Representational support for collaborative inquiry

Abstract

Empirical work with Belvedere, a software environment for the construction of diagrammatic representations of evidential relations, is summarized, leading to the hypothesis that variation in features of representational tools can have a significant effect on the learners' discourse and on learning outcomes. For example, by manipulating the concepts used by a toolkit, it is possible to manipulate the distinctions attended to by learners. Once learners have constructed some representations, their learning interactions appear to be further guided by the objects and relationships (expressed or potential) that these representations make salient. These kinds of design considerations are critical for collaborative learning software, yet are insufficiently studied. This paper describes the work that led to this position, sketches a theoretical analysis of the roles of constraint and salience in the effect of representational bias on collaborative learning discourse and describes initial studies now underway that varies textual, diagrammatic and tabular representations, and investigates their effects on the quality of discourse between learners.

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