电子大规模评估的数据管道:更好的自动化、质量保证和效率

IF 0.8 Q3 EDUCATION & EDUCATIONAL RESEARCH International Journal of Assessment Tools in Education Pub Date : 2023-11-20 DOI:10.21449/ijate.1321061
Ryan Schwarz, H. Bulut, Charles Ani̇fowose
{"title":"电子大规模评估的数据管道:更好的自动化、质量保证和效率","authors":"Ryan Schwarz, H. Bulut, Charles Ani̇fowose","doi":"10.21449/ijate.1321061","DOIUrl":null,"url":null,"abstract":"The increasing volume of large-scale assessment data poses a challenge for testing organizations to manage data and conduct psychometric analysis efficiently. Traditional psychometric software presents barriers, such as a lack of functionality for managing data and conducting various standard psychometric analyses efficiently. These challenges have resulted in high costs to achieve the desired research and analysis outcomes. To address these challenges, we have designed and implemented a modernized data pipeline that allows psychometricians and statisticians to efficiently manage the data, conduct psychometric analysis, generate technical reports, and perform quality assurance to validate the required outputs. This modernized pipeline has proven to scale with large databases, decrease human error by reducing manual processes, efficiently make complex workloads repeatable, ensure high quality of the outputs, and reduce overall costs of psychometric analysis of large-scale assessment data. This paper aims to provide information to support the modernization of the current psychometric analysis practices. We shared details on the workflow design and functionalities of our modernized data pipeline, which provide a universal interface to large-scale assessments. The methods for developing non-technical and user-friendly interfaces will also be discussed.","PeriodicalId":42417,"journal":{"name":"International Journal of Assessment Tools in Education","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A data pipeline for e-large-scale assessments: Better automation, quality assurance, and efficiency\",\"authors\":\"Ryan Schwarz, H. Bulut, Charles Ani̇fowose\",\"doi\":\"10.21449/ijate.1321061\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The increasing volume of large-scale assessment data poses a challenge for testing organizations to manage data and conduct psychometric analysis efficiently. Traditional psychometric software presents barriers, such as a lack of functionality for managing data and conducting various standard psychometric analyses efficiently. These challenges have resulted in high costs to achieve the desired research and analysis outcomes. To address these challenges, we have designed and implemented a modernized data pipeline that allows psychometricians and statisticians to efficiently manage the data, conduct psychometric analysis, generate technical reports, and perform quality assurance to validate the required outputs. This modernized pipeline has proven to scale with large databases, decrease human error by reducing manual processes, efficiently make complex workloads repeatable, ensure high quality of the outputs, and reduce overall costs of psychometric analysis of large-scale assessment data. This paper aims to provide information to support the modernization of the current psychometric analysis practices. We shared details on the workflow design and functionalities of our modernized data pipeline, which provide a universal interface to large-scale assessments. The methods for developing non-technical and user-friendly interfaces will also be discussed.\",\"PeriodicalId\":42417,\"journal\":{\"name\":\"International Journal of Assessment Tools in Education\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2023-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Assessment Tools in Education\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21449/ijate.1321061\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Assessment Tools in Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21449/ijate.1321061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

摘要

大规模测评数据量的不断增加,给测试机构高效管理数据和进行心理测量分析带来了挑战。传统的心理测量软件存在一些障碍,如缺乏管理数据和有效进行各种标准心理测量分析的功能。这些挑战导致成本高昂,难以实现预期的研究和分析成果。为了应对这些挑战,我们设计并实施了一个现代化的数据管道,使心理测量学家和统计学家能够高效地管理数据、进行心理测量分析、生成技术报告并执行质量保证以验证所需的输出结果。事实证明,这一现代化管道可以扩展大型数据库,通过减少人工流程来降低人为错误,有效地使复杂的工作量具有可重复性,确保产出的高质量,并降低大规模评估数据心理测量分析的总体成本。本文旨在为当前心理测量分析实践的现代化提供信息支持。我们分享了现代化数据管道的工作流程设计和功能细节,为大规模评估提供了一个通用接口。本文还将讨论开发非技术性和用户友好界面的方法。
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
A data pipeline for e-large-scale assessments: Better automation, quality assurance, and efficiency
The increasing volume of large-scale assessment data poses a challenge for testing organizations to manage data and conduct psychometric analysis efficiently. Traditional psychometric software presents barriers, such as a lack of functionality for managing data and conducting various standard psychometric analyses efficiently. These challenges have resulted in high costs to achieve the desired research and analysis outcomes. To address these challenges, we have designed and implemented a modernized data pipeline that allows psychometricians and statisticians to efficiently manage the data, conduct psychometric analysis, generate technical reports, and perform quality assurance to validate the required outputs. This modernized pipeline has proven to scale with large databases, decrease human error by reducing manual processes, efficiently make complex workloads repeatable, ensure high quality of the outputs, and reduce overall costs of psychometric analysis of large-scale assessment data. This paper aims to provide information to support the modernization of the current psychometric analysis practices. We shared details on the workflow design and functionalities of our modernized data pipeline, which provide a universal interface to large-scale assessments. The methods for developing non-technical and user-friendly interfaces will also be discussed.
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
International Journal of Assessment Tools in Education
International Journal of Assessment Tools in Education EDUCATION & EDUCATIONAL RESEARCH-
自引率
11.10%
发文量
40
期刊最新文献
The difference between estimated and perceived item difficulty: An empirical study Current Effect of Mother-Child Memory Talk on Emotion Regulation, Self-Esteem, and Memory Anxiety scale for parents of gifted children (ASPGC): Validity and reliability study Examining the validity and reliability of the academic entitlement scale in Turkish culture The examination of mediating role of distributed leadership in the relationship between school structure and accountability
×
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