# Student Dropout Prediction for University with High Precision and Recall
## 5 Experiment and Analysis
### Classification by Reason for Dropout  
Once the SDP system predicted the students who are about to drop out of the university, we classified the reasons for dropping out. We used a total of 1269 dropout student records for the classification. We encoded the categorical data using ordinal encoding. We conjectured that if dropouts shared similar reasons for their dropout, their data would  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Academic** & **Model** & **Accuracy** & \begin{tabular}{c} **Dropout** \\ **Recall** \\ \end{tabular} & \begin{tabular}{c} **Dropout** \\ **Precision** \\ \end{tabular} & \
\begin{tabular}{c} **Dropout** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Dropout F1** \\ \end{tabular} \\ \hline \multirow{2}{*}{Freshman} & Model 1 & 99\% & 76\% & 95\% & 85\% \\  & Model 2 & 98\% & 79\% & 78\% & 78\% \\ \hline \multirow{2}{*}{Sophomore} & Model 1 & 98\% & 65\% & 96\% & 77\% \\ \cline{2-6}  & Model 2 & 98\% & 69\% & 75\% & 72\% \\ \cline{2-6}  & Model 1 & 99\% & 80\% & 98\% & 88\% \\ \cline{2-6}  & Model 2 & 99\% & 82\% & 89\% & 85\% \\ \hline \hline \end{tabular}
\end{table}
Table 13: Performance of SDP with Respect to Academic Year.  
also be similar. Thus, we used PCA to identify the fields associated with the reasons for dropping out of the university. We have categorized the reasons in to the following labels: \"Employment\", \"Did Not Register\", \"Admitted to Other University\", and \"Personal Issues.\" We used PCA to compress the features listed in Table 6 into five principal components. Figure 6 illustrates the scatter plot of five principal components of PCA. The component in PC1 was \"Number of Scholarships Received\" with a value of 1.000; in PC2, there was \"Residence Postal Code\" and \"Login Count\" with values of 0.917 and 0.386, respectively; PC3 had \"Login Count\" with a value of 0.900; PC4 had \"Department\" with a value of 0.995; and PC5 had \"Grade Ranking\" and \"Completed Credits\" with values of 0.580 and 0.643, respectively.  
We used the PCA-compressed results as input to the K-means clustering algorithm to cluster the data by reason. We randomly selected the initial value of the cluster's center and used 80% of the data for training and 20% for validation with 10-fold cross-validation. We used multiple rounds of K-means with \(K=2\) to identify the reasons. By using K = 2 in the K-means, we could simplify the modeling process and improve computational efficiency. We created two groups for each reason and determined if a value was included in the corresponding group. For instance, we created the categories of \"Employment\" and \"Other Reasons\" and assigned a value of True or False depending on whether they were included. In the second round, we ran another round of K-means with the labels \"Did not register\" and \"Other Reasons\". In this way, we can understand the intention of students who have chosen multiple reasons for dropping out. After the four rounds of K-means, we identified the main reason for dropping out based on the resulting cluster.  
Dropout precision is crucial in this experiment because the SDP system first selects dropout students with a hybrid model and then conducts \"classification by reason\" for dropout. The \"Employment\" category showed the lowest accuracy at 0.238 because the volume of data was too small. However, we are not too concerned about the \"Employment\" category because these students successfully began professional careers. The \"Personal Issues\" category was 0.405, which did not show high dropout precision for various reasons. The precision of  
Figure 6: Scatter Plot of Top 5 Principal Components of PCA.