Prepped for Success: Identifying the factors that lead to student success in Economics Principles Courses Prior to Course Enrollment

The United States is challenged with drastically improving student completion rates in higher education, especially community colleges, without sacrificing quality or access. Decades of research have examined factors that lead students to be successful in college, yet completion rates remain persistently low. This paper argues that the problem is not with understanding student success at the macroscale (i.e., student success in general), but a lack of understanding at the microscale (i.e., at individual course level). Predictive analytics could help educational researchers uncover answers at this scale. This study examined the surveys and records of students enrolled in Economics Principles courses at one community college during one academic year to discover what factors are related to their success in the course (i.e., earning a grade of “C” or better).
Four research questions were proposed to develop an understanding of the relation between these factors and student success. The fourth research question developed binary logistic regression models to predict students’ success in their course, for the current sample of students and a subsequent cohort. Results show that 16 factors were related to students’ success in the course, including GPA, the instructor, if they had completed an economics course in a prior term, and the number of withdrawals they had in the prior semester. Results indicate that these factors differ depending on the Economics Principles course students complete (Microeconomics vs. Macroeconomics). Evidence also suggests these factors could predict student success in the course better than the course overall pass rate.
ii
These findings could inform prospective students, instructors, and academic advisors on factors that students can develop prior to course enrollment to maximize their likelihood of passing the course, and therefore improving their chances of completing their degree program. Discussion includes expanding this study model to other disciplines to further understand student completion outcomes.The United States is challenged with drastically improving student completion rates in higher education, especially community colleges, without sacrificing quality or access. Decades of research have examined factors that lead students to be successful in college, yet completion rates remain persistently low. This paper argues that the problem is not with understanding student success at the macroscale (i.e., student success in general), but a lack of understanding at the microscale (i.e., at individual course level). Predictive analytics could help educational researchers uncover answers at this scale. This study examined the surveys and records of students enrolled in Economics Principles courses at one community college during one academic year to discover what factors are related to their success in the course (i.e., earning a grade of “C” or better).
Four research questions were proposed to develop an understanding of the relation between these factors and student success. The fourth research question developed binary logistic regression models to predict students’ success in their course, for the current sample of students and a subsequent cohort. Results show that 16 factors were related to students’ success in the course, including GPA, the instructor, if they had completed an economics course in a prior term, and the number of withdrawals they had in the prior semester. Results indicate that these factors differ depending on the Economics Principles course students complete (Microeconomics vs. Macroeconomics). Evidence also suggests these factors could predict student success in the course better than the course overall pass rate.
ii
These findings could inform prospective students, instructors, and academic advisors on factors that students can develop prior to course enrollment to maximize their likelihood of passing the course, and therefore improving their chances of completing their degree program. Discussion includes expanding this study model to other disciplines to further understand student completion outcomes.