Open Data in Australian Schools: Taking Statistical Literacy and the Practice of Statistics Across the Curriculum

Open Data in Australian Schools: Taking Statistical Literacy and the Practice of Statistics Across the Curriculum

Jane Watson (University of Tasmania, Australia)
Copyright: © 2017 |Pages: 26
DOI: 10.4018/978-1-5225-2512-7.ch002
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Abstract

This chapter focuses on statistical literacy and the practice of statistics from the perspective of middle school students and how their experiences can be enhanced by the availability of open data. The open data sets selected illustrate the types of contexts that are available and their connections to the Australian school curriculum. The importance of visualisation is stressed and the software TinkerPlots is the tool used for students to create representations and develop the understanding necessary to analyse data and draw conclusions. Building appreciation of the practice of statistics in this way further assists students to become critical thinkers in judging the claims of others later as statistically literate adults.
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Introduction

This chapter focuses on statistical literacy and the practice of statistics from the perspective of middle school students and how their experiences can be enhanced by the availability of open data. The availability of open data and big data has provided wonderful opportunities for statisticians, scientists, and social scientists to develop new techniques and explore new problems (e.g., Kitchin, 2014; Mayer-Shonberger & Cukier, 2013). What has not been clear is the influence these data should have on the school curriculum, particularly the middle school where students are coming to terms with statistical literacy and the components of the practice of statistics. The suggestions made for the introduction of large, open data sets in schools (e.g., Engel, 2014; Ridgway, 2015; Ridgway, Nicholson, & McCusher, 2013; Ridgway & Smith, 2013) include two aspects relevant to this chapter, which is focussed on the middle school level. First is that open data, by their very existence, provide context for a statistical investigation, and the practice of statistics cannot take place without context (Rao, 1975). At the school level context implies cross-curriculum activities linking the mathematics curriculum meaningfully with other areas such as science, social science, and health and wellbeing. Second is the importance of visualisation and its implications for interpreting graphical presentations. In particular, the need to conceptualise multi-variate data and their representations is recognised in making sense of real world problems.

Ridgway (2015) and others base their definition of statistical literacy on the work of Gal (2002), who defined the phrase for adults as:

  • People’s ability to interpret and critically evaluate statistical information, data-related arguments, or stochastic phenomena, which they may encounter in diverse contexts, and when relevant;

  • Their ability to discuss or communicate their reactions to such statistical information, such as their understanding of the meaning of the information, their opinions about the implications of this information, or their concerns regarding the acceptability of given conclusions (pp. 2–3).

Further, the Guidelines for Assessment and Instruction in Statistics Education (GAISE) for both school (Franklin et al., 2007) and college (GAISE College Report ASA Revision Committee [GAISECollegeComm], 2016) stress statistical literacy in terms of real data. In today’s world, context with real data is likely to involve large open data sets. At the middle school level statistical literacy is seen as “the meeting point of the data and chance curriculum and the everyday world, where encounters involve unrehearsed contexts and spontaneous decision-making based on the ability to apply statistical tools, general contextual knowledge, and critical literacy” (Watson, 2006, p. 11). Reference to the curriculum places some constraints compared with what might be expected of adults. In terms of developing statistical literacy at this level Watson further suggests a three-tiered progression where students must make judgements based on

  • 1.

    Understanding the statistical terminology/tools used in claims;

  • 2.

    Understanding the use of the terminology/tools within the context of the claim; and

  • 3.

    Possessing the ability and confidence to challenge claims made without proper statistical foundation or to support those that are legitimate.

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