A Two-Level Adaptive Test Battery

IF 1.9 3区心理学 Q2 EDUCATION & EDUCATIONAL RESEARCH

Journal of Educational and Behavioral Statistics Pub Date : 2023-11-06 DOI:10.3102/10769986231209447

Wim J. van der Linden, Luping Niu, Seung W. Choi

引用次数: 0

Abstract

A test battery with two different levels of adaptation is presented: a within-subtest level for the selection of the items in the subtests and a between-subtest level to move from one subtest to the next. The battery runs on a two-level model consisting of a regular response model for each of the subtests extended with a second level for the joint distribution of their abilities. The presentation of the model is followed by an optimized MCMC algorithm to update the posterior distribution of each of its ability parameters, select the items to Bayesian optimality, and adaptively move from one subtest to the next. Thanks to extremely rapid convergence of the Markov chain and simple posterior calculations, the algorithm can be used in real-world applications without any noticeable latency. Finally, an empirical study with a battery of short diagnostic subtests is shown to yield score accuracies close to traditional one-level adaptive testing with subtests of double lengths.

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双电平自适应测试电池

提出了一个具有两种不同适应水平的测试组:子测试内水平用于选择子测试中的项目，子测试间水平用于从一个子测试移动到下一个子测试。电池组在一个两级模型上运行，该模型由每个子测试的规则响应模型组成，并扩展了用于其能力联合分布的第二级模型。在给出模型之后，采用优化的MCMC算法更新模型各能力参数的后验分布，选择贝叶斯最优的项目，并自适应地从一个子测试移动到下一个子测试。由于马尔可夫链的快速收敛和简单的后验计算，该算法可以在实际应用中使用，没有任何明显的延迟。最后，一组短诊断子测试的实证研究表明，其得分准确性接近传统的双长度子测试的单水平自适应测试。

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.