Content uploaded by Adib Ahmadi Mahjoob
Author content
All content in this area was uploaded by Adib Ahmadi Mahjoob on Jan 04, 2022
Content may be subject to copyright.
Students' and Instructors' Views of an Online Graduate
Program of TEFL: Contribution of Motivation, Readiness,
and Barriers to Satisfaction
Adib Ahmadi Mahjoub1, Mahboubeh Taghizadeh2*
1 M.A. in TEFL, Department of Foreign Languages, Iran University of Science and
Technology, Tehran, Iran, adib.mahjub061@gmail.com
2* Assistant Professor in TEFL, Department of Foreign Languages, Iran University of
Science and Technology, Tehran, Iran, mah_taghizadeh@iust.ac.ir
Article Info
ABSTRACT
Article Type:
Research Article
Received:
05/09/2020
Accepted:
26/10/2020
Satisfaction of online students is one of the important issues of online
education, and identifying the predictors of satisfaction can improve the
effectiveness and success of online education. This study thus aimed to
investigate online students’ and instructors’ perceptions of the influential
factors such as motivation, e-readiness, and barriers for online satisfaction
and to determine the contribution of these factors to satisfaction. The
participants were 114 online graduate students of TEFL and 5 online
instructors at Iran University of Science and Technology. Four questionnaires
on learning barriers, motivation, e-readiness, and satisfaction along with 4
open-ended questions were administered in this descriptive study. The results
revealed that most of online students were satisfied with the online graduate
program of TEFL as online instructors provided students with lesson
summary and class material prior to the session, had interaction with students
through social networks, and held the online classes after office hours. The
results also showed that online students' motivation was mostly instrumental,
and some were not completely ready for online education. The results of
multiple regression analysis also indicated that the contribution of motivation
to satisfaction was higher than that of readiness and barriers. Online
instructors need to solve students’ educational problems to make them ready
for online instruction and to foster students’ motivation, which are influential
in enhancing their satisfaction with online learning programs.
Keywords: Barriers, Online Learning, Online Motivation, Readiness,
Satisfaction
Cite this article: Ahmadi Mahjoub, A., Taghizadeh, M. (2022). Students' and instructors'
views of an online graduate program of TEFL: Contribution of motivation, readiness, and
barriers to satisfaction. Journal of Modern Research in English Language Studies, 9(1), 73-
95. DOI: 10.30479/jmrels.2020.14020.1725.
© The Author(s).
Publisher: Imam Khomeini International University
74 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
1. Introduction
The development of technology, especially the Internet resulted in the
easy access of people to educational resources and the emergence of online
education, which could enhance the interaction between instructors and
students around the world (Carey, 2012; Casey, 2008) and the development
of a more cost-effective mode of education (Dibiase, 2000). Given the
advantages of online education, such as the flexibility of time and creating
the chance to work and study simultaneously, adult students have become the
main users of online education (Perry & Pilati, 2011; Young & Norgard,
2006). Flexibility and potential opportunities of online education have also
increased students' interest for continuing their higher education (Moore et
al., 2011), and satisfaction of these students is one of the important issues of
online education, which is found to be influenced by various factors,
including level of motivation (Chute et al., 1999), readiness for online
education (Choy et al., 2002), barriers to online education (Kim et al., 2005),
instructor of the online course (Neumann, 1994; Williams & Ceci, 1997),
timely response and accessibility of instructors to students (Hiltz, 1993),
efficacy beliefs of online students (Hodges, 2008; Pintrich & De Groot,
1990), and students’ access to reliable facilities (Belanger & Jordan, 2000).
Accordingly, the focus of this study was to investigate the influential factors,
including barriers, motivation, and readiness for online students’ satisfaction.
It is also argued that students' motivation is the most essential
determinant of their outcomes, affecting their satisfaction with online course
(Svanum & Aigner, 2011). According to Hegarty (2011), the number of
dropouts can be decreased by considering students' motivation and designing
online instruction in accordance with their needs and interest. Readiness for
online education as another factor influencing learner satisfaction is referred
to as the ability in the utilization of technologies on which the quality of
online program and the success of students is dependent (Choucri et al.,
2003). In addition, adult learners may be found dissatisfied and demotivated
for further education due to some barriers such as feeling deprived of
educational opportunities (Goulding, 2013), bad experiences at school and
low self-esteem (Whitnall & Thompson, 2007), low motivation (Eilers,
1989), conflict between work and family commitments (Evans, 1994), and
financial issues (Bird & Morgan, 2003). Given the literature on factors
contributing to online satisfaction, it seems there is no study focusing on the
satisfaction of Iranian online graduate students of TEFL and identifying the
elements contributing to their satisfaction. The objectives of this study were
thus to determine online students’ and instructors’ perceptions of influential
factors, such as barriers, motivation, and e-readiness for online satisfaction
and to determine the correlation and contribution of these factors to learner
satisfaction. Accordingly, the following research questions were formulated
in this study.
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …75
1.What are perceptions of graduate students and instructors of TEFL
for online satisfaction, motivation, readiness, and barriers?
2.To what extent do barriers, motivation, and e-readiness correlate to
satisfaction of online students?
3. To what extent do barriers, motivation, and e-readiness of online
students contribute to their satisfaction?
2. Literature Review
2.1. Satisfaction with Online Learning
Online students' satisfaction is one of the important issues of online
education, which results in the formation of a community of practice to
evaluate social, cognitive, and teaching presence in online education
(Garrison & Vaughan, 2008). According to Cocea and Weibelzahl (2006),
success of an online program can be determined based on the level of learner
satisfaction. It is also argued that students who are satisfied with their
learning experience are more likely to be successful in continuing their
education (Chang & Smith, 2008) as their satisfaction positively impacts the
effective learning and could increase students' competence, ensuring their
appropriate performance (Muylle et al., 2004).
Online students' satisfaction is found to be influenced by a number of
factors: level of students’ familiarity and the suitability of course content
(Beqiri et al., 2010), interaction between instructor and students (Marks et al.,
2005), instructor's performance, namely timely response and accessibility to
students (Hiltz, 1993; Neumann, 1994), and access to reliable facilities as
well as familiarity with applied technology in the course (Belanger & Jordan,
2000). Beqiri et al. (2010) found that the satisfaction level of graduate
students was higher than that of undergraduate students. On the other hand,
Rodriguez Robles (2006) showed that students' educational level and their
satisfaction are not related to each other and that educational level cannot be
considered a predictor of students’ satisfaction for adult online learners. It
was also found that the potential flexibility of class schedule is one of the
other important factors affecting learner satisfaction (Seaberry, 2008).
According to Dabbagh (2007), students' performance can be enhanced by
sufficient and appropriate instructional methods, support, course structure,
and design.
2.2. Motivation for Online Learning
Since motivation represents the commitment, engagement, and
success of students, some researchers (e.g., Guay et al., 2008; Lopéz-Pérez et
al., 2011) have stressed its significance in learning contexts. It is
recommended that online instructors should obtain a vivid understanding of
the motivation and reason for online students' participation (Fryer et al.,
76 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
2014) as motivation is the most essential determinant of students' learning
outcomes (e.g., Hegarty, 2011; López-Pérez et al., 2011), course persistence
(Hegarty, 2011), and satisfaction with instruction (Svanum & Aigner, 2011).
According to Lim and Kim (2003), when students are able to promote
their ability or when there are some motivating forces for their endeavors,
such as score and instructional feedback, they become motivated. Deci and
Ryan (1985), however, emphasized the significance of self-determination for
motivation, suggesting that individuals need to feel they have the right to
select among their favorite learning activities, which are related to their
intrinsic motivation (Cheng & Yeh, 2009). Another factor influencing
motivation is the age of online students. Chyung (2007) showed that older
learners have higher motivation compared to younger ones; however, Ke and
Xie (2009) found that students' age was not an effective indicator for adult
online students' satisfaction and performance.
It was found that online students' motivation and satisfaction are
significantly interrelated with each other (Lim, 2004), and in the online
learning environment, motivation has a crucial role in student participation
and overall academic experience (Xie & Huang, 2014). Likewise, Topal
(2016) notes that if learners have high readiness and motivation, then their
satisfaction would also be high. According to Bekele (2010), motivation and
learner satisfaction can be influenced by success factors, such as technology,
course, and support.
2.3. Readiness for Online Learning
Success in online education is dependent on the notion of e-readiness,
which refers to the ability to make use of the required technology and
multimedia of learning management system to increase the learning quality
(Choucri et al., 2003). According to Smith (2005), online learning readiness
can also be described as students' ability in time management, management
of their own learning, having intrinsic motivation, as well as knowing the
styles and experiences of self-learning. In addition, students' readiness is
comprised of their preferences for learning, learning style, learning strategies,
earlier learning experience, technological knowledge, and interest in course
material (Eastmond, 1994). Wang et al. (2009) argue that readiness is the
most significant factor for successful online learning, which can be evaluated
by assessing the technical knowledge of students and their ability in using
computer (Schreurs et al., 2008). Students' readiness can be measured by
estimating two other variables, which are technology and attributes of
students (Dray et al., 2011).
The online students' readiness was found to be influenced by learners'
satisfaction with their learning experience (Gunawardena & Duphorne,
2000); learning experience and comfort in using online education (Fogerson,
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …77
2005); and social and emotional development and self-control (Davis, 2006).
It is also argued that online students' readiness can positively impact their
perceived learning and interaction with their instructors and peers (Demir,
Kaymak, & Horzum, 2013), academic motivation and collaborative learning
(Leigh & Watkins, 2005), and experience, learning satisfaction, and self-
esteem (Fogerson, 2005).
2.4. Barriers to Online Learning
Cross (1981) classified student barriers into situational, institutional,
and dispositional barriers. Situational barriers are defined as a wide range of
conditions that make a sort of obstructions in the ability of adult learners in
the process of continuing their education (MacKeracher et al., 2006). Adult
students may face various situational barriers related to their expenses, family
life, wellbeing, work struggle, and transportation (Goto & Martin, 2009).
Another type of barrier is institutional barrier, which is related to the
techniques institutions apply to plan, deliver, and implement learning
activities (MacKeracher et al., 2006), resulting in adult students' emotional
damage (Osam et al., 2017).
Dispositional barriers, on the other hand, are related to attitudes and
feelings of students about their ability and capacity to enroll, participate, and
finish learning activities successfully (MacKeracher et al., 2006). The most
frequent types of dispositional barriers are found to be low self-confidence,
negative feelings about being an adult student or being too old, too busy, too
sick, not savvy enough, being poor, having limited time, having no interest
for higher education, lack of sufficient language abilities, and getting bored
with program (MacKeracher et al., 2006).
3. Methods
3.1. Participants
The participants of this study included 114 Iranian online graduate
students at Iran University of Science and Technology (IUST). They were 98
female and 16 male students whose age range were 22 to 50 years. All
participants were native speakers of Persian and were studying TEFL at MA
level and were selected from the online enrollees of 2016 to 2018. In
addition, five instructors teaching online graduate courses at the e-learning
campus of IUST were also the other participants of this study. Approval from
the e-learning campus was obtained to conduct the research and to collect the
required data from online students.
3.2. Instruments
The first instrument used in this study was a scale on the satisfaction
of online students developed by Wang (2003). This scale consisted of 17
78 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
items on four dimensions: learner interface (4 items), learning community (4
items), content (4 items), and personalization (4 items). The second
instrument was a scale on online motivation developed by Yoo and Huang
(2013), which consisted of 12 items with four dimensions, including intrinsic
(3 items), short-term extrinsic (3 items), long-term extrinsic (3 items), and
willingness to learn new technologies (2 items). The third instrument was a
questionnaire on the readiness of online students developed by Hung, Chou,
Chen, and Own (2010) with18 items on five dimensions, including self-directed
learning (5 items), learner control (3 items), motivation for learning (4 items),
computer/Internet self-efficacy (3 items), and online communication self-
efficacy (3 items).
The fourth instrument developed by Wang et al. (2017) was the elder
learning barriers scale consisting of 37 items with five dimensions of barriers,
including dispositional (items 1-9), informational (items 10-16), physical
(items 17-23), situational (items 24-32), and institutional (items 33-37). It
should be noted that all items of the scales were based on a 5-point Likert
scale with values ranging from 1 = strongly disagree) to 5 = strongly agree.
In addition to the questionnaires, four open-ended questions on satisfaction,
motivation, readiness, and barriers were administered to online students, and
the same questions were also asked from the online instructors in a semi-
structured interview.
3.3. Procedure
This study was conducted in June 2018 at the e-learning campus of
IUST. Reviewing the literature, the researchers first selected the
questionnaires on barriers, motivation, readiness, and satisfaction with online
learning. After that, the instruments were translated into participants’ first
language, and then back translation was conducted to check the accuracy of
the Persian versions of the instruments. In addition, the instruments were
piloted with 20 online graduate students, and based on their feedback, the
wording of some sentences was modified.
The questionnaires were administered through an online survey tool
(i.e., see googleforms.com). Prior to their administration, the researchers
provided the required information to the participants. Then, the survey link
was sent to the students through WhatsApp. To increase students' response
rate, the survey was available for a month in an online survey and follow-up
emails and messages were sent to the students as a reminder. An interview
with four questions was also conducted with five online instructors.
The reliability coefficients for the scales were elder learning barriers
(α = .88), motivation (α = 83), readiness (α = .843), and satisfaction (α = .91).
The reliability coefficients for categories of each questionnaire were as
follows: elder learning barriers (dispositional barriers = .728, informational
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …79
barriers = .853, physical barriers = .733, situational barriers = .645, &
institutional barriers = .795); motivation (intrinsic motivation = .925, short-
term extrinsic motivation = .659, long-term extrinsic motivation = .673, &
willingness to learn new technologies = .727); readiness (computer/internet
self-efficacy = .827; self-directed learning = .627; learner control = .605;
motivation for learning = .664; & online communication self-efficacy =
.754); and readiness (learner interface = .84, learning community = .84,
content = .80, & personalization = .82).
3.4. Data Analysis
To investigate online students' views of satisfaction, motivation,
readiness, and barriers, descriptive statistics of the categories and items of the
questionnaires were run. To determine the relationship between satisfaction
and motivation, readiness, and barriers of online students, Pearson product-
moment correlation was performed. In addition, multiple regression analysis
was run to investigate the contribution of motivation, readiness, and barriers
to satisfaction of online students. Finally, content analysis was done on
students’ and instructors' responses to the open-ended questions; that is,
patterns in their responses were identified, and then quantitative analysis was
conducted to indicate the frequency and percentage for each pattern.
4. Results and Discussion
4.1. Results
4.1.1. Learners’ Satisfaction with Online Education
Considering learners’ responses to the satisfaction scale, the highest
agreements were obtained by the following statements, respectively: ‘The e-
learning system enables me to learn the content I need’ (73.6%); ‘The e-
learning system is easy to use’ (71.9%); ‘The e-learning system provides
useful content’ (70.2%); ‘The e-learning system provides up-to-date content’
(68.5%); ‘The e-learning system is user-friendly’ (63.2%); and ‘The e-
learning system makes it easy for us to access the shared content from the
learning community’ (59.7%). On the contrary, students disagreed more with
the following statements: ‘The operation of the e-learning system is stable’
(50.9%); ‘The e-learning system provides sufficient content’ (35.8%); ‘The
e-learning system enables me to choose what I want to learn’ (34.7%); and
‘The e-learning system makes it easy for me to share what I learn with the
learning community’ (21.1%). The descriptive statistics of the categories of
learner satisfaction scale are provided in Table 1.
80 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
Table 1
Descriptive Statistics of Categories of Satisfaction Scale (N = 114)
Categories
Min
Max
M
SD
Learner Interface
1.00
5.00
3.39
.83
Learning Community
1.50
5.00
3.22
.82
Content
2.00
5.00
3.72
.72
Personalization
2.00
5.00
3.45
.71
As indicated in Table 1, the highest mean was 3.72, which was related
to the ‘content category’, whereas the category of the ‘learning community’
received the lowest mean (M = 3.22). Table 1 also shows that the responses
to the ‘personalization’ were the most homogeneous (SD = .71), while those
to the ‘learner interface’ category were the most heterogeneous (SD = .83).
An open-ended question, 'Are you satisfied with studying graduate
program of TEFL online? What factors can influence your satisfaction with
this program?' was administered to the participants whose responses are
hierarchically presented as follows: very satisfied (n = 4, % = 6.55), satisfied
(n = 41, % = 67.21), somewhat satisfied (n = 12, % = 19.67), and not
satisfied (n = 3, % = 4.91). The responses to the second part of the question
are as follows: ‘the possibility to work and study simultaneously’ (n = 43, %
= 70.49), ‘saving time’ (n = 29, % = 47.54), ‘archived files of classes’ (n =
26, % = 42.62), ‘no need for physical attendance’ (n = 25, % = 40.98),
‘saving expenses’ (n = 13, % = 21.31), ‘appropriate class schedule’ (n = 10,
% = 16.39), ‘interaction with instructors’ (n = 8, % = 13.11), ‘convenient
education’ (n = 6, % = 9.83), ‘knowledgeable instructors’ (n = 5, % = 8.19),
‘lower educational content’ (n = 4, % = 6.55), ‘being motivated by
instructors’ (n = 4, % = 6.55), ‘summary of the lessons’ (n = 3, % = 4.91),
‘no need for dormitory’ (n = 3, % = 4.91), ‘instructor feedback’ (n = 2, % =
3.27), ‘having education without stress’ (n = 2, % = 3.27), ‘environmental
support’ (n = 2, % = 3.27), ‘use of L1’ (n = 2, % = 3.27), ‘quality of classes’
(n = 1, % = 1.63), and ‘higher level of learning’ (n = 1, % = 1.63).
An interview question, ‘What are the factors that result in the
satisfaction of online students? What are your actions for the satisfaction of
the students?’ was asked from the five online instructors. The responses of
the instructors are hierarchically presented as follows: ‘less materials for
study’ (n = 4, % = 80), ‘understanding students’ problems and getting along
with them’ (n = 3, % = 60), ‘simple and understandable content’ (n = 3, % =
60), ‘less assignments’ (n = 2, % = 40), ‘holding classes after office hours’ (n
= 2, % = 40), ‘Use of L1’ (n = 2, % = 40), ‘sample questions’ (n = 2, % =
40), ‘lenient evaluation’ (n = 1, % = 20), ‘giving high scores’ (n = 1, % = 20),
and ‘punctuality’ (n = 1, % = 20).
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …81
The responses of the five instructors about their actions to promote
students’ satisfaction are as follows: ‘providing students with books,
pamphlets, pdf, and PowerPoint related to syllabus’ (n = 4, % = 80),
‘providing students with lesson summary’ (n = 4, % = 80), ‘simplifying the
content’ (n = 4, % = 80), ‘using L1’ (n = 4, % = 80), ‘uploading the class
material prior to beginning of the session or semester’ (n = 3, % = 60),
‘keeping in touch with students through social networks’ (n = 3, % = 60),
‘getting along with students and understanding them’ (n = 2, % = 40),
‘having an adjustable syllabus’ (n = 2, % = 40), ‘working on one book’ (n =
2, % = 40), ‘answering students’ comments and questions and providing
them with feedback’ (n = 2, % = 40), ‘defining the course objectives clearly’
(n = 1, % = 20), ‘providing students with exam’s sample questions’ (n = 1, %
= 20), ‘taking teacher assistant’ (n = 1, % = 20), and ‘scaffolding and
supporting students’ (n = 1, % = 20).
4.1.2. Learners’ Motivation for Online Education
With regard to the items of the motivation scale, the highest
agreements were obtained by the following statements, respectively: ‘I want
to apply what I learn in my job’ (98.3%); ‘I enjoy learning how to use new
technologies’ (87.7%); ‘The online learning experience would be useful for
my job’ (78.9%); ‘I expect to create or expand my professional network in
the field through the online program’ (78.9%); ‘Online learning might be a
pleasant experience for me’ (77.2%); ‘Online learning would improve my
performance in the job’ (73.7%); ‘I might enjoy online learning’ (70.2%);
and ‘The advantages of online learning outweigh its disadvantages’ (70.1%).
However, the students disagreed more with the following statements,
respectively: ‘I expect to have a career change after earning a degree from the
online program’ (31.6%), ‘I am tech-savvy’ (29.8%), ‘It might be fun to take
online courses’ (26.5%), and ‘I have a concrete career plan for what I will do
after earning a degree from the online program’ (26.1%). The descriptive
statistics of the categories of online learning motivation are provided in Table
2.
Table 2
Descriptive Statistics for Categories of Online Motivation Scale (N = 114)
Types of Motivation
Min
Max
M
SD
Intrinsic
1.50
5.00
3.90
.87
Short-Term Extrinsic
3.00
5.00
4.25
.56
Long-Term Extrinsic
2.00
5.00
3.79
.68
Willingness to Learn New Technologies
1.00
5.00
3.65
.86
With regard to different categories of motivation scale, the highest
mean was 4.25, which was related to the ‘short-term extrinsic’, whereas the
category of the ‘willingness to learn new technologies’ received the lowest
82 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
mean (M = 3.65). Table 2 also indicates that responses to the ‘short-term
extrinsic’ were the most homogeneous (SD = .56), while those to ‘intrinsic’
category were the most heterogeneous (SD = .87).
An open-ended question, ‘Who chooses online courses to continue
education? What are their goals and motivation for getting a master degree
using online courses?’ was administered to online students whose responses
are hierarchically presented as follows: ‘employed students’ (n = 57, % =
93.44), ‘students not having enough time to attend face to face classes’ (n =
30, % = 49.18), ‘students living in other cities’ (n = 20, % = 32.78), ‘married
students’ (n = 18, % = 29.50), ‘students getting low score on university
entrance exam’ (n = 14, % = 22.95), ‘those having personal problems’ (n = 8,
% = 13.11), ‘elderly individuals’ (n = 6, % = 9.83), and ‘disabled individuals’
(n = 2, % = 3.27). The students’ responses to their goals are hierarchically
presented as follows: ‘job promotion’ (n = 30, % = 49.18), ‘enhancing
knowledge’ (n = 25, % = 40.98), ‘continuing education at PhD level’ (n = 15,
% = 24.59), ‘convenient type of education’ (n = 13, % = 21.31), ‘saving time
and expenses’ (n = 11, % = 18.03), ‘having work and education at the same
time’ (n = 10, % = 16.39), ‘learning English’ (n = 8, % = 13.11), ‘personal
interest’ (n = 4, % = 6.55), ‘finding more job opportunities’ (n = 4, % =
6.55), ‘improving listening and writing skills’ (n = 3, % = 4.91),
‘immigration’ (n = 2, % = 3.27), ‘efficient use of time’ (n = 2, % = 3.27), and
‘force of family’ (n = 1, % = 1.63).
Another question, ‘Who chooses the online education? What are the
goals and motivation of these students?’ was asked from the five online
instructors to highlight the motivation of online students. The responses of
the instructors are hierarchically presented as follows: ‘employed and busy
students’ (n = 5, % = 100), ‘elderly students’ (n = 3, % = 60), ‘those who
could not get the required score for face to face classes’ (n = 3, % = 60),
‘those who are in need of a quick and unproblematic degree’ (n = 1, % = 20),
‘married students’ (n = 1, % = 20), and ‘students with personal problems’ (n
= 1, % = 20). The responses of the instructors about the goals and motivation
of online students are hierarchically presented as follows: ‘job promotion’ (n
= 5, %=100), ‘getting a university degree’ (n = 4, % = 80), ‘retirement with
higher salary’ (n = 4, % = 80), ‘continuing education at PhD level’ (n = 4, %
= 80), and ‘social competition’ (n = 2, % = 40).
4.1.3. Learners’ Readiness for Online Education
Considering learners’ responses to items of the readiness scale, the
highest agreements were obtained by the following statements, respectively:
‘I improve from my mistakes’ (98.3%); ‘I feel confident in expressing myself
(e.g., emotions and humor) through text’ (98.3%); ‘I like to share my ideas
with others’ (93%); ‘I set up my learning goals’ (87.7%); ‘I carry out my own
study plan’ (80.7%); ‘I feel confident in my knowledge and skills of how to
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …83
manage software for online learning’ (80.7%); and ‘I feel confident in
performing the basic functions of Microsoft Office programs (e.g., MS Word,
MS Excel, and MS PowerPoint)’ (79%). However, the highest disagreements
were found with the following statements: ‘I feel confident in using online
tools to effectively communicate with others’ (31.6%); ‘I manage time well’
(29.8%); ‘I seek assistance when facing learning problems’ (24.1%); and ‘I
feel confident in using the Internet to find or gather information for online
learning’ (23.6%). The descriptive statistics for the categories of online
learning readiness scale are provided in Table 3.
Table 3
Descriptive Statistics for Categories of Readiness Scale (N = 114)
Categories
Min
Max
M
SD
Computer/Internet Self-Efficacy
2.33
5.00
3.86
.49
Self-Directed Learning
2.60
4.80
3.74
.66
Learner Control
1.67
4.67
3.94
.80
Motivation for Learning
3.00
5.00
4.32
.56
Online Communication Self-Efficacy
2.33
5.00
3.90
.60
As shown in Table 3, the highest mean was related to ‘motivation for
learning’ (M = 4.32), while the ‘self-directed learning’ received the lowest
mean (M = 3.74). Table 3 also indicates that the responses to the
‘computer/Internet self-efficacy’ were the most homogeneous (SD = .49),
while the responses to ‘learner control’ category were the most
heterogeneous (SD = .80).
An open-ended question, ‘Do you consider yourself ready for online
education? What are the actions that the university can do to increase your e-
readiness?’ was administered to students and their responses are
hierarchically presented as follows: ‘completely ready’ (n = 4, % = 6.55),
‘somewhat ready’ (n = 41, % = 67.21), and ‘not ready’ (n = 15, % = 24.58).
Their responses to the second part of the question are as follows: ‘holding
workshop to increase students' computer literacy’ (n = 21, % = 34.42),
‘determining the readiness of students at the beginning of the program’ (n =
17, % = 27.86), ‘providing students with answers to frequently asked
questions (FAQ)’ (n = 15, % = 24.59), ‘informing students of the possible
problems and giving them solutions’ (n = 14, % = 22.95), ‘offering technical
support’ (n = 14, % = 22.95), ‘teaching students how to work with learning
management system (LMS)’ (n = 12, % = 19.67), and ‘solving students’
problems by knowledgeable professors’ (n = 5, % = 8.19).
The third question, ‘Do you consider the online students ready for
online education? What actions can be done to increase the online students’
readiness?’ was asked from the online instructors whose responses were as
follows: ‘ready’ (n = 3, % = 60), and ‘unready’ (n = 2, % = 40). Their
responses to the second part of the question are hierarchically presented as
84 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
follows: ‘offering technical support’ (n = 3, % = 60), ‘holding workshop to
increase their computer and the Internet literacy’ (n = 2, % = 40),
‘instructors’ guidance’ (n = 2, % = 40), ‘providing students with FAQ’ (n =
1, % = 20), and ‘providing students with a CD, including problems and their
solutions’ (n = 1, % = 20).
4.1.4. Learners’ Barriers to Online Education
In order to determine which barriers received more positive replies, and
which ones received few positive replies, the percentage of students’
agreement and disagreement about each barrier in the questionnaire was
calculated. The highest frequencies were obtained by the following barriers,
respectively: ‘occupying with home responsibilities’ (80.7%); ‘lack of time’
(75.5%); ‘too much information to choose for learning’ (49.1%); and
‘inappropriate class time’ (43.9%). However, the students did not consider
the following items as their barriers: ‘nothing I want or need to learn’
(100%); ‘being afraid to be too old to learn’ (94.7%); ‘not enjoying learning’
(91.2%); ‘not good at learning’ (91.2%); ‘mobility problems caused by
negative health condition’ (91.2%); ‘suffering from chronic illness’ (86%);
‘bad learning experience in the past’ (82.5%); ‘refusing learning how to do
activities because of physical insensitivity’ (82.4%); ‘suffering from failing
eyesight’ (79%); ‘being tired of going to university’ (77.2%); ‘poor academic
achievement in school’ (73.7%); and ‘no encouragement from family
members’ (68.4%). The average of the replies to items of each category was
used to determine the mean for each category. The descriptive statistics for
the categories of elder learning barriers scale are provided in Table 4.
Table 4
Descriptive Statistics of Categories of Learning Barriers Scale (N = 114)
Types of Barriers
Min
Max
M
SD
Dispositional Barriers
1.00
3.22
1.87
.51
Informational Barriers
1.29
5.00
2.86
.78
Physical Barriers
1.00
4.00
2.03
.60
Situational Barriers
1.00
3.78
2.67
.54
Institutional Barriers
1.00
5.00
2.67
.80
Table 4 shows that the highest mean was obtained by the
‘informational barriers’ (M = 2.86), while ‘dispositional barriers’ received the
lowest mean (M = 1.87). Table 4 also indicates that the responses to the
‘dispositional barriers’ were the most homogeneous (SD = .51), while those
to the ‘institutional barriers’ were the most heterogeneous (SD = .80).
An open-ended question, ‘What are your problems with online
education? What actions can instructors do to solve these problems?’ was
administered to highlight students' problems with online learning. The
students' responses are hierarchically presented as follows: ‘technical
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …85
problems’ (n = 28, % = 45.90), ‘lack of interaction between instructors and
students’ (n = 25, % = 40.98), ‘incomplete uploaded files’ (n = 18, % =
25.90), ‘frequent change of class time’ (n = 15, % = 24.59), ‘time-consuming
mode of interaction and participation (i.e., typing)’ (n = 9, % = 14.76),
‘mandatory attendance for final exams’ (n = 8, % = 13.11), ‘high university
fee’ (n = 8, % = 13.11), ‘no problem’ (n = 8, % = 13.11), ‘inappropriate
announcement of class or exam date’ (n = 7, % = 11.47), ‘too many
educational resources’ (n = 7, % = 11.47), 'too many classes a week’ (n = 6,
% = 9.83), ‘instructors' absenteeism and unpunctuality’ (n = 6, % = 9.83),
‘teacher-centered classes’ (n = 6, % = 9.83), ‘postponement of make-up
sessions’ (n = 6, % = 9.83), ‘receiving late or no reply from instructors’ (n =
5, % = 8.19), ‘having average above 17 for getting thesis’ (n = 5, % = 8.19),
‘interference between class and work time’ (n = 4, % = 6.55), and ‘lack of
skillful instructors for online classes’ (n = 3, % = 4.91).
Students' responses to instructors' roles in solving their problems are
hierarchically presented as follows: ‘considering more time for question and
answer sessions’ (n = 20, % = 32.78), ‘holding more face to face sessions’ (n
= 18, % = 29.50), ‘uploading class files prior to beginning of the semester’ (n
= 17, % = 27.86), ‘using teacher assistant for online classes’ (n = 15, % =
24.59), ‘offering training programs for teaching online courses’ (n = 14, % =
22.95), ‘using social network to compensate for limited online interaction’ (n
= 14, % = 22.95), ‘setting advice time by instructors for online students’ (n =
13, % = 21.31), ‘allowing students to speak in the class’ (n = 13, % = 21.31),
‘giving more time for assignments’ (n = 12, % = 19.67), ‘holding some
workshops on online education and computer use’ (n = 12, %= 19.67),
‘technical support’ (n = 12, % = 19.67), ‘holding online classes after office
hours’ (n = 11, % = 18.03), ‘providing online students with feedback’ (n =
10, % = 16.39), ‘making appropriate announcements about class or exam
dates’ (n = 5, % = 18.19), ‘clarifying objectives of online courses’ (n = 3, %
= 4.91), and ‘giving some scores for class attendance and participation’ (n =
1, % = 1.63).
The last question, ‘What are online graduate students' problems?
What measures do you take to solve these problems?’ was asked from the
online instructors whose responses are hierarchically presented as follows:
‘lack of time’ (n = 5, % = 100), ‘financial problem’ (n = 2, % = 40), ‘lack of
technological knowledge’ (n = 2, % = 40), ‘lack of face to face interaction’ (n
= 2, % = 40), ‘family problems’ (n = 2, % = 40), ‘students' boredom’ (n = 1,
% = 20), ‘holding classes during office hours’ (n = 1, % = 20), ‘lack of
content’ (n = 1, % = 20), ‘low language level’ (n = 1, % = 20), ‘high age’ (n
= 1, % = 20), ‘no familiarity with learning strategies’ (n = 1, % = 20), ‘old
infrastructure’ (n = 1, % = 20), ‘frequent change of class time’ (n = 1, % =
20), and ‘materialistic vision of policymakers’ (n = 1, % = 20). Instructors'
86 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
responses to their roles in solving students' problems are hierarchically
presented as follows: ‘teaching learning strategies’ (n = 2, % = 40),
‘answering all students' questions’ (n = 2, % = 40), ‘keeping in touch with
students’ (n = 2, % = 40), ‘simplifying the content’ (n = 1, % = 20),
‘highlighting important points’ (n = 1, % = 20), ‘enhancing instructors'
technical knowledge’ (n = 1, % = 20), ‘multi-tasking ability of instructor’ (n
= 1, % = 20), ‘using various tasks, content, and evaluation methods’ (n = 1,
% = 20), and ‘providing students with summary’ (n = 1, % = 20).
4.1.5. Contribution of Barriers, Motivation, and Readiness to Online
Satisfaction
To determine the relationship between online satisfaction, barriers,
motivation, and readiness, Pearson-product-moment correlation was
conducted. To ensure no violation of the assumptions (i.e., normality,
linearity, and homoscedasticity) related to this statistical analysis, preliminary
analyses were conducted. The results are indicated in Table 5.
Table 5
Pearson Product-Moment Correlation among Variables of This Study
Variables
1
2
3
4
1. Satisfaction
-.495**
.702**
.623**
2. Barriers
-.415**
-.378**
3. Motivation
.959**
4. Readiness
As indicated in Table 5, considering students’ satisfaction, the highest
correlation was obtained between satisfaction and motivation (r = .702),
while the lowest correlation was found between satisfaction and barriers (r =
-495). Table 5 also shows a high, positive correlation between readiness and
motivation (r = .959). Multiple regression analysis was used to determine the
extent to which barriers, motivation, and readiness could predict satisfaction
with online education. Preliminary analyses were conducted to ensure no
violation of the assumptions of normality, homoscedasticity, linearity, and
multicollinearity. The results of this technique are provided in Table 6.
Table 6
Summary of Multiple Regression Analysis
Model
R
R Square
Adjusted R Square
SD. The error of the
Estimate
1
.755a
.571
.559
.42064
a. Predictors: (Constant), Readiness, Barriers, Motivation
b. Dependent Variable: Satisfaction
As Table 6 indicates, readiness, barriers, and motivation contributed
to the model, explaining 57 percent of the variance in the satisfaction of
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …87
online students. To assess the statistical significance of these results, an
ANOVA was conducted. The result is shown in Table 7.
Table 7
ANOVA Results of Readiness, Motivation, and Barriers to Satisfaction of Online Students
Model
Sum of
Squares
df
Mean Square
F
p
1
Regression
25.862
3
8.621
48.721
.000b
Residual
19.463
110
.177
Total
45.325
113
a. Dependent Variable: Satisfaction
b. Predictors: (Constant), Readiness, Barriers, and Motivation
As shown in Table 7, the contribution of predictors (i.e., readiness,
barriers, and motivation) was statistically significant, producing R2 = .571,
F(3, 110) = 48.721, p = .000. To investigate the relative contribution of each
predictor to satisfaction, the coefficients of the predictors were calculated,
and the results are provided in Table 8.
Table 8 shows that the contribution of barriers to satisfaction was
21%, while that of motivation and readiness to satisfaction was 32% and
16%, respectively. Of these three variables, motivation indicated the most
significant contribution (beta = 1.166, p = .000), while readiness (beta = -
.583, p = .010) and barriers (beta = -.232, p = .001) contributed less to
satisfaction, respectively.
Table 8
Coefficients of Contribution of Readiness, Barriers, and Motivation to the Satisfaction of
Online Students
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
p
95.0%
Confidence
Interval for B
Correlations
Collinearity
Statistics
B
Std. Error
Beta
Lower
Bound
Upper
Bound
Zero
order
Partial
Part
Tolerance
VIF
1
(Constant)
2.240
.519
4.317
.00
1.212
3.269
Barriers
-.327
.097
-.232
-3.37
.00
-.519
-.135
-.49
-.306
-.21
.823
1.21
Motivation
1.330
.258
1.166
5.16
.00
.819
1.840
.702
.442
.322
.077
13.06
Readiness
.816
.311
.583
2.62
.01
1.432
.201
.623
.243
.164
.079
12.62
a. Dependent Variable: Satisfaction of Online Students
4.2. Discussion
The findings of this study showed that online students had more
motivation for updating their information and applying theoretical aspects
they learn in online classes in their teaching practice. The second influential
factor for their high level of motivation was related to technology
88 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
enhancement, which makes learning an interesting experience, allowing them
to continue their education easily without need for physical presence. The
highest mean was related to short-term extrinsic motivation, which might be
due to students' instrumental motivation for job promotion or retirement with
higher salary. On the contrary, the category of willingness to learn new
technologies received the lowest mean, which can be justified by students'
high age and lack of digital literacy and technical knowledge to utilize
technology for their learning.
The contribution of motivation to satisfaction supported the findings of
a number of studies (e.g., Guay et al., 2008; Lopéz-Pérez et al., 2011; Schunk
et al., 2008), indicating that motivation is a powerful construct, which can
influence all parts of education, including students’ satisfaction. Moreover,
the results are in line with those of other researchers (e.g., Bird & Morgan,
2003; Muilenburg & Berge, 2005; Simonson et al., 2006) who have found
that low students' motivation resulted in dissatisfaction with online education.
The results revealed that learners considered themselves ready for
sharing their ideas with others, which can be related to their teaching
experience as they think their experience is valuable and for which they can
receive compliment from their instructors. With regard to different categories
of readiness scale, the highest mean was related to motivation for learning,
which can be explained by the high motivation of students' high motivation
for achieving various educational goals. On the contrary, self-directed
learning category received the lowest mean, which can be due to students'
past experience with traditional methods of education and no experience with
online self-directed learning strategies and their implementation for their
learning.
A positive correlation was found between readiness and online
satisfaction and motivation. This might be due to the fact that when students
know what they want and what they need, they would be motivated to learn
new content and try to achieve their educational goals. This finding supports
that of Kaymak and Horzum (2013), who found that online students'
readiness can directly influence their attendance, dropout, and satisfaction.
Likewise, this finding supports that of other studies (e.g., Boeglin &
Campbell, 2002; Fogerson, 2005; Gunawardena & Duphorne, 2000;
Haverila, 2010; Horzum et al., 2015; Bird & Morgan, 2003), indicating that
students’ readiness is correlated with their satisfaction.
The results of the study showed a negative correlation between
satisfaction and barriers of online students, since if the barriers in online
education reduce, then the satisfaction can increase. The most frequent
problems of online students were found to be occupying with home
responsibilities and not having enough time, which might be due to the fact
that most online students are married and responsible for their family; hence,
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …89
home responsibilities have priority for them compared to education. In
addition, since these students are busy with their jobs, they do not have
enough time to study their courses deeply. This finding is in line with that of
some studies (e.g., Haber & Mills, 2008; Maguire, 2005; Schifter, 2002) in
which participants mostly addressed the barrier of time commitment in online
education. Technical problem was also the most cited problems in students'
responses, which is related to their high age and their lack of technical
knowledge, which is a prerequisite for online education. In addition, most
students considered instructors' late reply to their questions and emails as
another problem, which can be considered as another reason for the lack of
interaction between students and teachers.
The results of the study revealed that online students were most
satisfied with the content offered to them, which can be related to up-to-date
materials, educational clips, and summary of the books provided by the
instructors. In contrast, the category of learning community received the
lowest mean, which might be related to the traditional teaching methods
online instructors use and the lack of interaction among students. For
instance, sending email which could be a good way for communication is
replied late by instructors due to lack of time and their heavy workload,
resulting in less learner satisfaction with interaction in online classes.
5. Conclusion and Implications
This study aimed to investigate the factors contributing to satisfaction
of online graduate program of TEFL in the e-learning campus of IUST. The
findings revealed that most students were satisfied with this program and
stated some reasons, such as having the opportunity for both work and study,
saving time and expenses, appropriate class schedule, convenient form of
education, and no need to commute to university for their satisfaction with
online graduate program of TEFL. Among the three variables investigated in
this study, motivation was found to have the most contribution to online
students' satisfaction. Students’ motivation was also found to be mostly
instrumental as they preferred job promotion or retirement with higher salary,
although some were really interested in continuing their education and aimed
to increase their knowledge about teaching English language. In addition,
most students did not consider themselves completely ready for online
education and lacked the required Internet skills, which were found to be
removed by providing appropriate technical support as well as teaching
students how to work with LMS.
Online students’ satisfaction was mostly related to their motivation and
less related to their readiness and barriers. Therefore, much effort should be
made to motivate online students to promote their satisfaction level. It can
also be stated that the decrease in students’ barriers can result in an increase
90 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
in their satisfaction with online instruction. To enhance online learning
community, the instructors should also design appropriate collaborative
learning tasks and activities and in order to reduce their dissatisfaction with
online education, factors, such as barriers, motivation, and e-readiness, which
contribute to online satisfaction should not be neglected. The results of this
study can help online instructors to solve online students’ problems through
holding more face to face sessions, uploading the class files sooner to give
more opportunity for prestudy; using social networks to keep in touch with
the students to answer their questions, holding classes after the office hours,
and not changing the class time fixed at the beginning of the semester. To
increase the readiness of online students, policymakers and administrators
can determine the e-readiness level of students at the beginning of the
program and based on the results, each student can then be provided with the
required instruction. To enhance students’ satisfaction, online instructors are
suggested to provide feedback to students’ comments, use their first language
or give more explanation when the contents are vague, and define the course
objectives clearly at the beginning of the program.
More research is required on online students' demographic
characteristics, including age and gender to discover the possible effect of
these factors on online satisfaction, barriers, motivation, and readiness. In
addition, to investigate online students’ satisfaction, other variables, namely
self-regulation, self-directed learning, and Internet self-efficacy can be
considered. Another study can explore the satisfaction of undergraduate
students with online general English courses. Instead of open-ended
questions, future researchers can conduct an interview with students and
policymakers about factors contributing to online satisfaction, gaining more
insight into the roadblocks to online students' success.
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …91
References
Bekele, T. A. (2010). Motivation and satisfaction in internet-supported
learning environments: A review. Educational Technology & Society,
13(2), 116-127.
Belanger, F., & Jordan, D. H. (2000). Evaluation and implementation of
distance learning: Technologies, tools, and techniques. Idea
Publishing Group.
Beqiri, M. S., Chase, N. M., & Bishka, A. (2010). Online course delivery: An
empirical investigation factors affecting student satisfaction. Journal
of Education for Business, 85(2), 95-100.
Bird, J., & Morgan, C. (2003). Adults contemplating university study at a
distance: Issues, themes and concerns. The International Review of
Research in Open and Distance Learning, 4(1), 1-17.
Carey, K. (2012). Into the future with MOOCs. Chronicle of Higher
Education, 58(32), 60-68.
Casey, D. (2008). A journey to legitimacy: The historical development of
distance education through technology. TechTrends, 52(2), 45-51.
Chang, S. H., & Smith, R. A. (2008). The effectiveness of personal
interaction in a learner-centered paradigm distance education class
based on student satisfaction. Journal of Research on Technology in
Education, 40(4), 407-426.
Cheng, Y. C., & Yeh, H. T. (2009). From concepts of motivation to its
application in instructional design: Reconsidering motivation from an
instructional design perspective. British Journal of Educational
Technology, 40, 597-605.
Choucri, N., Maugis, V., Madnick, S., & Siegel, M. (2003). Global e-
readiness for what? MIT School of Management.
Choy, S., McNickle, C., & Clayton, C. (2002). Learner expectations and
experiences: An examination of student views of support in online
learning. Australian National Training Authority.
Chute, A. G., Thompson, M. M., & Hancock, B. W. (1999). The McGraw-
Hill handbook of distance learning. McGraw-Hill.
Chyung, Y. S. (2007). The invisible motivation of online adult learners
during contract learning. The Journal of Educators Online, 4, 1-22.
Cocea, M., & Weibelzahl, S. (2006). Motivation: Included or excluded from
E-learning. In K. Kinshuk, D. Sampson, J. Spector, & P. Isaias (Eds.),
Cognition and exploratory learning in digital age (pp.435-437).
IADIS Press.
Cross, K. P. (1981). Adults as learners: Increasing participation and
facilitating learning. Jossey-Bass publishing.
Dabbagh, N. (2007). The online learner: Characteristics and pedagogical
implications. Contemporary Issues in Technology and Teacher
Education, 7(3), 217-226.
92 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
Davis, T. S. B. (2006). Assessing online readiness: Perceptions of distance
learning stakeholders in three Oklahoma community colleges
(Unpublished doctoral dissertation), Oklahoma State University,
Stillwater, Oklahoma.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-
determination in human behavior. Plenum.
Dibiase, D. (2000). Is distance education a Faustian bargain? Journal of
Geography in Higher Education, 24(1), 130-136.
Dray, B. J., Lowenthal, P. R., Miszkiewicz, M. J., Ruiz-Primo, M. A., &
Marczynski, K. (2011). Developing an instrument to assess student
readiness for online learning: A validation study. Distance Education,
32(1), 29-47.
Eastmond, D. V. (1994). Adult distance study through computer
conferencing. Distance Education, 15(1), 128-152.
Eilers, M. L. (1989). Older adults and computer education: Not to have the
world a closed door. International Journal of Technology and Aging,
2(1), 56-76.
Evans, T. N. (1994). Understanding open and distance learners. Kogan Page.
Fogerson, D. L. (2005). Readiness factors contributing to participant
satisfaction in online higher education courses (Doctoral
dissertation). The University of Tennessee, Knoxville.
Fryer, L., Bovee, N., & Nakao, K. (2014). E-learning: Reasons students in
language learning courses don't want to. Computers & Education, 74,
26-36.
Garrison, D., & Vaughan, N., D. (2008). Blended learning in higher
education: Framework, principles and guidelines. Jossey-Bass.
Goto, S. T., & Martin, C. (2009). Psychology of success: Overcoming
barriers to pursuing further education. The Journal of Continuing
Higher Education, 57, 10-21.
Goulding, A. (2013). Older people learning through contemporary visual art:
Engagement and barriers. The International Journal of Art & Design
Education, 32(1), 18-32.
Guay, F., Ratelle, C. F., & Chanal, J. (2008). Optimal learning in optimal
contexts: The role of self-determination in education. Canadian
Psychology, 49(3), 233-240.
Gunawardena, C. N., & Duphorne, P. L. (2000). Predictors of learner
satisfaction in an academic computer conference, Distance Education,
21(1), 101-117.
Haber, J., & Mills, M. (2008). Perceptions of barriers concerning effective
online teaching and policies: Florida community college faculty.
Community College Journal of Research and Practice, 32(4-6), 266-
283.
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …93
Haverila, M. (2010). Factors related to perceived learning outcomes in an
undergraduate e-learning course. International Journal of Knowledge
and Learning, 6(4), 308-328.
Hegarty, N. (2011). Adult learners as graduate students: Underlying
motivation in completing graduate programs. The Journal of
Continuing Higher Education, 59, 146-151.
Hiltz, S. R. (1993). Correlates of learning in a virtual classroom.
International Journal of Man-Machine Studies, 39, 71-98.
Hodges, C. (2008). The form of class and representative actions in European
legal systems: A new framework for collective redress in Europe.
Oxford: Hart Publishing.
Horzum, M. B., Kaymak, Z. M., & Gungoren, O. C. (2015). Structural
equation modeling towards online learning readiness, academic
motivations, and perceived learning. Educational Sciences: Theory &
Practice. 15(3), 759-770.
Hung, M., Chou, C., Chen, C., & Own, Z. (2010). Learner readiness for
online learning: Scale development and student perceptions.
Computers & Education, 55, 1080-1090.
Kaymak, D. A., & Horzum, M. B. (2013). Relationship between online
learning readiness and structure and interaction of online learning
students. Educational Sciences: Theory and Practice, 13(3), 1792-
1797.
Ke, F. F., & Xie, K. (2009). Toward deep learning for adult students in online
courses. Internet and Higher Education, 12, 136-145.
Kim, K. J., Liu, S., & Bonk, C. J. (2005). Online MBA students' perceptions
of online learning: Benefits, challenges, and suggestions. The Internet
and Higher Education, 8(4), 335-344.
Leigh, D., & Watkins, R. (2005). E-learner success: Validating a self-
assessment of learner readiness for online training, Research-to-
Practice Conference Proceedings, Midwest United States.
Lim, D. H. (2004). Cross-cultural differences in online learning motivation.
Educational Media International, 41(2), 163-173.
Lim, D. H., & Kim, H. J. (2003). Motivation and learner characteristics
affecting online learning and learning application. Journal of
Educational Technology Systems, 31, 423-439.
López-Pérez, V., Pérez-López, C., & Rodríguez-Ariza, L. (2011). Blended
learning in higher education: Students' perceptions and their relation
to outcomes. Computers & Education, 56(3), 818-826.
MacKeracher, D., Suart, T., & Potter, J. (2006). State of the field report:
Barriers to participation in adult learning. Canadian Council of
Learning.
94 Journal of Modern Research in English Language Studies 9(1), 73-95, (2022)
Maguire, L. L. (2005). Literature review: Faculty participation in online
distance education: Barriers and motivators. Online Journal of
Distance Learning Administration, 8(1), 67-83.
Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation
model of predictors for effective online learning. Journal of
Management Education, 29, 531-563.
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). E-learning, online
learning, and distance learning environments: Are they the same?
Internet and Higher Education, 14, 129-135.
Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning:
A factor analytic study. Distance Education, 26(1), 29-48.
Muylle, S., Moenaert, R., Despontin, M. (2004). The conceptualization and
empirical validation of website user satisfaction. Information &
Management, 41(5), 543-560.
Neumann, F. E. (1994). Course work characteristics and students' satisfaction
with instructions. Journal of Instructional Psychology, 21(2), 14-19.
Osam, E. K., Bergman, M., & Cumberland, M, D., (2017). An integrative
literature review on the barriers impacting adult learners' return to
college. The Journal of Adult Learning, 28(2), 54-60.
Perry, E. H., & Pilati, M. L. (2011). Online learning. New Directions for
Teaching & Learning, 128, 95-104.
Pintrich, R. R., & DeGroot, E. V. (1990). Motivational and self-regulated
learning components of classroom academic performance. Journal of
Educational Psychology, 82, 33-40.
Rodriguez Robles, F. M. (2006). Learner characteristic, interaction and
support service variables as predictors of satisfaction in web-based
distance education. Dissertation Abstracts International, 67(7), 187-
196.
Schifter, C. (2002). Perception differences about participating in distance
education. Online Journal of Distance Learning Administration, 5(1),
76-94.
Schreurs, J., Sammour, G., & Ehlers, U. (2008). E-learning readiness analysis
(ERA): An e-health case study of e-learning readiness. Knowledge
and Learning, 4(5), 496-508.
Schunk, D. H., Pintrich, P. R., & Meece, J. L. (2008). Motivation in
education: Theory, research, and applications. Pearson Prentice Hall.
Seaberry, B. J. (2008). A case study of student and faculty satisfaction with
online courses at a community college (An unpublished doctoral
dissertation). University of California, Oakland, California.
Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2006). Teaching
and learning at a distance: Foundations of distance education.
Pearson Prentice Hall.
Ahmadi Mahjoub, Taghizadeh / Students’ and instructors’ view of an online …95
Smith, P. J. (2005). Learning preferences and readiness for online learning.
Educational Psychology: An International Journal of Experimental
Educational Psychology, 25(1), 3-12.
Svanum, S., & Aigner, C. (2011). The influences of course effort, mastery
and performance goals, grade expectancies, and earned course grades
on student ratings of course satisfaction. British Journal of
Educational Psychology, 81, 667-679.
Topal, A. D. (2016). Examination of university students' level of satisfaction
and readiness for e-courses and the relationship between them.
European Journal of Contemporary Education, 15(1), 38-49.
Wang, Q., Zhu, Z., Chen, L. & Yan, H. (2009). E-learning in China. Campus-
Wide Information. Systems, 26, 47-61.
Wang, R., Donder, D. L., De Backer, F., Shihua, L., Honghui, P., Thomas,
V., & Lombaerts, K. (2017). Development and validation of the elder
learning barriers scale among older Chinese adults. The International
Journal of Aging and Human Development, 1(18), 157-171.
Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous
electronic learning systems. Information & Management 41, 75-86.
Whitnall, A., & Thompson, V. (2007). Older people and lifelong learning:
Choices and experiences. Economic and Social Resource Council.
Williams, W. M., & Ceci, S. J. (1997). How'm I doing? Problems with
student ratings of instructors and courses. Change, 29, 12-23.
Xie, K., & Huang, K. (2014). The role of beliefs and motivation in
asynchronous online learning in college-level classes. Journal of
Educational Computing Research, 50(3), 315-341.
Yoo, S. J., & Huang, W. D. (2013). Engaging online adult learners in higher
education: Motivational factors impacted by gender, age, and prior
experiences. The Journal of Continuing Higher Education, 61(3),
151-164.
Young, A., & Norgard, C. (2006). Assessing the quality of online courses
from the students' perspective. Internet and Higher Education, 9, 107-
115.
