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Discourse cohesion: a signature of collaboration

Published:16 March 2015Publication History

ABSTRACT

As Computer Supported Collaborative Learning (CSCL) becomes increasingly adopted as an alternative to classic educational scenarios, we face an increasing need for automatic tools designed to support tutors in the time consuming process of analyzing conversations and interactions among students. Therefore, building upon a cohesion-based model of the discourse, we have validated ReaderBench, a system capable of evaluating collaboration based on a social knowledge-building perspective. Through the inter-twining of different participants' points of view, collaboration emerges and this process is reflected in the identified cohesive links between different speakers. Overall, the current experiments indicate that textual cohesion successfully detects collaboration between participants as ideas are shared and exchanged within an ongoing conversation.

References

  1. Austin, J. L., 1962. How to Do Things With Words. Harvard University Press, Cambridge, MA.Google ScholarGoogle Scholar
  2. Bereiter, C., 2002. Education and mind in the knowledge age. Lawrence Erlbaum Associates, Mahwah, NJ.Google ScholarGoogle Scholar
  3. Blei, D. M., Ng, A. Y., and Jordan, M. I., 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 4--5, 993--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Budanitsky, A. and Hirst, G., 2006. Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32, 1, 13--47. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Dascalu, M., 2014. Analyzing discourse and text complexity for learning and collaborating, Studies in Computational Intelligence. Springer, Switzerland. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Dascalu, M., Trausan-Matu, S., and Dessus, P., 2013. Cohesion-based analysis of CSCL conversations: Holistic and individual perspectives. In CSCL 2013, N. Rummel, M. Kapur, M. Nathan and S. Puntambekar Eds. ISLS, Madison, USA, 145--152.Google ScholarGoogle Scholar
  7. Denhière, G., Lemaire, B., Bellissens, C., and Jhean-Larose, S., 2007. A semantic space for modeling children's semantic memory. In Handbook of Latent Semantic Analysis, T. K. Landauer, D. S. McNamara, S. Dennis and W. Kintsch Eds. Erlbaum, Mahwah, 143--165.Google ScholarGoogle Scholar
  8. Galley, M. and Mckeown, K., 2003. Improving word sense disambiguation in lexical chaining. In IJCAI'03, G. Gottlob and T. Walsh Eds. Morgan Kaufmann Publishers, Inc., Acapulco, Mexico, 1486--1488. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Halliday, M.a.K. and Hasan, R., 1976. Cohesion In English. Longman, London.Google ScholarGoogle Scholar
  10. Holmer, T., Kienle, A., and Wessner, M., 2006. Explicit Referencing in Learning Chats: Needs and Acceptance. In EC-TEL 2006, W. Nejdl and K. Tochtermann Eds. Springer, Crete, Greece, 170--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jurafsky, D. and Martin, J. H., 2009. An introduction to Natural Language Processing. Computational linguistics, and speech recognition. Pearson Prentice Hall, London. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Koschmann, T., 1999. Toward a dialogic theory of learning: Bakhtin's contribution to understanding learning in settings of collaboration. In CSCL'99, C. M. Hoadley and J. Roschelle Eds. ISLS, Palo Alto, 308--313. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Landauer, T. K. and Dumais, S. T., 1997. A solution to Plato's problem: the Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review 104, 2, 211--240.Google ScholarGoogle ScholarCross RefCross Ref
  14. Manning, C. D. and Schütze, H., 1999. Foundations of statistical Natural Language Processing. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. McNamara, D. S., Graesser, A. C., Mccarthy, P., and Cai, Z., 2014. Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press, Cambridge. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. McNamara, D. S., Louwerse, M. M., Mccarthy, P. M., and Graesser, A. C., 2010. Coh-Metrix: Capturing linguistic features of cohesion. Discourse Processes 47, 4, 292--330.Google ScholarGoogle ScholarCross RefCross Ref
  17. Miller, G. A., 1995. WordNet: A lexical database for English. Communications of the ACM 38, 11, 39--41. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Raghunathan, K., Lee, H., Rangarajan, S., Chambers, N., Surdeanu, M., Jurafsky, D., and Manning, C. D., 2010. A Multi-Pass Sieve for Coreference Resolution. In EMNLP '10 ACL, Cambridge, MA, 492--501. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Sagot, B. and Darja, F., 2008. Building a free French wordnet from multilingual resources. In Ontolex 2008, Marrakech, Maroc, 6.Google ScholarGoogle Scholar
  20. Scardamalia, M., 2002. Collective cognitive responsibility for the advancement of knowledge. In Liberal Education in a Knowledge Society, B. Smith and C. Bereiter Eds. Open Court Publishing, Chicago, 67--98.Google ScholarGoogle Scholar
  21. Searle, J., 1969. Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge, UK.Google ScholarGoogle ScholarCross RefCross Ref
  22. Stahl, G., 2006. Group cognition. Computer support for building collaborative knowledge. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Trausan-Matu, S., 2010. Automatic Support for the Analysis of Online Collaborative Learning Chat Conversations. In 3rd Int. Conf. on Hybrid Learning, P. M. Tsang, S. K. S. Cheung, V. S. K. Lee and R. Huang Eds. Springer, Beijing, China, 383--394. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Trausan-Matu, S., 2010. Computer support for creativity in small groups using chats. Annals of the Academy of Romanian Scientists, Series on Science and Technology of Information 3, 2, 81--90.Google ScholarGoogle Scholar
  25. Trausan-Matu, S., Dascalu, M., and Dessus, P., 2012. Textual complexity and discourse structure in Computer-Supported Collaborative Learning. In ITS 2012, S. A. Cerri, W. J. Clancey, G. Papadourakis and K. Panourgia Eds. Springer, Chania, Grece, 352--357. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Trausan-Matu, S., Stahl, G., and Sarmiento, J., 2007. Supporting polyphonic collaborative learning. Indiana University Press, E-service Journal 6, 1, 58--74.Google ScholarGoogle Scholar
  27. Wu, Z. and Palmer, M., 1994. Verb semantics and lexical selection. In ACL '94 ACL, New Mexico, USA, 133--138. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Other conferences
        LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge
        March 2015
        448 pages
        ISBN:9781450334174
        DOI:10.1145/2723576

        Copyright © 2015 ACM

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        Publication History

        • Published: 16 March 2015

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        LAK '15 Paper Acceptance Rate20of74submissions,27%Overall Acceptance Rate236of782submissions,30%

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