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Presentation 6D3. Statistics Assessment in Multidisciplinary Contexts

Susanne P. Lajoie (Canada)
Andrew Chiarella (Canada)


Presentation Abstract

As statistical education evolves as a discipline more research involving the examination of statistical reasoning across disciplines is anticipated. For example, statistical investigations can cross into areas of scientific reasoning quite easily. In both situations, research questions are posed, data are collected, analyzed, graphed and interpreted. The similarities and differences in how such investigations are conducted across disciplines, how data are collected, used, and interpreted are important for refining our definition of statistical literacy and how it is assessed. It is quite possible that instruction that crosses the curriculum may tie the concepts of statistical reasoning and statistical literacy more tightly, especially if statistics is seen from a problem solving perspective. Statistics can be valuable to almost any discipline where people engage in inquiry tasks and the data collected can be quantified. Given this statement one might expect that a basic education in statistics would be a pre or co-requisite for physical science students in middle school and high school. Generally, this is not the case. Instead of integrating statistics in the curriculum there is still a division of labour, whereby math educators are responsible for the teaching of statistics, and science teachers the teaching of scientific inquiry. This division often creates cross-disciplinary misconceptions. When and what students learn about statistics is oftentimes related to how statistics, as a formalised mathematical system, coheres with other mathematics topics rather than their relationship to other disciplines. Students in the science class may need to make sense of ideas like the center and the dispersion of a data set long before they are exposed to the statistics related to these concepts in the mathematics classroom. Some studies suggest that important statistical concepts like error variation (Schauble, 1996) and simply the ability to note patterns in data (Lehrer & Romberg, 1996) can prevent science students from making any sense of their science data. The result is that inquiry activities in the middle school and high school science classroom become unintelligible and without meaning due to lack instruction regarding basic statistical concepts. Lehrer and Schauble (2000) have looked at the relationship between mathematical concepts and science, as has Cobb (in press). However, these cross-disciplinary relationships need to be further examined in terms of our definitions of statistical reasoning and how we assess learning and problem-solving across disciplines. Assessment will consist of gathering multiple forms of evidence of statistical understanding across different domains to indicate emerging knowledge.


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