A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques von Albreiki, Balqis, Zaki, Nazar, Alashwal, Hany

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Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rule-based models von Albreiki, Balqis, Habuza, Tetiana, Zaki, Nazar

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Extracting topological features to identify at-risk students using machine learning and graph convolutional network models von Albreiki, Balqis, Habuza, Tetiana, Zaki, Nazar

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Correction to: Framework for automatically suggesting remedial actions to help students at risk based on explainable ML and rulebased models von Albreiki, Balqis, Habuza, Tetiana, Zaki, Nazar

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Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions von Albreiki, Balqis, Habuza, Tetiana, Shuqfa, Zaid, Serhani, Mohamed Adel, Zaki, Nazar, Harous, Saad

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Clustering-based knowledge graphs and entity-relation representation improves the detection of at risk students von Albreiki, Balqis, Habuza, Tetiana, Palakkal, Nishi, Zaki, Nazar

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