This paper proposes a multiplex network approach to analyze the Italian students’ mobility choices from bachelor’s to master’s degrees. We rely upon administrative data on Italian students’ careers by focusing on those who decide to enroll in a different university for their master’s studies once they graduate in a bachelor’s program. These flows are explored by defining a multiplex network approach where the ISCED-F fields of education and training are the layers, the Italian universities are the nodes, and the weighted and directed links measure the number of students moving between nodes. Network centrality measures and layers similarity indexes are computed to highlight the presence of core universities and verify if the network structures are similar across the layers. The results indicate that each field of study is characterized by its network structure, with the most attractive universities usually located in the Center-North of the country. The community detection algorithm highlights that graduates’ mobility between universities is encouraged by the geographical proximity, with different intensities depending on the field of study.
A multiplex network approach for analyzing university students’ mobility flows
Usala, C.
2022-01-01
Abstract
This paper proposes a multiplex network approach to analyze the Italian students’ mobility choices from bachelor’s to master’s degrees. We rely upon administrative data on Italian students’ careers by focusing on those who decide to enroll in a different university for their master’s studies once they graduate in a bachelor’s program. These flows are explored by defining a multiplex network approach where the ISCED-F fields of education and training are the layers, the Italian universities are the nodes, and the weighted and directed links measure the number of students moving between nodes. Network centrality measures and layers similarity indexes are computed to highlight the presence of core universities and verify if the network structures are similar across the layers. The results indicate that each field of study is characterized by its network structure, with the most attractive universities usually located in the Center-North of the country. The community detection algorithm highlights that graduates’ mobility between universities is encouraged by the geographical proximity, with different intensities depending on the field of study.File | Dimensione | Formato | |
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