Adamson, D.Bharadwaj, A.Singh, A.Ashe, C.Yaron, D.Rosé, C.2026-02-062014Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, Vol.8474 LNCS, , p. 220-2293029743https://doi.org/10.1007/978-3-319-07221-0_26https://idr.nitk.ac.in/handle/123456789/32581In 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.Computer-Supported Collaborative LearningDiscourse AnalysisEducational Data Mining