Productive failure in CSCL groups



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.


Ill-structured problem solving Well-structured problem solving Synchronous collaboration Problem-solving failure 



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.


  1. 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
  2. Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
  3. Bakeman, R., & Gottman, J. M. (1997). Observing interaction: An introduction to sequential analysis. New York: Cambridge University Press.Google Scholar
  4. 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
  5. 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
  6. 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
  7. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42.Google Scholar
  8. Chatterji, M. (2003). Designing and using tools for educational assessment. Boston: Allyn & Bacon.Google Scholar
  9. 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
  10. 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
  11. 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
  12. Cohen, J. (1977). Statistical power analysis for the behavioral sciences. New York: Academic Press.Google Scholar
  13. Collins, H. (1985). Changing order. London: Sage.Google Scholar
  14. Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.Google Scholar
  15. de Groot, A. D. (1965). Thought and choice in chess. The Hague, NL: Mouton.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, NL: Open Universiteit Nederland.Google Scholar
  17. 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
  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: Elsevier Science.Google Scholar
  19. 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
  20. 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
  21. 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
  22. 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
  23. Giles, J. (2006). The trouble with replication. Nature, 442, 344–347.CrossRefGoogle Scholar
  24. Goel, V., & Pirolli, P. (1992). The structure of design problem spaces. Cognitive Science, 16, 395–429.CrossRefGoogle Scholar
  25. 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
  26. 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
  27. Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266.CrossRefGoogle Scholar
  28. Holland, J. H. (1995). Hidden order: How adaptation builds complexity. New York: Addison-Wesley.Google Scholar
  29. Jonassen, D. H. (2000). Towards a design theory of problem solving. Educational Technology, Research and Development, 48(4), 63–85.CrossRefGoogle Scholar
  30. 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
  31. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
  32. 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
  33. 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
  34. 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
  35. Kauffman, S. (1995). At home in the universe. New York: Oxford University Press.Google Scholar
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. Marton, F. (2007). Sameness and difference in transfer. The Journal of the Learning Sciences, 15(4), 499–535.CrossRefGoogle Scholar
  43. 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
  44. 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
  45. Mestre, J. P. (2005). Transfer of learning from a modern multidisciplinary perspective. Greenwich, CT: Information Age.Google Scholar
  46. 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
  47. Petroski, H. (2006). Success through failure: The paradox of design. Princeton, NJ: Princeton University Press.Google Scholar
  48. Poole, M. S., & Holmes, M. E. (1995). Decision development in computer-assisted group decision making. Human Communications Research, 22(1), 90–127.CrossRefGoogle Scholar
  49. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models. Thousand Oaks, CA: Sage Publications.Google Scholar
  50. 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
  51. 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
  52. Sandberg, I. (1994). Human competence at work: An interpretative approach. Göteborg, Sweden: BAS.Google Scholar
  53. 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
  54. Scardamalia, M., & Bereiter, C. (2003). Knowledge building. In J. W. Guthrie (Ed.), Encyclopedia of Education. New York, NY: Macmillan Reference.Google Scholar
  55. 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
  56. 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
  57. Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–522.CrossRefGoogle Scholar
  58. 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
  59. 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
  60. Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis. London: Sage Publications.Google Scholar
  61. 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
  62. Stahl, G. (2005). Group cognition in computer-assisted collaborative learning. Journal of Computer Assisted Learning, 21, 79–90.CrossRefGoogle Scholar
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. Voss, J. F. (2005). Toulmin’s model and the solving of ill-structured problems. Argumentation, 19, 321–329.CrossRefGoogle Scholar
  70. 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
  71. 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
  72. 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

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2008

Authors and Affiliations

  1. 1.National Institute of EducationNanyang Technological UniversitySingaporeSingapore
  2. 2.Teachers CollegeColumbia UniversityNew YorkUSA

Personalised recommendations