Adaptation to Online Education: An Educational Data Mining Application

IF 0.3 Q4 COMPUTER SCIENCE, THEORY & METHODS Computer Science-AGH Pub Date : 2022-11-30 DOI:10.53070/bbd.1199055
Cengiz Hark, Hatice Okumuş, Taner Uçkan
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Abstract

Despite space, time, and financial limitations, people who want to receive education participate intensively in online education programs that have emerged with the development of technology. With the Covid-19 outbreak, this interest has increased exponentially. In today's societies, where online education, which is preferred for different reasons, has become essential, examining the factors affecting success in online learning is a very important research topic. The study examined the level of adaptation to online education in terms of demographic variables. Experimental studies and necessary analyzes were carried out on the open-access ‘Students Adaptability Level in Online Education’ dataset. The results obtained using association rules, among the most widely used data mining techniques, have provided remarkable results regarding factors affecting success in distance education. It is thought that the study and the reported results will be a guide in creating education plans suitable for the demographic characteristics of the students enrolled in the online education program.
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适应在线教育:一个教育数据挖掘应用
尽管受到空间、时间和资金的限制,想要接受教育的人还是会集中参与随着技术发展而出现的在线教育项目。随着Covid-19疫情的爆发,这种兴趣呈指数级增长。在当今社会,由于各种原因,在线教育已经成为人们的首选,研究影响在线学习成功的因素是一个非常重要的研究课题。该研究从人口统计变量的角度考察了对在线教育的适应程度。对开放获取的“学生在线教育适应水平”数据集进行了实验研究和必要的分析。关联规则是使用最广泛的数据挖掘技术之一,它提供了影响远程教育成功因素的显著结果。人们认为,这项研究和报告的结果将为制定适合参加在线教育计划的学生的人口特征的教育计划提供指导。
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来源期刊
Computer Science-AGH
Computer Science-AGH COMPUTER SCIENCE, THEORY & METHODS-
CiteScore
1.40
自引率
0.00%
发文量
18
审稿时长
20 weeks
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