期刊名称：The International Review of Research in Open and Distributed Learning
印刷版ISSN：1492-3831
出版年度：2015
卷号：16
期号：5
DOI：10.19173/irrodl.v16i5.2244
语种：English
出版社：AU Press
摘要：In China, microblogging is an extremely popular activity and is proving to be an effective mechanism to gauge perceptions about social phenomena. Between 2010 and 2015 Sina Weibo, China’s largest microblogging website, generated 95,015 postings from 62,074 users referencing the term massive open online courses (MOOCs), a method of online course delivery popularized in North America that has spread globally. Time series analyses revealed distinct patterns in the volume of postings during a four-year period, and subsequently by month, by week, and by the time of day. The volume of postings during the week, for example, peaked on Monday and declined daily to a low point on Saturday. Relative to maximizing learner engagement, the findings may provide insight to parties who deliver MOOCs to employ or test strategies on timing (i.e., time of year to offer/not offer a MOOC, time of week to release/not release new material, time of day to schedule/not schedule chat sessions). The paper also serves to demonstrate a mechanism to retrieve big data from social media sources, otherwise underutilized in educational research.
其他摘要：In China, microblogging is an extremely popular activity and is proving to be an effective mechanism to gauge perceptions about social phenomena. Between 2010 and 2015 Sina Weibo, China’s largest microblogging website, generated 95,015 postings from 62,074 users referencing the term massive open online courses (MOOCs), a method of online course delivery popularized in North America that has spread globally. Time series analyses revealed distinct patterns in the volume of postings during a four-year period, and subsequently by month, by week, and by the time of day. The volume of postings during the week, for example, peaked on Monday and declined daily to a low point on Saturday. Relative to maximizing learner engagement, the findings may provide insight to parties who deliver MOOCs to employ or test strategies on timing (i.e., time of year to offer/not offer a MOOC, time of week to release/not release new material, time of day to schedule/not schedule chat sessions). The paper also serves to demonstrate a mechanism to retrieve big data from social media sources, otherwise underutilized in educational research.
