• There is a considerable confusion in the educational literature about the nature of qualitative research. In this article I argue that a major source of the confusion arises from discussing qualitative research as if it is one approach. The discussion in the educational literature concerning qualitative research can be clarified by recognizing that qualitative research comes in many differenct varieties, which can be more clearly identified and understood by using the notion of research traditions. To apply this concept to the discussion of qualitative research, I describe briefly and compare six traditions from the disciplines of psychology, holistic ethnography, cognitive anthropology, ethnography of communication, and symbolic interactionism. I conclude that we may increase our understanding of qualitative research by focusing our dicussions at the level of traditions.

  • The appendeces from a report on teaching statistics which presents the Statistics Focus Group's recommendations:<br>Appendix A: Helping Students Learn (Garfield)<br>Appendix B: Examples<br>--Classroom experiments (Taylor)<br>--Project NABs (Gunst)<br>Appendix C: Making it Happen<br>--Interesting and Available Data (Lock)<br>--EDSTAT-L Discussion List (Arnold)<br>Appendix D: Report of workshop of Statistical education (Hogg)

  • We shall describe episodes of middle school students working on Exploratory Data<br>Analysis (EDA) developed within an innovative curriculum. We outline the program and<br>its rationale, analyze the design of the tasks, present extracts from students' activities and speculate about their learning processes. Finally, from our observations, we propose a new construct -- learning arena, which is suggested as a curriculum design principle, which may also facilitate research.

  • The Handbook of Research Design in Mathematics and Science Education is based on results from an NSF-supported project (REC 9450510) aimed at clarifying the nature of principles that govern the effective use of emerging new research designs in mathematics and science education. A primary goal is to describe several of the most important types of research designs that:<br>* have been pioneered recently by mathematics and science educators;<br>* have distinctive characteristics when they are used in projects that focus on mathematics and science education; and<br>* have proven to be especially productive for investigating the kinds of complex, interacting, and adapting systems that underlie the development of mathematics or science students and teachers, or for the development, dissemination, and implementation of innovative programs of mathematics or science instruction.<br>The volume emphasizes research designs that are intended to radically increase the relevance of research to practice, often by involving practitioners in the identification and formulation of the problems to be addressed or in other key roles in the research process. Examples of such research designs include teaching experiments, clinical interviews, analyses of videotapes, action research studies, ethnographic observations, software development studies (or curricula development studies, more generally), and computer modeling studies. This book's second goal is to begin discussions about the nature of appropriate and productive criteria for assessing (and increasing) the quality of research proposals, projects, or publications that are based on the preceding kind of research designs. A final objective is to describe such guidelines in forms that will be useful to graduate students and others who are novices to the fields of mathematics or science education research. The NSF-supported project from which this book developed involved a series of mini conferences in which leading researchers in mathematics and science education developed detailed specifications for the book, and planned and revised chapters to be included. Chapters were also field tested and revised during a series of doctoral research seminars that were sponsored by the University of Wisconsin's OERI-supported National Center for Improving Student Learning and Achievement in Mathematics and Science. A Web site with additional resource materials related to this book can be found at

  • The papers in this report represent the imagination, analysis, and experiences of many people involved in recent curricular studies in secondary school mathematics. It differs from reports that seek broad agreement on conservative, traditional curricula, representing instead the point of view of those working with emerging electronic technology that this technology offers striking opportunities and challenges to reshape content and pedagogy. The introductory chapter considers the potential impact of the new information technologies in relation to mathematics, setting the stage for the other papers. These address the impact of computing on algebra, geometry, calculus, and discrete mathematics and algorithmic methods. A sixth chapter considers prospects and strategies for change in school mathematics. A bibliography is included.

  • Statistical literacy is essential in our personal lives as consumers, citizens and professionals. Statistics plays a role in our health and happiness. Sound statistical reasoning skills take a long time to develop. They cannot be honed to the level needed in the modern world through one high school course. The surest way to reach the necessary skill level is to begin the educational process in the elementary grades and keep strengthening and expanding these skills throughout the middle and high school years. A statistically literate high school graduate will know how to interpret the data in the morning newspaper and will ask the right questions about statistical claims. He or she will be comfortable handling quantitative decisions that come up on the job, and will be able to make informed decision about quality of life issues.<br><br>The remainder of this document lays out a framework for educational programs designed to help students achieve this noble end.

  • There have been many changes in educational assessment in recent years, both within the fields of mesurement and evaluation and in specific disciplines. In this article, we summarize current assessment practices in statistics education, distinguishing between assessment for different purposes and assessment at different educational levels. To provide a context for asessment of statistical learning, we first describe current learning goals for students. We then highlight recent assessment methods being used for different puposes: indicvdual student ecvaluation, large-scale group evaluation, and as a researc tool. Example of assessment used in teaching statistics in primary schools, secondary schools, and teriary schools are given. We then focus on 3 examples of effetive uses of assessment and conclude with a description of some current assessment challenges.

  • Presents an alternative to the research-development-diffusion model of mathematics education based on an integration of curriculum research and design embedded in educational development. Explicates the characteristics of developmental research and discusses its methodological aspects.

  • Discussion of graphing software for educational purposes focuses on a study in which three versions of an original computer graphing program were used by inner-city high school students to solve scientific data analysis problems. Variations in degrees of flexibility and feedback in the software are explored.

  • We need to look beyond the view of computer-based technology as a means of enhancing the teaching and learning of current curricula; the end result of such activities is often no more than a translation of what are essentially pencil-and-paper-based activities onto a computer screen, albeit often done in an exciting and enlightening manner. As we move into an era in which computer-based technology becomes the new pencil and paper, such developments will become of historical interest at most (Kaput, 1992). Although there is undoubted benefit in using computer-based technology to reduce the time students spend on statistical computation, or in using it to illustrate the Central Limit Theorem, for example, the ultimate power in the technology lies in its ability to reshape the nature of intellectual activity in the statistics classroom. To see why this might be, we need to look generally at the ways in which interacting with technology of this sort has the potential to affect human intellectual performance. We will do this by using a theoretical framework proposed by Salamon, Perkins, and Globerson (1991) which has implications for both future classroom practice and research.