• Constructivism is a philosophy that supports student construction of knowledge. Since students uniquely construct their knowledge, instructional strategies that support constructivist philosophies naturally advocate student understanding. Instructional trends in the mathematics and statistics education communities support the active-learning orientation of constructivist philosophy. I posit that, while not the only philosophy of teaching and learning, constructivism is one of the best such philosophies. One question remains: "How do instructional strategies that support student knowledge construction address the needs of all students?" I first examine learning styles in general, then enumerate a collection of instructional strategies that support constructivism, and conclude with an analysis of how instructional strategies that support constructivism address the needs of the learning styles previously examined.

  • The use of significance tests in science has been debated from the invention of these tests until the present time. Apart from theoretical critiques on their appropriateness for evaluating scientific hypotheses, significance tests also receive criticism for inviting misinterpretations. Although these misinterpretations are well documented, until now there has been little research on pedagogical methods to remove them. Rather, they are considered "hard facts" that are impervious to correction. We discuss the roots of these misinterpretations and propose a pedagogical concept to teach significance tests, which involves explaining the meaning of statistical significance in an appropriate way. The present contribution is based on Krauss and Wassner (2001) and Haller and Krauss (in press).

  • Applied researchers are often interested in obtaining confidence intervals for key nonlinear model parameters so as to answer important research questions, and the usual "plus and minus 2 SE's" confidence interval leads easily into the usual Wald hypothesis test covered in most introductory statistics courses. However, since information about a specific parameter is often asymmetric, a skewed confidence interval is often more appropriate and reasonable in practice. This leads to the use of likelihood-based tests, typically introduced in intermediate undergraduate and basic graduate course. This paper argues that the superiority (in terms of for example increased power) of likelihood-based and score hypothesis tests over the Wald test is most easily conveyed and appreciated by first providing a reasonable motivation (as well as examples) using confidence intervals, and then exploiting the confidence interval-hypothesis test equivalence.

  • In this paper I consider the characteristics of a statistically literate (a "statisticate") person. I suggest that a statisticate person should be able to read and understand statistical arguments of moderate complexity, and to carry out statistical analyses to some degree. Significantly, the truly statisticate person should also have developed the habit of thinking quantitatively. Furthermore, he or she does not rely on rigid rules to make statistical decisions, but uses informed judgment. In particular, he or she should understand the concepts of modelling and selection between models, and recognise their importance. Consideration is given to one of the major barriers to developing statistacy: the vocabulary used, in particular, the common use of two words that should only be used with the greatest of care (if used at all). These words are "prove" and "true". An important illustration of the way that vocabulary hinders the development of understanding is the case of hypothesis testing, a vital statistical tool that is widely misunderstood. It represents a mode of thought that is fundamental to statistical analysis, and so belongs in the kit bag of any statisticate person.

  • This paper examines the ways technology is being used in a variety of college-level statistics courses: introductory statistics, probability, mathematical statistics, and intermediate statistics. Although there is some overlap in the types of technological resources being used in these different courses, an attempt is made to isolate the particular types of technology or software that are most appropriate or most used in each type of course.

  • This paper describes the conceptual base for the development of a computer-based expert system. After reviewing developments in computer-based learning and experiments with computer-assisted learning in statistics, the paper describes the nature of expert systems and desired attributes of expert systems in statistics. An overview of proposed research projects to develop a computer-based expert system research outliner/statistical tutor is presented. Current progress, anticipated timelines and methodological concerns are provided. Two figures--The Changing Focus of Attention in Technology for Computer-Assisted Learning and System Delivery Tools--are included. (Contains 37 references.) (Author/ALF)

  • During the last three decades the psychological exploration of subjective probability has produced a wide range of empirical findings and theoretical developments. In this book, prominent authorities from multiple disciplines analyse and document the human ability to deal with uncertainty. Contributions range from discussions of the philosophy of axiom systems through studies in the psychological laboratory to the real world of business decision making.

  • In 1994-95 the Department of Statistics at Iowa State University first offered a new two-semester sequence of distance education courses, Applied Statistics for Industry I & II. The courses were designed to meet the needs of engineers and managers in industrial settings. The courses are filmed during the on-campus delivery of the class and videotapes are sent to off-campus students for viewing the following week. Over the past 10 years, a major emphasis in statistics education has been the active participation of students in the practice of statistics. The Present paper will discuss strategies for incorporating activities and other practical experiences into a distance education course. We will also explore the use of technology to enhance the active statistics experience for students at a distance.

  • Recent fascinating developments of information and telecommunication technology have made vast amounts of data available to many millions throughout the world. This and the widespread increased use of conceptually and methodologically complex analytical procedures and tools require appropriate training of users. The paper therefore focuses on the question how the modern information and telecommunication technology could increase the quality and efficiency of statistical training at the workplace from learners' point of view. In this framework, general pedagogical issues and challenges of distance learning in a modern e-environment are addressed, and a model of a general technology-based course is proposed. Assessment of the present state of affairs is based on an extensive survey of technology-based statistical courses, and followed by an identification and discussion of future challenges in the field.

  • Yes, and yes. Success is dependent on developing conceptual understanding, easing interpretation, simplifying validation and facilitating corrective action. Focusing on statistical theory, data manipulation and increasing mathematical sophistication blocks the road to improvement. Lessons, learned over the past quarter century, in instructional strategies, in defining learning objectives and in using interactive examples, will be reviewed. Experience, demonstrating that developing EDA, measurement, DOE, prediction and control skills are critical, will be shared. Driven by the paradigm of statistical thinking and the concepts of quantitative literacy, compatible with the philosophies of Deming, Juran and Box, these approaches can ease implementation of ISO standards and accelerate the effectiveness of Six Sigma technologies.