Research

  • We explore the effects of optional community-based projects on students and particularly on motivation and learning in an applied statistics course. We consider how the nature and structure of community-based projects enhance student learning in a constructivist classroom. We critically assess the intellectual challenges of a community-based project and the nature of the statistical problems that arise. We review students' evaluations and our own estimation of their ability to learn from experience and from the community.

  • New results in research on judgment under uncertainty show a way of how to improve the teaching of statistical reasoning. The implications of this research are that (i) successful learning needs doing, and (ii) that the format in which information is represented plays a decisive role. Statistical problems are, for instance, solved much better if the relevant pieces of information are presented as frequencies rather than probabilities. It also helps a lot if random processes can be observed rather than only read about. A computer program is presented that incorporates these implications from psychological research. The software accompanies an elementary text book on probability theory to be used in high school.

  • 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.

  • This study investigated the knowledge base necessary for choosing appropriate statistical techniques in applied research. In this study, we compared knowledge used by six experts and six novices in two types of statistical tasks. The tasks were: 1) comparing research scenarios from the perspective of choosing a statistical technique, and 2) direct comparison of statistical techniques. The framework was based on expert knowledge in inferential statistics using the repertory grid technique for data collection. A qualitative analysis of data showed that of the three types of expert knowledge, research design knowledge comprised the biggest portion, with theoretical and procedural knowledge comprising relatively smaller parts. Little difference was observed between experts and novices in extensiveness of knowledge use, although experts' knowledge use was found to be more integrated than novices'. Finally, two implications were drawn regarding how to better teach selection skills in statistics education: (1) statistical techniques should be taught in relation to relevant research designs, and (2) conceptual connections between statistical techniques should be explicitly taught.

  • In this article, we focus primarily on what we have learned more recently from research about how younger students reason about data, concentrating on ideas that begin developing in early elementary school. We therefore do not review the literature related to statistical inference. One reason for not reviewing that literature here is that a reasonable treatment would require us to review as well the development of probabilistic thinking (see Shaughnessy's review, this volume). But more importantly, there are core ideas in reasoning about data that tend to get shoved to the wings as soon as statistical inference takes the stage. The issues we discuss here, though basic, are still critical to statistical reasoning in the upper grades.

  • An approach used to assess project team work in a condensed (half-term) elective course is discussed. The instructor's evaluation method signals appropriate course goals to students. The scheme described encourages student groups to prepare presentations that will be attractive to people who will evaluate their work in the real world. Colleague comments determine one-half of each student's course grade. Students are randomly selected to lead the presentations, ensuring that all students are thoroughly involved in the process (including assessment). A report on the projects (and comments) completed by Masters of Business Administration (MBA) students at a midwestern school of management is provided, along with the inventory used to assess each team's work.

  • The teaching and learning of statistics has impacted the curriculum in elementary, secondary, and post-secondary education. Because of this growing movement to expand and include statistics into all levels of education, there is also a considerable interest in employing effective instructional methods, especially for statistics concepts that tend to be very difficult or abstract. Researchers have recommended using computer simulation methods (CSMs) to teach these concepts; however, a review of the literature reveals very little empirical research to support the recommendations. The purpose of this paper is to summarize and critically evaluate the literature on how CSMs are used in the statistics classroom and their potential impact on student achievement. The recommendation is that more empirically and theoretically grounded research studies are needed to determine if these methods improve student learning.

  • This study investigated the relationship between a constructivist learning environment and students' attitudes toward statistics. The Constructivist Learning Environment Survey (CLES) and the Attitude Toward Statistics scale (ATS) were used to measure the environment and attitudes respectively. Participants were undergraduate students of an introductory college statistics course. They were drawn from Seattle Pacific University in the US and the University of Zimbabwe.<br>The study had two components. One component addressed hypotheses examining potential differences between groups and the other explored relationships between variables. The environment was not manipulated and the data was collected from courses that already existed in the form studied. For this reason, the overall design of the study had causal comparative and correlational elements. A constructivist learning environment was found to be significantly related to students' attitude toward statistics. Furthermore, there were significant differences between the groups based on location.<br>The study examined the similarities and differences in perceptions and attitudes of students from two very different learning milieus. Cross-cultural comparisons have the potential to generate new insights into statistical pedagogy and the role noncognitive socio cultural variables play in teaching statistics to college-age students.

  • This study examined the extent to which statistics and mathematics anxiety, attitudes toward mathematics and statistics, motivation and mathematical aptitude can explain the achievement of Arabic speaking pre-service teachers in introductory statistics. Complete data were collected from 162 pre-service teachers enrolled in an academic teacher-training program for elementary and middle schools in Israel. The data, except for the two achievement tests, were collected during statistics classes prior to the midterm examination. The majority (96%) of participants were female students with a mean age of 21. As regards variables examined in this study, only the hypothesized effect of mathematical aptitude on achievement in statistics was relatively large. The results also indicated that mathematical aptitude, mathematics anxiety, attitudes toward mathematics and statistics, and motivation, together accounted for 36% of the variance in achievement in introductory statistics for the current sample.

  • The purposes of this paper are to illustrate the use of several assessment strategies in an advanced course in statistics, and to present the results of student ratings for each assessment strategy in terms of difficulty, appropriateness, level of learning achieved, and preference. The assessment strategies used include structured data analysis assignments, open-ended data analysis assignments, reviews of applied research articles, and annotating computer output from multivariate software procedures. Findings indicate that students "prefer" instructor-directed or structured assignments overall, but feel they learn the most when the assessment is unstructured and requires greater self-direction. Suggestions for incorporating these assessment strategies into the multivariate classroom, as well as examples of each strategy, are included in this study.

Pages

register