# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 6 Discussion  
The results illustrated in previous sections unveil the effectiveness of the autoML methods in educational data mining processes. Without any human intervention, the suggested models report performance often much better of classic supervised learning techniques with default hyperparameters settings. Even from the early half of the semester, predictions have satisfactory values. The result models can help educators and instructors to identify weak students, improve retention and reduce academic failure rates. It can also lead to improved educational outcomes. Finding appropriate models and hyperparameter configurations is one of the most difficult processes when building a machine learning solution and indeed, it is central to the pursuit of precision. The main advantage of autoML and tools like Auto-WEKA is that they provide an out-of-the-box mechanism to build reliable machine learning models without the need for advanced data science knowledge. People in IT, educational administration, teaching, research or learning support roles, \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 Naïve Bayes & 0.463 & 0.680 & 0.710 * & 0.699 & 0.743 & 0.744 \\ \hline Random Forest & 0.487 & 0.697 & 0.704 & 0.689 & 0.708 & 0.716 \\ \hline Bagging & 0.495 & 0.723 * & 0.690 & 0.702 & 0.747 * & 0.760 * \\ \hline PART & 0.477 & 0.590 & 0.660 & 0.722 * & 0.651 & 0.742 \\ \hline SMO & 0.500 * & 0.500 & 0.500 & 0.500 & 0.496 & 0.496 \\ \hline IBk-5 NN & 0.486 & 0.649 & 0.632 & 0.655 & 0.740 & 0.737 \\ \hline Auto-WEKA & \begin{tabular}{c} 0.545 \\ Decision \\ Stump \\ \end{tabular} & \begin{tabular}{c} 0.883 \\ Random \\ Tree \\ \end{tabular} & \begin{tabular}{c} 0.778 \\ Random \\ J48 \\ \end{tabular} & \begin{tabular}{c} 0.801 \\ LMT \\ \end{tabular} & \
\begin{tabular}{c} 0.842 \\ LMT \\ \end{tabular} &
\begin{tabular}{c} 0.784 \\ LMT \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Overall ROC area results regarding the “Physics III” course and dropout classification task. Figure 6: Comparative results of the effectiveness of autoML over classic ML methods. The results indicate that in all cases, the effectiveness of the best classic classifier was improved when we applied autoML. who want to explore how their students perform, are not always experts in educational data mining. Therefore, despite having basic knowledge, novice ML users can converge easily to a suitable algorithm and its related hyperparameter settings and develop trusted machine learning models to support their educational institutions. In our comparative study we focus on classification and regression using educational data. We evaluated widely known ML algorithms from 6 different categories versus 2 allowed categories in Auto-WEKA, on 51 datasets exported from 3 compulsory courses, using the Moodle LMS as a complement to face-to-face lectures. For each course, we collected 6 samples of data--one for each month of the semester--in order to predict students' final performance as soon as possible. The total number of students (591) who attended the courses produced more than 130,000 log events between February 2018 and July 2018. Logs were triggered by a variety of learning modules that structured the courses--fora, pages, resources, assignments, folders, and URLs. Assignment grades were scaled to a common metric. The final performance for each ML task--pass/fail, regression and dropout--was adjusted to represent the class attribute appropriately. We further indicated the informative features for each course using the extremely randomized trees. We concluded that attributes related to assignment grades were more important than views counters when performing regression tasks. For pass/fail classification, apart from assignment grades, some features related to how many times a student accessed a resource or a page also had high importance scores.