Forgetting-aware Linear Bias for Attentive Knowledge Tracing
This addresses a specific issue in educational technology for personalized learning by enhancing knowledge tracing models, though it is incremental as it builds on existing attentive models.
The paper tackles the problem that existing attention-based knowledge tracing models neglect learner forgetting behavior, especially with long interaction histories, by proposing Forgetting-aware Linear Bias (FoLiBi), which improves AUC by up to 2.58% over state-of-the-art models on four benchmark datasets.
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.