## 3 Methodology
### Data Pre-Processing  
There are six pre-processing methods used, as shown in Figure 1: data collection and integration, data filtering, data cleaning, data transformation, and attribute selection. After the data were collected, the different dataset was then integrated based on the student number, and overall, the dataset has a size of 200. For the data cleaning, if there were too many missing values for certain features, the student was removed from the dataset, as this probably occurred if the student missed providing the information by not answering one of the surveys. This results in a lot of missing values in a certain section. The students \begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Question** \\ \hline SE1 & I ask questions during lecture \\ \hline SE2 & I respond to questions during lecture \\ \hline SE3 & I prepare study schedule and study plan \\ \hline SE4 & I ask and seek help from lecturers \\ \hline SE5 & I make additional lecture notes \\ \hline SE6 & I plan my time for examination/test/quiz \\ \hline SE7 & I ask for help from my friends if I face problems or confusion with regards to any particular topic \\ \hline SE8 & I give my best during examination/test/quiz \\ \hline SE9 & I involve myself in academic discussions with friends \\ \hline SE10 & I take note of/accept assignment feedback \\ \hline SE11 & I can explain subject-related contents to my friends \\ \hline SE12 & I try to answer questions well the first time \\ \hline SE13 & I always meet assignment deadlines \\ \hline SE14 & I try to meet the deadlines of group assignments \\ \hline SE15 & I pay attention during each lecture \\ \hline SE16 & I would make it clear in class if I do not understand any part of the lecture \\ \hline SE17 & I feel nervous whenever I am doing a presentation \\ \hline SE18 & I would volunteer myself to present during any group assignment \\ \hline SE19 & I am confident that I can complete my studies within four years \\ \hline SE20 & I take note of/accept feedback that I receive for any examination/test \\ \hline \hline \end{tabular} Note. From [PERSON] et al. [8].  
\end{table} 
Table 4: Survey Questions for Part B (Self-efficacy). \begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & **Attributes** & **Values** \\ \hline
1 & Program & Computer Science, Software Engineering Multimedia, Software Engineering Information Science, Information Technology \\ \hline
2 & Gender & Female, Male \\ \hline
3 & Race & Malay, Indian, Chinese, and Others \\ \hline
4 & Highest Education & Matriculation, STPM, Asasi, Diploma, International \\ \hline
5 & Type of Previous Secondary School/High School & Eight different types of schools \\ \hline
6 & Stream of Study & Pure Science, Technical, Art, Religious, Psychical Science, \\  & & Technical Science, \\  & & Humanities, Physics, Chemistry, ICT, \\  & & Computer Science \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The information on the demographic features. who did not answer one of the surveys were removed entirely. However, if there are only a few missing values, they will be filled in by the mean value. In addition to that, duplicated information was also removed. After cleaning, the dataset is reduced to 140 students. Most of the values in the dataset are nominal except for the learning program outcome features. Therefore, for data transformation, the nominal value was converted into numerical values. This is important as machine learning requires numerical values for the prediction model. In addition to that, StandardScaler was used to normalize this data; this is to ensure the individual features resemble normally distributed data. This is important before performing machine learning to ensure it performs well. Before proceeding to