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This study proposes a framework using heterogeneous graph deep learning models to predict student success early in the semester, addressing the challenge of leveraging diverse student data. By integrating dynamic assessment features, the approach achieved a validation F1 score of 68.6% with only 7% of the semester completed, outperforming traditional machine learning models. The findings highlight the importance of dynamic features and graph representations in enhancing early predictions and supporting time

Author

nguyenthihongnhung.dhcl

Language

EN