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Cited by 1,015

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Learning by Collaborating: Convergent Conceptual Change

Roschelle, Jeremy·1992·Journal of the Learning Sciences

Abstract

The goal of this article is to construct an integrated approach to collaboration and conceptual change. To this end, a case of conceptual change is analyzed from the point of view of conversational interaction. It is proposed that the crux of collaboration is the problem of convergence: How can two (or more) people construct shared meanings for conversations, concepts, and experiences? Collaboration is analyzed as a process that gradually can lead to convergence of meaning. The epistemological basis of the framework of analysis is a relational, situated view of meaning: Meanings are taken to be relations among situations and verbal or gestural actions. The central claim is that a process described by four primary features can account for students' incremental achievement of convergent conceptual change. The process is characterized by (a) the production of a deep-featured situation, in relation to (b) the interplay of physical metaphors, through the constructive use of (c) interactive cycles of conversational turn-taking, constrained by (d) the application of progressively higher standards of evidence for convergence.

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Metadata

Authors
Roschelle, J.
Venue
Journal of the Learning Sciences
Published
1992-07
Volume
2
Issue
3
Pages
235-276
ISSN
  • 1050-8406 (ISSN-L)
  • 1532-7809
OpenAlex
W2009915208
Cited by
1,015
References
2
FWCI
60.17
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning by Collaborating: Convergent Conceptual Change. https://app.cochat.ai/papers/10.1207/s15327809jls0203_1
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Roschelle, J. (1992). Learning by Collaborating: Convergent Conceptual Change. Journal of the Learning Sciences. https://doi.org/10.1207/s15327809jls0203_1

Data sources

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.