Advertisement

A Complexity-Grounded Model for the Emergence of Convergence in CSCL Groups

  • Manu Kapur
  • John Voiklis
  • Charles K. Kinzer
Chapter
Part of the Computer-Supported Collaborative Learning Series book series (CULS, volume 12)

Abstract

We advance a complexity−grounded, quantitative method for uncovering temporal patterns in CSCL discussions. We focus on convergence because understanding how complex group discussions converge presents a major challenge in CSCL research. From a complex systems perspective, convergence in group discussions is an emergent behavior arising from the transactional interactions between group members. Leveraging the concepts of emergent simplicity and emergent complexity (Bar-Yam 2003), a set of theoretically-sound yet simple rules was hypothesized: Interactions between group members were conceptualized as goal-seeking adaptations that either help the group move towards or away from its goal, or maintain its status quo. Operationalizing this movement as a Markov walk, we present quantitative and qualitative findings from a study of online problem-solving groups. Findings suggest high (or low) quality contributions have a greater positive (or negative) impact on convergence when they come earlier in a discussion than later. Significantly, convergence analysis was able to predict a group’s performance based on what happened in the first 30–40% of its discussion. Findings and their implications for CSCL theory, methodology, and design are discussed.

Keywords

Group Discussion Group Performance Emergent Behavior Problem Scenario Early Exchange 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The work reported herein was funded by a Spencer Dissertation Research Training Grant from Teachers College, Columbia University to the first author. This chapter reports work that has, in parts, been presented at the International Conference of the Learning Sciences in 2006, and the Computer-Supported Collaborative Learning Conference in 2007. Our special thanks go to the students and teachers who participated in this project. We also thank June Lee and Lee Huey Woon for their help with editing and formatting.

References

  1. Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97, 4463–4468.CrossRefGoogle Scholar
  2. Akhras, F. N., & Self, J. A. (2000). Modeling the process, not the product, of learning. In S. P. Lajoie (Ed.), Computers as cognitive tools (No more walls, Vol. 2, pp. 3–28). Mahwah: Erlbaum.Google Scholar
  3. Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). Small groups as complex systems: Formation, coordination, development, and adaptation. Thousand Oaks: Sage.Google Scholar
  4. Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.CrossRefGoogle Scholar
  5. Barab, S. A., Hay, K. E., & Yamagata-Lynch, L. C. (2001). Constructing networks of action-relevant episodes: An in-situ research methodology. Journal of the Learning Sciences, 10(1&2), 63–112.CrossRefGoogle Scholar
  6. Barron, B. (2000). Achieving coordination in collaborative problem-solving groups. Journal of the Learning Sciences, 9, 403–436.CrossRefGoogle Scholar
  7. Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
  8. Bar-Yam, Y. (2003). Dynamics of complex systems. New York: Perseus.Google Scholar
  9. Bransford, J. D., & Nitsch, K. E. (1978). Coming to understand things we could not previously understand. In J. F. Kavanaugh & W. Strange (Eds.), Speech and language in the laboratory, school, and clinic (pp. 267–307). Harvard: MIT Press.Google Scholar
  10. Brennan, S. E., & Clark, H. H. (1996). Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(6), 1482–1493.CrossRefGoogle Scholar
  11. Burtsev, M. S. (2003). Measuring the dynamics of artificial evolution. In W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, & J. Ziegler (Eds.), Advances in artificial life. Proceedings of the 7th European conference on artificial life (pp. 580–587). Berlin: Springer.CrossRefGoogle Scholar
  12. Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. Journal of the Learning Sciences, 6(3), 271–315.CrossRefGoogle Scholar
  13. Clark, H., & Brennan, S. (1991). Grounding in communication. In L. B. Resnick, J. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington, DC: APA.CrossRefGoogle Scholar
  14. Clark, H. H., & Lucy, P. (1975). Understanding what is meant from what is said: A study in conversationally conveyed requests. Journal of Verbal Learning and Verbal Behavior, 14(1), 56–72.CrossRefGoogle Scholar
  15. Cohen, E. G., Lotan, R. A., Abram, P. L., Scarloss, B. A., & Schultz, S. E. (2002). Can groups learn? Teachers College Record, 104(6), 1045–1068.CrossRefGoogle Scholar
  16. Collazos, C., Guerrero, L., Pino, J., & Ochoa, S. (2002). Evaluating collaborative learning processes. Proceedings of the 8th international workshop on groupware (CRIWG’2002) (pp. 203–221). Berlin: Springer.Google Scholar
  17. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC/Harvard: Brookings Institution Press/MIT Press.Google Scholar
  18. Erkens, G., Kanselaar, G., Prangsma, M., & Jaspers, J. (2003). Computer support for collaborative and argumentative writing. In E. De Corte, L. Verschaffel, N. Entwistle, & J. van Merrienboer (Eds.), Powerful learning environments: Unravelling basic components and dimensions (pp. 157–176). Amsterdam: Pergamon, Elsevier Science.Google Scholar
  19. Fischer, F., & Mandl, H. (2005). Knowledge convergence in computer-supported collaborative learning: The role of external representation tools. Journal of the Learning Sciences, 14(3), 405–441.CrossRefGoogle Scholar
  20. Gureckis, T. M., & Goldstone, R. L. (2006). Thinking in groups. Pragmatics and Cognition, 14(2), 293–311.CrossRefGoogle Scholar
  21. Hmelo-Silver, C. E., Jordan, R., Liu, L., & Chernobilsky, E. (this book). Representational tools for understanding complex computer-supported collaborative learning environments. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 83–106) Springer.Google Scholar
  22. Holmes, M. E. (1997). Optimal matching analysis of negotiation phase sequences in simulated and authentic hostage negotiations. Communication Reports, 10, 1–9.CrossRefGoogle Scholar
  23. Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34.CrossRefGoogle Scholar
  24. Jeong, H., & Chi, M. T. H. (2007). Knowledge convergence during collaborative learning. Instructional Science, 35, 287–315.CrossRefGoogle Scholar
  25. Jonassen, D. H. (2000). Towards a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.CrossRefGoogle Scholar
  26. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
  27. Kapur, M. (2009). Productive failure in mathematical problem solving. Instructional Science. doi: 10.1007/s11251-009-9093-x.Google Scholar
  28. Kapur, M. (2010). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science. DOI:  10.1007/s11251-010-9144-3.
  29. Kapur, M., Hung, D., Jacobson, M., Voiklis, J., Kinzer, C., & Chen, D.-T. (2007). Emergence of learning in computer-supported, large-scale collective dynamics: A research agenda. In C. A. Clark, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the international conference of computer-supported collaborative learning (pp. 323–332). Mahwah: Erlbaum.Google Scholar
  30. Kapur, M., & Jacobson, M. J. (2009). Learning as an emergent phenomenon: Methodological implications. Paper presented at the annual meeting of the American educational research association. San Diego.Google Scholar
  31. Kapur, M., & Kinzer, C. (2007). The effect of problem type on interactional activity, inequity, and group performance in a synchronous computer-supported collaborative environment. Educational Technology Research and Development, 55(5), 439–459.CrossRefGoogle Scholar
  32. Kapur, M., & Kinzer, C. (2009). Productive failure in CSCL groups. International Journal of Computer-Supported Collaborative Learning, 4(1), 21–46.CrossRefGoogle Scholar
  33. Kapur, M., Voiklis, J., & Kinzer, C. (2005). Problem solving as a complex, evolutionary activity: A methodological framework for analyzing problem-solving processes in a computer-supported collaborative environment. In Proceedings the computer supported collaborative learning (CSCL) conference. Mahwah: Erlbaum.Google Scholar
  34. Kapur, M., Voiklis, J., & Kinzer, C. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers & Education, 51, 54–66.CrossRefGoogle Scholar
  35. Kapur, M., Voiklis, J., Kinzer, C., & Black, J. (2006). Insights into the emergence of convergence in group discussions. In S. Barab, K. Hay, & D. Hickey (Eds.), Proceedings of the international conference on the learning sciences (pp. 300–306). Mahwah: Erlbaum.Google Scholar
  36. Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. New York: Oxford University Press.Google Scholar
  37. Lemke, J. L. (2000). Across the scales of time: Artifacts, activities, and meanings in ecosocial systems. Mind, Culture and Activity, 7(4), 273–290.CrossRefGoogle Scholar
  38. Newell, A., & Simon, H. (1972). Human problem solving. Englewood Cliffs: Prentice Hall.Google Scholar
  39. Nowak, A. (2004). Dynamical minimalism: Why less is more in psychology. Personality and Social Psychology Review, 8(2), 183–192.CrossRefGoogle Scholar
  40. Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.CrossRefGoogle Scholar
  41. Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257.CrossRefGoogle Scholar
  42. Reimann, P., Yacef, K., & Kay, J. (this book). Analyzing collaborative interactions with data mining methods for the benefit of learning. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 161–185). Springer.Google Scholar
  43. Roschelle, J. (1996). Learning by collaborating: Convergent conceptual change. In T. Koschmann (Ed.), CSCL: Theory and practice of an emerging aradigm (pp. 209–248). Mahwah: Erlbaum.Google Scholar
  44. 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.CrossRefGoogle Scholar
  45. Ross, S. M. (1996). Stochastic processes. New York: Wiley.Google Scholar
  46. Rourke, L., & Anderson, T. (2004). Validity in quantitative content analysis. Educational Technology Research and Development, 52(1), 5–18.CrossRefGoogle Scholar
  47. Schelling, T. C. (1960). The strategy of conflict. Cambridge: Harvard University Press.Google Scholar
  48. Schultz-Hardt, S., Jochims, M., & Frey, D. (2002). Productive conflict in group decision making: Genuine and contrived dissent as strategies to counteract biased information seeking. Organizational Behavior and Human Decision Processes, 88, 563–586.CrossRefGoogle Scholar
  49. Soller, A., Wiebe, J., & Lesgold, A. (2002). A machine learning approach to assessing knowledge sharing during collaborative learning activities. In G. Stahl (Ed.), Proceedings of computer support for collaborative learning (pp. 128–137). Hillsdale: Erlbaum.Google Scholar
  50. Stahl, G. (2005). Group cognition in computer-assisted collaborative learning. Journal of Computer Assisted Learning, 21, 79–90.CrossRefGoogle Scholar
  51. Suthers, D. D. (2006). Technology affordances for intersubjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1(3), 315–337.CrossRefGoogle Scholar
  52. Teasley, S. D., & Roschelle, J. (1993). Constructing a joint problem space: The computer as a tool for sharing knowledge. In S. P. Lajoie & S. D. Derry (Eds.), Computers as Cognitive Tools (pp. 229–258). Hillsdale, NJ: Erlbaum.Google Scholar
  53. Voiklis, J. (2008). A thing is what we say it is: Referential communication and indirect category learning. PhD thesis, Columbia University, New York.Google Scholar
  54. Voiklis, J., Kapur, M., Kinzer, C., & Black, J. (2006). An emergentist account of collective cognition in collaborative problem solving. In R. Sun (Ed.), Proceedings of the 28th annual conference of the cognitive science society (pp. 858–863). Mahwah: Erlbaum.Google Scholar
  55. Wampold, B. E. (1992). The intensive examination of social interaction. In T. R. Kratochwill & J. R. Levin (Eds.), Single-case research design and analysis: New directions for psychology and education (pp. 93–131). Hillsdale: Erlbaum.Google Scholar
  56. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.CrossRefGoogle Scholar
  57. Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry and Allied Disciplines, 17, 89–100.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.National Institute of EducationSingaporeSingapore

Personalised recommendations