根据过去的成绩和当前的日常努力,在考试中发现个别学生的意外分数

IF 3.8 Q2 MANAGEMENT TQM Journal Pub Date : 2023-01-10 DOI:10.1108/tqm-07-2022-0226
Nursuhana Alauddin, Saki Tanaka, S. Yamada
{"title":"根据过去的成绩和当前的日常努力,在考试中发现个别学生的意外分数","authors":"Nursuhana Alauddin, Saki Tanaka, S. Yamada","doi":"10.1108/tqm-07-2022-0226","DOIUrl":null,"url":null,"abstract":"PurposeThis paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.Design/methodology/approachThe scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.FindingsThe score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.Originality/valueUnlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.","PeriodicalId":40009,"journal":{"name":"TQM Journal","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting unexpected scores of individual students in an examination based on past scores and current daily efforts\",\"authors\":\"Nursuhana Alauddin, Saki Tanaka, S. Yamada\",\"doi\":\"10.1108/tqm-07-2022-0226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"PurposeThis paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.Design/methodology/approachThe scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.FindingsThe score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.Originality/valueUnlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.\",\"PeriodicalId\":40009,\"journal\":{\"name\":\"TQM Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"TQM Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/tqm-07-2022-0226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"TQM Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/tqm-07-2022-0226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一个基于学生个人过去成绩、当前日常努力和当前成绩趋势的意外考试成绩检测模型。在当前考试结束后立即进行检测,这有助于在下学期开始日常评估之前立即采取行动提高学生的能力。设计/方法/方法使用方差分析、主成分分析和多元回归分析相结合的方法分析过去考试和当前日常评估的分数。本研究以日本一所国际学校中学生的评核成绩为研究对象进行个案研究。本次考试的分数是根据过去的分数、当前每天的努力和当前分数的趋势来预测的。根据预测分数推导出检测意外分数的控制下限。低于控制下限的实际分数被认为是意外分数。本案例研究验证了组合使用数据检测意外分数的有效性。独创性/价值与以往的研究利用属性和背景数据来预测学生成绩不同,本研究结合了过去的考试成绩、当前相关科目的日常努力以及当前分数的趋势。
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
Detecting unexpected scores of individual students in an examination based on past scores and current daily efforts
PurposeThis paper proposes a model for detecting unexpected examination scores based on past scores, current daily efforts and trend in the current score of individual students. The detection is performed soon after the current examination is completed, which helps take immediate action to improve the ability of students before the commencement of daily assessments during the next semester.Design/methodology/approachThe scores of past examinations and current daily assessments are analyzed using a combination of an ANOVA, a principal component analysis and a multiple regression analysis. A case study is conducted using the assessment scores of secondary-level students of an international school in Japan.FindingsThe score for the current examination is predicted based on past scores, current daily efforts and trend in the current score. A lower control limit for detecting unexpected scores is derived based on the predicted score. The actual score, which is below the lower control limit, is recognized as an unexpected score. This case study verifies the effectiveness of the combinatorial usage of data in detecting unexpected scores.Originality/valueUnlike previous studies that utilize attribute and background data to predict student scores, this study utilizes a combination of past examination scores, current daily efforts for related subjects and trend in the current score.
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
TQM Journal
TQM Journal Business, Management and Accounting-Business, Management and Accounting (all)
CiteScore
9.10
自引率
0.00%
发文量
114
期刊介绍: Commitment to quality is essential if companies are to succeed in a commercial environment which will be virtually unrecognizable in less than a decade. Changing attitudes, changing perspectives and changing priorities will revolutionise the structure and philosophy of future business practice - and TQM will be at the heart of that metamorphosis. All aspects of preparing for, developing, introducing, managing and evaluating TQM initiatives.
期刊最新文献
Looking good or doing good? Define the U.S. university's public mission by analyzing mission statements and strategic planning Leadership characteristics for implementation and sustainability of quality: an exploratory study and directions for further research Industry 4.0 technologies integration with lean production tools: a review Tourists' satisfaction and sense of belonging in adopting responsible behaviors: the role of on-site and social media involvement in cultural tourism The misplacement of ISO 18404:2015 in organisational improvement: a point-counterpoint article
×
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