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The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development

Kinchin, Ian M., Hay, David B., Adams, Alan·2000·Educational Research

Abstract

This paper describes a qualitative approach to analysing students' concept maps. The classification highlights three major patterns which are referred to as 'spoke', 'chain' and 'net' structures. Examples are given from Year 8 science classes. The patterns are interpreted as being indicators of progressive levels of understanding. It is proposed that identification of these differences may help the classroom teacher to focus teaching for more effective learning and may be used as a basis for structuring groups in collaborative settings. This approach to analysing concept maps is of value because it suggests teaching approaches that help students integrate new knowledge and build upon their existing naive concepts. We also refer to the teacher's scheme of work and to the National Curriculum for science in order to consider their influence in the construction of understanding. These ideas have been deliberately offered for early publication to encourage debate and generate feedback. Further work is in progress to better understand how students with different conceptual structures can be most appropriately helped to achieve learning development.

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Crossref · 10.1080/001318800363908

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Metadata

Authors
Kinchin, I. M., Hay, D. B., & Adams, A.
Venue
Educational Research
Published
2000-01
Volume
42
Issue
1
Pages
43-57
ISSN
  • 0013-1881 (ISSN-L)
  • 1469-5847
OpenAlex
W2000830595
Cited by
638
References
3
FWCI
42.98
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.