In this paper, we delve into the dynamics of higher education dropouts through a novel approach that combines time-to-event processes analysis with multilevel functional decomposition of recurrent events. Utilizing data from Politecnico di Milano’s administrative archive, we explore dropout patterns across bachelor’s degree programs and schools. By leveraging models for recurrent events and employing functional principal component analysis techniques, our methodology expands upon existing frameworks to accommodate hierarchical data structures. Through simulations and analysis, we demonstrate the efficacy of our approach in reconstructing dropout dynamics, shedding light on critical periods, disentangling the effects given by degree programs and schools and informing proactive strategies for educational institutions.