Is optimization an opportunity ? an assessment of the impact of class size and school size on the performance of Ukrainian secondary schools

Using a rich data set of almost the entire population of Ukrainian secondary schools, the authors estimate the effect of school size and class size on the performance of secondary schools on Ukraine's External Independent Test. They find that larger schools tend to have somewhat better performance, both in terms of test scores and in terms of test participation. The size of this effect is relatively small, however, especially in rural areas for which the estimates are likely to be more clean estimates. Class size is found to be insignificant in most specifications and, if significant, of negligible size.Tertiary Education,Secondary Education,Teaching and Learning,Education For All,Primary Education

Is Optimization an Opportunity? An Assessment of the Impact of Class Size and School Size on the Performance of Ukrainian Secondary Schools

Using a rich data set of almost the entire population of Ukrainian secondary schools, the authors estimatethe effect of school size and class size on the performance of secondary schools on Ukraine's External Independent Test. They find that larger schools tend to have somewhat better performance, both in terms of test scores and in terms of test participation. The size of this effect is relatively small, however, especially in rural areas for which the estimates are likely to be more clean estimates. Class size is found to be insignificant in most specifications and, if significant, of negligible size.Tertiary Education, Secondary Education, Teaching and Learning, Education For All, Primary Education

Learning Bayesian network classifiers for multidimensional supervised classification problems by means of a multiobjective approach

A classical supervised classification task tries to predict a single class variable based on a data set composed of a set of labeled examples. However, in many real domains more than one variable could be considered as a class variable, so a generalization of the single-class classification problem to the simultaneous prediction of a set of class variables should be developed. This problem is called multi-dimensional supervised classification. In this paper, we deal with the problem of learning Bayesian net work classifiers for multi-dimensional supervised classification problems. In order to do that, we have generalized the classical single-class Bayesian network classifier to the prediction of several class variables. In addition, we have defined new classification rules for probabilistic classifiers in multi-dimensional problems. We present a learning approach following a multi-objective strategy which considers the accuracy of each class variable separately as the functions to optimize. The solution of the learning approach is a Pareto set of non-dominated multi-dimensional Bayesian network classifiers and their accuracies for the different class variables, so a decision maker can easily choose by hand the classifier that best suits the particular problem and domain

Job shop scheduling biobjetivo mediante enfriamiento simulado y enfoque de Pareto.

El problema del scheduling es uno de los problemas más ampliamente tratados en la literatura; sin embargo, es un problema complejo NP hard. Cuando, además, se involucra más de un objetivo, este problema se convierte en uno de los más complejos en el campo de la investigación de operaciones. Se presenta entonces un modelo biobjetivo para el job shop scheduling que incluye el makespan y el tiempo de flujo medio. Para resolver el modelo se ha utilizado una propuesta que incluye el uso del meta heurístico Recocido Simulado (SA) y el enfoque de Pareto. Este modelo es evaluado en tres problemas presentados en la literatura de tamaños 6×6, 10×5 y 10×10. Los resultados del modelo se comparan con otros meta-heurísticos y se encuentra que este modelo presenta buenos resultados en los tres problemas evaluados

A sensitivity study of bias and variance of k-fold cross-validation in prediction error estimation

In the machine learning field the performance of a classifier is usually measured in terms of prediction error. In most real-world problems, the error cannot be exactly calculated and it must be estimated. Therefore, it’s important to choose an appropriate estimator of the error. This paper analyzes the statistical properties (bias and variance) of the k-fold cross-validation classification error estimator (k-cv). Our main contribution is a novel theoretical decomposition of the variance of the k-cv considering its sources of variance: sensitivity to changes in the training set and sensitivity to changes in the folds. The paper also compares the bias and variance of the estimator for different values of k. The empirical study has been performed in artificial domains because they allow the exact computation of the implied quantities and we can specify rigorously the conditions of experimentation. The empirical study has been performed for two different classifiers (naïve Bayes and nearest neighbor), different number of folds (2, 5, 10, n) and sample sizes, and training sets coming from assorted probability distributions

Real Time Teaching with Java: JPR3

This paper describes a development platform built around a digital railroad scale{model: JPR3 (Java Platform for Realtime Railway Research). The laboratory equipment and software aims to achieve two goals: help and motivate students of real{time systems and as support for postgraduate students. Students nd the scale{model really challenging and are very motivated by it; thus it's easy for them to really learn and practice all the concepts of real{time systems. But it's not only for students use: it also serves as a research platform for postgraduate students, thanks to the possibilities o ered by the scale{model. Java has been chosen as the programming language codify the platform and the implementation of the system is described in this work.This work was supported in part by the Spanish Ministry of Education (with reference ACF2000{0037{IN) and the Regional Government of Murcia (Séneca Programmes with reference PB/8/FS/02

Impact of askling L-PROTOCOL on biceps femoris architecture, hamstring flexibility and sprint performance

Eccentric training has been shown to be important for hamstring strain injuries rehabilitation and prevention. The Askling L-PROTOCOL (L-P), comprising three exercises aimed at eccentric training and hamstring lengthening, was shown to improve this injuries recovery and relapse times in comparison with other traditional exercise-based protocols. However, the causes of these results remain unclear. This study looks at the impact of an 8-week L-P followed by 4 weeks of detraining on the architecture of the biceps femoris long head, hamstring flexibility and sprint performance. Twenty-eight healthy individuals were divided into two groups: an experimental group, which carried out the L-P, and a control group with no training. Muscle architecture was measured using 2D ultrasound, hamstring flexibility using goniometry and sprint performance using sports radar equipment before (M1) and after (M2) the training period and after detraining (M3). No significant changes were observed between M1 and M2 in the experimental group with regard to fascicle length (t=− 0.79, P>0.05), theoretical maximum speed (t=− 1.43, P>0.05), horizontal force (t=0.09, P>0.05), force application during sprint running (t=− 0.09, P>0.05) and horizontal power (t=− 0.97, P>0.05), but, however, changes were observed in hamstring flexibility (t=− 4.42, d=0.98, P0.05). L-P has been shown to be an eccentric protocol of moderate intensity and easy implementation that could be interesting to include throughout a sports season

Ambulatory Blood Pressure Monitoring in the Elderly

The incidence of hypertension is high in the elderly and is present in 2/3 of the patients older than 65 years. Prevalence can reach 90% in patients older than 80 years. The presence of isolated systolic hypertension (ISH) is characteristic of this population. However, the prevalence of hypertension by ambulatory blood pressure monitoring (ABPM) is not well known. In this study, we analyzed the special characteristics of hypertension in this population, giving special emphasis on ABPM readings