Mathematical modelling in mathematics education and instruction

This paper aims at giving a concise survey of the present state-of-the-art of mathematical modelling in mathematics education and instruction. It will consist of four parts. In part 1, some basic concepts relevant to the topic will be clarified and, in particular, mathematical modelling will be defined in a broad, comprehensive sense. Part 2 will review arguments for the inclusion of modelling in mathematics teaching at schools and universities, and identify certain schools of thought within mathematics education. Part 3 will describe the role of modelling in present mathematics curricula and in everyday teaching practice. Some obstacles for mathematical modelling in the classroom will be analysed, as well as the opportunities and risks of computer usage. In part 4, selected materials and resources for teaching mathematical modelling, developed in the last few years in America, Australia and Europe, will be presented. The examples will demonstrate many promising directions of development.

Analysis of applications and of conceptions for an application-oriented mathematics instruction

In connection with the (revived) demand for considering applications in the teaching of mathematics, various schemata or lists of criteria have been developed since the end of the sixties, which set up requirements about closeness to the real world or about the type of mathematics being used, and which have made it possible to analyze the available applications in their light. After having stated the problem (in section 1), we present (in section 2) a sketch of some of the best known of these and of some earlier schemata, although we are not aiming for a complete picture. Then (in section 3) we distinguish among different dimensions.in the analysis of applications. With this as a basis, we develop (in section 4) our own suggestion for categorizing types of applications and conceptions for an application-oriented mathematics instruction. Then (in section 5) we illustrate our schemata by some examples of performed evaluations. Finally (in section 6), we present some preliminary first results of the analysis of teaching conceptions.

ONTIC: A Knowledge Representation System for Mathematics

Ontic is an interactive system for developing and verifying mathematics. Ontic's verification mechanism is capable of automatically finding and applying information from a library containing hundreds of mathematical facts. Starting with only the axioms of Zermelo-Fraenkel set theory, the Ontic system has been used to build a data base of definitions and lemmas leading to a proof of the Stone representation theorem for Boolean lattices. The Ontic system has been used to explore issues in knowledge representation, automated deduction, and the automatic use of large data bases.

Translation Invariance in Object Recognition, and Its Relation to Other Visual Transformations

Human object recognition is generally considered to tolerate changes of the stimulus position in the visual field. A number of recent studies, however, have cast doubt on the completeness of translation invariance. In a new series of experiments we tried to investigate whether positional specificity of short-term memory is a general property of visual perception. We tested same/different discrimination of computer graphics models that were displayed at the same or at different locations of the visual field, and found complete translation invariance, regardless of the similarity of the animals and irrespective of direction and size of the displacement (Exp. 1 and 2). Decisions were strongly biased towards same decisions if stimuli appeared at a constant location, while after translation subjects displayed a tendency towards different decisions. Even if the spatial order of animal limbs was randomized ("scrambled animals"), no deteriorating effect of shifts in the field of view could be detected (Exp. 3). However, if the influence of single features was reduced (Exp. 4 and 5) small but significant effects of translation could be obtained. Under conditions that do not reveal an influence of translation, rotation in depth strongly interferes with recognition (Exp. 6). Changes of stimulus size did not reduce performance (Exp. 7). Tolerance to these object transformations seems to rely on different brain mechanisms, with translation and scale invariance being achieved in principle, while rotation invariance is not.

A comparison of the alr and ilr transformations for kernel density estimation of compositional data

In a seminal paper, Aitchison and Lauder (1985) introduced classical kernel density estimation techniques in the context of compositional data analysis. Indeed, they gave two options for the choice of the kernel to be used in the kernel estimator. One of these kernels is based on the use the alr transformation on the simplex SD jointly with the normal distribution on RD-1. However, these authors themselves recognized that this method has some deficiencies. A method for overcoming these dificulties based on recent developments for compositional data analysis and multivariate kernel estimation theory, combining the ilr transformation with the use of the normal density with a full bandwidth matrix, was recently proposed in Martín-Fernández, Chacón and Mateu- Figueras (2006). Here we present an extensive simulation study that compares both methods in practice, thus exploring the finite-sample behaviour of both estimators

Inputs for the incorporation of the UNESCO guidelines on ICT competency standards for teachers : the training of teachers of mathematics in Central America

Resumen tomado de la publicaci??n

Introduction to Network Mathematics

Introduction to Network Mathematics provides college students with basic graph theory to better understand the Internet

School of Mathematics Computing Guide

Guide for computing in the School of Mathematics. Intended for new staff and PG students. Originally written by Anton Prowse from a number of earlier documents.

WAIS seminar:Mathematics for Web Science Part3-1

ABSTRACT In the first two seminars we looked at the evolution of Ontologies from the current OWL level towards more powerful/expressive models and the corresponding hierarchy of Logics that underpin every stage of this evolution. We examined this in the more general context of the general evolution of the Web as a mathematical (directed and weighed) graph and the archetypical “living network” In the third seminar we will analyze further some of the startling properties that the Web has as a graph/network and which it shares with an array of “real-life” networks as well as some key elements of the mathematics (probability, statistics and graph theory) that underpin all this. No mathematical prerequisites are assumed or required. We will outline some directions that current (2005-now) research is taking and conclude with some illustrations/examples from ongoing research and applications that show great promise.

WAIS seminar:Mathematics for Web Science Part 3-2

ABSTRACT In the first two seminars we looked at the evolution of Ontologies from the current OWL level towards more powerful/expressive models and the corresponding hierarchy of Logics that underpin every stage of this evolution. We examined this in the more general context of the general evolution of the Web as a mathematical (directed and weighed) graph and the archetypical “living network” In the third seminar we will analyze further some of the startling properties that the Web has as a graph/network and which it shares with an array of “real-life” networks as well as some key elements of the mathematics (probability, statistics and graph theory) that underpin all this. No mathematical prerequisites are assumed or required. We will outline some directions that current (2005-now) research is taking and conclude with some illustrations/examples from ongoing research and applications that show great promise.

WAIS seminar: Mathematics for Web Science: Part 3-3

ABSTRACT In the first two seminars we looked at the evolution of Ontologies from the current OWL level towards more powerful/expressive models and the corresponding hierarchy of Logics that underpin every stage of this evolution. We examined this in the more general context of the general evolution of the Web as a mathematical (directed and weighed) graph and the archetypical “living network” In the third seminar we will analyze further some of the startling properties that the Web has as a graph/network and which it shares with an array of “real-life” networks as well as some key elements of the mathematics (probability, statistics and graph theory) that underpin all this. No mathematical prerequisites are assumed or required. We will outline some directions that current (2005-now) research is taking and conclude with some illustrations/examples from ongoing research and applications that show great promise.