Implementing a pedagogical cycle to support data modelling and statistical reasoning in years 1 and 2 through the Interdisciplinary Mathematics and Science (IMS) project Article Swipe
Joanne Mulligan
,
Russell Tytler
,
Vaughan Prain
,
Melinda Kirk
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s13394-023-00454-0
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s13394-023-00454-0
This paper illustrates how years 1 and 2 students were guided to engage in data modelling and statistical reasoning through interdisciplinary mathematics and science investigations drawn from an Australian 3-year longitudinal study: Interdisciplinary Mathematics and Science Learning ( https://imslearning.org/ ). The project developed learning sequences for 12 inquiry-based investigations involving 35 teachers and cohorts of between 25 and 70 students across years 1 through 6. The research used a design-based methodology to develop, implement, and refine a 4-stage pedagogical cycle based on students’ problem posing, data generation, organisation, interpretation, and reasoning about data. Across the stages of the IMS cycle, students generated increasingly sophisticated representations of data and made decisions about whether these supported their explanations, claims about, and solutions to scientific problems. The teacher’s role in supporting students’ statistical reasoning was analysed across two learning sequences: Ecology in year 1 and Paper Helicopters in year 2 involving the same cohort of students. An explicit focus on data modelling and meta-representational practices enabled the year 1 students to form statistical ideas, such as distribution, sampling, and aggregation, and to construct a range of data representations. In year 2, students engaged in tasks that focused on ordering and aggregating data, measures of central tendency, inferential reasoning, and, in some cases, informal ideas of variability. The study explores how a representation-focused interdisciplinary pedagogy can support the development of data modelling and statistical thinking from an early age.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s13394-023-00454-0
- https://link.springer.com/content/pdf/10.1007/s13394-023-00454-0.pdf
- OA Status
- hybrid
- Cited By
- 10
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W4378801590
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4378801590Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s13394-023-00454-0Digital Object Identifier
- Title
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Implementing a pedagogical cycle to support data modelling and statistical reasoning in years 1 and 2 through the Interdisciplinary Mathematics and Science (IMS) projectWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-31Full publication date if available
- Authors
-
Joanne Mulligan, Russell Tytler, Vaughan Prain, Melinda KirkList of authors in order
- Landing page
-
https://doi.org/10.1007/s13394-023-00454-0Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s13394-023-00454-0.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s13394-023-00454-0.pdfDirect OA link when available
- Concepts
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Mathematics education, Construct (python library), Representation (politics), Scientific modelling, Science education, Computer science, Psychology, Epistemology, Politics, Law, Philosophy, Programming language, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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