There are many challenges associated with student retention. Saint Mary-of-the-Woods College (SMWC) has focused on determining the factors affecting student retention across its campus and distance course delivery formats in order to improve student retention. The purpose in this study was to explore the extent to which age, course delivery, technical ability, and financial background determine retention at SMWC. Retention and attrition models of Tinto and Walleri laid the foundation of this study. Qualitative data on technical ability were collected from 69 students who responded to the survey instrument on Survey Monkey. Quantitative data on retention, age, course delivery, and financial background on students who had graduated over the past 10 years were gathered from the offices of financial aid and the registrar. For quantitative data analysis, the influences of age and financial background on student retention were examined through multiple regression; the influence of course delivery on student retention was examined through 2-tailed t tests for comparing the 2 population means. Qualitative data were analyzed through a narrative approach. The findings of quantitative data analysis were that student age and financial standing were not significant predictors of student retention and that retention for distance course delivery was significantly lower than that of online delivery. The finding for qualitative analysis was that students with higher technical ability showed higher retention across both course deliveries. The social change implications include a better understanding by SMWC's administrators and faculty of course delivery and design in order to improve student retention, thus benefitting the local economy and community by creating a more skilled and employable workforce and a stronger reputation for SMWC.
| Identifer | oai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-2384 |
| Date | 01 January 2015 |
| Creators | McCracken, Jamie Esther |
| Publisher | ScholarWorks |
| Source Sets | Walden University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Walden Dissertations and Doctoral Studies |
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