Building from In Vivo Research to the Future of Research on Relational Thinking and Learning
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This concluding commentary takes the perspective of research on practicing scientists and engineers to consider what open areas and future directions on relational thinking and learning should be considered beyond the impressive research presented in the special issue. Areas for more work include (a) a need to examine educational applications of relational thinking in divergent reasoning, rather than primarily in convergent reasoning; (b) considerations of when to not focus on relational reasoning in learning; (c) more research on the distributed nature of relational reasoning across students in a class, and to embedded physical, social, and historical contexts; (d) treatment of the hot components of relational reasoning including motivational and emotional processes; and (e) more attention to how relational reasoning is changed by the details of modalities rather than treating all contents as abstract symbols.
KeywordsSTEM learning Relational thinking Analogy Science Design
Compliance with Ethical Standards
This work was funded by grant DUE-1524575 from the National Science Foundation.
Conflict of Interest
The author declares that he has no conflict of interest.
- Alexander, P. A. (2016). Relational reasoning in stem domains: a foundation for academic development. Educational Psychology Review. doi: 10.1007/s10648-016-9383-1.
- Chan, J., Fu, K., Schunn, C. D., Cagan, J., Wood, K., & Kotovsky, K. (2011). On the benefits and pitfalls of analogies for innovative design: ideation performance based on analogical distance, commonness, and modality of examples. Journal of Mechanical Design, 133(8). doi: 10.1115/1.4004396.
- Chinn, C. A., & Brewer, W. F. (1992). Psychological responses to anomalous data. In Paper presented at the 14th Annual Meeting of the Cognitive Science Society. Bloomington: IN.Google Scholar
- Danielson, R. W., & Sinatra, G. M. (2016). A relational reasoning approach to text-graphic processing. Educational Psychology Review. doi: 10.1007/s10648-016-9374-2.
- Dumas, D. (2016). Relational reasoning in science, medicine, and engineering. Educational Psychology Review. doi: 10.1007/s10648-016-9370-6.
- Dunbar, K. (1995). How scientists really reason: scientific reasoning in real-world laboratories. In R. J. Sternberg & J. E. Davidson (Eds.), The nature of insight (pp. 365–395). Cambridge, MA: MIT Press.Google Scholar
- Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.Google Scholar
- Harrison, A. M., & Schunn, C. D. (2002). ACT-R/S: a computational and neurologically inspired model of spatial reasoning. In Paper presented at the 24th Annual Meeting of the Cognitive Science Society. Fairfax: VA.Google Scholar
- Holyoak, K. J., & Thagard, P. (1995). Mental leaps: analogy in creative thought. Cambridge, MA: MIT Press.Google Scholar
- Hutchins, E. (1995). Cognition in the wild. Cambridge: MIT Press.Google Scholar
- Kendeou, P., Butterfuss, R., Van Boekel, M., & O’Brien, E. J. (2016). Integrating relational reasoning and knowledge revision during reading. Educational Psychology Review. doi: 10.1007/s10648-016-9381-3.
- Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: putting Learning by Design™ into practice. Journal of the Learning Sciences, 12(4), 495–547. doi: 10.1207/S15327809JLS1204_2.CrossRefGoogle Scholar
- Linsey, J. S., Tseng, I., Fu, K., Cagan, J., Wood, K. L., & Schunn, C. D. (2010). A study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design, 132(4). doi: 10.1115/1.4001110.
- Newell, A. (1994). Unified theories of cognition. Harvard University Press.Google Scholar
- Resnick, I., Davatzes, A., Newcombe, N. S., & Shipley, T. F. (2016). Using relational reasoning to learn about scientific phenomena at unfamiliar scales. Educational Psychology Review. doi: 10.1007/s10648-016-9371-5.
- Reynolds, B., Mehalik, M. M., Lovell, M. R., & Schunn, C. D. (2009). Increasing student awareness of and interest in engineering as a career option through design-based learning. International Journal of Engineering Education, 25(4), 788–798.Google Scholar
- Richland, L. E., Begolli, J. N., Simms, N., Frausel, R. R., & Lyons, E. (2016). Supporting mathematical discussions: the roles of comparison and cognitive load. Educational Psychology Review. doi: 10.1007/s10648-016-9382-2.
- Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1988). Parallel distributed processing (Vol. 1): IEEE.Google Scholar
- Schunn, C. D., & Trafton, J. G. (2012). The psychology of uncertainty in scientific data analysis. In G. Feist & M. Gorman (Eds.), Handbook in the psychology of science. New York: Springer.Google Scholar
- Schunn, C. D., Saner, L. D., Kirschenbaum, S. K., Trafton, J. G., & Littleton, E. B. (2007). Complex visual data analysis, uncertainty, and representation. In M. C. Lovett & P. Shah (Eds.), Thinking with data. Mahwah, NJ: Erlbaum.Google Scholar
- Schunn, C. D., Silk, E. M., & Apedoe, X. S. (2012). Engineering in/&/or/for science education. In J. Shrager, S. Carver, & K. Dunbar (Eds.), From child to scientist. Washington, DC: APA Press.Google Scholar
- Simon, H. A. (1977). Models of discovery: and other topics in the methods of science (Vol. 54): Springer Science & Business Media.Google Scholar
- Thagard, P. (2008). Hot thought: mechanisms and applications of emotional cognition. MIT Press.Google Scholar
- Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action. MIT Press.Google Scholar