In the realm of intelligent education, cognitive diagnosis plays a crucial role in subsequent recommendation tasks attributed to the revealed students' proficiency in knowledge concepts. Although neural network-based neural cognitive diagnosis models (CDMs) have exhibited significantly better performance than traditional models, neural cognitive diagnosis is criticized for the poor model interpretability due to the multi-layer perception (MLP) employed, even with the monotonicity assumption. Therefore, this paper proposes to empower the interpretability of neural cognitive diagnosis models through efficient kolmogorov-arnold networks (KANs), named KAN2CD, where KANs are designed to enhance interpretability in two manners. Specifically, in the first manner, KANs are directly used to replace the used MLPs in existing neural CDMs; while in the second manner, the student embedding, exercise embedding, and concept embedding are directly processed by several KANs, and then their outputs are further combined and learned in a unified KAN to get final predictions. To overcome the problem of training KANs slowly, we modify the implementation of original KANs to accelerate the training. Experiments on four real-world datasets show that the proposed KA2NCD exhibits better performance than traditional CDMs, and the proposed KA2NCD still has a bit of performance leading even over the existing neural CDMs. More importantly, the learned structures of KANs enable the proposed KA2NCD to hold as good interpretability as traditional CDMs, which is superior to existing neural CDMs. Besides, the training cost of the proposed KA2NCD is competitive to existing models.

翻译：在智慧教育领域，认知诊断因能揭示学生对知识概念的掌握程度，在后续推荐任务中起着关键作用。尽管基于神经网络的神经认知诊断模型（CDMs）已展现出显著优于传统模型的性能，但由于其采用了多层感知机（MLP），即使引入单调性假设，神经认知诊断仍常因模型可解释性差而受到质疑。为此，本文提出通过高效的Kolmogorov-Arnold网络（KANs）来增强神经认知诊断模型的可解释性，该方法命名为KAN2CD。KANs主要通过两种方式提升可解释性：其一，直接将其用于替代现有神经CDMs中的MLP；其二，将学生嵌入、习题嵌入和概念嵌入直接输入多个KANs进行处理，再将它们的输出在统一的KAN中进一步融合与学习，以得到最终预测。为克服KANs训练速度慢的问题，我们改进了原始KAN的实现以加速训练。在四个真实数据集上的实验表明，所提出的KAN2CD不仅性能优于传统CDMs，甚至较现有神经CDMs仍保持一定性能优势。更重要的是，通过学习得到的KANs结构，KAN2CD能够具备与传统CDMs相当的良好可解释性，这显著优于现有神经CDMs。此外，KAN2CD的训练成本与现有模型相比具有竞争力。