# Implementing AutoML in Educational Data Mining for Prediction Tasks
## 4 Method
### Data Collection  
For the collection of the datasets, we developed a custom plugin for Moodle (Figure 1). The implemented extension computes an outline report of the course's activities. For each student, the outline calculates the number of views for each available module (e.g., activity, resource, folders, forum), the grades of each activity (e.g., assign, workshop, choice, quiz), the number of created posts (if any), aggregated event counters, and her/his final grade. By default, the report is computed from  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Course** & **Female** & **Male** & **Total** & **Course Modules in Moodle** \\ \hline Physical Chemistry I & 122 & 160 & 282 & forum, resource, page, assign, folder \\ Physics III & 90 & 90 & 180 & forum, resource, page, assign \\ Analytical Chemistry Lab & 57 & 72 & 129 & forum, resource, page, assign, folder, URL \\ \hline
**Total** & **269** & **322** & **591** & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary statistics for each course.  
the course starting date to the current date, but results can also be filtered by a specified date. Data are exported in various formats and for regression and classification machine learning tasks. Most of the data are retrieved from the log's table in conjunction with aggregate functions.  
For each machine learning experiment (dropout, pass/fail, regression) we collected six samples of the exported reports, one for each month of the semester. We aimed at experimenting in order to be aware of the precision of the results at the time and to predict failures as soon as possible during the semester. Table 3 lists the total number of logs that were available per course in the Moodle platform. In total, more than 130,000 log events were parsed. As it is expected, the number of events is increased during the semester (Figure 2). The students' interest in the course varies, depending on the course activities. In general, course registrations start in February, final examinations take place in June, and final grades are announced by professors in July. An exception is the \"Physical Chemistry I\" course, by which the logged interactions had not started until March. As such, we will not build models for the first period.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **February 2018** & **March 2018** & **April 2018** & **May 2018** & **June 2018** & **July 2018** \\ \hline Physical & 0 & 2075 & 7617 & 13,175 & 9298 & 787 \\ \cline{2-7} Chemistry I & 0 & 2075 & 9692 & 22,867 & 32,165 & 32,952 \\ \hline \multirow{2}{*}{Physics III} & 1156 & 8007 & 7259 & 10,369 & 7760 & 617 \\ \cline{2-7}  & 1156 & 9163 & 16,422 & 26,791 & 34,551 & 35,168 \\ \hline \multirow{2}{*}{Analytical Chemistry Lab} & 5800 & 28,564 & 8499 & 17,054 & 1623 & 416 \\ \cline{2-7} Chemistry Lab & 5800 & 34,364 & 42,863 & 59,917 & 61,540 & 61,956 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of activity logs per month for each course (separate and aggregated). e.g., 7617 records were created for Physical Chemistry I in the logs table during the third month (April), and 9692 records were created until the end of the third month (i.e., 2075 + 7617).  
Figure 1: Screenshot of the custom report plugin on Moodle. The report outline lists all the available learning activities of the course (modules) along with a corresponding score for each participant (student). Scores are calculated according to the resource type (e.g., for the ’page’ module we count the total number of student access). The report can be exported in two formats (arff, sls) and for various data mining tasks (classification, regression). Results can be calculated until a specified date.