Learning path recommendation based on forgetting factors and knowledge graph awareness

Abstract

Learning path recommendation involves generating sequences of learning objects that are adapted to learners’ needs, goals, abilities, and other factors through recommendation algorithms. Reinforcement learning (RL) has become an important approach for this task; however, it primarily emphasizes recommending new knowledge concepts while neglecting the necessity of revisiting forgotten ones. To overcome this limitation, FKGRec is introduced as a learning path recommendation framework that incorporates forgetting factors and knowledge graph awareness. To address the forgetting problem, a novel method named MemGNN is proposed, which integrates forgetting and knowledge graph features and employs a graph neural network with a memory gate structure to predict both new and previously learned knowledge concepts at each learning step. To further optimize the sequencing of new and previously learned knowledge concepts, an action space is constructed based on knowledge concept prediction, taking learners’ cognitive states into account. An RL algorithm is then applied to recommend optimal learning paths by balancing new and previously learned knowledge concepts using a designed reward function. Experiments conducted on three datasets demonstrate that FKGRec surpasses existing state-of-the-art frameworks. A case analysis shows that the FKGRec framework can recommend learning paths that integrate new and previously learned knowledge concepts, aligned with learners’ current cognitive state and forgetting factors.