International Journal of Educational Technology in Higher Education

Table 5 Performance of ML models (AUC ROC) classifying students into Good, AtRisk, and Failed groups

From: Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models

	XGB	LightGBM	SVM	GaussianNB	ExtraTrees	Bagging	RandomForest	MLP
D1	0.9274	0.9452	0.8460	0.8115	0.9627	0.8907	0.9620	0.8089
D2	0.9230	0.9195	0.8430	0.8499	0.9489	0.8498	0.905	0.8509

\(-\)Checkpoints before the MT were used as model inputs

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