Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/173
Title: In Search of Conversational Grain Size: Modeling Semantic Structure Using Moving Stanza Windows
Authors: Siebert-Evenstone, Amanda L.
Arastoopour, Golnaz
Collier, Wesley
Swiecki, Zachari
Ruis, Andrew R.
Shaffer, David Williamson
Issue Date: Jul-2016
Publisher: Singapore: International Society of the Learning Sciences
Citation: Siebert-Evenstone, A. L., Arastoopour, G., Collier, W., Swiecki, Z., Ruis, A. R., & Shaffer, D. W. (2016). In Search of Conversational Grain Size: Modeling Semantic Structure Using Moving Stanza Windows In Looi, C. K., Polman, J. L., Cress, U., and Reimann, P. (Eds.). Transforming Learning, Empowering Learners: The International Conference of the Learning Sciences (ICLS) 2016, Volume 1. Singapore: International Society of the Learning Sciences.
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.
URI: https://repository.isls.org/handle/1/173
https://dx.doi.org/10.22318/icls2016.82
Appears in Collections:ICSL 2016

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