Data Mining for Improving Online Higher Education Amidst COVID-19 Pandemic: A Case Study in the Assessment of Engineering Students
Data Mining for Improving Online Higher Education Amidst COVID-19 Pandemic: A Case Study in the Assessment of Engineering Students
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COVID-19 大流行期间用于改善在线高等教育的数据挖掘：工科学生评估的案例研究
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
C. Sgouropoulou
中科院分区：
文献类型：
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作者：
Z. Kanetaki;C. Stergiou;G. Bekas;C. Troussas;C. Sgouropoulou
教学材料、互联网的可访问性、学生的参与性和交流性一直是电子学习不可或缺的特点。在从面对面到新冠肺炎新的在线学习环境的过渡过程中，大学的讲座和实验室要么同步进行(使用MS Team等平台)，要么异步进行(使用Moodle等平台)。本研究以希腊一所大学为例，对学习者的在线评估进行了研究。作为这项研究的试验床，MS Teams被用作并测试为一个学习管理系统，用于评估单一平台的使用，以避免在大流行期间同时进行LMS操作时扰乱教育程序。统计分析包括相关性分析和可靠性分析，用于从在线调查问卷中挖掘和过滤数据。研究发现，37个变量对任务分配到同时用于同步授课的单一平台的测试有显著影响。Cronbach‘s Alpha系数的计算表明，89%的调查问题被发现是内部一致和可靠的变量，抽样充分性测量(巴特利特检验)被确定为良好的0.816。根据学生的勤奋程度、沟通能力和知识植入水平，对两组学生进行了区分。已经执行了等级聚类分析，提取了树状图，该树状图表示在上分支中有2个大的簇，在下分支中有3个簇，以及随后的下分支中包含5个簇。
Instructional materials, internet accessibility, student involvement and communication have always been integral characteristics of e-learning. During the transition from face-to-face to COVID-19 new online learning environments, the lectures and laboratories at universities have taken place either synchronously (using platforms, like MS Teams) or asynchronously (using platforms, like Moodle). In this study, a case study of a Greek university on the online assessment of learners is presented. As a testbed of this research, MS Teams was employed and tested as being a Learning Management System for evaluating a single platform use in order to avoid disruption of the educational procedure with concurrent LMS operations during the pandemic. A statistical analysis including a correlation analysis and a reliability analysis has been used to mine and filter data from online questionnaires. 37 variables were found to have a significant impact on the testing of tasks’ assignment into a single platform that was used at the same time for synchronous lectures. The calculation of Cronbach’s Alpha coefficient indicated that 89% of the survey questions have been found to be internally consistent and reliable variables and sampling adequacy measure (Bartlett’s test) was determined to be good at 0.816. Two clusters of students have been differentiated based on the parameters of their diligence, communication abilities and level of knowledge embedding. A hierarchical cluster analysis has been performed extracting a dendrogram indicating 2 large clusters in the upper branch, three clusters in the lower branch and an ensuing lower branch containing five clusters.