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 is a bound copy of a group of background readings from the Conference on Assessment Issues in Statistics Education.
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.
My purpose in this paper is to analyze how students in one middle-school classroom came to understand the data creation process and the importance of that process to the drawing of conclusions from statistical data.
My purpose in this paper is to document the learning of one teacher as she interacted with a group of seventh-grade students over the course of a twelve-week research project focused on statistical data analysis.
This article proposes a new strategy for the teaching of probability.
It is the purpose of this paper to present a visual analogy that may be employed by instructors to teach the concept of power to their students in statistical courses. It is anticipated that this analogy will then be useful to students in helping them to construct, in their own minds, the concept of statistical power.
This exploratory study examined errors that students commit solving multiple-choice questions about descriptive statistics and basic concepts in research methods. The sample consisted of 81 undergraduate students in an introductory statistics course. The results indicated that the most frequently detected errors were confusing concepts, misinterpreting descriptive information, applying inappropriate procedures and applying partial information. Analysis reveal potential sources of students' errors include assimilation of statistical concepts into inappropriate schemata, failure to use knowledge sources, and lack of ability to relate and combine knowledge from different sources.
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.
The meaning of success as experienced by students in statistical methods courses is described. Six social science graduate students who had completed several statistical methods courses were interviewed. The qualitative method of phenomenology was used to understand the essence of success by analyzing the students' experiences and perceptions. The students described success as an accumulation of conceptual knowledge that they are able to apply and communicate to others. They experienced success predominantly in the context of working in study groups. Success was precipitated by and coupled with positive feelings such as confidence and happiness.