多级测试中项目标定方法的研究

IF 0.6 Q3 SOCIAL SCIENCES, INTERDISCIPLINARY Measurement-Interdisciplinary Research and Perspectives Pub Date : 2021-07-03 DOI:10.1080/15366367.2021.1878778
L. Cai, Anthony D. Albano, L. Roussos
{"title":"多级测试中项目标定方法的研究","authors":"L. Cai, Anthony D. Albano, L. Roussos","doi":"10.1080/15366367.2021.1878778","DOIUrl":null,"url":null,"abstract":"ABSTRACT Multistage testing (MST), an adaptive test delivery mode that involves algorithmic selection of predefined item modules rather than individual items, offers a practical alternative to linear and fully computerized adaptive testing. However, interactions across stages between item modules and examinee groups can lead to challenges in item calibration with MST. This study used simulated data based on an operational program to investigate the performance of four item calibration methods under a 1–3 MST design. Conditions included routing module length, routing rule, and sample size. Calibration methods were evaluated based on item and person parameter recovery and classification accuracy. Results indicated that calibration with fixed common item parameters and concurrent calibration assuming a single ability distribution similarly outperformed both separate calibration with linking and concurrent calibration with the multiple-group procedure.","PeriodicalId":46596,"journal":{"name":"Measurement-Interdisciplinary Research and Perspectives","volume":"25 1","pages":"163 - 178"},"PeriodicalIF":0.6000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An Investigation of Item Calibration Methods in Multistage Testing\",\"authors\":\"L. Cai, Anthony D. Albano, L. Roussos\",\"doi\":\"10.1080/15366367.2021.1878778\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Multistage testing (MST), an adaptive test delivery mode that involves algorithmic selection of predefined item modules rather than individual items, offers a practical alternative to linear and fully computerized adaptive testing. However, interactions across stages between item modules and examinee groups can lead to challenges in item calibration with MST. This study used simulated data based on an operational program to investigate the performance of four item calibration methods under a 1–3 MST design. Conditions included routing module length, routing rule, and sample size. Calibration methods were evaluated based on item and person parameter recovery and classification accuracy. Results indicated that calibration with fixed common item parameters and concurrent calibration assuming a single ability distribution similarly outperformed both separate calibration with linking and concurrent calibration with the multiple-group procedure.\",\"PeriodicalId\":46596,\"journal\":{\"name\":\"Measurement-Interdisciplinary Research and Perspectives\",\"volume\":\"25 1\",\"pages\":\"163 - 178\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement-Interdisciplinary Research and Perspectives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15366367.2021.1878778\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement-Interdisciplinary Research and Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15366367.2021.1878778","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
引用次数: 2

摘要

多阶段测试(MST)是一种自适应测试交付模式,它涉及预先定义的项目模块的算法选择,而不是单个项目,提供了线性和完全计算机化的自适应测试的实际替代方案。然而,项目模块和考生群体之间的跨阶段互动可能会导致项目与MST校准的挑战。本研究使用基于操作程序的模拟数据,研究了1-3 MST设计下四种项目校准方法的性能。条件包括路由模块长度、路由规则和样本大小。基于项目和人的参数恢复和分类精度对标定方法进行了评价。结果表明,采用固定的共同项目参数进行标定和采用单一能力分布进行并行标定的效果与采用连接方法进行单独标定和采用多组方法进行并行标定的效果相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
An Investigation of Item Calibration Methods in Multistage Testing
ABSTRACT Multistage testing (MST), an adaptive test delivery mode that involves algorithmic selection of predefined item modules rather than individual items, offers a practical alternative to linear and fully computerized adaptive testing. However, interactions across stages between item modules and examinee groups can lead to challenges in item calibration with MST. This study used simulated data based on an operational program to investigate the performance of four item calibration methods under a 1–3 MST design. Conditions included routing module length, routing rule, and sample size. Calibration methods were evaluated based on item and person parameter recovery and classification accuracy. Results indicated that calibration with fixed common item parameters and concurrent calibration assuming a single ability distribution similarly outperformed both separate calibration with linking and concurrent calibration with the multiple-group procedure.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Measurement-Interdisciplinary Research and Perspectives
Measurement-Interdisciplinary Research and Perspectives SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
1.80
自引率
0.00%
发文量
23
期刊最新文献
A Latent Trait Approach to the Measurement of Physical Fitness Application of Machine Learning Techniques for Fake News Classification The Use of Multidimensional Item Response Theory Estimations in Controlling Differential Item Functioning Opinion Instability and Measurement Errors: A G-Theory Analysis of College Students Predicting the Risk of Diabetes and Heart Disease with Machine Learning Classifiers: The Mediation Analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1