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Analyzing the Curricular Complexity of Engineering Programs Across Disciplines and Time
Journal article   Peer reviewed

Analyzing the Curricular Complexity of Engineering Programs Across Disciplines and Time

David Reeping, Hossein Ebrahiminejad, Matthew W. Ohland, Kenneth J. Reid and Nahal Rashedi
IEEE transactions on education, v 69(2), pp 131-140
Apr 2026
DOI: https://doi.org/10.1109/TE.2026.3658085

Abstract

Codes Complexity theory Curricular analytics Data collection Databases Delays Descriptive statistics Engineering students multiple-institution database for investigating engineering longitudinal development (MIDFIELD) Solids Computer Science Mechanical Engineering
Contribution: This article describes an effort to quantitatively characterize the "complexity" of engineering programs across the United States using a burgeoning framework called curricular analytics. A new dataset is introduced to facilitate these analyses for researchers and practitioners, tied to an existing data-sharing agreement. Several avenues for future research using this dataset are outlined, such as connecting curricular data with student course-taking data. Background: As curriculum development and evaluation adopt a more data-driven approach to understanding how to best retain and graduate engineers, curricular analytics offers the engineering education community a method for uncovering the complexities of curricula that may be deterring students from the field. Curricular analytics involves representing program requirements as a network to assign a measure of complexity, called "structural complexity," which has been empirically shown to correlate with completion rates. Research question: How does the complexity of engineering programs vary across institutions, disciplines, and time? Methodology: The research question was approached using a quantitative research design. The sampling frame consisted of the multiple-institution database for investigating engineering longitudinal development (MIDFIELD), a data-sharing agreement encompassing 21 institutions (and still expanding). Plans of study were collected from 13 institutions for five disciplines-Mechanical, Electrical, Chemical, Industrial, and Civil-spanning a decade since their most recent record in MIDFIELD ( n = 494). Then, network analysis was applied to calculate the structural complexity of the programs. The results were explored using descriptive statistics, boxplots, and plotting the complexities longitudinally. Findings: Across the years 2012-2022, a range of 307-372 was observed for structural complexity, with a mean of 325 and a median of 323. In the sample, Chemical Engineering was found to be the most structurally complex discipline, followed by Mechanical Engineering. The remaining disciplines were more tightly clustered together. Over time, chemical engineering and civil engineering marginally increased in complexity by 4%, whereas electrical and mechanical engineering decreased by 2% and 0.7%, respectively. Industrial engineering exhibited the most significant decrease of 11%.

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