相关论文: Regularity as Structural Amplifier, Not Trap: A Ca…
High dropout rates in engineering programmes are conventionally attributed to student deficits: lack of academic preparation or motivation. However, this view neglects the causal role of "normative friction": the complex system of…
This study provides a causal validation of the dual-stressor hypothesis in a long-cycle engineering programme in Argentina, testing whether academic staff strikes (proximal shocks) and inflation (distal shocks) jointly shape student…
This study extends the CAPIRE framework with a macro-shock module to analyse the impact of teacher strikes and inflation on student trajectories in engineering education. Using data from 1,343 students across 15 cohorts (2004-2019) in a…
Engineering programmes in Latin America combine high structural rigidity, intense assessment cultures and persistent socio-economic inequality, producing dropout rates that remain stubbornly high despite increasingly accurate early-warning…
Dropout in higher education is commonly analysed through observable academic events such as course failure or repetition. However, these event-based perspectives may obscure the underlying structural dynamics that shape student…
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…
Curricula in long-cycle programmes are usually recorded in institutional databases as linear lists of courses, yet in practice they operate as directed graphs of prerequisite relationships that constrain student progression through complex…
Predictive models for student dropout, while often accurate, frequently rely on opportunistic feature sets and suffer from undocumented data leakage, limiting their explanatory power and institutional usefulness. This paper introduces a…
Progression and assessment rules are often treated as administrative details, yet they fundamentally shape who is allowed to remain in higher education, and on what terms. This article uses a calibrated agent-based model to examine how…
Engineering degrees are often perceived as "hard", yet this hardness is usually discussed in terms of content difficulty or student weaknesses rather than as a structural property of the curriculum itself. Recent work on course-prerequisite…
Social integration theories suggest that students embedded in supportive peer networks are less likely to drop out. In learning analytics, this has motivated the use of social network analysis (SNA) from institutional co-enrolment data to…
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…
Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability discourages over-specific co-adaptations of feature detectors, preventing overfitting and improving network…
University dropout remains a persistent challenge in higher education systems, yet causal evidence on the mechanisms triggering early disengagement is limited. This study estimates the causal effect of first-time academic failure on…
Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discrimination over temporal interpretability and calibration. This…
In recent years, a surge in the popularity of trap music among adolescents has prompted concerns about its potential influence on youth behavior and educational outcomes. In this study, we develop a novel compartmental model using a system…
Dropout is a regularization technique widely used in training artificial neural networks to mitigate overfitting. It consists of dynamically deactivating subsets of the network during training to promote more robust representations. Despite…
Continual learning in artificial neural networks is fundamentally limited by the stability--plasticity dilemma: systems that retain prior knowledge tend to resist acquiring new knowledge, and vice versa. Existing approaches, most notably…
Universities are widely expected to respond to technological transitions through rapid reconfiguration of programme demand and curricular supply. Using four decades of longitudinal administrative cohorts (1980-2019) from a large public…
Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that inflates computational cost and degrades conditioning, unstable weighting under…