Abstract
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.
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References
Allaire, J. C., & Marisiske, M. (1999). Everyday cognition: Age and intellectual ability correlates. Psychology and Aging, 14, 627–644.
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.
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.
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.
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.
Brown, S. I., & Walter, M. I. (2005). The art of problem posing (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
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.
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.
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.
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.
Elliott, S. N. (1995). Creating Meaningful Performance Assessments. http://www.ed.gov/databases/ERIC_Digests/ed381985.html; ERIC Digest E531; (ED381985).
Ericsson, K. A., & Simon, H. A. (1993) Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.
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.
Halpern, D. F. (2003). Thought and knowledge: An introduction to critical thinking (4th ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
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.
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.
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.
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.
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.
Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85.
*Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York, NY: Routledge.
Jonassen, D. H., & Cho, Y. H. (2011). Fostering argumentation while solving engineering ethics problems. Journal of Engineering Education, 100(4), 1–23.
Jonassen, D. H., & Grabowski, B. L. (1993). Handbook of individual differences, learning and instruction. Hillsdale, NJ: Lawrence Erlbaum Associates.
Jonassen, D. H., & Hung, W. (2008). All problems are not equal: Implications for PBL. Interdisciplinary Journal of Problem-Based Learning, 2(2), 6–28.
*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.
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.
Jonassen, D. H., & Ionas, I. G. (2008). Designing effective supports for reasoning causally. Educational Technology Research & Development, 56(3), 287–308.
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.
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.
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.
Kahn, H. (1965). On escalation: Metaphor and scenarios. New York, NY: Praeger.
Kitchner, K. S. (1983). Cognition, metacognition, and epistemistic cognition: A three-level model of cognitive processing. Human Development, 26, 222–232.
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.
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. Cambridge, UK: Cambridge University Press.
*Low, R., & Over, R. (1989) Detection of missing and irrelevant information within algebraic story problems. British Journal of Educational Psychology, 59, 296–305.
Low, R., & Over, R. (1990). Text editing of algebraic word problems. Australian Journal of Psychology, 42(1), 63–73.
Low, R., & Over, R. (1992). Hierarchical ordering of schematic knowledge relating to the area-of-rectangle problem. Journal of Educational Psychology, 84, 62–69.
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.
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.
Mestre, J. (2002). Probing adults’ conceptual understanding and transfer of learning via problem posing. Journal of Applied Developmental Psychology, 23(1), 9–50.
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.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.
Ngu, B. H., Lowe, R., & Sweller, J. (2002). Text editing in chemistry instruction. Instructional Science, 30, 379–402.
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.
Norris, S. P., & Ennis, R. H. (1989). Evaluating critical thinking. Pacific Grove, CA: Critical Thinking Press.
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.
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.
Rich, B. (1960). Schaum’s principles of and problems of elementary algebra. New York, NY: Schaum’s.
Rogoff, B., & Lave, J. (Eds.) (1984). Everyday cognition: Its development in social context. Cambridge, MA: Harvard University Press.
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.
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.
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.
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.
Smith, M. U. (Ed.) (1991). Toward a unified theory of problem solving: Views from the content domains. Hillsdale, NJ: Lawrence Erlbaum Associates.
Toulmin, S. (1958). The uses of argument. Cambridge, England: Cambridge University Press.
*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.
Van Heuvelen, A., & Maloney, D. P. (1999). Playing physics jeopardy. American Journal of Physics, 67(3), 252–256.
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.
Wood, P. K. (1983). Inquiring systems and problem structures: Implications for cognitive development. Human Development, 26, 249–265.
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Jonassen, D.H. (2014). Assessing Problem Solving. In: Spector, J., Merrill, M., Elen, J., Bishop, M. (eds) Handbook of Research on Educational Communications and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3185-5_22
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