Anticipated Date of Graduation

Summer 2019

Document Type

Thesis

Degree Name

Master of Science in Mathematical Sciences

Department

Mathematics

First Advisor

Douglas Darbro

Abstract

High school students across the country continue to enroll in universities without the proper mathematical skills and knowledge to be successful in college level mathematics courses. Universities are faced with a challenging question: what do we do with students who are not prepared to take our mathematics courses? For most universities the answer is by placing them in developmental mathematics. The aim is for the students to build a foundation of mathematics that will allow them to succeed in their future college level gateway mathematic courses. However, recent studies are beginning to show that developmental math, while expensive and resource exhausting, may not be achieving what it intends to achieve. 'Evaluating Developmental Math Courses at Shawnee State University in Predicting Success in Gateway Courses' is a study that takes a close look at how Shawnee State University's developmental math paths impact students in their gateway math courses. The aim is to determine whether the performance of a student in a developmental math course is predictive of a student's success in the successive college level gateway course. The study also briefly models whether performance in a developmental math and gateway math is predictive of earning a degree at Shawnee State University within a specific time period. Shawnee State University offers five different paths from developmental math to gateway math. The researchers of this study used direct logistic regression to test whether developmental math (among other independent variables) could predict success in gateway math. The researchers first defined success in the gateway course as a student earning a C or higher and ran the five models on this definition. The researchers then defined success in a gateway course as students earning a D-or higher and reran all of the logistic regression models. In total, ten logistic regression models were ran to decide if performance in a developmental math course was predictive of success in a college level gateway course. Of the 10 models, 7 brought back developmental grades as a significant predictor of success, or more importantly, predictors of nonsuccess. This study also ran a logistic regression model with earning a degree or not predicted from seven independent variables. The intent was to decide if the developmental and gateway math grades were significant predictors of earning a degree. The model returned gateway grades as a statistically significant predictor, but not developmental grades. The results imply much of what recent studies in the area conclude. That is, a student's success in developmental courses will predict their success in later courses. However, more students are failing or choosing to not move on then succeeding or persevering through the gateway courses. Seventy percent of students who took a developmental math course from 2011 to 2018 did not take a successive gateway math course. Thus indicating developmental courses are not achieving their intended purpose. Perhaps the fact that developmental performance is not a significant predictor of earning a degree is also valuable information. Why place a student in a developmental course if there is no significant data predicting that they will earn a degree? While gateway performance is a significant predictor, it makes more sense to spend resources to help students pass these courses as it may more likely predict whether they complete their degree. The bottom line is that, like Shawnee State University, other universities need to look at their developmental math programs critically and decide, is the time and money expended on these courses truly benefitting the respective students and respective university?

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