Abstract
This research focuses on developing and implementing a continuous Naïve Bayesian classifier for GEAR courses at Rio Salado Community College. Previous implementation efforts of a discrete version did not predict as well, 70%, and had deployment issues. This predictive model has higher prediction, over 90%, accuracy for both at-risk and successful students while easing interpretation and implementation. Predictive results across eleven courses and cumulative gain charts show potential improvements to be made in students’ academic success by focusing on high level risk students. Researchers at other colleges might find this empirical application relevant for implementation of early alert systems.
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Ornelas, F., Ordonez, C. Predicting Student Success: A Naïve Bayesian Application to Community College Data. Tech Know Learn 22, 299–315 (2017). https://doi.org/10.1007/s10758-017-9334-z
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DOI: https://doi.org/10.1007/s10758-017-9334-z