Concept Maps Decrease Students’ Neurocognitive Demand When Thinking about Engineering Problems
Publication: Construction Research Congress 2022
ABSTRACT
The research presented in this paper explores the effect of concept maps on students’ neurocognition when constructing engineering problem statements. In total, 66 engineering students participated in the experiment. Half of the students were asked to create a concept map illustrating all of the systems and stakeholders represented in a building on campus. The other half of students were not asked to draw a concept map. Both groups were then asked to construct an engineering problem statement about improvements to the building. While performing the problem statement task, their neurocognitive activation in their prefrontal cortex (PFC) was measured using a non-intrusive neuroimaging technique called functional near-infrared spectroscopy. The students that were asked to complete the concept mapping task required less cognitive effort to formulate and analyze their problem statements. The specific regions that were less activated were regions of the brain generally associated with working memory and problem evaluation. These results provide new insight into the changes in mental processing that occurs when using tools like concept maps and may provide helpful techniques for students to structure engineering problems.
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REFERENCES
Asimow, M. (1962). Introduction to design. Englewood Cliffs, N.J., Prentice-Hall.
Aziz-Zadeh, L., Kaplan, J. T., and Iacoboni, M. (2009). “Aha!”: The neural correlates of verbal insight solutions. Human Brain Mapping, 30(3), 908–916. https://doi.org/10.1002/hbm.20554.
Aziz-Zadeh, L., Liew, S.-L., and Dandekar, F. (2013). Exploring the neural correlates of visual creativity. Social Cognitive and Affective Neuroscience, 8(4), 475–480. https://doi.org/10.1093/scan/nss021.
Beamish, T. D., and Biggart, N. W. (2012). The role of social heuristics in project-centred production networks: Insights from the commercial construction industry. Engineering Project Organization Journal, 2(1–2), 57–70.
Bunce, S. C., Izzetoglu, K., Ayaz, H., Shewokis, P., Izzetoglu, M., Pourrezaei, K., and Onaral, B. (2011). Implementation of fNIRS for Monitoring Levels of Expertise and Mental Workload. In D. D. Schmorrow and C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition. Directing the Future of Adaptive Systems (pp. 13–22). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-21852-1_2.
Cieslik, E. C., Zilles, K., Caspers, S., Roski, C., Kellermann, T. S., Jakobs, O., Langner, R., Laird, A. R., Fox, P. T., and Eickhoff, S. B. (2013). Is There “One” DLPFC in Cognitive Action Control? Evidence for Heterogeneity From Co-Activation-Based Parcellation. Cerebral Cortex (New York, NY), 23(11), 2677–2689. https://doi.org/10.1093/cercor/bhs256.
Dalton, R. C., Hölscher, C., and Spiers, H. J. (2015). Navigating Complex Buildings: Cognition, Neuroscience and Architectural Design. In J. S. Gero (Ed.), Studying Visual and Spatial Reasoning for Design Creativity (pp. 3–22). Springer Netherlands. https://doi.org/10.1007/978-94-017-9297-4_1.
Dorst, K. (2011). The core of ‘design thinking’ and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006.
Dorst, K., and Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6.
Ellis, G. W., Rudnitsky, A., and Silverstein, B. (2004). Using concept maps to enhance understanding in engineering education. International Journal of Engineering Education, 20(6), 1012–1021.
Erban, L. E., and Walker, H. A. (2019). Beyond Old Pipes and Ailing Budgets: Systems Thinking on Twenty-First Century Water Infrastructure in Chicago. Frontiers in Built Environment, 5. https://doi.org/10.3389/fbuil.2019.00124.
Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., Neuper, C., Ebner, F., and Neubauer, A. C. (2009). The creative brain: Investigation of brain activity during creative problem solving by means of EEG and FMRI. Human Brain Mapping, 30(3), 734–748. https://doi.org/10.1002/hbm.20538.
Gabora, L. (2010). Revenge of the “Neurds”: Characterizing creative thought in terms of the structure and dynamics of memory. Creativity Research Journal, 22(1), 1–13. https://doi.org/10.1080/10400410903579494.
Gero, J. S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–36. https://doi.org/10.1609/aimag.v11i4.854.
Glimcher, P. W., and Fehr, E. (2013). Neuroeconomics: Decision Making and the Brain. Academic Press.
Goel, V. (2014). Creative brains: Designing in the real world. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00241.
Goel, V., and Dolan, R. (2004). Differential involvement of left prefrontal cortexin inductive and deductive reasoning. Cognition, 93(3), B109–B121. https://doi.org/10.1016/j.cognition.2004.03.001.
Goel, V., and Grafman, J. (2000). Role of the right prefrontal cortex in ill-structured planning. Cognitive Neuropsychology, 17(5), 415–436. https://doi.org/10.1080/026432900410775.
Goel, V., and Vartanian, O. (2005). Dissociating the roles of right ventral lateral and dorsal lateral prefrontal cortex in generation and maintenance of hypotheses in set-shift problems. Cerebral Cortex, 15(8), 1170–1177. https://doi.org/10.1093/cercor/bhh217.
Goldschmidt, G. (2016). Linkographic evidence for concurrent divergent and convergent thinking in creative design. Creativity Research Journal, 28(2), 115–122. https://doi.org/10.1080/10400419.2016.1162497.
Grohs, J., Shealy, T., Maczka, D., Hu, M., Panneton, R., and Yang, X. (2017). Evaluating the potential of fNIRS neuroimaging to study engineering problem solving and design. 2017 ASEE Annual Conference & Exposition Proceedings, 28305. https://doi.org/10.18260/1-2--28305.
Hay, L., Duffy, A. H. B., McTeague, C., Pidgeon, L. M., Vuletic, T., and Grealy, M. (2017a). A systematic review of protocol studies on conceptual design cognition: Design as search and exploration. Design Science, 3. https://doi.org/10.1017/dsj.2017.11.
Hay, L., Duffy, A. H. B., McTeague, C., Pidgeon, L. M., Vuletic, T., and Grealy, M. (2017b). Towards a shared ontology: A generic classification of cognitive processes in conceptual design. Design Science, 3. https://doi.org/10.1017/dsj.2017.6.
Henson, R. N. A. (2003). Neuroimaging studies of priming. Progress in Neurobiology, 70(1), 53–81. https://doi.org/10.1016/S0301-0082(03)00086-8.
Hu, M., and Shealy, T. (2018a, June 24). Methods for Measuring Systems Thinking: Differences Between Student Self-assessment, Concept Map Scores, and Cortical Activation During Tasks About Sustainability. ASEE, Salt Lake City, UT. https://www.asee.org/public/conferences/106/papers/22718/view.
Hu, M., and Shealy, T. (2019). Application of Functional Near-Infrared Spectroscopy to Measure Engineering Decision-Making and Design Cognition: Literature Review and Synthesis of Methods. Journal of Computing in Civil Engineering, 33(6), 04019034. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000848.
Hu, M., and Shealy, T. (2018b). Methods for Measuring Systems Thinking: Differences Between Student Self-assessment, Concept Map Scores, and Cortical Activation During Tasks About Sustainability. 2018 ASEE Annual Conference & Exposition Proceedings, 30807. https://doi.org/10.18260/1-2--30807.
Hu, M., Shealy, T., Grohs, J., and Panneton, R. (2019). Empirical evidence that concept mapping reduces neurocognitive effort during concept generation for sustainability. Journal of Cleaner Production, 238, 117815. https://doi.org/10.1016/j.jclepro.2019.117815.
Maani, K. E., and Maharaj, V. (2004). Links between systems thinking and complex decision making. System Dynamics Review, 20(1), 21–48. https://doi.org/10.1002/sdr.281.
Maher, M. L., and Poon, J. (1996). Modeling design exploration as co-evolution. Computer-Aided Civil and Infrastructure Engineering, 11(3), 195–209. https://doi.org/10.1111/j.1467-8667.1996.tb00323.x.
May, M. E. (2006). Elegant Solutions: Breakthrough Thinking the Toyota Way. http://changethis.com/manifesto/29.01.ElegantSolutions/pdf/29.01.ElegantSolutions.pdf.
Naseer, N., and Hong, K.-S. (2015). Corrigendum “fNIRS-based brain-computer interfaces: A review.” Frontiers in Human Neuroscience, 9. https://doi.org/10.3389/fnhum.2015.00172.
Novak, J. (1998). Learning, Creating, and Using Knowledge: Concept Maps as Facilitative Tools in Schools and Corporations/J.D. Novak. Journal of E-Learning and Knowledge Society, 6. https://doi.org/10.4324/9780203862001.
Novak, J. D., and Cañas, A. J. (2006). The Theory Underlying Concept Maps and How to Construct and Use Them (p. 36). Florida Institute for Human and Machine Cognition.
O’Donnell, A. M., Dansereau, D. F., and Hall, R. H. (2002). Knowledge Maps as Scaffolds for Cognitive Processing. Educational Psychology Review, 14(1), 71–86. https://doi.org/10.1023/A:1013132527007.
Pochon, J. B., Levy, R., Fossati, P., Lehericy, S., Poline, J. B., Pillon, B., Le Bihan, D., and Dubois, B. (2002). The neural system that bridges reward and cognition in humans: An fMRI study. Proceedings of the National Academy of Sciences, 99(8), 5669–5674. https://doi.org/10.1073/pnas.082111099.
Rosen, D. S., Erickson, B., Kim, Y. E., Mirman, D., Hamilton, R. H., and Kounios, J. (2016). Anodal tDCS to Right Dorsolateral Prefrontal Cortex Facilitates Performance for Novice Jazz Improvisers but Hinders Experts. Frontiers in Human Neuroscience, 10. https://doi.org/10.3389/fnhum.2016.00579.
Santosa, H., Aarabi, A., Perlman, S. B., and Huppert, T. J. (2017). Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy. Journal of Biomedical Optics, 22(5), 55002. https://doi.org/10.1117/1.JBO.22.5.055002.
Sato, T., Hokari, H., and Wade, Y. (2011). Independent component analysis technique to remove skin blood flow artifacts in functional near-infrared spectroscopy signals. Annual Conference of the Japanese Neural Network Society. http://jnns.org/conference/misc/camera_ready/P3-04.pdf.
Schön, D. (1983). The reflective practitioner: How professionals think in action. Temple Smith.
Schön, D. A., and Wiggins, G. (1992). Kinds of seeing and their functions in designing. Design Studies, 13(2), 135–156.
Shealy, T., Gero, J., Hu, M., and Milovanovic, J. (2020). Concept generation techniques change patterns of brain activation during engineering design. Design Science, 6, e31. https://doi.org/10.1017/dsj.2020.30.
Shealy, T., and Klotz, L. (2014). Encouraging Elegant Solutions by Applying Choice Architecture to Infrastructure Project Delivery. 574–583. https://doi.org/10.1061/9780784413517.059.
Sowden, P. T., Pringle, A., and Gabora, L. (2015). The shifting sands of creative thinking: Connections to dual-process theory. Thinking & Reasoning, 21(1), 40–60. https://doi.org/10.1080/13546783.2014.885464.
Tak, S., and Ye, J. C. (2014). Statistical analysis of fNIRS data: A comprehensive review. NeuroImage, 85, 72–91. https://doi.org/10.1016/j.neuroimage.2013.06.016.
Turns, J., Atman, C. J., and Adams, R. (2000a). Concept maps for engineering education: A cognitively motivated tool supporting varied assessment functions. IEEE Transactions on Education, 43(2), 164–173. https://doi.org/10.1109/13.848069.
Turns, J., Atman, C. J., and Adams, R. (2000b). Concept maps for engineering education: A cognitively motivated tool supporting varied assessment functions. IEEE Transactions on Education, 43(2), 164–173. https://doi.org/10.1109/13.848069.
Watson, M. K., Pelkey, J., Noyes, C. R., and Rodgers, M. O. (2016). Assessing Conceptual Knowledge Using Three Concept Map Scoring Methods. Journal of Engineering Education, 105(1), 118–146. https://doi.org/10.1002/jee.20111.
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Construction Research Congress 2022
Pages: 244 - 253
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Published online: Mar 7, 2022
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