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This is a Quantitative Study study.

Addressing Dropout through Personalization: A Graph Neural Network Approach to Modelling Learner Interactions

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This framework predicts at-risk students using graph neural networks, suggesting novel approaches for personalized digital education.

The predictive framework achieved an accuracy of 0.89, indicating strong performance in identifying at-risk students.
Results outperformed standard models like logistic regression and random forest, highlighting the efficacy of graph neural networks.
The model uses a bipartite structure to represent interactions between students and course materials effectively.
Findings support adaptive practices in education, enhancing how instructors can visualize learner engagement.

Hamdane et al. (2025) studied this question.

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