Date of Award

12-2019

Document Type

Dissertation

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Degree Name

Doctor of Education (Ded)

Department

Division of Education and Counseling

First Advisor

Renee Akbar

Second Advisor

Carroll Diaz

Third Advisor

Rachel Davis-Haley

Keywords

non-traditional student, first semester GPA, community college, non-cognitive factors, nontraditional, adult students, adult education, NCQ, noncognitive questionnaire

Abstract

Ninety percent of the students enrolled at community colleges in the United States are nontraditional (NCES, 2001). According to Horn and Carroll (1996), characteristics of nontraditional student include: delayed college enrollment, financially independent of parents, worked at least thirty-five hours per week, had dependents other than a spouse, were single parents, and had not received a standard high school diploma. Because of these characteristics, nontraditional students can face a plethora of challenges in higher education and feel underprepared for college coursework. Upon enrollment into the community college with an open admissions policy, these underprepared students must take high stakes standardized placement testing to determine their abilities to complete college-level course work (Hodara & Cox, 2016). The exclusive use of these cognitive skills tests to determine the preparedness of nontraditional students is problematic because of the inaccuracy of cognitive skills tests at assessing their abilities (Sedlacek, 2005). Additionally, the repercussions of not performing well on the high-stakes placement test(s) are numerous (Bailey, 2009). These tests not only fail high-achieving students, but especially, academically high-risk students. Adebayo (2008) stated that the exclusive use of cognitive factors as predictors of academic success in academically high-risk students have had mixed results. As measuring cognitive skills alone does not accurately assess the potential of all students, basing their college preparedness on high-stakes placement tests can potentially lead to additional costs, coursework, and time for nontraditional students. Researchers have found the Non-cognitive Questionnaire (NCQ) measures eight non-cognitive variables as reliable in predicting the academic success of minorities, student athletes, and students with nontraditional experiences at four-year universities (Sedlacek & Adams-Gaston, 1992). The researcher sought to find if any of the eight non-cognitive variables measured by the NCQ and other variables created due to data collection could be used to predict the first semester GPA of nontraditional students at community colleges. The researcher administered the NCQ to 96 students enrolled in a college success skills course at a community college in the Spring of 2019. The NCQ measured the students’ first semester GPAs, genders, ethnicities, and number of remedial or developmental courses they were enrolled in. The researcher created subgroups by analyzing and grouping the data by gender, age and ethnicity, then controlled for number of remedial courses enrolled. Step-wise multiple regression analysis and an analysis of variance were used to determine if there were any correlation(s) between the variables measured and the first semester GPAs of the students. The step-wise multiple regression analysis generated predictive models based on the correlations between the variables and first semester GPA for the subgroups that community colleges could use in the future. The researcher generated a predictive model based on a correlation between the NCQ variable deals with racism (DWR), and first semester GPAs in the original sample (n = 96). When the researcher grouped the sample by gender the female only (n = 72) sample analysis resulted in correlations between the number of remedial courses enrolled (RC), the non-cognitive variable, DWR, and a predictive model was generated using both variables. The male only sample (n=24) analysis resulted in no significant relationships between the NCQ variables and GPA. Therefore, no predictive model was generated. Additionally, the researcher grouped students by ethnicity. The analysis of the Black, non-Hispanic sample (n = 57) resulted in a correlation between the non-cognitive variable, availability of support (AS) and GPA—allowing the researcher to generate a predictor model. There was no significant correlation between deals with racism (DWR) and GPA as displayed in the original sample. Lastly, the researcher selected students by ethnicity and gender. The analysis of the Black, non-Hispanic and female sample (n = 47) resulted in statistically significant correlations between AS and GPA, and RC and GPA. The researcher generated a predictive model for that group using AS only. The researcher saw that the correlations between the NCQ variables were stronger, as these variables were controlled and decided to control for RC, the number of remedial courses enrolled at the number two, as that was the most frequent number in the sample. The analysis of the Black, non-Hispanic, female participants enrolled in two or more remedial courses represented the most statistically significant correlation between AS and GPA, generating a predictive model that included it.

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