THE PERFORMANCE OF AFFIRMATIVE ACTION STUDENTS AND ANALYSIS OF SUBJECTS GRADES USING LATENT CLASS AND LATENT BUDGET MODELS

Q4 Medicine
E. Jelihovschi
{"title":"THE PERFORMANCE OF AFFIRMATIVE ACTION STUDENTS AND ANALYSIS OF SUBJECTS GRADES USING LATENT CLASS AND LATENT BUDGET MODELS","authors":"E. Jelihovschi","doi":"10.28951/RBB.V36I4.309","DOIUrl":null,"url":null,"abstract":"Latent class analysis (LCA) is used to analyse data about performance of students. The original performance variables in the data set are course grade and approval status. However, those variables were not used directly, instead four new variables were calculated from those previous two, variables which are much more informative about the student performance. Coupled with another variable, say afirmative action, the results give light to an understanding about the performance of the divided by afirmative action, yes or no. Besides of showing the results it is also shown how the changing of the original variables by some suitable transformation of the original ones gives more reliable results. The main result is that armative action students have a lower performance than those coming from private schools. The paper also analyses the subjects grades using latent budget analysis (LBA), and it is found that the variables cited above have a real effect in characterizing the subjects. It is also shown that those results can be used in a process of evaluation on how the subject is being taught.","PeriodicalId":36293,"journal":{"name":"Revista Brasileira de Biometria","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Brasileira de Biometria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.28951/RBB.V36I4.309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

Abstract

Latent class analysis (LCA) is used to analyse data about performance of students. The original performance variables in the data set are course grade and approval status. However, those variables were not used directly, instead four new variables were calculated from those previous two, variables which are much more informative about the student performance. Coupled with another variable, say afirmative action, the results give light to an understanding about the performance of the divided by afirmative action, yes or no. Besides of showing the results it is also shown how the changing of the original variables by some suitable transformation of the original ones gives more reliable results. The main result is that armative action students have a lower performance than those coming from private schools. The paper also analyses the subjects grades using latent budget analysis (LBA), and it is found that the variables cited above have a real effect in characterizing the subjects. It is also shown that those results can be used in a process of evaluation on how the subject is being taught.
平权行动学生的表现及使用潜在阶级和潜在预算模型的学科成绩分析
潜类分析(LCA)用于分析学生的表现数据。数据集中的原始绩效变量为课程成绩和审批状态。然而,这些变量并不是直接使用的,而是从之前的两个变量中计算出四个新的变量,这些变量对学生的表现有更多的信息。再加上另一个变量,比如平权行动,结果让我们了解了平权行动对学生表现的影响,是或否。除了给出结果外,还说明了如何通过对原始变量进行适当的变换来改变原始变量,从而得到更可靠的结果。主要结果是,私立学校的学生比私立学校的学生表现更差。本文还利用潜在预算分析(latent budget analysis, LBA)对被试的成绩进行了分析,发现上述变量对被试的特征具有真实的影响。研究还表明,这些结果可以用于评估该学科的教学情况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Revista Brasileira de Biometria
Revista Brasileira de Biometria Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
自引率
0.00%
发文量
0
审稿时长
53 weeks
×
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