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.

这篇论文提出了一种简单而有效的解决方案，即遗忘感知线性偏差（FoLiBi），以反映遗忘行为作为线性偏差，并通过有效地解耦问题之间的相关性与遗忘行为，FoLiBi与几种知识追踪模型相结合，在四个基准数据集上一致改善了最高2.58％的AUC。

记忆感知线性偏差对于专注式知识追踪的效果