Examining the Relationship Between Self Efficacy and Achievement for Developmental Mathematics Students at Two Alabama Community Colleges
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2019-10-14
Department
Community College Leadership Program
Program
Doctor of Education
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Abstract
Due to growing demands for online course delivery platforms, community college administrators and faculty need more data and information to assist with the goal of increasing success for students in developmental math courses. The purpose of this quantitative study was to address the issue of high fail rate by students in developmental mathematics courses, MTH 090 and MTH 098, and to establish ways to reduce the fail rate thereby increasing student success. This study was designated to examine the relationship of Bandura’s self-efficacy and social cognitive theories to student achievement in developmental mathematics. The target population for this study was students enrolled in two public two-year community colleges that serve several counties in the Alabama Community College System (ACCS).
The study was framed as a quantitative research design utilizing binomial logistic regression. The independent variables for this study were math self-efficacy, technology self-efficacy and student demographic characteristics. The dependent variable was student math achievement scores. Both the OTSES and MSES surveys were used to gather student data.
Results suggested that students currently enrolled in MTH 090, Basic Mathematics, were approximately 12 times more likely to pass their final assessment test than students enrolled in MTH 098: Elementary Algebra. Multiple linear regressions showed that age and course level were good predictors of Mathematics Self-Efficacy. Namely, students aged 25 years and older and MTH 090, Basic Mathematics, were reliable predictors of Mathematics Self-Efficacy. Multiple linear regressions also revealed that race and age were good predictors of Technology Self-Efficacy. Specifically, students aged 25 years and older and students who identify as Black/African Americans were reliable predictors of Technology Self-Efficacy.