Literature Index

This paper begins by summarizing the research on attitudes toward and anxiety about statistics, distinguishing between studies in the two areas. Next, a research project that explores the relationship between attitudes toward statistics and a particular instructional method is described. A discussion of the results is followed by implications and suggestions for future research.<br>There are three main questions that this study addresses.<br>1. Are there differences in attitudes toward statistics for students in classes that use multimedia software, other software, or no technology?<br>2. Are there differences in how students in these settings view the role of technology in doing statistics?<br>3. Are there gender differences in attitudes toward statistics and views of technology in doing statistics?

It is my intention to demonstrate in a very pragmatic way that statistics is a subject of vital importance; it enters substantially into the quantitative content of many (if not all) other professional studies. Statistical information is pervasive: a detailed analysis of The New York Times newspaper for Saturday 22 May 1982 will leave us in little doubt of this fact. The intelligent reading of such a newspaper, or of any other current information material, therefore makes a basic grounding in statistics essential for all citizens. This premise leads very naturally to a discussion of what a common core of statistical training should consist of, and how it might best be imparted to students in schools and colleges throughout the world.

Data analysis and statistics have emerged as major topics in primary and secondary (K-12) school mathematics curricula during the 1900's (NCTM, 1989; NCTM, 2000). Statistics - a discipline addresses primarily at the post-secondary level prior to the Curriculum and Evaluation Standards for School Mathamatics (NCTM, 1989( - has lacked definition at the K-12 levels. The lack of clarity about what content to address has resulted in initial work focusing on how we might take more traditional statistics and translate the content for use with younger students. However, instructional practices have not been well defined. Increased attention has been given by researchers and curriculum developers to setting better directions for what we want K-12 students to know and be able to do with respect to data analysis and statistics and to defining the nature of instruction needed to support these directions.More recently, the interaction of technology with efforts to redefine both the content and instructional practices regarding data analysis and statistics in K-12 has provided new directions. Educational technology affords us a greater variety of strategies for teaching statistics and, at the same time, offers us new ways of doing statistics (Garfield & Burrill, 1997). Today, computers, software, and the Internet are essential tools for practice in this domain. The role of research must be addressed now, and the opportunity for defining and teaching a new content area with this kind of technological support must be grounded in research as this content is incorporated into school curricula.This chapter provides an overview related to this need by addressing the following questions:(1) What do we know about the content of data analysis and statistics to be developed at different levels, K-12?(2) In what ways can technology tools enhance current and new directions in teaching and learning data analysis and statistics?(3) What is the role of empirical research in clarifying the interactions between software development and use and the teaching and learning trajectories K-12 in data analysis and statistics?(4) What are the needs and directions that can help frame a research agenda?

The differential effect of two activity-based instructional treatments on subjects' concepts of probability was investigated. The concepts of interest were a classical/frequentist interpretation of probability and three misconceptions cited in the literature: law of averages, law of small numbers and availability. All subjects completed a workbook which presented a long-run frequency interpretation of probability. After completion of the workbook, subjects participated in a probability-matching activity where the task was to predict correctly the outcomes for 100 coin tosses of a fair coin. Half the subjects ( the No-Evaluation group) recorded only the outcome of each toss but not their guess. After the 100 tosses, No-Evaluation subjects were presented statements which pointed out congruities between the observed outcomes and the theory presented in the workbook. Evaluation subjects recorded both their guess and the outcome of each toss. In addition to the same statements presented to the No-Evaluation subjects, Evaluation subjects were asked questions which pointed out incongruitities between their recorded data and the three misconceptions. Evaluation subjects showed an increase in classical/frequentist responses from pretest to posttest. In contrast, No-Evaluation subjects showed an increase in law of averages responses with a consequent decrease in classical/ frequentist responses. These findings support the idea that misconceptions formed before or during instruction can be reinforced by experience with stochastic phenomena since subjects may be biased to attend information which confirms the misconceptions.