Patterns of university progression and social inequalities: delving into complex trajectories in higher education

This article analyses the trajectories followed by university students throughout their degrees using longitudinal data from a cohort entering eleven on-site universities in the Catalan system in 2012–2013 (<i>n</i> = 30,392). The analysis is carried out in two steps. First, types of academic trajectories are identified and described using Group Based Trajectory Modelling, and each type is related to students’ final academic outcomes over a seven-year period. Then, the probability of students belonging to each trajectory type is estimated according to academic and socio-demographic variables, disaggregated by field of study, to detect inequalities and specific patterns across disciplinary areas. The results indicate that the most frequent trajectory is stable high achievers, though trajectories marked by academic difficulties are observed. Academic variables are the strongest predictors of trajectory type, while among socio-demographic factors, age stands out, reflecting the greater time constraints often faced by older students in relation to their broader life course, which can limit ability or willingness to extend their studies when difficulties arise. Gender also has an effect, while social background has a more limited role. Patterns are most marked in STEM, where even high-entry-grade students face greater challenges, and older students show a reduced tendency towards recovery trajectories.

本文采用2012-2013学年入学的加泰罗尼亚地区11所实体高校的同队列学生（n=30392）的纵向追踪数据，分析大学生在整个本科阶段的学业轨迹。分析分为两个步骤：首先采用群组轨迹建模（Group Based Trajectory Modelling）方法识别并刻画各类学业轨迹，并将各轨迹类型与学生7年周期内的最终学业表现进行关联分析；随后，根据学生的学业与社会人口学变量，估算其归属各轨迹类型的概率，并按学科领域细分，以探究不同学科领域间的学业公平性差异与特定模式。研究结果显示，最常见的学业轨迹类型为表现稳定优异者，但同时也存在伴有学业困难的轨迹类型。学业变量是轨迹类型最强的预测因子；在社会人口学因素中，年龄最为显著，这反映出年长学生往往因自身更复杂的人生历程面临更多时间约束，进而在遭遇学业困难时，其继续完成学业的能力或意愿会受到限制。性别同样会对轨迹类型产生影响，而家庭社会背景的作用则相对有限。上述模式在理工科（STEM）领域表现最为显著：即便入学成绩优异的学生也会面临更大挑战，而年长学生的学业轨迹恢复倾向则有所降低。