University Dropout Driven by Administrative Rules, Not Student Failure

University dropout is often framed as a personal failure, attributed to students' lack of preparation or motivation. However, a new study using agent-based modeling reveals that administrative rules, not academic shortcomings, are the primary driver of student attrition. The research, simulating over 134,000 student trajectories, shows that 86.4% of dropouts result from time-to-live expiry regulations—deadlines that remove students from courses if they don't meet procedural requirements within set windows. This finding upends conventional wisdom, suggesting that institutional friction, rather than individual deficits, creates a 'regularity trap' that ensnares students, particularly those from disadvantaged backgrounds.

The study's key finding is the overwhelming dominance of normative dropout, accounting for 86.37% of attrition events, compared to just 5.33% caused by academic failure. This means that most students who leave university do so because of procedural deadlines, not because they can't pass exams. The simulation, which modeled 1,343 students across a 42-course civil engineering curriculum, isolated the effect of time-to-live constraints by controlling for academic ability. Students lose course regularity not due to poor performance but because life circumstances—like work or family obligations—prevent them from meeting administrative deadlines within the allotted two examination cycles. This triggers a cascade: each expiry leads to re-enrollment, schedule conflicts, and psychological stress, creating a self-reinforcing cycle that the researchers term the 'regularity trap'.

Ologically, the study employed an agent-based model grounded in empirical data from 15 cohorts at Universidad Nacional de Tucumán. The model defined 13 student archetypes based on clustering analysis, each with distinct attributes like planning horizon, academic ability, and psychological resilience. These agents navigated a curriculum structured as a directed acyclic graph with prerequisite dependencies, where courses had parameters like difficulty coefficients and workload units. The core innovation was formalizing the regularity regime as a time-decaying validity function: when agents achieved regular status in a course, a time-to-live counter started at 2, decrementing each examination period. If it reached zero before the course was credited, expiry occurred, triggering administrative removal, psychological depletion, and strategic complications. This design allowed the researchers to isolate the causal impact of expiry rules from academic performance, using 100 Monte Carlo replications to ensure statistical robustness.

Analysis revealed stark patterns. The overall dropout rate stabilized at 32.4%, with normative mechanisms driving 86.4% of these cases. Temporal dynamics showed early-period concentration, with a median time-to-event of 4 periods, indicating that most dropouts happen within the first few semesters as initial expiries cascade. Archetype heterogeneity was pronounced: dropout rates ranged from 13.2% for strategic agents with long planning horizons to 49.0% for myopic agents with short horizons, despite comparable academic ability. For vulnerable archetypes like PSICO_10, normative dropout accounted for 97.1% of attrition, while resilient archetypes like PSICO_01 had lower rates but still experienced mean expiries of 9.7. The data, illustrated in figures such as the Kaplan-Meier survival curve and dropout cause decomposition, underscores that expiry frequency correlates with dropout risk, with students who dropped out averaging 9.8 expiries compared to 5.1 for those who persisted.

Of these are profound for higher education policy and equity. They deficit-oriented models that blame students for dropout, instead highlighting how institutional structures inadvertently penalize vulnerable populations. Students with shorter planning horizons, often from under-resourced backgrounds, face disproportionately high attrition due to rigid deadlines, amplifying pre-existing inequalities. The study suggests practical interventions: extending time-to-live windows from 2 to 3 examination cycles could reduce dropout by about 6 percentage points, with post-expiry bridging support and coordinated exam scheduling offering additional benefits. These reforms could shift focus from remediation to structural flexibility, potentially narrowing achievement gaps without compromising academic standards.

Limitations of the study include parameter uncertainty, as alternative model specifications might yield similar outcomes, and omitted mechanisms like peer influences or financial shocks that affect real-world attrition. The research is based on data from a single institution, limiting generalizability, though time-to-live constraints are common in Latin American and European systems. Validation gaps exist, as the model hasn't been tested with prospective prediction, and it simplifies psychological dynamics, though sensitivity analyses show robustness. Future research should explore multi-intervention experiments, heterogeneous treatment effects, and cross-institutional comparisons to refine these insights and guide equitable policy reforms.