Abstract: Knowledge tracing is a core task in intelligent tutoring systems,which aims to model a learner’s mastery of knowledges based on their historical interaction behaviors and predict the next exercise answer.However,existing approaches suffer from three main limitations:1)Most methods rely heavily on exercise or knowledge IDs,lacking the ability to capture complex structural dependencies between exercises and knowledge concepts to enhance exercise representation;2)They fail to effectively utilize multi-dimensional exercise attributes to enrich exercise embeddings;3)They overlook the impact of forgetting patterns in learners’ cognitive processes,which limits predictive performance.To address these issues,Multi-view Exercise representation and Forgetting mechanism for deep Knowledge Tracing(MEFKT)is proposed.Specifically,MEFKT leverages pre-trained models to learn high-quality exercise embeddings and predicts learner responses based on learning dynamics.Firstly,a graph-based contrastive learning strategy is adopted to pre-train exercise representations enriched with structural information.At the same time,it constructs attribute-enhanced pre-trained embeddings that capture multi-dimensional features such as exercise/knowledge simila-rity,exercise difficulty,exercise type,and response time.These multiple perspectives are then fused into a unified representation space via a learnable linear alignment module.Finally,a behavior prediction module incorporating a forgetting mechanism is designed to dynamically update knowledge states and predict the next response.Extensive experiments on two public benchmark datasets demonstrate that MEFKT significantly outperforms existing state-of-the-art knowledge tracing models,validating the effectiveness of integrating multi-view exercise representations and forgetting-aware learning dynamics.

Key words: Knowledge tracing, Exercise representation, Graph contrastive learning, Forgetting mechanism