Proceedings

  • A series of examples from experiences taken from experiments that have taken place in The Netherlands and the USA were discussed. This work has been carried out by the Research Group on Mathematics Education of Utrecht University, with the USA work a collaboration with the National Center for Research in Mathematical Sciences Education of the University of Wisconsin in Madison. Materials were developed on the subject of Data Visualisation. This subject treats skills such as drawing basic plots and calculating basic numerical summaries of data, but it also treats questions such as the following as being of at least equal importance. What graphical representation is best for this set of data? What do these data tell me? How can I communicate the message through a picture? This approach has implications for testing and evaluation as well as teaching.

  • This talk described how a year of Descriptive Statistics could fit in a high school's course offerings. Topics include the following: building on a student's successes; games of chance; opinion surveys; US Census 1990; report writing; Galton Board Models; and much more. Classroom handouts were shown and discussed. It is also important to tell students and parents that Descriptive Statistics is necessary and fun.

  • A report on work that has been undertaken with stem-and-leaf plots and box plots. The report was supported with the representation of childrens' work. This presentation provides an excellent model of what could be achieved in statistical education. It shows how statistical concepts could be utilised to develop other significant mathematical ideas and, particularly through the childrens' work, indicates the high level of understanding and graphical comprehension that can be achieved.

  • A hierarchical model is developing graphicacy in the primary curriculum. The model stresses the importance of a progression in graphical work. As indicated, it was not meant to be a rigid, finalised version; rather it is designed to provide an initial framework for discussion. Readers interested in teaching statistics to young children will find this article an excellent basis for developing their own curriculum plan.

  • This paper examines the importance of the social context, particularly the place of discussion, for learning statistics in New Zealand primary classrooms.

  • This paper will report some of the findings of a study of grade four and grade six students' understanding of the information conveyed by bar graphs. The total study examined the effects of various characteristics of graphical displays on students' ability to read, interpret, and predict from such displays, and discusses the results within Davis's Frame Theory.

  • At every point in their development, students are engaged in serious intellectual work as they attempt to construct their own understanding of the world and their relation to it. As part of this work, they are immersed in mathematical ideas which are just at the edge of their understanding. In this paper, I will first discuss the nature of the mathematics in which the child in the primary grades can engage in the context of data analysis, and then give some examples of children's work in this area to illustrate how young children must construct for themselves key processes which are the building blocks of collecting, describing, and interpreting data.

  • This curriculum was so designed that it could be integrated into the effective mathematical curriculum for this age group at all secondary schools in West Germany. A report was given on the pedagogical and psychological rationales for why this age group must be instructed in probability and statistics. Furthermore, the presentation reported on the different empirical research methods used during the development and evaluation phase. Finally, information was given on how this curriculum was integrated into mathematics instruction in this age group.

  • Usually the PC is used in statistics to do quickly and conveniently what we have always been doing. This is a misuse of the PC since it has the potential to change statistical practice fundamentally. Historically, statistics was developed when computation was hard and expensive. To avoid massive computation a lot of sophisticated theory based on asymptotics was developed. Now computation is cheap and easy. The PC should replace sophisticated theory by simple computations, and make statistics more comprehensible. We should strive for understanding. Sophisticated statistical software is pedagogically harmful. We do not want to solve a dozen problems a day by using recipes. We want to solve a few paradigmatic examples in a few weeks. School statistics should solve a small number of fundamental problems, not quickly, but leisurely. We should derive programs for the solutions, which are general enough, so that they solve a whole class of problems. I do not advocate much deep programming, which is quite difficult. On the other hand, statistical software is for professionals. To learn its use requires an effort comparable to the effort of learning a new programming language. Design of a program is an important part of the learning process. A problem is solved if you have an efficient algorithm for its solution, which you understand.

  • This paper describes the background and rationale for the project, its goals and objectives, and the instructional strategy utilised by SIM-PAC. An example of a typical learning activity and the capabilities of the software are illustrated. The ICOTS presentation included a demonstration of two learning activities.

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