重新排序任务的部分评分:线性矩阵的Excel

Amma Kazuo
{"title":"重新排序任务的部分评分:线性矩阵的Excel","authors":"Amma Kazuo","doi":"10.1109/ICICT50521.2020.00008","DOIUrl":null,"url":null,"abstract":"Estimating a partial score of item reordering tasks has long been neglected in language testing and education sciences. A psychologically valid means of scoring, MRS (Maximal Relative Sequence) was proposed by the author and transplanted to Excel. As the protocol simply picks up elements in relative ascending order, it made easier the non-specialists’ access to and analysis of the calculation process resulting in educational as well as practical significance. However, since the Excel enumeration replicated each step of MRS precisely, the number of columns consumed explodes as the number of elements increases. Moreover, MRS merely counts the number of elements to be relocated; it fails to consider the distance of relocation for recovery. This paper proposes an alternative solution LM (Linearity Matrix), also executable with Excel’s basic functions, with far fewer columns to consume. Further, LM is advantageous over MRS in that it is a general protocol of estimating relative similarity of two sequences of which Kendall’s tau is a special case; LM is adjustable as to the degree of adjacency constraint by changing the distance weight for all combinations of elements.","PeriodicalId":445000,"journal":{"name":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Partial Scoring of Reordering Tasks Revisited: Linearity Matrix by Excel\",\"authors\":\"Amma Kazuo\",\"doi\":\"10.1109/ICICT50521.2020.00008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Estimating a partial score of item reordering tasks has long been neglected in language testing and education sciences. A psychologically valid means of scoring, MRS (Maximal Relative Sequence) was proposed by the author and transplanted to Excel. As the protocol simply picks up elements in relative ascending order, it made easier the non-specialists’ access to and analysis of the calculation process resulting in educational as well as practical significance. However, since the Excel enumeration replicated each step of MRS precisely, the number of columns consumed explodes as the number of elements increases. Moreover, MRS merely counts the number of elements to be relocated; it fails to consider the distance of relocation for recovery. This paper proposes an alternative solution LM (Linearity Matrix), also executable with Excel’s basic functions, with far fewer columns to consume. Further, LM is advantageous over MRS in that it is a general protocol of estimating relative similarity of two sequences of which Kendall’s tau is a special case; LM is adjustable as to the degree of adjacency constraint by changing the distance weight for all combinations of elements.\",\"PeriodicalId\":445000,\"journal\":{\"name\":\"2020 3rd International Conference on Information and Computer Technologies (ICICT)\",\"volume\":\"82 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Conference on Information and Computer Technologies (ICICT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICT50521.2020.00008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Information and Computer Technologies (ICICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICT50521.2020.00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在语言测试和教育科学中,对项目重新排序任务的部分分数估计一直被忽视。笔者提出了一种心理上有效的评分方法——MRS (maximum Relative Sequence),并将其移植到Excel中。由于该协议简单地以相对升序拾取元素,它使非专业人员更容易访问和分析计算过程,从而具有教育和实际意义。但是,由于Excel枚举精确地复制了MRS的每一步,因此所消耗的列数随着元素数量的增加而激增。此外,MRS仅仅计算要重新定位的元素的数量;它没有考虑到回收的搬迁距离。本文提出了另一种解决方案LM(线性矩阵),也可以用Excel的基本函数执行,需要消耗的列要少得多。此外,LM优于MRS,因为它是估计两个序列相对相似性的一般协议,其中Kendall 's tau是一个特殊情况;LM可以通过改变所有元素组合的距离权重来调节邻接约束的程度。
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
Partial Scoring of Reordering Tasks Revisited: Linearity Matrix by Excel
Estimating a partial score of item reordering tasks has long been neglected in language testing and education sciences. A psychologically valid means of scoring, MRS (Maximal Relative Sequence) was proposed by the author and transplanted to Excel. As the protocol simply picks up elements in relative ascending order, it made easier the non-specialists’ access to and analysis of the calculation process resulting in educational as well as practical significance. However, since the Excel enumeration replicated each step of MRS precisely, the number of columns consumed explodes as the number of elements increases. Moreover, MRS merely counts the number of elements to be relocated; it fails to consider the distance of relocation for recovery. This paper proposes an alternative solution LM (Linearity Matrix), also executable with Excel’s basic functions, with far fewer columns to consume. Further, LM is advantageous over MRS in that it is a general protocol of estimating relative similarity of two sequences of which Kendall’s tau is a special case; LM is adjustable as to the degree of adjacency constraint by changing the distance weight for all combinations of elements.
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
自引率
0.00%
发文量
0
期刊最新文献
Significance of Agile Software Development and SQA Powered by Automation Improved Generalizability of Deep-Fakes Detection using Transfer Learning Based CNN Framework A New Homomorphic Message Authentication Code Scheme for Network Coding Conspiracy and Rumor Correction: Analysis of Social Media Users' Comments A Novel System for Ammonia Gas Control in Broiler Production Environment
×
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