STRATEGIES FOR MANAGING STATISTICAL COMPLEXITY WITH NEW SOFTWARE TOOLS

TLDR

New software tools offer rich opportunities for data representation and understanding, yet research on how learners use them to think about data and its impact on teaching remains limited. The study examines how learners employ new software tools to manage variability when comparing groups. The authors analyze two approaches—binning to reduce variability and proportion‑based interpretation of bin size versus group size—based on observations of teachers, students, and a six‑week experiment. The paper concludes that these software‑based strategies have implications for both research and teaching.

Abstract

New Software tools for data analysis provide rich opportunities for representing and understanding data. However, little research has been done on hoe learners use these tools to think about data, nor how that affects teaching. This paper describes several ways that learners use new software tools to deal with variability in analyzing data, specifically in the context of comparing groups. The two methods we discuss are 1) reducing the apparent variability in a data set by grouping the values using numerical bins or cut points and 2) using proportions to interpret the relationship between bin size group size. This work is based on our observations of middle- and high-school teachers in a professional development seminar, as well as of students in these teachers’ classrooms, and in a 13-week sixth grade teaching experiment. We conclude with remarks on the implications of these uses of new software tools for research and teaching.
 First published November 2004 at Statistics Education Research Journal: Archives