Abstract
Since the advent of Web 2.0 and embedded social computing, there has been a widespread adoption of social computing applications such as tags. Tags can extract concepts from content and act as navigational cues that enable users to find meaningful and relevant information. This is especially important for domain novices in understanding formal academic or scientific articles written at varying domain expertise levels. In this study, three topics focusing on tag technology are discussed. First study elicited differences in tag assignments by Expert and Novice groups, and discussed tag quality problems in terms of similarity and relevance measures. The results show that experts can provide a more consistent and representative set of tags for academic and scientific documents than novices can generate, suggesting that tags chosen by experts reflect better understanding of the content.The second part of the study discusses the convergence variation of tag distributions that are affected by the social influence of a group of domain experts or a group of domain novices. This study compares three measures of the convergence rate of tagging behavior in Expert and Novice groups. The results show that the convergence rate of tagging behavior was better in the Expert group than it was in the Novice group. The one-bit comparison proposed by this research can accurately distinguish mature tags generated by experts with high consensus from other tags. The final part validated the effectiveness of using mature and high quality tags to facilitate self-directed learning. The experiment measured whether or not students can increase learning performance through these tags that had been extracted by domain experts. The result revealed that tags chosen by experts helped students’ better understanding of the content.This dissertation investigates the roles of expertise during convergence of consensus of a rank-ordered tagging distribution. The results support tag-based learning and provide insights and tools toward the design of interface involving tags in the Web 2.0 environment.