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Predicting student learning from conversational cues
(Springer Verlag service@springer.de, 2014) Adamson, D.; Bharadwaj, A.; Singh, A.; Ashe, C.; Yaron, D.; RosÃ©, C.
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. Â© 2014 Springer International Publishing Switzerland.