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).