Literature Index

Definitions of research in statistical education are discussed. A system of keywords for categorising statistical education research is outlined. Proposals for a Web-based survey of statistical education researchers to collect details of their research activities in statistical education, and the design of a database to store these details are described.

Earlier studies on sampling distribution, its founding concepts, misconceptions about sampling distributions, and the use of simulation highlighted that (1) learning of statistics requires an understanding of multifaceted issues and relations among them; (2) learning may be examined in terms of task, technique, theory, and learner's profile, each of which is influenced by instructional context; and (3) learning environments should be designed to stimulate flexible travelling along the network of these issues. Considering these emerging findings we attempt to outline a possible instructional design to teach sampling distribution with technology. Suggestions for training teachers in statistics education are included.

This study focuses on the feasibility of implementing independent learning in a traditional university and the feasibility of providing this independent learning by means of an electronic interactive learning environment. Three experimental variables were designed: learning environment, delivery, and support. This created five different learning conditions to which subjects were assigned at random.

There has been an increasingly strong call from practicing statisticians for statistical education to focus more on statistical thinking (e.g., Bailar, 1988; Snee, 1993; Moore, 1998). They maintain that the traditional approach of teaching, which has focused on the development of skills, has failed to produce an ability to think statistically: "Typically people learn methods, but not how to apply them or how to interpret the results" (Mallows, 1998, p. 2).<br>Solutions offered for changing this situation include employing a greater variety of learning methods at undergraduate level and compelling students to experience statistical thinking by dealing with real-world problems and issues. A major obstacle, as Bailar (1988) points out, is teacher inexperience. We believe this is greatly compounded by the lack of an articulated, coherent body of knowledge on statistical thinking that limits the pedagogical effectiveness even of teachers who are experienced statisticians. Mallows (1998) based his 1997 Fisher Memorial lecture on the need for effort to be put into developing a theory for understanding how to think about applied statistics, since the enunciation of these principles would be useful for teaching.<br>This chapter focuses on thinking in statistics rather than probability. Although statistics as a discipline uses mathematics and probability, as Moore (1992b) states, probability is a field of mathematics, whereas statistics is not. Statistics did not originate within mathematics. It is a unified logic of empirical science that has largely developed as a new discipline since the beginning of the 20th century. We will follow the origins of statistical thinking through to an explication of what we currently understand to be statistical thinking from the writings of statisticians and statistics educationists.