Electronics 2026, 15(9), 1898; https://doi.org/10.3390/electronics15091898 (registering DOI) - 30 Apr 2026
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The lack of graph information caused by ignoring the association between learners often affects the accuracy of graph-based learning. This paper proposes an approach called attention-based multi-topological graph convolution (A-MTGCN) to address this. It uses a graph neural network to predict academic tasks.
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The lack of graph information caused by ignoring the association between learners often affects the accuracy of graph-based learning. This paper proposes an approach called attention-based multi-topological graph convolution (A-MTGCN) to address this. It uses a graph neural network to predict academic tasks. The method involves an attention mechanism that assigns weights to different academic characteristics to reflect their effects on prediction. Additionally, the topology between learners is constructed from multiple perspectives to capture potential interactions and collaboration, forming a weighted learner association diagram. This reduces redundancy and information dispersion in the graph, while retaining the correlation features. The approach divides learners into four types. Experiments show the enhanced GCN performs well in learner node classification, with an accuracy of 92.53%, precision of 89.15%, recall of 92.27%, and F1-score of 87.83%. The evolution process of learners’ learning state is reflected by constructing learners’ state transition matrix.
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