Abstract
Knowledge Tracing (KT) aims to trace changes in students' knowledge states throughout their entire learning process by analyzing their historical learning data and predicting their future learning performance. Existing forgetting curve theory based knowledge tracing models only consider the general forgetting caused by time intervals, ignoring the individualization of students and the causal relationship of the forgetting process. To address these problems, we propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) which integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities. First, we integrate the students' personalized capabilities into both the learning and forgetting processes to explicitly distinguish students' individual learning gains and forgetting rates according to their cognitive abilities. Second, we take into account the hierarchical relationships between knowledge points and design a precursor-successor knowledge concept matrix to simulate the causal relationship in the forgetting process, while also integrating the potential impact of forgetting prior knowledge points on subsequent ones. The proposed personalized forgetting mechanism can not only be applied to the learning of specifc knowledge concepts but also the life-long learning process. Extensive experimental results on three public datasets show that our CPF outperforms current forgetting curve theory based methods in predicting student performance, demonstrating CPF can better simulate changes in students' knowledge status through the personalized forgetting mechanism.
Abstract (translated)
知识追踪（KT）旨在通过分析学生整个学习过程中的历史学习数据，预测他们的未来学习表现，来追溯学生在学习过程中的知识状态变化。现有基于知识追踪的遗忘曲线理论模型仅考虑时间间隔造成的普遍遗忘，而忽略了学生个性的差异和遗忘过程的因果关系。为了解决这些问题，我们提出了一个以概念驱动的学生个性化遗忘知识追踪模型（CPF），该模型将知识概念之间的层次关系与学生的个性化认知能力相结合。 首先，我们将学生的个性化能力集成到学习和忘记过程中，明确区分学生根据其认知能力获得的个性化学习收益和遗忘速率。其次，我们考虑知识点的层次关系，设计了一个前驱-成功者知识概念矩阵，以模拟遗忘过程中的因果关系，并考虑遗忘先前的知识点对后续知识点的潜在影响。 所提出的个性化遗忘机制不仅可以应用于对具体知识概念的学习，还可以应用于终身学习过程。在三个公共数据集上的大量实验结果表明，我们的CPF在预测学生表现方面优于基于遗忘曲线理论的方法，这表明通过个性化遗忘机制，CPF可以更好地模拟学生知识状态的变化。
URL
https://arxiv.org/abs/2404.12127
