Literature Index

We describe the design and iterative implementation of a learning progession for supporting statistical reasoning as students construct data and model chance. From a disciplinary perspective, the learning trajectory is informed by the history of statistics, in which concepts of distribution and variation first arose as accounts of the structure inherent in the variability of measurements. Hence, students were introduced to variability as they repeatedly measured and attribute (most often, length), and then developed statistics as ways of describing "true" measure and precision. Both of these developments have historic parallels, and the intricate relation of measure and data are also key components of ongoing professional practice (see Hall et al., this symposium). From a learning perspective, the learning trajectory reflects a commitment to several related principles: (a) constituting a learning progession as encounters with a series of problematics; (b) representational fluency and meta-repesentational competence as constituents of conceptual development in a discipline; (c) invented measures as grounding students' understanding of statistics and (d) an agentive perspective for orienting student activity, according to which distribution of measures emerges as a result of the collective activity of measurer-agents. Instructional design and assessment design (see Wilson et al., this symposium) were developed in tandem, so that what we took as evidence for the instructional design was subjected to test a a model of assessment, resulting in revision to each.

The First International Conference on the Teaching of Mathematics was held in Samos, Greece, in July 1998. Presentations by the attendees reflected a recent debate on reforms of the mathematics curriculum and related pedagogy. Chief among these was a greater emphasis on connecting the mathematics curriculum with applications, to make courses in mathematics more "relevant" to students. This manuscript notes that mathematicians tend to teach students to approach data analysis in a constructive manner, proceeding from an understanding of the basic science, while statisticians concentrate on reductive approaches, whereby models are generated upon consideration of the data themselves. It is suggested that departments of mathematics and statistics will need to adopt a new spirit of cooperation, and partner with colleagues in application areas, if curricular enhancements in either domain are to have a reasonable chance at success.

This article proposes a new strategy for the teaching of probability.

As we consider how we might improve our introductory statistics course we are constrained by a variety of environmental/logistical and pedagogical issues that must be addressed if we want our students to complete the course saying it was useful, it was relevant and practical, and that it increased their communicational, computational, technological and analytical skills. If not properly considered, such issues may result in the course being considered unsatisfying, incomprehensible, and/or unnecessarily obtuse.<br><br>This Webinar will focus on key course content concerns that must be addressed and will engage participants in discussing resolutions. Participants will also have the opportunity to describe and discuss other content barriers to effective statistical pedagogy.