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Textual Complexity and Discourse Structure in Computer-Supported Collaborative Learning

  • Stefan Trausan-Matu
  • Mihai Dascalu
  • Philippe Dessus
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

Abstract

Computer-Supported Collaborative Learning (CSCL) technologies play an increasing role simultaneously with the appearance of the Social Web. The polyphonic analysis method based on Bakhtin’s dialogical model reflects the multi-voiced nature of a CSCL conversation and the related learning processes. We propose the extension of the model and the previous applications of the polyphonic method to both collaborative CSCL chats and individual metacognitive essays performed by the same learners. The model allows a tight correlation between collaboration and textual complexity, all integrated in an implemented system, which uses Natural Language Processing techniques.

Keywords

Computer-Supported Collaborative Learning metacognition polyphonic model dialogism knowledge building textual complexity NLP 

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References

  1. 1.
    Sfard, A.: On reform movement and the limits of mathematical discourse. Mathematical Thinking and Learning 2(3), 157–189 (2000)CrossRefGoogle Scholar
  2. 2.
    Bakhtin, M.M.: Problems of Dostoevsky’s poetics. University of Minnesota Press, Minneapolis (1993)Google Scholar
  3. 3.
    Stahl, G.: Group cognition. MIT Press, Cambridge (2006)Google Scholar
  4. 4.
    Trausan-Matu, S., Stahl, G., Sarmiento, J.: Supporting polyphonic collaborative learning. E-service Journal 6(1), 58–74 (2007)CrossRefGoogle Scholar
  5. 5.
    Bakhtin, M.M.: Speech genres and other late essays. University of Texas, Austin (1986)Google Scholar
  6. 6.
    Dessus, P., Trausan-Matu, S.: Implementing Bakhtin’s dialogism theory with NLP techniques in distance learning environments. In: Trausan-Matu, S., Dessus, P. (eds.) Proc. 2nd Workshop on Natural Language Processing in Support of Learning: Metrics, Feedback and Connectivity (NLPsL 2010), pp. 11–20. Matrix Rom, Bucharest (2010)Google Scholar
  7. 7.
    Landauer, T.K., Dumais, S.T.: A solution to Plato’s problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychol. Rev. 104(2), 211–240 (1997)CrossRefGoogle Scholar
  8. 8.
    Dong, A.: The language of design: Theory and computation. Springer, New York (2009)Google Scholar
  9. 9.
    Jurafsky, D., Martin, J.H.: An introduction to natural language processing. Computational linguistics, and speech recognition. Pearson Prentice Hall, London (2009)Google Scholar
  10. 10.
    Nguyen, Q.H., Hong, S.-H.: Comparison of centrality-based planarisation for 2.5D graph drawing. NICTA technical report, Sidney (2006)Google Scholar
  11. 11.
    Dascalu, M., Trausan-Matu, S., Dessus, P.: Utterances assessment in chat conversations. Research in Computing Science 46, 323–334 (2010)Google Scholar
  12. 12.
    Page, E.: The imminence of grading essays by computer. Phi Delta Kappan 47, 238–243 (1966)Google Scholar
  13. 13.
    Wresch, W.: The imminence of grading essays by computer—25 years later. Computers and Composition 10(2), 45–58 (1993)CrossRefGoogle Scholar
  14. 14.
    Gervasi, V., Ambriola, V.: Quantitative assessment of textual complexity. In: Merlini Barbaresi, L. (ed.) Complexity in Language and Text, pp. 197–228. Plus, Pisa (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefan Trausan-Matu
    • 1
  • Mihai Dascalu
    • 1
  • Philippe Dessus
    • 2
  1. 1.University Politehnica of BucharestBucharestRomania
  2. 2.Grenoble UniversityGrenoble CEDEX 9France

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