Cultivating Logical Problem-Solving in Undergraduate Thermodynamics through Structured Instruction#

Developing structured problem-solving skills is essential in engineering education, especially in thermodynamics, a foundational yet conceptually challenging course. This paper presents a five-step framework that guides students through identifying knowns and unknowns, constructing schematics, defining control volumes, applying models, and conducting analysis. The method emphasizes logical reasoning, unit consistency, and conceptual clarity, distinguishing it from traditional plug-and-chug approaches. Two instructional practices support this framework: (1) a rubric-aligned grading policy applied consistently across assessments, and (2) targeted scaffolding of difficult steps, particularly schematics and symbolic modeling. Although students initially express concern about the method’s time demands, feedback suggests increased clarity, confidence, and deeper understanding over time. Implementation challenges such as cognitive overload and resistance to process-heavy methods are addressed through in-class modeling and collaborative activities. Future work includes integrating digital tools, like interactive simulations and automated feedback systems, to further enhance engagement and structured reasoning. These strategies offer transferable insights for promoting analytical thinking across STEM disciplines.

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Publication Details

Published
2025-07-30
DOI
https://doi.org/10.1109/istem-ed65612.2025.11129360
Primary Topic
Experimental Learning in Engineering
Type
article
Field-Weighted Citation Impact
0.00

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article

Cultivating Logical Problem-Solving in Undergraduate Thermodynamics through Structured Instruction#

Huidan Yu
Experimental Learning in Engineering
article

Cultivating Logical Problem-Solving in Undergraduate Thermodynamics through Structured Instruction#

Huidan Yu
article en

Abstract

Developing structured problem-solving skills is essential in engineering education, especially in thermodynamics, a foundational yet conceptually challenging course. This paper presents a five-step framework that guides students through identifying knowns and unknowns, constructing schematics, defining control volumes, applying models, and conducting analysis. The method emphasizes logical reasoning, unit consistency, and conceptual clarity, distinguishing it from traditional plug-and-chug approaches. Two instructional practices support this framework: (1) a rubric-aligned grading policy applied consistently across assessments, and (2) targeted scaffolding of difficult steps, particularly schematics and symbolic modeling. Although students initially express concern about the method’s time demands, feedback suggests increased clarity, confidence, and deeper understanding over time. Implementation challenges such as cognitive overload and resistance to process-heavy methods are addressed through in-class modeling and collaborative activities. Future work includes integrating digital tools, like interactive simulations and automated feedback systems, to further enhance engagement and structured reasoning. These strategies offer transferable insights for promoting analytical thinking across STEM disciplines.

University of Indianapolis (US), Indiana University – Purdue University Indianapolis (US)
Purdue University
Openalex Percentile: Top 67%
Experimental Learning in Engineering
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