What Works: Creating Adaptive and Intelligent Systems for Collaborative Learning Support

  • Nia M. Dowell
  • Whitney L. Cade
  • Yla Tausczik
  • James Pennebaker
  • Arthur C. Graesser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)


An emerging trend in classrooms is the use of collaborative learning environments that promote lively exchanges between learners in order to facilitate learning. This paper explored the possibility of using discourse features to predict student and group performance during collaborative learning interactions. We investigated the linguistic patterns of group chats, within an online collaborative learning exercise, on five discourse dimensions using an automated linguistic facility, Coh-Metrix. The results indicated that students who engaged in deeper cohesive integration and generated more complicated syntactic structures performed significantly better. The overall group level results indicated collaborative groups who engaged in deeper cohesive and expository style interactions performed significantly better on posttests. Although students do not directly express knowledge construction and cognitive processes, our results indicate that these states can be monitored by analyzing language and discourse. Implications are discussed regarding computer supported collaborative learning and ITS’s to facilitate productive communication in collaborative learning environments.


collaborative interactions learning computational linguistics Coh-Metrix 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nia M. Dowell
    • 1
  • Whitney L. Cade
    • 1
  • Yla Tausczik
    • 2
  • James Pennebaker
    • 3
  • Arthur C. Graesser
    • 1
  1. 1.Institute for Intelligent SystemsThe University of MemphisMemphisUSA
  2. 2.Department of Social ComputingCarnegie Mellon UniversityPittsburgUSA
  3. 3.Department of PsychologyUniversity of Texas at AustinAustinUSA

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