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2026 ASEE Annual Conference & Exposition

How Engineering Students Describe Good Teammates: A Thematic Analysis of Peer Evaluations Across Multiple Capstone Cohorts

Presented at Teamwork and Feedback

This full research paper analyzes 2,489 peer and self-evaluation responses from senior engineering capstone design courses across 14 evaluation waves and four cohort years (2019--2025) to examine how students evaluate teammate effectiveness in their own words. Using an inductive thematic analysis of open-ended peer and self-evaluation comments alongside descriptive summaries of productivity ratings, we identify eight recurring themes in student-authored evaluations of teammate performance. Contrary to assumptions common in engineering education research, students most frequently describe effective teammates in terms of concrete technical outputs rather than communication or collaboration process. Peer and self-evaluations diverge systematically, with students describing their own professional conduct far more prominently than peers do. Thematic structure was stable across all cohort years including the recent emergence of generative AI tools, with saturation reached early and sustained through 2025. These findings provide a
student-centered taxonomy of teammate behaviors, reveal gaps between student evaluative language and professional teamwork frameworks, and suggest ways to refine peer evaluation instruments and instructional practices to surface under-recognized competencies.

Authors

Dr. Tracy Anne Hammond http://orcid.org/https://0000-0001-7272-0507 Texas A&M University [biography]

Dr. Tracy Anne Hammond is a Professor of Computer Science and Engineering at Texas A&M University and Director of the Sketch Recognition Lab. She earned her Ph.D. in Electrical Engineering and Computer Science from the MIT Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology, along with additional degrees from Columbia University in computer science, mathematics, applied physics, and anthropology.

Her research focuses on artificial intelligence, sketch recognition, intelligent user interfaces, haptics, and engineering education, with applications in education, health, accessibility, and related domains. She contributes to research, teaching, and professional service in AI and engineering education, with an emphasis on responsible and inclusive design.
Jobin Varughese http://orcid.org/https://0009-0002-9322-4179 Texas A&M University [biography]

Jobin Varughese is a doctoral student in the College of Engineering at Texas A&M University, where he also serves as Senior Educational Data Analyst at the Center for Teaching Excellence. He holds an M.S. in Management Information Systems with an emphasis in Data Science from Oklahoma State University and a B.S. in Electronics and Communication Engineering from Christ University. His research draws on learning analytics, AI in education, and human-computer interaction to observe, build, and study systems that learn how people learn. In this work, he partners closely with faculty, instructional staff, and students — believing that the most meaningful scholarship emerges not from research alone, but from collaborations with the people closest to the learning experience.

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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