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贝叶斯网在小样本认知诊断中的应用

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认知诊断的一个理想应用场景是在小样本情境的课堂测试中提供学生的诊断信息,进而促进补救教学;当前多数的认知诊断模型需较大的样本量进行参数估计,并且有些参数估计方法存在计算效率问题不能提供及时的诊断反馈.基于贝叶斯网的认知诊断方法可以实现小样本情况下诊断分类,并且能够提供及时的诊断反馈,这对于推进认知诊断在实践中的应用提供了可能.研究尝试使用贝叶斯网络方法进行小样本认知诊断分类,模拟研究表明:贝叶斯网络方法的诊断分类性能优于同样适用于小样本的海明距离法.
The Application of Bayesian Networks in Cognitive Diagnosis with Small Sample Size
In this study,Bayesian networks(BN)are proposed to conduct cognitive diagnosis in a small sample.The combination of IRP(Ideal Response Pattern)and EM parameter estimating methods can overcome the shortcomings of IRP and EM algorithms respectively,and can realize the BN application in a small sample size.The Monte Carlo simulation study is used to examine the performance of the BN-IRP-EM method in a small sample size,compared with the hamming distance method.In the simulation study,the pattern match ratio and average attribute match ratio are used as criteria to evaluate the classification accuracy of different approaches.To demonstrate the effectiveness of the BN-IRP-EM method,the BN based purely on the IRP method is adopted as the controlling method,another controlling method is the hamming distance(H-D)method.The results are as follows:the classification rate of the BN-IRP method is slightly higher than that of the H-D method which is based on the same IRP information except for some conditions.The classification rate of the BN-IRP-EM method is higher than the BN-IRP method and the H-D method in all circumstances.In the BN-IRP-EM condition,due to the incorporation of the empirical information,the classification rate is gradually increasing with the increase in sample size.These outcomes demonstrated that the BN-IRP-EM method could be used in a small sample size and can promote the application of CDA in classroom assessment.

cognitive diagnostic assessmentbayesian networksclassroom evaluationsmall sample size

汪玲玲

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沈阳师范大学教育科学学院,沈阳 110034

认知诊断评估 贝叶斯网络 课堂评估 小样本

2024

心理学探新
江西师范大学

心理学探新

CHSSCD北大核心
影响因子:0.566
ISSN:1003-5184
年,卷(期):2024.44(3)