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Theory

  • This report discusses how factors such as sex, major, mathematics background, and dominant learning style can affect student performance in statistics. It is almost unarguable that the introductory statistics course is the most widely feared course on most university campuses. Dropout and failure rates are extremely high. Students come into the course with low expectation of success., and I have often wondered and talked with colleagues about this fear and lack of success. Can we identify any factors that affect our students' performance in the "Introduction to Statistics" course? Can we determine "what makes a student's statistical clock tick?" Or perhaps more precisely, "what prevents a student's statistical clock from ticking?" Do factors such as sex, major [field of study], class [freshman (first year), sophomore (second year), junior (third year), senior (fourth year)] or mathematics background have a bearing on student performance? For the above factors, no big surprise were found. But another factor, suggested to me by a colleague in the Psychology Department produced a rather stunning result. That factor is the student's dominant learning style.

  • This report describes four kinds of understanding that students rely on in statistics--logical/deductive, computational/algorithmic, graphical/dynamic, and verbal/interpretive. These kinds of understanding will be illustrated, as will four unifying themes (production, exploration, repetition, and inference) that instructors can use to give students a better sense of the subject of statistics as a structured whole. Structured concept maps will also be illustrated, as will four topics (transforming, adjusting, blocking, and crossing/interaction) that are often missing in introductory statistics courses.

  • This paper presents five principles of learning, derived from cognitive theory and supported by empirical results in cognitive psychology. To bridge the gap between theory and practice, each of these principles is transformed into a practical guideline and exemplified in a real teaching context. It is argued that this approach of putting cognitive theory into practice can offer several benefits to statistics education: a means for explaining and understanding why reform efforts work, a set of guidelines that can help instructors make well-informed design decisions when implementing these reforms, and a framework for generating new and effective instructional innovations.

  • Analogical transfer is transfer of a basic structure acquired through one or more instances to another instance. A basic structure like this is sometimes called a paradigm. Paradigmatic teaching, i.e. teaching for analogical transfer, requires the teaching of a basic structure by appropriate exemplars as well as the teaching of its application in various fields and contexts. This is demonstrated using research on teaching for problem-solving, inductive thinking, and learning-to-learn.

  • In this talk I will give some principles and many examples of "objective" questions that test statistical concepts. In some of our large classes we must of necessity use machine-gradable tests. Is it still possible to test concepts (and not just computations) in such a setting? I will try to argue (mostly by example) that we can test concepts this way. The most important principle for any testing is: Test what you believe is important!

  • This paper examines the need for continuous quality improvement in higher education; the role of academic statisticians in changes in higher education; some of the strategies and techniques colleges and universities are employing related to TQM at college and departmental levels; what individual instructors can do in terms of making improvements in higher education; and the role and importance of a personal quality vision in such an overall effort for organizational change. In addition, it is the authors' intent that the paper be a source for ideas about improving teaching and ways to think about issues related to TQM on campus.

  • In this article, I attempt to explicate the ethical prinicples of data analysis, to suggest some characteristics of research and researchers that give rise to ethical difficulties, and to provide recommendations for improved practice.

  • This paper examines the role of assessment in research studies focused on the teaching and learning of statistics at the undergraduate or graduate level. Some advantages and limitations for types of assessment methods typically used in statistics education research are summarized. An alternative framework is offered for conceptualizing assessment and its role in studies of statistics education. This framework is based on the theory of conceptual change. An illustration will be offered: a study of the impact of the use of computer simulations on learning statistical inference. Examples of the types of assessment embedded in this ongoing research project will be shared.

  • This article describes the need to reexamine the undergraduate discipline of statistics in light of society's needs. Traditionally, there has been too much focus on the mathematical aspects of the discipline. The article suggests courses that would meet important needs of the undergraduate statistics major and set the discipline of statistics apart from mathematics.

  • The objective of this chapter is to consider the practical implications of the biases observed in experimental studies of human reasoning and to discuss the ways in which we might limit the potential damage that such biases inflict on real life thinking and decision making.

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