PAGTPRP: Position-Aware Graph Transformer for Prerequisite Relationship Prediction

Predicting prerequisite relationships is crucial in educational applications such as curriculum planning and intelligent tutoring. Although unified modeling of concept and document prerequisite prediction has been achieved, existing approaches still face limitations in integrating global structural context and edge-weight information when modeling heterogeneous prerequisite networks. We propose a Position-Aware Graph Transformer for Prerequisite Relationship Prediction (PAGTPRP). Specifically, PAGTPRP firstly generates embeddings for knowledge concepts and documents and constructs a global heterogeneous graph, which contains two types of nodes (documents and knowledge concepts) and three types of edges (“document-document”, “concept-concept”, and “document-concept”). In addition, we calculate the edge weights and use the graph Transformer to encode them as relational encodings, which are integrated into the multi-head self-attention mechanism. Finally, we employ a Siamese network to more accurately predict the prerequisite relationships. On LectureBank and UCD, we compare PAGTPRP with several baseline methods. The experimental results demonstrate that PAGTPRP achieves excellent performance in predicting prerequisite relationships. Our code is available at https://anonymous.4open.science/r/PAGTPRP-4E44.