dc.contributor.author	Taylor, Brandy D.
dc.coverage.spatial	Georgia	en_US
dc.date.accessioned	2021-05-03T20:15:37Z
dc.date.available	2021-05-03T20:15:37Z
dc.date.issued	2021-03
dc.identifier.other	84E7593B-9C39-0691-4CB7-850B1167B30E	en_US
dc.identifier.uri	https://hdl.handle.net/10428/4757
dc.description.abstract	State and federal governments regularly focus on improving student retention and completion in higher education as a means of increasing the skills of the workforce to better meet the challenges of a global economy. The findings of this research present a statewide picture of retention for nontraditional students in the Technical College System of Georgia and generalizations could be used to specifically improve processes and procedures on how colleges recruit and respond to this growing and diverse student population. With a specific focus on nontraditional students in diploma and certificate programs, the outcomes of this research will allow decision-makers to consider how student factors, and the relationship between those factors, influence nontraditional student progression in order to make informed decisions on how to better serve the needs of this specific student population. The purpose of this nonexperimental, ex post facto, correlational study was to examine the predictability of academic factors (student GPA and program type), background factors (age, race or ethnicity, gender, high school diploma type, high school graduation date), and environmental factors (Pell eligibility, single parent status, displaced homemaker status) on the retention of nontraditional students enrolled in diploma and certificate programs in the Technical College System of Georgia. To do so, this study addressed which prediction model, out of two data modeling approaches (logistic regression and linear discriminant analysis) and three data mining approaches (classification tree, random forest, and support vector machine models), best predicts whether a student was retained or not retained. The predictor variables GPA, programs related to Transportation and Logistics, female students, Black students, and Pell eligibility were influential in students being retained. Being out of high school for five years or more and being enrolled in Cyber, Engineer, or Healthcare programs or Industrial Technology programs were influential predictors of students not being retained. The support vector machine will generate an accurate classification model based on the goal of correctly identifying students who will not be retained so adequate assistance and resources can be provided to them. Keywords: Retention, Nontraditional, Technical College System of Georgia, Prediction Model, Diploma Programs, Certificate Programs	en_US
dc.description.tableofcontents	Chapter I: INTRODUCTION 1 -- Statement of the Problem 5 -- Purpose of the Study 7 -- Research Questions 7 -- Research Methodology 9 -- Significance of the Study 11 -- Conceptual Framework of the Study 13 -- Limitations of the Study 16 -- Definition of Terms 17 -- Organization of the Study 22 -- Chapter II: LITERATURE REVIEW 23 -- Nontraditional Students at Community Colleges 28 -- Retention and Nontraditional Students 33 -- Student Retention Theories, Models, and Frameworks 36 -- Factors Related to Student Retention 43 -- Pell Eligibility 43 -- Single Parent or Displaced Homemaker Status 45 -- Age 47 -- Race or Ethnicity 49 -- Gender 53 -- High School Diploma Type 55 -- High School Graduation Date 56 -- Grade Point Average 58 -- Program Type 61 -- Data Modeling and Data Mining Approaches Related to Student Retention 62 -- Summary 66 -- Chapter III: METHODOLOGY 68 -- Research Design 69 -- Population 72 -- Data Collection 73 -- Data Analysis 74 -- Descriptive Statistics 74 -- Statistical Considerations and Assumptions 75 -- Inferential Statistics 76 -- Summary 83 -- Chapter IV: RESULTS 84 -- Data Screening and Descriptive Statistics 86 -- Data Preprocessing and Feature Engineering 98 -- Model Training and Significant Predictors 112 -- Research Question 1A 113 -- Research Question 1B 129 -- Research Question 1C 143 -- Research Question 1D 157 -- Model Comparisons for Research Question 1 171 -- Accuracy of the Classification Models 173 -- Research Question 2 173 -- Model Comparisons for Research Question 2 194 -- Summary 197 -- Chapter V: SUMMARY, CONCLUSIONS, AND IMPLICATIONS 203 -- Summary of Findings 205 -- Conclusions for Research Question 1A 207 -- Conclusions for Research Question 1B 212 -- Conclusions for Research Question 1C 217 -- Conclusions for Research Question 1D 221 -- Conclusions for Research Question 2 225 -- Limitations 227 -- Implications 229 -- Conceptual Implications 229 -- Practical Implications 229 -- Recommendations for Future Research 232 -- Conclusion 234 -- REFERENCES 236 -- APPENDIX A: Institutional Review Board Protocol Exemption Report 258 -- APPENDIX B: R Code for Data Analysis 260	en_US
dc.format.extent	1 electronic document (PDF/A), 301 pages. 4717207 bytes.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en_US	en_US
dc.rights	This dissertation is protected by the Copyright Laws of the United States (Public Law 94-553, revised in 1976). Consistent with fair use as defined in the Copyright Laws, brief quotations from this material are allowed with proper acknowledgement. Use of the materials for financial gain with the author's expressed written permissions is not allowed.	en_US
dc.subject	College dropouts--Prevention	en_US
dc.subject	Diplomas	en_US
dc.subject	Dissertations, Academic--United States	en_US
dc.subject	Nontraditional college students	en_US
dc.subject	Social prediction--Mathematical models	en_US
dc.subject	Technical College System of Georgia	en_US
dc.title	The Predictability of Nontraditional Student Retention in the Technical College System of Georgia	en_US
dc.type	Dissertation	en_US
dc.contributor.department	Department of Curriculum, Leadership, and Technology of the Dewar College of Education and Human Services	en_US
dc.description.advisor	Brockmeier, Lantry L.
dc.description.committee	Pate, James L.
dc.description.committee	Bochenko, Michael J.
dc.description.degree	Ed.D.	en_US
dc.description.major	Education in Leadership	en_US