## Appendix A \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} \\  & & & & & & & & & \\ \hline
[11] & Random forest & 77.29 & NS & 75.6 & 75.6 & 75.6 & xAPI-Edu-Data & 9 \\ \hline
[12] & Random forest & 86 & 68 & 86 & 85 & 85 & Self-data from 3 different universities & 9 \\ \hline
[24] & Decision tree & 79 & NS & 75.1 & 70.3 & 72 & Webpage & 9 \\ \hline
[29] & Decision tree & 98.94 & 99.4 & 100 & 85.7 & 92 & Self-collected & 9 \\ \hline
[31] & Decision tree & 95.82 & NS & NS & NS & NS & UCI repository & 8 \\ \hline
[34] & Decision tree & 96.5 & NS & NS & 93 & NS & Collected data & 9 \\ \hline
[44] & Decision tree & 75 & NS & NS & NS & NS & Self-data & 8 \\ \hline
[46] & Decision tree & 98.5 & 92.1 & 97.3 & 94 & 95.6 & University of Stanford & 9 \\ \hline
[54] & Genetic algorithm-based decision tree & 94.39 & NS & NS & NS &
\begin{tabular}{c} Federal Board \\ of Pakistan \\ \end{tabular} & 8 \\ \hline
[55] & Random forest & 79.8 & 93.8 & 79.8 & 78.8 & 79 & University of Nigeria & 7 \\ \hline
[58] & Random forest & NS & 93 & NS & NS & NS & Self-data & 9 \\ \hline
[59] & Multiple linear regression & 90 & NS & 90 & 89 & 89 & Kaggle & 7 \\ \hline
[60] & Decision tree & 87.21 & NS & 93.65 & 89.39 & NS &
\begin{tabular}{c} University of \\ Phayao \\ \end{tabular} & 9 \\ \hline
[62] & Random forest & 70.1 & NS & NS & NS & NS &
\begin{tabular}{c} University of Li’ege \\ (Belgium) \\ \end{tabular} & 9 \\ \hline
[64] & Decision tree & 94.63 & NS & 95.76 & 98.33 & 71.9 & Open University of China & 10 \\ \hline
[79] & Decision tree & 67.71 & NS & NS & NS & NS &
\begin{tabular}{c} NEDUET, \\ Pakistan \\ \end{tabular} & 9 \\ \hline \hline \end{tabular} Note: NS—not specified.  
\end{table} 
Table 1: Estimation metrics (decision-tree-based models). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \hline
[7] & Ensemble (J48, real AdaBoost) & 95.78 & NS & NS & NS & 0.958 & 0.958 & 0.96 &
\begin{tabular}{c} UCI Student \\ Performance \\ \end{tabular} & 8 \\ \hline
[15] & Ensemble (reptree bagging) & 97.5 & NS & NS & NS & 96.3 & 96.4 & 96.2 & Self-data & 8 \\ \hline
[27] & Ensemble [DT, boosting] & 96.96 & NS & NS & NS & 95.97 & 94.97 & 95.5 & Self-data & 8 \\ \hline
[32] & Ensemble [NN, RF-boosting] & NS & NS & NS & NS & NS & NS & NS & 6 \\ \hline
[43] & Ensemble [NB + AdaBoost] & 98.12 & 96 & 100 & NS & NS & NS & 98 &
\begin{tabular}{c} Directorate of \\ Higher \\ Secondary \\ \end{tabular} & 7 \\ \hline
[44] & Ensemble [DT-XGBoost] & NS & NS & NS & 92.5 & 89 & 89 &
\begin{tabular}{c} OULAD \\ \end{tabular} & 8 \\ \hline
[47] & Ensemble [DT, SMO] & 90.13 & NS & NS & NS & NS & NS &
\begin{tabular}{c} Microsoft showcase \\ school “Ayoulea- \\ Linardatou” \\ \end{tabular} & 9 \\ \hline
[57] & SVM-boosting & 90.6 & NS & NS & NS & 97 & NS & North Carolina & 5 \\ \hline
[61] & [DT, ANN, SVM] & \multirow{2}{*}{98} & NS & NS & 77.7 & 74.52 & NS & 76.1 & Self-data & 10 \\ Stacking ensemble & & & & & & & & & \\ \hline
[63] & Ensemble learning [random forest (RF) and adaptive boosting (AdaBoost)] & \multirow{2}{*}{98} & NS & NS & NS & 91 & 69 & 78 & Self-data & 7 \\ \hline
[66] & RF, KNN, and adaptive boosting & 70 & NS & NS & NS & 70 & 70 & 79 & University of Léon & 9 \\ \hline
[74] & Ensemble [RF, boosting] & 98.22 & NS & NS & NS & NS & NS & Self-data & 10 \\ \hline
[76] & Hybrid linear vector quantization (LVQ + AdaBoost) & 92.6 & NS & NS & NS & 95.6 & 91 & 92.3 & NS & 8 \\ \hline
[78] & Ensemble (DT + K means clustering) & 75.47 & NS & NS & NS & 72.2 & 47.27 & 57.1 & NS & 8 \\ \hline
[80] & Ensemble learning (SVM, RF, AdaBoost + logistic regression via stacking) & NS & NS & NS & 91.9 & 86 & 85.5 & 85 & Hankou University & 10 \\ \hline \hline \end{tabular} Note: NS—not specified.  
\end{table}