Retrospective Landmark Classification of Recorded Administrative Student Dropout Status from University Records

This study advances the methodology of educational data mining by establishing a leakage-controlled retrospective landmark evaluation framework that exposes the analytical boundaries of predicting student attrition from administrative records. Rather than treating repeated final administrative categories as dated withdrawal events, this research demonstrates why high random-split discrimination should not be mistaken for prospective intervention readiness.