Exploring Student Characteristics as Predictors of College Math Success in Corequisite Environments

The purpose of this study and question was to find out if age, gender, or student residency predicted the success in college credit-bearing courses when using the corequisite model. Student residency was defined, for the current study, as where the student lives, such as does the student reside on-campus or off-campus. The research questions for this study were as follows: (RQ1: Will age predict the success of a student in the college math course in a corequisite environment?) (RQ2: Will gender predict the success of a student in the college math course in a corequisite environment?) (RQ3: Will student residency predict the success of a student in the college math course in a corequisite environment?) (RQ4: Will age and gender predict the success of a student in the college math course in a corequisite environment?) (RQ5: Will gender and student residency predict the success of a student in the college math course in a corequisite environment?) (RQ6: Will age and student residency predict the success of a student in the college math course in a corequisite environment?) (RQ7: Will age, gender, and student residency predict the success of a student in the college math course in a corequisite environment?) This quantitative correlational study investigated age, gender, and student residency to determine if these variables were predictors of success in the college math course by students who were enrolled in the math course as well as a corequisite math course for support. The data were gathered through records that were available through the college for the predictors, age, gender, and student type, as well as the criterion, which is the final grade of the student in the college math course and the data were de-identified before it was given to the researcher. The students who attend community colleges and smaller universities in Ohio and need remediation in mathematics were the target population for this study. The students in the current study were those who could not place into a college-level math course and so had to take their math course with a corequisite math course for support and to provide the remediation needed during the 2017-2018 and 2018-2019 school years. The data were analyzed using logistic regression with the SPSS software. Logistical regression was performed for each predictor, the predictors in pairs, and then all three predictors at once time for all seven research questions. The researcher found that for each examination, the model did not predict success in college credit-bearing courses when using the corequisite model. However, gender was statistically significant individually and when paired with the other predictors. Future researchers should use gender as a predictor and use other predictor variables in models designed to predict success in college credit-bearing courses when using the corequisite model. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]