Literature Index

Although the use of a statistical computer package has become an integral part of modern statistics courses, the primary goal of traditional software has been to do statistics rather than to learn statistics. Fathom: Dynamic Statistics Software is one of several newly developed packages that focus a greater emphasis on providing an atmosphere in which students can investigate statistical concepts. To facilitate learning, a key premise is that all aspects of an analysis are linked so that students can see how changes in one area are reflected in another. Fathom's developers have made a special effort to produce an intuitive interface that allows students to "drag & drop" to construct analyses from basic building blocks. It also provides a convenient environment for instructors to develop effective demonstrations. We give examples of these sorts of dynamic illustrations and discuss how Fathom can be used to encourage student explorations.

Statistics education has become an increasingly important component of the mathematics education of today.s citizens. In part to address the call for a more statistically literate citizenship, The Guidelines for Assessment and Instruction in Statistics Education (GAISE) were developed in 2005 by the American Statistical Association. These guidelines provide a framework for statistics education towards the end of enabling students to achieve statistical literacy, both for their personal lives and in their careers. In order to achieve statistical literacy by adulthood, statistics education must begin at the elementary school level. However, many elementary school teachers have not had the opportunity to become statistically literate themselves. In addition, they are not equipped pedagogically to provide effective instruction in statistics. This article will discuss statistical concepts that have been identified as necessary for statistical literacy and describe how an undergraduate course in Probability and Statistics for pre-service elementary and middle school teachers was revised and implemented using the GAISE framework, in conjunction with the NCTM Standards for Data Analysis and Probability. The aims of the revised course were to deepen pre-service elementary and middle school teachers. conceptual knowledge of statistics; to provide them with opportunities to engage in, design, and implement pedagogical strategies for teaching statistics concepts to children; and, to help them make connections between the statistical concepts they are learning and the statistical concepts they will someday teach to elementary and middle school students.

The value to students of active learning has been recognized. This has led to the wide use of assignments in statistical methods courses where students use statistical software and computing equipment to analyze data. These assignments enable most students to master the mechanics of data analysis. The amount of experience that a student can get with such assignments, however, is limited. A sizable proportion of students have difficulty grasping some of the many concepts that are introduced in these courses. Nevertheless, these concepts are important for effective modeling and data analysis, and instructors should focus on them. By using current computing technology, it is possible to supplement standard data analysis assignments and algebraic derivations and have students become actively involved in the learning of important statistical concepts. The learning experience can be enhanced by giving students additional statistical "experiences" by using combinations of carefully designed and implemented multiple simulations and dynamic graphics to illustrate key ideas. In this article we describe and illustrate several instructional modules and corresponding software that have been designed to assist instructors in teaching introductory statistics courses.

We explore and discuss pedagogical opportunities presented by two subtle graphing calculator pitfalls that can be readily encountered in the secondary school classroom when doing statistics on common (TI) calculators: (1) confusion about bounds when computing cumulative probabilities for the normal distribution, and (2) confusion about the order of variables when computing regression lines of best fit to a dataset.