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）旨在根据学习者在在线学习系统（OIS）中与不同练习的交互，评估个体不断变化的知识状态，这对于后续智能服务（如个性化学习资源推荐）的决策支持至关重要。现有研究者已广泛研究KT并开发了许多有效方法。然而，大多数方法假设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具有明确边界的测验组织的，其中一次测验内的交互是连续完成的，而不同测验间的交互是离散的，可能间隔数天。在本文中，我们提出了基于测验的知识追踪（QKT）模型，根据学生基于测验的学习交互来监测其知识状态。具体地，由于学生在一次测验内的交互是连续的且涉及相同或相似的知识概念，我们设计了相邻门控层后接全局平均池化层，以捕获测验内的短期知识影响。随后，由于不同测验往往侧重于不同的知识概念，我们分别通过门控循环单元衡量测验间的知识替代性，并通过具有新颖近时性注意力机制的自注意力编码器衡量测验间的知识互补性。最后，我们整合不同测验间的长期知识替代性与互补性，输出学生不断变化的知识状态。在三个公开真实数据集上的广泛实验结果表明，与现有方法相比，QKT达到了最先进的性能。进一步分析证实，QKT在设计更有效的测验方面具有潜力。