Educational Technology Research and Development

, Volume 63, Issue 6, pp 975–994 | Cite as

Failing to learn: towards a unified design approach for failure-based learning

Development Article

Abstract

To date, many instructional systems are designed to support learners as they progress through a problem-solving task. Often these systems are designed in accordance with instructional design models that progress the learner efficiently through the problem-solving process. However, theories from various fields have discussed failure as a strategic way to engender learning. Although researchers suggest that failure may be an element of problem-solving, no models have discussed how to employ failure strategically within instructional design. Given this gap, we first present failure-based research from various theoretical frameworks. Based on the research, we proffer failure-based principles for learning systems design. Implications and future research are also discussed.

Keywords

Failure Problem-solving Unified theory Instructional design Case-based reasoning Productive failure 

References

  1. Argote, L., & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137.CrossRefGoogle Scholar
  2. Bar-Anan, Y., Wilson, T. D., & Gilbert, D. T. (2009). The feeling of uncertainty intensifies affective reactions. Emotion, 9(1), 123–127. doi:10.1037/a0014607.CrossRefGoogle Scholar
  3. Bauer, J., & Mulder, R. (2007). Modelling learning from errors in daily work. Learning in Health and Social Care, 6(3), 121–133.CrossRefGoogle Scholar
  4. Blumberg, F. C., Rosenthal, S. F., & Randall, J. D. (2008). Impasse-driven learning in the context of video games. Computers in Human Behavior, 24(4), 1530–1541. doi:10.1016/j.chb.2007.05.010.CrossRefGoogle Scholar
  5. Boud, D., Keogh, R., & Walker, D. (2013). Reflection: Turning experience into learning. New York: Routledge.Google Scholar
  6. Brown, J. S., & VanLehn, K. (1980). Repair theory: A generative theory of bugs in procedural skills. Cognitive Science, 4(4), 379–426. doi:10.1207/s15516709cog0404_3.CrossRefGoogle Scholar
  7. Casale, M. B., Roeder, J. L., & Ashby, F. G. (2012). Analogical transfer in perceptual categorization. Memory & Cognition, 40(3), 434–449.CrossRefGoogle Scholar
  8. Cope, J. (2011). Entrepreneurial learning from failure: An interpretative phenomenological analysis. Journal of Business Venturing, 26(6), 604–623. doi:10.1016/j.jbusvent.2010.06.002.CrossRefGoogle Scholar
  9. D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. doi:10.1016/j.learninstruc.2012.05.003.CrossRefGoogle Scholar
  10. De Jong, T., & Lazonder, A. (2014). The guided discovery learning principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 370–390). Cambridge University Press.Google Scholar
  11. Dugas, M. J., Hedayati, M., Karavidas, A., Buhr, K., Francis, K., & Phillips, N. A. (2005). Intolerance of uncertainty and information processing: Evidence of biased recall and interpretations. Cognitive Therapy and Research, 29(1), 57–70. doi:10.1007/s10608-005-1648-9.CrossRefGoogle Scholar
  12. Ellis, S. (2011). Learning from errors: The role of after-event reviews. In J. Bauer & C. Harteis (Eds.), Human fallibility: The ambiguity of errors for work and learning (pp. 215–314). Dordrecht: Springer.Google Scholar
  13. 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
  14. Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39.CrossRefGoogle Scholar
  15. 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
  16. 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
  17. Ge, X., & Land, S. (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
  18. Henry, H., Tawfik, A. A., Jonassen, D. H., Winholtz, R., & Khanna, S. (2012). “I know this is supposed to be more like the real world, but…”: Student perceptions of a PBL implementation in an undergraduate materials science course. Interdisciplinary Journal of Problem-Based Learning. doi:10.7771/1541-5015.1312.Google Scholar
  19. Herrington, J., Reeves, T. C., & Oliver, R. (2014). Authentic learning environments. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 453–464). New York, NY: Springer.Google Scholar
  20. Hmelo-Silver, C. E., & Eberbach, C. (2012).Learning theories and problem-based learning.In Bridges, S., McGrath, C., & Whitehill, T. L. (Eds.), Problem-based learning in clinical education (pp. 3–17). Springer Netherlands. Retrieved from http://link.springer.com/chapter/10.1007/978-94-007-2515-7_1.
  21. Hoeve, A., & Nieuwenhuis, L. F. (2006). Learning routines in innovation processes. Journal of Workplace Learning, 18(3), 171–185.CrossRefGoogle Scholar
  22. Holyoak, K., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory & Cognition, 15(4), 332–340.CrossRefGoogle Scholar
  23. Hong, Y.-C., & Choi, I. (2011). Three dimensions of reflective thinking in solving design problems: A conceptual model. Educational Technology Research and Development, 59(5), 687–710. doi:10.1007/s11423-011-9202-9.CrossRefGoogle Scholar
  24. Hung, W. (2011). Theory to reality: A few issues in implementing problem-based learning. Educational Technology Research & Development, 59(4), 529–552.CrossRefGoogle Scholar
  25. Jonassen, D. H. (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
  26. Jonassen, D. H. (2011). Supporting problem solving in PBL. Interdisciplinary Journal of Problem-Based Learning. doi:10.7771/1541-5015.1256.Google Scholar
  27. Jonassen, D. H., & 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
  28. Jones, R. M., & Vanlehn, K. (1994). Acquisition of children’s addition strategies: A model of impasse-free, knowledge-level learning. Machine Learning, 16(1–2), 11–36. doi:10.1007/BF00993172.Google Scholar
  29. Kapur, M. (2008). Productive failure. Cognition and Instruction, 38(6), 523–550.Google Scholar
  30. Kapur, M. (2010). Productive failure in mathematical problem solving. Instructional Science, 26(3), 379–424.Google Scholar
  31. Kapur, M. (2011). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science, 39(4), 561–579. doi:10.1007/s11251-010-9144-3.CrossRefGoogle Scholar
  32. Kapur, M. (2012). Productive failure in learning the concept of variance. Instructional Science, 40(4), 651–672. doi:10.1007/s11251-012-9209-6.CrossRefGoogle Scholar
  33. Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Indianapolis: Pearson FT Press.Google Scholar
  34. Kolodner, J. L., Owensby, J., & Guzdial, M. (2004). Case-based learning aids. In D. H. Jonassen (Ed.), Handbook of research on educational communications and technology: A project of the Association for Educational Communications and Technology (2nd ed., pp. 829–861). Mahwah: LEA.Google Scholar
  35. Lannin, J., Barker, D., & Townsend, B. (2007). How students view the general nature of their errors. Educational Studies in Mathematics, 66(1), 43–59. doi:10.1007/s10649-006-9067-8.CrossRefGoogle Scholar
  36. Lazonder, A. (2014). Inquiry learning. In J. M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 453–464). New York, NY: Springer.CrossRefGoogle Scholar
  37. Lorch, R. F, Jr, Lorch, E. P., Calderhead, W., Dunlap, E., Hodell, E., & Freer, B. (2010). Learning the control of variables strategy in higher and lower achieving classrooms: Contributions of explicit instruction and experimentation. Journal of Educational Psychology, 102(1), 90–101.CrossRefGoogle Scholar
  38. Mathan, S., & Koedinger, K. (2005). Fostering the intelligent novice: Learning from errors with metacognitive tutoring. Educational Psychologist, 40(4), 257–265.CrossRefGoogle Scholar
  39. Parviainen, J., & Eriksson, M. (2006). Negative knowledge, expertise and organisations. International Journal of Management Concepts and Philosophy, 2(2), 140–153.CrossRefGoogle Scholar
  40. Piaget, J. (1952). The origins of intelligence in children. New York: W W Norton & Co.CrossRefGoogle Scholar
  41. Piaget, J. (1977). The development of thought: equilibration of cognitive structures (Vol. viii). Oxford: Viking.Google Scholar
  42. Piaget, J., Brown, T., & Thampy, K. J. (1985). The equilibration of cognitive structures: the central problem of intellectual development (Vol. 985). Chicago: University of Chicago Press.Google Scholar
  43. Reiser, B. (2004). Scaffolding complex learning: the mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304.CrossRefGoogle Scholar
  44. Renner, J. W., Stafford, D. G., Lawson, A. E., McKinnon, J. W., Friot, F. E., & Kellogg, D. H. (1976). Research, teaching, and learning with the Piaget model. Norman: University of Oklahoma Press.Google Scholar
  45. Schank, R. (1982). Dynamic memory. Cambridge: Cambridge University Press.Google Scholar
  46. Schank, R. (1999). Dynamic memory revisited (2nd ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  47. Schmidt, H. G., & Rikers, R. M. J. P. (2007). How expertise develops in medicine: knowledge encapsulation and illness script formation. Medical Education, 41(12), 1133–1139. doi:10.1111/j.1365-2923.2007.02915.x.Google Scholar
  48. Schon, D. A. (1984). The reflective practitioner: How professionals think in action (1st ed.). New York: Basic Books.Google Scholar
  49. Schon, D. A. (1987). Educating the reflective practitioner: Toward a new design for teaching and learning in the professions (1st ed.). San Francisco, CA: Jossey-Bass.Google Scholar
  50. Spiro, R., Coulson, R. L., Feltovich, P., & Anderson, D. K. (1998). Cognitive flexibility theory: advanced knowledge acquisition in ill-structured domains.Google Scholar
  51. Tawfik, A. A., & Jonassen, D. H. (2013). The effects of successful versus failure-based cases on argumentation while solving decision-making problems. Educational Technology Research and Development, 61(3), 385–406. doi:10.1007/s11423-013-9294-5.CrossRefGoogle Scholar
  52. Tudge, J. (1993). Vygotsky, Piaget, and Bandura: Perspectives on the relations between the social world and cognitive development. Human Development, 36(2), 61–81.CrossRefGoogle Scholar
  53. VanLehn, K. (1988). Toward a theory of impasse-driven learning. In Mandl, D. H. & Lesgold, D. A. (Eds.) Learning Issues for Intelligent Tutoring Systems (pp. 19–41). Springer US. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4684-6350-7_2.
  54. 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. doi:10.1207/S1532690XCI2103_01.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2015

Authors and Affiliations

  1. 1.Northern Illinois UniversityDekalbUSA
  2. 2.The University of GeorgiaAthensUSA

Personalised recommendations