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An important element of student learning behaviors relates to students' self-

concept. This includes the ways in which students perceive their role as a

student. It provides a lens through which students interpret a learning

experience. Self-efficacy is identified as a way to summarize the beliefs an

individual student has about their ability to successfully learn from a course of

study (Shen, Cho, Tsai, & Marra, 2013). As such, self-efficacy is an important

part of their self-concept. High self-efficacy is closely associated with feelings of

autonomy and the ability to self-regulate a learning process (Bernard et al.,

2004). These are important in an online learning environment (Chu, 2010; Chu &

Chu, 2010). Encouraging self-efficacy is a key objective for teachers. Teachers

often try to inspire students to take ownership of their own learning experience

since it can increase the depth of student engagement while also reducing the

onus on the teacher to deliver student learning outcomes (Zimmerman, 2000,

2010). To support self-efficacy, teachers can take certain approaches to

designing online materials and assessments while also encouraging peer support

(Bandura, 2010; Shen et al., 2013). However, few studies consider the student

perspective in terms of self-efficacy antecedents, particularly in terms of attitudes

and online learning capabilities.

A greater understanding of these elements may reveal some of the influences of

self-efficacy on specific learning behaviors, where current studies only provide

general descriptions of self-directed or autonomous behaviors as self-efficacy

outcomes (Chu, 2010; Chu & Chu, 2010).

Student attitude can be a powerful influence on learning behavior (Arbaugh,

2010; Bernard et al., 2004). For example, lower performance is commonly

associated with a poor attitude (Sadik & Reisman, 2004). Attitudes also inform

student reactions to incentives (Love, Love, & Northcraft, 2010). Student

attitudes involve an underlying set of values regarding a phenomenon of interest.

(Andersen, Norton, & Nussbaum, 1981; Obermiller & Ruppert, 2012). Of

particular interest in the present study are student attitudes towards using online

distance learning process. More specifically, the study considers the role of

information and communications technology (ICT) as a means to achieve

learning outcomes. Student online learning experiences primarily involve

interactions with the online learning space (or learning management system).

However, other information technology uses are also important. Previous studies

identify email (Webster & Hackley, 1997), social media (Dabbagh & Kitsantas,

2012) and telecommunications (Valtonen et al., 2012) as significant learning

vehicles.

A key difficulty facing many teachers lies in creating positive student attitudes.

The payoffs in doing so include reductions in disruptive behaviors as well as

higher personal investment in learning processes (Bernard et al., 2004; Sadik &

Reisman, 2004). More specifically, student attitudes that associate learning

processes with positive attributes such as personal interest and enjoyment are

more likely to support positive learning behaviors. If an attitude is negative or

dismissive, there is littlechance that the student will engage in any learning

process. As earlier studies suggest, there is little actual difference between face-

to-face and distance learning in terms of knowledge outcomes, but student

experiences do differ (Cooper, 2001; Waldman, Perreault, Alexander, & Zhao,

2009). Therefore, the present study hypothesizes that a positive attitude towards

using ICT for online distance learning is likely to have positive effects on learning

experiences. This is likely to support the student's view that they will succeed in

the course and, thus, their self-efficacy.

Three types of online learning behaviors are the subjects of hypotheses three,

four and five respectively. Peer engagement is the subject of hypothesis three.

Peer engagement involves the ways in which students interact with classmates
during the online learning process.1 Peer support can help build and maintain

confidence while also encouraging student resolve. These outcomes are why

peer support is a key determinant of online learning success (Arbaugh, 2000;

Arbaugh & Benbunan-Finch, 2006). Two types of peer engagement behaviors

have become important in online distance learning contexts (Dabbagh &

Kitsantas, 2012; Koole, McQuilkin, & Ally, 2010; Shen et al., 2013). Social

interaction involves communicating with peers for the purpose of establishing

rapport.

This type of interaction is generally not for academic purposes. Instead, it is to

help build confidence while also providing opportunities for interactions beyond

the study context. Academic interaction, on the other hand, involves peer

communications so as to refine and build the student's understanding of study

content and requirements. Both types of peer interaction can provide

opportunities to build confidence and encourage greater depth of understanding.

Self-efficacy is likely to support productive peer engagement. The confidence

that high self-efficacy students experience probably translates to this setting.

Indeed, students with this are more likely to share their understanding, to

empathize with peers and to pose thought-provoking questions through online

means (Ting, 2015). Similarly, their high autonomy is likely to contribute to a

higher awareness of their learning needs. This awareness may help to focus

peer engagement activities in productive ways. Therefore, the present study

suggests that high self-efficacy has a significant, positive influence on peer

engagement.

Learning management system (LMS) interactions are the subject of hypothesis

four. Online distance learning involves a ‘learning space’, which is generally a

website that contains a structured set of learning materials, activities, discussion

boards/forums, and assessment tasks (Andrew, 2012; Yoany, 2006). Previous

studies suggest that the ways in which LMSs encourage online communication,

present course material and handle various pedagogical approaches are of

one of a suite of possible avenues for student learning. Recent studies suggest

that students can create their own ‘personal learningenvironments’ that comprise

social media, the LMS, interactions with peers, and the allocation of time and

space for study purposes (Dabbagh & Kitsantas, 2012; Valtonen et al., 2012).

Therefore, the LMS is one of a number of information sources available to

students.

Studies have documented both favorable and unfavorable students’ perceptions

in distance education. The authors reviewed the literature specifically on

students’ perceived barriers to online learning and more generally on students’

perceived barriers to learning. The goal was to seek out barriers, issues, and

success factors from the students’ perspectives that may affect the learning

outcomes (e.g., learning effectiveness, learner attitudes, and motivation). We

also searched for indications of what background characteristics and

demographics of the learner might affect the outcomes of their online learning.

Previous studies have found significant differences in learning, attitudes,

motivation, or experiences based on: (1) gender (e.g., Chen, 1986; Teo & Lim,

2000; Young, 2000); (2) age (e.g., Rekkedal, 1983); (3) ethnicity (e.g., Owens,

1998; Branden & Lambert, 1999; Chen, 1999); (4) ability and confidence with

online learning technology (from “not currently using these technologies” to being

“comfortable and confident with online learning technologies”). In other words,

students’ experiences with learning technologies (e.g., Koohang, 1989; Hara,

1998; Hara & Kling, 1999); (5) the type of learning institution they attend

(community college, undergraduate, graduate, business/corporate/non-profit, and

government/military) which may be compared outright, or which may also speak

to their prior educational level (e.g., Rekkedal, 1983; Sheets, 1992; Mungania,

2003); and

) learning effectiveness in the online environment (from “cannot learn as well

online,” through “no difference between online and traditional classroom,” to

successful student online (Mungania, 2003). To these we added several

variables we wanted to explore: (7) learning enjoyment in the online classroom

(“enjoy online learning significantly less,” to “enjoy online learning significantly

more than the traditional classroom”); (8) number of online courses completed;

(9) number of online courses dropped; (10) likelihood of taking a future online

course; and (11) whether students experienced prejudicial treatment in the

traditional classroom due to cultural background, disability or other personal

characteristics.

Literature review

Self-efficacy is a key component in student learning and satisfaction. It is defined

as “the level of confidence that someone has to perform a particular task, activity,

action or challenge” (Alqurashi, 2016, p. 45). If students believe that they cannot

achieve results, they will not make any effort to take the necessary steps to

achieve. However, students with high self-efficacy don’t regard difficult tasks as

obstacles to avoid, but rather as a challenge for developing their skills; this could

enhance learning and performance and lead to higher satisfaction with the

achieved results. Prior studies on self-efficacy within online learning

environments in the context of higher education have mostly focused on the

technological aspect of self-efficacy, such as Internet self-efficacy, learning

management system self-efficacy, computer selfefficacy, and web use self-

efficacy (Jan, 2015; Kuo et al., 2014; Martin & Tutty, 2008; Martin, Tutty, & Su,

2010; Simmering, Posey, & Piccoli, 2009).

Kuo et al. (2014) found that Internet self-efficacy does not relate to or predict

student satisfaction. Additionally, Tang and Tseng (2013) found that Internet self-

efficacy predicted students’ performance in a search test (i.e., the ability to

learning outcome). Martin and Tutty (2008) and Martin et al. (2010) found that

learning management system selfefficacy does not have an impact on course

performance. Also, self-efficacy to handle 134 E. ALQURASHI tools in a learning

management system does not predict student satisfaction (Shen, Cho, Tsai, &

Marra, 2013). When assessing the relationship between perceived self-efficacy

and perceived satisfaction with e-learning systems, Liaw (2008) found that

perceived self-efficacy does not predict perceived satisfaction

Computer self-efficacy and its relationship to student satisfaction were

investigated by many studies. For example, Wu, Tennyson, and Hsia (2010)

examined student satisfaction in a blended e-learning system environment and

found that computer selfefficacy does not significantly affect student satisfaction.

A recent study by Jan (2015) found no positive or significant relationship between

computer self-efficacy and student satisfaction. Similar results by Simmering et

al. (2009) found that computer self-efficacy has no relationship with students’

learning motivation. However, Lim’s (2001) study results showed that computer

self-efficacy was a significant predictor of student satisfaction. This also supports

Womble’s (2007) results that computer self-efficacy has a significant positive

relationship with student satisfaction. However, there are other studies that

focused on self-efficacy for learning rather than for technology. For example, a

study by Gunawardena, Linder-VanBerschot, LaPointe, and Rao (2010)

conducted in a corporate adult training setting found that online self-efficacy was

the strongest predictor of student satisfaction. Similarly, Shen et al. (2013)

investigated the relationship between online learning self-efficacy and student

satisfaction; they found that self-efficacy to complete an online course as well as

self-efficacy to interact with instructors in an online course were the strongest

predictors. Self-efficacy for learning was also investigated by Artino (2007a), who

found a positive and significant relationship between self-efficacy for learning and

student satisfaction in a self-paced online course.

interaction a learner has with course content, class instructor, and their peers.

Learner– content interaction (LCI) is the interaction that occurs between student

and the subject matter, and it is a highly individualized process facilitated by the

instructor. Learner– instructor interaction (LII) is a two-way communication

between learners and the instructor of the course. Learner-learner interaction

(LLI) is a two-way communication between or among learners for the purpose of

exchanging information or ideas related to course content. This can occur with or

without instructor supervision (Moore, 1989). DISTANCE EDUCATION 135 A

number of researchers have emphasized the importance of interaction (Abrami,

Bernard, Bures, Borokhovski, & Tamim, 2011; Chen & Chen, 2007; Cho & Kim,

2013; Kuo, Walker, Belland, & Schroder, 2013; Kuo et al., 2014). This is mainly

because of the essential role interaction plays in online formal education, and

also because interaction was mostly absent during the early stages of distance

education (Abrami et al., 2011). A meta-analysis by Bernard et al. (2009)

reviewed 74 studies on distance education and found that the three types of

interaction (LLI, LII, and LCI) are positively related to achievement outcomes.

However, it is important to note that effective interaction occurs only if learning

and instruction are designed and implemented well. It is about quality interaction,

not quantity.
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