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ARXIV:2604.08870 · STUDENT DROPOUT PREDICTION · SUBMITTED 13 APR · 20:27 UTC · FRESHNESS STALE
ARXIV:2604.08870STUDENT DROPOUT PREDICTIONSUBMITTED 13 APR · 20:27 UTCFRESHNESS STALERafael da Silva · Jeff Eicher · Gregory Longo · arXiv
A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations.
Opportunity summary
Pain A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations. This study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD).
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results are reported within each arm separately, as a single cross-arm ranking is not methodologically warranted. Code availability is flagged in the production record;…
Student Dropout Prediction moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
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A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations.
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10.48550/arXiv.2604.08870A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations.
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. This study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD). Two harmonized arms are compared: a dynamic weekly arm, with models in person-period representation, and a comparable continuous-time arm, with an expanded roster of families -- tree-based survival, parametric, and neural models. The evaluation protocol integrates four analytical layers: predictive performance, ablation, explainability, and calibration. Results are reported within each arm separately, as a single cross-arm ranking is not methodologically warranted. Within the comparable arm, Random Survival Forest leads in discrimination and horizon-specific Brier scores; within the dynamic arm, Poisson Piecewise-Exponential leads narrowly on integrated Brier score within a tight five-family cluster. No-refit bootstrap sampling variability qualifies these positions as directional signals rather than absolute superiority. Ablation and explainability analyses converged, across all families, on a shared finding: the dominant predictive signal was not primarily demographic or structural, but temporal and behavioral. Calibration corroborated this pattern in the better-discriminating models, with the exception of XGBoost AFT, which exhibited systematic bias. These results support the value of a harmonized, multi-dimensional benchmark in Learning Analytics and situate dropout risk as a temporal-behavioral process rather than a function of static background attributes.
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PROBLEM
A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations. This study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD).
METHOD
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 bench...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results are reported within each arm separately, as a single cross-arm ranking is not methodologically warranted. Code availability is flagged in the production record; the public repository link still ne...
WHY NOW
Student Dropout Prediction moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations. This study introduces a survival-oriented benchmark for temporal dropout risk modelling using the Open University Learning Analytics Dataset (OULAD).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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).
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Results are reported within each arm separately, as a single cross-arm ranking is not methodologically warranted. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Student Dropout Prediction moved forward this cycle; last verified April 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A survival-oriented benchmark for temporal student dropout risk modeling with harmonized representations.
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Student Dropout Prediction
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