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

Anne Patel & Maxine Pfannkuch

Statistics Education Research Journal2025https://doi.org/10.52041/serj.v24i1.714article
ABDC B
Weight
0.50

What the paper says

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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https://doi.org/https://doi.org/10.52041/serj.v24i1.714

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@article{anne2025,
  title        = {{AN INFERENTIALISM-BASED FRAMEWORK FOR CAPTURING STATISTICAL CONCEPT FORMATION OVER TIME}},
  author       = {Anne Patel & Maxine Pfannkuch},
  journal      = {Statistics Education Research Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.52041/serj.v24i1.714},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.