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

Amanda L. Siebert-Evenstone, Golnaz Arastoopour, Wesley Collier, Zachari Swiecki, Andrew R. Ruis, David Williamson Shaffer

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publication12th International Conference of the Learning Sciences, ICLS 2016
Subtitle of host publicationTransforming Learning, Empowering Learners, Conference Proceedings
EditorsChee-Kit Looi, Joseph L. Polman, Ulrike Cress, Peter Reimann
Place of PublicationSingapore Singapore
PublisherInternational Society of the Learning Sciences (ISLS)
Number of pages8
ISBN (Electronic)9780990355090
Publication statusPublished - 2016
Externally publishedYes
EventInternational conference of the learning sciences 2016 - Singapore, Singapore
Duration: 20 Jun 201624 Jun 2016
Conference number: 12th


ConferenceInternational conference of the learning sciences 2016
Abbreviated titleICLS 2016


  • Discourse Analysis
  • Epistemic Network Analysis
  • Segmentation
  • Sliding Window

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