A Leakage-Aware Data Layer For Student Analytics: The Capire Framework For Multilevel Trajectory Modeling

TL;DR

This paper introduces a leakage-aware data layer and framework for multilevel student trajectory modeling, improving robustness and interpretability of dropout prediction models by preventing data leakage and enabling archetype discovery.

Contribution

It proposes a novel data organization and the formalization of Value of Observation Time (VOT) to prevent data leakage in student analytics models.

Findings

Identified 13 stable student dropout archetypes

VOT-restricted features improve model robustness

Framework supports causal inference and agent-based modeling

Abstract

Predictive models for student dropout, while often accurate, frequently rely on opportunistic feature sets and suffer from undocumented data leakage, limiting their explanatory power and institutional usefulness. This paper introduces a leakage-aware data layer for student trajectory analytics, which serves as the methodological foundation for the CAPIRE framework for multilevel modelling. We propose a feature engineering design that organizes predictors into four levels: N1 (personal and socio-economic attributes), N2 (entry moment and academic history), N3 (curricular friction and performance), and N4 (institutional and macro-context variables)As a core component, we formalize the Value of Observation Time (VOT) as a critical design parameter that rigorously separates observation windows from outcome horizons, preventing data leakage by construction. An illustrative application in a long-cycle engineering program (1,343 students, ~57% dropout) demonstrates that VOT-restricted multilevel features support robust archetype discovery. A UMAP + DBSCAN pipeline uncovers 13 trajectory archetypes, including profiles of "early structural crisis," "sustained friction," and "hidden vulnerability" (low friction but high dropout). Bootstrap and permutation tests confirm these archetypes are statistically robust and temporally stable. We argue that this approach transforms feature engineering from a technical step into a central methodological artifact. This data layer serves as a disciplined bridge between retention theory, early-warning systems, and the future implementation of causal inference and agent-based modelling (ABM) within the CAPIRE program.