2026 ASEE Annual Conference & Exposition

WIP: Reflection Trends in Sequential First-Year Engineering Courses

Presented at First-Year Programs Division (FPD) Poster Session

WIP: This Work in Progress Paper explores how reflection trends vary across two sequential first-year engineering (FYE) courses at a large Midwest Public University. While prior studies found association between reflective activities in undergraduate STEM classrooms and learning outcomes, investigating reflection trends between sequential FYE courses remains a relatively underexplored topic. In this study, we analyzed student reflections on what they found confusing and interesting from two sequential FYE courses, referred to as ENGR 101 (N=80) and ENGR 102 (N=68). We used a mobile application to prompt students to reflect on what they found confusing and interesting after every lecture during two academic semesters. We conducted text analysis to uncover and analyze emerging patterns in student reflections. We created an initial codebook using Chi and VanLehn’s physics self-explanation framework and modified our codebook along the coding process. Two coders conducted independent coding on a subset of reflections and calculated inter-rater reliability using Cohen’s κ. After establishing reliability, the validated codebook was applied to a larger reflection dataset using human coding alongside large language model (LLM) coding. We quantified code frequencies and compared their distributions across courses and reflection types.
Our preliminary findings indicate that ENGR 101 reflections exhibit a more balanced mix of conceptual and procedural content, with frequent mentions of statistics and Excel. In contrast, ENGR 102 reflections are dominated by technical procedures related to MATLAB programming and file management. Confusing reflections highlight conceptual hurdles in ENGR 101 and procedural challenges in ENGR 102, supporting prior observations that programming courses elicit more procedural confusion. These patterns suggest that reflection content systematically differs between sequential FYE courses, offering insight into how instructors may better scaffold the transition from statistical reasoning to computational problem-solving.

Authors

Mr. Alfa Satya Putra http://orcid.org/0000-0003-0540-5819 Purdue University – West Lafayette (College of Engineering) [biography]

Alfa Satya Putra is a PhD candidate at School of Engineering Education at Purdue University. He has Bachelor's and Master's degree in Electrical and Computer Engineering from Purdue University. Before joining the PhD program, Alfa has served as a lecturer in Indonesia. Alfa is mainly interested in investigating the implementation of reflective activities in large classrooms and assessing how reflective activities affect student learning and academic performance.
Dr. Muhsin Menekse Purdue University – West Lafayette (College of Engineering) [biography]

Muhsin Menekse is an Associate Professor at Purdue University with a joint appointment in the School of Engineering Education and the Department of Curriculum & Instruction. Dr. Menekse's primary research focuses on exploring K-16 students' engagement and learning of engineering and science concepts by creating innovative instructional resources and conducting interdisciplinary quasi-experimental research studies in and out of classroom environments. Dr. Menekse is the recipient of the 2014 William Elgin Wickenden Award by the American Society for Engineering Education. He is also selected as an NSF SIARM fellow for the advanced research methods for STEM education research. Dr. Menekse received four Seed-for-Success Awards (in 2017, 2018, 2019, and 2021) from Purdue University's Excellence in Research Awards programs in recognition of obtaining four external grants of $1 million or more during each year. His research has been generously funded by grants from the Institute of Education Sciences (IES), the U.S. Department of Defense (DoD), Purdue Research Foundation (PRF), and the National Science Foundation (NSF).
Muyang Wu Southern Methodist University (Department of Computer Science & Engineering) [biography]

Muyang Wu is a PhD student in Computer Science at Southern Methodist University. Her research focuses on human-computer interaction and AI in education.

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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