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.
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Adami, C., Ofria, C., & Collier, T. C. (2000). Evolution of biological complexity. Proceedings of the National Academy of Sciences, 97, 4463–4468.
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.
Arrow, H., McGrath, J. E., & Berdahl, J. L. (2000). Small groups as complex systems: Formation, coordination, development, and adaptation. Thousand Oaks: Sage.
Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.
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.
Barron, B. (2000). Achieving coordination in collaborative problem-solving groups. Journal of the Learning Sciences, 9, 403–436.
Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.
Bar-Yam, Y. (2003). Dynamics of complex systems. New York: Perseus.
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.
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.
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.
Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. Journal of the Learning Sciences, 6(3), 271–315.
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.
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.
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.
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.
Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC/Harvard: Brookings Institution Press/MIT Press.
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.
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.
Gureckis, T. M., & Goldstone, R. L. (2006). Thinking in groups. Pragmatics and Cognition, 14(2), 293–311.
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.
Holmes, M. E. (1997). Optimal matching analysis of negotiation phase sequences in simulated and authentic hostage negotiations. Communication Reports, 10, 1–9.
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.
Jeong, H., & Chi, M. T. H. (2007). Knowledge convergence during collaborative learning. Instructional Science, 35, 287–315.
Jonassen, D. H. (2000). Towards a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.
Kapur, M. (2009). Productive failure in mathematical problem solving. Instructional Science. doi:10.1007/s11251-009-9093-x.
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.
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.
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.
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.
Kapur, M., & Kinzer, C. (2009). Productive failure in CSCL groups. International Journal of Computer-Supported Collaborative Learning, 4(1), 21–46.
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.
Kapur, M., Voiklis, J., & Kinzer, C. (2008). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers & Education, 51, 54–66.
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.
Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. New York: Oxford University Press.
Lemke, J. L. (2000). Across the scales of time: Artifacts, activities, and meanings in ecosocial systems. Mind, Culture and Activity, 7(4), 273–290.
Newell, A., & Simon, H. (1972). Human problem solving. Englewood Cliffs: Prentice Hall.
Nowak, A. (2004). Dynamical minimalism: Why less is more in psychology. Personality and Social Psychology Review, 8(2), 183–192.
Rabiner, L. R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286.
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.
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.
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.
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.
Ross, S. M. (1996). Stochastic processes. New York: Wiley.
Rourke, L., & Anderson, T. (2004). Validity in quantitative content analysis. Educational Technology Research and Development, 52(1), 5–18.
Schelling, T. C. (1960). The strategy of conflict. Cambridge: Harvard University Press.
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.
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.
Stahl, G. (2005). Group cognition in computer-assisted collaborative learning. Journal of Computer Assisted Learning, 21, 79–90.
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.
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.
Voiklis, J. (2008). A thing is what we say it is: Referential communication and indirect category learning. PhD thesis, Columbia University, New York.
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.
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.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.
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.
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.
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Kapur, M., Voiklis, J., Kinzer, C.K. (2011). A Complexity-Grounded Model for the Emergence of Convergence in CSCL Groups. In: Puntambekar, S., Erkens, G., Hmelo-Silver, C. (eds) Analyzing Interactions in CSCL. Computer-Supported Collaborative Learning Series, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7710-6_1
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