A novel technique was applied to a college student database to identify the cognitive and non-cognitive factors that predict college students’ course withdrawal behaviors. Predictors such as high school grade point average (HSGPA), standardized test scores (ACT–American College Test or SAT-Scholastic Aptitude Test), number of credit hours enrolled, and age were analyzed in this study. Data mining software algorithms were used to study information about undergraduate students at a west-south-central state university in the United States. The study results revealed that two factors, number of enrolled credit hours, and a student’s age have the most effect on collegiate course withdrawal behaviors, irrespective of HSGPA, and standardized test scores. Similar results have been found by other researchers when they applied t-test, simple regression, multiple regression, and discriminant analysis tools. Lastly, the empirical analysis shows that the data mining technique outperforms. These algorithms can be applied to similar studies on student databases and can be a useful tool for college administrators.
This article is originally published by the International Association of Journals and Conferences (IAJC) in Technology Interface International Journal (TIIJ). IAJC and TIIJ own the publishing rights. Posting of this article in Scholar Works was approved by the Editor-and-Chief of TIIJ.
International Association of Journals & Conferences
Date of publication
Ali, Mohammed, "Novel technique to analyze the effects of cognitive and non-cognitive predictors on students course withdrawal in college" (2020). Technology Faculty Publications and Presentations. Paper 5.