Time-Consistent Prediction in Higher Education: A Framework for Preventing Data Leakage in Longitudinal Models

Data leakage is a major source of overoptimistic performance estimates in machine learning-based predictive modelling. In higher education, dropout models are increasingly used to support interventions, yet leakage may arise not only from technical mistakes but also from an underspecified prediction task. This article conceptualizes predictive modelling as a temporally specified decision configuration and proposes Time-Consistent Dropout Prediction (TCDP) as a diagnostic framework for longitudinal educational data. TCDP defines four core components: an explicit prediction cutoff, a pre-cutoff information set, a risk-set-consistent target population, and a temporally appropriate validation design. The validation component comprises two design conditions: entity-level train–test separation and cohort-level temporal consistency. A structured methodological screening of recent dropout-prediction studies shows that temporal anchoring is becoming more common, whereas risk-set definition and validation hierarchy remain less consistently formalized. The empirical demonstration uses a pseudonymized longitudinal student panel from a Hungarian higher education institution, accompanied by a controlled synthetic reproduction package. Results show that row-wise validation in data with repeated student observations generates increasing train–test entity overlap across prediction cutoffs and inflates AUC, especially for high-capacity or instance-based models. Enforcing entity-level separation removes this inflation and yields lower, more realistic performance ranges. The paper contributes an operational validation grammar linking prediction time, admissible information, population eligibility, and validation design.