Analyzing Self-Explanations in Mathematics: Gestures and Written Notes Do Matter

Abstract

When learners self-explain, they try to make sense of new information. Although research has shown that bodily actions and written notes are an important part of learning, previous analyses of self-explanations rarely take into account written and non-verbal data produced spontaneously. In this paper, the extent to which interpretations of self-explanations are influenced by the systematic consideration of such data is investigated. The video recordings of 33 undergraduate students, who learned with worked-out examples dealing with complex numbers, were categorized successively including three different data bases: (a) verbal data, (b) verbal and written data, and (c) verbal, written and non-verbal data. Results reveal that including written data (notes) and non-verbal data (gestures and actions) leads to a more accurate analysis of self-explanations than an analysis solely based on verbal data. This influence is even stronger for the categorization of self-explanations as adequate or inadequate.