2026 ASEE Annual Conference & Exposition

Rethinking “Traditional Homework” in Thermodynamics: A Study on Optional Models and Their Impact on Student Success

Presented at Mechanical Engineering (MECH) Session 1: Thermodynamics and Heat Transfer Pedagogy

This ongoing study examines the impact of replacing traditional mandatory homework with an optional homework model supplemented by structured mini-projects in an undergraduate Thermodynamics course. Building upon prior research that questioned the effectiveness of graded homework in fostering meaningful learning, this study investigates whether autonomy—through optional homework and authentic, application-based projects—can enhance engagement, motivation, and conceptual understanding without compromising academic performance.
Two course sections are being compared during Spring 2025: a control group following traditional graded homework and a focus group using optional homework aligned with two discipline-specific mini-projects. Homework participation is monitored anonymously through Canvas analytics to measure voluntary engagement. Quantitative data, including exam and quiz scores and project grades, will be analyzed using descriptive statistics, t-tests, and ANOVA to detect performance differences.
Qualitative analysis will incorporate anonymous Likert-scale surveys and open-ended questions assessing motivation, workload perception, and perceived learning gains. Follow-up focus groups will explore student decision-making processes and attitudes toward optional assignments. Thematic coding using NVivo will identify key trends such as motivation, self-regulation, and learning behaviors. Baseline data (GPA and prior math/physics grades) are also collected to ensure section comparability.
Compared with hiring or relying heavily on teaching assistants for homework support, this model aims to reduce grading workload while fostering self-directed learning. Students receive targeted feedback through project-based mentoring rather than repetitive homework grading. Early classroom observations suggest that optional homework—when supported by structured mini-projects and feedback—can preserve rigor, improve time management, and enhance ownership of learning.
Findings from this study will inform evidence-based approaches to balancing flexibility and academic integrity in engineering education and provide practical guidance for implementing scalable, low-overhead assessment models in large technical courses.

Authors

Dr. Zahra Pournorouz Stevens Institute of Technology (School of Engineering and Science) [biography]

“Zahra Pournorouz received her Bachelor of Science degree in Aerospace Engineering from Amirkabir University of Technology (Tehran Polytechnic) in Tehran in the Fall of 2014. After finishing her bachelor’s studies, she got admitted directly to the Ph.D. program in Mechanical Engineering at the University of Texas at Arlington and graduated in August 2018.
Her research interests mainly focus on oil-based nanofluids and enhancing the thermophysical properties of synthetic oils. This was the first demonstration of the work ever done in this field and resulted in broad environmental and cost benefits, especially in energy storage and heat transfer applications. She has more than three years of experience teaching thermofluidic, mechanical design, and solid and structure courses and supervising senior capstone projects collaborating with industries such as Saint-Gobain, Klein Tools, and Parker. She also has served in leadership roles at the Society of Women Engineers and STEM advisory task force to represent diversity and inclusion and improve student success and retention for underrepresented students.”
Youmna Mahmoud http://orcid.org/0000-0002-2368-9950 Stevens Institute of Technology (School of Engineering and Science) [biography]

Youmna Mahmoud is a dedicated Mechanical Engineering Ph.D. candidate at Stevens Institute of Technology, with a strong passion for teaching and enhancing the student learning experience. She has been actively involved in teaching thermodynamics for several years, serving as a TA and once as a lecturer. Mahmoud is particularly invested in improving students’ understanding by fostering an empathetic approach to teaching, and creating a supportive learning environment. She values patience and clear communication as key tools to improve student outcomes. Outside of teaching, Mahmoud’s research focuses on Additive Manufacturing, where she leverages sensory data, thermal modeling, and machine learning to optimize the mechanical performance of fabricated parts.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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