Student satisfaction reflects how students perceive their learning experience. It is considered as one of the five pillars for the evaluation of the quality of online education as identified by the Online Learning Consortium (formerly The Sloan Consortium) (Moore, 2005). These pillars can be applied as a framework by educational institutions to evaluate and develop their online programs and courses.
Many previous studies have investigated the factors that influence and affect student satisfaction in online learning environments (Ali & Ahmad, 2011; Alshare & Lane, 2011; Artino, 2007a; Burgess, 2006; Chen & Chen, 2007; Croxton, 2014; DeBourgh, 1999; Dziuban et al., 2015; Gunawardena, Linder-VanBerschot, LaPointe, & Rao, 2010; Hassn, Hamid, & Ustati, 2013; Kuo et al., 2013; Kuo, Walker, Schroder, et al., 2014; J. Moore, 2014; Shen, Cho, Tsai, & Marra, 2013; Sher, 2009; Wu, Tennyson, & Hsia, 2010). A study by Gunawardena et al., (2010) that was done in a corporate adult training setting found that online self-efficacy was the strongest predictor of student satisfaction. The online self-efficacy’s scale they used includes technology usage, learning from discussions, and apply knowledge to work place. Similarly, Shen et al., (2013) investigated the relationship between online learning self-efficacy and student satisfaction, they found that online learning self-efficacy includes five dimensions, and all of them have a positive and significant relationship with student satisfaction. They found that self-efficacy to complete an online course, and self-efficacy to interact with instructors in an online course were the strongest predictors. However, self-efficacy to handle tools in a CMS was not a predictor at all. Learning Management system was also investigated by Martin & Tutty, (2008) and Martin et al., (2010), who found that LMS self-efficacy does not have a significant effect on course performance for the online learners. When assessing the relationship between perceived self-efficacy and perceived satisfaction with e-learning systems, Liaw, (2008) found that perceived self-efficacy was a predictor of learners’ perceived satisfaction.
Self-efficacy was also investigated by Artino (2007a) who explored the relationship between students’ motivational beliefs, their perceptions of the learning environment and student satisfaction with an online course. Results show a positive and significant relationship between task value, self-efficacy for learning with self-paced online courseware, perceived instructional quality, and students’ overall satisfaction with the online course. Also, task value and self-efficacy or learning with self-paced online courseware were significant positive predictors of student satisfaction.
Some types of interaction are found to be important elements in student satisfaction. Two studies, for example, by (Kuo et al., 2013) and (Kuo, Walker, Schroder, et al., 2014) examined some predictors that contribute to student satisfaction in online learning environments. They found that Internet self-efficacy, learner-learner interaction and self-regulated learning were not predictors of student satisfaction, but learner-content interaction was. Similar results by Burgess (2006), who explored student satisfaction with fully online courses and its relationship to two elements of Moore’s theory of transactional distance: (a) learner autonomy and (b) dialog between the instructor and student. Results show a significant relationship between student satisfaction with fully online courses and learner autonomy and instructor-student interaction.
Satisfaction with online courses may be related in part to the e-learning system. Liaw (2008) investigated learners’ satisfaction, behavioral intentions, and the effectiveness of the Blackboard e-learning system with university students. Findings from the study show that perceived self-efficacy was an important factor that influenced learners’ satisfaction with the Blackboard e-learning system. Also, the study showed that learners’ behavioral intention to use the e-learning system was influenced by both perceived usefulness and student satisfaction. In the past decade as well, Wu et al., (2010) examined student satisfaction in a blended e-learning system environment based on social cognitive theory. Findings of the study show that the main factors that contribute to student satisfaction were: computer self-efficacy, performance expectations, system functionality, content feature, interaction, and learning climate. However, performance expectations and learning climate significantly affected student satisfaction.