AN INFERENTIALISM-BASED FRAMEWORK FOR CAPTURING STATISTICAL CONCEPT FORMATION OVER TIME

Statistics education researchers have been challenged to consider the theory of inferentialism in understanding concept formation in students. A critique of inferentialism is that no comprehensive method has been formulated to use the theory in practice. In this paper an inferentialism-based framework is presented that appears to be capable of explicating the development of statistical concepts during learning. By following six 11-year-olds’ learning over several statistical modelling cycles using TinkerPlots, the framework was used to capture their interrogative cycles of noticing and wondering, giving and asking for reasons, and sanctioning and censuring, as well as oscillations between concretising language about actions and conceptualising language towards concept formation. Five teaching episodes occurring near the beginning of a 12-week learning sequence are used to illustrate how the framework might be able to capture student concept formation over time.

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Publication Details

Journal
Statistics Education Research Journal
Published
2025-02-12
DOI
https://doi.org/10.52041/serj.v24i1.714
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
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article

AN INFERENTIALISM-BASED FRAMEWORK FOR CAPTURING STATISTICAL CONCEPT FORMATION OVER TIME

Maxine Pfannkuch, Anne Patel
Statistics Education Research Journal
Bayesian Modeling and Causal Inference
article

AN INFERENTIALISM-BASED FRAMEWORK FOR CAPTURING STATISTICAL CONCEPT FORMATION OVER TIME

Maxine Pfannkuch, Anne Patel
article en

Abstract

Statistics education researchers have been challenged to consider the theory of inferentialism in understanding concept formation in students. A critique of inferentialism is that no comprehensive method has been formulated to use the theory in practice. In this paper an inferentialism-based framework is presented that appears to be capable of explicating the development of statistical concepts during learning. By following six 11-year-olds’ learning over several statistical modelling cycles using TinkerPlots, the framework was used to capture their interrogative cycles of noticing and wondering, giving and asking for reasons, and sanctioning and censuring, as well as oscillations between concretising language about actions and conceptualising language towards concept formation. Five teaching episodes occurring near the beginning of a 12-week learning sequence are used to illustrate how the framework might be able to capture student concept formation over time.

Statistics Education Research JournalVol. 24(1)
University of Auckland (NZ)
Openalex Percentile: Top 99%
Bayesian Modeling and Causal Inference
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