Chong Ho Yu
Applied Learning Technologies Institute
Arizona State University
alex.yu@asu.edu

Samuel Digangi
Applied Learning Technologies Institute
Arizona State University
sam@asu.edu

Angel Kay Jannasch-Pennell
Applied Learning Technologies Institute
Arizona State University
angel@asu.edu

Charles Kaprolet
Applied Learning Technologies Institute
Arizona State University
ckaps@mainex1.asu.edu

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.