On students’ covariational reasoning in Bayesian situations: do calculation strategies and visualizations play a role?
Bayesian reasoning has been intensively investigated, due to its high relevance. Prior research has focused on conventional Bayesian reasoning, i.e. the reasoning behind the calculation with probabilities. We extend this focus by studying covariational reasoning as part of extended Bayesian reasonin...
Gespeichert in:
| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
23 March 2025
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| In: |
Mathematical thinking and learning
Year: 2025, Pages: 1-22 |
| ISSN: | 1532-7833 |
| DOI: | 10.1080/10986065.2025.2468996 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1080/10986065.2025.2468996
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| Verfasserangaben: | Andreas Eichler, Theresa Büchter, Katharina Böcherer-Linder, and Markus Vogel |
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| 520 | |a Bayesian reasoning has been intensively investigated, due to its high relevance. Prior research has focused on conventional Bayesian reasoning, i.e. the reasoning behind the calculation with probabilities. We extend this focus by studying covariational reasoning as part of extended Bayesian reasoning. We analyze whether covariational reasoning varies with regard to four different visualizations known to facilitate calculation, i.e. tree diagram, double tree, unit square and 2 × 2-table (RQ1). Furthermore, we study how people’s strategy in calculation tasks affects their covariational reasoning in covariation tasks (RQ2). N = 221 undergraduate students participate in the experimental study. The results show significant differences in the covariational reasoning between visualizations: covariational reasoning is best with 2 × 2 tables and worst with tree diagrams. Also, the calculation strategy significantly influences covariational reasoning. Hence, the results provide insights about the support of visualizations for covariational reasoning and have implications on the teaching of extended Bayesian reasoning. | ||
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