Data-driven Group Formation for Informal Collaborative Learning PROCEEDINGS
Neil Rubens, Mikko Vilenius, Toshio Okamoto, University of Electro-Communications, Japan
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Vancouver, Canada ISBN 978-1-880094-76-1 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA
Ability to find appropriate collaborators and learning materials is crucial for informal collaborative learning. However, traditional group formation models are not applicable/effective in informal learning settings since little is known about learners and learning materials and teacher's assistance is not available. We proposed the data-driven group formation model that automatically extracts information about learners and learning materials from multiple data sources (databases of academic publications, wikis, social networking cites, blogs, forums, etc) and automatically forms collaborative learning groups. The open source implementation of the model (a part of WebClass-RAPSODY learning management system) consists of loosely coupled modules (implementing the proposed methods for data mashup, mining and inference) integrated through the web services interface; allowing for easy adaptation, extension and customization of the model.
Rubens, N., Vilenius, M. & Okamoto, T. (2009). Data-driven Group Formation for Informal Collaborative Learning. In T. Bastiaens, J. Dron & C. Xin (Eds.), Proceedings of E-Learn 2009--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 3127-3132). Vancouver, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved November 16, 2018 from https://www.learntechlib.org/primary/p/32933/.
© 2009 Association for the Advancement of Computing in Education (AACE)