Prerequisite Relation Learning: A Survey and Outlook

Abstract

Prerequisite relation (PR) learning is a fundamental task in educational technology that identifies dependencies between learning resources to facilitate personalized learning experiences and optimize educational content organization. This survey provides a systematic review of prerequisite relation learning, emphasizing both methodological advances and practical applications. We first explore two distinct granularities of learning resources: knowledge concepts (KCs) and learning objects (LOs), establishing their definitions and relationships. We then introduce a novel classification framework for prerequisite relation learning methods based on both feature types and enhancement relationships, categorizing existing approaches into four types: (1) multi-source knowledge features for KCs’ prerequisite relation learning; (2) semantic knowledge features for LOs’ prerequisite relation learning; (3) LOs-enhanced learning for KCs’ prerequisite relation learning; and (4) KCs-enhanced learning for LOs’ prerequisite relation learning. The survey highlights recent developments in modeling KCs’ prerequisite relations. We provide a comprehensive analysis of evaluation methodologies, including both intrinsic metrics and extrinsic evaluation. Furthermore, we analyze the practical impact of prerequisite relations in educational applications, from adaptive learning path generation to curriculum design. Finally, we discuss current challenges and future opportunities for prerequisite relation learning.