Learning the physics of electricity: A qualitative analysis of collaborative processes involved in productive failure

  • Suneeta A. Pathak
  • Beaumie Kim
  • Michael J. Jacobson
  • Baohui Zhang


Earlier quantitative studies in computer-supported collaborative learning identified ‘Productive Failure’ (Kapur, Cognition and Instruction 26(3):379–424, 2008) as a phenomenon in which students experiencing relative failures in their initial problem-solving efforts subsequently performed better than others who were in a condition not involving an initial failure. In this qualitative study, we examine the problem-solving dynamics of two dyads: a Productive Failure (PF) dyad who initially received a low-structured activity and a Non-Productive Failure (N-PF) dyad who initially received a high-structured activity. Both dyads then received an identical high-structured problem-solving activity. This process was repeated using multiple sets of problems, and this paper will discuss two sets. Interactions of the two dyads were logged. Data for this study included video conversations of the dyads, screen captures of their use of a computer model, and their submitted answers. Results indicated that initial struggle and failed attempts provided an opportunity to the PF dyad to expand their observation space and thus engage deeply with the computer model. Over-scripting proved to be detrimental in creation of a mutual meaning-making space for the N-PF dyad. This paper suggests that the relative success of the PF dyad might be viewed in terms of induction of reflective reasoning practices.


Collaboration Electricity Physics education Problem solving Productive failure Scientific inquiry 



This research was supported by the Learning Science Laboratory, Nanyang Technological University, Singapore (Grant # LSL 16/06 ZBH). The authors thank Pratim Sengupta and Uri Wilensky for supporting the use of the NIELS agent-based models in this research. We also thank the teachers who were involved in the design and enactment of this research and the students who participated in the study.


  1. American Association for the Advancement of Science. (1993). Benchmarks for science literacy. NewYork: Oxford University Press.Google Scholar
  2. Ashcoft, N. W., & Mermin, N. D. (1976). Solid state physics. Philadelphia: Saunders college.Google Scholar
  3. Barron, B. (2003). Why smart groups fail? The Journal of the Learning Sciences, 12(3), 307–359.CrossRefGoogle Scholar
  4. Bittinger, M. L., & Davic, E. (2001). Intermediate algebra: Concepts and applications (6th ed.). MA: Addison-Wesley.Google Scholar
  5. Basili, P. A., & Sanford, J. P. (1991). Conceptual change strategies and cooperative group work in chemistry. Journal of Research in Science Teaching, 28, 293–304.CrossRefGoogle Scholar
  6. Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1–49.Google Scholar
  7. Chinn, C. A., & Malhotra, B. A. (2002). Epistemologically authentic inquiry in schools: A theoretical framework for evaluating inquiry task. Science Education, 86(2), 175–218.CrossRefGoogle Scholar
  8. Denzin, N. K. (1978). The research act: A theoretical introduction to sociological methods. New York: McGraw Hill.Google Scholar
  9. diSessa, A., & Sherin, B. (1998). What changes in conceptual change? International Journal of Science Education, 20(10), 1155–1191.CrossRefGoogle Scholar
  10. Jacobson, M. J., Kim, B., Pathak, S. A., & Zhang, B. (2009). Learning the physics of electricity with agent-based models: Fail first and structure later? Paper presented at the 2009 Annual Meeting of the American Educational Research Association, San Diego, CA.Google Scholar
  11. Heller, P. M., & Finley, F. N. (1992). Variable uses of alternative conceptions: A case study in current electricity. Journal of Research in Science Teaching, 29(3), 259–268.CrossRefGoogle Scholar
  12. Jordan, B., & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4(1), 39–103.CrossRefGoogle Scholar
  13. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
  14. Kapur, M., & Kinzer, C. (2009). Productive failure in CSCL groups. International Journal of Computer-Supported Collaborative Learning, 4, 21–46.CrossRefGoogle Scholar
  15. Kapur, M. (2009). Productive failure in mathematical problem solving. Instructional Science. doi: 10.1007/s11251-009-9093-x.Google Scholar
  16. Kapur, M. (2010a). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science. doi: 10.1007/s11251-010-9144-3.Google Scholar
  17. Kapur, M. (2010b). Productive failure in learning the concept of variance. In S. Ohlsson & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2727–2732). Austin, TX: Cognitive Science Society.Google Scholar
  18. Klahr, D. (2000). Exploring science: The cognition and development of discovery process. Cambridge: MIT.Google Scholar
  19. Klahr, D., & Dunbar, K. (1988). Dual search space during scientific reasoning. Cognitive Science, 12, 1–48.CrossRefGoogle Scholar
  20. Pathak, S. A., Jacobson, M. J., Kim, B., Zhang, B. H., & Feng D. (2008). Learning the physics of electricity with agent based models: paradox of productive failure. In T.-W. Chan, G. Biswas, F.-C. Chen, S. Chen, C. Chou, M. Jacobson, Kinshuk, F. Klett, C.-K. Looi, T. Mitrovic, R. Mizoguchi, K. Nakabayashi, P. Reimann, D. Suthers, S. Yang & J.-C. Yang. International Conference on Computers in Education (pp. 221–228).Google Scholar
  21. Purcell, E. M. (1985). Electricity and magnetism. London: Mc-Grall Hill.Google Scholar
  22. Roth, W.-M., & Roychoudhury, A. (1992). The social construction of scientific concepts or the concept map as conscription device and tool for social thinking in high school science. Science Education, 76, 531–557.CrossRefGoogle Scholar
  23. Schwartz, R. S., Lederman, N. G., & Crawford, B. A. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science education, 88(4), 610–645.CrossRefGoogle Scholar
  24. Sengupta, P., & Wilensky, U. (2007a). NetLogo Ohm’s Law model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved October, 23, 2007 from'sLaw.
  25. Sengupta, P., & Wilensky, U. (2007b). NetLogo Parallel Circuit model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved October, 23, 2007 from
  26. Sengupta, P., & Wilensky, U. (2007c). NetLogo Series Circuit model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved October, 23, 2007 from
  27. Sengupta, P., & Wilensky, U. (2007d). NetLogo Electrostatics model. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University. Retrieved October, 23, 2007 from
  28. Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–522.CrossRefGoogle Scholar
  29. VanLehn, K., Siler, S., & Murray, C. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 2(3), 209–249.CrossRefGoogle Scholar
  30. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.CrossRefGoogle Scholar
  31. Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.CrossRefGoogle Scholar
  32. Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review, 20, 99–149.CrossRefGoogle Scholar

Copyright information

© International Society of the Learning Sciences, Inc.; Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Suneeta A. Pathak
    • 1
  • Beaumie Kim
    • 1
  • Michael J. Jacobson
    • 2
  • Baohui Zhang
    • 1
  1. 1.Learning Sciences Laboratory, National Institute of EducationNanyang Technological University1, Nanyang walkSingapore
  2. 2.Centre for Research on Computer Supported Learning and CognitionThe University of SydneySydneyAustralia

Personalised recommendations