2026 ASEE Annual Conference & Exposition

Student success and timely degree completion are two of the major goals of higher education institutions because they minimize the total cost of attendance and help the institution's reputation. The Heileman CurricularAnalytics software package in R affords graph-based metrics and visualization tools to help institutions investigate curricular structure. A delay factor metric quantifies how much a course influences degree completion time as measured by the longest chain of prerequisites. Blocking factor counts the number of future course registration blocks resulting from a course failure. Complexity is the sum of delay and blocking factors. High complexity, delay and blocking indicate a potential curricular bottleneck. This study aims to validate temporal curricular graph metrics with historical enrollment data, using ten years of anonymized mechanical engineering transcripts from a private R2 STEM-focused university. The framework uses prerequisite and corequisite relations from the directed acyclic graph and compares with how students actually take courses over time in order to reveal deviations between planned and observed pathways. The study develops comparable observational complexity validation metrics to identify how failing or delaying a course affects completion trajectory, whether students graduate on time, require additional semesters, or recover through adaptive strategies such as summer enrollment or out-of-sequence scheduling. By quantifying delays and recovery patterns associated with structural bottlenecks, courses whose position and connectivity create the highest degree of risk are identified and analyzed. Post-setback behaviors such as summer catch-up, out-of-sequence enrollment, concurrent enrollment, stop-outs, and course repeats are classified and analyzed to estimate their association with on-time completion versus added semesters. This curricular-structural validation approach provides insights for students, curriculum committees, and advisors, supporting data-driven insights of critical sequences, co-requisite policies, and scheduling flexibility to promote quicker and more equitable degree attainment.

This is a work-in-progress abstract seeking reviewer input. Current progress includes familiarization with CurricularAnalytics.org web-based and R-based tools, development of a university-specific mechanical engineering curricular graph, identification of crucial courses with elevated complexity metrics, acquisition of anonymized historical data, and exploratory data analysis. Next steps involve observational metric development and comparison to existing graph metrics.