Shen Shuanghong, Liu Qi, Huang Zhenya, Zhu Linbo, Lu Junyu, Zhang Kai
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui, China; State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui, China.
Neural Netw. 2025 Nov;191:107797. doi: 10.1016/j.neunet.2025.107797. Epub 2025 Jul 12.
Online learning has demonstrated superiority in connecting high-quality educational resources to a global audience. To ensure an excellent learning experience with sustainable and opportune learning instructions, online learning systems must comprehend learners' evolving knowledge states based on their learning interactions, known as the Knowledge Tracing (KT) task. Generally, learners practice through various quizzes, each comprising several exercises that cover similar knowledge concepts. Therefore, their learning interactions are continuous within each quiz but discrete across different quizzes. However, existing methods overlook the quiz structure and assume all learning interactions are uniformly distributed. We argue that learners' knowledge states should also be assessed in quiz since they practiced in quiz. To achieve this goal, we present a novel Quiz-based Knowledge Tracing (QKT) model, which effectively integrates the quiz structure of learning interactions. This is achieved by designing two distinct modules by neural networks: one for intra-quiz modeling and another for inter-quiz fusion. Extensive experimental results on public real-world datasets demonstrate that QKT achieves new state-of-the-art performance. The findings of this study suggest that incorporating the quiz structure of learning interactions can efficiently comprehend learners' knowledge states with fewer quizzes, and provides valuable insights into designing effective quizzes with fewer exercises.
在线学习在将优质教育资源与全球受众相连接方面已展现出优势。为了通过可持续且适时的学习指导确保卓越的学习体验，在线学习系统必须基于学习者的学习互动来理解其不断变化的知识状态，这一任务被称为知识追踪（KT）。一般来说，学习者通过各种测验进行练习，每个测验包含若干涵盖相似知识概念的练习题。因此，他们在每个测验中的学习互动是连续的，但在不同测验之间是离散的。然而，现有方法忽略了测验结构，并假设所有学习互动是均匀分布的。我们认为，由于学习者在测验中进行练习，他们的知识状态也应在测验中进行评估。为实现这一目标，我们提出了一种新颖的基于测验的知识追踪（QKT）模型，该模型有效地整合了学习互动的测验结构。这是通过神经网络设计两个不同的模块来实现的：一个用于测验内建模，另一个用于测验间融合。在公开真实世界数据集上的大量实验结果表明，QKT取得了新的最优性能。本研究结果表明，纳入学习互动的测验结构可以用更少的测验有效地理解学习者的知识状态，并为设计练习题更少的有效测验提供有价值的见解。
