dc.contributor.author	Clark, Andy T.
dc.coverage.spatial	Georgia, Valdosta, Valdosta State University	en_US
dc.coverage.temporal	2010-2016	en_US
dc.date.accessioned	2018-10-04T15:58:11Z
dc.date.available	2018-10-04T15:58:11Z
dc.date.issued	2016-12
dc.identifier.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.
dc.identifier.other	E512CC5D-E43E-3AB8-444A-42B9DA92DDEA	UUID
dc.identifier.uri	http://hdl.handle.net/10428/3240
dc.description.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.	en_US
dc.description.tableofcontents	Chapter I: STATE OF HIGHER EDUCATION 1 Background 1 Scope of Study 3 Significance of Problem 4 Research Question 7 Overview of the Dissertation 8 Chapter II: A STUDY OF COLLEGE STUDENT SUCCESS 11 The Impact of Financial Aid 25 Standardized Indicators 29 Chapter III: METHODOLOGY 39 Predictive Modeling 39 Faculty Portal 49 Chapter IV: RESULTS 55 Introduction 55 Predictive Modeling 55 Taking Math One Step Further 72 Financial Aid Predictive Models 84 Faculty Portal, Early Alert, and Interventions 88 Chapter V: PREDICTIVE ANALYTICS AND THE FUTURE OF STUDENT SUCCESS 92 Research Question and Key Findings 92 Lessons Learned 101 High-Impact Practices 102 Additional Avenues of Research and Study 107 Cautionary Warning and Conclusion 109 REFERENCES 111 APPENDIX A: Institutional Review Board Exemption 117 APPENDIX B: Complete Data Sheet 1190 -	en_US
dc.language.iso	en_US	en_US
dc.subject	Dissertations	en_US
dc.subject	Public Administration	en_US
dc.subject	Valdosta State University	en_US
dc.subject	College freshmen--Education	en_US
dc.subject	College dropouts--Prevention	en_US
dc.title	College Student Success: Using Predictive Modeling and Actionable Intelligence with a Faculty Centered Information Portal to Improve Student Academic Performance	en_US
dc.type	Dissertation	en_US
dc.contributor.department	The Department of Political Science of The College of Arts And Sciences	en_US
dc.description.advisor	LaPlant, James
dc.description.committee	Savoie, Michael P.
dc.description.committee	Yehl, Robert (Sherman)
dc.description.committee	Richards, Connie L.
dc.description.degree	D.P.A.	en_US
dc.description.major	Public Administration	en_US