arXiv:2604. 08874v3 Announce Type: replace-cross Abstract: 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.
arXiv:2604. 08870v3 Announce Type: replace-cross Abstract: 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.
arXiv:2608. 16914v1 Announce Type: cross Abstract: Digital learning platforms generate rich behavioural traces (digital markers) that offer the potential to identify struggling students early.
arXiv:2608. 15101v1 Announce Type: new Abstract: Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed.
arXiv:2608. 04408v1 Announce Type: cross Abstract: On-policy distillation (OPD) supervises student-visited trajectories, yet divergence-based rules cannot determine whether an erroneous prefix remains correctable.
arXiv:2607. 10466v1 Announce Type: new Abstract: Survival models can model time-to-event outcomes using partially observed data.
arXiv:2509. 21514v4 Announce Type: replace Abstract: Research on Knowledge Tracing (KT) models traditionally focuses on improving predictive accuracy.
arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.
arXiv:2607. 10633v1 Announce Type: cross Abstract: Explainable machine learning (XML) pipelines applied to composite mental health outcomes can produce apparently-robust, cross-population-stable risk hierarchies that are largely artefacts of how the outcome was constructed.
arXiv:2606. 03532v1 Announce Type: cross Abstract: Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs the \emph{temporal coupling} between teacher and student -- has not been systematically studied as a stability variable.
arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.
arXiv:2608. 17618v1 Announce Type: cross Abstract: Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and compatible with educational constraints.
arXiv:2607. 28608v1 Announce Type: new Abstract: Clinical risk models routinely achieve strong aggregate performance while producing materially different error rates across patient subgroups.