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）旨在根据个体在在线学习系统（OIS）中与不同习题的学习交互，评估其不断变化的知识状态，这对于支持后续智能服务（如个性化学习资源推荐）的决策至关重要。现有研究者已广泛研究知识追踪，并开发了多种有效方法。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这一事实：同一测验内的交互连续完成，而不同测验之间的交互是离散的，可能间隔数天。本文提出了基于测验的知识追踪（QKT）模型，根据学生在测验中的学习交互来监控其知识状态。具体而言，由于学生在同一测验内的交互是连续且涉及相同或相似的知识概念，我们设计了相邻门控层并随后加入全局平均池化层，以捕获测验内的短期知识影响。进而，由于不同测验往往聚焦于不同知识概念，我们分别通过门控循环单元衡量测验间的知识替代性，并通过引入新颖的近期性感知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。在三个公开真实世界数据集上的大量实验结果表明，与现有方法相比，QKT达到了最先进性能。进一步分析证实，QKT在设计更有效的测验方面具有潜力。