Quantised Academic Mobility: Network and Cluster Analysis of Degree Switching, Plan Changes, and Re-entries in an Engineering Faculty (1980-2019)

TL;DR

This paper introduces a novel framework for analyzing complex student mobility patterns over 40 years, revealing quantised trajectories and distinct archetypes that challenge traditional linear models of academic progression.

Contribution

It presents CAPIRE, a new analytical approach combining PCA and clustering to identify discrete student mobility archetypes and systemic hub majors in long-term academic data.

Findings

26.7% of students exhibit complex mobility patterns

Six distinct student archetypes identified

Hub majors like electronics act as attractors

Abstract

This study challenges the traditional binary view of student progression (retention versus dropout) by conceptualising academic trajectories as complex, quantised pathways. Utilising a 40-year longitudinal dataset from an Argentine engineering faculty (N = 24,016), we introduce CAPIRE, an analytical framework that differentiates between degree major switches, curriculum plan changes, and same-plan re-entries. While 73.3 per cent of students follow linear trajectories (Estables), a significant 26.7 per cent exhibit complex mobility patterns. By applying Principal Component Analysis (PCA) and DBSCAN clustering, we reveal that these trajectories are not continuous but structurally quantised, occupying discrete bands of complexity. The analysis identifies six distinct student archetypes, including 'Switchers' (10.7 per cent) who reorient vocationally, and 'Stable Re-entrants' (6.9 per cent) who exhibit stop-out behaviours without changing discipline. Furthermore, network analysis highlights specific 'hub majors' - such as electronics and computing - that act as systemic attractors. These findings suggest that student flux is an organised ecosystemic feature rather than random noise, offering institutions a new lens for curriculum analytics and predictive modelling.