Challenges To Cross-Disciplinary Curricula: Data Literacy And Divergent Disciplinary Perspectives

Introduction

Data literacy is the ability to ask and answer meaningful questions by collecting, analyzing and making sense of data encountered in our everyday lives. In our increasingly data-driven society, data literacy is arguably an important civic skill and one that we should be developing in our students. In addition, using data to connect school subjects with real-world events makes learning a richer and more meaningful experience. It can move students beyond simply learning facts to beginning to acquire skills in inquiry, critical reasoning, argumentation, and communication.
Much has been written about the importance of understanding quantitative data in today’s society (Briggs, 2002; Madison, 2002; Scheaffer, 2001; Steen, 2001). Unfortunately, the realization of this importance has not translated into classroom practice. While there has been significant research on the teaching and learning of data analysis and probability (e.g. Konold & Higgins, 2003; Lehrer & Schauble, 2002), and we have seen the inclusion of data analysis in mathematics education standards (NCTM, 2000), data analysis is still too often relegated to calculating measures of central tendency and reading simple graphs and tables, without aiming for true data literacy. Indeed, Rubin (2005, p. 22) writes, ” ‘Numerical literacy’ is woefully incomplete without ‘data literacy,’ yet we shortchange most students by leaving these topics out of the common series of math courses.”