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cs.LG2026
The C-index illusion: discrimination without calibration in published survival models
Rafael da Silva, Danilo Alvares
Recent work has argued normatively, on synthetic data, that evaluating survival models by discrimination alone (concordance index) yields systematically misleading model comparison…
cs.LG2026
A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics
Rafael da Silva, Jeff Eicher, Gregory Longo
Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heterogeneous protocols, prioritizing discriminat…
cs.LG2026
An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data
Rafael da Silva, Jeff Eicher, Gregory Longo
This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, using LMS engagement data and administrativ…