This paper discusses the kinds of software tools that should be available to help support student learning in statistics.
This paper discusses the kinds of software tools that should be available to help support student learning in statistics.
This paper discusses many different aspects of the concept of data handling, which is described as an holistic approach to dealing with data, involving a frame of mind as well as an environment within which one can explore data, rather than just covering a body of specific statistical content.
In this paper we will first explore some of the reasons for people's failure to solve problems, problem-solving "derailers" so to speak, and investigate the role of probabilistic misconceptions as one possible derailer. Second, we will consider the implications of what can "go wrong" when we solve probelms for research on the teaching of problem solving.
In this article, we argue that the focus on centers and distributions in current statistics instruction isn't too excessive, but rather of the wrong kind. Exploration of centers ought to be seen as part of a study of characteristics of complex, variable processes; too frequently, centers are portrayed as little more than summaries of groups of values. To highlight this difference, we examine how statisticians use and think about measures of center to compare two groups, and contrast this with what researchers have observed students doing. We also present various commonly-held interpretations of averages and show how most of these interpretations provide little or no conceptual basis for comparing groups. Based on our analyses, we offer several recommendations about how to help students come to see measures of center and spread as co-constructed ideas.
This paper explores the phenomenological and curricular dynamics of implicit mathematical structures embodied in "transparent" computer-based tools. Examples from a clinical study of students working with the Tabletop database/data analysis environment illustrate the process by which disruptions of transparency can provoke increasingly reflective use of a tool and bring students into engagement with valuable mathematical ideas. The interaction among learner, medium and curriculum is seen to have important implications for pedagogy, tool design, and evolving conceptions of mathematics.
The purpose of my presentation is to review trends in assessment in quantitative courses and illustrate several options and appropaches to assessment for advanced courses at the graduate level, particularly multivariate analysis.
In this chapter, we concentrate on statistics education at the college level. We summarize the literature related to women and math; women and statistics in college, including statistics performance and attitudes toward statistics; and two cognitive models relevant to learning statistics. We then discuss a number of topics relevant to teaching college statistics: the overall approach for the course, structural and organizational issues, presentation of numbers and formulas, computers and technology, process issues, recommended study strategies, counseling and advising, sexism, and classroom assessment. Finally, we provide some overall conclusions. Readers who want a more detailed review and a greatly expanded reference list are requested to contact us.
This chapter focuses on the problem of improving young adults' statistical reasoning skils, with particular emphasis on transfer outside the original learning context.
This paper defines statistical reasoning, provides a model of statistical reasoning, and discusses the assessment of statistical reasoning.
This paper examines the goals of an introductory statistics course, statistical literacy, statistical competence, data awareness, data production, and communication. It also discusses some of the misconceptions that statistics education research is trying to dispel.