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The Italian National Institute of Statistics (ISTAT) declined a multidimensional approach to measure equitable and sustainable well-being (Benessere Equo Sostenibile, BES) at a detailed territorial level, that is, at the provincial level (NUTS3) entailing a wide spectrum of indicators grouped into domains related to Health, Education, Work and life balance, Economic well-being, Social relationships, Politics and institutions, Security, Landscape and cultural heritage, Environment, Innovation research and creativity, and Quality of services. These indicators can help in describing the territories because they can spot situations of concern, such as in the South of Italy. The gap between North and South Italy has increased over time, a picture we can describe using each of the indicators, but also by jointly considering them by using a factor analysis and clustering and constructing a composite indicator. Contrary to some researchers who have suggested the employment of time series indicators for the period 2004–2016 looking for a latent factor, that takes into account the joint temporal trend of the indicators, in this paper, the situation among three different moments over time is compared: before the 2008 crisis and a few years after it. To do this, we have chosen a certain number of indicators on the basis of their features. [ABSTRACT FROM AUTHOR]

This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Although, great efforts have been made to diagnose dyslexia and to mitigate its effects at primary and secondary school, little has been done at the university level in particular in the Italian context. Indeed in the university context, the availability and possibility to use of support tools, that enable the student to achieve educational success, is still not sufficiently adequate. In this paper we used bivariate association tests and cluster analysis, in order to identify the most suitable compensatory tools and support strategies that can facilitate the students' performance in higher education. The data were obtained through the voluntary participation of Italian students, enrolled in a bachelor degree course, with certified diagnosis of dyslexia. Six groups of students were identified from the cluster analysis, defining specific support tools and learning strategies for each group. Furthermore, through the creation of these six groups, it was possible to describe "profiles" that highlight the risk factors (late diagnosis) and-or protection factors (such as associations, support from friends and family) in analyzing the academic career of students with dyslexia. Therefore, starting from these data, through artificial intelligence it will be possible to identify and suggest study methodologies and create specific support tools for each student that can enable her/him to achieve educational success in her/his academic career. [ABSTRACT FROM AUTHOR]