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Diagrammatic reasoning and hypostatic abstraction in statistics education

  • Arthur Bakker

    Arthur Bakker (b. 1970). His research interests include statistics education, workplace learning, technology, history of statistics and semiotics. His recent publications include ‘Reasoning about shape as a pattern in variability’ (2004); ‘Diagrammatic reasoning as the basis for developing concepts: A semiotic analysis of students' learning about statistical distribution’ (with M. H. G. Hoffmann, 2005); and ‘A historical phenomenology of mean and median’ (with K. P. E. Gravemeijer, 2006).

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Published/Copyright: April 16, 2007
Semiotica
From the journal Volume 2007 Issue 164

Abstract

Peirce's notions of diagrammatic reasoning and hypostatic abstraction are relevant to educational research in areas where diagrams and abstraction play an important role. In this paper, I analyze an example from statistics education in which diagrammatic reasoning created opportunities for hypostatic abstraction. For instance, where students initially characterized data points as being ‘spread out,’ they later said, ‘the spread is large.’ This is a prototypical example of hypostatic abstraction — taking a predicate as a new object that can have predicates itself. More generally, the notion of diagrammatic reasoning proved helpful to identify the key learning processes involved in learning to reason about statistical concepts.

About the author

Arthur Bakker

Arthur Bakker (b. 1970). His research interests include statistics education, workplace learning, technology, history of statistics and semiotics. His recent publications include ‘Reasoning about shape as a pattern in variability’ (2004); ‘Diagrammatic reasoning as the basis for developing concepts: A semiotic analysis of students' learning about statistical distribution’ (with M. H. G. Hoffmann, 2005); and ‘A historical phenomenology of mean and median’ (with K. P. E. Gravemeijer, 2006).

Published Online: 2007-04-16
Published in Print: 2007-04-19

© Walter de Gruyter

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