Enhancing Weak Supervision for Concept Prerequisite Relation Learning

Abstract

Concept prerequisite relation learning is used to identify dependency relations between knowledge concepts, which helps learners choose effective learning paths. Currently, most of the mainstream methods utilise deep learning algorithms to capture the prerequisite relations between concepts through supervised or semi-supervised learning. However, these methods are highly dependent on labelled data, which is scarce and costly to annotate in reality. To address this problem, we propose a framework called Weakly Supervised Enhanced Concept Prerequisite Relation Learning (WSECPRL). Specifically, we first generate an enhanced concept pseudo-relation graph without labeled data using the pre-trained language model and the large knowledge base as auxiliary information. Second, we propose an improved variational graph auto-encoder model to correctly determine the concept prerequisite relations. We incorporate a multi-head attention mechanism to enhance the representation learning capability of weakly supervised learning. The model reconstructs a directed graph into multiple undirected graphs by splitting the adjacency matrix and determines the direction of the concept prerequisite relation based on the strength of the dependency relation between concepts. Finally, experimental results on several publicly available datasets demonstrate the effectiveness of our proposed framework, with WSECPRL outperforming existing baseline models in terms of F1 scores and AUC.