## 3 Methodology
### Data Preparation  
The dataset used in this study was obtained from the Faculty of Technology and Information Science at the National University of Malaysia. The dataset consists of information from 200 students in their first semester of the first year during the 2019/2020 session with records of their characteristics based on their demographic, academic, and behavior with 41 attributes. All features which are used to build the model of prediction are listed in the tables below.  
The input features can be divided into four sections which are Part A (Learning Style), Part B (Self-efficacy), Part C (Demographic), and Part D (learning program outcome). Referring to Tables 3-5, information was obtained from the study of [PERSON] et al. [8]. This paper will improve his research, and therefore, new features of a holistic nature will be added. Table 3 is the research questionnaire for Part A (Learning Style), and Table 4 is the research questionnaire for Part B (Self-efficacy). For Parts A and B, these questions were handed out weekly from week 6 to week 9. Both sections are categorical and require students to pick an option most suitable for them. A five-point scale is used in Part B ranges from Strongly disagree (1), Disagree (2), Neutral (3), Agree (4) to Strongly agree (5). Next, Table 5 includes the demographic information of the students. Lastly, Table 6 includes new features obtained from the UKM system based on the learning program outcome score or known as Hail Program Pelajar (HPP) as used by [PERSON] et al. [30] and [PERSON] et al. [5]. The values for this study were obtained from the assignment and lab marks that were then calculated by the system to measure their learning program outcome. Therefore, values for the learning program outcome are between 0.00 to 4.00. The Faculty of Technology and Information Science's learning program outcome was employed in this study. There are eight different types of HPP; however, only HPP1, HPP2, HPP3, HPP4, HPP6, and HPP7 were measured during the student's first semester. At the same time, the output feature is the student's SGPA.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Variable** & **Question** & **Values** \\ \hline X1 & What is your level of involvement during last \\  & week’s lecture? & Very active, Active, Less active, Not active \\ \hline X2 & What is your level of focus during last week’s lecture? & Very focus, Focus, Moderate, Less focus, Not focus \\ \hline X3 & How much have your learned from last week’s lecture? & Very high, High, Moderate, Low, Very low \\ \hline X4 & What would be your course of action if you had & Ask the lecturer, Ask a friend, Find your own \\  & questions during lecture? & information, Silent \\ \hline X5 & How would you describe the conditions of last week’s & Very condusive, Conclusive, Less condusive, \\  & lecture room? & Not condusive \\ \hline  & How much time would you allocate for learning, \\  & including doing revision, assignment, exercise, \\  & discussion and reading? (Interaction during \\  & lecture/lab/tutorial is not considered) & \\ \hline X7 & When did you complete the \\  & exercise/homework/assignment given during last \\  & week’s lecture? & A day after lecture, 2–3 days after lecture, 4–5 days after lecture, A day before next lecture \\ \hline X8 & When was the most effective learning period for you \\  & during the entirety of last week? & In the morning, In the evening, At night, After midnight \\ \hline X9 & Where did you conduct revisions on the subject over the \\  & past week? & Residential college, Library, Others \\ \hline \hline \end{tabular} Note. From [PERSON] et al. [8].  
\end{table}
Table 3: Survey Questions for Part A (Learning Style).