No abstract

No abstract

No abstract

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.

This study aims to identify the priority of the roles and competencies of tutors working in the e-learning environments where the tutors are experiencing the changes brought by reforming traditional TV and broadcasting university to open universities. The mixed methods, DACUM, non-participatory observation, and questionnaires were used to identify the priority of the roles and competencies of tutors. The findings suggested that the priority of the roles and competencies has significantly changed accompanied to the shift of pedagogy from cognitive behaviorist to social-constructivist and connectivist. Changes in the roles of the instructional designer and instructor were highlighted.Significant differences in perceptions of the importance of the roles and competencies corresponding to learning management and technology use also merit further attention.

The rapid advancements in online education have pointed to a new open learning approach using open educational resources (OER). In this approach, educators and learners can freely access or redistribute educational resources that have been released online in the public domain under an open licence. Whereas this approach looks appealing in reducing learning costs, as well as in enhancing learning quality and facilitating knowledge sharing, several challenges might hinder the adoption of OER, such as locating and selecting the most appropriate resources among the thousands that are published and that are available online, and trusting them. This paper elaborates on those challenges and suggests an emerging technologies-based perspective for addressing the efficient inclusion of OER. To this end, this paper discusses how the integration of emerging yet essential technologies, such as Artificial Intelligence (AI) and blockchain, with big learning data and educational data mining algorithms could have a profound impact on enhancing OER-based learning and teaching. The dynamics of incorporating these technologies to solve several OER challenges are demonstrated through numerous examples, and the potential limitations are also discussed. The paper concludes with visions of the future, possible research challenges and directions.

Research on grit indicates that perseverance positively predicts academic achievement. Yet, the mechanisms through which perseverance might lead to academic success remain less explored, particularly in cross-cultural research. The current study investigated such mechanisms by examining possible mediating effects of students' use of self-regulated learning strategies (control, memorisation, and elaboration) on the predictive relation of students' perseverance on their academic achievement, in students from East Asian and Anglo-Saxon English speaking Western countries. The sample came from the OECD PISA study and included 24,352 population-representative 15-year-old students from

We consider multiagent consensus problems in a decentralized fashion. The interconnection topology among the agents is switching and directed. The agent dynamics is expressed in the form of a double-integrator model. Two different cases are considered: one is the leaderfollowing case and the other is the leaderless case. Based on graph theory and the common Lyapunov function method, some sufficient conditions are established for the consensus stability of the considered systems with the neighbor-based feedback laws in both leader-following case and leaderless case, respectively. As special cases, the consensus conditions for balanced and undirected interconnection topology cases can be established directly. Finally, two numerical examples are given to illustrate the obtained results.