TQM in higher education : continuously improving the teaching and learning of introductory statistics
2017-11-10T01:28:56Z (GMT) by
The study of statistics is regarded as sufficiently important for it to be included as a component of most tertiary courses in the 1990's. However, it is often the most disliked and least understood subject of all those studied. The poor regard in which statistics education is frequently held and the resultant concern of professional and academic statisticians is well documented. The dissatisfaction with introductory statistics education is the focus of the research presented in this thesis.Parallelling the concerns about statistics education has been the growth of interest in the quality of teaching and learning and in quality assurance in universities generally. This has led a number of universities and university departments to apply the management philosophy of Total Quality Management within their organisations. As a result, there has been a welcome move away from the common view of students as simply inputs and outputs of the educational process. In some universities, there has been an increased focus on students as customers and instructors as providers of a service. This focus on students as customers has been intensified by the current trend to 'user pays' funding models for universities. However, this thesis argues that the view of students as customers is confining and fails to acknowledge the multiplicity of roles played by students in the educational process- as inputs, outputs, customers and co-workers.TQM provides the enabling context as well as the problem solving methodology within which these issues are considered. The field of econometrics provides the tools of analysis. In an effort to determine the importance of the contribution of the student to the teaching and learning process, regression models were estimated to establish the relationship between students' attributes and effort, and their results in the introductory statistics subject. To estimate these models, individual student data were collected within two university departments over three years. The nature of this data, including non-normality of the dependent variable, errors in variables and simultaneity, created estimation problems which were addressed by the use of selected econometric techniques.The results for the regression models provided a finer definition of the problems associated with introductory statistics education as well as strong support for the argument that students are more than passive customers of the educational process. Learning factors unique to the two university departments were identified as well as attributes consistent with the findings of learning models for other university cultures. Significant factors included ability, as measured by score on a specially designed mathematics pre-test and performance in secondary school studies, language spoken at home, type of school attended, living arrangements during semester, course of enrolment and tutorial attendance. Factors that did not significantly influence learning outcomes included gender, age and whether or not students were in the course of their first preference. The attendance variable is regarded as a proxy for motivation and preparation and is the source of the strongest support for regarding students as co workers.The research concludes with recommendations for course design in introductory statistics courses to ensure high quality offerings that fully involve and empower students as co-workers in the educational process. In the face of a rapidly changing educational culture, a glimpse is provided of how these recommendations may be applied in a future which includes budgetary stringencies, more full-feepaying students, increasing use of technology and intensifying international competition.