LAPSE:2026.0535v1
Published Article
LAPSE:2026.0535v1
Understanding Student's Preferences for Computational Tools in Chemical Engineering Assessment
June 12, 2026
Abstract
Computational tools are widely used in solving engineering problems and are now embedded within chemical engineering education. At the UCL Department of Chemical Engineering, students are taught gPROMS ModelBuilder in modules requiring coding; however, many choose alternative tools such as MATLAB, Python, or Polymath for coursework and capstone design project reactor design. This study investigates the reasons behind these preferences using a survey of fourth-year students who had completed their third-year design project. The results show that perceived ease of use, availability of external resources, and ease of debugging could strongly influence tool selection. The findings highlight the importance of accessibility, community support, and perceived relevance in shaping sustained student engagement with computational tools.
Computational tools are widely used in solving engineering problems and are now embedded within chemical engineering education. At the UCL Department of Chemical Engineering, students are taught gPROMS ModelBuilder in modules requiring coding; however, many choose alternative tools such as MATLAB, Python, or Polymath for coursework and capstone design project reactor design. This study investigates the reasons behind these preferences using a survey of fourth-year students who had completed their third-year design project. The results show that perceived ease of use, availability of external resources, and ease of debugging could strongly influence tool selection. The findings highlight the importance of accessibility, community support, and perceived relevance in shaping sustained student engagement with computational tools.
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Suggested Citation
Badmos S. Understanding Student's Preferences for Computational Tools in Chemical Engineering Assessment. Systems and Control Transactions 5:2646-2651 (2026) https://doi.org/10.69997/sct.109721
Author Affiliations
Badmos S: Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
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Journal Name
Systems and Control Transactions
Volume
5
First Page
2646
Last Page
2651
Year
2026
Publication Date
2026-06-12
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Original Submission
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PII: 2646-2651-132-SCT-5-2026, Publication Type: Journal Article
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LAPSE:2026.0535v1
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https://doi.org/10.69997/sct.109721
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References Cited
- Shacham M, Cutlip MB. Selecting the appropriate numerical software for a chemical engineering course. Computers & Chemical Engineering 23:S645-S648 (1999) https://doi.org/10.1016/s0098-1354(99)80158-6
- Edgar, T.F., Himmelblau, D.M., & Lasdon, L.S. (2006). Optimization of Chemical Processes (2nd ed.). McGraw-Hill.
- Udugama, I.A., Heintz, S., & Gernaey, K.V. (2023). Digitalisation in chemical engineering education: Status, challenges and future perspectives. Education for Chemical Engineers, 42, 1-16 https://doi.org/10.1016/j.ece.2022.10.003
- Brenner A, Shacham M, Cutlip MB. Applications of mathematical software packages for modelling and simulations in environmental engineering education. Environmental Modelling & Software 20:1307-1313 (2005) https://doi.org/10.1016/j.envsoft.2004.09.007
- Lee, K., et al., 2014. Survey: MATLAB & MathCAD education in biochemical engineering. Chem. Eng. Educ. 48 (1), 59.
- Richelle A, Bogaerts P. Systematic methodology for bioprocess model identification based on generalized kinetic functions. Biochemical Engineering Journal 100:41-49 (2015) https://doi.org/10.1016/j.bej.2015.04.003
- Talbert, R., 2012. Learning MATLAB in the Inverted Classroom. Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2-21640.
- Tang, C., 2021. Computer-aided linear algebra course on jupyter-python notebook for engineering undergraduates. J. Phys.: Conf. Ser. 1815, 012004-012009. https://doi. org/10.1088/1742-6596/1815/1/012004.
- Inguva P, Bhute VJ, Cheng TNH, Walker PJ. Introducing students to research codes: a short course on solving partial differential equations in python. Education for Chemical Engineers 36:1-11 (2021) https://doi.org/10.1016/j.ece.2021.01.011
- Lorandi Medina AP, Ortigoza Capetillo GM, Saba GH, Perez MAH, Garcia Ramirez PJ. A simple way to bring python to the classrooms. 2020 IEEE International Conference on Engineering Veracruz (ICEV) :1-6 (2020) https://doi.org/10.1109/icev50249.2020.9289692
- Hunter JD. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9:90-95 (2007) https://doi.org/10.1109/mcse.2007.55
- Lee Y, Cho J. The influence of python programming education for raising computational thinking. IJUNESST 10:59-72 (2017) https://doi.org/10.14257/ijunesst.2017.10.8.06
- Siemens Process Systems Engineering, 2022. gPROMS, https://www.psenterprise.com/products/gproms/process, 1997-2022.
- Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13:319-340 (1989) https://doi.org/10.2307/249008
- Institution of Chemical Engineers (2021). 'Accreditation of chemical engineering programmes - A guide for education providers and assessors.' Available at https://www.icheme.org/media/26729/university-accreditation-guide.pdf [last accessed: 20th March 2026]
- Kamel D, Tsatse A, Badmos S. Teaching computational tools in chemical engineering curriculum in preparation for the capstone design project. Systems and Control Transactions 4:2197-2202 (2025) https://doi.org/10.69997/sct.126494
- Biggs J. Enhancing teaching through constructive alignment. High Educ 32:347-364 (1996) https://doi.org/10.1007/bf00138871
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