Dropout in an Online Training for In-service Teachers

Abstract

High dropout rates are a problem in online learning (Lee & Choi, 2011). Student dropout has been described and analyzed in the contexts of whole study programs (Grau-Valldosera & Minguillón, 2014) and single online courses (Lee & Choi, 2011). Determinants of learner attrition and persistence with online training have been shaped in various models with different levels of complexity (e.g., Lee & Choi, 2011). It appears to be a complex phenomenon depending on numerous factors (e.g., Lee & Choi, 2011). In addition to features of the online training course and the learning conditions, Lee and Choi (2011) strongly suggested that learner characteristics influence the decision to persist in an online course or to drop out. Therefore, we explored the extent that learners dropping out at various stages from an online training for in-service teachers differ from successful learners in domain-specific prior knowledge, motivation, learning skills, computer attitude and computer anxiety. In the context of complex learning environments and online learning, the domain-specific prior knowledge is known to influence program usage, information processing and performance often in a straightforward way (Amadieu, Tricot, & Mariné, 2009). Studies from hypertext research reported prior knowledge having a positive impact on a diversity of performance measures (Amadieu et al., 2009; McDonald & Stevenson, 1998; Stiller, 2003; 2009; 2015). Students having higher prior knowledge can more easily study because of having less new information connected to prior knowledge. Consequently, learners might experience a lower level of work load and be less threatened by learning difficulties. Thus the level of prior knowledge might influence a learner’s decision to drop out. Intrinsic motivation refers to engaging in behaviours, because the acts are inherently interesting or enjoyable (Ryan & Deci, 2000). Intrinsic motivation is also connected to high-quality learning (Ryan & Deci, 2000). Motivation is one of the most frequently studied variables in relation to dropout, and it was shown to be correlated to course persistence and dropout (Castles, 2004; Grau-Valldosera & Minguillon, 2014; Hart, 2012; Hartnett, St. George, & Drone, 2011; Ivankova & Stick, 2007; Osborn, 2001; Park & Choi, 2009; Parker, 2003). Learners who are intrinsically motivated might have an advantage in preventing learning difficulties. Their greater involvement in deeper learning might contribute to reduced dropout rates.Self-regulated learning is a key component of successful online learning (Barnard et al., 2009) comprising, according to Pintrich (1999), the use of cognitive and metacognitive learning strategies and resource management strategies. Metacognitive strategies, time management and creating a supporting learning environment are considered to be particularly relevant for online learning (Lee, Choi, & Kim, 2013). Metacognitive strategies include the planning, monitoring and regulation of cognitive processes (Pintrich, 1999). Resource management strategies are self-management strategies that support learning in general and shield against external disturbances and other detrimental influences (Pintrich, 1999). The strategies of time management (i.e., assigning adequate time periods to learning) and learning environment strategies (i.e., creating a supportive learning environment) belong to this category. Higher levels of these learning skills might contribute to reducing dropout. It was shown that management skills are significant predictors of dropout (Lee, Choi, & Kim, 2013), especially managing time effectively and having comfortable conditions for studying (Castles, 2004; Hart, 2012; Holder, 2007; Ivankova & Stick, 2007; Osborn, 2001; Shin & Kim, 1999). Computer attitude and anxiety might influence a learner’s decision to drop out by affecting learning. Attitudes consist of affective, conative and cognitive components (Richter, Naumann, & Horz, 2010). Computer anxiety is considered to be a trait, which comprises both cognitive and affective components such as feelings of anxiety and worrisome thoughts (Richter et al., 2010). Negative computer attitudes and computer anxiety might disturb learning because of negative emotions and thoughts associated with the computer, such as disturbing thoughts about the computer malfunctioning or even crashing. The limited studies investigating the effects of computer attitudes on course dropout have found positive effects of positive attitudes on course usage and persistence (Bernard et al., 2004; Stiller & Köster, 2016). Only two studies have investigated computer anxiety and course dropout / persistence. Long et al. (2009) presented no differences in drop-out rates between employees of a U.S. Midwest-based landscaping company who completed an online course, and Stiller and Köster (2016) showed that dropout employees had a higher level of computer anxiety than successful learners.