Knowledge tracing (KT) plays a crucial role in predicting students' future performance by analyzing their historical learning processes. Deep neural networks (DNNs) have shown great potential in solving the KT problem. However, there still exist some important challenges when applying deep learning techniques to model the KT process. The first challenge lies in taking the individual information of the question into modeling. This is crucial because, despite questions sharing the same knowledge component (KC), students' knowledge acquisition on homogeneous questions can vary significantly. The second challenge lies in interpreting the prediction results from existing deep learning-based KT models. In real-world applications, while it may not be necessary to have complete transparency and interpretability of the model parameters, it is crucial to present the model's prediction results in a manner that teachers find interpretable. This makes teachers accept the rationale behind the prediction results and utilize them to design teaching activities and tailored learning strategies for students. However, the inherent black-box nature of deep learning techniques often poses a hurdle for teachers to fully embrace the model's prediction results. To address these challenges, we propose a Question-centric Multi-experts Contrastive Learning framework for KT called Q-MCKT. We have provided all the datasets and code on our website at https://github.com/rattlesnakey/Q-MCKT.

翻译：知识追踪（KT）通过分析学生的历史学习过程，在预测其未来表现方面发挥着关键作用。深度神经网络（DNNs）在解决KT问题上已展现出巨大潜力。然而，将深度学习技术应用于建模KT过程时，仍存在一些重要挑战。第一个挑战在于将问题的个体信息纳入建模。这一点至关重要，因为尽管问题可能共享相同的知识成分（KC），但学生在同类问题上的知识获取情况可能存在显著差异。第二个挑战在于解释现有基于深度学习的KT模型的预测结果。在实际应用中，虽然可能不需要模型参数具备完全的透明度和可解释性，但以教师可理解的方式呈现模型的预测结果至关重要。这能使教师接受预测结果背后的逻辑，并利用它们来设计教学活动以及为学生制定个性化的学习策略。然而，深度学习技术固有的黑盒特性常常成为教师完全接受模型预测结果的障碍。为了应对这些挑战，我们提出了一种用于KT的、基于问题中心的多专家对比学习框架，称为Q-MCKT。我们已在网站 https://github.com/rattlesnakey/Q-MCKT 上提供了所有数据集和代码。