In search of conversational grain size: Modeling semantic structure using moving stanza windows

Abstract

Analyses of learning based on student discourse need to account not only for the content of the utterances but also for the ways in which students make connections across turns of talk. This requires segmentation of the discourse data to define when connections are likely to be meaningful. In this paper, we present a novel approach to segmenting data for the purposes of modeling connections in discourse. Specifically, we use epistemic network analysis to model connections in student discourse using a temporal segmentation method adapted from recent work in the learning sciences. We compare the results to a purely topic-based segmentation method to examine the affordances of temporal segmentation for modeling connections in discourse.