The network of student transfers within the system of the University of Bologna adapts a constraint scale-free topology. Despite the presence of “hubs,” their role is strongly influenced by different institutional decisions and choices applied to courses. Therefore, the macro model of this network is not useful for previewing its evolution over time, particularly in the creation of critical points, which are courses with high out transfer rates. The idea is to introduce a probability of transfer function for each course, in order to preview the creation of critical points. The proposed model is fundamentally a binary regression logistic model. This rough model allows us to identify the possible creation of critical points in our complex system, these being “escape” courses, and to preview the impact of institutional decisions. At the same time, it may suggest how to remove the existing critical points in order to optimize the academic courses on offer.

A FORECAST MODEL TO ASSESS THE CRITICAL POINTS IN UNIVERSITY SYSTEMS

MONARI, PAOLA;STRACQUALURSI, LUISA
2012

Abstract

The network of student transfers within the system of the University of Bologna adapts a constraint scale-free topology. Despite the presence of “hubs,” their role is strongly influenced by different institutional decisions and choices applied to courses. Therefore, the macro model of this network is not useful for previewing its evolution over time, particularly in the creation of critical points, which are courses with high out transfer rates. The idea is to introduce a probability of transfer function for each course, in order to preview the creation of critical points. The proposed model is fundamentally a binary regression logistic model. This rough model allows us to identify the possible creation of critical points in our complex system, these being “escape” courses, and to preview the impact of institutional decisions. At the same time, it may suggest how to remove the existing critical points in order to optimize the academic courses on offer.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/109408
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