Leveraging Cognitive Load Theory to Enhance Problem Solving Skills in Python Programming for Core Engineering Disciplines

Authors

  • Pankaj Beldar Mechanical Engineering Department, K. K. Wagh Institute of Engineering Education and Research, Savitribai Phule Pune University, Maharashtra, India

DOI:

https://doi.org/10.16920/jeet/2026/v39i4/26111

Keywords:

Problem Solving, Python, CLT, Cognitive Level

Abstract

Python programming is becoming essential across engineering disciplines such as mechanical, civil, electrical, and chemical engineering due to its applications in automation, data analysis, and problem-solving. However, students from non-software backgrounds often face challenges related to abstract thinking, debugging, and applying programming concepts to domain-specific problems. This study presents a case study-based instructional intervention grounded in Cognitive Load Theory (CLT) to enhance problem-solving skills in Python programming. The study was conducted on a sample of 485 undergraduate engineering students from mechanical (144), electrical (132), chemical (67), and civil (142) disciplines. A structured instructional design incorporating intrinsic load management, extraneous load reduction, and germane load enhancement was implemented across five units. The assessment approach included unit-wise performance evaluation, problem-solving skill analysis, and student feedback using quantitative and qualitative measures. Results indicate a significant improvement in student performance, with average scores increasing from 45.36% in Unit 1 to 85.79% in Unit 5. Similarly, problem-solving skills improved from 50.36% to 80.85% across the course. Student feedback also reflected high satisfaction levels, with over 85% positive responses toward CLT-based instructional strategies. The findings demonstrate that CLT-driven instructional design effectively enhances Python learning and problem-solving abilities among non-software engineering students, making complex programming concepts more accessible and applicable to real-world engineering problems.

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Published

2026-04-30

How to Cite

Beldar, P. (2026). Leveraging Cognitive Load Theory to Enhance Problem Solving Skills in Python Programming for Core Engineering Disciplines. Journal of Engineering Education Transformations, 39(4), 99–113. https://doi.org/10.16920/jeet/2026/v39i4/26111

Issue

Volume 39, Issue 4, April 2026

Section

Articles
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