The mechanics of CSCL macro scripts

Article

Abstract

Macro scripts structure collaborative learning and foster the emergence of knowledge-productive interactions such as argumentation, explanations and mutual regulation. We propose a pedagogical model for the designing of scripts and illustrate this model using three scripts. In brief, a script disturbs the natural convergence of a team and in doing so increases the intensity of interaction required between team members for the completion of their collaborative task. The nature of the perturbation determines the types of interactions that are necessary for overcoming it: for instance, if a script provides students with conflicting evidence, more argumentation is required before students can reach an agreement. Tools for authoring scripts manipulate abstract representations of the script components and the mechanisms that relate components to one another. These mechanisms are encompassed in the transformation of data structures (social structure, resources structure and products structure) between script phases. We describe how this pedagogical design model is translated into computational structures in three illustrated scripts.

Keywords

Scripts Pedagogical design model 

References

  1. Aronson, E., Blaney, N., Sikes, J., Stephan, G., & Snapp, M. (1978). The jigsaw classroom. Beverly Hills, CA: Sage.Google Scholar
  2. Ayala, G., & Yano, Y. (1998). A collaborative learning environment based on intelligent agents. Expert Systems with Applications, 14, 129–137.CrossRefGoogle Scholar
  3. Baker, M. J. (1995). Negotiation in collaborative problem-solving dialogues. In R.-J. Beun, M. J. Baker, & M. Reiner (Eds.), Dialogue and instruction (pp. 39–55). Berlin: Springer.Google Scholar
  4. Baker, M. J., & 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: Edicoes Colibri Sept. 20–Oct. 2.Google Scholar
  5. Barros, B., & Verdejo, M. F. (2000). Analysing student interaction processes in order to improve collaboration. The DEGREE approach. International Journal of Artificial Intelligence in Education, 11, 221–241.Google Scholar
  6. Berger, A., Moretti, R., Chastonay, P., Dillenbourg, P., Bchir, A., Baddoura, R., et al. (2001). Teaching community health by exploiting international socio-cultural and economical differences. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), Proceedings of the first European conference on computer supported collaborative learning (pp. 97–105). Maastricht, March 2001.Google Scholar
  7. Betbeder, M. L, & Tchounikine, P. (2003). Symba: A framework to support collective activities in an educational context. In Proceedings of the international conference on computers in education (pp. 188–196), Hong Kong.Google Scholar
  8. Blaye, A., Light, P., Joiner, R., & Sheldon, S. (1991). Collaboration as a facilitator of planning and problem solving on a computer based task. British Journal of Psychology, 9, 471–483.Google Scholar
  9. Brousseau, G. (1998). Théorie des situations didactiques. Grenoble: La Pensée Sauvage.Google Scholar
  10. Cherubini, M., & van der Pol, J. (2005). Grounding is not shared understanding: Distinguishing grounding at an utterance and knowledge level. In CONTEXT’05, the fifth international and interdisciplinary conference on modeling and using context, 2005.Google Scholar
  11. Clark, H. H., & Wilkes-Gibbs, D. (1986). Referring as a collaborative process. Cognition, 22, 1–39.CrossRefGoogle Scholar
  12. Constantino-Gonzalez, M., & Suthers, D. (2000). A coached collaborative learning environment for Entity-Relationship modeling. In Proceedings of the 5th international conference on intelligent tutoring systems (pp. 324–333). Montreal: Canada.Google Scholar
  13. De Jong, T., & van Jooligen (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68, 179–202.CrossRefGoogle Scholar
  14. 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
  15. Dillenbourg, P., Baker, M., Blaye, A., & O’Malley, C. (1996). The evolution of research on collaborative learning. In E. Spada & P. Reiman (Eds.), Learning in humans and machine: Towards an interdisciplinary learning science (pp. 189–211). Oxford: Elsevier.Google Scholar
  16. Dillenbourg, P., & Fischer, F. (2007). Basics of computer-supported collaborative learning. Zeitschrift für Berufs-und Wirtschaftspädagogik, 21, 111–130.Google Scholar
  17. Dillenbourg, P., & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, H. Mandl, J. Haake, & I. Kollar (Eds.), Scripting computer-supported collaborative learning––Cognitive, computational, and educational perspectives (pp. 275–301). New York: Springer Computer-supported Collaborative Learning Series.CrossRefGoogle Scholar
  18. Dillenbourg, P., Ott, D., Wehrle, T., Bourquin, Y., Jermann, P., Corti, D., et al. (2002). The socio-cognitive functions of community mirrors. In F. Flückiger, C. Jutz, P. Schulz, & L. Cantoni (Eds.), Proceedings of the 4th international conference on new educational environments. Lugano, May 8–11, 2002.Google Scholar
  19. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro CSCl scripts. Journal of Computer Assisted Learning, 23(1), 1–13.CrossRefGoogle Scholar
  20. Dillenbourg, P., & Traum, D. (2006). Sharing solutions: persistence and grounding in multi-modal collaborative problem solving. Journal of the Learning Sciences, 15(1), 121–151.CrossRefGoogle Scholar
  21. Doise, W., & Mugny, G. (1984). The social development of the intellect. Oxford: Pergamon.Google Scholar
  22. Friesen, M. (2005). Interoperability and learning objects: An overview of E-learning standardization. Interdisciplinary Journal of Knowledge and Learning Objects, 1, 23–30.Google Scholar
  23. Gijlers, H., & de Jong, T. (2005). Confronting ideas in collaborative scientific discovery learning. Paper presented at AERA 2005, Montreal, CA.Google Scholar
  24. Hoppe, U. H., & Ploetzner, R. (1999). Can analytic models support learning in groups? In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and computational approaches (pp.147–168). Oxford: Elsevier.Google Scholar
  25. Inaba, A., & Okamoto, T. (1996). Development of the intelligent discussion support system for collaborative learning. In Proceedings of ED-TELECOM’96 (pp. 137–142). Boston, MA.Google Scholar
  26. Inaba, A., Supnithi, T., Ikeda, M., Mizoguchi, R., & Toyoda, J. (2000). How can we form effective collaborative learning groups? In Proceedings of the 5th international conference on intelligent tutoring systems (pp. 282–291), June 19–23, 2000.Google Scholar
  27. Jermann, P., & Dillenbourg, P. (2003). Elaborating new arguments through a CSCL scenario. In G. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (pp. 205–226). Amsterdam: Kluwer CSCL Book Series.Google Scholar
  28. Jermann, P., & Dillenbourg, P. (2007). Group mirrors to support interaction regulation in collaborative problem solving. Computers and Education, in press.Google Scholar
  29. Jermann, P., Dillenbourg, P., & Brouze, J. C. (1999). Dialectics for collective activities: An approach to virtual campus design. In Proceedings of the 9th international conference on AI in education. Le Mans, France, July 1999.Google Scholar
  30. Jermann, P., Soller, A., & Mühlenbrock, M. (2001). From mirroring to guiding: A review of the state of art technology for supporting collaborative learning. In Proceedings of Euro-CSCL (pp. 324–331). Maastricht, NL.Google Scholar
  31. Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., & Fischer, F. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224.Google Scholar
  32. McManus, M., & Aiken, R. (1995). Monitoring computer-based problem solving. Journal of Artificial Intelligence in Education, 6(4), 307–336.Google Scholar
  33. Miyake, N. (1986). Constructive interaction and the iterative process of understanding. Cognitive Science, 10, 151–177.CrossRefGoogle Scholar
  34. Muehlenbrock, M. (2006). Learning group formation based on learner profile and context. International Journal on E-Learning, 5(1), 19–24.Google Scholar
  35. O’Malley, C. (1987). Understanding explanation. Cognitive science research report no. CSRP-88. Great Britain: University of Sussex.Google Scholar
  36. Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.CrossRefGoogle Scholar
  37. Randolph, C. H., & Evertson, C. M. (1994). Images of management in a learner-centered classroom. Action in Teacher Education, 16(1), 55–65.Google Scholar
  38. Ronen, M., Kohen-Vacs, D., Raz-Fogel, N. (2006). Structuring, sharing and reusing asynchronous collaborative pedagogy. In International conference of the learning sciences. Bloomington, IN: Indiana University.Google Scholar
  39. Roschelle, J. (1990). Designing for conversations. Paper presented at the AAAI Symposium on knowledge-based environments for learning and teaching. Standford, CA, March 1990.Google Scholar
  40. Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In C. E. O’Malley (Ed.), Computer-supported collaborative learning (pp. 69–197). Berlin: Springer.Google Scholar
  41. Ross, L., Greene, D., & House, P. (1977). The false consensus phenomenon: An attributional bias in self-perception and social perception processes. Journal of Experimental Social Psychology, 13, 279–301.CrossRefGoogle Scholar
  42. Rummel, N., & Spada, H. (2007). Can people learn computer-mediated collaboration by following a script. In F. Fischer, H. Mandl, J. Haake, & I. Kollar (Eds.), Scripting computer-supported communication of knowledge. Cognitive, computational, and educational perspectives. New York: Springer CSCL Book Series.Google Scholar
  43. Schwartz, D. L. (1995). The emergence of abstract dyad representations in dyad problem solving. The Journal of the Learning Sciences, 4(3), 321–354.CrossRefGoogle Scholar
  44. Soller, A. L. (2001). Supporting Social Interaction in an Intelligent Collaborative Learning System. International Journal of Artificial Intelligence in Education, 12(1), 40–62.Google Scholar
  45. Suthers, D. (1999). Representational bias as guidance for learning interactions: A research agenda. In Proceedings of the 9th world conference on artificial intelligence in education (AIED‘97) (pp. 121–128). Le Mans, France, July 19–23, 1999.Google Scholar
  46. Veerman, A. L., & Treasure-Jones, T. (1999). Software for problem solving through collaborative argumentation. In P. Coirier & J. E. B. Andriessen (Eds.), Foundations of argumentative text processing (pp. 203–230). Amsterdam: Amsterdam University Press.Google Scholar
  47. Wasson, B. (1998). Identifying coordination agents for collaborative telelearning. International Journal of Artificial Intelligence in Education, 9, 275–299.Google Scholar
  48. Weinberger, A., Fischer, F., & Mandl, H. (2002). Fostering computer supported collaborative learning with cooperation scripts and scaffolds. In G. Stahl (Ed.), Computer support for collaborative learning: Foundations for a CSCL community. Proceedings of the conference on computer support for collaborative learning (pp. 573–574). Boulder, CO.Google Scholar
  49. Wessner, M., & Pfister, H. (2001). Group formation in computer-supported collaborative learning. In Proceedings of the 2001 international ACM SIGGROUP conference on supporting group work. Boulder, Colorado, USA, September 30–October 03, 2001.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science+ Business Media, LLC 2007

Authors and Affiliations

  1. 1.School of Computer and Communication SciencesEcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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