Abstract:
Large service courses have become commonplace at land-grant universities. These courses present unique challenges for advisors and instructors. Students possess a wide range of academic abilities, experience with coursework, and other factors that affect their performance; the disparity between strong-and weak-performing students is often pronounced in traditional agriculture-related programs. Predicting student performance a priori can aid advisor decisions and instructor course design, ultimately improving student success rates. The objective of this study is to evaluate the use of registrar data to predict student performance in a large, agriculture-related service course. We use registrar data for 307 students enrolled in Farm and Agribusiness Management over four semesters at Oklahoma State University to parameterize models that predict course performance. Cumulative university grade point average (GPA), major, gender, and performance in prerequisites are significant predictors of student performance, while race, residency status, transfer status, and high school GPA are not. We find significant interaction effects between gender and major, ACT math score, and cumulative GPA; between major and university GPA, grade in agricultural economics prerequisite, and grade in math prerequisite; and between university GPA and prerequisites. University GPA dominates the effects, but agricultural economics students outperform other majors, and grades in the prerequisites notably influence student performance.
Keywords:
student performance, agricultural economics
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