Journal Article

  • Explored 5th graders' reasoning about data modeling by conducting 2 design experiments. In Exp 1, 10 Ss assumed the role of data analysts and developed a survey, collected and coded data, and used the dynamic notations of hypermedia to compare the lifestyles of American colonists to their own. In Exp 2, 2 5th graders and their teacher developed and used a randomized distribution to reason about the likelihood of ESP. Analysis of student conversations, including their dialogue with the teacher-researcher, indicated that the construction of data was an important preamble to description and inference. Students' ideas about many elements of data modeling were related to forms of notation. Experimentation afforded a framework for teaching about inference, grounded by the creation of a randomization distribution of the students' data.

  • We present a study of the meanings of average and variation displayed by paediatirc nurses. We trace how these meanings shape, and are shaped by, nurses' interpretations of trends in patient and population data. We suggest a theoretical framework for making sense of the data which compares and contrasts nurses' epistemology with that of official mathematics. Finally, we outline some provisional didactial implications.

  • Clarifies how students' mathematical reasoning as acts of participation are analyzed in the mathematical practices established by the classroom community. Presents episodes from a recently completed classroom teaching experiment that focused on statistics. Discusses change, diversity, and equity.

  • Determining the main research questions in statistics education is not an easy task, because there are so many important and unanswered questions relating to the teaching and learning of statistics. Nevertheless, in SERN 1 (2) we proposed a list of questions that we considered important to investigate, given the current state of research in statistics education as well as our own ideas and research traditions. We reflected on the diversity of people involved in statistics education research, the difficulties of having access to the literature in this area, and the challenges of training statistics education researchers within different disciplines. Our short note was complemented in SERN 2(1) by reactions from a number of colleagues from different countries who represent different backgrounds and experiences. These differences as well as the interdisciplinary nature of statistics education research were visible in the variety of responses and suggestions in the written responses. In this rejoinder, we attempt to synthesise the main points raised by the different reactors to whom we are very grateful, as they provided many important complementary ideas. It would be a too big a task to reply in detail to each of the points raised as some of them deserve a full issue of the Newsletter. We are therefore only offering remarks here regarding a few of the main points raised. We plan to focus on some of the remaining topics in future issues of our Newsletter.

  • In this paper we explore issues surrounding university students' experiences of statistics drawing on data related to learning statistics as a compulsory component of psychology. Over 250 students completed a written survey which included questions on their attitudes to learning statistics and their conceptions of statistics. Results indicated that most students were studying statistics unwillingly. A minority of students acknowledged that statistics was necessary for psychology, but statistics was seen by many as boring or difficult. Students' conceptions of statistics were analysed from a perspective developed from phenomenography (Marton & Booth, 1997). The aim of phenomenographic research is to describe the qualitative variation in the ways people experience or conceptualise a phenomenon - in this case students' interpretations of the topic statistics. The conceptions fell into five categories of description including: statistics as decontextualised processes and algorithms, statistics as a tool for professional life and statistics as a way to self-development and enhanced perspectives on our world. Excerpts from interviews with selected students indicate the diversity of experiences in learning statistics. The perceptions of two teachers flesh out the learning and teaching environment. The findings raise challenges for supporting the learning of "occasional users" (Nicholls, 2001) of statistics in higher education.

  • The status of Statistics teaching has not been sufficiently explored in Agricultural Colleges in Argentina. Although Statistics is considered as an important subject in different academic institutions, there is very little information about the way that it is taught in different university curricula. The aims of this study were to (a) gather information about the place of Statistics in college programs through different indicators, and (b) explore different issues concerning the teaching of Statistics in agricultural colleges, such as epistemological views, academic organization, etc. For this purpose, a survey was conducted in the main agricultural colleges in Argentina. Twenty-three teaching teams from different university answered a questionnaire. The responses were analyzed and categories were built in order to draw some conclusions.

  • Little is known about the provision of statistics teaching for PhD students in UK medical schools. A recent survey found that statistics courses were available to PhD students in 13 of 21 schools responding. The provision across these 13 schools was variable in terms of contact hours and content. At a meeting of 27 medical statistics teachers, consensus was reached that such teaching should be undertaken by a subject specialist, however there was no consensus as to the best mode of delivery. We describe the rationale for, content of, and student feedback from our newly developed course programme which emphasises aspects of both design and analysis of research projects.

  • This case study covers several exploratory data analysis ideas, the histogram and boxplot, kernel density estimates, the recently introduced bagplot - a two-dimensional extension of the boxplot - as well as the violin plot, which combines a boxplot with a density shape plot. We apply these ideas and demonstrate how to interpret the output from these tools in the context of data on living standards in Vietnam. The level of the presentation is suitable for an upper-level undergraduate or beginning graduate course in applied statistics. We use data from the Vietnam Living Standards Survey of 1998 (VLSS98) and from the 2000 Vietnam statistical yearbook, the statistical package Stata, and special programs provided by the authors who introduced the bagplot and the violin plot.

  • This paper extends work on the construction of instructional modules that use graphical and simulation techniques for teaching statistical concepts (Marasinghe, et al. 1996; Iversen and Marasinghe 2001). These modules consist of two components: a software part and a lesson part. A computer program written in LISP-STAT with a highly interactive user interface that the instructor and the students can use for exploring various ideas and concepts comprises the software part. The lesson part is a prototype document providing guidance for instructors for creating their own lessons using the software module. This includes a description of concepts to be covered, instructions on how to use the module and some exercises. The regression modules described here are designed to illustrate various concepts associated with regression model fitting such as the use of residuals and other case diagnostics to check for model adequacy, the assessment of the effects of transforming the response variable on the regression fit using well-known diagnostic plots and the use of statistics to measure effects of collinearity on model selection.

  • In Bayesian statistics, the choice of the prior distribution is often controversial. Different rules for selecting priors have been suggested in the literature, which, sometimes, produce priors that are difficult for the students to understand intuitively. In this article, we use a simple heuristic to illustrate to the students the rather counter-intuitive fact that flat priors are not necessarily non-informative; and non-informative priors are not necessarily flat.

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