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Designing effective supports for causal reasoning

Educational Technology Research and Development volume 56, pages 287–308 (2008)Cite this article

Abstract

Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the “cement of the universe” [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and inferences, and explaining phenomena. Causal relations are usually more complex than learners understand. In order to be able to understand and apply causal relationships, learners must be able to articulate numerous covariational attributes of causal relationships, including direction, valency, probability, duration, responsiveness, as well as mechanistic attributes, including process, conjunctions/disjunctions, and necessity/sufficiency. We describe different methods for supporting causal learning, including influence diagrams, simulations, questions, and different causal modeling tools, including expert systems, systems dynamics tools, and causal modeling tools. Extensive research is needed to validate and contrast these methods for supporting causal reasoning.

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References

  • Ahn, W., & Kalish, C. W. (2000). The role of mechanism beliefs in causal reasoning. In F. C. Keil, & R. A. Wilson (Eds.), Explanation and cognition (pp. 199–225). Cambridge, MA: MIT Press.

    Google Scholar 

  • Ahn, W., Kalish, C. W., Medin, D. L., & Gelman, S. (1995). The role of covariation versus mechanism information in causal attribution. Cognition, 54, 299–352.

    Article  Google Scholar 

  • Amsel, E., Langer, R., & Loutzenhiser, L. (1991). Do lawyers reason differently from psychologists? A comparative design for studying expertise. In R. J. Sternberg, & P. A. Frensch (Eds.), Complex problem solving: Principles and mechanisms (pp. 223–250). Hillsdale, NJ, England: Lawrence Erlbaum Associates Inc.

    Google Scholar 

  • Anderson, J. (1990). The adaptive character of thought. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Biswas, G., Schwartz, D., Bransford, J. D., & The Teachable Agents Group at Vanderbilt (2001). Technology support foor complex problem solving: From SAD environment to AI. In K. D. Farbus, & P. J. Feltovoch (Eds.), Smart machines in education: The learning revolution in educational technology. Menlo Park, CA: AAAI/MIT Press.

  • Brewer, W. F., Chinn, C. A., & Samarapungavan, A. (2000). Explanation in scientists, children. In F. C. Keil, & R. A. Wilson (Eds.), Explanation and cognition (pp. 279–298). Cambridge, MA: MIT Press.

    Google Scholar 

  • Bullock, M., Gelman, R., & Baillargeon, R. (1982). The development of causal reasoning. In W. Friedman (Eds.), The developmental psychology of time (pp. 209–254). New York: Academic Press.

    Google Scholar 

  • Bunge, M. (1979). Causality and modern science (3rd ed.). New York: Dover Publications.

    Google Scholar 

  • Carey, S. (1995). On the origin of causal understanding. In D. Sperber, D. Premack, & A. J. Premack (Eds.), Causal cognition: A multidisciplinary debate (pp. 268–302). Oxford, England: Clarendon Press.

    Google Scholar 

  • Carey, S. (2002). The origin of concepts: Continuing the conversation. In N. L. Stein, P. J. Bauer, & M. Rabinowitz (Eds.), Representation, memory, and development: Essays in honor of Jean Mandler (pp. 43–52). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Cheng, P. W. (1997). From covariation to casation: A causal power theory. Psychological Review, 104(2), 367–405.

    Article  Google Scholar 

  • Cheng, P. W., & Nisbett, R. E. (1993). Pragmatic constraints on causal deduction. In R. E. Nisbett (Eds.), Rules for reasoning (pp. 207–227). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Cheng, P. W., & Novick, L. R. (1992). Covariation in natural causal induction. Psychological Review, 99(2), 365–382.

    Article  Google Scholar 

  • Corrigan, R., & Denton, P. (1996). Causal understanding as a developmental primitive. Developmental Review, 16, 162–202.

    Article  Google Scholar 

  • de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computers simulations of conceptual domains. Review of Educational Research, 68(2), 179–201.

    Google Scholar 

  • Fugelsang, J. A., & Thompson, V. A. (2003). A dual-process model of belief and evidence interactions in causal reasoning. Memory & Cognition, 31(5), 800–815.

    Google Scholar 

  • Glennan, S. (2002). Rethinking mechanistic explanation. Philosophy of Science, 69, S342–S353.

    Article  Google Scholar 

  • Graesser, A. C., Baggett, W., & Williams, K. (1996). Question-driven explanatory reasoning. Applied Cognitive Psychology, 10, S17–S31.

    Article  Google Scholar 

  • Graesser, A. C., Langston, M. C., & Lang, K. L. (1992). Designing educational software around questioning. Journal of Artificial Intelligence in Education, 3, 235–241.

    Google Scholar 

  • Graesser, A. C., Swamer, S. S., Baggett, W. B., & Sell, M. A. (1996). New models of deep comprehension. In B. K. Britton, & A. C. Graesser (Eds.), Models of understanding text. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Guenther, R. K. (1998). Human cognition. Upper Saddle River, NJ: Prentice Hall.

  • Hagmayer, Y., & Waldmann, M. R. (2002). How temporal assumptions influence causal judgments. Memory and Cognition, 30(7), 1128–1137.

    Google Scholar 

  • Hedstrom, P., & Swedberg, R. (Eds.). (1998). Social mechanisms: An analytical approach to social theory. Cambridge, MA: Cambridge University Press.

  • Hogan, K., & Thomas, D. (2001). Cognitive comparisons of students’ systems modeling in ecology. Journal of Science Education and Technology, 10(4), 319–345.

    Article  Google Scholar 

  • Howard, R. A., & Matheson, J. E. (1989). Influence diagrams. In R. A. Howard, & J. E. Matheson (Eds.), Readings on the principles and applications of decision analysis (pp. 721–762). Menlo Park, CA: Strategic Decisions Group.

    Google Scholar 

  • Hume, D. (1739/2000). A treatise of human nature. Oxford, UK: Oxford University Press.

  • Hung, W., & Jonassen, D. H. (2006). Conceptual understanding of causal reasoning in physics. International Journal of Science Education, 28(5), 1–21.

    Google Scholar 

  • Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide. San Francisco, CA: Pfeiffer/Jossey-Bass.

    Google Scholar 

  • Jonassen, D. H. (2006a). On the role of concepts in learning and instructional design. Educational Technology: Research and Development, 54(2), 177–196.

    Article  Google Scholar 

  • Jonassen, D. H. (2006b). Facilitating case reuse during problem solving. Technology, Instruction, Cognition, and Learning. 3, 51–62.

    Google Scholar 

  • Kant, I. (1996). Critique of pure reason. Indianapolis, IN: Hackett Publishing Company (Translated by Werner S. Pluhar; Original work published in 1781).

  • Keil, F. C. (1989). Concepts, kinds, and cognitive development. Cambridge, MA: MIT Press.

    Google Scholar 

  • Kelley, H. H. (1973). The process of causal attribution. American Psychologist, 28, 107–128.

    Article  Google Scholar 

  • Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koslowski, B., Okagaki, L., Lorenz, C., & Umbach, D. (1989). When covariation is not enough: The role of causal mechanism, sampling method, and sample size in causal reasoning. Child Development, 60, 1316–1327.

    Article  Google Scholar 

  • Kuhn, T. (1977). The essential tension. Chicago: University of Chicago Press.

    Google Scholar 

  • Kuhn, D. (2002). What is scientific reasoning and and how does it develop. In U. Goswami (Eds.), Handbook of childhood cognitive development (pp. 371–393). Oxford, UK: Blackwell.

    Google Scholar 

  • Kuhn, D., & Dean, D. (2004). Connecting scientific reasoning and causal inference. Journal of Cognition and Development, 5(2), 261–288.

    Article  Google Scholar 

  • Mahoney, J. (2001). Beyond correlational analysis: Recent innovations in theory and method. Sociological Forum, 16(3), 575–593.

    Article  Google Scholar 

  • Marini, M. M., & Singer, B. (1988). Causality in the social sciences. Sociological Methodology, 18, 347–409.

    Article  Google Scholar 

  • Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Printice-Hall.

    Google Scholar 

  • Patel, V. L., Arocha, J. F., & Zhang, J. (2005). Thinking and reasoning in medicine. In K. J. Holyoak, & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (Vol xiv, pp. 727–750, 858). New York, NY: Cambridge University Press.

  • Rapus, T. L. (2004). Integrating information about mechanism and covariation in causal reasoning. Dissertation Abstracts International, 65(2-B), 1047.

    Google Scholar 

  • Rehder, B. (2003). Categorization as causal reasoning. Cognitive Science, 27(5), 709–748.

    Article  Google Scholar 

  • Salmon, W. C. (1984). Scientific explanation and the causal structure of the world. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Salmon, W. C. (1989). Four decades of scientific explanation. In P. P. Kitcher, & W. C. Salmon, (Eds.), Scientific explanation: Minnesota studies in the philosophy of science (Vol. 13, pp. 3–219). Minneapolis, MN: University of Minnesota Press.

  • Schlottmann, A. (2001). Perception versus knowledge of cause and effect in children: When seeing is believing. Current Directions in Psychological Science, 10(4), 111–115.

    Article  Google Scholar 

  • Sembugmorthy, V., & Chandrasekeran, B. (1986). Functional representations of devices and compilation of diagnostic problem-solving systems. In J. Kolodner, & C. K. Riesbeck (Eds.), Experience, memory, and reasoning (pp. 47–53). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Shapiro, B. R., van den Broek, P., & Fletcher, C. R. (1995). Using story-based causal diagrams to analyze disagreements about complex events. Discourse Processes, 20, 51–77.

    Article  Google Scholar 

  • Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453–489.

    Article  Google Scholar 

  • Thagard, P. (2000a). Probabilistic networks and explanatory coherence. Cognitive Science Quarterly, 1(1), 91–114.

    Google Scholar 

  • Thagard, P. (2000b). Explaining disease: Correlations, causes, and mechanisms. In F. C. Keil, & R. A. Wilson (Eds.), Explanation and cognition (pp. 254–276). Cambridge, MA: MIT Press.

    Google Scholar 

  • Verschueren, N., Schroyens, W., & d’Ydewalle, G. (2004). The interpretation of the concepts ‘necessity’ and ‘sufficiency’ in forward unicausal relations. Current Psychology Letters: Behaviour, Brain and Cognition, 14(3), 1–28.

    Google Scholar 

  • Waldmann, M. R., & Hagmayer, Y. (2001). Estimating causal strength: The role of structural knowledge and processing effort. Cognition, 82(1), 27–58.

    Article  Google Scholar 

  • Waldmann, M. R., & Holyoak, K. J. (1992). Predictive and diagnostic learning within causal models: Asymmetries in cue competition In Proceedings of the twelfth annual cognitive science society (pp. 190–197). Hillsdale, NJ: Lawrnce Erlbaum Associates.

  • Waldmann, M. R., Holyoak, K. J., & Fratianne, A. (1995). Causal models and the acquisition of category structure. Journal of Experimental Psychology: General, 124, 181–206.

    Article  Google Scholar 

  • Wellman, H. M., & Gelman, S. A. (1998). Knowledge acquisition in foundational domains. In W. Damon, D. Kuhn, & R. S. Siegler (Eds.), Handbook of child psychology: Cognition, perception and language (pp. 523–573). New York: Wiley.

    Google Scholar 

  • White, P. A. (1989). A theory of causal processing. British Journal of Psychology, 80, 161–188.

    Google Scholar 

  • Yoon, W. C., & Hammer, J. M. (1988). Deep-reasoning fault diagnosis: An aid and a model. IEEE Transactions on Systems, Man, and Cybernetics, 18(4), 659–676.

    Article  Google Scholar 

  • Zimmerman, C. (2000). The development of scientific reasoning skills. Developmental Review, 20, 99–149.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Missouri, 221C Townsend Hall, Columbia, MO, 65211, USA

    David H. Jonassen

  2. University of Missouri, 111 London Hall, Columbia, MO, 65211, USA

    Ioan Gelu Ionas

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  1. David H. Jonassen
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  2. Ioan Gelu Ionas
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Correspondence to David H. Jonassen.

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Jonassen, D.H., Ionas, I.G. Designing effective supports for causal reasoning. Education Tech Research Dev 56, 287–308 (2008). https://doi.org/10.1007/s11423-006-9021-6

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Keywords

  • Causal reasoning
  • Causality
  • Instructional strategies