Literature Index

As statistics continues to increase its presence in the school curriculum, particularly the mathematics one, it becomes increasingly more difficult for teachers to be able to fit everything in. They complain that if statistics must be included, then something must go. One suggestion to solve their problem is to combine the topics of statistics and mathematics so that both are presented together. The NSF-funded project Data-Driven Mathematics has done precisely that. The series of eleven modules motivates mathematics topics found in pre-algebra, algebra, geometry, advanced algebra, and advanced mathematics from a data point of view that involves students. This paper presents some insight as to how this may be done with the advanced mathematics topic of matrices. (See Burrill, Burrill, Landwehr, & Witmer, 1998).

In this exploratory study, we followed approximately 1000 students (Economics and Business) in their freshman year at the University of Maastricht (Netherlands). Those students attended three compulsory courses in Quantitative Methods, each having an important component of statistics. Our population of students exhibits a strong heterogeneity with respect to several aspects: attitude towards and prior knowledge of mathematics and statistics, nationality, type of prior education and the mastery of languages. To study the impact of this heterogeneity on learning introductory statistics, the development of a model of students' learning of introductory statistics was chosen as the goal of the project. In order to develop a relational model, several surveys were taken and data sources were used with regard to the students' characteristics, learning context, students' perceptions and the approaches students took. The major contribution of this study is the broad range of different determinants of learning that is considered, which allows investigation of the interrelation between several factors influencing learning besides studying the direct impact of each factor on learning.

At its August 1992 meeting in Boston, the Committee on Applied and Theoretical Statistics (CATS) noted widespread sentiment in the statistical community that upper-level undergraduate and graduate curricula for statistics majors and postdoctoral training for statisticians are currently structured in ways that do not provide sufficient exposure to modern statistical analysis, computational and graphical tools, communication skills, and the ever-growing interdisciplinary uses of statistics. Approaches and materials once considered standard are being rethought. The growth that statistics has undergone is often not reflected in the education that future statistician receive. There is a need to incorporate more meaningfully into the curriculum the computational and graphical tools that are today so important to many professional statisticians. There is a need for improved training of statistics students in written and oral communication skills, which are crucial for effective interaction with scientists and policy makers. More realistic experience is needed in various application areas for which statistics is now a key to further progress. In response to this sentiment, CATS initiated a project on modern interdisciplinary university statistics education. With support from the National Science Foundation, CATS organized and held a one-and-one-half-day symposium on that topic in conjunction with the August 1993 San Francisco Joint Statistical Meetings. The symposium's focus was what changes in statistics education are needed to (1) incorporate interdisciplinary training into the upper-undergraduate, graduate, and postdoctoral statistics programs, (2) bring the upper-undergraduate and graduate statistics curricula up to date, and (3) improve apprenticing of statistics graduate and postdoctoral students and appropriately reward faculty mentors. These proceedings have been compiled to capture the timely and important presentations and discussions that took place at that symposium. It should be noted that the opinions expressed in this volume are those of the speakers of discussants and do not necessarily represent the views of CATS or of the National Research Council.

Students' attitudes and beliefs can impede (or assist) learning statistics, and may affect the extent to which students will develop useful statistical thinking skills and apply what they have learned outside the classroom. This chapter alerts educators to the importance of assessing student attitudes and beliefs regarding statistics, describes and evaluates different methods developed to assess where students stand in this regard, provides suggestions for using and extending existing assessments, and outlines future research and instructional needs.