Discriminating factors between completers of and dropouts from online learning courses

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. Introduction In recent years, online learning has been the fastest growing area in education in terms of the number of students and the market revenue of related industry. The enrollment figures for online courses have increased substantially. In 2008, almost 4 million students were enrolled in at least one online course (Allen & Seaman, 2008). With increasing enrollment figures, the worldwide market for online learning products and services reached over $27.1 billion in 2009, and is expected to reach $49.6 billion by 2014 (Ambient Insight Research, 2010). Despite the seeming popularity of online education, there is a growing concern about the significantly higher student dropout rates of students in online courses compared with those of conventional courses (Clay, Rowland & Packard, 2009). Research on online learning indicates that the dropout experience of students leads to frustration and lowers their confidence in learning (Moore & Kearsley, 1996; Poellhuber, Chomienne & Karsenti, 2008). Such failure can cause social isolation as well as economic loss (Rumberger, 1987). Review of literature Studies have examined reasons for dropout and suggested several conceptual models to describe the processes. Tinto’s (1975) Student Integration Model (SIM) is a pioneering work that describes the process of a student’s decision to drop out, which has influenced subsequent models. SIM British Journal of Educational Technology (2012) doi:10.1111/j.1467-8535.2012.01306.x © 2012 The Authors. British Journal of Educational Technology © 2012 BERA. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. describes that students are likely to persist when they achieve successful academic and social integration. However, this model is derived from analysis of traditional undergraduate students; thus, it is not best suited to apply to online learning environments (Rovai, 2003). In response to the limitation of Tinto’s SIM, Bean and Metzner (1985) developed the Student Attrition Mode (SAM) for nontraditional students. The nontraditional student is one who qualifies for at least one of the three conditions: being older than 24, residing off campus and attending school part-time. The model included five factors that influence students’ decisions to drop out: Background Variables (eg, age), Academic Variables (eg, study habits), Environmental Variables (eg, finances), Academic Outcomes (eg, GPA) and Psychological Outcomes (eg, goal commitment). Unlike Tinto’s model, SAM emphasized the role of the external environment, which could weaken the contribution of socialization to persistence. For instance, if students lacked financial support or had excessive workloads and family responsibilities, they were likely to drop out as they experienced high stress and low levels of goal commitment. Seeking a compromise of Tinto’s SIM, and Bean and Metzner’s SAM, Rovai (2003) proposed a synthesis of the two models called the Composite Persistence Model (CPM). CPM is comprised of four components: two elements required prior to admission, Student Characteristics and Student Skills; and two after admission, External Factors and Internal Factors (see Figure 1). In this study, we chose Rovai’s CPM as the theoretical frame to identify significant predictors influencing a student’s decision to drop out of online courses, since it is the most updated model 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. 2 British Journal of Educational Technology © 2012 The Authors. British Journal of Educational Technology © 2012 BERA. for online dropout that has been widely approved by empirical evidence. Specifically, we focused on the three components of the model: External Factors, Internal Factors and Student Skills. This study excluded examining the Student Characteristics variable because there is no consensus among researchers about the importance of students’ background characteristics on students’ persistence in online courses, particularly demographics such as age, gender or ethnicity (Lee & Choi, 2011; Park, 2007; Willging & Johnson, 2004). With regard to External Factors, previous literature emphasized that support from family and work is an important predictor of a student’s persistence in online learning (Park & Choi, 2009; Perry, Boman, Care, Edwards & Park, 2008; Pierrakeas, Xenos, Panagiotakopoulos & Vergidis, 2004). Most students enrolled in online courses are partor full-time workers who need to balance their work, family and study responsibilities. Therefore, positive support from family, friends, employers and colleagues is necessary for students to succeed in online courses (Holder, 2007). Related to Internal Factors, a significant number of papers suggested that the locus of control influences students’ academic persistence (Dollinger, 2000; Joo, Joung & Sim, 2011; Morris, Finnegan & Wu, 2005; Parker, 2003). Locus of control refers to an individual’s belief concerning where to attribute outcomes of an event and the degree of control over that subsequent event (Rotter, 1966). For instance, those who have an internal locus of control tend to believe that the outcomes of actions are contingent on their decisions and efforts, while those who externalize attribution think that the outcomes of their actions are out of their personal control and depend on chance, fate or other external circumstances. To measure this personal belief, Rotter (1966) developed a scale for general locus of control. Later, focusing on specific aspects of academic settings, Trice (1985) revised Rotter’s scale to measure Academic Locus of Control (ALOC). Research on general locus of control indicates that students who have an internal locus of control are more self-motivated and self-directed, and achieve higher than the externals in online courses Figure 1: Rovai’s Composite Persistence Model (CPM, 2003) Dropout factors in online courses 3 © 2012 The Authors. British Journal of Educational Technology © 2012 BERA. (Chang & Ho, 2009; Liu, Lavelle & Andris, 2002; Parker, 2003). However, there is a limited number of studies that have examined the impact of ALOC on a student’s decision to drop out of online education. Levy’s (2007) recent study showed that in contrast with previous literature on the effects of general locus of control, the ALOC was not significantly predicting dropouts in an online course. Thus, the inconsistency of results, and scarcity of studies using the new scale, requires further investigation of its impact on student retention and achievement. Another important Internal Factor that has been emphasized in the literature is academic selfefficacy. Bandura (1997) defines this as a student’s belief in his or her capability to successfully perform the provided tasks. Researchers indicate that academic self-efficacy affects academic performance, efforts, persistence and motivation (Britner & Pajares, 2006; Caprara, Vecchione, Alessandri, Gerbino & Barbaranelli, 2011; Gore, 2006; Multon, Brown & Lent, 1991; Pajares, 1996). For example, Collins (1982) found that students with high self-efficacy outperformed those with low self-efficacy in solving math problems. In addition, students with high self-efficacy adopted various learning strategies during the learning process compared with students with low self-efficacy. The impact of self-efficacy was evidenced in online learning as well. Wang and Newlin (2002) revealed that academic self-efficacy is a crucial factor in the success of online learning. Finally, in terms of the Student Skills component, metacognitive self-regul