Exploring the construction of social class in educational discourse: the rational order of the nation state versus global uncertainties

This article aims to create intellectual space in which issues of social inequality and education can be analyzed and discussed in relation to the multifaceted and multi-levelled complexities of the modern world. It is divided into three sections. Section One locates the concept of social class in the context of the modern nation state during the period after the Second World War. Focusing particularly on the impact of 'Fordism' on social organization and cultural relations, it revisits the articulation of social justice issues in the United Kingdom, and the structures put into place at the time to alleviate educational and social inequalities. Section Two problematizes the traditional concept of social class in relation to economic, technological and sociocultural changes that have taken place around the world since the mid-1980s. In particular, it charts some of the changes to the international labour market and global patterns of consumption, and their collective impact on the re-constitution of class boundaries in 'developed countries'. This is juxtaposed with some of the major social effects of neo-classical economic policies in recent years on the sociocultural base in developing countries. It discusses some of the ways these inequalities are reflected in education. Section Three explores tensions between the educational ideals of the 'knowledge economy' and the discursive range of social inequalities that are emerging within and beyond the nation state. Drawing on key motifs identified throughout, the article concludes with a reassessment of the concept of social class within the global cultural economy. This is discussed in relation to some of the major equity and human rights issues in education today.

Pressure-relieving equipment: promoting its correct use amongst nurses via differing modes of educational delivery

Aims and objectives. To examine the impact of written and verbal education on bed-making practices, in an attempt to reduce the prevalence of pressure ulcers. Background. The Department of Health has set targets for a 5% reduction per annum in the incidence of pressure ulcers. Electric profiling beds with a visco-elastic polymer mattress are a new innovation in pressure ulcer prevention; however, mattress efficacy is reduced by tightly tucking sheets around the mattress. Design. A prospective randomized pre/post-test experimental design. Methods. Ward managers at a teaching hospital were approached to participate in the study. Two researchers independently examined the tightness of the sheets around the mattresses. Wards were randomized to one of two groups. Groups A and B received written education. In addition, group B received verbal education on alternate days for one week. Beds were re-examined one month later. One researcher was blinded to the educational delivery received by the wards. Results. Twelve wards agreed to participate in the study and 245 beds were examined. Before education, 113 beds (46%) had sheets tucked correctly around the mattresses. Following education, this increased to 215 beds (87.8%) (chi(2) = 68.03, P < 0.001). There was no significant difference in the number of correctly made beds between the two different education groups: 100 (87.72%) beds correctly made in group A vs. 115 (87.79%) beds in group B (chi(2) = 0, P 0.987). Conclusions. Clear, concise written instruction improved practice but verbal education was not additionally beneficial. Relevance to clinical practice. Nurses are receptive to clear, concise written evidence regarding pressure ulcer prevention and incorporate this into clinical practice.

Semantic organization to enhance active learning based on distributed multimedia educational resources

In order to organize distributed educational resources efficiently, to provide active learners an integrated, extendible and cohesive interface to share the dynamically growing multimedia learning materials on the Internet, this paper proposes a generic resource organization model with semantic structures to improve expressiveness, scalability and cohesiveness. We developed an active learning system with semantic support for learners to access and navigate through efficient and flexible manner. We learning resources in an efficient and flexible manner. We provide facilities for instructors to manipulate the structured educational resources via a convenient visual interface. We also developed a resource discovering and gathering engine based on complex semantic associations for several specific topics.

Students' assessment of immersive virtual reality as a pedagogic medium

Over the past few years a number of research studies, mainly involving desktop-based or head-mounted Virtual Reality (VR) systems, have been undertaken to determine what VR can contribute to the education process. In our study we have used the findings from a number of these studies to help in formulating a new study into the perceived merits and limitations of using VR in general, and immersive CAVE-like systems in particular, as an education tool. We conducted our study with a group of final year undergraduate students who were registered on a module that described VR in terms of the scientific issues, application areas, and strengths and weaknesses of the technology.

Sparse modeling using orthogonal forward regression with PRESS statistic and regularization

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.