Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8826
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dc.contributor.authorAdamson, D.
dc.contributor.authorBharadwaj, A.
dc.contributor.authorSingh, A.
dc.contributor.authorAshe, C.
dc.contributor.authorYaron, D.
dc.contributor.authorRos�, C.P.
dc.date.accessioned2020-03-30T10:22:49Z-
dc.date.available2020-03-30T10:22:49Z-
dc.date.issued2014
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, Vol.8474 LNCS, , pp.220-229en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8826-
dc.description.abstractIn the work here presented, we apply textual and sequential methods to assess the outcomes of an unconstrained multiparty dialogue. In the context of chat transcripts from a collaborative learning scenario, we demonstrate that while low-level textual features can indeed predict student success, models derived from sequential discourse act labels are also predictive, both on their own and as a supplement to textual feature sets. Further, we find that evidence from the initial stages of a collaborative activity is just as effective as using the whole. � 2014 Springer International Publishing Switzerland.en_US
dc.titlePredicting student learning from conversational cuesen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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