Productive failure in CSCL groups
- 999 Downloads
- 55 Citations
Abstract
This study was designed as a confirmatory study of work on productive failure (Kapur, Cognition and Instruction, 26(3), 379–424, 2008). N = 177, 11th-grade science students were randomly assigned to solve either well- or ill-structured problems in a computer-supported collaborative learning (CSCL) environment without the provision of any external support structures or scaffolds. After group problem solving, all students individually solved well-structured problems followed by ill-structured problems. Compared to groups who solved well-structured problems, groups who solved ill-structured problems expectedly struggled with defining, analyzing, and solving the problems. However, despite failing in their collaborative problem-solving efforts, these students outperformed their counterparts from the well-structured condition on the individual near and far transfer measures subsequently, thereby confirming the productive failure hypothesis. Building on the previous study, additional analyses revealed that neither preexisting differences in prior knowledge nor the variation in group outcomes (quality of solutions produced) seemed to have had any significant effect on individual near and far transfer measures, lending support to the idea that it was the nature of the collaborative process that explained productive failure.
Keywords
Ill-structured problem solving Well-structured problem solving Synchronous collaboration Problem-solving failureNotes
Acknowledgements
The research reported in this paper was funded in part by the Spencer Research Training Grant and the Education Policy Research Fellowship from Teachers College, Columbia University to the first author. The authors would like to thank the students, teachers, and principals of the participating schools for their support for this project. We are also grateful to David Hung, Donald J. Cunningham, Katerine Bielaczyc, Katherine Anderson, Liam Rourke, Michael Jacobson, Rebecca Mancy, Rogers Hall, Sarah Davis, Steven Zuiker, and John Voiklis for their insightful comments and suggestions.
References
- Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1), 1–14.CrossRefGoogle Scholar
- Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
- Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.Google Scholar
- Bielaczyc, K. (2006). Designing social infrastructure: Critical issues in creating learning environments with technology. The Journal of the Learning Sciences, 15(3), 301–329.CrossRefGoogle Scholar
- Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. In A. Iran-Nejad, & P. D. Pearson (Eds.), Review of research in education, 24 (pp. 61–101). Washington, DC: American Educational Research Association.Google Scholar
- Bromme, R., Hesse, F. W., & Spada, H. (2005). Barriers and biases in computer-mediated knowledge communication-and how they may be overcome. New York, NY: Springer.Google Scholar
- Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.Google Scholar
- Chatterji, M. (2003). Designing and using tools for educational assessment. Boston: Allyn & Bacon.Google Scholar
- Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315.CrossRefGoogle Scholar
- Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
- Cho, K. L., & Jonassen, D. H. (2002). The effects of argumentation scaffolds on argumentation and problem solving. Educational Technology, Research and Development, 50(3), 5–22.CrossRefGoogle Scholar
- Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York: Academic Press.Google Scholar
- Collins, H. (1985). Changing order. London: Sage.Google Scholar
- Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.Google Scholar
- de Groot, A. D. (1965). Thought and choice in chess. The Hague, NL: Mouton.Google Scholar
- 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, NL: Open Universiteit Nederland.Google Scholar
- Dillenbourg, P., & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 275–302). New York, NY: Springer.CrossRefGoogle Scholar
- 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: Elsevier Science.Google Scholar
- Ertl, B., Kopp, B., & Mandl, H. (2007). Supporting collaborative learning in videoconferencing using collaboration scripts and content schemes. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 213–236). New York, NY: Springer.CrossRefGoogle Scholar
- Fischer, F., Kollar, I., Mandl, H., & Haake, J. (2007). Perspectives on collaboration scripts. In F. Fischer, H. Mandl, J. Haake, & I. Kollar (Eds.), Scripting computer-supported collaborative learning (pp. 1–10). New York, NY: Springer.CrossRefGoogle Scholar
- Fischer, F., & Mandl, H. (2005). Knowledge convergence in computer-supported collaborative learning: The role of external representation tools. The Journal of the Learning Sciences, 14(3), 405–441.CrossRefGoogle Scholar
- Ge, X., & Land, S. M. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology, Research and Development, 51(1), 21–38.CrossRefGoogle Scholar
- Giles, J. (2006). The trouble with replication. Nature, 442, 344–347.CrossRefGoogle Scholar
- Goel, V., & Pirolli, P. (1992). The structure of design problem spaces. Cognitive Science, 16, 395–429.CrossRefGoogle Scholar
- Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma, & K. Hakuta (Eds.), Child Development and Education in Japan (pp. 262–272). New York: Freeman.Google Scholar
- Hewitt, J. (2005). Towards an understanding of how threads die in asynchronous computer conferences. The Journal of the Learning Sciences, 14(4), 567–589.CrossRefGoogle Scholar
- Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.CrossRefGoogle Scholar
- Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York: Addison-Wesley.Google Scholar
- Jonassen, D. H. (2000). Towards a design theory of problem solving. Educational Technology, Research and Development, 48(4), 63–85.CrossRefGoogle Scholar
- Jonassen, D. H., & Kwon, H. I. (2001). Communication patterns in computer-mediated vs. face-to-face group problem solving. Educational Technology, Research and Development, 49(1), 35–52.CrossRefGoogle Scholar
- Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
- Kapur, M., Dickson, L., & Toh, P. Y. (2008). Productive failure in mathematical problem solving. In B. C. Love, K. McRae, & V. M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (pp. 1717–1722). Austin, TX: Cognitive Science Society.Google Scholar
- Kapur, M., & Kinzer, C. (2007). The effect of problem type on collaborative problem solving in a synchronous computer-mediated environment. Educational Technology, Research and Development, 55(5), 439–459.CrossRefGoogle Scholar
- Kapur, M., Voiklis, J., & Kinzer, C. (2007). Sensitivities to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers and Education, 51, 54–66.CrossRefGoogle Scholar
- Kauffman, S. (1995). At home in the universe. New York: Oxford University Press.Google Scholar
- King, A. (2007). Scripting collaborative learning processes: A cognitive perspective. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 13–38). New York, NY: Springer.CrossRefGoogle Scholar
- Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.CrossRefGoogle Scholar
- Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hamalainen, R., Hakkinen, P., & Fischer, F. (2007). Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(2–3), 211–224.CrossRefGoogle Scholar
- Kyllonen, P. C., & Lajoie, S. P. (2003). Reassessing aptitude: Introduction to a special issue in honor of Richard E. Snow. Educational Psychologist, 38, 79–83.CrossRefGoogle Scholar
- Lin, X., Hmelo, C., Kinzer, C., & Secules, T. J. (1999). Designing technology to support reflection. Educational Technology, Research and Development, 47(3), 43–62.CrossRefGoogle Scholar
- Lund, K., Molinari, G., Sejourne, A., & Baker, M. (2007). How do argumentation diagrams compare when students pairs use them as a means for debate or as a tool for representing debate? International Journal of Computer-Supported Collaborative Learning, 2(2–3), 273–296.CrossRefGoogle Scholar
- Marton, F. (2007). Sameness and difference in transfer. The Journal of the Learning Sciences, 15(4), 499–535.CrossRefGoogle Scholar
- McNamara, D. S. (2001). Reading both high-coherence and low-coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology, 55(1), 51–62.Google Scholar
- McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14(1), 1–43.CrossRefGoogle Scholar
- Mestre, J. P. (2005). Transfer of learning from a modern multidisciplinary perspective. Greenwich, CT: Information Age.Google Scholar
- Mirza, N. M., Tartas, V., Perret-Clermont, A., & de Pietro, J. (2007). Using graphical tools in a phased activity for enhancing dialogical skills: An example with Digalo. International Journal of Computer-Supported Collaborative Learning (ijCSCL), 2(2–3), 247–272.CrossRefGoogle Scholar
- Petroski, H. (2006). Success through failure: The paradox of design. Princeton, NJ: Princeton University Press.Google Scholar
- Poole, M. S., & Holmes, M. E. (1995). Decision development in computer-assisted group decision making. Human Communications Research, 22(1), 90–127.CrossRefGoogle Scholar
- Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks, CA: Sage Publications.Google Scholar
- Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. The Journal of the Learning Sciences, 13(3), 423–451.CrossRefGoogle Scholar
- Rummel, N., & Spada, H. (2007). Can people learn in computer-mediated collaboration by following a script? In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 39–56). New York, NY: Springer.CrossRefGoogle Scholar
- Sandberg, I. (1994). Human competence at work: An interpretative approach. Göteborg, Sweden: BAS.Google Scholar
- Sandoval, W. A., & Millwood, K. A. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23–55.CrossRefGoogle Scholar
- Scardamalia, M., & Bereiter, C. (2003). Knowledge building. In J. W. Guthrie (Ed.), Encyclopedia of Education. New York, NY: Macmillan Reference.Google Scholar
- Schellens, T., Van Keer, H., De Wever, B., & Valcke, M. (2007). Scripting by assigning roles: Does it improve knowledge construction in asynchronous discussion groups? International Journal of Computer-Supported Collaborative Learning (ijCSCL), 2(2–3), 225–246.CrossRefGoogle Scholar
- 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
- Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–522.CrossRefGoogle Scholar
- Schwartz, D. L., Bransford, J. D., & Sears, D. (2005). Efficiency and innovation in transfer. In J. P. Mestre (Ed.), Transfer of learning from a modern multidisciplinary perspective (pp. 1–52). Greenwich, CT: Information Age Publishing.Google Scholar
- Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129–184.CrossRefGoogle Scholar
- Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis. London: Sage Publications.Google Scholar
- Spiro, R. J., Feltovich, R. P., Jacobson, M. J., & Coulson, R. L. (1992). Cognitive flexibility, constructivism, and hypertext. In T. M. Duffy, & D. H. Jonassen (Eds.), Constructivism and the technology of instruction: A conversation (pp. 1–5). Hillsdale, NJ : Erlbaum.Google Scholar
- Stahl, G. (2005). Group cognition in computer-assisted collaborative learning. Journal of Computer Assisted Learning, 21, 79–90.CrossRefGoogle Scholar
- Stahl, G. (2007). Scripting group cognition. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 327–336). New York, NY: Springer.CrossRefGoogle Scholar
- Suthers, D. D. (2006). Technology affordances for intersubjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning (ijCSCL), 1(3), 315–337.CrossRefGoogle Scholar
- Suthers, D. D., & Hundhausen, C. (2003). An empirical study of the effects of representational guidance on collaborative learning. The Journal of the Learning Sciences, 12(2), 183–219.CrossRefGoogle Scholar
- VanLehn, K. (1999). Rule learning events in the acquisition of a complex skill: An evaluation of cascade. The Journal of the Learning Sciences, 8(1), 71–125.CrossRefGoogle Scholar
- VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. B. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21(3), 209–249.CrossRefGoogle Scholar
- Voss, J. F. (1988). Problem solving and reasoning in ill-structured domains. In C. Antaki (Ed.), Analyzing everyday explanation: A casebook of methods pp. 74–93. London: Sage Publications.Google Scholar
- Voss, J. F. (2005). Toulmin’s model and the solving of ill-structured problems. Argumentation, 19, 321–329.CrossRefGoogle Scholar
- 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, NJ: Erlbaum.Google Scholar
- Weinberger, A., Stegmann, K., Fischer, F., & Mandl, H. (2007). Scripting argumentative knowledge construction in computer-supported learning environments. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning (pp. 191–212). New York, NY: Springer.CrossRefGoogle Scholar
- 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