MMCPL:Multimodal Embedding Enhancement for Concept Prerequisite Learning

Concept prerequisite relation learning aims to identify directed dependencies between concepts, which is crucial for personalized education and learning path planning in intelligent education. Existing methods rely heavily on text modalities to model concepts, neglecting the crucial implicit information embedded within image modalities. We propose a MultiModal Embedding Enhancement method for Concept Prerequisite Learning (MMCPL). Specifically, MMCPL extracts textual and image modal information of concepts from open authoritative knowledge bases and utilizes pretrained models to obtain multimodal representations of these concepts. Furthermore, we employ a cross-modal attention mechanism to facilitate bidirectional information interaction between textual and image modalities. By integrating Gaussian noise based intra-modal contrastive learning with inter-modal contrastive learning, we synergistically optimize the cross-modal consistency of representations while further enhancing the discriminative power within each modality. Extensive experiments on three benchmark datasets demonstrate that MMCPL consistently outperforms competitive baseline methods across all evaluation metrics.