Abstract

Problem solving is a common activity for engineering students and practicing engineers as they learn and practice the use of engineering concepts. Understanding the approach to a problem provides a glimpse at unique problem-solving behaviors that can be used as a means to compare different problem solvers. Engineering education research has focused on particular behaviors to compare problem solvers and problem types, which has led to a greater understanding of the gap in student preparedness for the workplace and advances in better teaching practices. This study further explores the similarities and differences in problem-solving behaviors of engineering practitioners and students as they solve problems with multiple representations. An exploratory study was done using eye-tracking techniques to observe the problem-solving behaviors of engineering practitioners and engineering students as they solve three problems with four equivalent representations as means to solve the problems. Problem-solving behaviors were compared and explored using descriptive statistics to understand unique similarities and differences and search for patterns across multiple problems. The results show that engineering practitioners appear to be more consistent, efficient, and rigid in their approach and that students are more likely to adjust their problem-solving approach and use different representations. No observable trends in time spent solving the problems or problem correctness with respect to a particular representation were observed, which suggests that there is not one representation that is better than another. Students used formulaic representations more often than visual representations, whereas engineering practitioners had no observable use based on representation typology. Trends related to problem correctness across the three problems were observed for both students and engineering practitioners, and students began exploring additional representations as they solved more problems. These similarities and differences suggest that there is more to learn about the problem-solving behaviors of students and engineering practitioners. Understanding more about these behaviors can assist in understanding the gap in student preparedness for the workplace and help educators in further developing better teaching practices.

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Data Availability Statement

All data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. This includes data (SPSS analysis and results, Excel tables, and Excel graphs). We do not have approval from the participants to share the data based on the approved IRB protocol, so data could only be shared if we were able to acquire consent from the participants.

Acknowledgments

This material is based on work supported by the National Science Foundation under Grant No. 1463769. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

References

Abadi, M. G., S. L. Gestson, S. Brown, and D. Hurwitz. 2019. “Traffic signal phasing problem-solving rationales of professional engineers developed from eye-tracking and clinical interviews.” Transp. Res. Rec. 2673 (4): 685–696. https://doi.org/10.1177/0361198119837506.
Ahmed, A., D. Hurwitz, S. Gestson, and S. Brown. 2021. “Differences between professionals and students in their visual attention on multiple representation types while solving an open-ended engineering design problem.” J. Civ. Eng. Educ. 147 (3): 04021005. https://doi.org/10.1061/(ASCE)EI.2643-9115.0000044.
Atman, C. J., R. S. Adams, M. E. Cardella, J. Turns, S. Mosborg, and J. Saleem. 2007. “Engineering design processes: A comparison of students and expert practitioners.” J. Eng. Educ. 96 (4): 359–379. https://doi.org/10.1002/j.2168-9830.2007.tb00945.x.
Aurigemma, J., S. Chandrasekharan, N. J. Nersessian, and W. Newstetter. 2013. “Turning experiments into objects: The cognitive processes involved in the design of a lab-on-a-chip device.” J. Eng. Educ. 102 (1): 117–140. https://doi.org/10.1002/jee.20003.
Berg, B. L., and H. Lune. 2001. Qualitative research methods for the social sciences: Pearson new international edition. Upper Saddle River, NJ: Pearson.
Biernacki, P., and D. Waldorf. 1981. “Snowball sampling: Problems and techniques of chain referral sampling.” Sociol. Methods Res. 10 (2): 141–163. https://doi.org/10.1177/004912418101000205.
Bolden, D., P. Barmby, S. Raine, and M. Gardner. 2015. “How young children view mathematical representations: A study using eye-tracking technology.” Educ. Res. 57 (1): 59–79. https://doi.org/10.1080/00131881.2014.983718.
Chi, M. T. H., P. J. Feltovich, and R. Glaser. 1981. “Categorization and representation of physics problems by experts and novices.” Cogn. Sci. 5 (2): 121–152. https://doi.org/10.1207/s15516709cog0502_2.
Cirstea, M. 2003. “Problem-based learning (PBL) in microelectronics.” Int. J. Eng. Educ. 19 (5): 738–741.
Cook, M., E. N. Wiebe, and G. Carter. 2008. “The influence of prior knowledge on viewing and interpreting graphics with macroscopic and molecular representations.” Sci. Educ. 92 (5): 848–867. https://doi.org/10.1002/sce.20262.
Downey, G. L. 2009. “What is engineering studies for? Dominant practices and scalable scholarship.” Eng. Stud. 1 (1): 55–76. https://doi.org/10.1080/19378620902786499.
Elby, A. 2000. “What students’ learning of representations tells us about constructivism.” J. Math. Behav. 19 (4): 481–502. https://doi.org/10.1016/S0732-3123(01)00054-2.
Felder, R. M. 2012. “Engineering education: A tale of two paradigms.” In Proc., Int. Conf. Shaking the Foundations of Geo-Engineering Education, edited by B. McCabe, M. Pantazidou, and D. Phillips. Leiden, Netherlands: CRC Press/Balkema.
Ferk, V., M. Vrtacnik, and A. Blejec. 2003. “Student’s understanding of molecular structure representations.” Int. J. Sci. Educ. 25 (10): 1227–1245. https://doi.org/10.1080/0950069022000038231.
Gestson, S. L., M. S. Barner, M. G. Abadi, D. S. Hurwitz, and S. A. Brown. 2019a. Problem solving personas of civil engineering practitioners using eye tracking techniques. Dublin, Ireland: Tempus Publications.
Gestson, S. L., S. A. Brown, M. S. Barner, M. G. Abadi, and D. S. Hurwitz. 2019b. “Factors contributing to the problem-solving heuristics of civil engineering students.” In Proc., ASEE Annual Conf. and Exposition. Washington, DC: American Society of Engineering Education.
Gestson, S. L., B. D. Lutz, S. A. Brown, M. S. Barner, D. S. Hurwitz, and M. G. Abadi. 2018. “Developing an understanding of civil engineering practitioner problem-solving rationale using multiple contextual representations.” In Proc., ASEE Annual Conf. and Exposition. Washington, DC: American Society of Engineering Education.
Hamilton, E., R. Lesh, F. Lester, and M. Brilleslyper. 2008. “Model-eliciting activities (MEAs) as a bridge between engineering education research and mathematics department of mathematical sciences.” Adv. Eng. Educ. 1 (2): 1–25.
Hegarty, M., R. E. Mayer, and C. A. Monk. 1995. “Comprehension of arithmetic word problems: A comparison of successful and unsuccessful problem solvers.” J. Educ. Psychol. 87 (1): 18–32. https://doi.org/10.1037/0022-0663.87.1.18.
Hill, M., and M. D. Sharma. 2015. “Students’ representational fluency at university: A cross-sectional measure of how multiple representations are used by physics students using the representational fluency survey.” Eurasian J. Math. Sci. Technol. Educ. 11 (6): 1633–1655. https://doi.org/10.12973/eurasia.2015.1427a.
Hurwitz, D., S. Brown, M. Islam, K. Daratha, and M. Kyte. 2014. “Traffic signal system misconceptions across three cohorts.” Transp. Res. Rec. 2414 (1): 52–62. https://doi.org/10.3141/2414-07.
Jarodzka, H., K. Scheiter, P. Gerjets, and T. Van Gog. 2010. “In the eyes of the beholder: How experts and novices interpret dynamic stimuli.” Learn. Instr. 20 (2): 146–154. https://doi.org/10.1016/j.learninstruc.2009.02.019.
Johnson, P. A. 1999. “Problem-based, cooperative learning in the engineering classroom.” J. Civ. Eng. Educ. 125 (1): 8–11. https://doi.org/10.1061/(ASCE)1052-3928(1999)125:1(8).
Johri, A., W. M. Roth, and B. M. Olds. 2013. “The role of representations in engineering practices: Taking a turn towards inscriptions.” J. Eng. Educ. 102 (1): 2–19. https://doi.org/10.1002/jee.20005.
Jonassen, D., J. Strobel, and C. B. Lee. 2006. “Everyday problem solving in engineering: Lessons for engineering educators.” J. Eng. Educ. 95 (2): 139–151. https://doi.org/10.1002/j.2168-9830.2006.tb00885.x.
Just, M. A., and P. A. Carpenter. 1980. “A theory of reading: From eye fixations to comprehension.” Psychol. Rev. 87 (4): 329–354. https://doi.org/10.1037/0033-295X.87.4.329.
Kalyuga, S. 2007. “Enhancing instructional efficiency of interactive e-learning environments: A cognitive load perspective.” Educ. Psychol. Rev. 19 (3): 387–399. https://doi.org/10.1007/s10648-007-9051-6.
Kim, S., V. Aleven, and A. K. Dey. 2014. “Understanding expert-novice differences in geometry problem-solving tasks.” In Proc., Extended Abstracts of the 32nd Annual ACM Conf. on Human Factors in Computing Systems–CHI EA ’14, 1867–1872. New York: ACM SIGCHI Special Interest Group on Computer-Human Interactions.
Kindfield, A. C. H. 1994. “Biology diagrams: Tools to think with.” J. Learn. Sci. 3 (1): 1–36. https://doi.org/10.1207/s15327809jls0301_1.
Kohl, P. B., and N. D. Finkelstein. 2008. “Patterns of multiple representation use by experts and novices during physics problem solving.” Phys. Rev. Spec. Top. Phys. Educ. Res. 4 (1): 1–13. https://doi.org/10.1103/PhysRevSTPER.4.010111.
Kozma, R., E. Chin, J. Russell, and N. Marx. 2000. “The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning.” J. Learn. Sci. 9 (2): 105–143. https://doi.org/10.1207/s15327809jls0902_1.
Kozma, R. B., and J. Russell. 1997. “Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena.” J. Res. Sci. Teach. 34 (9): 949–968. https://doi.org/10.1002/(SICI)1098-2736(199711)34:9%3C949::AID-TEA7%3E3.0.CO;2-U.
Lee, C. S., N. J. Mcneill, E. P. Douglas, M. E. Koro-Ljungberg, and D. J. Therriault. 2013. “Indispensable resource? A phenomenological study of textbook use in engineering problem solving.” J. Eng. Educ. 102 (2): 269–288. https://doi.org/10.1002/jee.20011.
Lorenzo, M. 2005. “The development, implementation, and evaluation of a problem solving heuristic.” Int. J. Sci. Math. Educ. 3 (1): 33–58. https://doi.org/10.1007/s10763-004-8359-7.
McIntyre, C. 2002. “Problem-based learning as applied to the construction and engineering capstone course at North Dakota State university.” In Proc., Frontiers in Education Conf. New York: IEEE.
Mohd, C. K. N. C. K., and F. Shahbodin. 2015. “Personalized learning environment: Alpha testing, beta testing & user acceptance test.” Proc. Soc. Behav. Sci. 195 (Jun): 837–843. https://doi.org/10.1016/j.sbspro.2015.06.319.
Moore, T. J., R. L. Miller, R. A. Lesh, M. S. Stohlmann, and Y. R. Kim. 2013. “Modeling in engineering: The role of representational fluency in students’ conceptual understanding.” J. Eng. Educ. 102 (1): 141–178. https://doi.org/10.1002/jee.20004.
Pande, P., and S. Chandrasekharan. 2014. “Eye-tracking in STEM education research: Limitations, experiences and possible extensions.” In Proc., IEEE 6th Int. Conf. on Technology for Education, T4E 2014, 116–119. New York: IEEE.
Pande, P., and S. Chandrasekharan. 2017. “Representational competence: Towards a distributed and embodied cognition account.” Stud. Sci. Educ. 53 (1): 1–43. https://doi.org/10.1080/03057267.2017.1248627.
Patrick, M. D., G. Carter, and E. N. Wiebe. 2005. “Visual representations of DNA replication: Middle grades students’ perceptions and interpretations.” J. Sci. Educ. Technol. 14 (3): 353–365. https://doi.org/10.1007/s10956-005-7200-6.
Prince, M. J., and R. M. Felder. 2006. “Inductive teaching and learning methods: Definitions, comparisons, and research bases.” J. Eng. Educ. 95 (2): 123–138. https://doi.org/10.1002/j.2168-9830.2006.tb00884.x.
Rosengrant, D., C. Thomson, and T. Mzoughi. 2009. “Comparing experts and novices in solving electrical circuit problems with the help of eye-tracking.” AIP Conf. Proc. 1179 (1): 249–252. https://doi.org/10.1063/1.3266728.
Schoenfeld, A. H. 1985. “Metacognitive and epistemological issues in mathematical understanding.” In Teaching and learning mathematical problem solving: Multiple research perspectives. New York: Routledge.
Schoenfeld, A. H. 1992. “Learning to think mathematically: Sense-making in mathematics.” In Handbook for research on mathematics teaching and learning, 334–370. New York: Macmillan Publishing Company.
Simon, H. 1978. “On the forms of mental representation.” In Minnesota studies in the philosophy of science. Minneapolis, MN: University of Minnesota Press.
Stieff, M., M. Hegarty, and G. Deslongchamps. 2011. “Identifying representational competence with multi-representational displays.” Cognit. Instr. 29 (1): 123–145. https://doi.org/10.1080/07370008.2010.507318.
Stieff, M., S. Scopelitis, M. E. Lira, and D. Desutter. 2016. “Improving representational competence with concrete models.” Sci. Educ. 100 (2): 344–363. https://doi.org/10.1002/sce.21203.
Stull, A. T., and M. Hegarty. 2016. “Model manipulation and learning: Fostering representational competence with virtual and concrete models.” J. Educ. Psychol. 108 (4): 509–527. https://doi.org/10.1037/edu0000077.
Sweller, J. 1988. “Cognitive load during problem solving.” Cognit. Process. 12 (2): 257–285. https://doi.org/10.1207/s15516709cog1202_4.
Tien, T., P. H. Pucher, M. H. Sodergren, K. Sriskandarajah, G.-Z. Yang, and A. Darzi. 2014. Eye tracking for skills assessment and training: A systematic review. Amsterdam, Netherlands: Elsevier.
Urlacher, M. A., S. A. Brown, P. S. Steif, and F. Bornasal. 2015. “Practicing civil engineers’ understanding of statics concept inventory questions.” In Proc., ASEE Annual Conf. and Exposition. Washington, DC: American Society of Engineering Education.
Venters, C., and L. Mcnair. 2010. “Learning statics: A cognitive approach.” In Proc., ASEE Southeast Section Conf., 1–10. Washington, DC: American Society of Engineering Education.

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Go to Journal of Civil Engineering Education
Journal of Civil Engineering Education
Volume 150Issue 1January 2024

History

Received: Aug 14, 2022
Accepted: May 22, 2023
Published online: Aug 25, 2023
Published in print: Jan 1, 2024
Discussion open until: Jan 25, 2024

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Assistant Professor, Shiley School of Engineering, Univ. of Portland, Portland, OR 97203 (corresponding author) ORCID: https://orcid.org/0000-0002-4796-1410. Email: [email protected]
Professor, School of Civil and Construction Engineering, Oregon State Univ., Corvallis, OR 97331. ORCID: https://orcid.org/0000-0003-3669-8407. Email: [email protected]
Ananna Ahmed [email protected]
Graduate Research Assistant, School of Civil and Construction Engineering, Oregon State Univ., Corvallis, OR 97331. Email: [email protected]
Professor, School of Civil and Construction Engineering, Oregon State Univ., Corvallis, OR 97331. ORCID: https://orcid.org/0000-0001-8450-6516. Email: [email protected]
Assistant Professor, The Hal and Inge Marcus School of Engineering, Saint Martin’s Univ., Lacey, WA 98503. ORCID: https://orcid.org/0000-0003-3951-2321. Email: [email protected]
Postdoctoral Scholar, School of Civil and Construction Engineering, Oregon State Univ., Corvallis, OR 97331. ORCID: https://orcid.org/0000-0002-4052-2423. Email: [email protected]

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