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Cited by 591

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Macro, Submicro, and Symbolic: The many faces of the chemistry “triplet”

Talanquer, Vicente·2011·International Journal of Science Education

Abstract

The idea that chemical knowledge can be represented in three main ways: macro, submicro, and symbolic (chemistry triplet) has become paradigmatic in chemistry and science education. It has served both as the base of theoretical frameworks that guide research in chemical education and as a central idea in various curriculum projects. However, this triplet relationship has been the subject of different adaptations and reinterpretations that sometimes lead to confusion and misunderstanding, which complicates the analysis of the triplet’s nature and scope. Thus, the central goal of this paper is to describe some of the existing views of the triplet relationship in chemistry and science education and critically analyse their underlying assumptions. We also propose a general structure of our chemistry knowledge intended to better situate the chemistry triplet in relationship with the different types, scales, dimensions, and approaches that seem to characterise such knowledge. Our proposed model may be useful in the analysis, evaluation, and reflection of educational research results and teaching practices centred on the triplet relationship.

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Metadata

Authors
Talanquer, V.
Venue
International Journal of Science Education
Published
2010-01-08
Volume
33
Issue
2
Pages
179-195
ISSN
  • 0950-0693 (ISSN-L)
  • 1464-5289
OpenAlex
W2097503300
Cited by
591
References
39
FWCI
55.09
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Macro, Submicro, and Symbolic: The many faces of the chemistry “triplet”. https://app.cochat.ai/papers/10.1080/09500690903386435
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Talanquer, V. (2011). Macro, Submicro, and Symbolic: The many faces of the chemistry “triplet”. International Journal of Science Education. https://doi.org/10.1080/09500690903386435

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.