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

High dropout rates continue to be one of the main barriers to the effectiveness of online learning. The objective of this study is to develop a predictive framework that identifies at-risk students early enough to enable timely intervention. The proposed approach relies on graph neural networks (GNNs) to capture how learners interact with digital resources over time. The learning environment is represented as a bipartite structure where students and course materials form nodes, and their connections are defined by frequency, type, and recency of interactions.

The model was tested on a dataset of 3,000 students enrolled in 20 online courses over two academic semesters. A graph convolutional network (GCN) was implemented with embedding, layered convolution, dropout regularization, and a softmax output classifier. The results show that this framework outperforms commonly used models such as logistic regression, random forest, long short-term memory networks, and gradient boosting. It achieved strong predictive performance, with accuracy of 0.89, F1-score of 0.86, and area under the ROC curve of 0.91.

In addition to improving predictive accuracy, the framework offers a dashboard that allows instructors to visualize learner engagement and detect borderline-risk profiles. These findings demonstrate that relational and temporal modeling with GNNs can provide a more reliable basis for early-warning systems, while supporting adaptive and learner-centered practices in digital education.