A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics
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 study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD). Two arms are compared: Family A: Dynamic Weekly, with models in person-period representation, and Family B: Static Early-Window, with an expanded roster of families: tree-based survival, parametric, and neural models. The evaluation protocol integrates four analytical layers: predictive performance, ablation, expl...
| Authors | Rafael da Silva, Jeff Eicher, Gregory Longo |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 21 July 2026 |
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