Knowledge tracing (KT) aims to assess individuals' evolving knowledge states according to their learning interactions with different exercises in online learning systems (OIS), which is critical in supporting decision-making for subsequent intelligent services, such as personalized learning source recommendation. Existing researchers have broadly studied KT and developed many effective methods. However, most of them assume that students' historical interactions are uniformly distributed in a continuous sequence, ignoring the fact that actual interaction sequences are organized based on a series of quizzes with clear boundaries, where interactions within a quiz are consecutively completed, but interactions across different quizzes are discrete and may be spaced over days. In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions. Specifically, as students' interactions within a quiz are continuous and have the same or similar knowledge concepts, we design the adjacent gate followed by a global average pooling layer to capture the intra-quiz short-term knowledge influence. Then, as various quizzes tend to focus on different knowledge concepts, we respectively measure the inter-quiz knowledge substitution by the gated recurrent unit and the inter-quiz knowledge complementarity by the self-attentive encoder with a novel recency-aware attention mechanism. Finally, we integrate the inter-quiz long-term knowledge substitution and complementarity across different quizzes to output students' evolving knowledge states. Extensive experimental results on three public real-world datasets demonstrate that QKT achieves state-of-the-art performance compared to existing methods. Further analyses confirm that QKT is promising in designing more effective quizzes.
翻译:知识追踪(KT)旨在根据个人在在线学习系统(OIS)中与不同练习的学习交互来评估其不断发展的知识状态,这对于支持随后的智能服务(例如个性化的学习源推荐)是至关重要的决策制定。现有的研究人员广泛研究了KT并开发了许多有效的方法。然而,它们大多数假定学生的历史交互在连续序列中均匀分布,忽略了实际交互序列是基于一系列带有明确边界的测验组织的,其中测验内的交互是连续完成的,但不同测验之间的交互是离散且可能间隔数天的。在本文中,我们提出了基于测验的知识追踪(QKT)模型,以根据学生基于测验的学习交互监测学生的知识状态。具体而言,由于学生在测验内的交互是连续的,并具有相同或类似的知识概念,因此我们设计了相邻门,后跟全局平均池化层,以捕获测验内的短期知识影响。然后,由于各种测验往往关注不同的知识概念,我们分别通过门控循环单元测量测验间的知识替代和通过具有新颖的最近关注机制的自我注意编码器测量测验间的知识互补性。最后,我们将不同测验之间的知识替代和互补性整合到一起,以输出学生不断发展的知识状态。对三个公共真实数据集的广泛实验结果表明,QKT相对于现有方法实现了最先进的性能。进一步的分析证实了QKT在设计更有效的测验方面的潜力。