# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 4 Method
### Feature Importance  
During our research, additional procedures to understand more comprehensively the datasets were performed. More specifically, we used extremely randomized trees [65] to evaluate the importance of features on the classification and regression tasks (We used ExtraTreesClassifier and ExtraTreesRegressor ensemble methods from sklearn Python library, that return the feature importance (e.g., see [[https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html)))). We therefore indicated the informative features for each course (Figure 3). Table 6 summarizes the results found by listing the module categories that were estimated above each average per course and per task. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Physical Chemistry I**} & \multicolumn{3}{c}{**Physics III**} & \multicolumn{3}{c}{**Analytical Chemistry Lab**} \\ \hline \multirow{4}{*}{Classification (Pass/Fail)} & 14 & \multirow{4}{*}{13} & \multirow{4}{*}{2 assign views, total activity, 4 resources, 3 assigns, 1 folder, forums} & 13 & \multirow{4}{*}{2 assign views, total activity, gender} & 10 \\  & & total views, 3 assign & & & & & 8 assigns, 1 resources, \\ \cline{1-1}  & & total views, 3 assign & & & & & 1 assign views \\ \cline{1-1}  & & total views, 2 assign & & & & & & \\ \cline{1-1}  & views, total activity, & 3 resource, total views, & & & & & 7 assigns \\ \cline{1-1}  & 2 resources, 3 assigns & & & & & & & \\ \hline \multirow{4}{*}{Classification (Dropout/No Dropout)} & 17 & \multirow{4}{*}{10} & \multirow{4}{*}{15} & \multirow{4}{*}{7 assign views, total activity, 2 resources, total views, 4 total activity} & 15 \\  & & total views, 3 assign & & & & & 7 assign, 5 folder, 3 assign \\ \cline{1-1}  & & views, total activity, & & & & & 7 assigns, total views \\ \cline{1-1}  & & 1 folder, 1 forum, 2 pages & & & & & & \\ \hline \multirow{4}{*}{Common Important Features} & 9 & \multirow{4}{*}{8 total views, total activity, 2 assign views, 2 assigns} & \multirow{4}{*}{10} & \multirow{4}{*}{7 assign views, total activity, 2 resources, total activity, 2 resources, total views, total views} \\  & & total views, total activity, & & & & & 6 \\ \cline{1-1}  & & 2 assign views, & & & & 4 assign & 6 assigns \\ \cline{1-1}  & & 2 resources, 2 assigns & & & & 1 assign & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Features above the average importance per course and per task. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Regression**} & \multicolumn{3}{c}{**Classification**} & \multicolumn{3}{c}{**Classification**} \\ \cline{2-9}  & **Min** & **Max** & **Mean** & **Std** & **Pass** & **Fail** & **Dropout** & **No Dropout** \\ \hline Physical Chemistry I & 0 & 10 & 3.98 & 2.92 & 134 & 148 & 78 & 204 \\ Physics III & 0 & 10 & 3.62 & 3.17 & 74 & 106 & 44 & 136 \\ Analytical Chemistry Lab & 0 & 10 & 6.27 & 2.73 & 105 & 24 & 8 & 121 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive statistics for each course. Figure 3: The plot suggests the features that are informative for each task. However, such data approaches rarely can lead to conclusive results. As expected, features related to assignment grades were far more informative that views counters when considering regression tasks. A representative example is the laboratory course of Analytical Chemistry; as such type of courses concentrate on conducting assignments related to the theory of a related course. In-class exams were indicated as good predictors also by [PERSON] et al. [32]. On the other hand, pass/fail classification tasks list among the effective and some features that are related to how many times a student accessed resources and pages.