ANGEL Mining

ANGEL Mining

Tyler Swanger (Yahoo! & The College at Brockport: State University of New York, USA), Kaitlyn Whitlock (Yahoo!, USA), Anthony Scime (The College at Brockport: State University of New York, USA) and Brendan P. Post (The College at Brockport: State University of New York, USA)
DOI: 10.4018/978-1-60960-884-2.ch005
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This chapter data mines the usage patterns of the ANGEL Learning Management System (LMS) at a comprehensive college. The data includes counts of all the features ANGEL offers its users for the Fall and Spring semesters of the academic years beginning in 2007 and 2008. Data mining techniques are applied to evaluate which LMS features are used most commonly and most effectively by instructors and students. Classification produces a decision tree which predicts the courses that will use the ANGEL system based on course specific attributes. The dataset undergoes association mining to discover the usage of one feature’s effect on the usage of another set of features. Finally, clustering the data identifies messages and files as the features most commonly used. These results can be used by this institution, as well as similar institutions, for decision making concerning feature selection and overall usefulness of LMS design, selection and implementation.
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There is a similarity between LMS and Knowledge Management Systems (KMS); both provide a repository for knowledge which is valuable for the user. In a KMS the knowledge is kept and used by an organization’s employees. In a LMS the purpose is to disseminate knowledge from instructors to students and to share knowledge in a way to enhance student learning (Haldane, 1998). There are also similarities between Learning Management Systems and distance learning. Distance learning uses LMS like software to provide students with learning materials and activities while tracking student activity (Falvo & Johnson, 2007).

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