面向学习行为的大语言模型增强的自监督认知诊断方法

By integrating large language models and graph neural networks through a multi-view contrastive learning framework, the problems of data sparsity and noise interference in cognitive diagnostic models are solved, improving the accuracy and generalization ability of cognitive diagnosis. It is applicable to scenarios such as K-12 education, higher education and vocational education.

CN122197972BActive Publication Date: 2026-07-17CHINA UNIV OF MINING & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF MINING & TECH
Filing Date
2026-05-18
Publication Date
2026-07-17

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Abstract

本发明属于认知诊断技术领域，具体公开了一种面向学习行为的大语言模型增强的自监督认知诊断方法。首先利用学生答题记录与题目‑知识点关联矩阵构建交互图，使用模式分解图卷积的方法构建锚点视图，利用大模型推理构建语义增强视图，引入随机翻转机制构建结构增强视图；然后采用图对比学习预训练方法，将锚点视图与结构增强视图、语义增强视图进行对比学习预训练，使锚点视图学习到两个增强视图中的信息；最后将对比学习预训练完成后新锚点视图的表示作为固定的初始化嵌入，并将其转换到与认知诊断模型的原始嵌入相同的维度，对认知诊断模型进行训练，并利用训练好的模型执行认知诊断任务。本发明提升了认知诊断精度与泛化能力。
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