# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 5 Results
### Predicting Students' Academic Performance  
In addition, we conducted a series of experiments to identify the effectiveness of classic data mining algorithms to predict students' grades at early enough stages. Next, we compared the results with the predictions given by models created by applying the autoML. Similar to Section 5.1, the experiments comprise of six phases, one for each month of the semester. Tables 11-13 present the regression results, represented by the mean absolute error measure. Depending on the dataset, we observe that Bagging and the Random Forest algorithms outperform the others in most cases in the first course, Bagging and SMOreg algorithms outperform the others in most cases in the second course, and Random Forest algorithm outperforms the others in most cases in the third course. In total, Bagging is noted to be among the best performers 10 times, Random Forest 9 times, and SMOreg 5 times. Overall, from the predictions generated we could not conclude one best performer for our given datasets. In addition, we used Auto-WEKA to run the automated machine learning experiments for the corresponding datasets. From the results, we identify that in all cases, the mean absolute error was significantly decreased. Auto-WEKA was able to minimize the error from 0.0188 to 0.4055 (Figure 5). The suggested classifiers and hyperparameters again vary across the datasets, including M5P, Random Tree, REPTree and M5 Rules. Overall, tree-based methods were primarily suggested. More specifically, the M5P and Random Tree were the outperformers 5 out of 17 times. Figure 4: Comparative results of the effectiveness of autoML over classic EDM methods. The results indicate that in most cases, the effectiveness of the best classic classifier was improved when we applied autoML. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **February 2018** & **March 2018** & **April 2018** & **May 2018** & **June 2018** & **July 2018** \\ \hline Random Forest & 2.1137 & 1.7123 * & 1.62 * & 1.381 * & 1.3825 * & 1.3864 * \\ \hline M5 Rules & 2.1347 & 1.9989 & 1.6982 & 1.4233 & 1.5202 & 1.5492 \\ \hline Bagging & 2.0715 * & 1.795 & 1.7555 & 1.4894 & 1.4902 & 1.4864 \\ \hline SMOreg & 2.1972 & 1.9377 & 1.7385 & 1.5705 & 1.5401 & 1.5303 \\ \hline IBk-5 NN & 2.3073 & 1.8891 & 1.7302 & 1.4574 & 1.4434 & 1.4426 \\ \hline Auto-WEKA & 1.7935 & 1.6067 & 1.4947 & 1.2135 & 1.0402 & 1.2135 \\ REPTree & REPTree & M5P & M5P & M5P & M5 Rules \\ \hline \hline \end{tabular} \(t\)-test: \(p\)-value = 0.0016, a = 0.05.  
\end{table} 
Table 13: Overall MAE results regarding the “Analytical Chemistry Lab” course and regression task. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **February 2018** & **March 2018** & **April 2018** & **May 2018** & **June 2018** & **July 2018** \\ \hline Random Forest & - & 2.4264 & 1.9346 & 1.7334 * & 1.5784 * & 1.5731 * \\ \hline M5 Rules & - & 2.4504 & 2.0541 & 1.8244 & 1.8169 & 1.782 \\ \hline Bagging & - & 2.3903 * & 1.901 * & 1.7495 & 1.6081 & 1.6136 \\ \hline SMOreg & - & 2.4604 & 2.1352 & 1.9962 & 1.8998 & 1.9037 \\ \hline IBk-5 NN & - & 2.4433 & 2.1556 & 1.9011 & 1.7521 & 1.774 \\ \hline Auto-WEKA & - & 1.9848 & 1.8822 & 1.6715 & 1.4017 & 1.4255 \\ REPTree & REPTree & M5P & M5P & Random & Random \\ Tree & Tree & Tree & Tree & Tree \\ \hline \hline \end{tabular} \(t\)-test: \(p\)-value = 0.0366, a = 0.05.  
\end{table} 
Table 11: Overall MAE results regarding the “Physical Chemistry I” course and regression task. Figure 5: _Cont._