The effect of multilingual facilitation on active participation in MOOCs Authors:

A new approach for overcoming the language and culture barriers to participation in MOOCs is reported. It is hypothesised that the juxtaposition of English as the language of instruction, used for interacting with course materials, and one’s preferred language as the language of participation, used for interaction with peers and facilitators, is preferable to ‘English only’ for participation in a MOOC. The HANDSON MOOC included seven teams of facilitators, each catering for a different language community. Facilitators were responsible for promoting active participation and peer tutoring. Comparing language groups revealed a series of predictors of intention to learn, some of which became apparent in the first days of the MOOC already. The comparison also uncovered four critical factors that influence participation: facilitation, language of participation, group size, and a pre-existing sense of community. Especially crucial was reaching a sufficient number of active participants during the first week. We conclude that multilingual facilitation activates participation in MOOCs in various ways; and that synergy between the four aforementioned factors is critical for the formation of the learning network that supports a social dynamics of active participation. Our approach suggests future targets for the development of the multilingual and community potential of MOOCs.

Finding the Needle in a Haystack: Who are the Most Central Authors Within a Domain?

The speed at which new scientific papers are published has increased dramatically, while the process of tracking the most recent publications having a high impact has become more and more cumbersome. In order to support learners and researchers in retrieving relevant articles and identifying the most central researchers within a domain, we propose a novel 2-mode multilayered graph derived from Cohesion Network Analysis (CNA). The resulting extended CNA graph integrates both authors and papers, as well as three principal link types: coauthorship, co-citation, and semantic similarity among the contents of the papers. Our rankings do not rely on the number of published documents, but on their global impact based on links between authors, citations, and semantic relatedness to similar articles. As a preliminary validation, we have built a network based on the 2013 LAK dataset in order to reveal the most central authors within the emerging Learning Analytics domain.