Wednesday, May 6, 2020

The Impact Of Technology On The World Of Statistical...

Introduction Technology is constantly changing and evolving – so it can be no surprise that it is having an effect on the world of statistical education. This paper takes a look at three different articles – The Guidelines for Assessment and Instruction in Statistics Education (GAISE) Report (Franklin et al., 2007), that presents information on statistical education as a whole and two other articles, Evaluating Data Analysis Software: The Case of TinkerPlots (Fitzallen, 2007) and The Role of Technology in Improving Student Learning of Statistics (Ben-Zvi, Chance, Garfield, and Medina, 2007) that provide a look at how technology is playing a role in facilitating that education. At a glance – it may seem as if The GAISE Report does not fit†¦show more content†¦It shows how statistics can influence our personal choices, citizenship, and our workplace/professions. The report then goes on to describe what statistical education should look like. The framework presented in the report is based off the National Council of Teachers of Mathematics (NCTM) and its Principles and Standards for School Mathematics published in 2000. The framework breaks down statistical education into four main components: 1. Formulate Questions †¢ Clarify the problem at hand †¢ Formulate one (or more) questions that can be answered with data. 2. Collect Data †¢ Design a plan to collect appropriate data †¢ Employ the plan to collect data 3. Analyze Data †¢ Select appropriate graphical and numerical methods †¢ Use these methods to analyze the data 4. Interpret Results †¢ Interpret the analysis †¢ Relate the interpretation to the original question (Franklin et al, 2007, p. 11) From there each component is broken down further into three different levels of understanding (A, B, C – with C being the highest level). Examples of satisfying or obtaining each level within each component are given and explained. The article also spends some time distinguishing what makes statistics different from mathematics and that difference is variability in the data. The framework provides an understanding for how variability effects each of the four components, in order; anticipating, acknowledging,

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