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Given the expected advantages for individuals and societies, financial literacy is high on the policy agenda in many countries. This paper reports the results from a unique survey conducted on a sample of 13-16-year-old students in five European countries, aimed at measuring and comparing their level of financial literacy skills. The results indicate significant differences in the level of financial literacy across countries that are interpreted in the light of the differences in the countries' educational policies and experiences with financial education. The findings suggest that supra-national coordinated action is needed to guarantee better financial knowledge and safeguard economic stability in future crises.

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university.

This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the time to dropout as soon as possible and to observe how the dropout phenomenon varies across time and degree programs. The innovative contributions of this work are methodological and managerial. First, the adoption of shared frailty Cox models with time-varying covariates is relatively new to the student dropout literature and it allows to consider the student career evolution and the heterogeneity across degree programs. Second, understanding the dropout pattern over time and identifying the earliest moment for obtaining its accurate prediction allow policy makers to set timely interventions for students at risk of dropout. [ABSTRACT FROM AUTHOR]

Study success in Higher Education is of primary importance in the European policy agenda. Yet, given the diverse educational landscape across countries and institutions, more coordinated action is needed to gain a more solid knowledge of the dropout phenomenon. This study aims to gain a better insight into students' dropout based on an integrated comparative study of two universities located in two different European countries: Politecnico di Milano (Italy) and Vrije Universiteit Amsterdam (the Netherlands). This research aims at assessing whether the factors affecting dropout are similar in the Italian and the Dutch contexts by testing the predictive capacity of ad-hoc models trained in other university-country settings at three different stages of the student's university journey: (i) enrolment, (ii) end of the first semester, and (iii) end of the first year. Results show that the predictive capacity of models is exchangeable across different contexts, and it improves dramatically once data on university performance becomes available. We find that the models trained in the Dutch context have a better ability to identify dropouts in the Italian context than the other way around. Models trained on Dutch data allow us to better understand the relationship between educational credits obtained, the most important variable across models, and students' dropout. This study contributes to creating a European common arena for discussing Higher Education success issues. [ABSTRACT FROM AUTHOR]

This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading Italian university. [ABSTRACT FROM AUTHOR]