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Applying Academic Analytics: Developing a Process for Utilizing Bayesian Networks to Predict Stopping Out Among Community College Students

Abstract Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion of each of their first six terms. The study utilized exploratory a... (more)
Created Date 2015
Contributor Arcuria, Phil (Author) / Levy, Roy (Advisor) / Green, Samuel B (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Subject Educational evaluation / Statistics / Educational psychology / Academic Analytics / Bayesian / Bayesian Networks / Community College / Stopping Out / Student Success
Type Doctoral Dissertation
Extent 179 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Educational Psychology 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis