华东师范大学学报（自然科学版）Issue(5)：76-86,11.DOI:10.3969/j.issn.1000-5641.2025.05.008
基于知识点关系增强的静态认知诊断模型
Static cognitive diagnosis model enhanced by knowledge point relations
摘要
Abstract
Cognitive diagnosis,a core task in personalized education,aims to evaluate students'mastery of knowledge points using historical response records.Existing static cognitive diagnosis models are typically based on manually annotated key knowledge points,ignoring potential correlations between knowledge points within items as well as differences in how items emphasize specific knowledge points.To address these limitations,this study proposes a static cognitive diagnosis model improved by knowledge point relations(Q-matrix Enhanced Neural Cognitive Diagnosis,QENCD)model.The model optimizes the item-knowledge point association vector by constructing knowledge point dependency relationships and item emphasis information,then integrating these features through residual connections.The experimental results showed that QENCD model significantly outperforms existing models on the ASSIST09,ASSIST17,and Junyi datasets significantly outperforming state-of-the-art baselines.This study provides a more precise knowledge modeling method for static cognitive diagnosis.关键词
认知诊断/知识点关联/注意力机制/残差连接/静态模型Key words
cognitive diagnosis/knowledge point correlation/attention mechanism/residual connection/static model分类
社会科学引用本文复制引用
梁恒贵,朱益辉,唐晓雯,朱命冬..基于知识点关系增强的静态认知诊断模型[J].华东师范大学学报（自然科学版）,2025,(5):76-86,11.基金项目
国家自然科学基金(62377012,61802116) （62377012,61802116）
河南省科技攻关项目(252102211028) （252102211028）
