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.
Original language | English |
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Title of host publication | 12th International Conference of the Learning Sciences, ICLS 2016 |
Subtitle of host publication | Transforming Learning, Empowering Learners, Conference Proceedings |
Editors | Chee-Kit Looi, Joseph L. Polman, Ulrike Cress, Peter Reimann |
Place of Publication | Singapore Singapore |
Publisher | International Society of the Learning Sciences |
Pages | 631-638 |
Number of pages | 8 |
Volume | 1 |
ISBN (Electronic) | 9780990355090 |
Publication status | Published - 2016 |
Externally published | Yes |
Event | International Conference of the Learning Sciences 2016 - Singapore, Singapore Duration: 20 Jun 2016 → 24 Jun 2016 Conference number: 12th |
Conference
Conference | International Conference of the Learning Sciences 2016 |
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Abbreviated title | ICLS 2016 |
Country/Territory | Singapore |
City | Singapore |
Period | 20/06/16 → 24/06/16 |
Keywords
- Discourse Analysis
- Epistemic Network Analysis
- Segmentation
- Sliding Window