This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of difficulty, previous KT research has yet to exploit its potential for model optimization and has struggled to predict difficulty from unseen data. To address these problems, we propose a difficulty-centered contrastive learning method for KT models and a Large Language Model (LLM)-based framework for difficulty prediction. These innovative methods seek to improve the performance of KT models and provide accurate difficulty estimates for unseen data. Our ablation study demonstrates the efficacy of these techniques by demonstrating enhanced KT model performance. Nonetheless, the complex relationship between language and difficulty merits further investigation.

翻译：本文提出了一系列旨在提升知识追踪模型性能的新技术，重点聚焦于问题与概念难度这一关键因素。尽管难度的重要性已得到广泛认可，但既往知识追踪研究尚未充分利用其模型优化潜力，且难以实现对未知数据难度的预测。为解决上述问题，我们提出了一种以难度为核心的对比学习方法用于知识追踪模型，并构建了基于大语言模型的难度预测框架。这些创新方法旨在提升知识追踪模型性能，同时为未知数据提供准确的难度估计。消融实验结果表明，所提技术通过增强知识追踪模型性能验证了其有效性。然而，语言与难度之间的复杂关系仍需进一步探究。