# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 4 Method
### Data Analysis  
Data related to 6 types of learning activities were collected and they are presented in Table 4. For each module, we calculated a numeric representation. Specifically, for the forum module, we counted the total number of times a student viewed the forum threads. Respectively, we counted the number of times a page, a resource, a folder, and a URL module had been accessed by each student. For the assign module, we counted the number of times a student accessed its description, and the grade that s/he took using the 0-10 scale. We also include a counter that aggregates the number of times a student viewed the course (course total views) and the total number of every kind of log written for a student for the specific course (course total activity). We were not able to examine additional demographic values apart from gender. The class attribute was the final grade that the student achieved, transformed to 0-10 scale, or nominal according to the supervised machine learning problem in question. Students that finally succeeded in a course are the ones that scored above or equal to 5. Dropout students were considered to be the ones whose final grade was an empty value. The dataset does not contain missing values. Students that did not access a learning activity score for 0. Learning resources that were not accessed by any student were not included in the experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Physical Chemistry I** & **Physics III** &
\begin{tabular}{c} **Analytical** \\ **Chemistry Lab** \\ \end{tabular} & **Description** & **Possible Values** \\ \hline gender & gender & gender & Student’s gender & \{female, male\} \\ \hline
7 pages & 1 forum & 2 pages & & \\
17 resources & 6 pages & 4 resources & Total number of times a student & 0 or positive integer \\
2 folders & 15 resources & 17 folders & accessed the resource & \\
8 assign views & 9 assign views & 1 URL & & \\  & & 8 assign views & & \\ \hline
3 assigns & 9 assigns & 8 assigns & Student grade & [0,10] \\ \hline course total views & course total views & course total views & The total number of course views for an individual student & 0 or positive integer \\ \hline course total activity & course total activity & course total activity & The total number of every kind of log written for an individual student & 0 or positive integer \\ \hline
42 attributes & 44 attributes & 45 attributes & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variables extracted from students’ Moodle use for each course.  
Figure 2: The total number of logs per month for each course. Logged activities are increased during the semester, as could be expected.  
The descriptive statistics are represented in Table 5. The mean (average) and standard deviation of the final grades in \"Physical Chemistry I\" were, respectively, 3.98 and 2.92, in \"Physics III\" 3.62 and 3.17, while in \"Analytical Chemistry Laboratory\" 6.27 and 2.73. The 48% of students passed the first course, 41% the second and 81% the third course. Only the 28% of students dropped out of the first course, 24% dropped out of the second and 6% dropped out of the third. It is concluded that Physical Chemistry I and Physics III were more challenging courses, while the high grades on average in the laboratory of Analytical Chemistry affirm that this course was easier.