation between the incidence of sexual violence and other indicators of misogyny, and the availability and accessibility of pornography within a number of societies. This article develops that work with a specific focus on the regulatory environment as it relates to pornography and sexual representation. Does a liberal regulatory regime in sexual culture correlate with a relatively advanced state of sexual politics in a given country? Conversely, does an illiberal regime, where pornography and other forms of sexual culture are banned or severely restricted, correlate with relatively strong patriarchal structures? A comparative cross-country analysis seeks to explain the correlations identified, and to assess the extent to which the availability of porn can be viewed as a causal or a consequential characteristic of those societies where feminism has achieved significant advances.

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In this environmental scan of research and policy literature, the authors consider various definitions of trust as they seek to address and inform the hypothesis that ‘a culture of trust enhances performance’ in schools. The discussion draws on the work of the Australian Institute for Teaching and School Leadership in school leadership and research by Caldwell and Harris (2008) to consider ways in which trust is linked to performance in schools and organisations. It explores the concept of trust as a form of capital available to schools, and highlights how successful school leaders work to align trust, strategy and passion in implementing effective change. At the heart of the review is the relationship between trust and school improvement. An adapted model of the elements of a culture of trust developed by Bryk and Schneider in their influential study of trust in school reform in Chicago is used to frame this aspect of the review. The authors identify four essential categories of relationships that enable schools to be described as having a culture of trust. The review concludes with a discussion of the relationship between trust and school governance.

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Educational Transformations Pty Ltd was commissioned by The Song Room (TSR) to conduct a study of the impact of TSR programs in government schools in relatively disadvantaged communities in New South Wales (NSW) on indicators of student performance that have been identified in previous research as related to potential engagement in juvenile crime. Students in Grades 5 and 6 were the subjects of study. TSR is a not-for-profit organisation in receipt of grants from public and private sources that conducts free programs in the performing arts in schools where these are not currently offered. These programs are conducted by mutual agreement between TSR and participating schools. Across Australia, approximately 200 schools and 40,000 students are engaged for a minimum of six months each year. Students typically participate for approximately one hour per week in each class. Instruction is provided by a Teaching Artist (TA), contracted to TSR and working in partnership with the classroom teacher at the school of placement. TSR received a three-year grant from the Macquarie Group Foundation to investigate the efficacy of its interventions in improving social and education outcomes for children in a range of high need target group areas participating in its program...

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The effects of oxygen availability and induction culture biomass upon production of an industrially important monoamine oxidase (MAO) were investigated in fed-batch cultures of a recombinant E. coli. For each induction cell biomass 2 different oxygenation methods were used, aeration and oxygen enriched air. Induction at higher biomass levels increased the culture demand for oxygen, leading to fermentative metabolism and accumulation of high levels of acetate in the aerated cultures. Paradoxically, despite an almost eight fold increase in acetate accumulation to levels widely reported to be highly detrimental to protein production, when induction wet cell weight (WCW) rose from 100% to 137.5%, MAO specific activity in these aerated processes showed a 3 fold increase. By contrast, for oxygenated cultures induced at WCW's 100% and 137.5% specific activity levels were broadly similar, but fell rapidly after the maxima were reached. Induction at high biomass levels (WCW 175%) led to very low levels of specific MAO activity relative to induction at lower WCW's in both aerated and oxygenated cultures. Oxygen enrichment of these cultures was a useful strategy for boosting specific growth rates, but did not have positive effects upon specific enzyme activity. Based upon our findings, consideration of the amino acid composition of MAO and previous studies on related enzymes, we propose that this effect is due to oxidative damage to the MAO enzyme itself during these highly aerobic processes. Thus, the optimal process for MAO production is aerated, not oxygenated, and induced at moderate cell density, and clearly represents a compromise between oxygen supply effects on specific growth rate/induction cell density, acetate accumulation, and high specific MAO activity. This work shows that the negative effects of oxygen previously reported in free enzyme preparations, are not limited to these acellular environments but are also discernible in the sheltered environment of the cytosol of E. coli cells.

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Development of researchers through higher degree research studies is a high priority in most universities. Yet, research about supervision as pedagogy and models of supervision is only recently gained increasing attention. Charged with producing good researchers within very limited resources, academics are constantly looking for more efficient models of supervision for higher degree research students. A cohort model of supervision promises several efficiencies, but we argue that its success lies importantly on how well the cohort is developed specifically for higher degree research studies. We drew on a growing body of literature on higher degree research supervision to design, implement and evaluate our approach to developing a cohort of seven students enrolled in the Master of Education (Research) degree. Our approach included four provisions: initial residential workshop, development of a learning community, nourishing scholarship, and ongoing learning opportunities. The four provisions resulted in gradually developing an environment and culture that students found very supportive and nurturing. This paper is based on the findings from data collected from student evaluations in the first year of studies, feedback from the cohort’s sponsor, and our reflective notes. The evaluation substantiated the value in investing time and resources for purposely developing a cohort for higher degree research studies. Whether the cohorts are sponsored or not, universities will still need to invest time and resources for cohort development if a cohort model is intended to gain wider efficiencies in supervision of higher degree research students.

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Ramp signalling is an access control for motorways, in which a traffic signal is placed at on-ramps to regulate the rate of vehicles entering the motorway and thus to preserve the motorway capacity. In general, ramp signalling algorithms fall into two categories: local control and coordinated control by their effective scope. Coordinated ramp signalling strategies make use of measurements from the entire motorway network to operate individual ramp signals for the optimal performances at the network level. This study proposes a multi-hierarchical strategy for coordinated ramp signalling. The strategy is structured in two layers. At the higher layer with a longer update interval, coordination group is assembled and disassembled based on the location of high-risk breakdown flow. At the lower layer with a shorter update interval, individual ramps are hired to serve the coordination and are also released based on the prevailing congestion level on the ramp. This strategy is modelled and applied to the northbound Pacific Motorway micro-simulation platform (AIMSUN). The simulation results show an effective congestion mitigation of the proposed strategy.

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Over several decades, academics around the world have investigated the necessary tools, techniques, and conditions which would allow BIM (building information modeling) to become a positive force in the world of construction. As the research results matured, BIM started to become commercially available. Researchers and many in industry soon realized that BIM, as a technological innovation, was, in and of itself, not the end point in the journey. The technical adoption of BIM has to be supported by process and culture change within organizations to make a real impact on a project (for example, see AECbytes Viewpoint #35 by Chuck Eastman, Paul Teicholz, Rafael Sacks and Kathleen Liston). Current academic research aims to understand the steps beyond BIM, which will help chart the future of our industry over the coming decades. This article describes an international research effort in this area, coordinated by the Integrated Design and Delivery Solutions (IDDS) initiative of the CIB (International Council for Research and Innovation in Building and Construction). We hope that it responds to and extends the discussion initiated by Brian Lighthart in AECbytes Viewpoint #56, which asked about who is charting future BIM directions.

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Unstable density-driven flow can lead to enhanced solute transport in groundwater. Only recently has the complex fingering pattern associated with free convection been documented in field settings. Electrical resistivity (ER) tomography has been used to capture a snapshot of convective instabilities at a single point in time, but a thorough transient analysis is still lacking in the literature. We present the results of a 2 year experimental study at a shallow aquifer in the United Arab Emirates that was designed to specifically explore the transient nature of free convection. ER tomography data documented the presence of convective fingers following a significant rainfall event. We demonstrate that the complex fingering pattern had completely disappeared a year after the rainfall event. The observation is supported by an analysis of the aquifer halite budget and hydrodynamic modeling of the transient character of the fingering instabilities. Modeling results show that the transient dynamics of the gravitational instabilities (their initial development, infiltration into the underlying lower-density groundwater, and subsequent decay) are in agreement with the timing observed in the time-lapse ER measurements. All experimental observations and modeling results are consistent with the hypothesis that a dense brine that infiltrated into the aquifer from a surficial source was the cause of free convection at this site, and that the finite nature of the dense brine source and dispersive mixing led to the decay of instabilities with time. This study highlights the importance of the transience of free convection phenomena and suggests that these processes are more rapid than was previously understood.

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A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

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Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.

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Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

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This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

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Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points (SANP) and Manifold Discriminant Analysis (MDA).