Geography of Italian student mobility: A network analysis approach

Columbu, Silvia; Porcu, Mariano; Primerano, Ilaria; Sulis, Isabella; Vitale, María Prosperina

doi:10.1016/j.seps.2020.100918

Cited by 11 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study on the geographic mobility of Italian students, Silvia Columbu et al built network models using social network analysis (SNA) and illustrated the intricacy of students' geographic movement [15]. The study combines social network analysis methods with other clustering techniques methods.…”

Section: Economic Geographymentioning

confidence: 99%

See 1 more Smart Citation

The use of social network analysis in different fields

Zhou

2023

ACE

View full text Add to dashboard Cite

Social network analysis is a well-established research method in sociology. Some scholars also refer to this quantitative approach to graph theory as network science. As research intensified in the 1970s, it gained the attention of researchers in many other fields and has become an interdisciplinary research method. Social network analysis collects data from multiple sources and unearths new information by building network structures.The constructed network topology diagram and the calculated network properties are eventually analysed and generalised to produce further research results. The use of social network analysis (SNA) in a few different fieldstourism, society, transportation, citation, viral communication, medical education, and economic geographyis reviewed in this essay. Social network analysis is a quantitative analysis method that blends multiple disciplines. Various relationships are quantified through the construction of networks and property analysis. The paper is categorized according to the different fields of application. The different applications of social network analysis in various fields show its general and specific analytical approach.

show abstract

Section: Economic Geographymentioning

confidence: 99%

“…The use of social network analysis allows for the identification of core regions, provinces, and universities in the network where students flow in and out based on measures of network centrality. In addition, modeling analysis is used to show the network structure of regional student mobility [15].…”

Section: Economic Geographymentioning

confidence: 99%

The use of social network analysis in different fields

Zhou

2023

ACE

View full text Add to dashboard Cite

show abstract

“…Students want to anticipate the job market, moving soon towards those places able to give them better chances of being collocatated on a suitable employment, in many cases facing several 'settling costs' from economic, psychological, and social points of view (D'Agostino et al, 2021). They seem also very interested in the competencies they are going to acquire, showing high selectivity in their choice of the educational curricula (Columbu et al, 2021) Thus, students are not attracted by departments providing on average a higher level of grades. If any effect emerges, it is only in comparison with particularly low levels of average grading (Lombardi and Ghellini, 2019).…”

Section: Soft Grading Policies: Comparing Different Perspectivesmentioning

confidence: 99%

Universities’ Attractiveness and Grading Policies: A Spatial Competition Model

Lombardi

Vicario

2022

8th International Conference on Higher Education Advances (HEAd'22)

View full text Add to dashboard Cite

This article compares two different views about universities’ attractiveness and grading policies. On the one side, inflating grades should be a suitable strategy in order to attract students to a certain university. On the other side, soft grading policies damage departments’ reputation, so discouraging students’ applications. From this perspective, students are really interested in the quality of courses and the socio-economic characteristics of the territories in which universities are settled. We propose a spatial competition model, showing that grading inflation is a reaction to adverse conditions, aimed at balancing the distortions generated by fees level and differences in the socio-economic characteristics of the locations.

show abstract

“…Most of the concern has been devoted to mobility at undergraduate level. This represents the ''usual'' choice for a student who attends a university faraway from his/her region, while less attention was devoted to mobility between the undergraduate and the master's level (Enea 2018;Columbu et al 2021). Indeed, the aim of this paper is to look at this ''second term'' mobility, which has not been studied thoroughly because it is more difficult to analyse: single universities do not record the master's of their undergraduates when these move to another university.…”

Section: Introductionmentioning

confidence: 99%

A network analysis of student mobility patterns from high school to master’s

et al. 2021

View full text Add to dashboard Cite

Human migration involves the movement of people from one place to another. An example of undirected migration is Italian student mobility where students move from the South to the Center-North. This kind of mobility has become of general interest, and this work explores student mobility from Sicily towards universities outside the island. The data used in this paper regards six cohorts of students, from 2008/09 to 2013/14. In particular, our goal is to study the 3-step migration path: the area of origin (Sicilian provinces), the regional university for the bachelor's degree, and the regional university for the master's. Our analysis is conducted by building a multipartite network with four sets of nodes: students; Sicilian provinces; bachelor region of studies; and the master region of studies. By projecting the students' set onto the others, we obtain a tripartite network where the number of students represents the link weight. Results show that the big Sicilian cities-Palermo, Catania, and Messina-have different preferential paths compared to small Sicilian cities. Furthermore, the results reveal preferential paths of 3-step mobility that only, in part, reflect a south-north orientation in the transition from the region of study for the bachelor degree to that for the master's.

show abstract

Geography of Italian student mobility: A network analysis approach

Cited by 11 publications

References 19 publications

The use of social network analysis in different fields

The use of social network analysis in different fields

Universities’ Attractiveness and Grading Policies: A Spatial Competition Model

A network analysis of student mobility patterns from high school to master’s

Contact Info

Product

Resources

About