dc.contributor.advisor | Laffey, James M. (James Michael), 1949- | eng |
dc.contributor.author | Ai, Jiye | eng |
dc.date.issued | 2009 | eng |
dc.date.submitted | 2009 Summer | eng |
dc.description | Title from PDF of title page (University of Missouri--Columbia, viewed on Sept.8, 2010). | eng |
dc.description | The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. | eng |
dc.description | Dissertation advisor: Dr. James Laffey. | eng |
dc.description | Vita. | eng |
dc.description | Ph. D. University of Missouri--Columbia 2009. | eng |
dc.description.abstract | [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Since E-learning in Course Management System (CMS) is a growing form for learning and teaching in higher education, it is key for us to identify and describe student behavior and patterns of activity in CMS. Understanding student behaviors and patterns of activities may lead to better approaches for supporting online learning. These approaches in turn can support more effective teaching and improve learning outcomes. Data mining (including web mining) is a recognized approach for building knowledge and value in business and commercial information systems. Multiple data mining techniques have potential for application in a comprehensive course management system. Three main web mining methods (Classification, Association Rule and Clustering) have been used on the data from a CMS (WebCT).The primary finding of this research was to suggest that web mining can be an approach that educational researchers can use, and when combined with other forms of data collection, has potential for adding to the way we build knowledge about e-learning. A second contribution of the current study was to draw implications for how to improve the process of web mining e-learning data sets. | eng |
dc.description.bibref | Includes bibliographical references. | eng |
dc.format.extent | ix, 93 pages | eng |
dc.identifier.oclc | 694794360 | eng |
dc.identifier.uri | https://hdl.handle.net/10355/9575 | |
dc.identifier.uri | https://doi.org/10.32469/10355/9575 | eng |
dc.language | English | eng |
dc.publisher | University of Missouri--Columbia | eng |
dc.relation.ispartofcommunity | University of Missouri--Columbia. Graduate School. Theses and Dissertations | eng |
dc.rights | Access is limited to the campus of the University of Missouri--Columbia. | eng |
dc.subject.lcsh | Data mining | eng |
dc.subject.lcsh | Universities and colleges -- Computer networks | eng |
dc.subject.lcsh | Internet in higher education | eng |
dc.subject.lcsh | Education, Higher -- Computer-assisted instruction | eng |
dc.subject.lcsh | Education, Higher -- Effect of technological innovations on | eng |
dc.subject.lcsh | Information technology | eng |
dc.subject.lcsh | Educational technology | eng |
dc.title | Using web mining to discover learning patterns in course management systems | eng |
dc.type | Thesis | eng |
thesis.degree.discipline | Information science and learning technologies (MU) | eng |
thesis.degree.grantor | University of Missouri--Columbia | eng |
thesis.degree.level | Doctoral | eng |
thesis.degree.name | Ph. D. | eng |