NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
PDF on ERIC Download full text
ERIC Number: EJ1163809
Record Type: Journal
Publication Date: 2017
Pages: 17
Abstractor: As Provided
ISSN: EISSN-1929-7750
In Search of Conversational Grain Size: Modelling Semantic Structure Using Moving Stanza Windows
Siebert-Evenstone, Amanda L.; Irgens, Golnaz Arastoopour; Collier, Wesley; Swiecki, Zachari; Ruis, Andrew R.; Shaffer, David Williamson
Journal of Learning Analytics, v4 n3 p123-139 2017
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 discourse data to define when connections are likely to be meaningful. In this paper, we present an approach to segmenting data for the purposes of modeling connections in discourse using epistemic network analysis. 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 of this study to a purely conversation-based segmentation method to examine the affordances of temporal segmentation for modeling connections in discourse.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Evaluative
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: DRL0918409; DRL0946372; DRL1247262; DRL1418288; DRL1661036; DRL1713110; DUE0919347; DUE1225885; EEC1232656; EEC1340402; REC0347000