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This article addresses the position taken by Clark (1983) that media do not influence learning under any conditions. The article reframes the questions raised by Clark to explore the conditions under which media will influence learning. Specifically, it posits the need to consider the capabilities of media, and the methods that employ them, as they interact with the cognitive and social processes by which knowledge is constructed. This approach is examined within the context of two major media-based projects, one which uses computers and the other video. The article discusses the implications of this approach for media theory, research, and practice. Do media influence learning? Ten years ago, Richard Clark (1983) reviewed the results of comparative research on educational media and claimed that they provide consistent evidence "... for the generalization that there are no learning benefits to be gained from employing any specific medium to deliver instruction " (p. 445). According to Clark, the results of those studies that appear to favor one medium over another are due not to the medium but to the method or content that are introduced along with the

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.

This paper considers numerical algorithms for finding local minimizers of metric multidimensional scaling problems. Both the STRESS and SSTRESS criteria are considered, and the leading algorithms for each are carefully explicated. A new algorithm, based on Newton's method, is proposed. Translational and rotational indeterminancy is removed by a parametrization that has not previously been used in multidimensional scaling algorithms. In contrast to previous algorithms, a very pleasant feature of the new algorithm is that it can be used with either the STRESS or the SSTRESS criterion. Numerical results are presented. Key words: Metric multidimensional scaling, STRESS criterion, SSTRESS criterion, unconstrained optimization, Newton's method. Department of Computational and Applied Mathematics, Rice University, Houston, TX 77251-1892. This author was generously supported by a Patricia R. Harris Fellowship. y Department of Computational and Applied Mathematics and Center for Research in...

... In this paper we present a multigrid framework for MDS problems. We demonstrate the performance of our algorithm on dimensionality reduction and isometric embedding problems, two classical problems requiring efficient large-scale MDS. Simulation results show that the proposed approach significantly outperforms conventional MDS algorithms.

Multidimensional scaling (MDS) is a collection of data analytic techniques for constructing configurations of points from dissimilarity information about interpoint distances. A popular measure of the fit of the constructed distances to the observed dissimilarities is the stress criterion, which must be minimized by numerical optimization. Empirical evidence concerning the existence of nonglobal minimizers of the stress criterion is somewhat contradictory. We report a configuration that we have demonstrated to be a nonglobal minimizer. 1 Preliminaries Multidimensional scaling (MDS) is a collection of techniques for fitting distance models to distance data. The data are called dissimilarities. Formally, a symmetric n \Theta n matrix \Delta = (ffi ij ) is a dissimilarity matrix if ffi ij 0 and ffi ii = 0. In this report, we restrict attention to the case of a single dissimilarity matrix (two-way MDS). The goal of MDS is to construct a configuration of points in a target metric (usually...

Classical multidimensional scaling (MDS) is a method for visualizing high-dimensional point clouds by mapping to low-dimensional Euclidean space. This mapping is defined in terms of eigenfunctions of a matrix of interpoint dissimilarities. In this paper we analyze in detail multidimensional scaling applied to a specific dataset: the 2005 United States House of Representatives roll call votes. Certain MDS and kernel projections output “horseshoes” that are characteristic of dimensionality reduction techniques. We show that, in general, a latent ordering of the data gives rise to these patterns when one only has local information. That is, when only the interpoint distances for nearby points are known accurately. Our results provide a rigorous set of results and insight into manifold learning in the special case where the manifold is a curve. 1. Introduction. Classical

: In this paper we describe multivariate statistical visualization techniques designed to improve the quality, accuracy and satisfaction of the statistical data analysis process. We describe techniques for visualizing multivariate data structure, for visualizing multivariate data models, and for visualizing multivariate data analysis sessions. We illustrate these techniques with ViSta, our statistical visualization research and development testbed. 1 Introduction Statistical data analysis systems have long included graphics to help users see the results of analyses. Such statistical graphics have also been used to help users explore data for structure. Dynamic statistical graphics -- graphics which incorporate motion -- can be powerful tools for exploring data structure. They can be powerful because they help the scientific explorer visually analyze -- to visualize -- structure. Dynamic statistical graphics are especially powerful for visualizing structure in multivariate data. This i...

Multidimensional scaling (MDS) is a class of methods used to find a low-dimensional representation of a set of points given a matrix of pairwise distances between them. Problems of this kind arise in various applications, from dimensionality reduction of image manifolds to psychology and statistics. In many of these applications, efficient and accurate solution of an MDS problem is required. In this paper, we propose using vector extrapolation techniques to accelerate the numerical solution of MDS problems. Vector extrapolation is used to accelerate the convergence of fixed-point iterative algorithms. We review the problem of multidimensional scaling and vector extrapolation techniques, and show several examples of our accelerated solver for multidimensional scaling problems in various applications. 1

Abstract. Do schools influence the values of the students who attend them? This study examines the influence of parents, peer groups, teachers and the schools on student values as assessed by the Schwartz Value Survey. A sample of students at Grade 12 was chosen from 11 South Australian secondary schools of a wide variety of types, and the students, their parents and their teachers completed the Value Survey, and students also provided sociodemographic information. The data were analysed using the HLM multilevel analysis program with respect to the four higher order value components, namely self-transcendence, self-enhancement, conservation and openness to change in order to identify student level and school level effects on the values held by the students. Results showed that sex of student, language background, the Christian involvement of the student, parental social position and the values held by parents and peer groups had much greater effects upon the students ’ values than the schools and their teachers. Further research involving a larger number of schools in a wider variety of settings is suggested to build on this exploratory study. 1.