Contribution of deep learning to predictive models for early dropout detection: the case of high school students in the Rahmna region.

Despite improved investment in the education sector, a paradox persists which can be summed up as the inability of the system to provide basic skills training and harmful repetition practices that lead to early school leaving for a large part of the school population [1]. Thus, school dropout is one of the challenges faced by most schools in developing countries, particularly in Africa. In order to solve the dropout problem, a thorough understanding of the underlying factors is essential. 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 trend seems to be continuing in the education systems of several developing countries. In Morocco, for example, the dropout rate has 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. In addition, machine learning has attracted a lot of attention when it comes to solving societal problems in different sectors, including the education sector [2,3].

In order to contribute to the analysis and reduction of the phenomenon, this research uses recent advances in data science and educational technology to understand and model the dropout phenomenon in order to lay the foundation for an early dropout detection system in junior high schools in the Rahmna region.

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. Second, we make a Moroccan adaptation of the questionnaire developed by the Quebec team for dropout screening [4], followed by the training of a predictive model for early school dropout screening in the Rahmna region. Finally, we propose a model of argumentation applied to the case of school dropout by providing justifications for the steps leading to an outcome and making explicit the arguments that support the decisions.