A Model for Predicting Learning Flow and Achievement in Corporate e-Learning

Introduction E-learning has been around for more than a decade and has become widely regarded as a viable option for a variety of educational contexts. Especially, it forms the core of numerous business plans, as new technologies provide a new set of tools that can add value to traditional leaning modes, such as accessibility to content, efficient management of courseware and learners, and enhanced delivery channels (Wild, Griggs, & Downing, 2002). In addition to these positive benefits, economic savings have made e-learning a high priority for many corporations (Strother, 2002). Given that as much as 40% of money spent on in-person corporate learning is eaten up by travel cost (Zhang & Nunamaker, 2003), companies using online training can expect plenty of time and cost savings, compared with conventional face-to face training. Despite the rapid growth of e-learning in the corporate training sector, this quantitative growth has not always guaranteed an equivalent improvement in the quality of learning. Especially, learners participating in online training in a corporate setting are likely to have their own job tasks to perform, which makes it difficult for them to concentrate on the learning itself. Hence, cognitive engagement of learners has drawn keen attention from researchers interested in the learners' experience during online learning as well as the learning outcome (Herrington, Oliver, & Reeves, 2003). More specifically, learning flow has been reported as a construct related to learners' engagement, predicting learning achievement. According to Csikszentmihalyi (1997), learning flow is characterized by complete absorption during learning. In other words, flow is the optimal experience as a mental state of extremely rewarding concentration that emerges in-between frustration and boredom. Flow becomes more important in the elearning environment where there is no physical limitation in terms of time and space. When the learners do not experience flow, they may produce low engagement throughout learning, or even worse, fail to complete the elearning (Skadberg & Kimmel, 2004). Considering that learning flow is a potential indicator of online learning achievement, more discussion on flow is necessary to expand our understanding of the phenomenon of corporate elearning. The primary objective of this study is to investigate the determinants of learning flow and achievement in corporate online training. Factors related to learning flow: Self-efficacy, intrinsic value, perceived usefulness and ease of use Based on an extensive review of prior research, self-efficacy, intrinsic value, and perceived usefulness and ease of use of the e-learning program have been identified as critical variables predicting learning flow. Self-efficacy is a belief in one's capabilities to organize and execute the courses of action (Bandura, 1977). Since these beliefs are determinants of how people think, behave, and feel, they play important roles during the course of learning. Zimmerman and Schunk (1989) described self-efficacy as an important factor that resides within the learner, mediates between cognition and affect, and affects academic performance. The relationship between self-efficacy and learning flow has been reported by previous studies. Meece, Blumenfeld and Hoyle (1988) examined the levels of self-efficacy of 275 fifth and sixth graders in a traditional classroom environment, and divided them into two groups of high and low self-efficacy. The former students showed higher outcome expectation, deeper engagement during learning for a longer period of time, and higher participation. In an online learning environment, Puzziferro (2008) investigated 815 undergraduate-level students' self-efficacy and self- regulated learning skills, and reported that self-efficacy was a significant predictor of learning flow and achievement as well. Learners' intrinsic value has been identified as another important factor influencing learning flow. …