## 3 Methodology  
In this study, a sequential methodology was adopted, encompassing two parts. The first one focused on collecting data via a survey, while the second part used the gathered dataset to develop predictive machine learning models aimed at identifying factors related to student dropout.  
A quantitative analysis was conducted using a structured survey to examine the factors influencing student dropout from educational institutions. A modified questionnaire was used to collect data from 120 students at an open access faculty of a Moroccan university. The survey collected data on several dimensions, including socioeconomic background, academic performance, choice of university, and students' feelings toward the university and professors (see Table 1).  
\begin{table}
\begin{tabular}{c c l} \hline \hline
**Factors** & & **Main Questions** \\ \hline \multirow{6}{*}{Socioeconomic} & \(\bullet\) & What is your parents’ occupation? \\  & \(\bullet\) & During this academic year, where are you living? \\  & \(\bullet\) & Do you have a university scholarship? \\  & \(\bullet\) & Do you have a job outside of school during the academic year? \\  & \(\bullet\) & What is your main source of monthly income? \\ \hline \multirow{2}{*}{Academic Background} & \(\bullet\) & In what year did you obtain your bacalaureate? \\  & \(\bullet\) & What grade did you get on your high school diploma? \\  & \(\bullet\) & Did you repeat a grade in elementary, middle, or high school? \\ \hline \multirow{6}{*}{Choice of university} & \(\bullet\) & After graduating from high school, how did you discover your choice of studies? \\  & \(\bullet\) & What led you to enroll in university? \\ \cline{1-1}  & \(\bullet\) & Would you have preferred another type of orientation? \\ \cline{1-1}  & \(\bullet\) & For what reason(s) did you choose this field of study? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Factors related to student dropout.  
Figure 1: Word cloud using NVivo.  
SPSS (version 26), an appropriate statistical tool, was used to validate and analyze the data collected from the sample respondents. In addition, a comprehensive dataset was constructed from the survey responses by extracting and organizing all the factors related to student dropout.  
This dataset was used in the second phase of the study, which involved the proposed framework for prediction, structured into three main phases: data input, processing, and results (see Figure 2).  
In the data input phase, information was collected from multiple sources. Due to challenges in obtaining permission to use personal data for predictive modeling, the study relied on data acquired from the survey responses, ensuring privacy and ethical requirements. The processing phase involved data cleaning, encoding categorical variables, normalization, and splitting the dataset into training and testing subsets.  
Several machine learning algorithms, such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and decision tree (DTree), were applied to develop predictive models. The model performance was evaluated using metrics including the accuracy, precision, recall, F1-score, and confusion matrix.  
Finally, the results phase of predictive model building focused on interpreting the models and identifying the most significant predictors of student dropout. This integrated methodology not only allows for the identification of critical dropout factors but also provides a predictive mechanism to identify at-risk students, enabling timely interventions.