Literature Index

The power of computing technology has increased at an astounding rate in the last decade. Today, the personal computer plays a key role in most introductory statistics courses, freeing students from "computational drudgery" as well as enabling a sharper instructional focus on data analysis and the interpretation of statistical results. Computers have also come to play an important role in teaching statistical concepts through simulations. Despite the increased popularity of computer-based statistical simulations, there have been few empirical evaluations of their effectiveness. In this paper, I describe and evaluate three computer-assisted simulations developed for use with SPSS and Microsoft Excel. The simulations are designed to reinforce and enhance students' understanding of sampling distributions, confidence intervals, and significance tests. Results of the evaluation reveal that these simulations can help improve students' comprehension of some of the most difficult material they encounter in the introductory social statistics course.

In order to investigate the impact of simulation software on students' understanding of sampling distributions, the Sampling Sim program (delMas, 2001) was developed. The use of this software with students has been the subject of several classroom research studies conducted in a variety of settings (see Chance, delMas, and Garfield, in press). This paper examines the effect of several versions of a structured activity on students' understanding of sampling distributions. The first version of the activity was created to guide the students' interaction with the simulation software based on ideas from previous studies as well as the research literature. Two subsequent versions introduced a sticker and scrapbook activity that allowed students to keep a visual record of the effects of change in population shape and sample size on the resulting distributions of sample means. Four questions guided the studies reported in this paper: how can the simulations be utilized most effectively, how can we best integrate the technology into instruction, which particular techniques appear to be most effective, and how is student reasoning of sampling distributions impacted by use of the program and activities. A variety of assessment tasks were used to determine the extent of students' conceptual understanding of sampling distributions. As classroom researchers, a main goal was to document student learning while providing feedback for further development and improvement of the software and the learning activity. Ongoing collection and analysis of assessment data indicated that despite students' engagement in the activity and apparent understanding of sampling distributions and the Central Limit Theorem,<br>they were unable to apply this knowledge to solve novel problems. In particular, they had<br>difficulty solving graphical items that resembled tasks in the activity as well as well as applying the Central Limit Theorem to different situations. We comment on the lessons we have learned from this research, our explanations for why so many students continue to have difficulties, and our plans for revising the activity.

This paper outlines the successful professional development workshops provided by Canada's National Statistical Agency, Statistics Canada, for the Census at School program. Workshops for this international in-class online survey program help teachers develop statistical knowledge and teaching competencies. Workshop participants develop more positive attitudes toward statistics teaching and learning through hands-on exploration. Furthermore, by analyzing the Census at School data with TinkerPlots&trade; dynamic statistical software, participants learn to use technology to maximize statistical learning.

The teaching and learning of statistics has impacted the curriculum in elementary, secondary, and post-secondary education. Because of this growing movement to expand and include statistics into all levels of education, there is also a considerable interest in employing effective instructional methods, especially for statistics concepts that tend to be very difficult or abstract. Researchers have recommended using computer simulation methods (CSMs) to teach these concepts; however, a review of the literature reveals very little empirical research to support the recommendations. The purpose of this paper is to summarize and critically evaluate the literature on how CSMs are used in the statistics classroom and their potential impact on student achievement. The recommendation is that more empirically and theoretically grounded research studies are needed to determine if these methods improve student learning.