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An International Journal on the Teaching and Learning of Statistics

JSE Volume 10, Number 2 Abstracts

Anna Reid and Peter Petocz
Students’ Conceptions of Statistics: A Phenomenographic Study

This paper reports on the results of an empirical study of students’ conceptions and understanding of statistics. Six qualitatively different conceptions are described, ranging from fragmented to inclusive views. Students expressing the more inclusive and holistic conceptions approach their study of statistics through a focus on ‘higher-order’ statistical thinking. Students expressing limited and fragmented views may not be able to understand the complexity or applications of the discipline. This paper describes the use of a qualitative methodology - phenomenography - that aims to explore the qualitatively different ways in which a group of people experience a specific phenomenon, in this case statistics. It also describes an overarching framework, the "Professional Entity," that relates students’ understanding of statistics and their perceptions of working as a statistician. Investigating and describing the ways in which students learn statistics, how they understand statistics, and how they perceive their own work will enable teachers to develop curricula that focus on enhancing the student learning environment and guiding student conceptions of statistics.

Key Words: Curriculum development; Student learning; Student work.

Jim Albert
A Baseball Statistics Course

An introductory statistics course is described that is entirely taught from a baseball perspective. Topics in data analysis, including methods for one batch, comparison of batches, and relationships, are communicated using current and historical baseball data sets. Probability is introduced by describing and playing tabletop baseball games. Inference is taught by first making the distinction between a player's "ability" and his "performance", and then describing how one can learn about a player's ability based on his season performance. Baseball issues such as the proper interpretation of situational and "streaky" data are used to illustrate statistical inference.

Key Words: Ability; Measures of batting performance; Situational statistics; Spinner probability model; Sports; Streakiness.

G. Rex Bryce
Undergraduate Statistics Education: An Introduction and Review of Selected Literature

A recent symposium on “Improving the Work Force of the Future: Opportunities in Undergraduate Statistics Education” was held to focus attention on the importance of undergraduate statistics education. The symposium and the approval of curriculum guidelines for undergraduate degrees by the Board of Directors of the American Statistical Association have done much to define the current state of undergraduate education in statistics and suggest directions for improvement. This article summarizes the activities leading up to the symposium and provides a brief summary of six papers from the symposium that have been published. The article concludes with a discussion of some of the outstanding issues that remain to be addressed.

Key Words: BA degree in statistics; BS degree in Statistics; Curriculum guidelines; Minors in statistics; Undergraduate Statistics Education Initiative (USEI).

Joan Garfield, Bob Hogg, Candace Schau, and Dex Whittinghill
First Courses in Statistical Science: The Status of Educational Reform Efforts

Over the past twenty years much has been written about the introductory or service course in statistics. Historically, this course has been viewed as difficult and unpleasant by many students and frustrating and unrewarding to teach by many instructors. Dissatisfactions with the introductory course have led people to suggest new models for the course, to lead workshops to reexamine this course (Hogg 1992), and to offer recommendations for how the course should be changed (Cobb 1992). This paper presents the results of a survey of teachers of the first statistics course, to determine the impact of reform efforts on the teaching of statistics. Suggestions and guidelines for teaching these courses are offered, based on the results of the survey.

Key Words: Introductory statistics; Undergraduate Statistics Education Initiative (USEI).

Ann Cannon, Brad Hartlaub, Robin Lock, William Notz, and Mary Parker
Guidelines for Undergraduate Minors and Concentrations in Statistical Science

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.

Key Words: Major programs; Minor programs; Undergraduate programs; Undergraduate Statistics Education Initiative (USEI).

Thaddeus Tarpey, Carmen Acuna, George Cobb, and Richard De Veaux
Curriculum Guidelines for Bachelor of Arts Degrees in Statistical Science

Curriculum guidelines for a bachelor of arts degree in statistical science are proposed. These guidelines are intended for liberal arts colleges, and other institutions where statistics is taught in departments of mathematics. A flexible curriculum is described consisting of three main parts: mathematics, core statistical topics and a substantive area of study. The curriculum guidelines permit and actively encourage the rethinking of traditional courses and the development of new courses. Guidelines for a minor in statistical science are also proposed. The guidelines are the result of an Undergraduate Statistics Education Initiative workshop held in Alexandria, Virginia in April 2000.

Key Words: Liberal arts; Statistics major; Undergraduate Statistics Education Initiative (USEI).

P. P. J. L. Verkoeijen, Tj. Imbos, M. W. J. van de Wiel, M. P. F. Berger, and H. G. Schmidt
Assessing Knowledge Structures in a Constructive Statistical Learning Environment

In this report, the method of free recall is put forward as a tool to evaluate a prototypical statistical learning environment. A number of students from the faculty of Health Sciences, Maastricht University, the Netherlands, were required to write down whatever they could remember of a statistics course in which they had participated. By means of examining the free recall protocols of the participants, insight can be obtained into the mental representations they had formed with respect to three statistical concepts. Quantitative as well as qualitative analyses of the free recall protocols showed that the effect of the constructive learning environment was not in line with the expectations. Despite small-group discussions on the statistical concepts, students appeared to have disappointingly low levels of conceptual understanding.

Key Words: Active learning; Constructive learning environment; Free recall; Knowledge representation.

Teaching Bits: A Resource for Teachers of Statistics

This department features information sampled from a variety of sources that may be of interest to teachers of statistics. Deb Rumsey abstracts information from the literature on teaching and learning statistics, while Bill Peterson summarizes articles from the news and other media that may be used with students to provoke discussions or serve as a basis for classroom activities or student projects.

James J. Cochran
Data Management, Exploratory Data Analysis, and Regression Analysis with 1969-2000 Major League Baseball Attendance

The 1969-2000 Major League Baseball Attendance dataset contains Runs Scored, Runs Allowed, Wins, Losses, Number of Games Behind the Division Leader, and Home Game Attendance of each major league franchise for the 1969 through 2000 seasons. Also included for each franchise are its location, league affiliation (National or American), and division affiliation (East, Central, or West). These data have been used in a project-based modeling course to instruct students in basic data management, the use of exploratory data analysis to "clean" data, and construction of regression models. The dataset, which is both cross-sectional and time-series, is of a manageable size and easily understood. Furthermore, it provides a useful, interesting, and realistic classroom example for discussing many important statistical concepts.

Key Words: Classroom data; Exploratory data analysis; Regression analysis.

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