Abstract
Prerequisite relations among concepts are important for a wide range of educational applications, such as intelligent tutoring and curriculum planning. However, concept prerequisite relation learning is not trivial due to the sparsity of prerequisite relations. In this paper, we propose a contextual-knowledge-aware concept prerequisite relation learning approach called ConLearn. Four unique properties of the proposed approach are: (1) It transfers knowledge from large language model BERT to improve contextual representations of concepts; (2) It captures concept prerequisite transition patterns by applying graph neural network on concept prerequisite graph; (3) It is equipped with self-attention mechanism to fuse information from related concepts for target concept prerequisite relation classification; (4) No handcrafted features are used in our model, which makes our model easy to implement in downstream applications. Extensive experiments on three representative datasets demonstrate that our approach significantly outperforms the state-of-the-art methods.
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