# Multi-Class Phased Prediction of Academic Performance and Dropout in Higher Education
## 4 Materials and Methods
### Data Description  
As one of the goals of this work is to study the best and earliest phase to predict students' performance, three different datasets were built from the base one. They all have in common the demographic, social- and macro- economic information but differ in the content of the academic information: a base dataset (S0), with academic information restricted to the students' path at the time of enrollment in higher education; a dataset that additionally includes the information regarding the students' academic results during the first academic semester in higher education (S1); and a dataset that additionally includes information regarding students' academic results during the first and second semesters (S2). The features in the base dataset are presented in Table 1. The additional features in dataset S1 are presented in Table 2. Dataset S2 additionally contains equivalent features for the second semester. The distribution of the records among the three categories described above depends on the moment considered and presents an imbalanced distribution among the three classes, as shown in Figure 1. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature Class** & **Feature Name** & **Type** \\ \hline \multirow{6}{*}{Demographic} & Age at enrollment & Discrete \\  & Sex & Binary \\  & Marital Status & Nominal \\  & Displaced Student & Binary \\  & Foreign student & Binary \\  & Nationality & Nominal \\ \hline \multirow{6}{*}{Social-Economic} & Mother’s qualification & Nominal \\  & Father’s qualification & Nominal \\  & Student’s previous qualification & Nominal \\  & Mother’s occupation & Nominal \\  & Father’s occupation & Nominal \\  & Scholarship holder & Binary \\  & Debtor & Binary \\  & Tuition fees up to date & Binary \\  & Educational Special Needs & Binary \\ \hline \multirow{6}{*}{Academic (at enrollment)} & Application Order & Discrete \\  & Application Mode & Nominal \\  & IPP School & Nominal \\  & Degree & Nominal \\  & Admission Grade & Continuous \\  & Daytime/Evening Attendance & Binary \\  & Number of National Exams taken by student & Discrete \\  & Student’s Average Grade at National Exams & Continuous \\ \hline \multirow{2}{*}{Macro-Economic (enrollment year)} & Unemployment Rate & Continuous \\  & Inflation Rate & Continuous \\  & Real Gross Domestic Product Rate & Continuous \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of features for the base dataset. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature Class** & **Feature Name** & **Type** \\ \hline \multirow{6}{*}{Demographic} & Number of CU enrolled & Discrete \\  & Number of CU attended & Discrete \\  & Number of CU approved & Discrete \\  & Average grade & Continuous \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of additional features for the dataset S1. Additional features for dataset S2 represent the same information but for the second semester. CU: Curricular Units. The lower number of samples with the label \"Dropout\" in dataset S2 is due to the fact that once the student drops out, those records are removed from the academic system. Although the entries regarding these students could be kept in the datasets, we opted to remove them because the final goal is to develop a system that can be used in the real scenario, where students' records are removed once they drop out. It is also worth noticing that most students who drop out do it during the second semester. Figure 1 shows that for datasets S0 and S1, there is one majority class (\"Graduate\") with half the number of total records and two equally distributed minority classes with approximately a quarter of the records each. However, for dataset S2, the two minority classes differ significantly from each other, with the label \"Dropout\" being the less represented class with only 10% of the registers. The most important classes for accurate classification are the minority ones since the students from these classes are the ones that might benefit the most from planned interventions for academic support and guidance.