2026 ASEE Annual Conference & Exposition

A Systematic Review of Structural Equation Modeling Practices in Engineering Education

Presented at Educational Research and Methods Division (ERM) Poster Session

This method work-in-progress paper presents a systematic review of structural equation modeling (SEM) practices in engineering education research. SEM has become an increasingly prominent quantitative method for modeling complex relationships among latent constructs such as motivation, engagement, and self-efficacy. As engineering education researchers aim to test theoretically grounded models of learning and behavior, SEM offers a framework that integrates measurement and structural modeling. However, as SEM has become more accessible through software such as AMOS, Mplus, SmartPLS, and lavaan, concerns have emerged about methodological rigor, transparency, and adherence to best practices. Despite the growing number of SEM-based publications, no comprehensive methodological review currently exists describing how SEM is applied, validated, and reported in engineering education.

This ongoing review follows the PRISMA framework. Searches were conducted across six databases ERIC (via EBSCO), APA PsycINFO, Engineering Village, Web of Science, ScienceDirect, and Education Research Complete using combinations of the terms “Structural Equation Modeling,” “SEM,” “Path Analysis,” and “Latent Variable Modeling” with “Engineering Education.” The search retrieved 6,513 records. Following de-duplication, 1,316 unique studies remained and are currently undergoing eligibility screening based on predefined inclusion and exclusion criteria using Rayyan. Articles are being coded for SEM family (covariance-based vs. variance-based), estimation method, software used, sample adequacy, reliability and validity reporting, fit indices, missing data treatment, model modification rationale, and testing of measurement invariance. Coding is conducted independently by two reviewers to ensure inter-rater reliability.

Preliminary findings suggest that most SEM applications in engineering education rely on covariance-based modeling and cross-sectional survey data. While internal consistency reliability (e.g., Cronbach’s α) is frequently reported, evidence of construct validity (e.g., AVE, HTMT) and justification for estimation techniques are often lacking. Several studies report post-hoc model modifications without theoretical grounding, and few conduct multigroup or longitudinal invariance testing. These trends highlight the need for improved methodological transparency and reporting standards in SEM research within engineering education.

Upon completion, this review will provide a systematic synthesis of SEM practices in engineering education. Findings aim to inform reporting guidelines, methodological training, and quantitative research capacity-building among engineering education researchers.

Keywords: Structural Equation Modeling (SEM); Confirmatory Factor Analysis (CFA); Path Analysis; Measurement Invariance; Methodological Rigor; Engineering Education

Authors

VINCENT OLUWASETO FAKIYESI University of Georgia [biography]

Vincent Oluwaseto Fakiyesi is a doctoral student in the Engineering Education program at the University of Georgia. He holds a bachelor’s degree in Chemical Engineering and is interested in advancing assessment practices to better understand student learning. His research focuses on cognitive diagnostic modeling, student misconceptions, and adaptive assessment systems. He applies diagnostic models to analyze large-scale educational assessment data and generate actionable insights that support teaching, learning, and personalized feedback in engineering education. He also examines assessment practices in the era of generative artificial intelligence, emphasizing adaptive, interpretable, and learning-centered systems.
Dr. Nathaniel Hunsu The University of Georgia [biography]

Nathaniel Hunsu is an Associate Professor of Engineering Education at the University of Georgia, affiliated with the Engineering Education Transformational Institute (EETI) and the School of Electrical and Computer Engineering. He leads the Engineering Student Learning and Engagement Research (ESLER) group, mentoring students in projects that explore how engineering learners engage with tasks, technologies, and learning environments. His research focuses on conceptual understanding, epistemic cognition, and student engagement, with an emphasis on evidence-based strategies that enhance learning in engineering education. He applies quantitative research designs, systematic reviews, meta-analysis, and assessment instrument development to advance both the theory and practice of engineering education. Dr. Hunsu’s work also investigates the use of emerging educational technologies, including AI, XR, and robotics, in fostering active and self-regulated learning. His goal is to bridge research and practice, equipping educators and learners to achieve meaningful, lasting learning outcomes.
Dr. Olanrewaju Paul Olaogun Merrimack College [biography]

Dr. Olanrewaju Paul Olaogun is an Assistant Instructional Professor of Electrical and Computer Engineering in the Department of Mechanical and Electrical Engineering at Merrimack College. He holds a Ph.D. in Engineering from the University of Georgia, Athens, GA, USA. His research examines the interplay of cognitive, affective, and motivational factors to improve learning outcomes among engineering students. He uses quantitative methods to study learning models and seeks to apply theoretical insights to practical educational interventions. Additionally, he is interested in understanding the role of technology on engineering education, particularly how meaningful learning occurs within virtual reality environments. His research contributions to engineering education have been published in reputable academic journals and international conference proceedings.
ISAAC DAMILARE DUNMOYE University of Georgia [biography]

Isaac Dunmoye
PhD in Engineering (in view), University of Georgia, USA,
M.Sc. in Electrical Engineering, University of Cape Town, South Africa, 2022.
B.Eng. in Agricultural and Biosystems Engineering, University of Ilorin, Nigeria, 2016.

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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