ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism

Dascalu, M., Trausan-Matu, S., McNamara, D.S., & Dessus, P. (2015). ReaderBench – Automated Evaluation of Collaboration based on Cohesion and Dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423. doi: 10.1007/s11412-015-9226-y

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.

Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.