The items covered in the paper are as follows: 1)teaching as an action leading the student towards a better understanding and interpretation of reality; 2)statistics as an area of knowledge that supplies scientific models closer to reality than classical deterministic ones; 3)the appeal of simplified models versus the risk of lack of rigour and of credibility; 4)the role of algorithmic computations versus laboratory simulation results.
The nature of these projects is enormously diverse, ranging from the development of completely new software to the integration of existing packages with lecture and laboratory classes.
This paper discusses graphical software which has been developed in Kalvelagen and Tijms, and is designed to introduce the beginning student in a motivating and coherent way to very basic concepts.
A computer program written in BASIC to simulate each solution is given and uses a built-in computer "random number generator".
In this paper we shall deal with the teaching of CDA in courses.
The present paper outlines a spreadsheet simulation model, suitable for teaching purposes, to simulate the operation of the Central Limit Theorem.
In this paper we will explain the development of our package, how and why it is used in the classroom, and why we feel it is a better way of teaching statistics.
In this paper I am going to concentrate on the core subject, Computing and Data Skills, which is taught by the School of Information and Computer Sciences.
To enhance student comprehension of basic sampling concepts, Arvanities and Reich have developed a Forest Sampling Simulator (FOSS) for microcomputer (Arvanities and Reich, 1989). It has been well-documented by numerous studies that computer simulation fosters understanding of complex systems by permitting students to manipulate individual parts and observe the effects of their action on the rest of the model (Heerman, 1988). This system is described in Section 2 below. In section 3 its possible augmentation by an expert system is described, which would automatically generate the most appropriate sampling strategy for a particular situation, given the appropriate input information.
Our approach to teaching this topic is described, and some implications for the future undergraduate curriculum in statistics are discussed.