6,643 research outputs found
Should we adjust for pupil background in school value-added models? A study of Progress 8 and school accountability in England
In the UK, US and elsewhere, school accountability systems increasingly
compare schools using value-added measures of school performance derived from
pupil scores in high-stakes standardised tests. Rather than naively comparing
school average scores, which largely reflect school intake differences in prior
attainment, these measures attempt to compare the average progress or
improvement pupils make during a year or phase of schooling. Schools, however,
also differ in terms of their pupil demographic and socioeconomic
characteristics and these also predict why some schools subsequently score
higher than others. Many therefore argue that value-added measures unadjusted
for pupil background are biased in favour of schools with more 'educationally
advantaged' intakes. But, others worry that adjusting for pupil background
entrenches socioeconomic inequities and excuses low performing schools. In this
article we explore these theoretical arguments and their practical importance
in the context of the 'Progress 8' secondary school accountability system in
England which has chosen to ignore pupil background. We reveal how the reported
low or high performance of many schools changes dramatically once adjustments
are made for pupil background and these changes also affect the reported
differential performances of region and of different school types. We conclude
that accountability systems which choose to ignore pupil background are likely
to reward and punish the wrong schools and this will likely have detrimental
effects on pupil learning. These findings, especially when coupled with more
general concerns surrounding high-stakes testing and school value-added models,
raise serious doubts about their use in school accountability systems
The Limitations of Using School League Tables to Inform School Choice
In England, so-called ‘league tables’ based upon examination results and test scores are published annually, ostensibly to inform parental choice of secondary schools. A crucial limitation of these tables is that the most recent published information is based on the current performance of a cohort of pupils who entered secondary schools several years earlier, whereas for choosing a school it is the future performance of the current cohort that is of interest. We show that there is substantial uncertainty in predicting such future performance and that incorporating this uncertainty leads to a situation where only a handful of schools’ future performances can be separated from both the overall mean and from one another with an acceptable degree of precision. This suggests that school league tables, including value-added ones, have very little to offer as guides to school choice.Examination results, Institutional comparisons, League tables, Multilevel modelling, Performance indicators, Ranking, School choice, School effectiveness, Value-added
Computational Approaches to Understanding Structure-Function Relationships at the Intersection of Cellular Organization, Mechanics, and Electrophysiology
The heart is a complex mechanical and electrical environment and small changes at the cellular and subcellular scale can have profound impacts at the tissue, organ, and organ system levels. The goal of this research is to better understand structure-function relationships at these cellular and subcellular levels of the cardiac environment. This improved understanding may prove increasingly important as medicine begins shifting toward engineered replacement tissues and organs. Specifically, we work towards this goal by presenting a framework to automatically create finite element models of cells based on optical images. This framework can be customized to model the effects of subcellular structure and organization on mechanical and electrophysiological properties at the cellular level and has the potential for extension to the tissue level and beyond. In part one of this work, we present a novel algorithm is presented that can generate physiologically relevant distributions of myofibrils within adult cardiomyocytes from confocal microscopy images. This is achieved by modelling these distributions as directed acyclic graphs, assigning a cost to each node based on observations of cardiac structure and function, and determining to minimum-cost flow through the network. This resulting flow represents the optimal distribution of myofibrils within the cell. In part two, these generated geometries are used as inputs to a finite element model (FEM) to determine the role the myofibrillar organization plays in the axal and transverse mechanics of the whole cell. The cardiomyocytes are modeled as a composite of fiber trusses within an elastic solid matrix. The behavior of the model is validated by comparison to data from combined Atomic Force Microscopy (AFM) and Carbon Fiber manipulation. Recommendations for extending the FEM framework are also explored. A secondary goal, discussed in part three of this work, is to make computational models and simulation tools more accessible to novice learners. Doing so allows active learning of complicated course materials to take place. Working towards this goal, we present CellSpark: a simulation tool developed for teaching cellular electrophysiology and modelling to undergraduate bioengineering students. We discuss the details of its implementation and implications for improved student learning outcomes when used as part of a discovery learning assignment
One year of monitoring the Vela pulsar using a Phased Array Feed
We have observed the Vela pulsar for one year using a Phased Array Feed (PAF)
receiver on the 12-metre antenna of the Parkes Test-Bed Facility. These
observations have allowed us to investigate the stability of the PAF
beam-weights over time, to demonstrate that pulsars can be timed over long
periods using PAF technology and to detect and study the most recent glitch
event that occurred on 12 December 2016. The beam-weights are shown to be
stable to 1% on time scales on the order of three weeks. We discuss the
implications of this for monitoring pulsars using PAFs on single dish
telescopes.Comment: 6 pages, 4 figures, 2 tables. Accepted for publication in PAS
A multilevel modelling approach to measuring changing patterns of ethnic composition and segregation among London secondary schools, 2001-2010
Multilevel binomial logistic regression has recently been proposed for the special case of statistically modelling changing composition and segregation of two groups of individuals over two occasions among organizational units, enabling inferences to be made about the underlying social processes which generate these patterns. A simulation method can then be used to re-express the model parameters in the metric of any desired two-group segregation index. We generalize this combined modelling and simulation approach by proposing multilevel random-coefficient multinomial logistic regression for the general case of statistically modelling multiple groups of individuals over multiple occasions and multiple organizational scales. We illustrate this combined approach with an application to modelling changing three-group white–black–Asian ethnic composition and segregation among London secondary schools and local authorities during the first decade of the 21st century
The evolution of school league tables in England 1992-2016:‘contextual value-added’, ‘expected progress’ and ‘progress 8’
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