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Collaboration is of increased importance in today’s society, with increased emphasis placed on working jointly with others, whether it is in the classroom, in the lab, in the workplace, or virtually across the world. The wiki is one particular virtual collaboration tool that is gaining particular prominence in recent years, enabling people – either in small project groups or as part of the wiki’s entire user base – to socially construct knowledge asynchronously on a wide variety of topics. However, there are few intelligent support tools for wikis available, particularly those providing recommendation-based support to users.
This thesis investigates the topic of user and data modeling for recommendation systems in a wiki environment. In addition to conventional usage data, the proposed model uses new metrics designed for the wiki domain, including active-passive activity level rating and minimalist-overachiever score. The active-passive activity level rating provides a quick overview of a participant’s collaborative activity composition and can be leveraged to alert moderators when participants aren’t meeting expectations. The minimalist-overachiever score strongly correlates to evaluations that participants have received and can be used as an aid in determining performance in future collaborations. These, along with other findings, serve as the foundation for improved virtual collaboration.
Adviser: Leen-Kiat Soh