DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2014-10-03 - Colloque/Présentation - poster - Anglais - 1 page(s)

Friant Nathanaël , Gagliolo Matteo, "School quasi-markets as social networks" in 4th Belgian Network Research Meeting, Brussels, Belgium, 2014

  • Codes CREF : Politique de l'éducation (DI4415), Sociologie de l'éducation (DI4142), Evaluation [sociologie] (DI4129)
  • Unités de recherche UMONS : Méthodologie et formation (P316)
  • Instituts UMONS : Institut de Recherche sur les Systèmes Complexes (Complexys), Institut de Recherche en Développement Humain et des Organisations (HumanOrg)
Texte intégral :

Abstract(s) :

(Anglais) Some educational systems are characterized by free school choice and a public funding of schools according to the number of pupils enrolled. This is what we call a school quasi-market (Le Grand, 1991). In these systems, schools are in competition with each other according to the number, and the characteristics, of pupils they enrol. Schools are said to be interdependent (Delvaux & Joseph, 2006). These interdependencies can be revealed by comparing the actual distribution of pupils between schools with what would happen if pupils simply attended the school closest to their home (Friant, 2012; Taylor, 2009). We can then analyse which schools attract pupils and which schools are avoided, thus characterizing a competition space. The problems arise when we want to broaden the analysis to a larger scale (e.g. the educational system as a whole). We need more advanced tools to analyse such a large network of interdependencies. This paper addresses this problem by applying social network analysis to better describe and analyse school quasi-markets. We use the results of an agent-based simulation of school choice in French-speaking Belgium (Friant, 2012) and consider the data as a network of schools exchanging pupils with each other. The resulting network is cyclic, directed, and weighted, with nodes representing schools, and edges weights representing fluxes of pupils. Using such metrics as weighted in- and out-degrees, clustering, betweennes centrality, and flows, we propose new ways of characterizing the position of schools in a hierarchical competition space, and in the educational system as a whole.