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DC Field | Value | Language |
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dc.contributor.author | Adamson, D. | |
dc.contributor.author | Bharadwaj, A. | |
dc.contributor.author | Singh, A. | |
dc.contributor.author | Ashe, C. | |
dc.contributor.author | Yaron, D. | |
dc.contributor.author | Ros�, C.P. | |
dc.date.accessioned | 2020-03-30T10:22:49Z | - |
dc.date.available | 2020-03-30T10:22:49Z | - |
dc.date.issued | 2014 | |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, Vol.8474 LNCS, , pp.220-229 | en_US |
dc.identifier.uri | http://idr.nitk.ac.in/jspui/handle/123456789/8826 | - |
dc.description.abstract | In 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.title | Predicting student learning from conversational cues | en_US |
dc.type | Book chapter | en_US |
Appears in Collections: | 2. Conference Papers |
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