The effects of successful versus failure-based cases on argumentation while solving decision-making problems

  • Andrew Tawfik
  • David Jonassen
Research Article


Solving complex, ill-structured problems may be effectively supported by case-based reasoning through case libraries that provide just-in-time domain-specific principles in the form of stories. The cases not only articulate previous experiences of practitioners, but also serve as problem-solving narratives from which learners can acquire meaning. The current study investigated the effects of different case-types (success, failures) on analogical transfer to similar problems. In the first week, undergraduate sales management students (N = 36) were assigned to different case library treatments (success, failure) and asked to construct a multifaceted argument (initial argument, counterargument, rebuttal) to resolve an ill-structured, decision-making hiring problem. In the following week, students constructed an argument to solve a novel case without the support of the case library. Data analysis revealed the failure-based case library condition produced significantly higher scores on measurements of counterarguments and holistic argumentation scores on both tasks. A discussion of the implications for pedagogy and instructional design are also presented.


Problem solving Decision making Argumentation Case-based reasoning Case libraries Failure Failure-driven memory 



We would like to express our gratitude for the helpful comments of the reviewers throughout the iterations of this manuscript. We would like also like to thank the editors for their detailed assistance at each stage of the review process.


  1. Aamodt, A., & Plaza, E. (1996). Case-based reasoning: Foundational issues, methodological variations, and systems approaches. Artificial Intelligence Communications, 7(1), 39–59.Google Scholar
  2. Asterhan, C. S. C., & Schwarz, B. B. (2009). Argumentation and explanation in conceptual change: Indications from protocol analyses of peer-to-peer dialog. Cognitive Science, 33(3), 374–400. doi: 10.1111/j.1551-6709.2009.01017.x.CrossRefGoogle Scholar
  3. Barrows, H. (1988). The tutorial process. Springfield: Southern Illinois University School of Medicine.Google Scholar
  4. Barrows, H. (1996). Problem-based learning in medicine and beyond: A brief overview. New Directions for Teaching and Learning, 1996(68), 3–12.CrossRefGoogle Scholar
  5. Barrows, H., & Tamblyn, R. (1980). Problem-based learning: An approach to medical education. New York: Springer Publishing Company.Google Scholar
  6. Bauer, J., & Mulder, R. (2007). Modelling learning from errors in daily work. Learning in Health and Social Care, 6(3), 121–133.CrossRefGoogle Scholar
  7. Dunlap, J., & Grabinger, R. S. (1996). Rich environments for active learning in the higher education classroom. In B. G. Wilson (Ed.), Constructivist learning environments: Case studies in instructional design (pp. 65–82). Englewood Cliffs: Educational Technology.Google Scholar
  8. Ellis, S., & Davidi, I. (2005). After-event reviews: Drawing lessons from successful and failed experience. Journal of Applied Psychology, 90(5), 857–871.CrossRefGoogle Scholar
  9. Ellis, S., Mendel, R., & Davidi, I. (2006). Learning from successful and failed experience: The moderating role of kind of after-event review. Journal of Applied Psychology, 91(3), 669–680.CrossRefGoogle Scholar
  10. Fullerton, A. S. (2009). A conceptual framework for ordered logistic regression models. Sociological Methods & Research, 38(2), 306–347. doi: 10.1177/0049124109346162.CrossRefGoogle Scholar
  11. Gartmeier, M., Bauer, J., Gruber, H., & Heid, H. (2008). Negative knowledge: Understanding professional learning and expertise. Vocations and Learning, 1(2), 87–103. doi: 10.1007/s12186-008-9006-1.CrossRefGoogle Scholar
  12. Gartmeier, M., Bauer, J., Gruber, H., & Heid, H. (2010). Workplace errors and negative knowledge in elder care nursing. Human Resource Development International, 13(1), 5–25.CrossRefGoogle Scholar
  13. Gijbels, D., Dochy, F., Van den Bossche, P., & Segers, M. (2005). Effects of problem-based learning: A meta-analysis from the angle of assessment. Review of Educational Research, 75(1), 27–61. doi: 10.3102/00346543075001027.CrossRefGoogle Scholar
  14. Gore, D. C. (2006). National survey of surgical morbidity and mortality conferences. The American Journal of Surgery, 191(5), 708–714. doi: 10.1016/j.amjsurg.2006.01.029.CrossRefGoogle Scholar
  15. Hernandez-Serrano, J., & Jonassen, D. H. (2003). The effects of case libraries on problem solving. Journal of Computer Assisted Learning, 19(1), 103–114.CrossRefGoogle Scholar
  16. Hoeve, A., & Nieuwenhuis, L. F. (2006). Learning routines in innovation processes. Journal of Workplace Learning, 18(3), 171–185.CrossRefGoogle Scholar
  17. Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97.CrossRefGoogle Scholar
  18. Jeong, A., & Joung, S. (2007). Scaffolding collaborative argumentation in asynchronous discussions with message constraints and message labels. Computers & Education, 48(3), 427–445. doi: 10.1016/j.compedu.2005.02.002.CrossRefGoogle Scholar
  19. Jeong, A. C., & Lee, J. (2008). The effects of active versus reflective learning style on the processes of critical discourse in computer-supported collaborative argumentation. British Journal of Educational Technology, 39(4), 651–665.CrossRefGoogle Scholar
  20. Jonassen, D. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94.CrossRefGoogle Scholar
  21. Jonassen, D. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.CrossRefGoogle Scholar
  22. Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York: Routledge.Google Scholar
  23. Jonassen, D. H., & Cho, Y. (2011). Fostering argumentation while solving engineering ethics problems. Journal of Engineering Education, 100(4), 680–702.CrossRefGoogle Scholar
  24. Jonassen, D. H., & Hernandez-Serrano, J. (2002). Case-based reasoning and instructional design: Using stories to support problem solving. Educational Technology Research and Development, 50(2), 65–77.CrossRefGoogle Scholar
  25. Jonassen, D., & Hung, W. (2006). Learning to troubleshoot: A new theory-based design architecture. Educational Psychology Review, 18(1), 77–114.CrossRefGoogle Scholar
  26. Jonassen, D., & Hung, W. (2008). All problems are not equal: Implications for problem-based learning. Interdisciplinary Journal of Problem-based Learning, 2(2), 6–28.CrossRefGoogle Scholar
  27. Kim, H., & Hannafin, M. J. (2008). Grounded design of web-enhanced case-based activity. Educational Technology Research and Development, 56(2), 161–179.CrossRefGoogle Scholar
  28. Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34. doi: 10.1007/BF00155578.CrossRefGoogle Scholar
  29. Kolodner, J. L., Cox, M., & Gonzalez-Calero, P. (2005). Case-based reasoning-inspired approaches to education. The Knowledge Engineering Review, 20(3), 299–303. doi: 10.1017/S0269888906000634.CrossRefGoogle Scholar
  30. Kolodner, J. L., Owensby, J., & Guzdial, M. (2004). Case-based learning aids. In D. Jonassen (Ed.), Handbook of research on educational communications and technology (2nd ed., pp. 829–861). Mahwah: LEA.Google Scholar
  31. Kuhn, D. (1993). Science as argument: Implications for teaching and learning scientific thinking. Science Education, 77(3), 319–337. doi: 10.1002/sce.3730770306.CrossRefGoogle Scholar
  32. Kuhn, D., & Udell, W. (2003). The development of argument skills. Child Development, 74(5), 1245–1260.CrossRefGoogle Scholar
  33. Kuhn, D., & Udell, W. (2007). Coordinating own and other perspectives in argument. Thinking & Reasoning, 13(2), 90–104.CrossRefGoogle Scholar
  34. Lin, T.-J., & Anderson, R. C. (2008). Reflections on collaborative discourse, argumentation, and learning. Contemporary Educational Psychology, 33(3), 443–448. doi: 10.1016/j.cedpsych.2008.06.002.CrossRefGoogle Scholar
  35. Mason, L., Gava, M., & Boldrin, A. (2008). On warm conceptual change: The interplay of text, epistemological beliefs, and topic interest. Journal of Educational Psychology, 100(2), 291–309. doi: 10.1037/0022-0663.100.2.291.CrossRefGoogle Scholar
  36. Mathan, S., & Koedinger, K. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.CrossRefGoogle Scholar
  37. Norman, G., & Schmidt, H. G. (1992). The psychological basis of problem-based learning: A review of the evidence. Academic Medicine, 67(9), 557–565.CrossRefGoogle Scholar
  38. Nussbaum, M. (2008). Collaborative discourse, argumentation, and learning: Preface and literature review. Contemporary Educational Psychology, 33(3), 345–359. doi: 10.1016/j.cedpsych.2008.06.001.CrossRefGoogle Scholar
  39. Nussbaum, M., & Schraw, G. (2007). Promoting argument-counterargument integration in students’ writing. Journal of Experimental Education, 76(1), 59–92.CrossRefGoogle Scholar
  40. Nussbaum, M., Kardash, C. A. M., & Graham, S. E. (2005). The effects of goal instructions and text on the generation of counterarguments during writing. Journal of Educational Psychology, 97(2), 157.CrossRefGoogle Scholar
  41. Parviainen, J., & Eriksson, M. (2006). Negative knowledge, expertise and organisations. International Journal of Management Concepts and Philosophy, 2(2), 140–153.Google Scholar
  42. Popper, K. (1987). All life is problem solving. London: Routledge.Google Scholar
  43. Rosenfeld, J. C. (2005). Using the morbidity and mortality conference to teach and assess the ACGME general competencies. Current Surgery, 62(6), 664–669. doi: 10.1016/j.cursur.2005.06.009.CrossRefGoogle Scholar
  44. Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (2001). Methodological issues in the content analysis of computer conference transcripts. International Journal of Artificial Intelligence in Education, 12, 8–22.Google Scholar
  45. Savery, J. R., & Duffy, T. M. (1996). Problem based learning: An instructional model and its constructivist framework. Educational Technology, 35(5), 31–38.Google Scholar
  46. Schank, R. (1999). Dynamic memory revisited (2nd ed.). Cambridge: Cambridge University Press.Google Scholar
  47. Schank, R., Berman, T., & Macpherson, K. (1999). Learning by doing. In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (1st ed., Vol. 2, pp. 241–261). Mahwah: Lawrence Erlbaum Associates.Google Scholar
  48. Schrader, P. G., Leu, D. J., Kinzer, C. K., Ataya, R., Teale, W. H., Labbo, L. D., et al. (2003). Using Internet delivered video cases, to support pre-service teachers’ understanding of effective early literacy instruction: An exploratory study. Instructional Science, 31(4), 317–340. doi: 10.1023/A:1024690111227.CrossRefGoogle Scholar
  49. Seel, N. (2008). Empirical perspectives on memory and motivation. In J. M. Spector, M. D. Merrill, J. van Merrienboer, & M. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 659–670). New York: Routledge.Google Scholar
  50. Shepherd, D. A. (2003). Learning from business failure: Propositions of grief recovery for the self-employed. The Academy of Management Review, 28(2), 318–328. doi: 10.2307/30040715.Google Scholar
  51. Sitkin, S. B. (1992). Learning through failure: The strategy of small losses. Research in Organizational Behavior, 14, 231–266.Google Scholar
  52. Spiro, R. J., Coulson, R. L., Feltovich, P. J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains. Tech Report No. 441. Champaign, IL: University of Illinois, Center for the Study of Reading.Google Scholar
  53. Stegmann, K., Weinberger, A., & Fischer, F. (2007). Facilitating argumentative knowledge construction with computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(4), 421–447. doi: 10.1007/s11412-007-9028-y.CrossRefGoogle Scholar
  54. Weinberger, A., Stegmann, K., & Fischer, F. (2010). Learning to argue online: Scripted groups surpass individuals (unscripted groups do not). Computers in Human Behavior, 26(4), 506–515. doi: 10.1016/j.chb.2009.08.007.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2013

Authors and Affiliations

  1. 1.Concodia University Chicago (Office CC 248 i)River ForestUSA
  2. 2.University of MissouriColumbiaUSA

Personalised recommendations