A student-risk model travelled better after adaptation—but the smallest programme had 46 records

A better benchmark score is not a fair student-support policy

The result shows that distribution shift matters and that a lightweight statistical correction can sometimes recover performance. It does not show that any student should be labelled at risk. CORAL used the unlabelled target programme's full feature distribution, while the same fixed target records were evaluated across 30 runs. The data are private, the smallest programme contained only 46 students, and the paper did not report prospective use, calibration by subgroup or consequences of false alerts.

For advisers, the useful lesson is to test portability before adopting a model trained elsewhere—and to keep support voluntary and human-led. Gender, school background and admission route can encode structural inequality even when they improve prediction. A credible next step would freeze the model, validate it prospectively at independent institutions, publish subgroup error and calibration, compare against simple rules, and measure whether any intervention improves retention without stigmatising students. The authors reported public Indonesian funding and no competing interests.[1]