长尾分布下基于层内相似关系的认知诊断模型

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中图分类号：TP391 文献标志码：ADOI：10. 13705/j. issn. 1671-6841. 2023115

文章编号：1671-6841（2025）03-0035-07

Cognitive Diagnosis Model Based on Intra-layer Similarity with Long Tail Distribution

WANG Mian， ZHANG Yuhong，LIU Fei，BU Chenyang，HU Xuegang （School of Computer Science and Information Engineering，Hefei University of Technology，Hefei 230601，China）

Abstract：Most of currnt 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.

Key words：cognitive diagnosis；long-tailed distribution；similarity； intra-layer relationships；graph convolutional networks

0 引言

近年来，人工智能技术应用于教育领域引起了广泛关注。（剩余11075字）