Analysis of higher education dropouts dynamics through multilevel functional decomposition of recurrent events in counting processes

This paper analyses higher education dropouts dynamics through an innovative approach that integrates recurrent events modelling and point process theory with functional data analysis. We propose a novel methodology that extends existing frameworks to accommodate hierarchical data structures, demonstrating its potential within a simulated setting. By analysing administrative data from student careers at Politecnico di Milano, we explore freshmen dropout patterns across different bachelor’s degree programmes and schools. Specifically, we model dropouts as recurrent events occurring across both programmes and schools using a Cox-based recurrent events model. Additionally, we leverage functional data analysis and multilevel principal component analysis to unravel the latent effects of degree programmes and schools on dropout trends, offering valuable insights for institutions seeking to implement strategies aimed at reducing dropout rates. The proposed methodology offers a groundbreaking approach to dropout analysis, opening a new perspective and avenues for modelling its dynamics.