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

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Intelligent Tutoring Systems (ITS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8474))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Institute for Intelligent Systems, The University of Memphis, Memphis, TN, 38152, USA

    Nia M. Dowell, Whitney L. Cade & Arthur C. Graesser

  2. Department of Social Computing, Carnegie Mellon University, Pittsburg, PA, 13289, USA

    Yla Tausczik

  3. Department of Psychology, University of Texas at Austin, Austin, TX, 78705, USA

    James Pennebaker

Authors
  1. Nia M. Dowell
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  2. Whitney L. Cade
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  3. Yla Tausczik
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  4. James Pennebaker
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  5. Arthur C. Graesser
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Editor information

Editors and Affiliations

  1. Computer Science Department, University Politehnica of Bucharest, Splaiul Independentei 313, 060042, Bucharest, Romania

    Stefan Trausan-Matu

  2. North Carolina State University, 890 Oval Dr., Campus Box 8206, 27695-8206, Raleigh, NC, USA

    Kristy Elizabeth Boyer

  3. Department of Information and Computer Sciences, University of Hawaii, 1680 East-West Road, POST 317, 96822, Honolulu, HI, USA

    Martha Crosby

  4. Neoanalysis Ltd., Marni 56, 10437, Athens, Greece

    Kitty Panourgia

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© 2014 Springer International Publishing Switzerland

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Dowell, N.M., Cade, W.L., Tausczik, Y., Pennebaker, J., Graesser, A.C. (2014). What Works: Creating Adaptive and Intelligent Systems for Collaborative Learning Support. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. https://doi.org/10.1007/978-3-319-07221-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-07221-0_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07220-3

  • Online ISBN: 978-3-319-07221-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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