Balancing theory and computational tools in undergraduate engineering education
DOI:
https://doi.org/10.32674/aga4kx82Keywords:
engineering education, computational tools, theoretical understanding, engineering judgement, hydraulic engineering, water resources engineeringAbstract
The growing use of computational tools in engineering education creates opportunities for applied learning but also raises concerns that software proficiency may be prioritized over theoretical understanding. This qualitative descriptive study examined how undergraduate students in water resources and hydraulic engineering courses demonstrated theoretical understanding, theory-tool integration, and engineering judgment across laboratory assignments. De-identified submissions collected with institutional review board approval were analyzed using a three-level coding framework. Results showed stronger performance in structured and software supported activities, although successful tool use did not always reflect strong conceptual understanding. Engineering judgment was most evident when assignments explicitly required interpretation, feasibility assessment, and justification. The findings support laboratory designs that connect analytical reasoning, computational tools, verification, and engineering decision making.
References
Álvarez Ariza, J., & Hernández Hernández, C. (2026). Investigating the impact of Education 4.0 and digital learning on students’ learning outcomes in engineering: A four-year multiple-case study. Informatics, 13(2), Article 18. https://doi.org/10.3390/informatics13020018
Bezzina, A., & Zammit, J. P. (2026). A comprehensive framework for designing metaverse-based learning for engineering education. Multimodal Technologies and Interaction, 10(7), Article 74. https://doi.org/10.3390/mti10070074
Doulougeri, K., Vermunt, J. D., Bombaerts, G., & Bots, M. (2024). Challenge-based learning implementation in engineering education: A systematic literature review. Journal of Engineering Education, 113(4), 1076–1106. https://doi.org/10.1002/jee.20588
Feijoo-Garcia, M. A., Zhang, Y., Gu, Y., Magana, A. J., Benes, B., & Popescu, V. (2025). Students’ conceptual explanations of neural networks enabled by extended reality learning: A multiple methods approach. Computer Applications in Engineering Education, 33(6), e70084. https://doi.org/10.1002/cae.70084
Feisel, L. D., & Peterson, G. D. (2002). A colloquy on learning objectives for engineering education laboratories. Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition, Montreal, Canada, 7.20.1–7.20.12. https://doi.org/10.18260/1-2--11246
Feisel, L. D., & Rosa, A. J. (2005). The role of the laboratory in undergraduate engineering education. Journal of Engineering Education, 94(1), 121–130. https://doi.org/10.1002/j.2168-9830.2005.tb00833.x
Giuliano, H. G., Giri, L. A., Nicchi, F. G., Weyerstall, W. M., Ferreira Aicardi, L. F., Parselis, M., & Vasen, F. (2022). Critical thinking and judgment on engineer’s work: Its integration in engineering education. Engineering Studies, 14(1), 6–16. https://doi.org/10.1080/19378629.2022.2042003
Glancy, A. W., & Moore, T. J. (2013). Theoretical foundations for effective STEM learning environments (School of Engineering Education Working Papers, Paper 1). Purdue University. https://docs.lib.purdue.edu/enewp/1/
Hidayat, H., Zulhendra, Z., Efrizon, E., Delianti, V. I., Dewi, F. K., Mohd Isa, M. R., Harmanto, D., & Tanucan, J. C. M. (2024). Computational thinking skills in engineering education: Enhancing academic achievement through innovations, challenges, and opportunities. TEM Journal, 13(4), 3454–3467. https://doi.org/10.18421/TEM134-78
Lampropoulos, G., Fernández-Arias, P., de Bosque, A., & Vergara, D. (2025). Virtual reality in engineering education: A scoping review. Education Sciences, 15(8), Article 1027. https://doi.org/10.3390/educsci15081027
Lavado-Anguera, S., Velasco-Quintana, P.-J., & Terrón-López, M.-J. (2024). Project-based learning (PBL) as an experiential pedagogical methodology in engineering education: A review of the literature. Education Sciences, 14(6), Article 617. https://doi.org/10.3390/educsci14060617
Li, R., Lund, A., & Nordsteien, A. (2023). The link between flipped and active learning: A scoping review. Teaching in Higher Education, 28(8), 1993–2027. https://doi.org/10.1080/13562517.2021.1943655
Litzinger, T. A., Lattuca, L. R., Hadgraft, R. G., Newstetter, W. C., Alley, M., Atman, C., DiBiasio, D., Finelli, C., Diefes-Dux, H., Kolmos, A., Riley, D., Sheppard, S., Weimer, M., & Yasuhara, K. (2011). Engineering education and the development of expertise. Journal of Engineering Education, 100(1), 123–150. https://doi.org/10.1002/j.2168-9830.2011.tb00006.x
Luo, J., Zheng, C., Yin, J., & Teo, H. H. (2025). Design and assessment of AI-based learning tools in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 22, 42. https://doi.org/10.1186/s41239-025-00540-2
Lyon, J. A., & Magana, A. J. (2021). The use of engineering model-building activities to elicit computational thinking: A design-based research study. Journal of Engineering Education, 110(1), 184–206. https://doi.org/10.1002/jee.20372
Magana, A. J., & Silva Coutinho, G. (2017). Modeling and simulation practices for a computational thinking-enabled engineering workforce. Computer Applications in Engineering Education, 25(1), 62–78. https://doi.org/10.1002/cae.21779
Moore, T. J., Miller, R. L., Lesh, R. A., Stohlmann, M. S., & Kim, Y. R. (2013). Modeling in engineering: The role of representational fluency in students’ conceptual understanding. Journal of Engineering Education, 102(1), 141–178. https://doi.org/10.1002/jee.20004
Pande, P., & Jepsen, P. M. (2025). Science lab safety goes immersive: An ecological media-comparison study with gender analyses assessing iVR’s learning effectiveness. Research and Practice in Technology Enhanced Learning, 20, Article 1. https://doi.org/10.58459/rptel.2025.20001
Ravan, K., & Huang, S. (2024). Assessing computational thinking in engineering education: A systematic review. Proceedings of the Canadian Engineering Education Association. https://doi.org/10.24908/pceea.2024.18632
Sheppard, S. D., Macatangay, K., Colby, A., & Sullivan, W. M. (2009). Educating engineers: Designing for the future of the field. Jossey-Bass.
Tenney, K., Stringer, B. P., LaTona-Tequida, T., & White, I. (2023). Conceptualizations and limitations of STEM literacy across learning theories. Journal of Microbiology & Biology Education, 24(1), e00168-22. https://doi.org/10.1128/jmbe.00168-22
Vieira, C., Magana, A. J., Roy, A., & Falk, M. L. (2019). Student explanations in the context of computational science and engineering education. Cognition and Instruction, 37(2), 201–231. https://doi.org/10.1080/07370008.2018.1539738
Zhang, J. (2026). Scaffolding probabilistic reasoning in civil engineering education: Integrating AI tutoring with simulation-based learning. Education Sciences, 16(1), Article 103. https://doi.org/10.3390/educsci16010103
Zhou, Z., Oveissi, F., & Langrish, T. (2024). Applications of augmented reality (AR) in chemical engineering education: Virtual laboratory work demonstration to digital twin development. Computers & Chemical Engineering, 188, Article 108784. https://doi.org/10.1016/j.compchemeng.2024.108784
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Dr. Thathsarani D.H. Herath Mudiyanselage

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) [year] [author]
This work is licensed under a Creative Commons Attribution 4.0 International License.
This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0
Call for Special Issue Proposals 






