Literature Index

In an effort to improve active learning in introductory statistics, we introduce the use of<br>concept mapping techniques as part of the course. While previous papers have touted the<br>use of this and other interactive teaching methods in statistics education, we add to this<br>literature by providing additional assessment of its efficacy. This comes through an<br>experimental design that involves a single instructor teaching two sections of the same<br>statistics course over the same semester. Both cover the same material in the same way<br>with the exception that concept mapping is used in one section, but not the other.<br>Assessment of learning outcomes is done through the use of pre-tests and post-tests of<br>understanding of statistical concepts. We also track changes in student's study habits over<br>the semester through additional surveys. We find only weak evidence that concept<br>mapping is effective in aiding student learning of statistics.

In this paper, an assessment project conducted in a class for future middle school teachers in statistics<br>is presented. The project consisted of designing and presenting a lesson that addressed a statistical<br>concept taught in middle school following the Japanese Lesson Study model. Future teachers were<br>asked to identify the big ideas covered, its connections to previous and future content by aligning the<br>lesson to state, national standards, and the GAISE recommendations, make predictions about students<br>misunderstandings based on readings about statistics education research, propose activities or<br>procedures on how to teach the concept, and create an evaluation plan. Lessons were presented in pairs<br>to the entire class and the "best" lesson presented to actual 5th graders at a local middle school. Final<br>reflections and evaluations from prospective teachers are discussed.

The dataset presented here illustrates to students the utility of logistic regression. Its analysis results in a fit that explains much of how senators vote on a particular bill, and allows for quantification of the effects of ideology and money on the vote. A number of interesting quantitative interpretations follow from a good fit. A successful analysis makes use of a number of ideas discussed in applied courses: descriptive statistics, inferential methods, transformation of variables, and the handling of outliers and special cases. All these issues arise in the context of data on variables that require of students no specialized knowledge. Students have strong qualitative preconceptions about the relationships among the variables. The final results quantify, and nicely confirm, many of those conceptions.

Using a framework for assessing dimensions of understanding in statistics a series of assessment tasks were developed by the researcher to address both procedural and conceptual understanding. This paper describes these tasks, together with the results of students' performance on the tasks. It will be shown that, while some of the tasks developed did assess the dimension of knowledge which they were developed to address, and some did not, overall it was possible to develop tasks to specifically assess both procedural and conceptual knowledge in statistical inference