GAT-CL: A Graph Attention and Contrastive Learning Framework for Multimodal Behavior Modeling in Intelligent Education Systems
Abstract
This study addresses the limitations of intelligent education systems in multimodal data fusion, scalability, and robustness by proposing a graph-based cognitive modeling framework enhanced with contrastive representation learning. Using interaction data from 186 students and 874,520 records over a semester, heterogeneous behavior graphs are constructed and encoded with a Multi-Head Graph Attention Network (GAT) to capture semantic and temporal dependencies. A contrastive learning module further strengthens embedding robustness, and the optimized representations drive a dynamic strategy engine for adaptive instructional resource allocation. Experimental results demonstrate 93.2% accuracy in learner behavior classification and 90.1% accuracy in clickstream prediction, with a 15.4% improvement in disengagement-signal retention compared to GCN, LSTM, Transformer, and GraphCL baselines. These findings validate the effectiveness and transferability of combining cognitive graph modeling with contrastive learning, advancing both theoretical foundations and practical capabilities of intelligent education systems to reduce dropout risk and enhance engagement.References
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https://doi.org/10.31449/inf.v49i36.10625Downloads
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