2026 ASEE Annual Conference & Exposition

This empirical full paper explores new ways to analyze and represent engineering curricula through the concept of Curricular Analytics. The engineering curriculum is notorious for its hierarchical structure of required technical content, the many prerequisites and corequisites requirements for upper-level courses, and numerous accreditation requirements, which make the curriculum complex. Understanding this complexity is essential for improving student progression and success, identifying structural barriers, and supporting curricular redesign. Curricular Analytics has emerged as a valuable tool in engineering education research for understanding structural barriers in degree programs. This analytical framework provides a way to quantify curricula by representing corequisite and prerequisite chains as a network, enabling the use of existing and adapted techniques from fields like social network analysis and graph theory to generate insights into students’ course-taking paths and bottlenecks within them.

Studies in the field that use Curricular Analytics often primarily focus on structural complexity captured through network analysis, conceptualized as course cruciality, to quantitatively describe the importance of individual courses in the context of the entire program. Recent work has expanded the analytical capabilities by developing a package of functions in the statistical programming platform R that enables examination of curricula from several angles.

To demonstrate the functionality of this package, we draw from two large curricular datasets (n = 494 and n = 155) representing five engineering disciplines (i.e., mechanical, electrical, civil, industrial, and chemical) from 13 institutions and three engineering disciplines (i.e., mechanical, electrical, and civil) from 53 institutions, respectively. We will showcase alternative metrics, including curriculum rigidity (a simpler characterization of structural complexity), core collapse sequence (a visual approach to understanding the curricular design patterns in a curriculum), bottleneck courses (an alternative view of cruciality), and deferment factor (which quantifies how failing a course influences the students’ time to degree).

This package offers new ways to analyze engineering curricula, and this work provides descriptive representations of alternative curriculum complexity measures and demonstrates their usefulness across two large datasets of engineering programs. Leveraging these metrics can be key to future research and practice focused on engineering curriculum design.