## 6 Discussion
The AUC-ROC is typically used to address imbalanced data because it demonstrates the trade-off between the true positive rate (TPR) and the false positive rate (FPR) in a two-dimensional space [23]. The ROC curve is obtained by plotting the values of the various thresholds calculated. The TPR represents the percentage of students' dropout that the model predicted as dropout states in this research. Vice versa, the FPR is the percentage of students' non-dropouts, but the classifier predicted them as a dropout. This metric summarizes the goodness of fit of a classifier [30]. Figure 3 shows that random forest (RF) (AUC = 0.968) and logistic regression (LR) (AUC = 0.924) classifiers are significantly better when measured by the AUC-ROC curve. Decision tree (DT) gained (AUC = 0.883), followed by a support vector machine (SVM) (AUC = 0.850) and Naive Bayes (NB) (AUC = 0.765). Considering the distribution of positive class (completers '1') and negative class (non-completers '0') in the researched dataset, each of the used classification algorithms is very good at capturing positive class occurrences. However, some of them, such as DT, SVM, and NB, generate lots of false-positive predictions, which lowers final predictive performance accuracy. Random forest classifier and logistic regression can distinguish little differences between the conditioning parameters that result in fewer incorrectly predicted failures. Therefore, their predictive performance is higher. This statement can also confirm the classification error rate, representing a proportion of cases misclassified over the whole set of cases. Again, the RF and LR classifiers achieved the best values in this performance metric. They misclassified only around 6% of the cases, which is in stark contrast to the NB classifier, which misclassified 23% of the cases. [PERSON]'s test provides another interesting point of view on the individual classifiers. The mutual comparison of the binary classifiers shown in Table 4 uncovered that the RF and LR classifiers classify the individual unseen cases to the same classes. There is no significant difference between them. Simultaneously, there is no significant difference between other classifiers, namely, between DT and SVM, DT, and LR, and between LR and SVM. Finally, the computational cost of running all the research algorithms was compared for completeness sake. The four classification algorithms were executed in less than 1.00 s, as shown in Table 5. Naturally, the NN recorded a significantly greater time because of hidden layers. However, given the time frame of the proposed problem, its runtime was not prohibitive. It took around nine seconds to run while using all the attributes in the worst case. The discussion about the different performance metrics confirms that the selected binary classification models can be used for predicting students' success or dropout at the individual course level, even in the case of the scarce dataset with a limited number of input features. However, it is necessary to explore a more extensive set of performance metrics before the classifiers should be deployed for students' dropout prediction. This statement is in line with the outcomes of other research papers published in the learning analytics and educational data mining domain. Nevertheless, the presented case study and its findings have some limitation. As was already mentioned, the limited size of the dataset is the first one. In contrast to many other application domains of machine learning models, the amount of data in the educational domain cannot be easily increased by combining different resources. The  
Figure 3: Comparison of the performance measures by area under the receiver operating characteristic curve (AUC-ROC) curves. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Execution Time** \\ \hline LR & 0.36 \\ DR & 0.84 \\ RF & 0.46 \\ NB & 0.38 \\ SVM & 0.52 \\ NN & 8.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Execution time (in seconds).