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.

翻译：知识追踪（KT）通过分析学生的历史学习过程来预测其未来表现，发挥着关键作用。深度神经网络（DNNs）在解决KT问题中展现出巨大潜力。然而，应用深度学习技术建模KT过程仍面临若干重要挑战。首要挑战在于将题目的个体信息纳入建模过程。这至关重要，因为即便题目共享相同的知识组件（KC），学生对同质题目的知识掌握程度也可能存在显著差异。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。实际应用中，虽然无需完全透明与可解释模型参数的细节，但以教师可理解的方式呈现模型预测结果至关重要。这能使教师认可预测结果的合理性，并据此设计教学活动及为学生定制个性化学习策略。然而，深度学习技术固有的黑箱特性往往成为教师全面接受模型预测结果的障碍。为解决上述挑战，我们提出了一种面向问题的多专家对比学习框架Q-MCKT。