基于文本语义增强图神经网络的计算机自适应测试方法

doi:10.16451/j.cnki.issn1003-6059.202509006

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Abstract Computerized adaptive testing(CAT) is designed to achieve efficient assessment through dynamic question selection. However, existing methods still suffer from insufficient accuracy in semantic modeling and ability estimation. To address these issues, a text-semantic enhanced graph neural network based CAT approach(TECAT) is proposed. Contextual semantic representations of questions and concepts are extracted using a pretrained language model, and multi-level dependencies among questions and concepts are captured by applying graph attention networks on the question-concept and concept-prerequisite graphs. To integrate semantic and structural information, a gated fusion mechanism based on additive attention and SiLU activation is introduced for the above two types of information to be adaptively combined. As a result, more expressive node representations are obtained. The CAT process is further formulated as a multi-objective reinforcement learning task to jointly optimize question quality, diversity, and novelty. A quality reward function based on changes in ability estimation error is designed to better reflect the contribution of each question to ability diagnosis. Experiments on two real-world datasets, Eedi and Junyi, show that TECAT achieves superior ability in estimation accuracy and concept representation quality compared with the existing methods.

Key words： Computerized Adaptive Testing(CAT) Textual Semantic Representation Multi-objective Reinforcement Learning Graph Attention Network

Received: 07 August 2025

ZTFLH:

G434

Fund:National Natural Science Foundation of China(No.62507039,62576287), Sichuan Science and Technology Program(No.2025ZNSFSC0506), Undergraduate Education and Teaching Reform Research Project of Xihua University(No.xjjg2025052)

Corresponding Authors: LI Yanli, Ph.D., lecturer. Her research interests include large language models, graph deep learning, recommendation systems, and educational data mining.

About author:: PU Qingsong, Master student. His research interests include data mining and inte-lligent education.
DU Yajun, Ph.D., professor. His research interests include deep learning methods, natural language processing, knowledge graphs, and social network analysis.
LI Xianyong, Ph.D., professor. His research interests include artificial intelligence, social network analysis, sentiment analysis, and evolution and guidance of online public opinion.
CHEN Xiaoliang, Ph.D., professor. His research interests include AI-generated content (AIGC), natural language processing, social computing, and theory and applications of membrane computing.
LIU Jia, Ph.D., lecturer. His research interests include artificial intelligence, data mining, and social network analysis.

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Cite this article:

PU Qingsong,LI Yanli,DU Yajun等. Text-Semantic Enhanced Graph Neural Network Based Computerized Adaptive Testing Approach[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(9): 837-850.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202509006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I9/837

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