Related papers: A Leakage-Aware Data Layer For Student Analytics: …
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…
Both student retention in higher education and artificial intelligence governance face a common structural challenge: the application of linear regulatory frameworks to complex adaptive systems. Risk-based approaches dominate both domains,…
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…
This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrative withdrawal records. Dropout is operationalized as a…
Engineering programmes, particularly in Latin America, are often governed by rigid curricula and strict regularity rules that are claimed to create a Regularity Trap for capable students. This study tests that causal hypothesis using the…
Student dropout is a global issue influenced by personal, familial, and academic factors, with varying rates across countries. This paper introduces an AI-driven predictive modeling approach to identify students at risk of dropping out…
The field of learning analytics needs to adopt a more rigorous approach for predictive model evaluation that matches the complex practice of model-building. In this work, we present a procedure to statistically test hypotheses about model…
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…
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…
Multi-agent systems achieve state-of-the-art outcomes through peer collaboration. However, when an agent in the pipeline silently drops a constraint, the system's final output may look correct even though the reasoning chain was quietly…
Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy. However, responsible real-world deployment requires models to know when to defer uncertain predictions to a human teacher. We introduce an…
The importance of retention rate for higher education institutions has encouraged data analysts to present various methods to predict at-risk students. The present study, motivated by the same encouragement, proposes a deep learning model…
Feature attribution methods identify which features of an input most influence a model's output. Most widely-used feature attribution methods (such as SHAP, LIME, and Grad-CAM) are "class-dependent" methods in that they generate a feature…
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…
As the scale and size of the data increases significantly nowadays, federal learning (Bonawitz et al. [2019]) for high performance computing and machine learning has been much more important than ever before (Abadi et al. [2016]). People…
This chapter bridges technical analysis and organizational preparedness by tracing the path from layered failure modes to reliability awareness in generative and agentic AI systems. We first introduce an 11-layer failure stack, a structured…
Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged…
Academic integrity continues to face the persistent challenge of examination cheating. Traditional invigilation relies on human observation, which is inefficient, costly, and prone to errors at scale. Although some existing AI-powered…
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…