College Student Success: Using Predictive Modeling and Actionable Intelligence with a Faculty Centered Information Portal to Improve Student Academic Performance
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Authors
Clark, Andy T.
Issue Date
2016-12
Type
Dissertation
Language
en_US
Keywords
Dissertations , Public Administration , Valdosta State University , College freshmen--Education , College dropouts--Prevention
Alternative Title
Abstract
This research project examines new models and approaches to student learning and success by concentrating on the first-year experience of beginning freshmen at Valdosta State University utilizing data from 2008-2014. With a fall freshman class ranging from 1,500 to 2,500 new students, the sample size is large enough to produce a much smaller confidence interval/sampling error, yet small enough to work with individual departments and faculty to implement and monitor the effect of changes employed through the use of predictive metrics and active intervention. The predictive metrics developed for this model use three specific indicators: (1) standardized test scores from the SAT or ACT, (2) high school grade point average and (3) where the student’s high school ranks in relation to the other high schools in the state of Georgia. The purpose of this research is to develop and defend the answer in response to the research question: Can predictive modeling be used to create actionable student intelligence to improve the grades in key English and math classes resulting in higher retention rates of traditional first-year students? The findings from this research demonstrate that predictive modeling can be very effective in identifying at-risk student populations. These models provide timely insight into students’ needs for additional support to be successful academically. There were five important clusters of results: (1) the pass/fail rates based upon the 1-4 rankings for high school rank, GPA, and SAT, with these data points proving to be very useful in predicting DFW rates, (2) the multivariate regression analysis also showed that these variables are statistically significant, (3) for math the difference of means test for the changes over time once the placement index was put in place improved the pass rate in math courses, (4) the analysis of financial grouping and employment index showed that these variables also impact student success, (5) student success improved with faculty that utilized the portal vs. faculty that did not utilize the portal. This research is very closely aligned with the “Complete College America” movement.
Description
Citation
Clark, Andy T., "College Student Success: Using Predictive Modeling and Actionable Intelligence with a Faculty Centered Information Portal to Improve Student Academic Performance." PhD diss., Valdosta State University, December 2016. http://hdl.handle.net/10428/3240.