Educational Psychology Review

, Volume 29, Issue 1, pp 97–104 | Cite as

Building from In Vivo Research to the Future of Research on Relational Thinking and Learning

  • Christian D. SchunnEmail author
Review Article


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.


STEM 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.


  1. Alexander, P. A. (2003). The development of expertise: the journey from acclimation to proficiency. Educational Researcher, 32(8), 10–14.CrossRefGoogle Scholar
  2. Alexander, P. A. (2016). Relational reasoning in stem domains: a foundation for academic development. Educational Psychology Review. doi: 10.1007/s10648-016-9383-1.
  3. Alexander, P. A., Dumas, D., Grossnickle, E. M., List, A., & Firetto, C. M. (2016). Measuring relational reasoning. Journal of Experimental Education, 84, 119–151. doi: 10.1080/00220973.2014.963216.CrossRefGoogle Scholar
  4. Apedoe, X. S., Reynolds, B., Ellefson, M. R., & Schunn, C. D. (2008). Bringing engineering design into high school science classrooms: the heating/cooling unit. Journal of Science Education and Technology, 17(5), 454–465. doi: 10.1007/S10956-008-9114-6.CrossRefGoogle Scholar
  5. Barsalou, L. W., Simmons, W. K., Barbey, A. K., & Wilson, C. D. (2003). Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences, 7(2), 84–91.CrossRefGoogle Scholar
  6. Bathgate, M., Crowell, A., Schunn, C. D., Cannady, M., & Dorph, R. (2015). The learning benefits of being willing and able to engage in scientific argumentation. International Journal of Science Education, 37(10), 1590–1612. doi: 10.1080/09500693.2015.1045958.CrossRefGoogle Scholar
  7. Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: the role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21(3), 399–432. doi: 10.1080/10508406.2011.651232.CrossRefGoogle Scholar
  8. Belenky, D. M., & Nokes-Malach, T. J. (2013). Knowledge transfer and mastery-approach goals: effects of structure and framing. Learning and Individual Differences, 25, 21–34. doi: 10.1016/j.lindif.2013.02.004.CrossRefGoogle Scholar
  9. Biederman, I. (1987). Recognition-by-components: a theory of human image understanding. Psychological Review, 94(2), 115–117.CrossRefGoogle Scholar
  10. Chan, J., & Schunn, C. D. (2015a). The impact of analogies on creative concept generation: lessons from an in vivo study in engineering design. Cognitive Science, 39(1), 126–155. doi: 10.1111/cogs.12127.CrossRefGoogle Scholar
  11. Chan, J., & Schunn, C. D. (2015b). The importance of iteration in creative conceptual combination. Cognition, 145, 104–115. doi: 10.1016/j.cognition.2015.08.008.CrossRefGoogle Scholar
  12. 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.
  13. Chan, J., Paletz, S. B. F., & Schunn, C. D. (2012). Analogy as a strategy for supporting complex problem solving under uncertainty. Memory & Cognitition, 40(8), 1352–1365. doi: 10.3758/s13421-012-0227-z.CrossRefGoogle Scholar
  14. Chan, J., Dow, S. P., & Schunn, C. D. (2015). Do the best design ideas (really) come from conceptually distant sources of inspiration? Design Studies, 36, 31–58. doi: 10.1016/j.destud.2014.08.001.CrossRefGoogle Scholar
  15. 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
  16. Christensen, B. T., & Schunn, C. D. (2007). The relationship of analogical distance to analogical function and preinventive structure: the case of engineering design. Memory & Cognition, 35(1), 29–38.CrossRefGoogle Scholar
  17. Christensen, B. T., & Schunn, C. D. (2009). The role and impact of mental simulation in design. Applied Cognitive Psychology, 23(3), 327–344. doi: 10.1002/acp.1464.CrossRefGoogle Scholar
  18. 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.
  19. Dumas, D. (2016). Relational reasoning in science, medicine, and engineering. Educational Psychology Review. doi: 10.1007/s10648-016-9370-6.
  20. 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
  21. Ellefson, M. R., Brinker, R. A., Vernacchio, V. J., & Schunn, C. D. (2008). Design-based learning for biology: genetic engineering experience improves understanding of gene expression. Biochemistry and Molecular Biology Education, 36(4), 292–298. doi: 10.1002/bmb.20203.CrossRefGoogle Scholar
  22. Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and Emotion, 30, 111–116.CrossRefGoogle Scholar
  23. Forbus, K. D., Gentner, D., & Law, K. (1995). MAC/FAC: a model of similarity-based retrieval. Cognitive Science, 19(2), 141–205.CrossRefGoogle Scholar
  24. Gibson, J. J. (1979). The ecological approach to visual perception. Boston: Houghton Mifflin.Google Scholar
  25. Goel, A. K. (1997). Design, analogy, and creativity. IEEE Expert, 12(3), 62–70.CrossRefGoogle Scholar
  26. Harackiewicz, J. M., Barron, K. E., Tauer, J. M., & Carter, S. M. (2000). Short-term and long-term consequences of achievement goals in college: predicting continued interest and performance over time. Journal of Educational Psychology, 92, 315–330.CrossRefGoogle Scholar
  27. 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
  28. Holyoak, K. J., & Thagard, P. (1995). Mental leaps: analogy in creative thought. Cambridge, MA: MIT Press.Google Scholar
  29. Hutchins, E. (1995). Cognition in the wild. Cambridge: MIT Press.Google Scholar
  30. Jang, J., & Schunn, C. D. (2014). A framework for unpacking cognitive benefits of distributed complex visual displays. Journal of Experimental Psychology. Applied, 20(3), 260–269. doi: 10.1037/xap0000022.CrossRefGoogle Scholar
  31. Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12, 3–11.CrossRefGoogle Scholar
  32. 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.
  33. 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
  34. Kosslyn, S. M., Ganis, G., & Thompson, W. L. (2001). Neural foundations of imagery. Nature Reviews Neuroscience, 2, 635–642.CrossRefGoogle Scholar
  35. Lemaire, P., & Siegler, R. S. (1995). Four aspects of strategic change: contributions to children’s learning of multiplication. Journal of Experimental Psychology: General, 124(1), 83–97.CrossRefGoogle Scholar
  36. 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.
  37. Mehalik, M. M., Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71–85.CrossRefGoogle Scholar
  38. Newell, A. (1994). Unified theories of cognition. Harvard University Press.Google Scholar
  39. Paletz, S. B. F., & Schunn, C. D. (2010). A social-cognitive framework of multidisciplinary team innovation. Topics in Cognitive Science, 2(1), 73–95. doi: 10.1111/j.1756-8765.2009.01029.x.CrossRefGoogle Scholar
  40. Paletz, S. B. F., Schunn, C. D., & Kim, K. H. (2011). Intragroup conflict under the microscope: micro-conflicts in naturalistic team discussions. Negotiation and Conflict Management Research, 4(4), 314–351. doi: 10.1111/J.1750-4716.2011.00085.X/Abstract.CrossRefGoogle Scholar
  41. Paletz, S. B. F., Kim, K. H., Schunn, C. D., Tollinger, I., & Vera, A. (2013a). Reuse and recycle: the development of adaptive expertise, routine expertise, and novelty in a large research team. Applied Cognitive Psychology, 27(4), 415–428. doi: 10.1002/Acp.2928.CrossRefGoogle Scholar
  42. Paletz, S. B. F., Schunn, C. D., & Kim, K. H. (2013b). The interplay of conflict and analogy in multidisciplinary teams. Cognition, 126(1), 1–19. doi: 10.1016/j.cognition.2012.07.020.CrossRefGoogle Scholar
  43. Paletz, S. B. F., Chan, J., & Schunn, C. D. (2016). Uncovering uncertainty through disagreement. Applied Cognitive Psychology, 30(3), 387–400.CrossRefGoogle Scholar
  44. Peffer, M. E., Beckler, M. L., Schunn, C. D., Renken, M., & Revak, A. (2015). Science classroom inquiry (SCI) simulations: a novel method to scaffold science learning. PloS One, 10(3), e0120638. doi: 10.1371/journal.pone.0120638.CrossRefGoogle Scholar
  45. Previc, F. H. (1998). The neuropsychology of 3-D space. Psychological Bulletin, 124(2), 123–164.CrossRefGoogle Scholar
  46. Purcell, A. T., & Gero, J. S. (1996). Design and other types of fixation. Design Studies, 17(4), 363–383.CrossRefGoogle Scholar
  47. 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.
  48. 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
  49. 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.
  50. Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1988). Parallel distributed processing (Vol. 1): IEEE.Google Scholar
  51. Schuchardt, A., & Schunn, C. D. (2016). Modeling scientific processes with mathematics equations enhances student qualitative conceptual understanding and quantitative problem solving. Science Education, 100(2), 290–320. doi: 10.1002/sce.21198.CrossRefGoogle Scholar
  52. Schunn, C. D., & Anderson, J. R. (1999). The generality/specificity of expertise in scientific reasoning. Cognitive Science, 23(3), 337–370. doi: 10.1207/S15516709cog2303_3.CrossRefGoogle Scholar
  53. 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
  54. Schunn, C. D., McGregor, M. U., & Saner, L. D. (2005). Expertise in ill-defined problem-solving domains as effective strategy use. Memory & Cognition, 33(8), 1377–1387.CrossRefGoogle Scholar
  55. 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
  56. 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
  57. Silk, E. M., Schunn, C. D., & Cary, M. S. (2009). The impact of an engineering design curriculum on science reasoning in an urban setting. Journal of Science Education and Technology, 18(3), 209–223. doi: 10.1007/S10956-009-9144-8.CrossRefGoogle Scholar
  58. Simon, H. A. (1977). Models of discovery: and other topics in the methods of science (Vol. 54): Springer Science & Business Media.Google Scholar
  59. Thagard, P. (2008). Hot thought: mechanisms and applications of emotional cognition. MIT Press.Google Scholar
  60. Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action. MIT Press.Google Scholar
  61. Trickett, S. B., Trafton, J. G., & Schunn, C. D. (2009). How do scientists respond to anomalies? Different strategies used in basic and applied science. Topics in Cognitive Science, 1(4), 711–729. doi: 10.1111/j.1756-8765.2009.01036.x.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of PittsburghPittsburghUSA

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