A Generalized Objective Function for Computer Adaptive Item Selection

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Harold Doran, Testsuhiro Yamada, Ted Diaz, Emre Gonulates, Vanessa Culver
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引用次数: 0

Abstract

Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and principled assessment design. The generalized nature of the algorithm permits a wide array of test requirements allowing experts to define what to measure and how to measure it and the algorithm is simply a means to an end to support better construct representation. This work also emphasizes the computational algorithm and its ability to scale to support faster computing and better cost‐containment in real‐world applications than other CAT algorithms. We make a significant effort to consolidate all information needed to build and scale the algorithm so that expert psychometricians and software developers can use this document as a self‐contained resource and specification document to build and deploy an operational CAT platform.
计算机自适应项目选择的通用目标函数
计算机自适应测试(CAT)是一种日益普遍的测试管理模式,它能提高测试的安全性、测量的精确性,并有可能缩短测试时间。本文介绍了一种基于通用目标函数的新项目选择算法,以支持多种类型的测试条件和有原则的评估设计。该算法的通用性可满足各种测试要求,让专家确定测量什么和如何测量,而算法只是一种手段,目的是支持更好的建构表征。与其他 CAT 算法相比,这项工作还强调了计算算法及其在实际应用中的扩展能力,以支持更快的计算和更好的成本控制。我们努力整合构建和扩展算法所需的所有信息,以便心理测量专家和软件开发人员可以将本文档作为自成一体的资源和规范文档,用于构建和部署可操作的 CAT 平台。
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
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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