Predicting Students Graduate on Time Using C4.5 Algorithm

H. Yuliansyah, Rahmasari Adi Putri Imaniati, Anggit Wirasto, Merlinda Wibowo
{"title":"Predicting Students Graduate on Time Using C4.5 Algorithm","authors":"H. Yuliansyah, Rahmasari Adi Putri Imaniati, Anggit Wirasto, Merlinda Wibowo","doi":"10.20473/JISEBI.7.1.67-73","DOIUrl":null,"url":null,"abstract":"Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.","PeriodicalId":16185,"journal":{"name":"Journal of Information Systems Engineering and Business Intelligence","volume":"56 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information Systems Engineering and Business Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20473/JISEBI.7.1.67-73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

Background: Facilitating an effective learning process is the goal of higher education institutions. Despite improvement in curriculum and resources, many students cannot graduate on time. Mostly, the number of students who graduate on time is lower than the number of new students enrolling to universities. This could dilute the chance for students to learn effectively as the ratio between faculty members and students becomes non-ideal.Objective: This study aims to present a prediction model for students’ on-time graduation using the C4.5 algorithm by considering four features, namely the department, GPA, English score, and age.Methods: This research was completed in three stages: data pre-processing, data processing and performance measurement. This predicting scheme make the prediction based on the department of study, age, GPA and English proficiency.Results: The results of this study have successfully predicted students’ graduation. This result is based on the data of students who graduated in 2008-2014. The prediction performance result achieved 90% of accuracy using 300 testing data.Conclusion: The finding is expected to be useful for universities in administering their teaching and learning process.
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
利用C4.5算法预测学生准时毕业
背景:促进有效的学习过程是高等教育机构的目标。尽管课程和资源有所改善,但许多学生无法按时毕业。大多数情况下,按时毕业的学生数量低于大学新生入学人数。这可能会稀释学生有效学习的机会,因为教师和学生之间的比例变得不理想。目的:本研究旨在结合院系、GPA、英语成绩、年龄四个特征,运用C4.5算法建立学生准时毕业的预测模型。方法:本研究分数据预处理、数据处理和性能测量三个阶段完成。该预测方案基于所学专业、年龄、GPA和英语水平进行预测。结果:本研究的结果成功地预测了学生的毕业情况。这一结果是基于2008-2014年毕业学生的数据得出的。使用300个测试数据,预测性能结果达到90%的准确率。结论:本研究结果可为高校的教与学管理提供参考。
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 去求助
来源期刊
CiteScore
0.30
自引率
0.00%
发文量
0
期刊最新文献
Sentiment Analysis on a Large Indonesian Product Review Dataset Leveraging Biotic Interaction Knowledge Graph and Network Analysis to Uncover Insect Vectors of Plant Virus Model-based Decision Support System Using a System Dynamics Approach to Increase Corn Productivity Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction A Practical Approach to Enhance Data Quality Management in Government: Case Study of Indonesian Customs and Excise Office
×
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