Higher education nowadays offer many opportunities to take online courses and complete degree programs online. This is to meet the demand of the incensement in online learning enrollment. The number of students enrolled in online courses has increased by 350% from 2002 to 2014. Allen & Seaman, (2014) have reported that in Fall 2002, 1.6 million students were enrolled in at least one online course in U.S. colleges and universities; this number increased to 7.1 million students by Fall 2012. By Fall 2014, there were additional 403,420 students enrolled in at least one online course; while the rate of on campus courses enrollment continues to decline (Allen, Seaman, Poulin, & Straut, 2016). Interestingly, the number of online enrollment increased by 3.9% in the year of 2015 (Allen & Seaman, 2017). As the number of enrollment in online courses in higher education increases, so does the need for research to identify factors that play an important role in student satisfaction and learning.
Student satisfaction with online learning is greatly associated with dropout rates, persistence, motivation in taking further online courses, student success and student commitment to an online course or program (Ali & Ahmad, 2011; Allen & Seaman, 2004; DeBourgh, 1999; Yukselturk & Yildirim, 2008). Similarly, student perceived learning has been considered as an indicator of learning, and it is one of the core elements for course evaluation (Wright, Sunal, & Wilson, 2006). For those potential benefits, student satisfaction and perceived learning in online learning should be studied and investigated in order to increase recruitment, retention and provide a better learning experience for prospective students. Evaluating student satisfaction and perceived learning allows educational institutions to detect areas for development and improvement for online learning (Kuo, Walker, Schroder, & Belland, 2014). This research focuses on the role of self-efficacy for completing an online course and interaction in predicting student satisfaction and perceived learning because of the important role they play in online learning environments in higher education.
Research on self-efficacy in online learning environments in higher education mostly focused on the technological aspect of self-efficacy, such as computer self-efficacy, Internet self-efficacy, Learning Management System self-efficacy, or web-user self-efficacy (Jan, 2015; Joo, Bong, & Choi, 2000; Kuo, 2010; Kuo, Walker, Belland, Schroder, & Kuo, 2014; Lee & Hwang, 2007; Lim, 2001; Lin, Liang, Yang, & Tsai, 2013; Martin & Tutty, 2008; Martin, Tutty, & Su, 2010; Simmering, Posey, & Piccoli, 2009). Previous studies show that students’ self-efficacy for technology has changed over the years (Alqurashi, 2016). College students are becoming more confident in performing web-based activities as years go by and as a result self-efficacy for technology is becoming less predictive of student learning experiences. For example, a recent study by Kuo, Walker, Belland, et al., (2014) looked into the relationship between Internet self-efficacy, interaction and students satisfaction. It was found that used Internet self-efficacy was not a predictor of student satisfaction.
Although technology skills are needed for online learning, it is important to consider how this generation of students is different compared to past generations in terms of their confidence level in their capabilities and fluency with technology (Alqurashi, 2016). With the evolution of technology, it seems that students are now more willing to use and interact with technology to communicate with other people. This should be taking in consideration when researching self-efficacy in online learning environments. In other words, the focus of research should be shifted from self-efficacy to use technology to focusing on students’ confidence in their ability to perform, learn and complete an online course successfully.
Another critical element in online learning is interaction. A number of researchers have emphasized its importance (Abrami, Bernard, Bures, Borokhovski, & Tamim, 2011; Anderson, 2003; Cho & Kim, 2013; Croxton, 2014; Jung, Choi, Lim, & Leem, 2002; Ke, 2013; Kožuh et al., 2015; Kuo, Walker, Belland, & Schroder, 2013; Kuo, Walker, Schroder, et al., 2014; Michael G. Moore, 1989; Sher, 2009; Woo & Reeves, 2008). This is mainly because of the essential role interaction play in online formal education, and also because interaction was mostly absent during early stages of distance education (Abrami et al., 2011). In spite of many forms of interaction that were developed by different researchers with different perspectives, Moore’s interaction model (i.e. learner-learner, learner-instructor, and learner-content) still leads and guides later related research on interaction in online learning environments.
Interaction becomes more complex in online learning environments due to the addition of technology. Although some in the mid 90s have argued that Moore’s interaction model overlooked the role of technology, which is the medium of all forms of interactions in online learning (Hillman, Willis, & Cunawardena, 1994). It is important to note that technology doesn’t necessary lead to effective interaction that results to a positive learning experience if learning and instruction weren’t designed and implemented well.
In order to fill the gap in research, this research intends to investigate the role of self-efficacy for completing an online course, learner-content interaction, learner-instructor interaction, and learner-learner interaction in online learning environments to predict both student satisfaction and perceived learning. Results from this study can support higher education instructors and instructional designers to improve planning, designing, developing, and delivering quality online education in order to improve students learning as well as their satisfaction.