The advancement of online learning platforms has intensified the demand for personalized learning. Knowledge tracing (KT) technology, which models students’ knowledge states to predict their problem-solving performance, serves as the foundation for personalization. Current KT models primarily construct learning sequences using platform-recorded exercise data, students’ learning behaviors outside the platform can also affect their knowledge states. However, comprehensive data collection of heterogeneous learning behaviors requires substantial resources, while modeling such behavioral diversity presents technical challenges, making it impractical to resolve these limitations through exhaustive behavioral logging and direct KT integration. To address these challenges, this paper proposes Imputing Knowledge States (IKT). Specifically, we first model fine-grained knowledge states at the concept level by analyzing direct and indirect relationships between knowledge concepts through students’ learning sequences. Subsequently, we reconstruct these knowledge states via a Variational Autoencoder (VAE) and predict students’ extra-platform learning activities by contrasting reconstructed-original knowledge states and analyzing inter-interaction time intervals. These predictions then guide knowledge state imputation to derive more plausible representations. Finally, extensive experiments on four real-world datasets demonstrate that IKT outperforms current state-of-the-art models.