Data literacy is recognized as a critically important skill in today’s society, but thinking with and about data is often ignored in K-12 curricula. The Thinking With Data (TWD) project advocates that data literacy is a skill that should be addressed in an interdisciplinary manner. This article provides an overview of the TWD curriculum for middle school, which uses an extended Preparation for Future Learning (PFL) framework to teach data literacy across subjects. Research conducted during field implementation of the curriculum in two middle schools investigated whether the approach worked, and if so, why. Results show evidence of student gains in data literacy skills as well as in mathematics and science. Our best evidence of learning of data literacy skills was found during the social studies and mathematics modules. While we have preliminary evidence of learning in science and language arts, potential gains in these modules need to be more closely examined.PERSPECTIVES/THEORETICAL FRAMEWORKMuch has been written about the importance of understanding quantitative data in today’s society (Briggs, 2002; Madison, 2002; Steen, 2001). Unfortunately, this realization has not translated into classroom practice. There has been significant research on the teaching and learning of data analysis and probability (Lehrer & Schauble, 2002) as well as on key aspects of data literacy that are age appropriate for middle school students, and data analysis has been included in mathematics education standards for many years (National Council for Teachers of Mathematics [NCTM], 2000). However, data analysis is still relegated to a few days in mathematics class in which students calculate measures of central tendency and read simple graphs and tables, without aiming for true data literacy. Indeed, Rubin (2005, p. 22) writes that, ‘”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.”Although unfortunate, this situation is perhaps inevitable. Most teachers in the United States cannot find time to cover their required material, with textbooks that are already “a mile wide and an inch deep” (Schmidt, McKnight, Cogan, Jakwerth, & Houang, 1999), leaving them with the impression that there is no time to address a topic as complex as data literacy. And data literacy is indeed complex, as it includes the ability to formulate and answer data-based questions; use appropriate data, tools, and representations; interpret information from data; develop and evaluate data-based inferences and explanations; and use data to solve real problems and communicate their solutions. Compounding the issue in addressing data literacy is the organization of today’s middle school itself: In many schools, individual subjects are still taught in relative isolation, as students move from class to class, with each class focusing on an individual discipline. However, true data literacy cannot be taught within classes that are focused on a single discipline (Briggs, 2002; Madison, 2002; Scheaffer, 2001 ; Steen, 2001 ). This is most obvious in considering the role of the context of investigation: Whereas in most mathematics “the context is part of the irrelevant detail … in data analysis, context provides meaning” (Cobb & Moore, 1997, p. 801 ; see also Rubin, 2005). We cannot expect the context for using the skills of data literacy to come solely from the mathematics classroom, nor can we expect the data analysis required for data literacy to come from the other disciplines. True data literacy, especially at the middle grade level, requires contributions from across the curriculum.While recognizing the challenges of implementing cross-disciplinary learning activities in middle school, there is reason to be hopeful that middle schools will be able to support this cross-curricular learning, as the importance of data literacy has been recognized across middle school disciplinary standards (Table 1).
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