Abstract: In the context of educational digitalization,accurately tracking students’ knowledge mastery has become one of the key approaches to improving teaching quality.Knowledge tracing seeks to analyze various types of student behavior data－such as responses to questions and online study duration－to evaluate their mastery of specific knowledge points.Although existing approaches have demonstrated good performance in predicting personalized learning behaviors,they still face two major challenges:1) the widespread issue of data sparsity in educational settings;2) the neglect of the complex and dynamic nature of students’ knowledge acquisition,resulting in an inability to effectively capture changes in knowledge and forgetting patterns.To address these challenges,this paper proposes a personalized knowledge tracing model,DCL-FKT,which integrates dual contrastive lear-ning with a forgetting mechanism.The model alleviates data sparsity through question masking and substitution,building upon the traditional contrastive learning framework,it introduces a feature-level contrastive learning module to eliminate redundant representations and enhance modeling efficiency.In addition,by incorporating a forgetting gate mechanism,the model dynamically simulates the human forgetting curve,allowing it to accurately capture the nonlinear decay of students’ knowledge over time and enabling dynamic modeling of the learning process.Experiments conducted on real-world datasets demonstrate that the proposed model achieves significant improvements in core metrics,such as prediction accuracy.It provides a more accurate reflection of students’ actual knowledge levels and offers reliable support for personalized online learning.

Key words: Intelligent education, Knowledge tracing, Dual contrastive learning, Forgetting mechanism, Performance prediction