Assessing Problem Solving

  • David H. Jonassen


Methods for assessing problem-solving learning outcomes vary with the nature of the ­problem. For simpler well-structured problems, answer correctness and process may be used along with assessments of comprehension of problem schemas, including problem classification, text editing, and analogical comparisons. For more complex and ill-­structured problems that have no convergent answers, solution criteria, or solution methods, problem solving may be assessed by constructing and applying solution rubrics to assess mental simulations (scenarios), arguments in support of solutions, and student-constructed ­problems. Problem solving processes are normally assessed by coding schemes. In addition to assessing problem solutions, assessments of critical cognitive skills, including causal reasoning and student models, may be used to infer problem-solving skills.


Problem solving Problem types Assessment methods Rubrics 


  1. Allaire, J. C., & Marisiske, M. (1999). Everyday cognition: Age and intellectual ability correlates. Psychology and Aging, 14, 627–644.Google Scholar
  2. Arlin, P. K. (1989). Problem solving and problem finding in young artists and young scientists. In M. L. Commons, J. D. Sinnott, F. A. Richards, & C. Amon (Eds.), Adult development volume 1: comparisons and applications of developmental models (pp. 197–216). New York: Praeger.Google Scholar
  3. Atman, C. J., & Turns, J. (2001). Studying engineering design learning: Four verbal protocol analysis studies. In C. Eastman, W. M. McCracken, & W. C. Newstetter (Eds.), Design knowing and learning: Cognition in design education (pp. 37–62). New York: Elsevier.Google Scholar
  4. Barab, S. A., & Duffy, T. M. (2000). From practice fields to communities of practice. In D. H. Jonassen & S. M. Land (Eds.), Theoretical foundations of learning environments (pp. 25–55). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  5. Barab, S. A., Squire, K. D., & Dueber, W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37–62.CrossRefGoogle Scholar
  6. Brown, S. I., & Walter, M. I. (2005). The art of problem posing (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  7. Chapman, M., McBride, M. L. (1992). The education of reason: Cognitive conflict and its role inintellectyural development. In C. U. Shantz & WW. Hartup (Eds.), Conflict in child and adolescent development (pp. 36–89). Cambridge, UK: Cambridge University Press.Google Scholar
  8. Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  9. 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
  10. Dufresne, R. J., Gerace, W. J., Hardiman, P. T., & Mestre, J. P. (1992). Constraining novices to perform expertlike problem analysis: Effects on schema acquisition. The Journal of the Learning Sciences, 2(3), 307–331.CrossRefGoogle Scholar
  11. Elliott, S. N. (1995). Creating Meaningful Performance Assessments.; ERIC Digest E531; (ED381985).
  12. Ericsson, K. A., & Simon, H. A. (1993) Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.Google Scholar
  13. Greeno, J. G. (1980). Trends in the theory of knowledge for problem solving. In D. T. Turna & F. Reif (Eds.), Problem solving and education: Issues in teaching and research (pp. 9–25). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  14. Halpern, D. F. (2003). Thought and knowledge: An introduction to critical thinking (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  15. Hong, N. S., Jonassen, D. H., & McGee, S. (2003). Predictors of well-structured and ill-structured problem solving in an astronomy simulation. Journal of Research in Science Teaching, 40(1), 6–33.CrossRefGoogle Scholar
  16. Hardiman, P. T., Dufresne, R., & Mestre, J. P. (1989). The relationship between problem categorization and problem solving among experts and novices. Memory and Cognition, 17(5), 627–638.CrossRefGoogle Scholar
  17. Jacobs, A. E. J. P., Dolmans, D. H. J. M., Wolfhagen, I. H. A. P., & Scherpbier, A. J. J. A. (2003). Validation of a short questionnaire to assess the degree of complexity and structuredness of PBL problems. Medical Education, 37(11), 1001–1007.Google Scholar
  18. Jacobson, M. J., & Archodidou, A. (2000). The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. The Journal of the Learning Sciences, 9(2), 149–199.CrossRefGoogle Scholar
  19. Jonassen, D. H. (1997). Instructional design model for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–95.CrossRefGoogle Scholar
  20. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.CrossRefGoogle Scholar
  21. *Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York, NY: Routledge.Google Scholar
  22. Jonassen, D. H., & Cho, Y. H. (2011). Fostering argumentation while solving engineering ethics problems. Journal of Engineering Education, 100(4), 1–23.CrossRefGoogle Scholar
  23. Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning and instruction. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  24. Jonassen, D. H., & Hung, W. (2008). All problems are not equal: Implications for PBL. Interdisciplinary Journal of Problem-Based Learning, 2(2), 6–28.Google Scholar
  25. *Jonassen, D. H., & Kim, B. (2010). Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology: Research & Development, 58(4), 439–457.Google Scholar
  26. Jonassen, D. H., & Kwon, H. I. (2001). Communication patterns in computer-mediated vs. face-to-face group problem solving. Educational Technology Research & Development, 49(1), 35–52.Google Scholar
  27. Jonassen, D. H., & Ionas, I. G. (2008). Designing effective supports for reasoning causally. Educational Technology Research & Development, 56(3), 287–308.Google Scholar
  28. Jonassen, D. H., Shen, D., Marra, R. M., Cho, Y. H., Lo, J. L., & Lohani, V. K. (2009). Engaging and supporting problem solving in engineering ethics. Journal of Engineering Education, 98(3), 235–254.CrossRefGoogle Scholar
  29. Jonassen, D. H., Strobel, J., & Ionas, I. G. (2008). The evolution of a collaborative authoring system for non-linear hypertext: A design-based research study. Computers and Education, 51, 67–85.CrossRefGoogle Scholar
  30. Jonassen, D. H., Strobel, J., & Lee, C. B. (2006). Everyday problem solving in engineering: Lessons for engineering educators. Journal of Engineering Education, 95(2), 1–14.Google Scholar
  31. Kahn, H. (1965). On escalation: Metaphor and scenarios. New York, NY: Praeger.Google Scholar
  32. Kitchner, K. S. (1983). Cognition, metacognition, and epistemistic cognition: A three-level model of cognitive processing. Human Development, 26, 222–232.Google Scholar
  33. Littlefield, J., & Rieser, J. J. (1993). Semantic features of similarity and children’s strategies for identifying relevant information in mathematical story problems. Cognition and Instruction, 11(2), 133–188.CrossRefGoogle Scholar
  34. Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. Cambridge, UK: Cambridge University Press.Google Scholar
  35. *Low, R., & Over, R. (1989) Detection of missing and irrelevant information within algebraic story problems. British Journal of Educational Psychology, 59, 296–305.Google Scholar
  36. Low, R., & Over, R. (1990). Text editing of algebraic word problems. Australian Journal of Psychology, 42(1), 63–73.CrossRefGoogle Scholar
  37. Low, R., & Over, R. (1992). Hierarchical ordering of schematic knowledge relating to the area-of-rectangle problem. Journal of Educational Psychology, 84, 62–69.CrossRefGoogle Scholar
  38. Low, R., Over, R., Doolan, L., & Michell, S. (1994). Solution of algebraic word problems following training in identifying necessary and sufficient information within problems. The American Journal of Psychology, 107(3), 423–439.CrossRefGoogle Scholar
  39. Meacham, J. A., & Emont, N. M. (1989). The interpersonal basis of everyday problem solving. In J. D. Sinnnott (Ed.), Everyday problem solving: Theory and applications (pp. 7–23). New York: Praeger.CrossRefGoogle Scholar
  40. Mestre, J. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Journal of Applied Developmental Psychology, 23(1), 9–50.CrossRefGoogle Scholar
  41. Morrison, M., & Morgan, M. S. (1999). Models as mediating instruments. In M. S. Morgan & M. Morrison (Eds.), Models as mediators: Perspectives on natural and social science (pp. 10–37). Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
  42. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.CrossRefGoogle Scholar
  43. Ngu, B. H., Lowe, R., & Sweller, J. (2002). Text editing in chemistry instruction. Instructional Science, 30, 379–402.CrossRefGoogle Scholar
  44. Nicaise, M., Gibney, T., & Crane, M. (2000). Toward an understanding of authentic learning: Student perceptions of an authentic classroom. Journal of Science Education and Technology, 9(1), 79–94.CrossRefGoogle Scholar
  45. Norris, S. P., & Ennis, R. H. (1989). Evaluating critical thinking. Pacific Grove, CA: Critical Thinking Press.Google Scholar
  46. Nussbaum, E. M., & Kardash, C. M. (2005). The effects of goal instructions and text on the generation of counterarguments during writing. Journal of Educational Psychology, 97(2), 157–169.CrossRefGoogle Scholar
  47. Radinsky, J., Buillion, L., Lento, E. M., & Gomez, L. (2001). Mutual partnership benefit: A curricular design for authenticity. Journal of Curriculum Studies, 33(4), 405–430.Google Scholar
  48. Rich, B. (1960). Schaum’s principles of and problems of elementary algebra. New York, NY: Schaum’s.Google Scholar
  49. Rogoff, B., & Lave, J. (Eds.) (1984). Everyday cognition: Its development in social context. Cambridge, MA: Harvard University Press.Google Scholar
  50. Rumelhart, D. E., & Ortony, A. (1977). The representation of knowledge in memory. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the acquisition of knowledge (pp. 99–135). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  51. Savelsbergh, E. R., de Jong, T., & Ferguson-Hessler, M. G. M. (1998). Competence related differences in problem representations. In M. van Someren, P. Reimann, T. de Jong, & H. Boshuizen (Eds.), The role of multiple representations in learning and problem solving (pp. 263–282). Amsterdam: Elservier.Google Scholar
  52. Silver, E. A., & Cai, J. (1996). An analysis of arithmetic problem posing by middle school students. Journal for Research in Mathematics Education, 27(6), 521–539.CrossRefGoogle Scholar
  53. Simon, H. A. (1978). What the knower knows: Alternative strategies for problem-solving tasks. In F. Klix (Ed.), Human and artificial intelligence (pp. 89–100). Berlin: VEB Deutscher Verlag der Wissenschafter.Google Scholar
  54. Smith, M. U. (Ed.) (1991). Toward a unified theory of problem solving: Views from the content domains. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  55. Toulmin, S. (1958). The uses of argument. Cambridge, England: Cambridge University Press.Google Scholar
  56. *Tversky, A., & Kahneman, D. (1980). Causal schemas in judgments under uncertainty. In M. Fishbein (Ed.), Progress in social psychology (Vol. 1, pp. 49–72). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  57. Van Heuvelen, A., & Maloney, D. P. (1999). Playing physics jeopardy. American Journal of Physics, 67(3), 252–256.CrossRefGoogle Scholar
  58. Voss, J. F., & Post, T. A. (1988). On the solving of ill-structured problems. In M. T. H. Chi, Rl Glaser, & M. J. Farr (Eds.), The nature of expertise. NJ: Lawrence Erlbaum.Google Scholar
  59. Wood, P. K. (1983). Inquiring systems and problem structures: Implications for cognitive development. Human Development, 26, 249–265.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Educational Psychology and Learning TechnologiesUniversity of MissouriColumbiaUSA

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