# University Student Dropout Prediction Using Pretrained Language Models
## 3 Methodology
### Data Description
#### 3.1.2 Data Modality  
Freshmen data can be expressed using demographic information that a student already possesses before admission (e.g., gender and age) and academic information that indicates the student's behavior during the semester (e.g., GPA and course evaluation). Table 1 describes each feature of the demographic and academic information. We divide the features into three categories based on data type: categorical, numerical, and textual. Categorical features belong to one of two or more categories, numerical features are values within a specific range, and textual features are text data. Categorical features included admission type, detailed admission type, student club, and academic activity. The admission type was determined by the university's admissions system, which consisted of evaluating students based on their Korean SAT scores, evaluating students based on their high school grades and extracurricular activities without taking an entrance exam, and evaluating foreign students. In addition, the detailed admission type was specifically applied to the second admission type (e.g., university admission interview and essay test). The student club indicated whether a student joined a club, while the academic activity indicated their involvement in university-held academic activities. In the numerical features, the parental financial income was calculated by national scholarships students received based on their income level, and the volunteer hours were the number of volunteer hours a student has performed off- and on-campus. Lastly, the course evaluation score, a numerical feature, was one of the course evaluation methods, and the course evaluation comment was written by students and was a textual feature. We formulated all the features in a natural language format that was predesigned for each feature (see Section 3.2.1). Table 3 lists the templates used in the formulation along with an example.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Feature** & **Type** & **Classes** & **Description** \\ \hline Birth year & Categorical & 24 & The birth year of the student \\ \hline Gender & Categorical & 2 & The gender of the student \\ \hline Nationality & Categorical & 12 & The nationality of the student \\ \hline Graduation year & Categorical & 23 & The year of high school \\ graduation \\ \hline High school & Categorical & 1564 & The graduating high school \\ \hline High school region & Categorical & 24 & The region of the graduating \\ high school & & & high school \\ \hline Admission year & Categorical & 5 & The year the student was \\ \hline Admission type & Categorical & 8 & The type of admission \\ \hline Detailed admission type & Categorical & 14 & The type of \\ \hline Major field & Categorical & 13 & The major field of the student \\ \hline Parental financial income & Numerical & - & The income level divided \\ \hline Major GPA & Numerical & - & The average grade point \\  & & & of majors \\ \hline Nonmajor GPA & Numerical & - & The average grade point \\  & & & of nomnajors \\ \hline Volunteer hours & Numerical & - & The hours of volunteering \\ \hline Academic information & & & Whether one participates in \\  & & & a student club or not \\ \hline Academic activity & Categorical & 2 & Whether one participates in \\  & & & an academic activity or not \\ \hline Course evaluation & & & Evaluation score \\  & & & for courses taken \\ \hline Course evaluation & & & Evaluation comments \\ comments & & & for courses taken \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.  
Figure 1: Distributions for the categorical data expressed as a percentage.