2026 ASEE Annual Conference & Exposition

Exploring Mechanical Engineering Student Degree Completion Outcomes at an HSI using Machine Learning and Counterfactuals

Presented at Persistence, Completion, and their Factors

This full research paper examines mechanical engineering student graduation outcomes at a Hispanic-Serving Institution (HSI) using machine learning and counterfactual analysis. Prior studies indicate that a substantial proportion of students leave engineering programs before completing their degrees, motivating ongoing efforts to improve persistence and graduation success. Because the majority of Latine students who earn STEM degrees do so at HSIs, these institutions represent critical contexts for understanding student success and identifying opportunities to improve graduation outcomes.

Results indicate that cumulative measures of academic progress, particularly the number of courses passed, are the strongest predictors of graduation but offer limited actionable insight. When this variable is removed, performance in Statics emerges as the most influential course-level predictor of engineering degree completion. Counterfactual analysis further demonstrates how hypothetical improvements in Statics performance alter predicted graduation outcomes.

Authors

Mr. Henry Salgado http://orcid.org/https://0009-0001-6506-5484 University of Texas at El Paso [biography]

Henry is a Ph.D. candidate (ABD) in Computer Science whose research focuses on explainable AI, particularly through modeling approaches such as causal inference. His work explores how machine learning models can be made more interpretable and scientifically useful. He also studies the science of science, with interests in faculty roles in education, student persistence in engineering, and the impact of institutional contexts on these outcomes.
Dr. Meagan R. Kendall http://orcid.org/0000-0002-9940-4405 University of Texas at El Paso [biography]

An Associate Professor at The University of Texas at El Paso, Dr. Meagan R. Kendall is a founding member of the Department of Engineering Education and Leadership. With a background in both engineering education and design thinking, her research focuses on how Latinx students develop identities as engineers and navigate moments of identity interference, student and faculty engineering leadership development through the Contextual Engineering Leadership Development framework, and promoting student motivation. Dr. Kendall is the Past Chair of the Engineering Leadership Development Division of ASEE.
Dr. Martine Ceberio University of Texas at El Paso [biography]

Dr. Ceberio is a professor of Computer Science at the University of Texas at El Paso, currently serving as Associate Dean of Engineering for People, Culture, and Environment. She joined UTEP in 2003 after obtaining her PhD in Computer Science from the University of Nantes, France (2003). Her research revolves around reliable decision-making under uncertainty, optimization, constraint solving, solving dynamical systems, and studying neural networks with uncertainty. Her passion lies in developing reliable tools to generate reliable information about otherwise uncertain situations. She is passionate about teaching and mentoring students. She is a fierce advocate of her students’ success and seizes every opportunity to provide advice and mentoring. At the University of Texas at El Paso, she is the co-lead of the Student Experience Project, which currently provides, within a community of practice, bringing together about 50 faculty and 15 graduates teaching assistants, and has so far reached over 4,500 students. As Associate Dean of Engineering, she leads a mentoring program for new faculty, which includes sessions on teaching. Outside UTEP, she mentors teachers of the local community and gets to share her passion beyond through contributed and invited talks about her teaching approach.
Dr. Angel Flores Abad University of Texas at El Paso [biography]

Dr. Angel Flores-Abad is an associate professor in the Aerospace and Mechanical Engineering Department at the University of Texas at El Paso. His areas of interest include robotics, autonomy, and workforce development for those domains. He is committed to mentoring and guiding students to successfully and on time complete their degrees and achieve a high level of readiness for the job market. Dr. Flores-Abad is a member of the AIAA Space Automation and Robotics Committee.

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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