Students’ difficulties in practicing computer-supported statistical inference: Some hypothetical generalizations from a study

Maxine Pfannkuch (), Chris J. Wild () and Matt Regan ()
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Maxine Pfannkuch: The University of Auckland, Department of Statistics
Chris J. Wild: The University of Auckland, Department of Statistics
Matt Regan: The University of Auckland, Department of Statistics

Chapter Chapter 28 in Mit Werkzeugen Mathematik und Stochastik lernen – Using Tools for Learning Mathematics and Statistics, 2014, pp 393-403 from Springer

Abstract: Abstract When introducing students to statistical inference using bootstrapping and randomization methods and new infrastructure such as dynamic visualizations, new conceptual development issues may be revealed. From a pilot study and a main study involving over 3000 students from the final year of high school and introductory university statistics, we use preliminary results to conjecture potential conceptual issues and obstacles. In imitation of an insightful paper of Biehler (1997), we identify seven problem areas and difficulties of students related to using bootstrapping and randomization inferential methods from our research. Although dynamic visualizations have the power to reveal chance variation and the depth of the conceptual structure underpinning the methods in ways that were not previously accessible, the identified areas indicate that attention to the necessity of precise verbal descriptions and the nature of the argumentation are important. In accord with Biehler we surmise that we may need to develop a habit of mind in students that is orientated towards a careful interpretation and understanding of graph visualizations.

Keywords: Problem Area; Bootstrap Confidence Interval; Dynamic Visualization; Bootstrap Distribution; Inferential Reasoning (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-658-03104-6_28

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DOI: 10.1007/978-3-658-03104-6_28

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