Leveraging Python and Cloud Computing to Innovate Mathematical Modeling Instruction in Engineering Education

Source abstract

Traditional mathematical modeling courses for engineering undergraduates, often reliant on tools like MATLAB, prioritize procedural skills over conceptual understanding, creating a gap between theory and modern engineering practice. This study evaluated a reformed curriculum that integrated Python and cloud computing within a project-based learning (PBL) framework, focusing on engineering-specific computational modeling tasks. We assessed its impact on both mathematical modeling proficiency and conceptual understanding compared to a traditional MATLAB-based approach. A quasi-experimental study was conducted with 146 engineering students (Mechanical, Electrical, and Civil). The experimental group (n=72) experienced the reformed curriculum, while the control group (n=74) followed the traditional syllabus. Data from pre/post tests and final projects were analyzed using Analysis of Covariance (ANCOVA) and t-tests. The experimental group showed significantly greater improvement in post-test scores (F(1, 143) = 24.75, p < .001, η₂² = 0.15) and higher final project scores (t(144) = 5.12, p < .001, d = 0.85). A fine-grained analysis confirmed superior conceptual understanding of engineering-relevant mathematical principles among experimental students. The reform successfully bridges the theory-practice gap by using computational tools to make mathematics tangible for engineering problem-solving. This study provides an empirically validated model for enhancing conceptual insight and technical proficiency in engineering education.