Use of artificial intelligence in the early detection of school dropout: Theoretical elaboration of a major problem in the college cycle in Morocco

In developing countries, the prospects for reducing dropout in the education system are still slim, given the magnitude of the socio-economic challenges that are considered essential to keeping students in school (Mduma et al., 2019). Dropping out of school is therefore one of the challenges faced by most schools in these countries. The development of solution approaches for the control of dropout requires a thorough understanding of the underlying factors. Several researchers have identified and proposed causes, methods and strategies that will help reduce or suppress the problem. However, most of the proposed solutions have not shown promising results and the dropout trend seems to continue in the education systems of several developing countries: in Morocco, the dropout rate increased from 10.8% in 2010-2011 to 10.4% in 2019-2020 in the college cycle according to data from the Ministry of National Education. Furthermore, to prevent dropout researchers have used supervised and unsupervised learning techniques, survival analysis methods, matrix factorization and neural networks (Hung et al., 2017; Elbadrawy et al., 2016). In addition, machine learning has attracted a lot of attention when it comes to solving societal problems in different sectors, including the education sector (Elbadrawy et al., 2016; Xu et al., 2017). In order to contribute to the analysis and reduction of the phenomenon, this research uses recent advances in data science and educational technologies to understand and model the dropout phenomenon in order to lay the foundation for an early dropout detection system in Moroccan junior high schools. From a methodological point of view, we use a four-step approach. We propose to conduct a systematic review of the determinants of school dropout in Africa on the one hand and the various options for combating school dropout, including the use of artificial intelligence, on the other. We then adapt the questionnaire developed by the Quebec team for dropout screening (Fortin et al., 2007) to Moroccan conditions, followed by the training of a predictive model for early detection of dropout in the college cycle in Morocco. Finally, we propose a model of argumentation applied to the case of school dropout by providing justifications for the steps leading to a result and making explicit the arguments that support the decisions.

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