Predicting Prerequisite Relations for Unseen Concepts
Yaxin Zhu, Hamed Zamani · 2022
Concept prerequisite learning (CPL) plays a key role in developing technologies that assist people to learn a new complex topic or concept.Previous work commonly assumes that all concepts are given at training time and solely focuses on predicting the unseen prerequisite relationships between them.However, many real-world scenarios deal with concepts that are left undiscovered at training time, which is relatively unexplored.This paper studies this problem and proposes a novel alternating knowledge distillation approach to take advantage of both content-and graph-based models for this task.Extensive experiments on three public benchmarks demonstrate up to 10% improvements in terms of F1 score.