2026 ASEE Annual Conference & Exposition

Improving Students’ Learning Experiences in Undergraduate Engineering Programs

Presented at Electrical and Computer Engineering Division (ECE) Poster Session

The increasing demand for computing and other STEM professionals in the United States highlights the need for innovative instructional approaches that improve student learning, retention, and diversity. Despite strong labor market growth and high earning potential in computing-related fields, participation among women, underrepresented minorities, and first-generation college students remains disproportionately low. At the same time, traditional lecture-centered teaching methods often fail to align with rapidly evolving technologies and students’ interests in hands-on, real-world problem solving.

This study examines the impact of contextualized, student-centered instruction integrated with maker-centered learning in undergraduate computer science and engineering courses at two university campuses. Guided by the How People Learn (HPL) framework, the project redesigned courses to incorporate active learning, collaborative design projects, maker space training, and participation in Mini Maker Faire events. To explore the effect of the instruction on students’ learning outcomes, we designed a demographic questionnaire and a student exit survey, and utilized an existing Life Long Learning (LLL) Scale. The demographic questionnaire and the student exit survey were administered once in a semester at the same time. LLL scale was administered two times, once early in the semester and later towards the completion of the semester. Students’ responses to the pre and post LLL Scale were matched and their gain scores were computed. Students’ LLL gain scores, their demographic characteristics including gender, ethnicity, and first generation college student status, and their responses to the student exit questionnaires were analyzed.

Results suggest that integrating evidence-based pedagogy with maker culture can enhance students’ learning experiences and foster motivation and problem-solving skills. These findings support the continued use of contextualized, student-centered approaches to broaden participation and strengthen persistence in computing-related STEM disciplines.

Authors

Dr. Yonghui Wang http://orcid.org/https://my-orcid?orcid=0000-0003-4334-0921 Prairie View A&M University [biography]

Dr. Yonghui Wang received his B.S. in Optoelectronics from Xidian University in 1993, his M.S. in electrical engineering from Beijing Polytechnic University in 1999; and his Ph.D. in computer engineering from Mississippi State University in 2003.
Dr. Suxia Cui http://orcid.org/0000-0003-1152-3272 Prairie View A&M University [biography]

Dr. Suxia Cui is a professor in the Department of Electrical and Computer Engineering at Prairie View A&M University (PVAMU). She received her B.S. and M.S. degrees in Electrical Engineering from Beijing University of Technology, Beijing, China. She received her Ph.D. degree in Computer Engineering from Mississippi State University, Starkville, MS.
Her research focuses on machine learning, computer vision, cybersecurity, and computing education. She applied AI algorithms for image super-resolution. The project covers a wide range of applications, and the datasets include historical films, remote sensing data, bio-medical data, and underwater image. Her recent sabbatical experience and summer research at Airforce Research Lab extended her research on how to apply AI into cybersecurity. Specifically, her research is focused on how to apply reinforcement learning and other algorithms to mitigate co-resident risks in data cloud. Dr. Cui’s work is supported by various prestigious agencies, including the National Science Foundation, U.S. Department of Agriculture, Department of Defense, and Department of Education. Additionally, Dr. Cui serves as an ACCESS campus champion, actively advocating for high-performance computing research and education at PVAMU. She also leads semiconductor workforce development initiatives at PVAMU through external grants, promoting computer hardware and VLSI research and education. Dr. Cui is a senior member of IEEE and a member of HKN, ACM, ASEE, and SPIE.
Dr. Wei Zhan http://orcid.org/0000-0002-9956-1910 Texas A&M University [biography]

Dr. Wei Zhan is a Professor of Electronic Systems Engineering Technology and Associate Department Head for Undergraduate Studies at Texas A&M University. Dr. Zhan earned his D.Sc. in Systems Science and Mathematics from Washington University in St. Louis in 1991. From 1991 to 1995, he worked at University of California, San Diego and Wayne State University as a postdoctoral researcher and visiting assistant professor, respectively. From 1995 to 2006, he worked in the automotive industry as a system engineer. In 2006 he joined the Electronic Systems Engineering Technology faculty at Texas A&M. Dr. Zhan holds a joint appointment position in the department of Electrical and Computer Engineering at Texas A&M University. He is an American Society for Quality certified Six Sigma Black Belt and an ASQ Fellow. His research activities include control system theory and applications to industry, fault prognosis, system engineering, robust design, modeling, simulation, quality control, and optimization.
Dr. Bugrahan Yalvac http://orcid.org/https://0000-0002-3675-1936 Texas A&M University

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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