Predicting Student Learning from Conversational Cues

  • David Adamson
  • Akash Bharadwaj
  • Ashudeep Singh
  • Colin Ashe
  • David Yaron
  • Carolyn P. Rosé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


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.


Computer-Supported Collaborative Learning Discourse Analysis Educational Data Mining 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • David Adamson
    • 1
  • Akash Bharadwaj
    • 2
  • Ashudeep Singh
    • 3
  • Colin Ashe
    • 4
  • David Yaron
    • 1
  • Carolyn P. Rosé
    • 1
  1. 1.Carnegie Mellon UniversityUSA
  2. 2.National Institute of Technology KarnatakaIndia
  3. 3.Indian Institute of Technology KanpurIndia
  4. 4.Indiana University of PennsylvaniaUSA

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