A Prompt-based Approach for Discovering Prerequisite Relations Among Concepts

The concept prerequisite learning task is a crucial study in smart education and plays an important role in online course design, course guidance, and learning material recommendation systems. In general, concept prerequisite relations are the determination of whether two concepts have a prerequisite relationship, usually as a binary task. Nowadays, learning concept representations from pre-trained models has become a new trend in concept prerequisite learning (CPL) tasks. However, too many handicraft features are still required to discover the concept features in previous studies. In this paper, we propose a concept prerequisite relationship discovery method based on prompt learning, in which we design four prompt functions, mapping the predicted labels to the existing labels through answer engineering after the model training. Conducting thorough experiments across three publicly available benchmarks reveals enhancements of up to 13% in F1 score. It shows that the prompt learning approach can effectively improve the prediction of prerequisite relations and provide a new idea for the study of concept prerequisite relations tasks.