Conference Program

School dropout is a complex and persistent phenomenon with significant and concerning repercussions at both the individual and social levels. At the individual level, it affects employment, health and well-being, while at the social level, it fosters greater economic and social inequalities and hinders the development of a more equitable and inclusive society. Given the potential consequences of school dropout for individuals and society, preventive intervention is not only a priority, but also an ethical necessity. However, although research shows that dropping out is not a sudden act, but rather the result of multiple factors, the data currently available at national and international levels is often inaccurate, fragmented and «ex post», i.e. collected months or even years later.

Consequently, longitudinal models that use Big Data to monitor students’ pathways over time are needed. This will allow us to identify situations of student hardship early on and intervene before they result in school dropout.

Based on these premises, the present study stems from a doctoral project carried out in collaboration with the municipality of X in Italy. The aim was to build a shared system for data collection and management among the various local educational agencies to improve monitoring of school dropout through longitudinal, continuous and updated data. In this context, the models used by five educational agencies in the X area were examined (the Ministry of Education, the Province, and the Vocational Education and Training department).

A comparative and documentary analysis of the surveyed data and systems highlights significant heterogeneity in the methods used to collect, manage and measure the school dropout rate. While the sources demonstrate some operational differences, they also exhibit structural issues that hinder an integrated interpretation of the phenomenon. In general, there is a lack of clear definition of the construct, a problem that has already been highlighted in the literature, which leads different agencies to measure the phenomenon using different and often non-overlapping variables, such as dropouts, withdrawals and transfers.

Similarly, most models use a data collection system at specific moments (e.g. the end of the school year or transition between cycles) which does not allow for timely intervention with students, but which, as previously mentioned, provides data referring to months or even years earlier.

Some differences instead concern the scope of coverage: these range from the Ministry of Education and Merit, which is responsible for schooling and ensures coverage across the entire territory, to three education and training systems (therefore relating to a more limited student population), up to the Province, which, despite having access to a large amount of data, responds more to planning purposes than evaluative ones and therefore does not deal with analyzing the phenomenon.

This fragmentation confirms what has emerged in national and international literature and makes the need for shared data systems among agencies operating in the same territory, including both education and training, even more urgent. From this perspective, data integration is essential for the effective and timely prevention of school dropout.