Predicting primary and middle-school students’ preferences for online learning with machine learning

IF 0.8 Q3 EDUCATION & EDUCATIONAL RESEARCH
V. Selvakumar, Tilak Pakki Venkata, Teja Pakki Venkata, Shubham Singh
{"title":"Predicting primary and middle-school students’ preferences for online learning with machine learning","authors":"V. Selvakumar, Tilak Pakki Venkata, Teja Pakki Venkata, Shubham Singh","doi":"10.4102/sajce.v13i1.1324","DOIUrl":null,"url":null,"abstract":"Background: The COVID-19 pandemic has brought attention to student psychological wellness. Because of isolation, lack of socialisation and intellectual and physical development from excessive media use, primary and secondary school students are at high risk for health problems.Aim: This study aimed to identify the most effective machine learning model for predicting the offline and online instructional strategies students would choose during a pandemic.Setting: The study was carried out at a number of primary and middle schools in Hyderabad, India.Methods: We evaluated the data using machine learning methods such as logistic regression, K-nearest neighbour (KNN), decision trees, bagging and boosting using the Python programming language.Results: In this study, 414 instances were collected from different schools. Exploratory data analysis showed that few students chose online courses. According to the research, very few students choose online classes, and the majority of students favoured offline classes over online because of physical and mental health difficulties; online education effects include a lack of social and peer relationships that affects young children psychologically, and they may not be disciplined enough to resist internet diversions. Smartphones, laptops, etc., affect their vision, causing headaches and impaired eyesight.Conclusion: The KNN was the most accurate machine learning algorithm, with 92.13% accuracy to fits the data to identify the preferences of online education.Contribution: This article examined the perspectives of primary and middle-school children on online education. Most students in this survey also reported experiencing mental or physical health issues that made online education difficult for them. Machine learning algorithms were applied to identify the most effective model for predicting students’ online and offline study preferences. This machine learning method will help schools improve their course delivery methods, allowing students to continue their studies without interruption.","PeriodicalId":55958,"journal":{"name":"South African Journal of Childhood Education","volume":" ","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"South African Journal of Childhood Education","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4102/sajce.v13i1.1324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
引用次数: 0

Abstract

Background: The COVID-19 pandemic has brought attention to student psychological wellness. Because of isolation, lack of socialisation and intellectual and physical development from excessive media use, primary and secondary school students are at high risk for health problems.Aim: This study aimed to identify the most effective machine learning model for predicting the offline and online instructional strategies students would choose during a pandemic.Setting: The study was carried out at a number of primary and middle schools in Hyderabad, India.Methods: We evaluated the data using machine learning methods such as logistic regression, K-nearest neighbour (KNN), decision trees, bagging and boosting using the Python programming language.Results: In this study, 414 instances were collected from different schools. Exploratory data analysis showed that few students chose online courses. According to the research, very few students choose online classes, and the majority of students favoured offline classes over online because of physical and mental health difficulties; online education effects include a lack of social and peer relationships that affects young children psychologically, and they may not be disciplined enough to resist internet diversions. Smartphones, laptops, etc., affect their vision, causing headaches and impaired eyesight.Conclusion: The KNN was the most accurate machine learning algorithm, with 92.13% accuracy to fits the data to identify the preferences of online education.Contribution: This article examined the perspectives of primary and middle-school children on online education. Most students in this survey also reported experiencing mental or physical health issues that made online education difficult for them. Machine learning algorithms were applied to identify the most effective model for predicting students’ online and offline study preferences. This machine learning method will help schools improve their course delivery methods, allowing students to continue their studies without interruption.
利用机器学习预测中小学生在线学习偏好
背景:新冠肺炎大流行引起了人们对学生心理健康的关注。由于与世隔绝、缺乏社交以及过度使用媒体导致的智力和身体发展,中小学生面临健康问题的高风险。目的:本研究旨在确定最有效的机器学习模型,用于预测学生在疫情期间选择的线下和在线教学策略。背景:这项研究在印度海得拉巴的一些中小学进行。方法:我们使用机器学习方法评估数据,如逻辑回归、K近邻(KNN)、决策树、使用Python编程语言进行装袋和提升。结果:在本研究中,从不同的学校收集了414个案例。探索性数据分析显示,很少有学生选择在线课程。根据研究,很少有学生选择网课,由于身心健康问题,大多数学生更喜欢线下课程而不是在线课程;网络教育的影响包括缺乏社交和同伴关系,这会影响幼儿的心理,他们可能没有足够的纪律来抵制网络转移。智能手机、笔记本电脑等会影响他们的视力,导致头痛和视力受损。结论:KNN是最准确的机器学习算法,拟合数据识别在线教育偏好的准确率为92.13%。贡献:本文考察了中小学生对在线教育的看法。这项调查中的大多数学生还报告说,他们遇到了心理或身体健康问题,这让他们很难接受在线教育。应用机器学习算法来确定预测学生在线和离线学习偏好的最有效模型。这种机器学习方法将帮助学校改进课程交付方法,让学生能够不间断地继续学习。
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
South African Journal of Childhood Education
South African Journal of Childhood Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
1.90
自引率
11.10%
发文量
50
审稿时长
25 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