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.
By Rafael da Silva, Jeff Eicher, Gregory Longo
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.
By Lighton Phiri, Mutune Chaibela, Ivy Chisha, David Pungwa, Danny Siabbaba, Bydon Simukoko
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...
By Nabit Bajwa, Seth B. Hunter, Sanmay Das
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.
By Elakkiya Rajasekar
arXiv:2608. 15101v1 Announce Type: new Abstract: Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed.
By Wesley Shu
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.
By De Jiang, Zhengyang Zhang, Kehong Yuan, Shaohua Ma