Application of graph neural networks (GNNs) in learning situation prediction and educational resource recommendation algorithms

Modeling the complex interrelationships among students, learning resources, and knowledge points remains a significant challenge in intelligent educational systems. To address this issue, this study proposes an improved Heterogeneous Graph Neural Network (HetGNN) framework for comprehensive learning behavior modeling and personalized educational recommendation. We define three distinct node types—students, resources, and knowledge points—and construct a multi-relational heterogeneous graph by incorporating diverse edge types derived from behavioral interactions (e.g., clicks, completions), knowledge dependencies (prerequisite relationships), and content associations (label-based matching). A student–resource–knowledge point meta-path is designed to capture composite relational patterns, and a Relational Graph Convolutional Network (R-GCN) is employed to aggregate neighborhood information while preserving semantic distinctions across relation types. To model temporal learning sequences, a Transformer encoder is integrated to generate dynamic attention weights that reflect evolving student engagement. A gated fusion mechanism is introduced to effectively combine dynamic sequential features with static structural representations, ensuring feature complementarity and minimizing interference. The model further incorporates two jointly optimized output branches—learning status prediction (including dropout risk and performance forecasting) and personalized resource recommendation—through shared parameter learning. Experimental results on real-world educational datasets demonstrate the superiority of the proposed approach: the F1-score for dropout risk prediction reaches 0.91, grade prediction achieves a stable RMSE of approximately 0.32, and resource recommendation attains an NDCG (Normalized Discounted Cumulative Gain) @10 of 0.93 in standard scenarios and 0.88 in knowledge gap scenarios. Moreover, the coverage of long-tail resources improves to 0.56, with a reduced recommendation bias coefficient of 0.29. The results validate the model’s effectiveness in capturing intricate student–resource–knowledge dynamics, offering a robust solution for learning analytics and adaptive educational systems.