Literature Index

In this research forum we present results from a research project concerning students' understanding of statitical association and its evolution after teaching experiments using computers. This research has been carried out at the Universities of Granada and Jaen over the years 1991-98. We have identified different incorrect preconceptions and strategies to assess statistical association and performed two different teaching experiments designed to overcome these difficulties and to identify the critical points, which arise in attempting to do this.

This paper will describe a project designed to enhance the numeracy skills of students at two educational levels - elementary and undergraduate. Under the guidance of the university students, students in grades four through six will formulate a research question, gather the appropriate data and summarize the data using graphs. The graphs along with a written summary of the project will be displayed in a poster, which will be sent to the national poster competition sponsored by the American Statistical Association.

COMPENSTAT, a menu-driven statistical program for IBM-compatible microcomputers, has two distinct versions: instructional and computational. The instructional version can be used by instructors as a classroom resource, and the computational version is used directly by students to calculate answers to problems. The software package is primarily used as an assignment generating and problem solving tool. Each student in a class is assigned unique data for a problem type. Since all data sets generate different answers, students can help each other learn but cannot simply copy answers. The instructor is not burdened with extra work, since each student's assignment is followed by a personalized answer key on which the student's answer is computed. An answer sheet is even provided to organize students' responses for easy checking. This paper provides instructions for using the menu-driven features of COMPENSTAT in a business statistics course including diagrams of the menu options, which facilitate building, viewing, or modifying data sets; generating individualized assignments for students in a class; and performing statistical calculations (e.g., frequency distributions, descriptive statistics, probability distributions, confidence intervals, hypothesis testing, chi-square and ANOVA, index numbers, regression and correlation, and nonparametric statistics). Examples of a COMPENSTAT answer sheet and statistical problems on regression, correlation, and ANOVA are also provided. (GL)

Biostatistics is traditionally a difficult subject for students to learn. While the mathematical aspects are challenging, it can also be demanding for students to learn the exact language to use to correctly interpret statistical results. In particular, correctly interpreting the parameters from linear regression is both a vital tool and a potentially taxing topic. We have developed a Calibrated Peer Review (CPR) module to aid in learning the intricacies of correct interpretation for continuous, binary, and categorical predictors. Student results in interpreting regression parameters for a continuous predictor on midterm exams were compared between students who had used CPR and historical controls from the prior course offering. The risk of mistakenly interpreting a regression parameter was 6.2 times greater before the introduction of the CPR module (p=0.04). We also assessed when learning took place for a specific item for three students of differing capabilities at the start of the assignment. All three demonstrated achievement of the goal of this assignment; that they learn to correctly evaluate their written work to identify mistakes, though one did so without understanding the concept. For each student, we were able to qualitatively identify a time during their CPR assignment in which they demonstrated this understanding.