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.

翻译：本研究通过将学术轨迹概念化为复杂的量化路径，挑战了传统二元对立的学生发展观（保留与辍学）。基于阿根廷某工程学院40年纵向数据集（N = 24,016），我们提出CAPIRE分析框架，以区分专业转换、培养计划调整及同计划再入学行为。虽然73.3%的学生遵循线性轨迹（稳定型），但仍有26.7%的学生呈现复杂的流动模式。通过主成分分析（PCA）与DBSCAN聚类方法，我们发现这些轨迹并非连续分布，而是呈现结构性量化特征，占据离散的复杂度区间。分析识别出六类典型学生群体，包括职业方向重构的'转换者'（10.7%），以及保持专业不变但存在学业中断的'稳定再入学群体'（6.9%）。网络分析进一步揭示电子与计算机等特定'枢纽专业'扮演着系统性吸引源的角色。这些发现表明，学生流动是高等教育系统的有序生态特征而非随机噪声，为院校的课程分析与预测建模提供了新的研究视角。