Discriminating factors between completers of and dropouts from online learning courses

Abstract This study examined the differences between persistent and dropout students enrolled in an online course with five factors: support from family and work, academic locus of control, academic self‐efficacy, time and environment management skills, and metacognitive self‐regulation skills. Moreover, this study investigated the most significant factors discriminating students' success in their online course completion. A total of 169 adult students participated in the study. We used online surveys, which consisted of 27 items adopted from the literature, to measure the level of five factors of which students perceive. The analysis showed persistent students had higher levels of academic locus of control and metacognitive self‐regulation skills than dropout students. Our finding suggests that these factors are the most significant factors influencing students' persistence in an online course. Practitioner Notes What is already known about this topic Student dropout rates in online learning are much higher than ones in conventional learning environment. To explain or predict dropout in online courses, several conceptual models were suggested: Tinto's (1975) Student Integration Model, Bean and Metzner's (1985) Student Attrition Mode and Rovai's (2003) Composite Persistence Model. Research found several significant predictors of dropout: locus of control, metacognitive strategies, self‐efficacy, resource management skills, and support from family and work. What this paper adds This study suggested an integrated approach to assess the effects of multiple predictors simultaneously using multivariate analysis of variance. Results of this study revealed the relative powers of these factors to predict dropout. In addition, this study provided empirical results for Rovai's conceptual model, supporting the original design partly. Implications for practice and/or policy Students' entry characteristics including academic locus of control and metacognitive strategies need to be assessed in the beginning of a semester to better address students' weakness during the course. Depending on students' preparedness for online courses, teachers and administrators could provide necessary support, such as technical supports, interactive learning activities and student‐centered web design. Interactive web design and learning activities could improve self‐regulated learning skills of online students.