Testing knowledge is an integral part of a summative assessment at schools. It can be performed in many different ways. In this study we propose assessment of physics knowledge by using a class tournament approach. Prior to a statistical analysis of the results obtained over a tournament organized in one of Polish high schools, all its specifics are discussed at length, including the types of questions assigned, as well as additional self-and peer-evaluation questionnaires, constituting an integral part of the tournament. The impact of the tournament upon student improvement is examined by confronting the results of a post-test with pre-tournament students' achievements reflected in scores earned in former, tests written by the students in experimental group and their colleagues from control group. We also present some of students' and teachers' feedback on the idea of a tournament as a tool of assessment. Both the analysis of the tournament results and the students' and teachers' opinions point to at least several benefits of our approach.

A simplified data analysis protocol, for dielectric spectroscopy use to study conductivity percolation in dehydrating granular media is discussed. To enhance visibility of the protonic conductivity contribution to the dielectric loss spectrum, detrimental effects of either low-frequency dielectric relaxation or electrode polarization are removed. Use of the directly measurable monofrequency dielectric loss factor rather than estimated DC conductivity to parameterize the percolation transition substantially reduces the analysis work and time.

Abstract

In the paper, we begin with introducing a novel scale mixture of normal distribution such that its leptokurticity and fat-tailedness are only local, with this “locality” being separately controlled by two censoring parameters. This new, locally leptokurtic and fat-tailed (LLFT) distribution makes a viable alternative for other, globally leptokurtic, fat-tailed and symmetric distributions, typically entertained in financial volatility modelling. Then, we incorporate the LLFT distribution into a basic stochastic volatility (SV) model to yield a flexible alternative for common heavy-tailed SV models. For the resulting LLFT-SV model, we develop a Bayesian statistical framework and effective MCMC methods to enable posterior sampling of the parameters and latent variables. Empirical results indicate the validity of the LLFT-SV specification for modelling both “non-standard” financial time series with repeating zero returns, as well as more “typical” data on the S&P 500 and DAX indices. For the former, the LLFT-SV model is also shown to markedly outperform a common, globally heavy-tailed, t-SV alternative in terms of density forecasting. Applications of the proposed distribution in more advanced SV models seem to be easily attainable.

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The goal of this conference is to set up a new research field to study IT value and its components, says co-chair Elliot Chikofsky, chief consultant of Engineering Management and Integration, an IT consultancy firm, in his opening statement. Looking at the high costs of IT made him wonder whether the research effort was maybe too late. Chikofsky predicts that concepts and methods need to be combined to discover how much is known about IT value and what elements are lacking. "This is a 'casa blanca' conference to round up the usual suspects: who are the people doing the research in this area?"

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SummaryWe compare predictive performance of a multitude of alternative Bayesian vector autoregression (VAR) models allowing for cointegration and time‐varying conditional covariances, described by different multivariate stochastic volatility (MSV) models, including their hybrids with multivariate GARCH processes (MSV‐MGARCH), as well as t‐GARCH and Markov‐switching structures. The forecast accuracy is evaluated mainly through predictive Bayes factors, but energy scores and the probability integral transform are also used. Two empirical studies, for the US and Polish economies, are based on a small model of monetary policy comprising inflation, unemployment and interest rate. The results indicate that capturing conditional heteroskedasticity by some MSV‐MGARCH specifications contributes the most to the forecasting power of the VAR/VEC model.