Table 3. Algorithms and metrics used in predicting student performance.

#	Type of algorithm	Algorithms	Metric	Reference
1	Classification	Naive Bayes (NB), Support Vector Machine (SVM), Support Vector Classification (SVC), Decision Trees (DT), K Neighbors Classifier (KNN), Multilayer Perceptron (MLP), and Random Forest (RF)	Area under the curve (AUC), validation cross, Spearman correlation, Pearson correlation, accuracy, precision, recall, and F1 score	Abdulwahhab & Abdulwahab (2017), Buenaño-Fernández, Gil & Luján-Mora (2019), Costa et al. (2017), Moreno-Marcos et al. (2020), Pereira et al. (2021), Pereira et al. (2019), Sivasakthi (2017), Sunday et al. (2020), Villagrá-Arnedo et al. (2017), Yoshino et al. (2020)
2	Regression	Linear regression (LR), and logistic regression	Mean square error (RMSE), absolute error medium (MAE), R square (R2)	de la Peña et al. (2017), Moreno-Marcos et al. (2020), Munson & Zitovsky (2018), Pereira et al. (2019), Yoshino et al. (2020)