发表机构
University of Science and Technology of China; iFLYTEK AI Research; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（中国科学技术大学; 讯飞AI研究院; 合肥综合性国家科学中心人工智能研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对多领域知识追踪场景，提出整合认知负荷与知识迁移的LT-MKT方法，利用LLMs构建多领域层次图并联合建模认知负荷与知识迁移，在真实数据集上实现了最优性能。
AI 中文摘要
知识追踪（Knowledge Tracing, KT）旨在从学生的学习历史中评估其动态知识状态。尽管现有大多数KT方法在单领域学习中取得了显著成功，但现实学习场景往往同时涉及多个领域，带来两个关键因素：1）认知负荷，源于在时间和知识维度上跨领域管理学习；2）知识迁移，即一个领域的知识状态会影响该领域内及跨领域的相关状态。本文聚焦于探索这些因素以改进多领域学习场景中学生的知识状态评估，提出一种整合认知负荷与知识迁移的多领域知识追踪新方法（LT-MKT）。具体而言，为连接孤立的领域，LT-MKT首先整合题目及其关联概念的文本信息以构建多领域层次图，利用大语言模型（large language models, LLMs）的强大表征能力。接着，显式建模时间和知识维度上的跨领域特征，以捕捉认知负荷的影响。此外，设计知识迁移模块以建模知识状态在领域内及跨领域的传播。通过联合建模这些因素，LT-MKT能够更准确地预测学生的未来表现。最后，在真实世界数据集上的大量实验表明，我们的方法达到了当前最优性能。
英文摘要
Knowledge Tracing (KT) aims to assess students' dynamic knowledge states from their learning histories. While most existing KT methods focus on single-domain learning with notable success, real-world learning scenarios often involve multiple domains simultaneously, introducing two critical factors: 1) Cognitive load, arising from managing learning across domains in both temporal and knowledge dimensions. 2) Knowledge transfer, where knowledge states in one domain influence related states both within and across domains. In this paper, we focus on exploring these factors to improve students' knowledge state assessment in multi-domain learning scenarios and propose a novel method incorporating cognitive Load and knowledge Transfer for Multi-domain Knowledge Tracing (LT-MKT). Specifically, to bridge isolated domains, LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs). Then, cross-domain features in both the temporal and knowledge dimensions are explicitly modeled to capture the effects of cognitive load. Additionally, a knowledge transfer module is designed to model the propagation of knowledge states within and across domains. By jointly modeling these factors, LT-MKT enables more accurate prediction of students' future performance. Finally, extensive experiments on real-world datasets demonstrate that our method achieves state-of-the-art performance.
CommentsAccepted as a CIKM 2026 Oral
