The ability to seek out data, organize it, and interpret it is an empowering skill, and that a person who truly understands data has a source of power to use in influencing the direction of important decisions. The goal of education in statistics and probability should be to impart this sense of power to students, Learning statistics does not mean merely mastering the fomulaic transformations that yield mean, standard deviation, and P value. A true understanding of statistics includes knowing how to use data to discover and evaluate important associations and to communicate these associations to others. It requires learning how to evaluate other people's use of data and to augment or challenge them with additional data. There are NCTM Teaching Standards (NCTM, 1991), which include a new view of pedagogy in mathematics teaching - a focus on understanding the underlying concepts of our number system rather than on memorizing addition and multiplication facts, on facility in spatial visualization rather than on learning formulas for the area of polygons, and on planning and on carrying out data analysis projects rather than on knowing the difference between mean and median. Integrated with these two major changes, researchers and practitioners are looking more to technology to support new approaches to mathematics learning, as "tools for enhancing [mathematical] discourse." (NCTM, 1991, p.52) How does the computer fit into the developing view of statistics education? At first glance, the answer seems obvious: computers free students (and teachers) from the tedious computations that are required to calculate means, standard deviations, confidence intervals, etc. They draw graphs quickly and accurately. They generate multitudes of samples in a single bound. But this list of accomplishments leaves two crucial questions unanswered: 1) Are there other more powerful ways in which computers can facilitate students' learning of statistics? 2) Are there any drawbacks to uses of computers in statistics classes? The remainder of this paper will address both of these questions.
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