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.
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