用扩散模型生成学生认知结构，再通过强化学习优化，提升学习建模效果。
Cognitive Structure Generation: From Educational Priors to Policy Optimization
- 先用教育先验预训练扩散模型生成认知结构
- 通过分层奖励信号优化生成过程，匹配真实学习发展水平
- 在4个真实数据集上显著提升知识追踪与认知诊断性能
认知结构是学生对客观知识体系的主观组织，体现在概念及其关系的心理建构中。然而，认知结构评估在学生建模和心理测量领域仍是一个长期挑战，始终是教育实践中的基础但难以评估的概念。本文提出一种新框架——认知结构生成（Cognitive Structure Generation, CSG），首先预训练一个认知结构扩散概率模型（CSDPM），从教育先验生成学生的认知结构；随后利用强化学习，通过分层奖励信号优化生成过程，使其与学生学习过程中的真实认知发展水平对齐。在四个主流真实教育数据集上的实验表明，CSG生成的认知结构为学生建模提供了更全面、有效的表征，在知识追踪（KT）和认知诊断（CD）任务上显著提升性能，同时增强可解释性。
原文摘要 · Abstract (English)
Cognitive structure is a student's subjective organization of an objective knowledge system, reflected in the psychological construction of concepts and their relations. However, cognitive structure assessment remains a long-standing challenge in student modeling and psychometrics, persisting as a foundational yet largely unassessable concept in educational practice. This paper introduces a novel framework, Cognitive Structure Generation (CSG), in which we first pretrain a Cognitive Structure Diffusion Probabilistic Model (CSDPM) to generate students' cognitive structures from educational priors, and then further optimize its generative process as a policy with hierarchical reward signals via reinforcement learning to align with genuine cognitive development levels during students' learning processes. Experimental results on four popular real-world education datasets show that cognitive structures generated by CSG offer more comprehensive and effective representations for student modeling, substantially improving performance on KT and CD tasks while enhancing interpretability.
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