Assessment Issues on the Teaching of Statistics


Book: 
Fourth International Conference on Teaching Statistics, July
Authors: 
Clark, M.
Category: 
Pages: 
10-Jan
Year: 
1994
Place: 
Marrakech, Morocco
Abstract: 

In New Zealand, end-of-course examination based assessment is rooted in our past and while it may have served the past well, it is clear that it does not adequately serve our present needs. In 1991 the Education Subcommittee of the New Zealand Statistical Association suggested that these examinations may not be valid (unbiased) or reliable (have low variability) measures of ability. Further there is a growing concern that our examinations do not function equitably across all groups of students, and that they do not adequately measure either those skills needed by the general population for their everyday needs, or the skills needed to contribute to the country's economic growth. The debate on assessment procedures has, in part, arisen because of the differential performance of girls and boys in traditional mathematics examinations. In New Zealand a number of analyses of secondary school mathematics examination performance have been done (Stewart, 1987; Reilly et al, 1987; Forbes, 1988; Morton et al, 1988 and 1989; Forbes et al, 1990). These results all show a greater range of achievement within each gender than between the genders but typically the top grades are dominated my males. There are a number of forms of assessment in current use in statistics. Some types of assessment may unfairly advantage one group of students over another. A limited amount of research has been done comparing assessment methods to determine those which may best suit women, Maori (indigenous New Zealanders), or ethnic minorities. Women themselves cannot be classified as just one group. Forbes (1992) showed that a reduction in gender differences in performance in mathematics of one group of the New Zealand population (European) does not necessarily lead to a similar reduction in another group (Maori).

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education