A three-level analysis of collaborative learning in dual-interaction spaces

Article

Abstract

CSCL systems which follow the dual-interaction spaces paradigm support the synchronous construction and discussion of shared artifacts by distributed or colocated small groups of learners. The most recent generic dual-interaction space environments, either model based or component based, can be deeply customized by teachers for supporting different collaborative learning tasks and different ways of performing them. This work stresses the importance of basing customization decisions on a socio-cognitive interpretation of how learners interact in a given learning situation. The central contribution of this article is a methodological approach for conducting qualitative interaction analysis oriented toward the improvement of the supporting environment which can be applied to any learning task and any environment configuration. This “generic analysis approach” is organized into three levels. At the dialog level, a task-independent dialogical model is proposed for analyzing action/communication traces as “generalized conversations.” A graphical notation is provided for visualizing the syntactical characteristics of collaborative sessions. At the knowledge level, a typology of task-independent collaborative knowledge-building episode types that can occur during such generalized conversations is proposed. Thanks to that classification scheme, recurrent meaningful elements that structure the low-level descriptions can be detected and characterized. These regularities help to pass from local interpretations to a global interpretation of the whole process. At the action level, task-dependent socio-cognitive interpretations of why the collaborative learning process unfolds as observed are proposed. They constitute a firm basis for improving the customization of the generic environment in order to support learners more efficiently.

Keywords

Dual-interaction spaces Interaction analysis Generic environment Generic analysis approach 

References

  1. Alonso, M., Py, D., & Lemeunier, T. (2008). A learning environment for object-oriented modeling, supporting metacognitive regulations. In Proceedings 8th IEEE International Conference on Advanced Learning Technologies (pp. 69–73). IEEE Computer Society.Google Scholar
  2. Amelsvoort, M. A. A., Andriessen, J. E. B., & Kanselaar, G. (2008). How students structure and relate argumentative knowledge. Computers in Human Behavior, 24, 1293–1313.CrossRefGoogle Scholar
  3. Arvaja, M., Salovaara, H., Häkkinen, P., & Järvelä, S. (2007). Combining individual and group-level perspectives for studying collaborative knowledge construction in context. Learning and Instruction, 17, 448–459.CrossRefGoogle Scholar
  4. Avouris N., Margaritis M., & Komis V. (2004). Modelling interaction during small-group synchronous problem-solving activities: The Synergo approach. 2nd International Workshop on Designing Computational Models of Collaborative Learning Interaction, 7th Conference on Intelligent Tutoring Systems (pp. 13–18). Maceio, Brazil.Google Scholar
  5. Baghaei, N., & Mitrovic, A. (2006). A constraint-based collaborative environment for learning UML class diagrams. In M. Ikeda, K. Ashley & T.-W. Chan (Eds.), Proceedings 8th International Conference on Intelligent Tutoring Systems 2006, LNCS 4053 (pp. 176–186). Berlin, Heidelberg, New York: Springer-Verlag.Google Scholar
  6. Baker, M., & Lund, K. (1996). Flexibly structuring the interaction in a CSCL environment. In P. Brna, A. Paiva & J. Self (Eds.), Proceedings of the European Conference on Artificial Intelligence in Education (pp. 401–407). Lisbon, Portugal: Edicôes Colibri.Google Scholar
  7. Bandura, A. (1977). Social learning theory. New York: General Learning.Google Scholar
  8. Beers, P. J., Boshuizen, H. P. A., Kirschner, P. A., & Gijselaers, W. H. (2007). The analysis of negotiation of common ground in CSCL. Learning and Instruction, 17, 427–435.CrossRefGoogle Scholar
  9. Çakir, M. P., Zemel, A., & Stahl, G. (2007). The organization of collaborative math problem solving activities across dual interaction spaces. In C. Hmelo-Silver & A. O’Donnell (Eds.), Proceedings of the 7th International Conference on Computer-Supported Collaborative Learning (pp. 107–109). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  10. Clark, H., & Schaefer, E. (1989). Contributing to discourse. Cognitive Science, 13, 259–294.CrossRefGoogle Scholar
  11. Constantino-Gonzales, M. A., & Suthers, D. (2001). Coaching collaboration by comparing solutions and tracking participation. In P. Dillenbourg, A. Eurelings & K. Hakkarainen (Eds.), European perspectives on computer-supported collaborative learning, proceedings of the first European conference on computer-supported collaborative learning (pp. 173–180). Maastricht, NL: Universiteit Maastricht.Google Scholar
  12. Cress, U., & Kimmerle, J. (2008). A systemic and cognitive view on collaborative knowledge building with wikis. International Journal of Computer-Supported Collaborative Learning, 3(2), 105–122.CrossRefGoogle Scholar
  13. De Chiara, R., Di Matteo, A., Manno, I., & Scarano, V. (2007). CoFFEE: Cooperative Face2Face Educational Environment. In Proceedings of the 3 rd International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2007). New York, NY.Google Scholar
  14. De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers & Education, 46, 6–28.CrossRefGoogle Scholar
  15. Dillenbourg, P. (1999). What do you mean by collaborative learning? Collaborative-learning: Cognitive and computational approaches (pp. 1–19). Oxford, U.K: Elsevier.Google Scholar
  16. Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL? (pp. 61–91). Heerlen: Open Universiteit Nederland.Google Scholar
  17. Dimitracopoulou, A., & Komis, V. (2005). Design principles for the support of modelling and collaboration in a technology-based learning environment. International Journal of Continuing Engineering Education and Lifelong Learning, 15(1/2), 30–55.CrossRefGoogle Scholar
  18. Erkens, G., & Janssen, J. (2008). Automatic coding of communication in collaboration protocols. International Journal of Computer-Supported Collaborative Learning, 3(4), 447–470.CrossRefGoogle Scholar
  19. Farnham, S., Chesley, H. R., McGhee, D. E., Kawal, R., & Landau, J. (2000). Structured online interactions: Improving the decision-making of small discussion groups. In Proceedings of ACM Computer Supported Cooperative Work 2000 (pp. 299–308). New York, NY: ACM Press.Google Scholar
  20. Fidas, C., Komis, V., Avouris, N., & Dimitracopoulou, A. (2002). Collaborative problem solving using an open modeling environment. In G. Stahl (Ed.), Computer support for collaborative learning: Foundations for a CSCL community, Proceedings of International Conference on Computer-Supported Collaborative Learning CSCL 2002 (pp. 654–655). Hillsdale, NJ: Erlbaum.Google Scholar
  21. Fischer, G. (2003). Meta-design—beyond user-centered and participatory design. In Proceedings of the 10th International Conference on Human-Computer Interaction (HCI 2003), Crete, Greece, 88–92.Google Scholar
  22. Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23.CrossRefGoogle Scholar
  23. Glassner, A., & Schwarz, B. (2005). The role of floor control and of ontology in argumentative activities with discussion-based tools. In T. Koschmann, D. Suthers & T. Chan (Eds.), Computer supported collaborative learning 2005: The next 10 years (pp. 170–179). Mahwah, NJ: Erlbaum.Google Scholar
  24. Gogoulou, A., Gouli, E., Grigoriadou, M., & Samarakou, M. (2005). ACT: A web-based adaptive communication tool. In T. Koschmann, D. Suthers & T. W. Chan (Eds.), Computer supported collaborative learning 2005: The next 10 years (pp. 180–189). Mahwah, NJ: Erlbaum.Google Scholar
  25. Henri, F. (1992). Computer conferencing and content analysis. In A. Kaye (Ed.), Collaborative learning through computer conferencing: The Najaden papers (pp. 117–136). London: Springer Verlag.Google Scholar
  26. Hernández-Leo, D., Asensio-Pérez, J. I., Dimitriadis, Y., Bote-Lorenzo, M. L., Jorrín-Abellán, I. M., & Villasclaras-Fernández, E. D. (2005). Reusing IMS-LD formalized best practices in collaborative learning structuring. Advanced Technology for Learning, 2(3), 223–232.Google Scholar
  27. Hutchby, I., & Wooffitt, R. (1998). Conversation analysis. Cambridge, UK: Polity.Google Scholar
  28. Jermann, P. (2004). Computer support for interaction regulation in collaborative problem-solving. Geneva: Dissertation, University of Geneva.Google Scholar
  29. Jones, C., Dirckinck-Holmfeld, L., & Lindström, B. (2006). A relational, indirect, meso-level approach to CSCL design in the next decade. International Journal of Computer-Supported Collaborative Learning, 1(1), 35–56.CrossRefGoogle Scholar
  30. Klausmeier, H. J. (1992). Concept learning and concept teaching. Educational Psychologist, 27(3), 267–286.CrossRefGoogle Scholar
  31. Kumpulainen, K., & Mutanen, M. (1999). The situated dynamics of peer group interaction: An introduction to an analytic framework. Learning and Instruction, 9, 449–474.CrossRefGoogle Scholar
  32. Landsman, S., & Alterman, R. (2003). Building Groupware on THYME. Technical Report CS-03-234. Waltham, MA: Brandeis University.Google Scholar
  33. Levinson, S. (1983). Pragmatics. Cambridge, UK: Cambridge University.Google Scholar
  34. Lipman, M. (1991). Thinking in education. New York: Cambridge Education.Google Scholar
  35. Lonchamp, J. (2006). Supporting synchronous collaborative learning: a generic, multi-dimensional model. International Journal of Computer-Supported Collaborative Learning, 1(2), 247–276.CrossRefGoogle Scholar
  36. Lonchamp, J. (2007a). Floor control in complex CSCL synchronous environments, In Proceedings of the Third International Conference on Web Information Systems and Technology (pp. 397–402). Barcelona, Spain.Google Scholar
  37. Lonchamp, J. (2007b). Linking conversation and task objects in complex synchronous CSCL environments. In Proceedings of the Third International Conference on Web Information Systems and Technology (pp. 281–288). Barcelona, Spain.Google Scholar
  38. Lonchamp, J. (2008). Interaction analysis supporting participants’ self-regulation in a generic CSCL system. In P. Dillenbourg & M. Specht (Eds.), Times of convergence, Proceedings of the Third European Conference on Technology Enhanced Learning, LNCS 5192 (pp. 262–273). Berlin, Heidelberg, New York: Springer Verlag.Google Scholar
  39. Maes, P. (1987). Concepts and experiments in computational reflection. Proceedings of the 2 nd ACM International Conference on Object-oriented Programming Systems, Languages and Applications (pp. 147–155). Orlando, Florida.Google Scholar
  40. Morris, C. (1938). Foundations of the theory of signs. Chicago University Press.Google Scholar
  41. Mühlenbrock, M. (2001). Action-based collaboration analysis for group learning. Amsterdam: Dissertations in Artificial Intelligence, IOS.Google Scholar
  42. Mühlpfordt, M., & Stahl, G. (2007). The integration of synchronous communication across dual interaction spaces. In C. Hmelo-Silver & A. O’Donnell (Eds.), Proceedings of the 7th International Conference on Computer Supported Collaborative Learning (pp. 525–534). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  43. Nonaka, I., & Takeushi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation (pp. 61–85). New York: Oxford University.Google Scholar
  44. Nosofsky, R. (1988). Exemplar-based accounts of relations between classification, recognition, and typicality. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14, 700–708.CrossRefGoogle Scholar
  45. Paavola, S., Lipponen, L., & Hakkarainen, K. (2002). Epistemological foundations for CSCL: A comparison of three models of innovative knowledge communities. In G. Stahl (Ed.), Computer Support for Collaborative Learning: Foundations for a CSCL community, Proceedings of International Conference on Computer-Supported Collaborative Learning CSCL 2002 (pp. 24–32). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  46. Pfister, H.-R., & Mühlpfordt, M. (2002). Supporting discourse in a synchronous learning environment: The learning protocol approach. In G. Stahl (Ed.), Computer Support for Collaborative Learning: Foundations for a CSCL community, Proceedings of International Conference on Computer-Supported Collaborative Learning CSCL 2002 (pp. 581–589). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  47. Piaget, J. (1977). The development of thought: Equilibration of cognitive structures. New York: The Viking.Google Scholar
  48. Pinkwart, N. (2003). A plug-in architecture for graph based collaborative modeling systems. In U. Hoppe, F. Verdejo & J. Kay (Eds.), Shaping the future of learning through intelligent technologies: Proceedings of the 11th Conference on Artificial Intelligence in Education (pp. 535–536). Amsterdam, NL: IOS.Google Scholar
  49. Polanyi, M. (1962). Personal knowledge: towards a post critical philosophy. London: Routledge.Google Scholar
  50. Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., Duncan, R., et al. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13(3), 337–386.CrossRefGoogle Scholar
  51. Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.CrossRefGoogle Scholar
  52. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104(3), 192–233.CrossRefGoogle Scholar
  53. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (1999). Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14, 51–70.Google Scholar
  54. Schegloff, E. A. (2006). Sequence organization in interaction: A primer in conversation analysis. Cambridge: Cambridge University.Google Scholar
  55. Schrire, S. (2006). Knowledge building in asynchronous discussion groups: Going beyond quantitative analysis. Computers & Education, 46, 49–70.CrossRefGoogle Scholar
  56. Soller, A., Wiebe, J., & Lesgold, A. (2002). A machine learning approach to assessing knowledge sharing during collaborative learning activities. In G. Stahl (Ed.), Computer Support for Collaborative Learning: Foundations for a CSCL community, Proceedings of International Conference on Computer-Supported Collaborative Learning CSCL 2002 (pp. 128–137). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  57. Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT.Google Scholar
  58. Stahl, G. (2007). Social practices of group cognition in virtual math teams. In S. Ludvigsen, A. Lund, & R. Säljö (Eds.), Learning in social practices. ICT and new artifacts—transformation of social and cultural practices: Pergamon.Google Scholar
  59. Strijbos, J.-W., & Stahl, G. (2007). Methodological issues in developing a multi-dimensional coding procedure for small-group chat communication. Learning and Instruction, 17, 394–404.CrossRefGoogle Scholar
  60. Strijbos, J.-W., Martens, R. L., Prins, F. J., & Jochems, W. M. G. (2006). Content analysis: What are they talking about? Computers & Education, 46, 29–48.CrossRefGoogle Scholar
  61. Suthers, D. D. (2006). A qualitative analysis of collaborative knowledge construction through shared representations. Research and Practice in Technology Enhanced Learning, 1(2), 1–28.Google Scholar
  62. Suthers, D. D., Dwyer, N., Vatrapu, R., & Medina, R. (2007). An abstract transcript notation for analyzing interactional construction of meaning in online learning. In Proceedings of the 40th Hawaii International Conference on the Systems Science (CD-ROM). IEEE Computer Society Press.Google Scholar
  63. Topping, K. (1998). Peer-assessment between students in colleges and universities. Review of Educational Research, 68(3), 249–276.Google Scholar
  64. Trausan-Matu, S., Stahl, G., & Sarmiento, J. (2006). Polyphonic Support for Collaborative Learning. In Y. Dimitriadis, I. Zigurs, & E. Gómez-Sánchez (Eds.) Groupware: Design, Implementation, and Use, 12th International Workshop CRIWG 2006 (pp.132–139), LNCS 4154, Berlin, Heidelberg, New York: Springer-Verlag.Google Scholar
  65. van Joolingen, W., de Jong, T., Lazonder, A., Savelsbergh, E., & Manlove, S. (2005). Co-Lab: research and development of an online learning environment for collaborative scientific discovery learning. Computers in Human Behavior, 21, 671–688.CrossRefGoogle Scholar
  66. Webb, N. M. (1982). Student interaction and learning in small groups. Review of Educational Research, 52(3), 421–445.Google Scholar
  67. Wee, J. D., & Looi, C. K (2007). Meaning-making paths as a unit of analysis in synchronous chat environments: Application of the collaboration interaction model. In Proceedings of the Redesigning Pedagogy: Culture, Knowledge and Understanding Conference, Singapore, May 2007.Google Scholar
  68. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33(1), 1–30.CrossRefGoogle Scholar
  69. Zumbach, J., Muehlenbrock, M., Jansen, M., Reimann, P., & Hoppe, H.-U. (2002). Multidimensional tracking in virtual learning teams. In G. Stahl (Ed.), Computer Support for Collaborative Learning: Foundations for a CSCL community, Proceedings of International Conference on Computer-Supported Collaborative Learning CSCL 2002 (pp. 650–651). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2009

Authors and Affiliations

  1. 1.LORIA-Nancy UniversitéVandœuvre-lès-Nancy CedexFrance

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