2026 ASEE Annual Conference & Exposition

This “student-led research on engineering education” research paper concerns establishing preliminary evidence of the relationship between measures to quantify the complexity of a curriculum and “ecosystem metrics” that expand on the concept of retention and graduation rates. Although prior research in engineering education has explored retention and course progression, few studies have addressed how the curriculum design itself influences the broader flow of students across disciplines. An emerging approach for quantifying the complexity of curricula called Curricular Analytics provides a new lens for this research gap. This approach treats the prerequisite and corequisite relationships in a curriculum as a network to calculate a total measure that can be used to compare programs and correlate with student outcomes of interest. Previous work has established that the total measure, called structural complexity, is negatively correlated with completion rates. However, correlations with broader metrics used in the literature, called ecosystem metrics, have not been explored. Unlike completion rates, ecosystem metrics seek to capture more nuanced ways in which students engage with the curriculum, such as major switching. Accordingly, we pose the following research question: “How do the measures of curricular complexity within Curricular Analytics relate to ecosystem metrics, and how do they vary among different genders/races?”

We explore two measures of curricular complexity: structural complexity and curricular rigidity. Structural complexity is the sum of the blocking and delay factors of all courses within a curriculum. The blocking factor for a course is the number of courses a student cannot take if they fail it; on the other hand, the delay factor for a course is the longest prerequisite chain to which the course belongs. We also use a simpler form of structural complexity as a comparison metric, called curriculum rigidity. Curriculum rigidity is the density of prerequisite relationships, the total number of prerequisites and corequisites divided by the number of courses. We used two student outcome measures: stickiness, defined as the percentage of students who ever declare a discipline and ultimately graduate in that same field, and migration yield, the percentage of students who transfer into and successfully graduate from a new discipline.

We used program plans of study (n = 40) collected from 13 institutions across five engineering disciplines (2012–2022) in the Multiple-Institution Database for Investigating Longitudinal Development (MIDFIELD), including student records (i.e., major and graduation indicators). Complexity measures were calculated for 2012 as the students’ entering year. We approached the data using a correlational design.

Our general results provide preliminary evidence that more structurally complex tend to correspond with lower stickiness (r(38) = -0.22) and migration yield (r(38) = -0.28), and the curriculum rigidity negatively correlate with stickiness (r(38) = -0.17) and migration yield (r(38) = -0.29), suggesting that curricular design can constrain, but not fully determine, student persistence and mobility. By identifying which structural features act as barriers or enablers, the findings provide actionable insights for curriculum reform, guiding departments toward more flexible and inclusive curricular ecosystems that support diverse pathways to success.