Literature Index

Representatives from academia, industry, and government met in May 1999 and in April 2000 at the ASA Headquarters to discuss issues concerning undergraduate education in statistical science. One outcome of these meetings was the symposium entitled "Improving the Workforce of the Future: Opportunities in Undergraduate Education," held August 12 through 13, 2000, in Indianapolis, Indiana. Among the topics discussed in the meetings and at the symposium were guidelines for minor programs in statistical science. This article summarizes the results of these discussions.

Designers of educational software tools inevitably struggle with the issue of complexity. In general, a simple tool will minimize the time needed to learn it at the expense of range of applications. On the other hand, designing a tool to handle a wide range of applications risks overwhelming students. I contrast the decisions we made regarding complexity when we developed DataScope 15 years ago with those we recently made in designing TinkerPlots, and describe how our more recent tack has served to increase student engagement at the same time it helps them see critical connections among display types. More generally, I suggest that in the attempt to not overwhelm students, too many educational environments managed instead to under whelm them and thus serve to stifle rather than foster learning.

Current curricular thinking in mathematics, science and computing displays a recurring theme: the value of working with data and the importance of learning the skills and concepts associated with such work. Recent statements issued by the National Council of Teachers of Mathematics (NCTM, 1987) and the Mathematical Sciences Education Board (Ralston 1988) advocate a sharply increased emphasis on data analysis in school mathematics at all levels. The concern with computer literacy of the early 1980s is maturing into a discussion of the kinds of "information studies" that are needed to prepare students for a society in which information technologies play an essential and ever-expanding role (White , 1987). The call for the use of real data in the natural and social sciences goes back considerably farther (Hawkins, 1964; Morrison, 1964; Taba, 1967), but has recently gained new impetus from technological advances which multiply the potential for powerful, realistic investigation by science students (Hawkins, Brunner, et al. 1987; Tinker, 1987). In support of curricular developments such as these, new technologies offer a potential that is largely untapped. The large bitmapped screens and fast processors which are available on today's new workstations, and will be available on the school computers of the middle to late 1990's make possible a whole new class of tools for working with data, tools whose transparency and rich interactivity can support qualitatively new styles of inquiry and bring unprecedented analytic power to students of all ages. We have designed and partially prototyped an exemplar of this new class: a highly visual, highly interactive environment for creating, organizing, exploring and analyzing "attribute data" -- the kinds of data that are used in statistics and many of the sciences, and which conventional database systems are designed to store. The environment achieves a striking combination of simplicity, directness, power and flexibility. We are truly excited by the potential for tools of this kind to support a new level of data analysis and theory building in mathematics and the sciences. More than tools are needed, however. Essential to all of these curricular trends, it seems, is a fundamental set of concepts and skills about which more needs to be known.

This paper describes different ideas that key on most of the important topics of the introductory statistics course.