A Graph Neural Network Model for Concept Prerequisite Relation Extraction

In recent years, with the emergence of online learning platforms and e-learning resources, many documents are available for a particular topic. For a better learning experience, the learner often needs to know and learn first the prerequisite concepts for a given concept. Traditionally, the identification of such prerequisite concepts is done manually by subject experts, which in turn, often limits self-paced learning. Recently, machine learning models have found encouraging success for the task, obviating manual effort. In this paper, we propose a graph neural network based approach that leverages node attention over a heterogeneous graph to extract the prerequisite concepts for a given concept. Experiments on a set of benchmark data show that the proposed model outperforms the existing models by large margins almost always, making the model a new state-of-the-art for the task.