## 2 Related Work
[13] carried out their study on a sample of 877 students from two Spanish universities asking them questions about their intention to drop out. A path model was used to find a correlation between three variables: satisfaction with the course, student commitment, and the use of self-regulated learning strategies. Therefore, many of these variables are directly related to the students themselves. Student demotivation and engagement are important variables in predicting the intention to drop out of university. If the level of engagement is higher, the student is less motivated to drop out. [PERSON]. [14] proposed a two-layer stacking set based on a hybrid of random forest (RF), gradient boosting (GB), external gradient boosting (XGBoost), and feed-forward neural networks (FNN) to design a predicy dropout system based on academic data collection at Constantine the Philosopher University in Nitra. A comparison of models based on the test accuracy and AUC demonstrated that the proposed method performed better under the same conditions. Along with this, gender and age are also significant predictors. According to previous research, female students are less likely to drop out of university than male students, due to their better study habits and higher education priorities; while those who are older than the college entrance standard or those who did not go directly to college after high school are likely to leave university [15]. [PERSON]. [16] compared the performance of three countries, Ethiopia, Australia, and Saudia Arabia, considering the [PERSON] national cultural model, which includes social, economic, psychological, and organizational factors. The overall results showed that the reasons for student dropout are country-specific and vary according to the country's culture and the economic and organizational resources it deploys. Therefore, the factors do not have the same impact and cannot be generalized. In Ref. [17], low academic performance was determined to be one of the major reasons behind dropping out. This issue aligns with a range of personal and institutional factors that contribute to university dropout rates as confirmed by the use of the Categorical Boosting classifier [18]. The study by [PERSON]. [19; 20] used naive Bayesian, support vector machine, logistic regression, and artificial neural networks models, which are effective in predicting student at risk of dropping out from university courses and programs. [PERSON]. [19] observed that the use of binary classification models can predict student dropout at the course level, even with a small dataset. Nonetheless, some limitations remain, including the need to explore performance indicators in depth, to collect data considering relevant characteristics, and to design learning activities. Moreover, the main limitation concerns the chosen time threshold for the prediction, which depends on the course. As recommended by Ref. [19], it is possible to choose an appropriate intervention strategy if the time dimension is incorporated into dropout prediction through the input features available in a specific period. According to Ref. [20], the accuracy rate is between 0.88 and 0.96, and the proposed method is accurate, which allows the advisor enough time to implement interventions. Furthermore, various studies highlight the potential of machine learning techniques to create new predictive systems aimed at solving the problem of university dropout. These systems help students plan their future and improve their academic careers. Despite the development of numerous theories and models by researchers, all the case studies and their findings have limitations and lack a general consensus on the factors that influence students' decisions to interrupt their studies. The reasons for dropping out remain linked to the student's general context. The problem of Moroccan university dropout is more observed in the first cycle consisting of three years of study leading to a _licence_ (bachelor's degree).