在多次尝试的诊断分类模型中建立分层属性结构模型

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Tae Yeon Kwon, A. Corinne Huggins-Manley, Jonathan Templin, Mingying Zheng
{"title":"在多次尝试的诊断分类模型中建立分层属性结构模型","authors":"Tae Yeon Kwon,&nbsp;A. Corinne Huggins-Manley,&nbsp;Jonathan Templin,&nbsp;Mingying Zheng","doi":"10.1111/jedm.12387","DOIUrl":null,"url":null,"abstract":"<p>In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical DCM (sequential HDCM), which combines a sequential DCM with the HDCM, and investigate classification accuracy of the model in the presence of hierarchies when multiple attempts are allowed in dynamic assessment. We investigated the model's impact on classification accuracy when hierarchical structures are correctly specified, misspecified, or overspecified. The results indicate that (1) a sequential HDCM accurately classified students as masters and nonmasters when the data had a hierarchical structure; (2) a sequential HDCM produced similar or slightly higher classification accuracy than nonhierarchical sequential LCDM when the data had hierarchical structures; and (3) the misspecification of the hierarchical structure of the data resulted in lower classification accuracy when the misspecified model had fewer attribute profiles than the true model. We discuss limitations and make recommendations on using the proposed model in practice. This study provides practitioners with information about the possibilities for psychometric modeling of dynamic classroom assessment data.</p>","PeriodicalId":47871,"journal":{"name":"Journal of Educational Measurement","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Hierarchical Attribute Structures in Diagnostic Classification Models with Multiple Attempts\",\"authors\":\"Tae Yeon Kwon,&nbsp;A. Corinne Huggins-Manley,&nbsp;Jonathan Templin,&nbsp;Mingying Zheng\",\"doi\":\"10.1111/jedm.12387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical DCM (sequential HDCM), which combines a sequential DCM with the HDCM, and investigate classification accuracy of the model in the presence of hierarchies when multiple attempts are allowed in dynamic assessment. We investigated the model's impact on classification accuracy when hierarchical structures are correctly specified, misspecified, or overspecified. The results indicate that (1) a sequential HDCM accurately classified students as masters and nonmasters when the data had a hierarchical structure; (2) a sequential HDCM produced similar or slightly higher classification accuracy than nonhierarchical sequential LCDM when the data had hierarchical structures; and (3) the misspecification of the hierarchical structure of the data resulted in lower classification accuracy when the misspecified model had fewer attribute profiles than the true model. We discuss limitations and make recommendations on using the proposed model in practice. This study provides practitioners with information about the possibilities for psychometric modeling of dynamic classroom assessment data.</p>\",\"PeriodicalId\":47871,\"journal\":{\"name\":\"Journal of Educational Measurement\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Educational Measurement\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jedm.12387\",\"RegionNum\":4,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PSYCHOLOGY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Educational Measurement","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jedm.12387","RegionNum":4,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PSYCHOLOGY, APPLIED","Score":null,"Total":0}
引用次数: 0

摘要

在课堂评估中,考生往往会多次回答测试题目,从而产生连续的多次尝试数据。针对此类数据开发了序列诊断分类模型(DCM)。由于学生的学习过程可能与所测特质的层次相一致,本研究旨在开发一种顺序层次诊断分类模型(顺序 HDCM),它将顺序 DCM 与 HDCM 相结合,并研究了在动态评估中允许多次尝试时,模型在存在层次的情况下的分类准确性。我们研究了当分层结构被正确指定、错误指定或过度指定时,模型对分类准确性的影响。结果表明:(1) 当数据具有层次结构时,顺序式 HDCM 能准确地将学生分类为硕士和非硕士;(2) 当数据具有层次结构时,顺序式 HDCM 的分类准确率与非层次式顺序 LCDM 相似或略高于后者;(3) 当错误指定的模型比真实模型具有更少的属性剖面时,对数据层次结构的错误指定会导致分类准确率降低。我们讨论了局限性,并就如何在实践中使用所提出的模型提出了建议。本研究为从业人员提供了有关动态课堂评估数据心理计量建模可能性的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling Hierarchical Attribute Structures in Diagnostic Classification Models with Multiple Attempts

In classroom assessments, examinees can often answer test items multiple times, resulting in sequential multiple-attempt data. Sequential diagnostic classification models (DCMs) have been developed for such data. As student learning processes may be aligned with a hierarchy of measured traits, this study aimed to develop a sequential hierarchical DCM (sequential HDCM), which combines a sequential DCM with the HDCM, and investigate classification accuracy of the model in the presence of hierarchies when multiple attempts are allowed in dynamic assessment. We investigated the model's impact on classification accuracy when hierarchical structures are correctly specified, misspecified, or overspecified. The results indicate that (1) a sequential HDCM accurately classified students as masters and nonmasters when the data had a hierarchical structure; (2) a sequential HDCM produced similar or slightly higher classification accuracy than nonhierarchical sequential LCDM when the data had hierarchical structures; and (3) the misspecification of the hierarchical structure of the data resulted in lower classification accuracy when the misspecified model had fewer attribute profiles than the true model. We discuss limitations and make recommendations on using the proposed model in practice. This study provides practitioners with information about the possibilities for psychometric modeling of dynamic classroom assessment data.

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
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.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信