# A Study on Dropout Prediction for University Students Using Machine Learning
## 3 Proposed Method
### Feature Selection  
To perform supervised learning, it is necessary to merge the tables in Table 2 into a single table. The new table used for learning is constructed as follows. 1. In the new table, records are stored by SID. To implement this, records with the same SID in the existing source tables need to be converted into a single record consisting of summarized values of attributes in the records. From this, the new table is called the summary table, as seen below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SID** & **Year** & **Semester** & **Status** & \(\cdots\) \\ \hline
2012 xxx012 & 2012 & 1 & Admission(0) & \(\cdots\) \\
2012 xxx012 & 2012 & 2 & Enrollment(1) & \(\cdots\) \\
2012 xxx012 & 2013 & 1 & Leave-of- & \\  & & & absence(2) & \\
2012 xxx012 & 2013 & 2 & Leave-of- & \\  & & & absence(2) & \\
2012 xxx012 & 2014 & 1 & Dropout(4) & \\
2012 xxx013 & 2012 & 1 & Transfer(3) & \\
2012 xxx013 & 2012 & 2 & Enrollment(1) & \(\cdots\) \\
2012 xxx013 & 2013 & 1 & Dropout(4) & \(\cdots\) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example records of dropout and non-dropout students in the AcademicStatus table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SID** & **Year** & **Semester** & **Grade** & **NumCourse** & \(\cdots\) \\ \hline
2012 xxx010 & 2012 & 1 & 3.53 & 17 & \(\cdots\) \\
2012 xxx010 & 2012 & 2 & 2.69 & 16 & \(\cdots\) \\
2012 xxx010 & 2013 & 1 & 3.17 & 18 & \(\cdots\) \\
2012 xxx010 & 2013 & 2 & 2.53 & 16 & \(\cdots\)  
2. When merging all source tables, 150 attributes could be added to the summary table. Not all of these attributes have a significant impact on student dropout. If attributes having less relevance to the dropout are used for learning, prediction accuracy may decrease. So, only attributes with a high correlation with the dropout are extracted and added to the summary table. Regarding 1, Table 5 shows the list of candidate attributes that can be extracted from the source tables in Table 2 and added to the summary table. First, from the Grade table, an average grade and the number of F grades can be obtained for each student, which are denoted as the Grade and NumF attributes in Table 5, respectively. To get the average grade from the multiple records with the same SID, the simple exponential smoothing function [34] was adopted. The function can give higher weight to recent values, as follows. \[y=\alpha y_{t}+\alpha(1-\alpha)y_{t-1}+\alpha(1-\alpha)^{2}y_{t-2}+\cdots \tag{1}\]  
In expression (1), the parameter \(\alpha\) denotes the weight, and it was set to 0.8 in the proposed method. \(y_{i}\) denotes the grade of the \(i\)-th record, where \(y_{t}\) is the most recent. Consequently, older records are multiplied by a weighting factor of 0.2. In the example of Table 3, when (1) is applied to the records of student 2012 xxx010, y is calculated as 3.73. For the NumF attribute, the number of F grades in the last semester of attendance is extracted and stored as an attribute value. From the AcademicStatus table, the number of semesters enrolled, the number of consecutive semesters of leave-of-absence, and dropout information can be obtained for each student, which attributes are denoted as NumSem, NumAbs, and Dropout in Table 5, respectively. The values of these attributes can be extracted from the Status attribute of the AcademicStatus table. For example, the value of NumSem can be calculated as the number of records whose values of the Status attribute are of Admission (0), Enrollment (1), or Transfer (3). Similarly, the value of NumAbs can be calculated as the number of records whose Status values are equal to Leave-of-absence (2). In the example of Table 4, the values of NumSem and NumAbs for student 2012 xxx012 are equal to 2. The value of the Dropout attribute is set to 1 if Status of the last semester record of a student is equal to Dropout (4). Otherwise, it is set to 0.