Anticipated Date of Graduation

Summer 2021

Document Type


Degree Name

Master of Science in Mathematical Sciences



First Advisor

Douglas Darbro


There is a high emphasis on gaining a college education in order to prosper and be successful in life. Student success is a high priority to all institutions, but many students enroll into college lacking the basic skills required for college level courses. This is especially true for mathematics. Developmental education started off as tutoring, however, it grew into something more than tutoring alone. Developmental courses were created to help students gain those basic skills so that they can take college level courses and hopefully obtain a college degree. There are concerns with students dropping out without a degree due to the financial burden and frustration related to taking developmental courses. This study seeks to see if there are any areas of improvement that should be made to developmental mathematics courses by examining a group of predictors. A group of predictors consisting of student characteristics, instructor characteristics, and classroom characteristics were selected to analyze. Student characteristics include gender, age, race, ACT Math score, ACT Reading score, math pretest score, 1st generation status, SES, and high school GPA. Instructor characteristics include gender, degree, and employment status. Classroom characteristics include class size, number of times a class meets per week, and time of day the class meets. The dependent variables in this study will be final exam score and overall grade in the developmental mathematics course. The theoretical framework of this study is Tinto’s Theory of Retention which seeks to find out why students drop out of college. In Tinto’s theory, students enter college with a background that could affect the way they integrate into college ultimately leading to the decision to stay in college or drop out. Meaning that if a student doesn’t integrate into college, then that could lead to a decision to leave college. Knowing what the predictors of success are for developmental mathematics is beneficial so that any improvements can be made to the course to help students be more iv successful and thus help students complete college. The sample consists of students who were previously enrolled in a developmental mathematics class at Shawnee State University, Math 0101: Basic Algebra with Geometry Application. The research design of this study is ex-post facto which means that the data already existed, but needed to be collected according to the needs of the study. Data came from student records, department records, class schedules, and from the Director of Developmental Mathematics at Shawnee State University. Regression and ANOVA techniques were implemented to examine the predictors. Standard logistics regression followed up by forward selection logistic regression was used to see any predictors were significant in predicting success in the course. The forward selection logistic regression model was a better fit model compared to the standard logistic regression based off the Akaike information criterion (AIC) and chi-square model comparison. ACT math score, pretest score, high school GPA, class size, and SES (determined by Pell-Grant status) were the predictors that remained in the reduced model. However, Pell-Grant status was not significant even though it remained in the model. There were no significant predictors in the multiple regression models in predicting for the final exam score. Relating back to the theoretical framework of this study, the predictors that were significant in predicting success in the course were all in the pre-college schooling background category. Institutions and the instructors of the developmental mathematics course can keep this in mind when making decisions about the course and help students to be successful in the developmental mathematics course, thus helping them succeed in their college career.

Included in

Mathematics Commons