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.

翻译：知识追踪（KT）旨在基于答题历史追踪学习者的能力水平，从而提供个性化课程。近年来的研究积极利用基于注意力机制的模型来捕捉问题之间的相关性，并将其与学习者的特征相结合，以预测答题表现。然而，我们的实证研究表明，现有基于注意力的知识追踪模型忽略了学习者的遗忘行为，尤其是在交互历史较长的情况下。这一问题的根源在于模型偏置过度优先考虑问题相关性，而无意中忽视了遗忘行为的影响。本文提出了一种简单而有效的解决方案，即面向遗忘感知的线性偏置（Forgetting-aware Linear Bias, FoLiBi），通过线性偏置来反映遗忘行为。尽管方法简单，FoLiBi通过有效解耦问题相关性与遗忘行为，可轻松集成至现有的注意力知识追踪模型中。将FoLiBi嵌入多个知识追踪模型后，在四个基准数据集上相比当前最优模型，AUC指标获得了一致性提升，最高达2.58%。