Urban mobility and learning: analyzing the influence of commuting time on students' GPA at Politecnico di Milano

TL;DR

This study investigates how commuting time affects first-year students' GPA at Politecnico di Milano, employing innovative machine learning and causal modeling techniques to establish a significant impact of travel duration on academic performance.

Contribution

It introduces a novel two-step methodology combining privacy-preserving GPS data analysis and causal modeling to explore the effect of commuting time on student performance.

Findings

Longer commuting times negatively impact GPA

Machine learning effectively estimates commuting durations

Causal analysis confirms the influence of travel time on academic success

Abstract

Despite its crucial role in students' daily lives, commuting time remains an underexplored dimension in higher education research. To address this gap, this study focuses on challenges that students face in urban environments and investigates the impact of commuting time on the academic performance of first-year bachelor students of Politecnico di Milano, Italy. This research employs an innovative two-step methodology. In the initial phase, machine learning algorithms trained on privacy-preserving GPS data from anonymous users are used to construct accessibility maps to the university and to obtain an estimate of students' commuting times. In the subsequent phase, authors utilize polynomial linear mixed-effects models and investigate the factors influencing students' academic performance, with a particular emphasis on commuting time. Notably, this investigation incorporates a causal framework, which enables the establishment of causal relationships between commuting time and academic outcomes. The findings underscore the significant impact of travel time on students' performance and may support policies and implications aiming at improving students' educational experience in metropolitan areas. The study's innovation lies both in its exploration of a relatively uncharted factor and the novel methodologies applied in both phases.