Centroid competitive learning approach for clustering and mapping the social vulnerability in Morocco

Abstract

Over the last three decades, growing inequalities in countries have compounded the issues faced by society's most vulnerable populations. Students are facing the brunt of the effects in particular. A student's social vulnerability emerges as a result of the interaction of a variety of individual and environmental factors that accumulate over time. Poverty, material deprivation, and a lack of parental education can all have an impact on student assessment in school. Previous research has focused on the impact of psychological, cognitive, and physical functioning on children's education, ignoring students’ social vulnerability and its impact on educational achievements in developing countries. This paper aims to identify vulnerable regions that need attention and intervention by clustering Moroccan students based on their social vulnerability using an unsupervised competitive learning approach “Centroid neural network,” subsequently displaying the results in a choropleth map to visualize the results. For this purpose, we used the PISA dataset which contains the full set of responses from individual students focusing on specific information such as their parent’s backgrounds, socioeconomic position, and school conditions. Based on our current findings, we concluded that social vulnerability has a detrimental impact on students’ cognitive development. Moreover, the choropleth map shows that 'Beni Mellal-Khenifra' has the highest level of social vulnerability among all regions, besides "Marrakech-Safi" "Eddakhla-Oued Eddahab" and "Guelmim-Oued Noun" all of which have a high level of social vulnerability as well, urging academicians to incorporate resilience building into the design of policies in these regions in order to improve student’s educational outcomes.