降低认知诊断计算机自适应测试的错误分类成本:最小预期风险的项目选择。

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL
Chia-Ling Hsu, Wen-Chung Wang
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引用次数: 0

摘要

认知诊断计算机化自适应测试(CD-CAT)旨在识别每个考生在潜在属性上的优势和劣势,并将其适当分类到属性概况中。由于CD-CAT错误分类的成本因用户需求而异(例如,补救计划与奖学金资格),项目选择可以纳入此类成本以提高测量效率。本文提出了一种基于贝叶斯决策理论的最小期望风险(MER)方法。模拟结果表明,使用MER识别未掌握(MER- u0)或完全掌握(MER- u1)考生的分类准确率和效率高于其他方法,特别是对于较短的考试或低质量的题库。对于其他属性文件,无论项目质量或终止标准如何,MER方法、改进后置加权Kullback-Leibler信息(MPWKL)、后置加权CDM判别指数(PWCDI)和Shannon熵(SHE)在分类精度和测试效率方面表现相似,且优于后置加权属性级CDM判别指数(PWACDI),特别是在短测试上。具有0 - 1损失函数的MER、MER- u0、MER- u1和PWACDI比其他方法更有效地利用了物库。总的来说,这些结果表明在CD-CAT中使用MER来提高特定属性概况的准确性以满足不同用户需求的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Reducing the Misclassification Costs of Cognitive Diagnosis Computerized Adaptive Testing: Item Selection With Minimum Expected Risk.

Reducing the Misclassification Costs of Cognitive Diagnosis Computerized Adaptive Testing: Item Selection With Minimum Expected Risk.

Reducing the Misclassification Costs of Cognitive Diagnosis Computerized Adaptive Testing: Item Selection With Minimum Expected Risk.

Cognitive diagnosis computerized adaptive testing (CD-CAT) aims to identify each examinee's strengths and weaknesses on latent attributes for appropriate classification into an attribute profile. As the cost of a CD-CAT misclassification differs across user needs (e.g., remedial program vs. scholarship eligibilities), item selection can incorporate such costs to improve measurement efficiency. This study proposes such a method, minimum expected risk (MER), based on Bayesian decision theory. According to simulations, using MER to identify examinees with no mastery (MER-U0) or full mastery (MER-U1) showed greater classification accuracy and efficiency than other methods for these attribute profiles, especially for shorter tests or low quality item banks. For other attribute profiles, regardless of item quality or termination criterion, MER methods, modified posterior-weighted Kullback-Leibler information (MPWKL), posterior-weighted CDM discrimination index (PWCDI), and Shannon entropy (SHE) performed similarly and outperformed posterior-weighted attribute-level CDM discrimination index (PWACDI) in classification accuracy and test efficiency, especially on short tests. MER with a zero-one loss function, MER-U0, MER-U1, and PWACDI utilized item banks more effectively than the other methods. Overall, these results show the feasibility of using MER in CD-CAT to increase the accuracy for specific attribute profiles to address different user needs.

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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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