Profiling Students Who Take Online Courses Using Data Mining Methods.

Abstract

The efficacy of online learning programs is tied to the suitability of the program in relation to the target audience. Based on the dataset that provides information on student enrollment, academic performance, and demographics extracted from a data warehouse of a large Southwest institution, this study explored the factors that could distinguish students who tend to take online courses from those who do not. To address this issue, data mining methods, including classification trees and multivariate adaptive regressive splines (MARS), were employed. Unlike parametric methods that tend to return a long list of predictors, data mining methods in this study suggest that only a few variables are relevant, namely, age and discipline. Previous research suggests that older students prefer online courses and thus a conservative approach in adopting new technology is more suitable to this audience. However, this study found that younger students have a stronger tendency to take online classes than older students. In addition, among these younger students, it is more likely for fine arts and education majors to take online courses. These findings can help policymakers prioritize resources for online course development and also help institutional researchers, faculty members, and instructional designers customize instructional design strategies for specific audiences.