Literature review
Online learning in higher education
Online learning has become an appropriate and attractive solution for students who
pursue their education while undergoing busy activities (Seaman et al., 2018).
Therefore, universities are constantly looking for ways to improve the quality of online
courses to increase student satisfaction, enrollment, and retention (Legon and Garrett,
2017).
A unique feature of online learning is that students and lecturers are physically far apart
and require a medium for delivering course material (Wilde and Hsu, 2019). The
interaction of students and lecturers is mediated by technology, and the design of virtual
learning environments significantly impacts learning outcomes (Bower, 2019; Gonzalez
et al., 2020). For decades, research on online learning has been studied, and the
effectiveness of online teaching results from instructional design and planning (Hodges
et al., 2020). The COVID-19 pandemic is forcing students worldwide to shift from offline
learning to online learning environments. Students and teachers have limited capacities
regarding information processing, and there is a chance that a combination of learning
modalities may result in the cognitive overload that impacts the ability to learn new
information effectively (Patricia Aguilera-Hermida, 2020). In addition, lack of confidence
in the new technology they use for learning or the absence of cognitive engagement
and social connections have less than the optimal impact on student learning outcomes
(Bower, 2019).
The existence of technology, if used effectively, can provide opportunities for students
and teachers to collaborate (Bower, 2019; Gonzalez et al., 2020). The success of the
transition from offline to online learning is strongly influenced by the intention and
usefulness of technology (Yakubu and Dasuki, 2018; Kemp et al., 2019), so the
effectiveness of online learning is highly dependent on the level of student acceptance
(Tarhini et al., 2015). Therefore, it is essential to analyze the factors related to online
learning to achieve student learning outcomes.
Course quality
As online learning continues to mature and evolve in higher education, faculty and
support staff (instructional designers, developers, and technologists) need guidance on
how to best design and deliver practical online courses. Course quality standards are a
valuable component in the instructional design process. They help guide course writers
and identify needed improvements in courses and programs and create consistency in
faculty expectations and student experience (Scharf, 2017).
In general, quality is an essential factor in online learning to provide a helpful learning
experience for students (Barczyk et al., 2017), while course quality supports university
learning performance. Quality Matters™ (QM) is an international organization that
involves collaboration between institutions and creating a shared understanding of
online course quality (Ralston-Berg, 2014). This research measures course quality by
three dimensions: course design, course content support, and course assessment
(Hadullo et al., 2018). These three dimensions are determinants in assessing the quality
of learning.
Instructor factor
The quality of the instructor in delivering the material becomes the input to achieve
learning performance (Ikhsan et al., 2019). To facilitate an active learning process,
instructors should use strategies to increase participation in learning. While the
responsibility for learning lies with the learner, the instructor plays an essential role in
enhancing learning and engagement in the online environment (Arghode et al., 2018).
As an essential actor in the classroom, instructors must have psychological similarities
with students to help academically lacking students by changing perceptions of external
barriers and stereotypes (Sullivan et al., 2021).
Several research results have proven the influence of instructor interactivity in the
classroom on online teaching, including active learning (Muir et al., 2019), instructor
presence (Roque-Hernández et al., 2021), discussion and assessment techniques
(Chakraborty et al., 2021; McAvoy et al., 2022), and feedback (Kim and Kim, 2021).
Some of these study areas are topics often studied with the needs and values of
instructor interaction. In this study, the importance of instructor interactivity in online
learning is related to online discussion forum activities and instructor interaction.
Student factor
The characteristics of students who take online learning education are different from
those who study conventionally (face to face). Several essential factors drive student
success in online learning: understanding computers and the internet, personal desires,
motivation from instructors, and reasonable access to online learning systems (Hadullo
et al., 2018; Bashir et al., 2021; Glassman et al., 2021; Rahman et al., 2021). Self-
efficacy is explained by social cognitive theory as the ability to self-regulation (Bandura,
2010). According to social cognitive theory, people can develop self-efficacy by
observing other people’s models of achieving goals and having had various successful
attempts in the past to achieve challenging goals (Duchatelet and Donche, 2019).
People who have high levels of self-efficacy tend to be confident in their ability to
succeed in challenging tasks, such as their own, and observe others to achieve goals.
Institutional factor
Institutional theory has been used to explore organizational behavior toward technology
acceptance, as it explains how institutions adapt to institutional change (Rohde and
Hielscher, 2021). Currently, most higher education institutions have migrated from
traditional to online learning systems, thereby changing traditional learning
environments such as the physical presence of teachers, classrooms, and exams
(Bokolo et al., 2020). Today’s developing technologies have improved education due to
online learning, teleconferencing, computer-assisted learning, web-based distance
learning, and other technologies (Bailey et al., 2022; Fauzi, 2022). Online learning
systems provide more flexibility and improve teaching and learning processes, offering
more opportunities for reflection and feedback (Archambault et al., 2022). Online
learning offers interactive teaching, easy access, and is cost-effective mainly (Sweta,
2021).
E-learning technology
E-learning technology in this study is defined as the learning media used by universities
in going online learning. The task-technology fit (TTF) model has been used to assess
how technology generates performance, evaluate the effect of use and assess the fit
between task requirements and technological competence (Wu and Chen, 2017). The
TTF model suggests that the user accepts the technology because it is appropriate to
the task and improves learning performance (Kissi et al., 2018). Technology acceptance
is determined by the individual’s understanding and attitude toward technology, but the
compatibility between task and technology must be considered necessary (Zhou et al.,
2010). When a student decides to use technology, such as an LMS, their decision is
very likely that the assignment and technology match.
Overall quality
Developments and challenges in information systems inspire researchers and
practitioners to improve the quality and functionality of a new system to take advantage
of its growth potential (Aldholay et al., 2018). Overall quality is understood as a new
construct that includes system quality, information quality, and service quality (Ho et al.,
2010; Isaac et al., 2017d). System quality is defined as the extent to which users
believe that the system is easy to use, easy to learn, easy to connect, and fun to use
(Jiménez-Bucarey et al., 2021). Information quality is understood as the extent to which
system users think that online learning information is up-to-date, accurate, relevant,
comprehensive, and organized (Raija et al., 2010). Service quality is referred to through
various attributes, such as tangible, reliability, responsiveness, assurance, functionality,
interactivity, and empathy (Preaux et al., 2022).
Student engagement
Student engagement in online learning is when they use online learning platforms to
learn, including behavioral, cognitive, and emotional engagement (Hu et al., 2016).
Student engagement in online learning is not only due to the behavioral performance of
reading teaching materials, asking questions, participating in interactive activities, and
completing homework, but more importantly, cognitive performance (Lee et al., 2015).
In this study, cognitive behavior is all mental activities that enable students to relate,
assess, and consider an event to gain knowledge afterward. In addition, cognitive
behavior is closely related to a person’s intelligence and skill level. For example: when
someone is studying, building an idea, and solving a problem.
Student behavioral engagement is essential in online learning but is difficult to define
clearly and fully reflect student efforts. So, it must consider students’ perception,
regulation, and emotional support in the learning process (ChanMin et al., 2015).
Students must fully enter online learning, including the quantity of engagement and
quality of engagement, communication with others and conscious learning, guidance,
assistance from others, and self-management and self-control.
Student satisfaction
Perceived satisfaction is not limited to marketing concepts but can also be used in the
context of online learning (Caruana et al., 2016). User satisfaction is one of the leading
indicators when assessing success in adopting a new system (Montesdioca and
Maçada, 2015; DeLone and McLean, 2016). User satisfaction also refers to perceiving a
system as applicable and wanting to reuse it. In the context of online learning, student
satisfaction is defined as the extent to which students who use online learning are
satisfied with their decision to use it and how well it meets their expectations (Roca et
al., 2006). Students who are satisfied while studying with an online learning system will
strive to achieve good academic scores.
Student performance impact
In the context of education, performance is the result of the efforts of students and
lecturers in the learning process and students’ interest in learning (Mensink and King,
2020). The essence of education is student academic achievement; therefore, student
achievement is considered the success of the entire education system. Student
academic achievement determines the success and failure of academic institutions
(Narad and Abdullah, 2016).
It is crucial to explore problems with online learning systems in higher education to
improve the student experience in learning. Therefore, the university’s ability to design
effective online learning will impact university performance and student performance.
The failure of online learning design and technology can frustrate students and lead to
negative perceptions of students (Gopal et al., 2021).
With rapidly changing technology and the introduction of many new systems,
researchers focus on the results of using systems in terms of performance improvement
to evaluate and measure system success (Montesdioca and Maçada, 2015; Isaac et al.,
2017a,b,c,d). Performance impact is defined as the extent to which the use of the
system improves the quality of work by helping to complete tasks quickly, enabling
control over work, improving job performance, eliminating errors, and increasing work
effectiveness (Isaac et al., 2017d; Aldholay et al., 2018). In this study, performance
impact is interpreted as an outcome of the use of technology in online learning.
Literature review
The impact of online learning on academic performance
Online learning in university education is thought to be one of the important variables to
increase academic performance in university studies.
Chun, & Heo (2018) pointed out that the flipped learning is an effective method in terms
of
both self-efficacy and academic performance, and Halabi, Essop, Carmichael, & Steyn
(2014)
provide empirical evidence to show that students who spent more time online
significantly
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improved their course mark. Students felt satisfied with their online learning and their
academic performance was correlated with their usage of the online content materials
(Kuo,
Luo, & Brielmaier, 2016); meanwhile, Wei, & Chou (2020) indicated that students'
computer
or internet self-efficacy and motivation for learning exerted a direct, positive effect on
their
online discussion score and course satisfaction. There is a significant but weak positive
correlation between the engagement of students in the online module and their
performances
in the final learning activity (Rajabalee, Santally, & Rennie, 2020); and instructional
strategies that facilitate cross-cultural collaborative online learning, including group
work,
self-introductions, and cultural awareness activity, computer-supported collaborative
learning
activity, the inclusion of global examples, and internationalized curriculum (Kumi-
Yeboah,
2018).
Shaw, MacIsaac, & Singleton-Jackson (2019) indicated that students had high test
scores
using the online tool compared to multiple-choice paper-and-pencil exam marks; and
Akhter,
& Mahmood (2018) show that improving online technology fosters dynamic learning
opportunities for students through online education. The relationship between learning
activities in the online package and assessment component grades was found to be
weak but
meaningful, and the total number of attempts and performance in individual online
learning
activities, are predictors of the final course grade (Foung, & Chen, 2019); meanwhile,
the
blended data set combining online, and traditional critical factors had the highest
predictive
students’performance (Lu et al., 2018). Learner interaction in an online web-based
course and
LMS use could be assessed concerning academic performance (Strang, 2017; Alkis, &
Temizel, 2018); meanwhile, Zhang et al. (2020) concluded that personalized learning
intervention can effectively improve students' learning behaviors, attitude, motivation,
self-
efficacy, and academic performance in a blended learning environment. Students'
engagement
from the online learning community were higher than the ones who used the English
learning
system only, although the learning achievement is not significant (Lai, Lin, Lin, & Tho,
2019;
Mercer (2018) confirmed the predictive effects of online learning attitudes, online
learning
readiness on student motivation; meanwhile, Bailie (2019) show the influence of learner
pre-
term access to graduate-level courses delivered entirely online.
There is a relationship among inquiry framework (social, teaching, and cognitive), and
students' learning-related outcomes (satisfaction, continuous academic-related online
performance, and academic achievement) (Choy, & Quek, 2016; Main, & Griffith, 2019);
and
Marshall (2017) indicated a high level of statistical significance in first-time online
students
with academic success as well as overall persistence in students who completed the
online
orientation. Online and flipped instructional approaches, as well as self-reported
adoption, had
a greater impact on student academic performance than the traditional approach
(Sharp, &
Sharp, 2017); Han, & Ellis, 2020); meanwhile, Mingfang, & Wang (2018) concluded that
students' online learning performance influence life satisfaction and social identity.
Through
online dynamic assessment using Google docs were evidenced academic writing skills
development, and, is also beneficial for student engagement (Ebadi, & Rahimi, 2019;
Sneed
(2019), and Harris (2017) indicated that ethnicity was systematically related to
academic
performance for online education at the undergraduate level.
In a different point of view, online pedagogy had a negative effect on student academic
performance when compared with the traditionally taught group, and online students
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underperform compared with face-to-face students (Bir, 2019; Dendir, 2019); and Stark
(2019) found that while online students reported lower levels of motivation compared to
face-
to-face students, particularly for online courses. Thus, it is evidenced that online
learning
impacts academic performance at university. In conclusion, the investigation of the
relationship between online learning and academic performance, as resulted in previous
research, is important. Therefore, based on the above literature review it is
hypothesized that:
H # 1: Academic performance is a function of online learning
The impact of online learning on students’ satisfaction
Online learning in university education is meant to be one of the important variables to
improve students' satisfaction. Umek, Aristovnik, Tomaževic, & Keržic (2015) reveal a
positive correlation between the proportion of the course implemented in the Moodle e-
learning platform and students' performance on one hand and their satisfaction on the
other;
and Wei, & Chou (2020) indicated that students' computer or internet self-efficacy and
motivation for learning exerted a direct, positive effect on their online discussion score
and
course satisfaction. Increasing student interaction, and orientation of students to an
online
learning environment, have been important components to enhance a sense of
community in
online learning and improve student satisfaction (Brown, Schroeder, & Eaton, 2016;
Boz, &
Adnan, 2017).Online learning influence student satisfaction and perceived learning
(Gray, &
DiLoreto, 2016; Ashford, 2014); meanwhile, Cakir, Karademir, & Erdogdu (2018) found
a
significant correlation between the students' motivation levels and their online learning
experiences and satisfaction.
Library and information science courses, as well as interactive course influence
students’motivation and attitude (Combes, & Carroll, 2012; Turley, & Graham, 2019),
and
Alexander, Lynch, Rabinovich, & Knutel (2014) found out that online learning has
generated
high student satisfaction. Cole, Shelley, & Swartz (2014) revealed that there were no
statistically significant differences in the level of satisfaction based on gender, age, or
level of
study of online instruction, and Alqurashi (2019) found that learner-content interaction
was
the strongest and most significant predictor of student satisfaction, while online learning
self-
efficacy was the strongest and most significant predictor of perceived learning.
From the other point of view, Swart, & MacLeod (2020) showed that there was no
significant
difference in satisfaction between the online and face-to-face offerings, and Tratnik,
Urh, &
Jereb (2019) indicate that the students taking the face-to-face course were generally
more
satisfied with the course on several dimensions than their online counterparts. Hence, it
is
evidenced that online learning influence students' satisfaction at university. In
conclusion, the
investigation of the relationship between online learning and students' satisfaction, as
resulted
in the above research, is important. Therefore, based on the above literature review it is
hypothesized that:
H # 2: Students’ satisfaction is a function of online learning
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