Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis
| dc.contributor.advisor | McCalla, Gordon I. | en_US |
| dc.contributor.committeeMember | Gutwin, Carl | en_US |
| dc.contributor.committeeMember | Fichter, Darlene | en_US |
| dc.contributor.committeeMember | Vassileva, Julita | en_US |
| dc.creator | Bateman, Scott | en_US |
| dc.date.accessioned | 2007-12-11T22:16:06Z | en_US |
| dc.date.accessioned | 2013-01-04T05:10:15Z | |
| dc.date.available | 2007-12-12T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T05:10:15Z | |
| dc.date.created | 2007-12 | en_US |
| dc.date.issued | 2007-12-12 | en_US |
| dc.date.submitted | December 2007 | en_US |
| dc.description.abstract | Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-12112007-221606 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | the World Wide Web | en_US |
| dc.subject | the Semantic Web | en_US |
| dc.subject | tag clouds | en_US |
| dc.subject | e-learning | en_US |
| dc.subject | information visualization | en_US |
| dc.subject | folksonomy | en_US |
| dc.subject | collaborative tagging | en_US |
| dc.subject | metadata | en_US |
| dc.title | Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Computer Science | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |