Developing prospective science teachers’ multivariable thinking capabilities
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Deep expertise develops through scaffolded engagement with progressively complex problems, in which practitioners struggle with difficulty, monitor and revise their own reasoning, and repeatedly correct flawed assumptions against evidence.
“Deep expertise develops through scaffolded engagement with progressively complex problems, in which practitioners struggle with difficulty, monitor and revise their own reasoning, and repeatedly correct flawed assumptions against evidence (Ericsson, 2006;Mezirow, 1991;Rind, 2022).”
From The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise (2026) · cites this paper as Rind, 2022
Cites
Direct instruction vs. discovery: The long view
Dean Jr., D., & Kuhn, D. (2007) · Science Education · doi:10.1002/sce.20194 · Cited by 325 · see citing works
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Lovett, N. (2026) · Human Resource Development Review · doi:10.1177/15344843261470602 · Cited by 7 · see citing works
- Authors
- Rind, I. A.
- Affiliations
- Sukkur IBA University (PK)
- Corresponding author
- Rind, Irfan Ahmed
- ORCID
- Rind, Irfan Ahmed
- Venue
- Thinking Skills and Creativity
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 46
- Pages
- 101168
- ISSN
- 1871-1871 (ISSN-L)
- OpenAlex
- W4304099308
- Cited by
- 10
- References
- 1
- Citations by year
- 2026: 6 · 2024: 2 · 2023: 2
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Thinking Skills and Creativity (publishedVersion)
- Topics
- Social Sciences › Education › Science Education and Pedagogy
- Social Sciences › Education › Education and Critical Thinking Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- prospective science teachers, elementary science teacher education, problem-based inquiry, metacognitive scaffolding, variable control, causal reasoning, scientific thinking
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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