When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction

In three linesDiagnostic study on offline model selectors for edX dropout prediction. Authors identify three failure causes (mismatched learner, non-predictive state, label shift) via three stages: oracle ceiling via k-NN, BC/DQN/CQL evaluation, state ablation. Across 5 models, oracle gains 9.7 accuracy points, but learners plateau due to local representational ambiguity.

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