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Scaffolding geography's conceptual ways of thinking using 'semantic waves'.

  • Published In: Curriculum Journal, 2025, v. 36, n. 2. P. 255 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Vernon, Esther; Dunphy, Alison 3 of 3

Abstract

This paper discusses the application of Karl Maton's notion of 'semantic waves' to the teaching of geography at the post‐16 phase (A‐level) in England. Drawing on evidence generated through a 2‐year close‐to‐practice case study, it illustrates its potential as a scaffold in two ways. Firstly, one that can help teachers face both ways: Towards their subject discipline, to see it in more naïve forms, and towards their novice students, to make that form explicit and thus accessible to them. Such a deliberative or hermeneutic orientation stands in sharp contrast to the atomistic mode of the technicist orientation to knowledge dominant in English education. Secondly, as a scaffold that can provide a visual language for students which makes explicit the implicit relationship between different components of an essay, and in this sense, helps scaffold for a more relational mode of thinking about the geography itself. Interviews with students suggested semantic waves was: (i) a practical but not a prescriptive tool, (ii) a framework that could extend their thinking and (iii) a scaffold that connects knowledge with essay writing. It appeared to be a useful tool to bring the geographical knowledge and its complexity to the fore by aiding metacognitive thought. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Curriculum Journal. 2025/06, Vol. 36, Issue 2, p255
  • Document Type:Article
  • Subject Area:Education
  • Publication Date:2025
  • ISSN:0958-5176
  • DOI:10.1002/curj.294
  • Accession Number:185525587
  • Copyright Statement:Copyright of Curriculum Journal is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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