Principles of Instructional Design for Supporting the Development of Students’ Statistical Reasoning

Abstract

This chapter proposes design principles for developing statistical reasoning in elementary school. In doing so, we will draw on a classroom design experiment that we conducted several years ago in the United States with 12-year-old students that focused on the analysis of univariate data. Experiments of this type involve tightly integrated cycles of instructional design and the analysis of students’ learning that feeds back to inform the revision of the design. However, before giving an overview of the experiment and discussing specific principles for supporting students’ development of statistical reasoning, we need to clarify that we take a relatively broad view of statistics. The approach that we followed in the classroom design experiment is consistent with G. Cobb and Moore’s (1997) argument that data analysis comprises three main aspects: data generation, exploratory data analysis (EDA), and statistical inference. Although Cobb and Moore are primarily concerned with the teaching and learning of statistics at the college level, we contend that the major aspects of their argument also apply to the middle and high school levels.