R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

Understand

The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.

• In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training.
• We propose a model called the Relational-Variational Graph AutoEncoder (R-VGAE) to predict concept relations within a graph consisting of concept and resource nodes.
• Results show that our unsupervised approach outperforms graph-based semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in terms of prerequisite relation prediction accuracy and F1 score.