## 6 Experimental Results and Simulations
### Results with Feature Selection
#### 6.3.5 The Most Relevant Features Selected by EWOA-V4  
To explore the most relevant features that impact students' performance, we employed ten independent runs using EWOA-V4 for both datasets. Table 14 shows the selected features for each run over Data1. Obviously, second-period grade (G2) appears in all runs, which means that tutors should give more attention to this feature, while traveling-time, absence, and first-period grade (G1) affect the student's performance. Moreover, the obtained average for selected features shows that at least two features have an effect, and one of them should be a second-period grade (G2). Table 15 explores the selected features for Data2. From the reported results, three features (i.e., instr, Attendance, and difficulty) are the most relevant features that tutors should pay attention to them to predict students' performance. Finally, Table 16 summarizes the selected features for each dataset based on the number of selections and ratios. We believe that each educational organization should examine their data carefully to find the most relevant features that affect their students' performance. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**WOA-S2**} & \multicolumn{2}{c}{**EWOA-S2**} & \multicolumn{2}{c}{**WOA-V4**} & \multicolumn{2}{c}{**EWOA-V4**} \\ \cline{3-10}  & & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** \\ \hline \multirow{3}{*}{Data1} & AUC & 0.90695 & 0.00187 & 0.91683 & 0.00133 & 0.91445 & 0.00156 & 0.91573 & 0.001046 \\ \cline{2-10}  & Features & 14.30 & 3.20 & 2.2 & 0.918937 & 1.40 & 0.70 & 1.70 & 1.251666 \\ \cline{2-10}  & Fitness & 0.09659 & 0.00209 & 0.08302 & 0.001421 & 0.08513 & 0.00157 & 0.08396 & 0.001289 \\ \hline \multirow{3}{*}{Data2} & AUC & 0.64625 & 0.00155 & 0.65468 & 0.002318 & 0.65526 & 0.00050 & 0.65569 & 0.000556 \\ \cline{2-10}  & Features & 19.50 & 2.63 & 3.9 & 2.84605 & 3.00 & 0.00 & 3.00 & 0.00 \\ \cline{1-1} \cline{2-10}  & Fitness & 0.35631 & 0.00115 & 0.34308 & 0.003179 & 0.34223 & 0.00050 & 0.34180 & 0.00055 \\ \hline \multicolumn{10}{l}{Overall Rank (F-Test)} & \multicolumn{2}{c}{4.00} & \multicolumn{2}{c}{2.33} & \multicolumn{2}{c}{2.08} & \multicolumn{2}{c}{**1.58**} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison between EWOA and WOA based on best TFs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Measure**} & \multicolumn{2}{c}{**AUC**} & \multicolumn{2}{c}{**No. Selected Features**} & \multicolumn{2}{c}{**Fitness**} \\ \cline{3-8}  & & **WOA-S2** & **WOA-V4** & **WOA-S2** & **WOA-V4** & **WOA-S2** & **WOA-V4** \\ \hline \multirow{3}{*}{Data1} & Avg & 0.90695 & 0.91445 & 14.30 & 1.40 & 0.09659 & 0.08513 \\  & Std & 0.00187 & 0.00156 & 3.19722 & 0.69921 & 0.00209 & 0.00157 \\ \hline \multicolumn{8}{c}{Wilcoxon (\(p\)-value)} & \multicolumn{2}{c}{1.81E-04} & \multicolumn{2}{c}{1.22E-04} & \multicolumn{2}{c}{1.82E-04} \\ \hline \multirow{3}{*}{Data2} & Avg & 0.64625 & 0.65526 & 19.50 & 3.00 & 0.35631 & 0.34223 \\  & Std & 0.00155 & 0.00050 & 2.62679 & 0.00000 & 0.00115 & 0.00050 \\ \hline \multicolumn{8}{c}{Wilcoxon (\(p\)-value)} & \multicolumn{2}{c}{1.83E-04} & \multicolumn{2}{c}{6.07E-05} & \multicolumn{2}{c}{1.83E-04} \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of top variants WOA-S2 and WOA-V4 in terms of AUC, selected features, and fitness rates.