Using web mining to discover learning patterns in course management systems
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[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.
Access is limited to the campus of the University of Missouri--Columbia.