斯坦伦博斯大学学生在2019冠状病毒病疫情期间阻碍其成功在线学习能力的挑战:人口和空间分析

A. Onwuegbuzie, Emmanuel O. Ojo, Annie Burger, Talitha Crowley, Samantha P. Adams, B. J. Bergsteedt
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引用次数: 11

摘要

这项元方法研究(即,涉及多种方法研究方法和混合方法研究方法的完全整合)的目的是研究Stellenbosch大学的学生在2020年4月因新冠肺炎大流行而开始的紧急远程教学期间所经历的挑战,这些挑战阻碍了他们成功在线学习的能力。具体来说,1932名学生完成了一份在线问卷。通过WordStat 8.0.29主题建模(即交叉混合分析)对开放式回答进行分析,确定了对学生挑战进行分类的七个(挑战)主题。反过来,通过对主题的主成分分析(即交叉混合分析),这七个主题被分解为四个维度(即元主题)。潜在的班级分析(即定性)产生了七个学生档案。根据人口统计和地点变量对主题进行分类,可以确定每种挑战类型风险最大的亚组。讨论了这些发现和其他发现的含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges Experienced by Students at Stellenbosch University that Hinder their Ability Successfully to Learn Online during the COVID-19 era: A Demographic and Spatial Analysis
The purpose of this meta-methods study (i.e., involving the full[er] integration of multiple methods research approaches and mixed methods research approaches) was to examine challenges experienced by students at Stellenbosch University that hinder their ability successfully to learn online during the emergency remote teaching that began in April 2020 due to the COVID-19 pandemic. Specifically, 1,932 students completed an online questionnaire. Analyses of the open-ended responses via WordStat 8.0.29 topic modeling (i.e., crossover mixed analysis) led to the identification of seven (challenge) themes that categorised the students’ challenges. In turn, via a principal component analysis of the themes (i.e., crossover mixed analysis), these seven themes were collapsed into four dimensions (i.e., meta-themes). A latent class analysis (i.e., qualitizing) yielded seven profiles of students. Disaggregating the themes by demographic and location variables led to the identification of subgroups who are most at risk for experiencing each challenge type. Implications of these and other findings are discussed.
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