Urban Mobility and Learning: Analyzing the Influence of Commuting Time on Students' GPA at Politecnico di Milano
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| Title: | Urban Mobility and Learning: Analyzing the Influence of Commuting Time on Students' GPA at Politecnico di Milano |
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| Language: | English |
| Authors: | Arianna Burzacchi (ORCID |
| Source: | Studies in Higher Education. 2025 50(7):1339-1364. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 26 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Commuting Students, Grade Point Average, Time, Higher Education, College Freshmen, Influences, Predictor Variables, Student Characteristics, Academic Achievement |
| Geographic Terms: | Italy (Milan) |
| DOI: | 10.1080/03075079.2024.2374005 |
| ISSN: | 0307-5079 1470-174X |
| 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 Grade Point Average (GPA) 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' GPA, with a particular emphasis on commuting time. Notably, this investigation incorporates causal inference analyses from the observational studies domain, which enable to establish the effect of commuting time on academic outcome. 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502747 |
| Database: | ERIC |
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| 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 Grade Point Average (GPA) 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' GPA, with a particular emphasis on commuting time. Notably, this investigation incorporates causal inference analyses from the observational studies domain, which enable to establish the effect of commuting time on academic outcome. 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. |
|---|---|
| ISSN: | 0307-5079 1470-174X |
| DOI: | 10.1080/03075079.2024.2374005 |
