  Knowledge Tracing (KT) aims to track proficiency based on a question-solving
history, allowing us to offer a streamlined curriculum. Recent studies actively
utilize attention-based mechanisms to capture the correlation between questions
and combine it with the learner's characteristics for responses. However, our
empirical study shows that existing attention-based KT models neglect the
learner's forgetting behavior, especially as the interaction history becomes
longer. This problem arises from the bias that overprioritizes the correlation
of questions while inadvertently ignoring the impact of forgetting behavior.
This paper proposes a simple-yet-effective solution, namely Forgetting-aware
Linear Bias (FoLiBi), to reflect forgetting behavior as a linear bias. Despite
its simplicity, FoLiBi is readily equipped with existing attentive KT models by
effectively decomposing question correlations with forgetting behavior. FoLiBi
plugged with several KT models yields a consistent improvement of up to 2.58%
in AUC over state-of-the-art KT models on four benchmark datasets.
