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Dimensionality Reduction of Social Media Application Attributes for Ubiquitous Learning Using Principal Component Analysis
Mobile Information Systems ( IF 1.863 ) Pub Date : 2021-01-25 , DOI: 10.1155/2021/6633223
Caitlin Sam 1 , Nalindren Naicker 1 , Marion Adebiyi 1
Affiliation  

Ubiquitous learning is anywhere and anytime learning using e-learning and m-learning platforms. Learning takes place regularly on mobile devices. School-based instructors and learners have capitalised on ubiquitous learning platforms in unprecedented times such as COVID-19. There has been a proliferation of social media applications for ubiquitous learning. There are a vast number of attributes of the social media applications that must be considered for it to be deemed suitable for education. Further to this, mobile and desktop accessibility criteria must be considered. The aim of this research study was to determine the high impacting and most pertinent criteria to evaluate social media applications for school-based ubiquitous learning. Data was collected from 30 experts in the field of teaching and learning who were asked to evaluate 60 criteria. Principal Component Analysis (PCA) was the method employed for the dimensionality reduction. PCA was implemented using singular value decomposition (SVD) on R-Studio. The results showed loading values from principal component one for the top 40 educational requirements and technology criteria of the 60 criteria used in the study. The implications of this research study will guide researchers in the field of Educational Data Mining (EDM) and practitioners on the most important dimensions to consider when evaluating social media applications for ubiquitous learning.

中文翻译:

使用主成分分析的普遍学习社交媒体应用属性降维

使用电子学习和m-learning平台随时随地进行无处不在的学习。学习定期在移动设备上进行。校本的讲师和学习者已经在空前的时代(例如COVID-19)利用了无处不在的学习平台。用于普遍学习的社交媒体应用已经激增。必须考虑社交媒体应用程序的众多属性,才能使其适合于教育。除此之外,必须考虑移动和桌面可访问性标准。这项研究的目的是确定影响最大的和最相关的标准,以评估社交媒体在学校普遍学习中的应用。从30名教学领域的专家那里收集了数据,他们被要求评估60条标准。主成分分析(PCA)是用于降维的方法。PCA是使用R-Studio上的奇异值分解(SVD)实现的。结果显示,主要成分一的负荷值用于研究中的前40个教育要求和60个标准中的技术标准。这项研究的意义将指导教育数据挖掘(EDM)领域的研究人员和从业人员,在评估针对普遍学习的社交媒体应用程序时要考虑的最重要方面。结果显示,主要成分一的负荷值用于研究中的前40个教育要求和60个标准中的技术标准。这项研究的意义将指导教育数据挖掘(EDM)领域的研究人员和从业人员,在评估针对普遍学习的社交媒体应用程序时要考虑的最重要方面。结果显示,主要成分一的负荷值用于研究中的前40个教育要求和60个标准中的技术标准。这项研究的意义将指导教育数据挖掘(EDM)领域的研究人员和从业人员,在评估针对普遍学习的社交媒体应用程序时要考虑的最重要方面。
更新日期:2021-01-25
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