论文标题
使用5 W的方法和1小时的方法调查不同国家 /地区的在线学习的出现
Investigating the Emergence of Online Learning in Different Countries using the 5 W's and 1 H Approach
论文作者
论文摘要
过去十年来,一切生活方式的兴起都对世界上几乎所有国家的在线学习的出现和采用都产生了重大影响。在未来几年中,电子学习3.0预计将成为几乎所有领域学习的规范。预计由互联网提供的语义网的普遍性预计将在无缝和更快地采用电子学习范式3.0的范式中发挥巨大作用。因此,本文提出了一项探索性研究,以分析语义Web行为数据的多模式组成部分,以调查世界各地在线学习的出现。这项工作特别涉及研究相关的网络行为数据以解释5 W和1 h-谁,什么,何时何时,为什么以及与在线学习的关系。在研究2021年的电子学习指数的基础上,该研究是针对所有属于经济合作与发展组织成员国的国家进行的。结果和讨论的结果有助于解释每个国家 /地区的在线学习的出现,以相关的公众看法,查询,观点,行为和观点。此外,为了支持该领域的研发,我们发布了与在线学习有关的基于Web行为的大数据,该数据均以数据集的形式为所有这38个国家/地区开采,该数据集可在https://dx.doi.org/10.21227/xbvs-0198上使用。
The rise of the Internet of Everything lifestyle in the last decade has had a significant impact on the increased emergence and adoption of online learning in almost all countries across the world. E-learning 3.0 is expected to become the norm of learning globally in almost all sectors in the next few years. The pervasiveness of the Semantic Web powered by the Internet of Everything lifestyle is expected to play a huge role towards seamless and faster adoption of the emerging paradigms of E-learning 3.0. Therefore, this paper presents an exploratory study to analyze multimodal components of Semantic Web behavior data to investigate the emergence of online learning in different countries across the world. The work specifically involved investigating relevant web behavior data to interpret the 5 W's and 1 H - Who, What, When Where, Why, and How related to online learning. Based on studying the E-learning Index of 2021, the study was performed for all the countries that are member states of the Organization for Economic Cooperation and Development. The results presented and discussed help to interpret the emergence of online learning in each of these countries in terms of the associated public perceptions, queries, opinions, behaviors, and perspectives. Furthermore, to support research and development in this field, we have published the web behavior-based Big Data related to online learning that was mined for all these 38 countries, in the form of a dataset, which is avail-able at https://dx.doi.org/10.21227/xbvs-0198.