This paper concentrates on the "Why?" parts of some of the simpler questions and discusses the usefulness of asking students to give a brief reason as to why they had chosen the answer they gave. Responses given by the UK students are used to illustrate points.
This paper provides a conceptual framework for generating assessment tasks which provide an instructor with a richer description of students' thinking and reasoning than is possible by just giving students problems to solve. Although the framework is general, its application is illustrated with material from college and beginning graduate level pre-calculus introductory statistics courses which focuses on the following topics: sampling, interval estimation, point estimation, and hypothesis testing.
In professional fields such as education, psychology, sociology, etc., applied statistics courses emphasise developing skills in planning quantitative research studies, properly analysing data, and correctly interpreting the results of analysis. In general, students lack the background which would be required to deal with mathematical derivations. Furthermore, it is doubtful that this background would materially benefit these students in the professional roles for which they are preparing. The vast majority of researchers in the behavioural sciences are able to conduct their data analyses using the sophisticated statistical packages that are readily available. Thus, it becomes critical that applied statistics courses realistically prepare them for their role as data analysts. Each academic year, our department enrolls about 1000 students in undergraduate and graduate applied statistics courses. Approximately 50% of these students are enrolled in an undergraduate elementary statistics course and the other 50% of the students are enrolled in a series of four graduate courses offered, primarily, for students in the College of Education.
Traditional courses in statistics generally approach inference from a theoretical probability based perspective. Since the mathematical backgrounds of students is often not strong, many courses use computer based simulations to empirically justify ideas which are too complex or too abstract for most students. However, eventually, students must move from the empirical to the theoretical understanding of the concept if they are to apply these ideas to traditional inference methodologies. This paper questions the effectiveness of some of these strategies, and discusses how computer based technologies may be used effectively to bring together these theoretical and empirical perspectives.
The purpose of this paper is to investigate gender differences in achievement in statistics, making use of the Population A data of the Second International Mathematics Study (SIMS) conducted by the International Association for the evaluation of Educational Achievement (IEA). In IEA terms, Population A means all the students in the grade in which most students attain the age of 13.0 to 13.11 years by the middle of the school year.
Our research looks at whether preferences in question choice, and differences in achievement, in Mathematics with Statistics papers are related to gender.
This research aims to replicate the study by Forbes (1988) who investigated gender differences in attainment in a Scholarship examination in mathematics. There are three major differences between this study and hers. First, this research is based on a mainstream Advanced Level examination paper rather than a Scholarship paper. Second, the study aims to discover whether the results found in New Zealand apply to pupils in Britain. Third, the examination paper includes questions on mechanics, which did not appear in the New Zealand examination, as well as statistics and pure mathematics. Following Forbes, the initial hypotheses are that girls and boys will perform equally well on some, at least, of the pure mathematics questions. There is also the opportunity to look for gender differences in attainment in mechanics questions.
This paper presents some of the salient features of that particular experiment which involved students of the FA2 Statistics class at Kinnaird College during the academic year 1988-1989. The experiment seems to have been successful, and it appears that if such an exercise is made an integral part of the teaching of statistics at the higher secondary/intermediate level, it may prove to be an effective means not only of consolidating in the students' minds some of the basic concepts of statistics, but also of promoting in the students self-confidence, self-expression, and the capability for teamwork.
We have an increasing number of students at Australian universities whose first language is not English. The English courses which many of these students take before being admitted are frequently inadequate for the study of technical subjects such as statistics which have a language of their own. Very little work has been done on the precise difficulties experienced by overseas students and how these difficulties could be alleviated. This paper discusses some of these problems which arise in first level university statistics courses and suggests some appropriate responses. In general these suggestions would probably benefit all students, not only those from overseas.
Statistics is taught in all Singapore schools as part of the compulsory subject mathematics. Only at the university level may statistics be offered as a subject by itself, in the Department of Economics and Statistics. In attempting to make every school-leaver numerate and literate the statistics taught at the school level is meant to equip an individual with the ability to interpret and understand correctly the data presented in tables, diagrams, charts, and graphs. It is the concern of every mathematics teacher in school that pupils should know enough about simple statistics to be able to interpret them correctly and not be deceived by them.