Proceedings

  • A study to investigate students' facility with proportions has been undertaken by the author and Fay Sharples of the University of Waikato in New Zealand over the period 1989 to 1992. The initial study was done during 1989 and 1990 and 64 students at the University of Waikato and 57 at Brunel University in the UK took part. Some results of this study have been reported elsewhere. We made some changes to our questionnaire after studying the results of the initial study. In the Spring of 1992, 127 students in New Zealand and 29 students in the UK, all of whom were taking statistics as a service course, completed the revised version of the questionnaire. As with the previous study, the results in this follow-up were interesting and not always what we expected. This paper discusses the results on three questions.

  • Traditional testing techniques (particularly those used in national or regional examinations) emphasize competitive perspectives of assessment where the main purpose is to differentiate between students for selection or relative ranking purposes (Suggett, 1985). For ranking purposes, information about knowledge possessed by individual students is largely irrelevant. The crucial dimension for both candidate and the examining authority is the position on the rank-order list. Such assessment approaches do not inform either the candidate (whether successful or not) or the teacher (whether past or future) about the level of conceptual development that has been reached or about the possible next steps in the learning process. This report describes some innovative assessment strategies used to explore conceptual development and to describe achievement in terms of the tasks that candidates can do (or not do) rather than in terms of rank order. Such mapping of a set of mathematics results provides more useful information for the parties to the assessment.

  • In Section 2 of this article, we give a brief introduction to the contents and general structure of the undergraduate requirement in probability theory and mathematical statistics, and in Section 3, the proposals from some experts in China will be presented. The efforts made by some teachers and some changes and opinions in some recent textbooks will be introduced in Section 4.

  • Brief descriptions of several model courses which have been developed by participants in a series of Statistics in the Liberal Arts Workshops (SLAW).

  • This consideration of the question "What is basic statistics?" was initiated by an examination of training needs for statistics faculty at certain target universities in eastern Indonesia.

  • This paper is a discussion of the contributions Charles Babbage made to Statistics.

  • I have selected the stated problem which may be found in Takacs (1960,p.21) with a brief reference to statistical mechanics, for several reasons: its source is an important test used by many engineering students; the problem lends itself well to illustrate the ideas on imagination presented in this paper and, with imagination unfettered, it can serve to generate quite exciting purely mathematical questions.

  • In South Australia mathematics students typically have had considerable exposure to calculus but little to probability on entering university. The amount of probability in school syllabuses has decreased, and our first year university students often associate the subject with fatuous but intricate examples. Some undertake a first year statistics subject, but many will come to a first course in applied probability/stochastic processes in second year applied mathematics without that background. They are ill at lease with any second year applied mathematics courses not based on calculus (forgetting the effort with which the comfort with calculus was won!) and will often demand evidence of meaningful applicability at an early stage. This creates some challenge and necessitates an examination of one's teaching philosophy.

  • This paper discusses the modeling process.

  • We shall discuss the software and hardware which are required to develop new models from data analysis.

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