PROGRAM FOR INTERNATIONAL STUDENT ASSESSMENT (PISA) ANALYSIS OF ASIAN COUNTRIES USING K-MEAN CLUSTERING ALGORITHMS

D. Pratama, Ihda Husnayaini
{"title":"PROGRAM FOR INTERNATIONAL STUDENT ASSESSMENT (PISA) ANALYSIS OF ASIAN COUNTRIES USING K-MEAN CLUSTERING ALGORITHMS","authors":"D. Pratama, Ihda Husnayaini","doi":"10.21009/jisae.v8i1.25445","DOIUrl":null,"url":null,"abstract":"The Organization for Economic Co-operation and Development (OECD) report shows that there are still some Asian countries at the lowest level in terms of achieving the Program for International Student Assessment (PISA) such as the Philippines, Lebanon, Indonesia, Kazakhstan, Azerbaijan, and Saudi Arabia. Based on these problems, it is necessary to group countries in Asia based on PISA indicators so that the characteristics of each country can be known through the k-mean clustering algorithm method. The data in this study are secondary data from the 2018 PISA results which include variables in reading, mathematics, and science. The sample in this study were Asian countries that participated in PISA in 2018 totaling 17 countries. Based on the results of clustering, there are 3 clusters formed, namely, cluster 1 is China and Singapore, which are countries with PISA capabilities above average. Cluster 2 is a cluster consisting of Malaysia, Brunei Darussalam, Qatar, Saudi Arabia, Thailand, Azerbaijan, Kazakhstan, Indonesia, Lebanon, and the Philippines with below-average PISA acquisition. Whereas in cluster 3 it is a cluster consisting of countries with medium capabilities in PISA acquisition such as Macau, Hong Kong, Korea, Japan, and China-Taipei.","PeriodicalId":262250,"journal":{"name":"JISAE: Journal of Indonesian Student Assessment and Evaluation","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JISAE: Journal of Indonesian Student Assessment and Evaluation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21009/jisae.v8i1.25445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The Organization for Economic Co-operation and Development (OECD) report shows that there are still some Asian countries at the lowest level in terms of achieving the Program for International Student Assessment (PISA) such as the Philippines, Lebanon, Indonesia, Kazakhstan, Azerbaijan, and Saudi Arabia. Based on these problems, it is necessary to group countries in Asia based on PISA indicators so that the characteristics of each country can be known through the k-mean clustering algorithm method. The data in this study are secondary data from the 2018 PISA results which include variables in reading, mathematics, and science. The sample in this study were Asian countries that participated in PISA in 2018 totaling 17 countries. Based on the results of clustering, there are 3 clusters formed, namely, cluster 1 is China and Singapore, which are countries with PISA capabilities above average. Cluster 2 is a cluster consisting of Malaysia, Brunei Darussalam, Qatar, Saudi Arabia, Thailand, Azerbaijan, Kazakhstan, Indonesia, Lebanon, and the Philippines with below-average PISA acquisition. Whereas in cluster 3 it is a cluster consisting of countries with medium capabilities in PISA acquisition such as Macau, Hong Kong, Korea, Japan, and China-Taipei.
使用k -均值聚类算法的亚洲国家国际学生评估(pisa)分析程序
经济合作与发展组织(OECD)的报告显示,在实现国际学生评估项目(PISA)方面,仍有一些亚洲国家处于最低水平,如菲律宾、黎巴嫩、印度尼西亚、哈萨克斯坦、阿塞拜疆和沙特阿拉伯。基于这些问题,有必要根据PISA指标对亚洲国家进行分组,通过k-mean聚类算法方法了解每个国家的特征。本研究中的数据是2018年PISA结果的二手数据,其中包括阅读、数学和科学方面的变量。本研究的样本是参加2018年PISA的亚洲国家,共有17个国家。根据聚类的结果,形成了3个集群,即,集群1是中国和新加坡,这是PISA能力高于平均水平的国家。集群2是由马来西亚、文莱达鲁萨兰国、卡塔尔、沙特阿拉伯、泰国、阿塞拜疆、哈萨克斯坦、印度尼西亚、黎巴嫩和菲律宾组成的集群,其PISA成绩低于平均水平。而在集群3中,它是一个由澳门、香港、韩国、日本和中国台北等在PISA获取方面具有中等能力的国家组成的集群。
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
自引率
0.00%
发文量
0
×
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