Global and adaptive split conformal prediction for capacity-constrained Top-K screening: a methodological evaluation using synthetic student performance data

Introduction Learning analytics models commonly produce predictive scores without specifying how uncertainty should inform prioritization when review capacity is limited. Methods This study developed a reproducible methodological workflow integrating predictive benchmarking, global and normalized adaptive split conformal calibration, hypothetical capacity-constrained Top- K screening, threshold-relative analytical routing, and deterministic audit recording. The workflow was evaluated on a public synthetic student performance benchmark containing 6,607 records, 19 mixed-type predictors, and one numerical examination score outcome. Seven regression models were compared over five repeated 60%/20%/20% train-calibration-test partitions. Results Regularized linear regression produced the most favorable observed mean performance, with a mean absolute error of 0.502 ± 0.016, a root mean squared error of 2.104 ± 0.149, a coefficient of determination of 0.713 ± 0.030, and a Spearman correlation of 0.976 ± 0.003. Global and adaptive conformal procedures both achieved complete-test empirical marginal coverage close to the nominal 90% level. Across all seven models, adaptive calibration generated record-varying intervals, reduced mean interval width and interval score, increased the mean split-wise minimum coverage across the examined encoded categories, and reduced mean coverage disparity, although several category-level estimates remained below 0.90. The common radius used by global calibration preserved the point-prediction rankings, whereas adaptive scores changed Top- K membership. However, the resulting changes in Precision@ K , Capture@ K , and FN@ K were numerically small at the reported precision and showed no consistent screening advantage. Training-quantile-derived robustness analyses supported the same interpretation. Discussion Within this synthetic benchmark, adaptive calibration contributed primarily through uncertainty differentiation and interval efficiency rather than improved retrospective screening utility. The study demonstrated computational reproducibility and record-level traceability; it did not validate real student screening, establish category- or selection-conditional coverage, or support automated or causal intervention decisions.