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Title: Predicting student learning from conversational cues
Authors: Adamson, D.
Bharadwaj, A.
Singh, A.
Ashe, C.
Yaron, D.
Ros�, C.P.
Issue Date: 2014
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
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.
Appears in Collections:2. Conference Papers

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