## 4 Experimental Results
### Experiment Results  
#### 4.3.1 Results of Case 1 and Case 2  
Table 3 shows results of Case 1, which is our original data. For Case 1, the mean values (standard deviation) of overall accuracy are 51.20% (0.44%), 47.86% (0.68%), 52.61% (0.7%), and 41.67% (1.70%) for CART, C5.0, RF, and MLP, respectively. From Table 3, we can find all models built by these algorithms cannot achieve an acceptable performance. The reason may be that we divided too many class labels (EX, VG, G, AVG, Poor). Therefore, we combined the majority (VG, G, AVG) into a new class label (Normal) for Case 2 because we expected the models can predict the minority.  
Table 3 also lists results of Case 2. For Case 2, the mean values (standard deviation) of overall accuracy are 87.50% (0.44%) for CART, 91.60% (0%) for C5.0, 89.62% (0%) for RF, and 89.91% (1.05%) for MLP. The prediction accuracies have been significantly improved. Among these four algorithms, C5.0 outperforms MLP, CART, and RF.  
Table 4 reports the confusion matrix of C5.0 in Case 2. It is obvious that C5.0 algorithm cannot recognize the minority classes (EX and Poor). In other words, the constructed prediction models by C5.0 algorithm cannot identify excellent and poor students. Those minority are usually important for HEIs' management to invest teaching resources and offer special assistance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Actual** & **Predicted** & **EX** & **Normal** & **Poor** \\ \hline EX & 0 & 93 & 0 \\ Normal & 0 & 2206 & 0 \\ Poor & 0 & 108 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix of C5.0 in Case 2 (fold 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Case 1: Origin**} & \multicolumn{6}{c}{**Case 2: Combination of Majority**} \\ \multicolumn{6}{c}{**EX-VG-G-AVG-Poor Classification**} & \multicolumn{6}{c}{**EX-Normal-Poor Classification**} \\ \cline{2-10}
**Experiment No.** & \multicolumn{2}{c}{**CART**} & **C5.0** & **RF** & **MLP** & **CART** & **C5.0** & **RF** & **MLP** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 51.86 & 47.80 & 52.69 & 42.32 & 87.75 & 91.60 & 89.62 & 90.66 \\
2 & 51.24 & 47.30 & 51.86 & 39.83 & 86.92 & 91.60 & 89.62 & 90.66 \\
3 & 50.62 & 47.90 & 52.48 & 41.28 & 87.34 & 91.60 & 89.62 & 90.66 \\
4 & 50.82 & 47.20 & 52.28 & 40.87 & 87.55 & 91.60 & 89.62 & 90.66 \\
5 & 51.45 & 49.10 & 53.73 & 43.15 & 87.13 & 91.60 & 89.62 & 90.66 \\
6 & 52.28 & 46.20 & 52.28 & 40.66 & 88.17 & 91.60 & 89.62 & 87.75 \\
7 & 52.07 & 49.10 & 52.69 & 41.70 & 88.17 & 91.60 & 89.62 & 90.24 \\
8 & 51.86 & 47.90 & 52.07 & 41.28 & 88.38 & 91.60 & 89.62 & 90.04 \\
9 & 50.82 & 47.30 & 53.11 & 45.58 & 86.92 & 91.60 & 89.62 & 88.58 \\
10 & 52.07 & 48.60 & 52.28 & 40.04 & 87.75 & 91.60 & 89.62 & 89.21 \\ Mean & 51.51 & 47.84 & 52.55 & 41.67 & 87.61 & 91.60 & 89.62 & 89.91 \\ Standard Deviation & 0.60 & 0.91 & 0.55 & 1.70 & 0.53 & 0.00 & 0.00 & 1.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of Case 1 and Case 2.  
For our research purposes, this prediction model can only find normal students. The students who need tutoring with poor learning effectiveness and the gifted students who need additional teaching resources to achieve higher achievements will not be identified. Therefore, we implemented another experiment similar to Case 3 in which we focused only the minority classes: Excellent and Poor.