ADULT EDUCATION: A PATH TO SOCIAL INCLUSION. A CASE STUDY IN MOLISE (ITALY)
Journal: Quality Issues and Insights in the 21st Century (Vol.3, No. 1)Publication Date: 2014-12-23
Authors : Daniela Grignoli;
Page : 31-40
Keywords : active citizenship; adult education; participation; social inclusion; quality of life;
Abstract
The society needs to rediscover education as a means for social inclusion. One of the most difficult challenges, at this respect, is to define the social inclusion of marginalized groups (the unemployed, the elderly, the low educated). Very seldom these people take an active part in society: having access to education can be a way to avoid marginalization.
This study will analyse the results of the European research project EDucation Against Marginalization (EDAM). Eight partners from as many European member States developed a database on life-long learning, which offers a perspective on whether and on how educational programs, aimed at improving the social inclusion of marginalized groups, can be optimized. In particular, the focus of this article is on the EDAM survey carried out in Molise, which involved 132 respondents out of a total of 995 at project level. The instruments of the survey were two questionnaires, addressed to social vulnerable people involved in educational programmes, and aimed at detecting to what extent attending such programmes is beneficial to their social inclusion. The questionnaires were explained and delivered in two different stages, 15 classes away from one another, by the respective teachers, specifically trained for the purpose. With respect to the social inclusion process, the analysis of the results in Molise showed an increase of 52.3% for participation and of 37.4 % for connection. Moreover, the results have pointed out that after participating in adult education programmes, the participants have experienced an increased capability of “history making” of society. In any case, although the results show to be fairly robust and the models fit is always adequate, using all the data as a single sample requires that we interpret the data with caution.
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