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The Stagnant Persistence Paradox: Survival Analysis and Temporal Efficiency in Exact Sciences and Engineering Education

Published: December 4, 2025 | arXiv ID: 2512.04828v1

By: H. R. Paz

Potential Business Impact:

Shows students waste years in college.

Business Areas:
STEM Education Education, Science and Engineering

Research on student progression in higher education has traditionally focused on vertical outcomes such as persistence and dropout, often reducing complex academic histories to binary indicators. While the structural component of horizontal mobility (major switching, plan changes, re-entries) has recently been recognised as a core feature of contemporary university systems, the temporal cost and efficiency of these pathways remain largely unquantified. Using forty years of administrative records from a large faculty of engineering and exact sciences in Argentina (N = 24,016), this study applies a dual-outcome survival analysis framework to two key outcomes: definitive dropout and first major switch. We reconstruct academic trajectories as sequences of enrolment spells and typed transitions under the CAPIRE protocol, and then deploy non-parametric Kaplan-Meier estimators to model time-to-event under right-censoring. Results uncover a critical systemic inefficiency: a global median survival time of 4.33 years prior to definitive dropout, with a pronounced long tail of extended enrolment. This pattern reveals a phenomenon of stagnant persistence, where students remain formally enrolled for long periods without commensurate curricular progression. In contrast, major switching follows an early-event regime, with a median time of 1.0 year among switchers and most switches concentrated within the first academic year. We argue that academic failure in rigid engineering curricula is not a sudden outcome but a long-tail process that generates high opportunity costs, and that institutional indicators should shift from static retention metrics towards measures of curricular velocity based on time-to-event analysis.

Country of Origin
🇦🇷 Argentina

Page Count
18 pages

Category
Computer Science:
Computers and Society