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已有的认知诊断模型大多基于充分的学生做题记录进行诊断。然而现实中学生的做题记录、题目与知识点等的层间关联呈现长尾分布，即部分学生做题数量少、部分习题仅包含少量知识点，给模型的训练带来挑战。为此，提出一个基于层内相似关系的认知诊断模型，通过学生作答记录使用简单匹配系数分别计算学生、习题、知识点的相似系数，并构建层内相似关系。利用这种层内相似关系通过关系图卷积网络将头节点信息传递给尾节点，改善尾节点层间关系的稀疏性，通过融合知识点表示的诊断函数进行诊断。
Abstract：Most of current cognitive diagnostic models that existed in the past predominantly relied on abundant student response records for diagnosis. However, in reality, the interconnections among students′ response records, items, and knowledge concepts exhibited a long-tail distribution. That meant that some students had a limited number of response records, and some items were covered by only a few knowledge concepts. This challenge was posed for model training. To address this issue, a cognitive diagnostic model based on intra-layer similarity relationships was proposed. Using a simple matching coefficient, the similarity coefficients of students, items, and knowledge concepts were calculated based on their response records. This process established intra-layer similarity relationships for students, items, and knowledge concepts. These intra-layer relationships were then utilized by the model, and a relational graph convolutional network was employed to propagate information from head nodes to tail nodes. This approach aimed to improve the sparsity of inter-layer relationships in the tail nodes. A diagnostic function that incorporated knowledge point representations was used for cognitive diagnosis.
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基本信息:
DOI：10.13705/j.issn.1671-6841.2023115
中图分类号:TP18
引用信息:
[1]王冕,张玉红,刘菲,等.长尾分布下基于层内相似关系的认知诊断模型[J].郑州大学学报(理学版),2025,57(03):35-41+48.DOI:10.13705/j.issn.1671-6841.2023115.
基金信息:
国家自然科学基金项目(61976077,62076085); 安徽省自然科学基金项目(2208085MF170); 安徽省高校协同创新项目(GXXT-2022-040)
2024-02-07
2024-02-07
2024-02-07