Literature Index

To support 11th-grade students' informal inferential reasoning, a teaching and learning strategy was designed based on authentic practices in which professionals use correlation or linear regression. These practices included identifying suitable physical training programmes, dyke monitoring, and the calibration of measurement instruments. The question addressed in this study is: How does a teaching and learning strategy based on authentic practices support students in making statistical inferences about authentic problems with the help of correlation and linear regression? To respond to this question we used video-recordings of lessons, audio-taped interviews, classroom field notes, and student work from a teaching experiment with 12 Dutch students (aged 16-17 years). The analysis provided insights into how the teaching and learning strategies based on authentic practices supported them to draw inferences about authentic problems using correlated data. The evidence illustrates how an understanding of the authentic problem being solved, collecting their own data to become acquainted with the situation, and learning to coordinate individual and aggregate views on data sets seemed to support these students in learning to draw inferences that make sense in the context.

I have found writing this response to be a difficult task, as evidenced by my inability to resist the combination of clichés in the title. As I read Menon's article I found myself agreeing with much of what he had written, although sometimes I wondered why it was considered to be worth stating. Then Menon would take a more extreme line which had not really been justified by what had preceded it, and I found myself frustrated by the lack of continuity as much as by the extreme view itself. I will give some examples of what I found to be problems with Menon's position, based around the themes of (a) was it worth saying anyway; (b) the function of over-statement; (c) methodology and the role of theory in educational research; and (d) the proposed ideal world of educational research. IN this response I have taken research in mathematics education to be entirely subsumed in educational research generally.

We identify the student characteristics most associated with success in an introductory business statistics class, placing special focus on the relationship between student math skills and course performance, as measured by student grade in the course. To determine which math skills are important for student success, we examine (1) whether the student has taken calculus or business calculus, (2) whether the student has been required to take remedial mathematics, (3) the student's score on a test of very basic mathematical concepts, (4) student scores on the mathematics portion of the ACT exam, and (5) science/reasoning portion of the ACT exam. The score on the science portion of the ACT exam and the math-quiz score are significantly related to performance in an introductory statistics course, as are student GPA and gender. This result is robust across course formats and instructors. These results have implications for curriculum development, course content, and course prerequisites.

Is it reasonable to teach the ideas and methods of Bayesian inference in a first statistics course for general students? This paper argues that it is, at best, premature to do so. Surveys of the statistical methods actually in use suggest that Bayesian techniques are little used. Moreover, Bayesians have not yet agreed on standard approaches to standard problem settings. Bayesian reasoning requires a grasp of conditional probability, a concept confusing to beginners. Finally, an emphasis on Bayesian inference might well impede the trend toward experience with real data and a better balance among data analysis, data production, and inference in first statistics courses