Chronotype, sleep quality and sleep duration in adult distance education: Not related to study progress

Research in traditional education shows chronotype, sleep duration and sleep quality to be related to learning performance. Research in adult students participating in distance education (DE) is scarce. This study aims to provide knowledge on these relationships in this educational setting. In an observational longitudinal study, chronotype, sleep duration (i.e., for work and free days separately) and sleep quality of 894 students were analyzed in a multiple regression analyses. Students provided information on sleep-relatedmeasures and important covariates at the start of their study and study progress was evaluated after 14 months (i.e., the number of successfully completed modules). In linewith previous research, chronotype did not predict study progress. Further, sleep duration did not predict study progress, neither as a linear nor as a polynomial term. Third, sleep quality did not predict study progress. Concluding, these results are in linewith previous research that DE provides a solution to the asynchrony problem. Findings regarding sleep duration and sleep quality are new and unexpected, asking for attention and further research. Despite the study's observational nature, findings suggest that students participating in DE may benefit from this type of education as the asynchrony problem appears not to apply here, as students can choose their own study schedule.

Sedentary behavior and not physical activity predicts study progress in distance education`

For the official publication, see: http://dx.doi.org/10.1016/j.lindif.2016.06.021

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.

The role of learning design and learning analytics in MOOCs

The last couple of years there has been a lot of attention for MOOCs. More and more universities start offering MOOCs. Although the open dimension of MOOC indicates that it is open in every aspect, in most cases it is a course with a structure and a timeline within which learning activities are positioned. There is a contradiction there. The open aspect puts MOOCs more in the non-formal professional learning domain, while the course structure takes it into the formal, traditional education domain. Accordingly, there is no consensus yet on solid pedagogical approaches for MOOCs. Something similar can be said for learning analytics, another upcoming concept that is receiving a lot of attention. Given its nature, learning analytics offers a large potential to support learners in particular in MOOCs. Learning analytics should then be applied to assist the learners and teachers in understanding the learning process and could predict learning, provide opportunities for pro-active feedback, but should also results in interventions aimed at improving progress. This paper illustrates pedagogical and learning analytics approaches based on practices developed in formal online and distance teaching university education that have been fine-tuned for MOOCs and have been piloted in the context of the EU-funded MOOC projects ECO (Elearning, Communication, Open-Data: http://ecolearning.eu) and EMMA (European Multiple MOOC Aggregator: http://platform.europeanmoocs.eu).