Utilizing Real-Time Test Data to Solve Attenuation Paradox in Computerized Adaptive Testing to Enhance Optimal Design

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2023-09-25 DOI:10.3102/10769986231197666

Jyun-Hong Chen, Hsiu-Yi Chao

引用次数: 0

Abstract

To solve the attenuation paradox in computerized adaptive testing (CAT), this study proposes an item selection method, the integer programming approach based on real-time test data (IPRD), to improve test efficiency. The IPRD method turns information regarding the ability distribution of the population from real-time test data into feasible test constraints to reversely assembled shadow tests for item selection to prevent the attenuation paradox by integer programming. A simulation study was conducted to thoroughly investigate IPRD performance. The results indicate that the IPRD method can efficiently improve CAT performance in terms of the precision of trait estimation and satisfaction of all required test constraints, especially for conditions with stringent exposure control.

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利用实时测试数据解决计算机自适应测试中的衰减悖论，提高优化设计

为了解决计算机自适应测试(CAT)中的衰减悖论，提出了一种基于实时测试数据的整数规划法(IPRD)项目选择方法，以提高测试效率。IPRD方法通过整数规划，将实时测试数据中关于总体能力分布的信息转化为可行的测试约束，反向组合阴影测试进行项目选择，防止衰减悖论。我们进行了一项模拟研究，以彻底调查知识产权开发的性能。结果表明，IPRD方法在特征估计精度和满足所有要求的测试约束方面可以有效地提高CAT性能，特别是在严格暴露控制的条件下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Educational and Behavioral Statistics Multiple-

CiteScore

4.40

自引率

4.20%

发文量

期刊介绍： Journal of Educational and Behavioral Statistics, sponsored jointly by the American Educational Research Association and the American Statistical Association, publishes articles that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also of interest. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority. The Journal of Educational and Behavioral Statistics provides an outlet for papers that are original and provide methods that are useful to those studying problems and issues in educational or behavioral research. Typical papers introduce new methods of analysis, provide properties of these methods, and an example of use in education or behavioral research. Critical reviews of current practice, tutorial presentations of less well known methods, and novel applications of already-known methods are also sometimes accepted. Papers discussing statistical techniques without specific educational or behavioral interest or focusing on substantive results without developing new statistical methods or models or making novel use of existing methods have lower priority. Simulation studies, either to demonstrate properties of an existing method or to compare several existing methods (without providing a new method), also have low priority.