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367
© 2021 by the authors; licensee Asian Online Journal Publishing Group
Journal of Education and e-Learning Research
Vol. 8, No. 4, 367-374, 2021
ISSN(E) 2410-9991 / ISSN(P) 2518-0169
DOI: 10.20448/journal.509.2021.84.367.374
© 2021 by the authors; licensee Asian Online Journal Publishing Group
Beyond Conventional Classroom Learning: Linking Emotions and Self-Efficacy to
Academic Achievement and Satisfaction with Online Learning during the COVID-
19 Pandemic
Chooi-Seong Lai1
Kah-Mun Au2
Chee-Seong Low3
(
Corresponding Author)
1,3Department of Social Science, Tunku Abdul Rahman University College, Malaysia.
1Email: laics@tarc.edu.my Tel: +6016 899816
3Email: lowcs@tarc.edu.my Tel: +6016 6650022
2Tunku Abdul Rahman University College, Malaysia.
2Email: p3698@tarc.edu.my Tel: +6017 3879211
Abstract
This study examined the impact of online learning on students’ mental well-being, distress,
anxiety, academic achievement and satisfaction with online learning. A total of 915
undergraduates in Malaysia and Indonesia responded to the Google Classroom Evaluation survey.
Results indicated that students’ mental well-being was negatively impacted by financial
difficulties, concern that their physical health may suffer due to exposure to Covid-19, and a lack
of motivation for online learning. Analysis of emotional factors revealed distress and feelings of
nervousness when classes were conducted online. Overall, participants were satisfied with the
online learning experience, as was evident from the academic achievement in courses conducted
with above average CGPA. Multiple regression analysis indicated that distress, anxiety and self-
efficacy in online learning were significant predictors of satisfaction with online learning, with
self-efficacy in online learning making the largest contribution. However, academic achievement
was not a significant predictor of satisfaction with online learning. It is recommended that higher
learning institutions provide counselling, behavioral adaptations and study techniques to support
students in managing their distress and anxiety surrounding online learning, which is anticipated
to continue throughout 2021 due to the increase in virus cases.
Keywords: Mental well-being, Distress, Anxiety, Academic achievement, Self-efficacy in online learning, Satisfaction with online learning.
Citation | Chooi-Seong Lai; Kah-Mun Au; Chee-Seong Low (2021).
Beyond Conventional Classroom Learning: Linking Emotions and
Self-Efficacy to Academic Achievement and Satisfaction with Online
Learning during COVID-19 Pandemic. Journal of Education and e-
Learning Research, 8(4): 367-374.
History:
Received: 4 August 2021
Revised: 7 September 2021
Accepted: 12 October 2021
Published: 9 November 2021
Licensed: This work is licensed under a Creative Commons
Attribution 3.0 License
Publisher: Asian Online Journal Publishing Group
Acknowledgement: All authors contributed to the conception and design of
the study.
Funding: This study received no specific financial support.
Competing Interests: The authors declare that they have no conflict of
interests.
Transparency: The authors confirm that the manuscript is an honest,
accurate, and transparent account of the study was reported; that no vital
features of the study have been omitted; and that any discrepancies from the
study as planned have been explained.
Ethical: This study follows all ethical practices during writing.
Contents
1. Introduction .................................................................................................................................................................................... 368
2. Methods ........................................................................................................................................................................................... 370
3. Findings ........................................................................................................................................................................................... 371
4. Discussion ........................................................................................................................................................................................ 372
5. Conclusion ....................................................................................................................................................................................... 373
References ............................................................................................................................................................................................ 373
Journal of Education and e-Learning Research, 2021, 8(4): 367-374
368
© 2021 by the authors; licensee Asian Online Journal Publishing Group
Contribution of this paper to the literature
This study contributes to existing literature by confirming that distress, anxiety and self-
efficacy in online learning were significant predictors of satisfaction with online learning of
university students in Malaysia and Indonesia. Students’ mental well-being was also negatively
impacted by financial difficulties and a lack of motivation for online learning.
1. Introduction
In October 2020, the number of Covid-19 cases in Malaysia increased drastically. This happened despite an
earlier decrease in the middle of 2020, after the initial lockdown was lifted. The Malaysian government responded
by placing many areas under a Conditional Movement Control Order (CMCO), aimed at controlling the viral outbreak
by controlling the movement of people. The implemented measures included quarantining infected people, social
distancing, the compulsory wearing of face masks and limiting the number of people in a car. Thus, the general
Malaysian population was once again confined to social isolation: employees were working from home, though
some were allowed to return to their workplace for a maximum of 4 hours per day, kindergartens, primary schools,
secondary schools and tertiary institutions were closed, and lessons were delivered online to ensure the continuity
of education. Traditional examinations were also replaced with other forms of assessment. Although online
teaching allows for a degree of flexibility, in which students can learn anywhere and at any time, every form of
education (face-to-face classes, laboratory practical, projects and internships) was affected by the Covid-19
pandemic. The pandemic has changed the education landscape, and online learning has become the new normal to
the extent that it has revolutionized teaching and learning in the higher education sector. It is certainly undeniable
that online learning has been the most sensible decision and probably the only option during this challenging time.
The shift to online learning was made at very short notice. While every effort was made to ensure that
teaching materials and assessments were available for online delivery of courses to run efficiently and effectively,
the higher education sector has also had to ensure that students were ready for online learning (Dhawan, 2020).
Students must cope with the fact that in online learning, time management, self-discipline, and proactive learning
are even more critical. In addition, students also face challenges such as access to internet, internet connectivity,
additional costs incurred for better internet access, lack of a sufficiently functional laptop, and computer literacy
and skills (Sundarasen et al., 2020). Students are also concerned about the efficiency and accountability of online
lessons and assessments. All these factors have significant impacts on students’ mental health and well-being, in
the form of distress and anxiety (Son, Hegde, Smith, Wang, & Sasangohar, 2020).
Nonetheless, there are also many students in Higher Learning Institutions (HLIs) who are skilled in
information and communication technology (ICT) and have adapted well to online learning environments, because
they allow learning to take place at home or anywhere via computers or mobile devices and students do not need to
travel to campus to attend physical classes (Gupta & Pathania, 2021). Generation Z, who are perceived to have
higher self-efficacy in technology, may be able to cope with online learning materials and may be satisfied with the
methods of online delivery and assessments.
The purpose of this study is to examine the impact of online learning on students’ levels of distress and
anxiety, including the positive factors of students’ achievement and satisfaction. To date, all the studies on the
impact of COVID-19 have concentrated on the negative emotions, and only a few (not from Malaysia and
Indonesia) have examined the impact of students' self-efficacy and academic achievement and satisfaction on the
effectiveness of online learning during this pandemic period.
1.1. Anxiety and Distress Related to Online Learning
Around the world, many studies on the impact of the COVID-19 pandemic on students in higher education
were conducted in 2020, at the peak of the crisis. Results of studies conducted in Indonesia (Irawan, Dwisona, &
Lestari, 2020; Rohman, Marji, Sugandi, & Nurhadi, 2020), China (Cao et al., 2020), Germany (Bäuerle et al., 2020),
Switzerland (Shanahan et al., 2020), Jordan (Al-Tammemi, Akour, & Alfalah, 2020), Bangladesh (Akhtarul-Islam,
Barna, Raihan, Khan, & Hossain, 2020; Hasan & Bao, 2020) and the USA (Son et al., 2020) all reported increased
levels of generalized anxiety, depression and distress. Specifically, factors causing higher levels of psychological
distress which were positively associated with students’ anxiety symptoms were problems with online learning,
including ineffective study plans and lack of technological skills, and academic delays. Additionally, Cao et al.
(2020) reported that Chinese college students' anxiety regarding the pandemic was significantly associated with
their place of residence, source of parental income, whether they lived with their parents, and whether any of their
relatives or acquaintances was infected with COVID-19.
Online learning, web-based learning, and distance learning are terms often used interchangeably in the
literature (Moore, Dickson-Deane, & Galyen, 2011). Distance learning or online learning have generally been
associated with reported symptoms of anxiety, distress and resistance (Ajmal & Ahmad, 2019; Kwaah & Essilfie,
2017; Saadé, Kira, Mak, & Nebebe, 2017; Tuncay & Uzunboylu, 2010). Such findings have also been discussed
recently within the literature.
Son et al. (2020) indicated that COVID-19 had increased students’ level of fear and worry about their own
health and the health of their loved ones and had led to increased difficulty in concentrating on academic work due
to various sources of distraction, such as family members and household chores at home; they also found that online
classes were subject to higher levels of distraction due to the lack of direct interactions and the necessity of paying
prolonged attention to a computer screen. The sudden changes in the syllabus, the quality of the classes, technical
issues with online applications, and the difficulty of learning online all served to increase students’ academic
workload and required them to increase their own efforts to catch up with online classes and class projects given
the lack of in-person support from instructors or teaching assistants.
Students also experienced increased boredom and considerable anxiety during research courses, as well as
emotional disturbances indicated by changes in mood caused by too many assignments that were considered
ineffective. It was more difficult to achieve an understanding of the topics because the communication process in
online methods was less effective, especially in practical courses, in which the process of communication and
Journal of Education and e-Learning Research, 2021, 8(4): 367-374
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© 2021 by the authors; licensee Asian Online Journal Publishing Group
discussion both with lecturers and fellow students traditionally place an important role. Students from low-income
demographics were also financially burdened, as they had to increase their expenditure on internet bandwidth to be
able to take part in online learning (Irawan et al., 2020; Rohman et al., 2020).
Evidence from several studies has indicated a significant gender difference in the levels of anxiety experienced.
Ajmal and Ahmad (2019); Kwaah and Essilfie (2017); Saadé et al. (2017) reported that male students experienced
slightly higher anxiety levels toward distance learning as compared to female students, while Sundarasen et al.
(2020) found that the anxiety level was higher among female students than among male students. Factors that
caused students anxiety included a lack of communication with tutors, poor feedback systems, a lack of interaction
with classmates, and a lack of knowledge and understanding of the course content. Students who lived alone
experienced the highest anxiety levels compared to those living with family and friends. Other contributing factors
to academic anxiety were multiple and continuous alternative assignments without flexible deadlines and
uncertainty regarding their exams; those students who were graduating felt helpless in their plans to launch their
careers.
The unanticipated shift to online learning also caused additional stress to students due to contributing factors
such as unstable internet connection, extra financial burden for internet quota, lack of readiness for the new
learning method, and difficulties with time management and focusing while learning through a computer for a long
period of time (Amir et al., 2020). The most commonly highlighted stressors were predominantly financial
constraints, poor internet connection, uncertainty about the future due to COVID-19, having to adjust to remote
learning and being isolated from their friends, which created undue frustration, anger, resentment and, ultimately,
anxiety. Some students also reported having to use their mobile phones for online classes, and that the need to
juggle household chores and take care of siblings while concurrently attending online classes further contributed to
stress and health issues (Sundarasen et al., 2020). These issues and worries caused disruptions to their sleep
patterns. Son et al. (2020) added that factors such as loneliness, insecurity or uncertainty, powerlessness or
hopelessness, and concerns about academic achievement led to depressive thoughts in some participants, and a
small number stated that the pandemic had led them to suicidal thoughts. A study on the impact of COVID-19 by
Bäuerle et al. (2020) specifically reported that respondents aged between 18 and 24 had a high prevalence of
generalized anxiety symptoms, depression symptoms, psychological distress and COVID-19 related fear. Shanahan
et al. (2020) also reported that pre-pandemic social stressors such as bully victimization, stressful life events and
feelings of social exclusion significantly predicted during-pandemic emotional distress on the same age group.
During-pandemic lifestyle and economic disruption and feelings of hopelessness were also strongly associated with
emotional distress in these young adults. These studies indicated that young adults have been more susceptible to
distress and anxiety during this pandemic period.
1.2. Satisfaction with Online Learning
Student satisfaction has been defined as the learner’s perception of the value of their educational experiences in
an educational setting (Roach & Lemasters, 2006). The most recently published study on Malaysian students’
satisfaction with the e-learning system was conducted by Shanahan et al. (2020); Raime, Shamsudin, Hashim, and
Rahman (2020) and was supported by the earlier study conducted by Goh, Leong, Kasmin, Hii, and Tan (2017) who
concluded that user satisfaction has a significant positive impact on the success of the e-learning portal for both
male and female respondents. Factors such as interaction with peers, interaction with and feedback from
instructors, and course design all led to better learning outcomes and satisfaction; and they further concluded that
the e-learning portal not only facilitates the learning process among students, but also provides socioemotional
support in a computer-mediated learning environment.
Other recent studies on student satisfaction with online learning have been conducted in South Korea and India
(Almusharraf & Khahro, 2020), China (Chen, Jin, Liang, & Liu, 2021) and Saudi Arabia (Almusharraf & Khahro,
2020). All the findings were similar and suggested that students’ perceived learning in online classes has a positive
influence on student satisfaction. The interactive quality (learner participation or engagement, practice feedback,
instructor support), service quality (course management, assessment plans, teaching pedagogies, course learning
outcome achievement) and availability of the online teaching platform all had significant positive effects on student
satisfaction with the online mode of lesson delivery.
Studies on the specific effectiveness of the e-learning platform Google Classroom have been conducted by
Gupta and Pathania (2021) in India, Heggart and Yoo (2018) in Australia, Al-Maroof and Al-Emran (2018) in
Oman, Abid Azhar and Iqbal (2018) in Pakistan, and Basher (2017) in Saudi Arabia. General feedback from student
users of the Google Classroom platform indicated both effectiveness and ease of use (not boring nor a waste of
time) in a relaxed learning atmosphere, and a high sense of students’ satisfaction and achievement. Students are
highly motivated to incorporate such pedagogical tools in their learning process, and they display satisfaction with
the uploaded course content; learning is flexible, as they can work at their own pace, and students are able to voice
their feedback and contributions in a way they feel comfortable with Gupta and Pathania (2021); Al-Maroof and Al-
Emran (2018); Heggart and Yoo (2018). On the other hand, the feedback from instructors using the Google
Classroom platform is mixed. The positive conclusions are that the Google Classroom platform did increase
student participation and learning, and improved classroom dynamics and academic achievement. Instructors also
indicated an improvement in the planning, execution, and evaluation of the course delivery and in academic
achievement with computers (Basher, 2017; Heggart & Yoo, 2018).
Instructors’ negative feedback on the effectiveness of the Google Classroom platform was focused on the lack of
a user-friendly interface as the main reason for its inefficiency; hence, they used the platform only for document
management and basic classroom management, without it having a significant impact on teaching methodologies
(Abid Azhar & Iqbal, 2018). Both students and instructors found the messages, discussions and activities posted in
the Google Classroom stream inefficient, as older messages moved further down the stream as more messages were
added and students could not have a meaningful learning experience when instructors shared their course
resources (Heggart & Yoo, 2018).
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© 2021 by the authors; licensee Asian Online Journal Publishing Group
1.3. Self-Efficacy in Online Learning
It is important to note that even before the COVID-19 pandemic, many Malaysian HLIs had already
incorporated e-learning, distance learning and blended learning in some common courses across faculties and
postgraduate programs. And considering that education has become increasingly borderless, particularly after the
COVID-19 pandemic, this mode of teaching delivery will continue to be employed. Thus, HLIs in Malaysia must
ensure that their e-learning portal is operational 24/7, provides up-to-date, error-free and high-quality
information, offers quality content, well-organized data and has a user-friendly design, so that students can
continue to learn effectively online. The other factor that HLIs must consider is student self-efficacy in online
learning, as not every student is comfortable using an e-learning portal, regardless of how user-friendly its design
may be (Almusharraf & Khahro, 2020). Self-efficacy refers to “people’s judgments of their capabilities to organize
and execute a course of action required to attain designated types of performances” (Bandura, 1982). According to
Taipjutorus, Hansen, and Brown (2012), self-efficacy in online learning is not only a good predictor of learners’
academic outcomes, but efficacious learners also tend to persist, cope, and adapt well, even when they have no prior
experience.
The most significant predictor of perceived satisfaction with online learning is computer self-efficacy (learners’
confidence in their ability to use computers and other types of technology), followed by self-efficacy in online
learning, prior online learning experience, number of online courses, and finally self-confidence with learner’s
control (Alqurashi, 2016; Landrum, 2020; Lee, 2015; Shen, Cho, Tsai, & Marra, 2013; Taipjutorus et al., 2012;
Zimmerman & Kulikowich, 2016). Additionally, Lee (2015), who measured self-efficacy in online learning 3 times in
a semester, concluded that students’ online technology self-efficacy improved with practice.
The aims of this study are to answer the following research questions: (1) What are the impacts of partial
lockdown or CMCO on students’ mental well-being with regard to their concerns about ICT facilities, ICT
hardware, motivation for online learning, physical health, living conditions and finances? (2) What are the impacts
of online learning on students’ distress, anxiety, academic achievement, and satisfaction in online learning? And (3)
What are the significant predictors of students’ satisfaction with online learning?
2. Methods
This is a cross-sectional survey study which involves collecting data from students of higher learning
institutions at one specific point in time (during the partial lockdown or CMCO period). The research design is
quantitative and data-oriented, whereby the collected information is analyzed with statistical techniques for the
description, explanation, and prediction of variables related to online learning. Information is collected through the
method of online survey questionnaires (Google Form) because all higher learning institutions are closed, and it is
inexpensive and requires a shorter time span. The Google Form will ensure that the voluntary survey participants
are able to give informed consent.
2.1. Samples and Procedures
This study is based on a secondary analysis of end-of-course evaluations completed through online surveys.
Data was collected from undergraduates between 18 and 24 years old from the public and private higher learning
institutions in Malaysia and Indonesia who volunteered to participate. Due to the difficulty of gaining a complete
sampling frame because of the large population and the possible lack of response to the Google Form, a
combination of convenience and snowball sampling was adopted in the data collection period. Convenience
sampling involves low cost and easy accessibility while snowball sampling involves having participants identify
other possible survey participants. This combination was used to ensure that the study achieved the response rate
needed for generalizability. Before this study was launched, approval from the University’s Research Ethics
Committee was obtained. Participants took part in the study on a voluntary basis without incentives.
Data were collected via Google Form from 11 January 2021 to 20 Feb 2021, a period of 6 weeks during which a
partial lockdown was in force in Malaysia and Indonesia, during which higher learning institutions were
conducting online classes. Nine hundred and fifteen (915) participants participated in this survey, of whom 325
(35.52%) were males and 590 (64.48%) were females. The age range of participants was from 17 to 41 years
(Mage=20.70, SD=1.91). The majority of the participants identified themselves as Malaysians (n=866) and the
balance (n=49) were from other countries. During the data collection period, 53 (5.79%), 375 (40.98), and 487
(53.22%) participants were pursuing foundation, diploma, and bachelor’s studies, respectively, of whom 25 (2.73%)
were staying alone in a rented room, 18 (1.97%) were staying with friends in a rented residence, and 872 (95.30%)
were staying with family.
2.2. Research Instruments
2.2.1. Kessler Psychological Distress Scale (K6)
The Kessler Psychological Distress Scale (K6) is a 6-item self-report instrument designed to measure
nonspecific psychological distress. Participants were asked to respond to each of the items using a 5-point scale
with response options ranging from 1 (All of the time) to 5 (None of the time). The K6 is one of the most widely used
indicators of psychological distress, with well-established reliability ( = .84) (Mewton et al., 2016).
2.2.2. Course Anxiety Scale
The Course Anxiety Scale is an instrument for measuring students’ perception of anxiety towards online
learning. It is a measure of the degree to which situations pertaining to online learning are appraised as anxiety-
inducing. The instrument comprises 18 items over three subscales – (a) computers, (b) the Internet, and (c) online
courses, with a 5-point response scale ranging from 1 (strongly disagree) to 5 (strongly agree). The overall internal
reliability for the course anxiety scale is high ( = .93). In addition, internal correlation coefficients for the three
subscales are also high – (a) computers ( = .89), (b) the Internet ( = .86), and (c) online courses ( = .90)
Journal of Education and e-Learning Research, 2021, 8(4): 367-374
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© 2021 by the authors; licensee Asian Online Journal Publishing Group
(Bolliger & Halupa, 2012). For the current study, only the subscale of online courses was utilized due to its relevancy
to the study.
2.2.3. Satisfaction with Online Learning Scale
The Satisfaction with Online Learning Scale (Roach & Lemasters, 2006) is a 13-item self-report measure of
participants’ level of satisfaction with online learning. In this study, participants responded to statements relating
to the content and delivery of the course using a 5-point response scale (ranging from 1=Strongly Disagree to
5=Strongly Agree).
2.2.4. Self-Efficacy Questionnaire for Online Learning
The Self-Efficacy Questionnaire for Online Learning (SeQoL) was developed by Shen et al. (2013) to measure
the multifaceted dimensions of self-efficacy in online learning. The SeQoL comprises 30 items, which measure five
dimensions of self-efficacy in online learning – (a)self-efficacy to complete an online course ( = .93), (b) self-
efficacy to interact socially with classmates ( = .92), (c) self-efficacy to handle tools in a course management
system ( = .93), (d) self-efficacy to interact with instructors in an online course ( = .94), and (6) self-efficacy to
interact with classmates for academic purposes ( = .93) (Tsai, Cho, Marra, & Shen, 2020). The SeQoL uses a 5-
point scale with response options ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The current study only
utilized the dimension of Self-Efficacy to Complete an Online Course, due to its relevancy. This dimension has an
internal consistency of .91 with 8 items.
2.2.5. Academic Achievement
Academic achievement was measured by students’ cumulative grade point average (CGPA) of the online
courses taken.
2.2.6. Impacts of Partial Lockdown (CMCO)
The effects of the partial lockdown (CMCO) on students’ mental well-being were evaluated through
demographic data focusing on statements such as (a) reliability of internet access, (b) use-worthiness or functioning
of personal computer, (c) financial difficulties, (d) motivation for online learning, (e) physical exercise, and (f) place
of residence. Participants responded to the questions using a 5-point response scale ranging from 1 (None of the
time) to 5 (All of the time).
3. Findings
Research Question 1: What are the impacts of Partial Lockdown (CMCO) on students’ mental well-being concerning ICT
facilities, ICT hardware, motivation to online learning, physical health, living condition and finances?
Overall, 79% of participants indicated that they had reliable internet access most/all of the time, with 21%
reporting that they had an unstable internet connection. In addition, 78% of participants claimed to have a
functioning laptop/ICT hardware most/all of the time to attend their online classes, with 22% reporting otherwise.
Furthermore, 40% of participants reported that they were motivated to learn online most/all of the time, while
59% reported being less motivated for online learning.
In examining the difficulties that students may be faced with during the pandemic, 43% of respondents
reported experiencing financial difficulties most/all of the time. On top of that, well over half of the participants
(69%) were concerned with their physical health in relation to exposure to Covid-19. On the other hand, 76% of
respondents reported satisfaction with their current living conditions. The complete data is summarized in Table 1.
Table-1. Impacts of Partial Lockdown/CMCO on Students’ Mental Well-being.
Items All of the time
None of the time
Some of the time
Most of the time
All of the time
I have reliable internet access
18 (1.97%)
178 (19.45%)
514 (56.17%)
205(22.40%)
My laptop/ICT hardware is functioning
well
19 (2.08%)
178 (19.45%)
440 (48.09%)
278 (30.38%)
I am motivated to learn online
170 (18.58%)
373 (40.77%)
260 (28.42%)
112 (12.24%)
I experience financial difficulties
148 (16.17%)
371 (40.55%)
298 (32.57%)
98 (10.71%)
I am concerned about my physical health
73 (7.98%)
211(23.06%)
307 (33.55%)
324 (35.41%)
I am satisfied with my living conditions
36 (3.93%)
177 (19.34%)
376 (41.09%)
326 (35.63%)
Note: n=915.
Research Question 2: What are the impacts of online learning on students’ distress, anxiety, academic achievement, and
satisfaction with online learning?
Table 2 revealed a slight level of distress (M=16.86, SD=5.21) experienced by participants when classes were
conducted online during the pandemic. In addition, the results also indicated a feeling of nervousness (M=17.07,
SD=4.43) towards online classes during the course of partial lockdown (CMCO). Nevertheless, participants were
generally satisfied with the online learning experience (M=46.37, SD=7.80). The satisfaction was evident in
students’ academic achievement for courses conducted online during the partial lockdown with an average CGPA
of 3.52 (SD=0.40).
Table-2. Descriptive statistics for student’s distress, anxiety, academic achievement and satisfaction with online learning.
Variable
Range
Mean
SD
Distress
6 – 30
16.86
5.21
Anxiety
6 – 30
17.07
4.43
Academic Achievement
0 – 4.0
3.52
0.40
Satisfaction with Online Learning
17 – 65
46.37
7.80
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Research Question 3: What are the significant predictors of students’ satisfaction with online learning?
To estimate the proportion of variance in satisfaction with online learning that can be accounted for by
distress, anxiety, academic achievement and self-efficacy in online learning, a standard multiple regression was
performed.
The results revealed significant and negative relationships between satisfaction with online learning and
distress (r= -.262, p<.01) and anxiety (r= -.436, p<.01); but significant and positive relationships between academic
achievement (r=.087, p<.01) and self-efficacy in online learning (r =.575, p<.01), as shown in Table 3. Multiple
regression analysis indicated that distress, anxiety and self-efficacy (β=-.072, p<.05; β= -.214, p<.05; β=.479, p<.05)
in online learning were significant predictors of satisfaction with online learning. However, academic achievement
was not a significant predictor of satisfaction with online learning. In combination, distress, anxiety and self-
efficacy in online learning accounted for a significant 38.5% of the variability in satisfaction with online learning, R2
= .385, adjusted R2 = .382, F (4, 910) = 142.41, p<.001, as shown in Table 4. By Cohen’s (1988) conventions, a
combined effect of this magnitude can be considered “large” (f2=.626). Unstandardized (B) and standardized ()
regression coefficients and squared semi-partial correlations (sr2) for each regression model are reported in Table 5.
Table-3. Correlational matrix between satisfaction with online learning, distress, anxiety, academic achievement and self-
efficacy in online learning.
No.
1
2
3
4
5
1
Satisfaction with Online Learning
-
2
Distress
-0.262**
-
3
Anxiety
-0.436**
0.417**
-
4
Academic Achievement
0.087**
-0.005
-0.068*
-
5
Self-Efficacy in Online Learning
0.575**
-0.212**
-0.405**
0.210**-
Note: *Correlation is significant at the 0.05 level (2-tailed).
**Correlation is significant at the 0.01 level (2-tailed).
Table-4. Model summary of regression analysis.
Model
R
R Square
Adjusted R Square
F
Sig.
1
0.620b
0.385
0.382
142.405
0.000b
a. Dependent Variable: Satisfaction with Online Learning.
b. Predictors: (Constant), Distress, Anxiety, Academic Achievement, Self-Efficacy in Online Learning.
Table-5. Unstandardized (B) and standardized () regression coefficients, and squared semi-partial correlations (sr2)
for each predictor in a regression model predicting satisfaction with online learning.
Variable
B
sr2
Distress
-0.108*
-0.072
0.007
Anxiety
-0.376**
-0.214
0.051
Self-Efficacy in Online Learning
0.883**
0.479
0.229
Academic achievement
-0.566
-0.029
0.001
Note: *p<.05
**p<.01.
4. Discussion
Of the 915 respondents, 59% reported feeling less motivated to learn online during this CMCO or partial
lockdown period. From the information collected, the lack of reliable internet access (21%) and functional laptop
devices (22%) may hinder online learning. These participants may be from the lower income households, who faced
difficulties in sustaining their family expenses during the pandemic, as 43% of participants reported experiencing
financial difficulties most/all of the time. Higher monthly internet expenses, extra costs for laptops and other ICT
hardware for online learning and living in areas with weak internet reception all added to students’ extra financial
burden. Lastly, the number of people living under the same roof also affected participants’ mental well-being and
hence motivation for online learning. The data collected indicated that 24% of respondents were dissatisfied with
their current living conditions, and 95% of the participants were living with their family with an average of 5
people in the home. This may be deemed as overcrowded, especially in the case of lower income households who
live in smaller homes, as a lack of privacy (little individual space) and too much noise can affect online learning
concentration. UNICEF has also noted that children from low-income families are at higher risk of dropping out of
school due to the combined financial and psychological impacts of the Covid-19 pandemic (Tan, 2020). More than
half of the participants (69%) were concerned about their physical health due to exposure to Covid-19, which added
to the negative mental well-being of the respondents.
The data analysis revealed that self-efficacy in online learning (β=.479, p <.05) is the most important predictor
of satisfaction with online learning, followed by anxiety (β=-.214, p <.05) and distress (β=-.072, p <.05) which
together explain 38.5% of variance in satisfaction with online learning. Students with higher self-efficacy in online
learning are more likely to experience online learning satisfaction. Similar results from studies on online learning
during the pandemic have been reported by Chen et al. (2021) in China, Fachmi, Alsolami, Sopacua, and Prajogo
(2021) in Indonesia, and Lin (2021) in Taiwan. Higher self-efficacy leading to higher satisfaction with online
learning was also reported by pre-pandemic studies conducted by Joo, Lim, and Kim (2013) in Korea, Artino
(2008); Wang, Shannon, and Ross (2013) and Zimmerman and Kulikowich (2016) in the U.S., and Ramsin and
Mayall (2019) in Thailand.
Students’ increased anxiety with online learning during the pandemic period has also been reported by Wang.,
Zhao, and Zhang (2020) in China, Fawaz and Samaha (2021) in Lebanon, Saddik et al. (2020) in United Arab
Emirates, Bahçecioğlu Turan, Özer, and Çiftçi (2021) in Turkey, Servidio, Bartolo, Palermiti, and Costabile (2021)
in Italy, Nishimura et al. (2021) in Japan, and Alonzi, La Torre, and Silverstein (2020) in Canada and the U.S.
Hasan and Bao (2020) confirmed that college students suffered psychological distress due to ineffective e-learning
systems and fear of academic year loss.
Journal of Education and e-Learning Research, 2021, 8(4): 367-374
373
© 2021 by the authors; licensee Asian Online Journal Publishing Group
Finally, studies conducted by Al-Dwaikat, Aldalaykeh, and Rababa (2020) and Al-Tammemi et al. (2020) in
Jordan, Hamza, Ewing, Heath, and Goldstein (2021) in Canada, and Hunt et al. (2021) in the U.S. all indicated that
students experienced psychological distress during the pandemic period, all of which are consistent with the result
of this study.
5. Conclusion
Heath experts globally have signaled their expectation of a prolonged course of Covid-19, due to the
emergence of new mutant strains of the virus. This indicates that tremendous uncertainties still lie ahead of us in
the education sector. Therefore, it is recommended that higher learning institutions collect comprehensive data on
students’ engagement and motivation in online learning, and monitor students’ mental and emotional well-being,
behavior and learning progress. Higher learning institutions can establish new ways of delivering quality education
to stimulate learning motivation in disengaged students, and adopt a flexible curriculum and other facilities to
support online learning, such as providing training to teach students about new technology, loaning laptops to
students in need, providing secure and conducive living spaces for effective learning (such as hostels). Finally they
should continuously conduct diagnostic assessments to identify students learning needs. These methods, in
combination with counselling and behavioral adaptation, can ease students’ distress and anxiety and will restore
students’ confidence and motivation in online learning and hence support them to complete their studies in this
difficult period.
References
Abid Azhar, K., & Iqbal, N. (2018). Effectiveness of google classroom: Teachers’ perceptions. Prizren Social Science Journal, 2(2), 52–66.
Ajmal, M., & Ahmad, S. (2019). Exploration of anxiety factors among students of distance learning: A case study of Allama Iqbal Open
University. Bulletin of Education and Research, 41(2), 67-78.
Akhtarul-Islam, M., Barna, S. D., Raihan, H., Khan, M. N. A., & Hossain, M. T. (2020). Depression and anxiety among university students
during the COVID-19 pandemic in Bangladesh: A web-based cross-sectional survey. PloS One, 15(8), e0238162. Available at:
https://doi.org/10.1371/journal.pone.0238162.
Al-Dwaikat, T. N., Aldalaykeh, M., & Rababa, M. (2020). The relationship between social networking sites usage and psychological distress
among undergraduate students during COVID-19 lockdown. Heliyon, 6(12), e05695. Available at:
https://doi.org/10.1016/j.heliyon.2020.e05695.
Al-Maroof, R. A. S., & Al-Emran, M. (2018). Students acceptance of google classroom: An exploratory study using PLS-SEM approach.
International Journal of Emerging Technologies in Learning, 13(6), 112–123. Available at: https://doi.org/10.3991/ijet.v13i06.8275.
Al-Tammemi, A. a. B., Akour, A., & Alfalah, L. (2020). Is it just about physical health? an online cross-sectional study exploring the
psychological distress among university students in jordan in the midst of COVID-19 pandemic. Frontiers in Psychology, 11, 562213-
562213. Available at: https://doi.org/10.1101/2020.05.14.20102343.
Almusharraf, N., & Khahro, S. (2020). Students satisfaction with online learning experiences during the COVID-19 pandemic. International
Journal of Emerging Technologies in Learning (IJET), 15(21), 246-267. Available at: https://doi.org/10.3991/ijet.v15i21.15647.
Alonzi, S., La Torre, A., & Silverstein, M. W. (2020). The psychological impact of preexisting mental and physical health conditions during
the COVID-19 pandemic. Psychological Trauma, 12(S1), S236-S238. Available at: https://doi.org/10.1037/tra0000840.
Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research (CIER), 9(1),
45-52. Available at: https://doi.org/10.19030/cier.v9i1.9549.
Amir, L. R., Tanti, I., Maharani, D. A., Wimardhani, Y. S., Julia, V., Sulijaya, B., & Puspitawati, R. (2020). Student perspective of classroom
and distance learning during COVID-19 pandemic in the undergraduate dental study program Universitas Indonesia. BMC
Medical Education, 20(1), 1-8. Available at: https://doi.org/10.21203/rs.3.rs-42334/v2.
Artino, A. R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training. Journal of
Computer Assisted Learning, 24(3), 260-270. Available at: https://doi.org/10.1111/j.1365-2729.2007.00258.x.
Bahçecioğlu Turan, G., Özer, Z., & Çiftçi, B. (2021). Analysis of anxiety levels and attitudes of nursing students toward the nursing
profession during the COVID-19 pandemic. Perspectives in Psychiatric Care. Available at: https://doi.org/10.1111/ppc.12766.
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147. Available at:
https://doi.org/10.1037/0003-066x.37.2.122.
Basher, S. A. O. (2017). The impact of Google classroom application on the teaching efficiency of pre-teachers. International Journal Social
Sciences and Education, 7(2), 45-54.
Bäuerle, A., Teufel, M., Musche, V., Weismüller, B., Kohler, H., Hetkamp, M., . . . Skoda, E.-M. (2020). Increased generalized anxiety,
depression and distress during the COVID-19 pandemic: A cross-sectional study in Germany. Journal of Public Health, 42(4), 672-
678. Available at: https://doi.org/10.1093/pubmed/fdaa106.
Bolliger, D. U., & Halupa, C. (2012). Student perceptions of satisfaction and anxiety in an online doctoral program. Distance Education, 33(1),
81-98. Available at: https://doi.org/10.1080/01587919.2012.667961.
Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college
students in China. Psychiatry Res, 287, 112934-112934. Available at: https://doi.org/10.1016/j.psychres.2020.112934.
Chen, G., Jin, Y., Liang, W., & Liu, Y. (2021). Study on the influence of middle school students’ self-efficacy on the willingness to use online
learning platform. The International Journal of Electrical Engineering & Education, 0020720920984030. Available at:
https://doi.org/10.1177/0020720920984030.
Cohen, J. (1988). Statistical power analysis for the behavioural sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5-22.
Available at: https://doi.org/10.1177/0047239520934018.
Fachmi, Y. P., Alsolami, B., Sopacua, I. O., & Prajogo, W. (2021). The role of community of inquiry and self-efficacy on accounting students’
satisfaction in online learning environment. Jurnal Siasat Bisnis, 25(1), 1-16. Available at:
https://doi.org/10.20885/jsb.vol25.iss1.art1.
Fawaz, M., & Samaha, A. (2021). E-learning: Depression, anxiety, and stress symptomatology among lebanese university students during
COVID-19 quarantine. Nursing Forum (Hillsdale), 56(1), 52-57. Available at: https://doi.org/10.1111/nuf.12521.
Goh, C., Leong, C., Kasmin, K., Hii, P., & Tan, O. (2017). Students’ experiences, learning outcomes and satisfaction in e-learning. Journal of
E-learning and Knowledge Society, 13(2), 117–128.
Gupta, A., & Pathania, P. (2021). To study the impact of Google Classroom as a platform of learning and collaboration at the teacher
education level. Education and Information Technologies, 26(1), 843-857. Available at: https://doi.org/10.1007/s10639-020-10294-1.
Hamza, C. A., Ewing, L., Heath, N. L., & Goldstein, A. L. (2021). When social isolation is nothing new: A longitudinal study psychological
distress during COVID-19 among university students with and without preexisting mental health concerns. Canadian Psychology,
62(1), 20-30. Available at: https://doi.org/10.1037/cap0000255.
Hasan, N., & Bao, Y. (2020). Impact of “e-Learning crack-up” perception on psychological distress among college students during COVID-19
pandemic: A mediating role of “fear of academic year loss”. Children and Youth Services Review, 118, 105355. Available at:
https://doi.org/10.1016/j.childyouth.2020.105355.
Heggart, K., & Yoo, J. (2018). Getting the most from Google Classroom: A pedagogical framework for tertiary educators. Australian Journal
of Teacher Education, 43(3), 140-153. Available at: https://doi.org/10.14221/ajte.2018v43n3.9.
Journal of Education and e-Learning Research, 2021, 8(4): 367-374
374
© 2021 by the authors; licensee Asian Online Journal Publishing Group
Irawan, A. W., Dwisona, D., & Lestari, M. (2020). Psychological impacts of students on online learning during the pandemic COVID-19.
KONSELI: Jurnal Bimbingan dan Konseling (E-Journal), 7(1), 53-60. Available at: https://doi.org/10.24042/kons.v7i1.6389.
Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university
context. Computers & Education, 62, 149-158. Available at: https://doi.org/10.1016/j.compedu.2012.10.027.
Kwaah, C. Y., & Essilfie, G. (2017). Stress and coping strategies among distance education students at the University of Cape Coast, Ghana.
Turkish Online Journal of Distance Education, 18(3), 120-134. Available at: https://doi.org/10.17718/tojde.328942.
Landrum, B. (2020). Examining students’ confidence to learn online, self-regulation skills and perceptions of satisfaction and usefulness of
online classes. Online Learning, 24(3), 128-146. Available at: https://doi.org/10.24059/olj.v24i3.2066.
Lee, C.-Y. (2015). Changes in self-efficacy and task value in online learning. Distance Education, 36(1), 59-79. Available at:
https://doi.org/10.1080/01587919.2015.1019967.
Lin, T.-J. (2021). Exploring the differences in Taiwanese university students’ online learning task value, goal orientation, and self-efficacy
before and after the COVID-19 outbreak. The Asia-Pacific Education Researcher, 30(3), 191-203. Available at:
https://doi.org/10.1007/s40299-021-00553-1.
Mewton, L., Kessler, R. C., Slade, T., Hobbs, M. J., Brownhill, L., Birrell, L., . . . Chapman, C. (2016). The psychometric properties of the
Kessler Psychological Distress Scale (K6) in a general population sample of adolescents. Psychological Assessment, 28(10), 1232-1242.
Available at: https://doi.org/10.1037/pas0000239.
Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). E-Learning, online learning, and distance learning environments: Are they the same?
The Internet and Higher Education, 14(2), 129-135. Available at: https://doi.org/10.1016/j.iheduc.2010.10.001.
Nishimura, Y., Ochi, K., Tokumasu, K., Obika, M., Hagiya, H., Kataoka, H., & Otsuka, F. (2021). Impact of the COVID-19 pandemic on the
psychological distress of medical students in Japan: Cross-sectional survey study. Journal of Medical Internet Research, 23(2), e25232.
Available at: https://doi.org/10.2196/25232.
Raime, S., Shamsudin, M. F., Hashim, R. A., & Rahman, N. A. (2020). Students’ self-motivation and online learning students’ satisfaction
among UNITAR college students. Asian Journal of Research in Education and Social Sciences, 2(3), 62-71.
Ramsin, A., & Mayall, H. J. (2019). Assessing ESL learners’ online learning self-efficacy in thailand: Are they ready? Journal of Information
Technology Education, 18, 467-479. Available at: https://doi.org/10.28945/4452.
Roach, V., & Lemasters, L. (2006). Satisfaction with online learning: A comparative descriptive study. Journal of Interactive Online Learning,
5(3), 317-332.
Rohman, M., Marji, D. A. S., Sugandi, R. M., & Nurhadi, D. (2020). Online learning in higher education during covid-19 pandemic: students’
perceptions. Journal of Talent Development and Excellence, 12(2s), 3644-3651.
Saadé, R. G., Kira, D., Mak, T., & Nebebe, F. (2017). Anxiety and performance in online learning. Paper presented at the Proceedings of the
Informing Science and Information Technology Education Conference, Vietnam.
Saddik, B., Hussein, A., Sharif-Askari, F. S., Kheder, W., Temsah, M.-H., Koutaich, R. A., . . . Hamid, Q. (2020). Increased levels of anxiety
among medical and non-medical university students during the COVID-19 pandemic in the United Arab Emirates. Risk
Management and Healthcare Policy, 13, 2395–2406. Available at: https://doi.org/10.2147/rmhp.s273333.
Servidio, R., Bartolo, M. G., Palermiti, A. L., & Costabile, A. (2021). Fear of COVID-19, depression, anxiety, and their association with
internet addiction disorder in a sample of Italian students. Journal of Affective Disorders Reports, 4, 100097. Available at:
https://doi.org/10.1016/j.jadr.2021.100097.
Shanahan, L., Steinhoff, A., Bechtiger, L., Murray, A. L., Nivette, A., Hepp, U., & Eisner, M. (2020). Emotional distress in young adults
during the COVID-19 pandemic: Evidence of risk and resilience from a longitudinal cohort study. Psychological Medicine, 1-10.
Available at: https://doi.org/10.1017/s003329172000241x.
Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning
satisfaction. The Internet and Higher Education, 19, 10-17. Available at: https://doi.org/10.1016/j.iheduc.2013.04.001.
Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college students’ mental health in the United
States: Interview survey study. Journal of Medical Internet Research, 22(9), e21279. Available at: https://doi.org/10.2196/21279.
Sundarasen, S., Chinna, K., Kamaludin, K., Nurunnabi, M., Baloch, G. M., Khoshaim, H. B., & Sukayt, A. (2020). Psychological impact of
COVID-19 and lockdown among university students in Malaysia: Implications and policy recommendations. International Journal of
Environmental Research and Public Health, 17(17), 6206. Available at: https://doi.org/10.3390/ijerph17176206.
Taipjutorus, W., Hansen, S., & Brown, M. (2012). Investigating a relationship between learner control and self-efficacy in an online learning
environment. Journal of Open, Flexible and Distance Learning, 16(1), 56-69.
Tsai, C.-L., Cho, M.-H., Marra, R., & Shen, D. (2020). The self-efficacy questionnaire for online learning (SeQoL). Distance Education, 41(4),
472-489. Available at: https://doi.org/10.1080/01587919.2020.1821604.
Tuncay, N., & Uzunboylu, H. (2010). Anxiety and resistance in distance learning. Cypriot Journal of Educational Sciences, 5(2), 142-150.
Wang, C.-H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course
outcomes in online learning. Distance Education, 34(3), 302-323. Available at: https://doi.org/10.1080/01587919.2013.835779.
Wang., C., Zhao, H., & Zhang, H. (2020). Chinese college students have higher anxiety in new semester of online learning during COVID-
19: A machine learning approach. Frontiers in Psychology, 11, 3465. Available at: https://doi.org/10.3389/fpsyg.2020.587413.
Zimmerman, W. A., & Kulikowich, J. M. (2016). Online learning self-efficacy in students with and without online learning experience.
American Journal of Distance Education, 30(3), 180-191. Available at: https://doi.org/10.1080/08923647.2016.1193801.
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