Adamson, D.Bharadwaj, A.Singh, A.Ashe, C.Yaron, D.Ros�, C.P.2020-03-302020-03-302014Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, Vol.8474 LNCS, , pp.220-229https://idr.nitk.ac.in/handle/123456789/8826In 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.Book chapter