Predictors of Success in Online Education
To address the quality and attrition rates of online and distance
education, researchers have examined the predictability of success by
measuring factors that are related to both the student and the
institution. For example, Dupin-Bryant (2004) have identified six
pre-entry variables that were related to student retention in online
distance education, including GPA, class rank, number of previous online
courses, information literacy, knowledge of learning management systems,
and digital communications (p. 202). However, among these six
predictors, of course completion and non-completion, information and
research literacy, as demonstrated by the student’s adequate computer
training, constituted the highest predictors of success and completion
(p. 204). In a similar study, Horzum, Kaymak and Gungoren (2015) have
examined the relationship between online learning readiness, motivation,
and students’ perceptions about learning. Results from this study showed
online learning readiness was a predictor of academic motivation
directly and of perceived learning indirectly (p. 764).
Self-efficacy, which is shaped in part by the student’s level of
satisfaction and readiness for online learning, has also been found to
be a significant predictor of program completion (Allen & Seaman, 2013;
Evan & Beverly, 2011; Farid, 2014; Steven and Elaine, 2008; Hung et
al., 2010; Squires, 2018). For example, Farid (2014) found that 28% of
students who dropped out of online courses at a community college have
cited personal reasons as the primary cause (p. 153). However, 18% of
students who participated in the same study have indicated that
technology and lack of institutional support services were the reasons
for not completing the online courses.
When it comes to self-efficacy, motivation for learning, and
self-directed learning online education is more suited for upper levels
and graduate education. For example, hung et al (2010) found that junior
and senior students exhibited significantly greater readiness on
self-directed learning and efficacy than did freshman and sophomore
students at a Taiwanese university (p. 1087). These findings are in
agreement with those of Dupin-Bryant (2004) who found that prior
learning experience and computer training to be significant predictors
of online completion. However, missing from the studies of Hung et al
(2010) and Dupin-Bryant (2004) were the potential impacts of
institutional retention strategies on student satisfaction and
motivation in online courses. In a more recent study, Cochran, Campbell,
Baker and Leeds, (2014) have examined the impacts of retention
strategies and found that cumulative GPA and class standing were
significant student characteristics related to student retention.
Furthermore, there was no empirical support for student engagement as a
strategy for retention in online courses (Cochran et al., 2014).
There is no doubt that online education has changed the landscape of
higher education institutions in the United States and around the world
(Allen & Seaman, 2017; Cochran et al., 2014; Horzum et al., 2015; Hung
et al., 2010). It is also evident that growth in online enrollment is
associated with high attrition rates as compared to face-to-face
learning environments (Allen & Seaman, 2013; Farid, 2014). In recent
studies, researchers have examined predictors of success in online
courses including, self-efficacy, prior learning experience (i.e., GPA),
and student familiarity with technology and research methods (Allen &
Seaman, 2017; Dupin-Bryant, 2004; Horzum et al., 2015). While factors
such as previous learning experience, academic rank, and self-efficacy
were consistent predictors of success across several studies, other
factors such as family, employment status, and academic support are
equally important (Bousbahi & Alrazgan, 2015; Squires, 2018).