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.
The study explores whether combining traditional and digital learning analytics can predict failure in a first‑year CS1 course. Using data from 284 students across four cohorts, the authors identified ten candidate factors and built a logistic regression model that achieved 74.7% accuracy and 0.742 macro F1, with 87% recall for failing students. Weighted academic momentum, basic demographics, and LMS activity emerged as the most predictive features, suggesting that simple digital markers can enable early‑warning systems by week five.
arXiv:2609.23836v1 Announce Type: new Abstract: A fundamentally challenging question in K-12 education is about the effects of taking more advanced or challenging classes. It is particularly complex...
OBER+ extends an institutional attainment platform to bridge the gap between measured learning outcome shortfalls and corrective actions. It aggregates attainment across course deliveries, flags shortfalls, grades them against regulator cutoffs, records decisions linked to evidence‑annotated practices, logs changes, and quantifies subsequent improvements. The system also ensures outcomes are compared only when unchanged, preventing misleading comparisons across redefined outcomes, and has identified real defects in institutional data through rule‑based analysis.
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.
The study analyzes 22 OULAD asynchronous online courses (over 22,000 learners) using Zigzag Persistent Homology to track the number of disconnected behavioral clusters, denoted $eta_0$. It finds that changes in $eta_0$ strongly co‑vary with active learner counts, indicating that $eta_0$ is a participation‑sensitive indicator rather than a cause of dropout. Assessment deadlines trigger fragmentation in 82.6% of cases and the full Fragment First, Converge Later cycle in 60.2%, with long‑term fragmentation dominating 90.9% of courses.
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.
EduRiskX is a neuro-symbolic framework that combines a temporal Transformer-based predictor with an F-Logic symbolic reasoning module to forecast students’ academic risk early in online courses. The neural part models longitudinal activity sequences, while the F-Logic rule base, grounded in Engagement Theory and the Student Integration Model, offers interpretable, rule-based explanations. On the Open University Learning Analytics Dataset, EduRiskX achieves an accuracy of 0.900 and an F1-score of 0.894 by week 38, detecting risk on average by week 9.32 with a 94.30% detection rate, outperforming state‑of‑the‑art time‑series and deep learning baselines.
arXiv:2609.08618v1 Announce Type: new Abstract: Benchmark scores describe what a checkpoint can do now, but they do not determine how it will respond to the next training episode. We measure this mis...