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ANGEL Mining

ANGEL Mining

Tyler Swanger, Kaitlyn Whitlock, Anthony Scime, Brendan P. Post
Copyright: © 2013 |Pages: 22
ISBN13: 9781466624559|ISBN10: 1466624558|EISBN13: 9781466624566
DOI: 10.4018/978-1-4666-2455-9.ch043
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MLA

Swanger, Tyler, et al. "ANGEL Mining." Data Mining: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2013, pp. 837-858. https://doi.org/10.4018/978-1-4666-2455-9.ch043

APA

Swanger, T., Whitlock, K., Scime, A., & Post, B. P. (2013). ANGEL Mining. In I. Management Association (Ed.), Data Mining: Concepts, Methodologies, Tools, and Applications (pp. 837-858). IGI Global. https://doi.org/10.4018/978-1-4666-2455-9.ch043

Chicago

Swanger, Tyler, et al. "ANGEL Mining." In Data Mining: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 837-858. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2455-9.ch043

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Abstract

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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