IJLTER.ORG Vol 20 No 6 June 2021

International Journal
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Vol.20 No.6

International Journal of Learning, Teaching and Educational Research
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Vol. 20, No. 6 (June 2021)
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VOLUME 20 NUMBER 6 June 2021
Table of Contents
Perceived Satisfaction of Emergency Remote Teaching: More Evidence from Thailand..............................................1
Kevin Fuchs
An Emergency Shift to e-Learning in Health Professions Education: A Comparative Study of Perspectives
between Students and Instructors ......................................................................................................................................16
Afrah Almuwais, Samiah Alqabbani, Nada Benajiba, Fatmah Almoayad
Impulsing the Development of Students' Competency Related to Mathematical Thinking and Reasoning through
Teaching Straight-Line Equations.......................................................................................................................................38
Bui Phuong Uyen, Lu Kim Ngan, Nguyen Phuong Thao, Duong Huu Tong
Exploring Effective Practices in Managing Distance Learning for Teaching Art and Design in Bahrain................. 66
Sama'a Al Hashimi
Improving Novice Students’ Computational Thinking Skills by Problem-Solving and Metacognitive Techniques
.................................................................................................................................................................................................88
Nor Hasbiah Ubaidullah, Zulkifley Mohamed, Jamilah Hamid, Suliana Sulaiman, Rahmah Lob Yussof
Examining Saudi Students’ Perceptions on the Use of the Blackboard Platform during the COVID-19 Pandemic
............................................................................................................................................................................................... 109
Elham Alzain
A Bibliometric Analysis of Blended Learning in Higher Education: Perception, Achievement and Engagement
............................................................................................................................................................................................... 126
Arumugam Raman, Raamani Thannimalai, Yahya Don, Mohan Rathakrishnan
The Role of Nurturing Technopreneurship Education and Building University Students’ Entrepreneurial
Mindsets and Skill Sets in Fostering Digital Innovation and Augmenting the Tech Start-Up Ecosystem in Bahrain
............................................................................................................................................................................................... 152
Sama'a Al Hashimi, Yasmina Zaki, Ameena Al Muwali, Nasser Mahdi
British National Corpus in English Language Teaching of University Students....................................................... 174
Nataliia Bober, Yan Kapranov, Anna Kukarina, Tetiana Tron, Tamara Nasalevych
Emerging Trends in Metaphoric Images of Curriculum Reform Implementation in Schools: A Critical Literature
Review.................................................................................................................................................................................. 194
Godsend T. Chimbi, Loyiso C. Jita
Is Decentralisation a Suitable Response to Improve South African Rural Education?.............................................. 211
Kevin Teise, Emma Barnett
Qualitative Content Analysis of Teachers’ Perceptions and Experiences in Using Blended Learning during the
COVID-19 Pandemic .......................................................................................................................................................... 225
Kenneth Ian Talosig Batac, Jonnedel Azucena Baquiran, Casper Boongaling Agaton

Teachers’ Perceptions of the Role of Entrepreneurship Education in the Career Choice Decision-Making of
Business Studies Learners in Gauteng South Africa ...................................................................................................... 244
Oluwakemi B. Ajayi
The Common Thinking Styles Based on the Mental Self-Government Theory Among Saudi University Students
According to Gender, Academic Achievement and Extracurricular Activities.......................................................... 258
Ali Tared Aldossari, Mahmoud Moh'd Ali Abu Jadou
Pre-service Science Teachers’ Integration of Constructivist Ideas in the Lecture Method........................................ 277
Rose Atieno Mutende, Rosemary K. Imonje, Winston Akala
Implementation of the Social Component of Higher Education: Bottom-up Approach........................................... 299
Alla A. Marushkevych, Iryna M. Zvarych, Natalia M. Lavrychenko, Liudmyla Ya. Biriuk, Olha M. Zaitseva
The Effects of Using a Case Study Method for Environmental Education..................................................................319
Sergii D. Rudyshyn, Inna A. Stakhova, Nataliia H. Sharata, Tetiana V. Berezovska, Tetiana P. Kravchenko
Exploring Vocational High School Students’ Entrepreneurial Intention: Preliminary Study ..................................341
Darma Rika Swaramarinda, Badrul Isa, Norhayati Mohd Yusof, Mohd Ali Bahari Abdul Kadir
School Support Received and the Challenges Encountered in Distance Learning Education by Filipino Teachers
during the Covid-19 Pandemic ......................................................................................................................................... 360
Angelito Palma Bautista Jr., Doris Gelvoligaya Bleza, Cielito Bernardino Buhain, Dianne Morta Balibrea
The Role of Teacher Educators in Curriculum Reforms in Lesotho Schools .............................................................. 386
Julia Chere-Masopha, Tebello Tlali, Tankie Khalanyane, Edith Sebatane
The Development of Digital Competences for University Tourism Teachers ............................................................ 403
Derling José Mendoza Velazco, Magda Francisca Cejas Martínez, Mercedes Navarro Cejas, María Hipatia Delgado Demera,
Silvia Marieta Aldaz Hernández

1
©Author
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
Vol. 20, No. 6, pp. 1-15, June 2021
https://doi.org/10.26803/ijlter.20.6.1
Perceived Satisfaction of Emergency Remote
Teaching: More Evidence from Thailand
Kevin Fuchs
Prince of Songkla University, Phuket, Thailand
https://orcid.org/0000-0003-3253-5133
Abstract. The sudden shift from physical classroom education towards
emergency remote teaching (ERT) in higher education during the
unprecedented global pandemic caused an abrupt change in the
learning environment for students and educators alike. The disruptive
overnight change and conversion of entire courses to emergency remote
teaching caused concern for not only educators, but also students that
had little time to adapt to the new circumstances. While the embedment
of technologies in the classroom is not a new concept, this quantitative
research expands a case study that sought to examine the perceived
satisfaction of undergraduate students with the emerging paradigm of
ERT. Responses based on empirical data (n=450) as well as secondary
data (n=219) were analyzed to conclude that, in particular, younger
freshmen students struggled more with online emergency remote
teaching than their older peers. Furthermore, the study identified
numerous similarities between both data samples. The current research
informs educators about student perceptions and preferences during
these extraordinary circumstances of uncertain duration. Furthermore,
the paper concludes with recommendations that aim to provide
institutions and educators with practical guidance on how to tackle the
outlined issues.
Keywords: Emergency remote teaching; Technology-enhanced learning;
Thailand; Online learning; Higher education
1. Introduction
The universality of information technology has been influencing almost all
aspects of our lives: the way we work, interact with others, process data into
information, analyze and share information, entertain ourselves, and enjoy
tourism (Palvia et al., 2018). Due to the threat of COVID-19, universities are
facing decisions about how to continue teaching and learning while keeping
their faculty, staff, and students safe from a public health emergency that is
moving fast and is not well understood. Many institutions have opted to cancel
all face-to-face classes, including lab-based classes and seminars. They have
mandated that faculties move their courses online to help prevent the spread of

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http://ijlter.org/index.php/ijlter
the virus that causes COVID-19 (Fuchs, 2021a). This unprecedented situation
created an entirely new phenomenon: due to the severe nature of the virus,
entire curricula were moved to online education overnight. The challenge herein
was not limited to the educators, who found themselves in a situation of needing
to teach their entire syllabus online, but also extended to the students, who
needed to adapt to a new learning environment instantaneously (Whalen, 2020).
As a response to the global education crisis, online emergency remote teaching
has been put into practice. It is a complex process that requires careful planning,
designing, and determination of aims in order to create an effective learning
ecology (Themelis & Sime, 2020). The temptation to compare online learning to
face-to-face instruction in these circumstances will be great. Online learning
carries a stigma of being lower in quality than face-to-face learning, despite
research showing otherwise. These hurried moves online by so many
institutions at once could seal the perception of online learning as a weak option
when, in truth, nobody making the transition to online teaching under these
circumstances will truly be designed to take full advantage of the affordances
and possibilities of the online format (Hodges et al., 2020).
This paper is an expansion of an earlier study done by Fuchs and Karrila (2021)
that sought to examine the perceived satisfaction of students in higher education
concerning emergency remote teaching amid COVID-19 in Thailand. Fuchs and
Karrila (2021) identified that most undergraduate students prefer a traditional
on-site classroom arrangement, but were satisfied with the alternative ERT that
was delivered fully online. The study highlighted that the students perceived
knowledge, friendliness, and patience as the most important characteristics of
their lecturer in these circumstances. However, the limited sample size from the
previous study (n=219) would not suffice to generalize the results to a larger
population, nor allow for validation in different geographical parts of Thailand.
This paper therefore aims to expand on the original research setting and to meet
the following research objectives:
1) To seek validation of previous research results through an increased
sample size
2) To identify whether the perceived satisfaction from undergraduate
students varies in a different geographical setting
3) To recognize a similarity or dissimilarity between specific factors based
on geographical location, i.e. in Northeastern vs. Southern Thailand
Moreover, the research was guided by the following research question: “How do
undergraduate students in Northeastern Thailand perceive satisfaction with the
emerging paradigm of emergency remote teaching during COVID-19?”
2. Literature Review
2.1. Online learning in higher education
The COVID-19 has resulted in schools shut all across the world. As a result,
education has changed dramatically, with the distinctive rise of e-learning,
whereby teaching is undertaken remotely and on digital platforms. As shown in
a previous study, effective time management was the second-highest-rated

3
advantage of online education, with students having more freedom to control
their time and not being constrained by predetermined schedules (Martin et al.,
2020). Another study found that, depending on the teaching methods used, the
ability to use multiple virtual classrooms at the same time could improve
student interest and involvement, allowing for smaller group discussions during
online lectures (Fuchs, 2021b). Furthermore, a combination of time and location
versatility was claimed as one of the key advantages of online education. The
benefit of place and time flexibility works both ways, allowing students and
educators to choose the best work environment for them.
Additionally, time saved by eliminating a daily commute can be spent in more
study time, increasing the likelihood of success. The variety of digital resources
that can be incorporated into the virtual classroom was mentioned as another
advantage of online education. According to Downes (2019) in his
Connectivism-based educational theory, the online medium provided an
opportunity and experience to connect with students from various disciplines,
backgrounds, and cultures (Downes, 2019). Since the early 2000s, the paradigm
of online education has changed radically. Most notably, the Internet is
connecting an ever-increasing number of people all over the world. According to
the United Nations nearly 4.68 billion people will have access to the Internet in
the year 2020. This figure reflects roughly 58 percent of the world's population,
so it is no wonder that online education is growing in tandem.
2.2. Challenges and opportunities related to online education
Earlier research by Fuchs and Karrila (2021), Sun and Chen (2016), Kyne and
Thompson (2020), Delnoij et al. (2020), and Fuchs (2021c) claim that online
education has numerous advantages, including the ability to study remotely
without having to engage in a daily rush hour in metropolitan areas.
Furthermore, another advantage listed was timely and frequent feedback from
the course instructor through digital evaluations and electronic communication
(Kyne & Thompson, 2020). Other advantages of online education include the
multi-media experience in a well-designed virtual classroom with various
technical elements (Fuchs, 2021c). Moreover, of course, there were monetary
benefits resulting from reduced costs and, therefore, lower tuition fees for
participating students (Sun & Chen, 2016).
Online education and technology-enhanced education are certainly not new
concepts; they have been around for quite some time. However, rising curiosity
has ignited a big trend in these fields (Sun & Chen, 2016; Kyne & Thompson,
2020). Educators were searching for a way to do some of their teachings online,
or at the very least incorporate technology into their classrooms (Fuchs, 2021b;
Fuchs, 2021c). The pedagogy of active learning, in particular, has intensified the
movement toward technology-enhanced education, which has the benefits of
increased student participation, improved learning outcomes, and, as a result,
higher retention rates (Delnoij et al., 2020). However, many of the outlined
benefits are not transferable to the paradigm of emergency remote teaching in a
crisis, wherein this emerging paradigm carries its own set of distinguishing
characteristics.

4
2.3. The emerging paradigm of ERT
As a result of crises, emergency remote teaching (ERT) is a temporary transition
in instructional delivery to an alternative delivery model, wherein it is implied
that teaching is carried out entirely online. It was also stated that online
education has been studied for decades, with a consensus on the elements that
do not contribute proportionally to the efficacy of online education. These
characteristics include but were not limited to modality, pacing, student-
instructor ratio, pedagogy, the role of assessment, the instructor’s role, the
student’s role, communication channels, and sources of feedback. These
characteristics will invariably be evident in an effective ERT class. The lack of
time available for educators to change their instructional materials – in the event
of a last-minute switch from classroom to online – may potentially indicate an
unsuitable learning atmosphere for students.
Kyne and Thompson (2020) conducted a case study that described many
challenges faced by students during their fully online semester. Completing lab-
based tasks, navigating Moodle (LMS), and engaging with online content were
among them. If the course content is not carefully and intentionally designed,
“undergraduate students claim a lack of socialization with peers and low
engagement with the course materials” as primary reasons for their
dissatisfaction, according to a similar study (Fuchs, 2021a). Furthermore, Wilcox
and Vignal (2020) discovered that the two most common difficulties students
faced as a result of ERT were (1) course inception and (2) learning environment.
The most frequently mentioned issue in the above group was unreliable Internet
access that hindered the students' learning experience.
Participants said the learning process was uncomfortable and unpleasant,
according to Gelles et al. (2020). Although there are many benefits and
opportunities in the online education paradigm, it should be recognized that it is
not without its difficulties and flaws. Certainly, lack of student engagement
(Fuchs, 2021c), willingness to meet learning results (Zlatkin-Troitschanskaia et
al., 2016), and involvement of low-performing students (Fay & Zavattaro, 2016)
were all difficulties found in previous studies. However, given the existence of
the substantial changes that emergency remote teaching could entail, there is the
potential for a new set of challenges to arise.
2.4. Defining students satisfaction
Satisfaction is a euphoric feeling that occurs when a person's needs and desires
have been met (Suikkanen, 2011). It is a state of mind of a person that has
achieved or perceived a result that has exceeded their expectations (Busacca &
Padula, 2015). As a result, satisfaction can be described as an experience of
receiving expected results. In related research, satisfaction is often portrayed as
the positive difference between the perceived importance and the perceived
performance of an attribute or action (Muhsin et al., 2020). In other words,
satisfaction refers to the satisfaction or dissatisfaction experienced as a result of
contrasting perceived results to expectations (Suikkanen, 2011; Padula, 2015).
Generally, students are satisfied when the perceived performance of a specific
service or action outranks the perceived expectation of the same service/action.

5
When a person perceives that service encountered as good, they will be satisfied.
When the perceived performance of the service or action is below the perceived
expectation, then that person would be dissatisfied with the result: Satisfaction
(S) = Perceived Importance (I) – Perceived Performance (P).
The measurement of a student's educational experiences leads to a short-term
disposition of satisfaction. It is the product and effect of an educational system
and is a positive indicator of student loyalty (Weerasinghe & Fernando, 2017;
Muhsin et al., 2020). In conclusion, student satisfaction can be understood as a
function of the relative level of experiences and perceived performance
concerning educational services during the study period (Suikkanen, 2011;
Padula, 2015; Weerasinghe & Fernando, 2017).
3. Methodology
3.1. Sample
The data were collected from undergraduate students of all years who were
enrolled in a full-time degree program. The sample included degree programs
that relate to Business and Management studies. After screening the collected
data, a total of 38 responses were discarded. The discarded responses included
13 from another Faculty (i.e. Faculty of Science). Moreover, 8 responses from
international exchange students were excluded. However, responses from
international degree students were included in the analysis. Finally, 17
inconclusive/incomplete responses were discarded.
Table 1: Socio-demographic characteristics of the participants
Characteristics University A1 University B2
Gender
Male 58 26% 125 28%
Female 159 73% 325 72%
Prefer not to say 2 1% - 0%
Total 219 100% 445 100%
Year of study
Year 1 50 23% 79 18%
Year 2 83 38% 208 46%
Year 3 43 20% 76 17%
Year 4 32 14% 68 15%
Year 5 or above 11 5% 19 4%
Total 219 100% 445 100%
Age range
18 years old or below 6 3% 7 2%
19 – 20 years old 122 56% 285 63%
21 – 22 years old 68 31% 114 25%
23 – 24 years old 16 7% 30 7%
25 years old or above 7 3% 14 3%
Total 219 100% 445 100%
1Secondary Data obtained from University A in Southern Thailand (n=219)
2Empirical Data obtained from University B in Northeastern Thailand (n=450)

6
An overall sample size (n=669) was included as a population sample for the data
analysis. The confidence level of accurate sampling was estimated at 95%
(p<0.05) and, based on the total student enrollment and sample size that were
included, the margin of error was quantified at 4.40%. Based on eligible
responses, the representative demographic profile in Table 1 and 2 summarizes
the respondents’ gender, year of study, age range (all in Table 1), nationality,
and preferred mode of study (in Table 2).
Table 2: Socio-demographic characteristics of the participants
Characteristics University A1 University B2
Nationality
Thai 184 84% 360 80%
Foreign 35 16% 90 20%
Total 219 100% 445 100%
Preferred study mode
Virtual classroom 54 25% 117 26%
Traditional classroom 165 75% 333 74%
Total 219 100% 445 100%
1Secondary Data obtained from University A in Southern Thailand (n=219)
2Empirical Data obtained from University B in Northeastern Thailand (n=450)
3.2. Research instrument
Convenience sampling was used to collect the data through a bilingual (Thai
and English) self-administered digital survey (e-survey). The e-survey was split
into three sections containing a total of 27 questions and was adapted from an
earlier case study (Fuchs & Karrila, 2021). The students were recruited on-site to
voluntarily participate in the data collection. Furthermore, the students were
prompted for assistance to further distribute the survey amongst their peers. The
first section sought to collect data on the participant’s socio-demographic profile.
Table 3: Summary of survey items
No. Item
1. The teacher begins the class with a review of the previous class
2. The teacher presents the material in an interesting and engaging way
3. The teacher presents the material in an organized and coherent way
4. The teacher is knowledgeable about the content of the course
5. The teacher is friendly and patient with the students
6. The course material is well and professionally prepared
7. The course material is easy to access in the LMS
8. Students are engaged to actively participate in the discussion
9. I am learning something which I consider valuable
10. I am finding the course challenging and stimulating

7
The second and third sections contained ten (10) items each, wherein the
participants were able to express their views on a 5-point Likert-type scale with
pre-coded responses for Not Important At All (1), Not Very Important (2),
Somewhat Important (3), Very Important (4), and Extremely Important (5) in the
second section. Similarly, the third section had pre-coded Likert-type responses
for Not At All Satisfied (1), Not Very Satisfied (2), Somewhat Satisfied (3), Very
Satisfied (4), and Extremely Satisfied (5). Otherwise, the items in the second and
third sections were similar in terms of comparing the perceived importance and
performance for each item (Table 3). The structure and content of the
administered e-survey were examined for validity by three university lecturers
and tested with ten students for comprehension of the survey. These preliminary
examinations yielded minor revisions to enhance the clarity of the survey.
3.3. Survey administration
The secondary data were taken from an earlier study conducted by Fuchs and
Karrila (2021) and were collected in the first quarter of 2021 at a large higher
educational institution in southern Thailand. They were collected amid a
countrywide ERT policy as a result of the imminent spread of COVID-19.
Hereafter, this sample is referred to as University A (n1=219).
The empirical data were collected in the second quarter of 2021 at a large higher
educational institution in northeastern Thailand and the survey accepted
responses for a duration of 96 hours before it was closed for new responses. It
was collected in the aftermath of a countrywide ERT policy that was
implemented and effectively replaced traditional face-to-face teaching.
Henceforth, the sample is referred to as University B (n2=450). Both institutions
are the largest universities in terms of size (i.e. student enrollment and curricula
offered) and recognition in the respective areas. Furthermore, they are
characterized as government-run institutions of higher education targeting
students from middle-class households.
3.4. Data analysis
The survey data were examined using JASP and software to obtain an average
value (Mean), standard deviation (SD), minimum value (Min), maximum value
(Max), the proportion of the data (i.e., a fraction of cases without missing data),
and distribution of data for each item. Independent T-tests were performed to
determine if there was a significant difference between the means of University
A and University B. The data analysis and findings are discussed and
interpreted in later sections of this paper.
4. Results and Discussion
The results from the empirical data collection and secondary data sample are
presented in two separate sections that allow for chronologic analysis and
presentation. The first section presents the demographic profiling that was
conducted to identify similarities or dissimilarities between the samples based
on gender, age range, year of study, nationality, or preferred mode of study.

8
The second section presents the mean values of both samples for each attribute
and allows for comparison of the results and analysis of student satisfaction with
emergency remote teaching. Moreover, the variance from each attribute between
the first and second samples was calculated and independent t-tests were
performed from both samples to determine whether there was a statistically
significant difference between the means in both samples.
4.1. Demographic profiling of both sample groups
The socio-demographic profile, consisting of gender, age range, year of study,
and nationality, was included in a rigorous cross-analysis wherein different
mean ratings based on gender or year of study were detected. However, the
results do not suggest a statistical significance or relevance that would further
provide value concerning the perceived satisfaction of students with emergency
remote teaching in Thailand. However, one particular criterion yielded a
noteworthy result. The enclosed graphic (Fig. 1) shows the preferred mode of
study based on 17 totally different socio-demographic filters that were applied.
Figure 1: Demographic profiling about the students preferred mode of studying
The blue bar signifies the percentage of students that prefer a traditional
classroom setting instead of emergency remote teaching, wherein the red bar
indicates the percentage of students that prefer emergency remote teaching to a
traditional on-site classroom arrangement. To draw a baseline for comparison,
13 of the 17 attributes that were examined yielded a similar proportional
response, wherein 74% of students prefer the traditional classroom and 26%
prefer the virtual classroom during ERT (No. 1). The majority of socio-
demographic filters validate this sentiment with a relatively small standard
deviation of not more than 2%. However, based on the 17 filters that were
applied, four particular settings yielded noteworthy results. Namely, these are:
26% 25% 28% 25% 26%
8%
24%
29% 30%
24%
16%
27% 25% 28%
43%
24%
34%
Total
Female
Male
University
A
University
B
18
or
younger
19-20
years
21-22
years
23-24
years
25
or
above
Year
1
Year
2
Year
3
Year
4
Year
5+
Thai
Foreign
No. 1 No. 2 No. 3 No. 4 No. 5 No. 6 No. 7 No. 8 No. 9 No. 10 No. 11 No. 12 No. 13 No. 14 No. 15 No. 16 No. 17
Traditional Classroom Virtual Classroom

9
No. 6 (18 or younger), No. 11 (Year 1), No. 15 (Year 5+), and No. 17 (Foreign).
The first three findings indicate opposing views of the respective groups of
participants. The students aged “18 years or younger” expressed their preference
toward the traditional classroom environment with 92%, whereas only 8% of
that same group prefer the virtual classroom as part of emergency remote
teaching (No. 6). A similar notion is shared amongst the first-year students,
wherein 84% prefer the traditional classroom and 16% prefer the virtual
classroom (No. 11). Generally, the students are aged 17-19 years old in their first
year of undergraduate studies. A possible hypothesis therefore derives that
freshmen students aged 18 years or younger have a strong preference toward
the traditional classroom environment. Another case study suggests that female
students in particular struggle with virtual classrooms and claim “lack of
socialization, peer interaction and technological challenges” as the main
difficulties (Fuchs, 2021a).
Students in their fifth year (and above) expressed an opposing sentiment
concerning the preferred mode of study. While 92% of first-year students prefer
the traditional classroom (No. 6), only 57% of their older peers share that same
sentiment (No. 15). A possible explanation for these opposing views could be
that freshmen students eagerly wait to commence their study experience upon
high school graduation, wherein students in their final years of study are
already more independent and have shifted their focus toward work-life-balance
rather than study experience, as claimed in a case study by Yamada and Yamada
(2018).
Lastly, another notable deviation from the baseline result (No. 1) was the result
recorded from foreign degree students (No. 17). 66% of foreign degree students
prefer the traditional classroom, whereas 34% of them prefer the virtual
classroom. While about two-thirds still favor an on-site arrangement, the result
deviates by 8% from the baseline and is even 10% less compared to their Thai
peers (No. 16). Trower and Lehmann (2017) suggest that personal development,
immersion into a new culture, and learning a new language are amongst the top
reasons for students to study abroad. Thus, a negative deviation from the
baseline result suggests a rather contradictory result from these findings and
offers room for further research in the future.
4.2. Importance-performance analysis
The three highest mean ratings concerning the perceived importance of the
attributes (Table 4) at University A are No. 4 (4.37), No. 5 (4.27), and No. 7 (4.13).
On the other hand, at University B the following three attributes received the
highest mean rating from the participants: No. 5 (4.18), No. 2 (4.17), and No. 4
(4.14). Similar to the results from University A, the participants at University B
valued two identical attributes in their top three of most important attributes
during ERT. Namely, these are “The teacher is knowledgeable about the content
of the course (No. 4)” as well as “The teacher is friendly and patient about the
content of the course (No.5)”. Both attributes directly address the virtues of the
lecturer, as students perceive being knowledgeable, friendly, and patient as the
most important characteristics at both institutions. Respectively, the weighted
mean ratings from both institutions rank almost identically with No. 4 (4.22) and

10
No. 5 (4.21) as the most important attributes during emergency remote teaching.
Contrary to the most important attributes, the participants at University A rated
item No. 1 (3.73) and No. 10 (3.58) as the least important attributes.
Table 4: Comparison of importance ratings
No.1
University A University B Total
Mean SD Mean SD Mean SD
1 3.73 0.99 4.02 1.02 3.93 1.02
2 4.03 1.01 4.17 0.98 4.12 0.99
3 4.05 0.92 4.05 0.98 4.05 0.96
4 4.37 0.89 4.14 0.99 4.22 0.97
5 4.27 0.96 4.18 1.02 4.21 1.00
6 4.12 0.99 4.11 1.01 4.11 1.00
7 4.13 1.03 4.11 1.01 4.12 1.02
8 3.98 0.95 3.91 1.08 3.93 1.04
9 3.95 0.98 4.07 1.02 4.03 1.01
10 3.58 1.24 4.00 1.06 3.87 1.14
1Ratings obtained from a Likert-type five points scale ranging from lowest rating
to highest rating, i.e. Not Important At All (1), Not Very Important (2), Somewhat
Important (3), Very Important (4), and Extremely Important (5).
Although these two attributes also rank in the bottom three for participants from
University B, the lowest mean rating was given to No. 8 (3.91), which asked the
participants about the importance of the statement “students are engaged to
actively participate in the discussion”. Evaluating the totality of both samples, it
can be concluded that the lowest to highest mean rating ranges from 3.87 (No
10.) to 4.22 (No. 4), which indicates relatively high importance for all ten
attributes. Furthermore, the findings from the first sample taken at University A
were affirmed with the second sample from University B, with the virtues of the
lecturer perceived by the students as the most important characteristics.
Table 5: Comparison of performance ratings
No.1
University A University B Total
Mean SD Mean SD Mean SD
1 3.52 0.99 3.92 1.05 3.79 1.05
2 3.62 1.04 3.80 1.04 3.74 1.04
3 3.79 1.00 3.81 1.04 3.81 1.03
4 4.12 0.96 3.82 1.08 3.92 1.05
5 4.05 0.94 3.76 1.01 3.85 1.00
6 3.84 0.98 3.63 1.08 3.70 1.05
7 3.87 1.00 3.73 1.01 3.77 1.01
8 3.81 0.96 3.83 1.03 3.82 1.01
9 3.76 1.01 3.90 1.00 3.86 1.01
10 3.54 1.20 3.87 1.03 3.76 1.09
1Ratings obtained from a Likert-type five points scale ranging from lowest
rating to highest rating, i.e. Not At All Satisfied (1), Not Very Satisfied (2),
Somewhat Satisfied (3), Very Satisfied (4), and Extremely Satisfied (5).

11
In addition to the attributes that received the lowest and highest mean ratings
about perceived importance, Table 5 indicates the perceived performance of the
same ten attributes per educational institution. The three highest-rated attributes
about perceived performance at University A are No. 4 (4.12), No. 5 (4.05), and
No. 7 (3.87). The results recorded from participants at University B differ in
terms of mean value, ranking, and mean value variance. Firstly, it can be noted
that the range for the mean value is relatively narrow. The lowest to highest
value range is from 3.63 (No. 6) to 3.92 (No. 1). Moreover, the highest-rated
attributes about perceived performance are No. 1 (3.92), No. 9 (3.90), and No. 10
(3.87), indicating that students at University B place more emphasis on the
perceived performance for a review of the previous class at the beginning of
their lecture (No. 1) and being able to learn something valuable (No. 9). Notably,
the lowest-rated attributes from the first sample at University A correspond to
No. 1 (3.52) and No. 9 (3.54), which were the highest-ranked at University B.
In summary, it can be noted that there is an agreement between both institutions
that the virtues and personal traits of the lecturer are perceived as the most
important attributes during emergency remote teaching. Attributes that
correspond to the lecturers’ friendliness, patience, or knowledge are rated higher
than, for example, the need for a stimulating or challenging course (Table 6).
Table 6. Comparison of importance-performance ratings (n=669)
No.
Importance rating1 Performance rating2
Mean SD Mean SD
1 3.52 0.99 3.92 1.05
2 3.62 1.04 3.80 1.04
3 3.79 1.00 3.81 1.04
4 4.12 0.96 3.82 1.08
5 4.05 0.94 3.76 1.01
6 3.84 0.98 3.63 1.08
7 3.87 1.00 3.73 1.01
8 3.81 0.96 3.83 1.03
9 3.76 1.01 3.90 1.00
10 3.54 1.20 3.87 1.03
Furthermore, while there is a relative agreement about the attributes perceived
as most important during emergency remote teaching, there is a discrepancy
amongst both institutions about perceived performance during emergency
remote teaching. The latter result is probably less surprising, considering that
the performance of an online class is largely dependent on the individual
lecturer, as well as how the institution manages the ERT. However, it can be
noted that participants from both institutions are generally satisfied with the
perceived performance during ERT.
The attributes related to perceived performance for both institutions range from
the lowest at 3.70 (No. 6) to the highest at 3.92 (No. 4). Also, the result is less
surprising as the lowest-ranked attribute is item No. 6, about professionally
prepared course material. This is understandable given the short notice to

12
convert educational material from traditional classroom teaching into an online
environment, as earlier stated by Hodges et al. (2020).
The underlying factors are not clear for the survey items that resulted in very
low comparative p-values, as summarized in Table 7, based on the analysis
conducted.
Table 7: Comparison and Independent T-Test’s
No. Mean1 Mean2 Variance t-value p-value
Importance
1 3.73 4.02 -0.29 -3.5379 <.001
2 4.03 4.17 -0.14 -1.6520 0.099
3 4.05 4.05 ±0.00 0.1027 0.918
4 4.37 4.14 0.23 2.8501 0.005
5 4.27 4.18 0.09 1.0329 0.302
6 4.12 4.11 0.01 0.0919 0.927
7 4.13 4.11 0.02 0.2802 0.779
8 3.98 3.91 0.07 0.8214a 0.412
9 3.95 4.07 -0.12 -1.4389 0.151
10 3.58 4.00 -0.42 -4.5389a <.001
Performance
1 3.52 3.92 -0.40 -4.684 <.001
2 3.62 3.80 -0.18 -2.071 0.039
3 3.79 3.81 -0.02 -0.223 0.824
4 4.12 3.82 0.30 3.419 <.001
5 4.05 3.76 0.29 3.608a <.001
6 3.84 3.63 0.21 2.343a 0.019
7 3.87 3.73 0.14 1.696 0.090
8 3.81 3.83 -0.02 -0.194 0.846
9 3.76 3.90 -0.14 -1.739 0.082
10 3.54 3.87 -0.33 -3.744a <.001
1Sample taken from University A; 2Sample taken from University B
aLevene’s test is significant (p < .05), suggesting a violation of the assumption of
equal variances.
5. Conclusion and Future Works
It was the aim of the study to seek validation of previous research results
through an increased sample size and to identify whether the perceived
satisfaction from undergraduate students varies in a different geographical
setting. Everyone involved in the temporary but sudden shift toward virtual
learning must recognize that these crises cause disturbances for students, staff,
and educators alike. While the coronavirus pandemic will hopefully soon be a
distant memory, we should not simply return to our pre-virus teaching and
learning practices and ignoring valuable lessons learned from ERT. There are a
few noteworthy findings from this study that outline where the educator’s
emphasis could be placed in a sudden and disruptive move toward virtual
teaching. Both data samples suggest an agreement amongst the attributes that
students value most and deem as most important in a time when educators
struggle to fulfill similar expectations as in the on-site traditional classroom

13
arrangement. Furthermore, the study revealed that Thai undergraduate
students’ perceived performance is generally ranked lower than their perceived
expectations, although it should be noted that the perceived satisfaction yielded
an overall good result at both institutions. Moreover, emphasis and more
attention should be given to younger undergraduate students in their first year
of study who struggle more with virtual classrooms than their older peers.
Lastly, limitations offer an opportunity for future research; while the authors
tried to mitigate possible limitations as far as possible, it is significant to point
out that the settings in which the results were collected are geographically
limited to the northeastern and southern region of Thailand and not
generalizable to a larger population. Furthermore, the demographic profiling of
students offers opportunities for future research to quantitatively validate the
results and possibly generalize the findings to a larger population.
6. Acknowledgments
6.1. Conflict of interest
The author would like to declare no potential conflicts of interest concerning the
research, authorship, or publication of this article.
6.2. Data availability
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
6.3. Funding
The Faculty of Hospitality and Tourism, Prince of Songkla University funded the
project under the Fast Track Data Collection Grant [Contract No. FHT 6400002].
Any opinions or conclusions expressed in this paper are solely the intellectual
result of the author(s) and do not reflect the viewpoint of the Faculty or
University.
6.4. Recognition
The author would like to thank the participants that contributed to the research
project by answering the questionnaire.
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©Authors
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License (CC BY-NC-ND 4.0).
Vol. 20, No. 6, pp. 16-37, June 2021
https://doi.org/10.26803/ijlter.20.6.2
An Emergency Shift to e-Learning in Health
Professions Education: A Comparative Study of
Perspectives between Students and Instructors
Afrah Almuwais
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences,
Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
https://orcid.org/0000-0002-2774-868X
Samiah Alqabbani
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences,
https://orcid.org/0000-0003-4495-5047
Nada Benajiba
Department of Basic Health Sciences, Deanship of Preparatory Year, Princess
Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
https://orcid.org/0000-0002-5533-7626
Fatmah Almoayad*
Department of Health Sciences, College of Health and Rehabilitation Sciences,
https://orcid.org/0000-0002-8424-5229
Abstract. This is a cross-sectional study which assessed the readiness to
shift to e-learning in correlation with perceived effectiveness and
satisfaction following the sudden shift caused by the coronavirus disease
2019 (COVID-19) pandemic among students and instructors. The study
compared perspectives between instructors (n = 47) and students (n = 254)
at the College of Health and Rehabilitation Sciences (CHRS) at Princess
Nourah bint Abdulrahman University (PNU; Riyadh, Kingdom of Saudi
Arabia). Data were collected using an online questionnaire using
convenient sampling method. The results showed a high level of
readiness to shift to e-learning among instructors and students, as well as
a positive correlation between perceived effectiveness and satisfaction.
However, instructors showed a higher satisfaction level and perceived
this shift to be effective more than students. This experience offers a
reasonable foundation for any future plans to implement e-learning in
health professions education and maximise its benefits without
*Corresponding author: Fatmah Almoayad; Email: ftm.myd@gmail.com

17
compromising the practical and clinical training provided via face-to-face
learning. Further studies are needed to explore e-learning experiences a
year after this shift, when educational institutions are expected to have
clearer plans and have better prepared for e-learning. In addition, effect
of e-learning shift on clinical training outcomes for different health
professions is also recommended.
Keywords: e-learning; COVID-19; health professions; Saudi Arabia
1. Introduction
Since the beginning of the 21st century, e-learning has been progressively
integrated within higher education systems worldwide (Aljaber, 2018; Hiltz &
Turoff, 2005). In Saudi Arabia, health colleges have participated in the e-learning
movement and many have embedded blended teaching strategies that combine
face-to-face learning with e-learning (Sajid et al., 2016; Zakaria et al., 2013). While
an extensive body of literature discusses several types of e-learning – such as
distance learning, blended learning and mobile learning – attempts to confirm
their effectiveness have been inconclusive in international research, specifically in
studies of e-learning in Saudi Arabia (Rajab, 2018). Nevertheless, blended learning
has shown effectiveness vis-à-vis skill and knowledge acquisition in health
professions education (Liu, et al 2016). Moreover, growing evidence suggests that
advances in virtual simulation are benefitting health profession training (Pottle,
2019; Skochelak & Stack, 2017).
1.1 The Importance of Preparedness in e-Learning
As the literature suggests, providing proper and effective e-learning requires
advanced planning (Nasiri et al., 2014; Rice & McKendree, 2014). e-Learning
infrastructure and support have been indicated as crucial to successful e-learning
experiences (Naveed et al., 2017). This importance was clearly demonstrated
when education shifted abruptly to e-learning in the early months of the
coronavirus disease 2019 (COVID-19) pandemic.
During this time, the existence of the required infrastructures and preparedness
to accommodate this shift to e-learning demonstrated a significant positive impact
on the learning process’s continuation. Countries with excellent and complete
infrastructure were better able to resume the teaching process with minimal or no
interruptions (Marinoni et al., 2020). Meanwhile, poor internet connections and a
lack of preparedness (such as a lack of electronic devices) were found to present
significant obstacles for both students and instructors during this emergency shift
to e-learning (Maatuk et al., 2021). Additionally, the literature showed that
satisfaction with e-learning is a key factor for the success of e-learning experiences
themselves (Bolliger, 2004; Liaw et al., 2007). Al-Samarraie et al. (2018)
investigated a unified perception of students’ and instructors’ satisfaction with an
e-learning system, demonstrating that steadily maintained satisfaction with e-
learning indicates a successful continuation of e-learning. Thus, instructors’
ability to utilise a learning management system is influenced by their satisfaction
levels (Yengin et al., 2011). On the other hand, students’ online readiness had a
mediated influence on learning perceptions and course satisfaction (Wei & Chou,
2020). Gopal et al. (2021) revealed that students’ satisfaction positively influenced

18
their performance during online education as a result of the pandemic-related
lockdown. Moreover, both students’ and instructors’ satisfaction influenced their
motivation in an online environment (Bolliger & Wasilik, 2009).
A previous study was conducted by Alqabbani et al. (2020) to assess the readiness
to shift to online learning at Princess Nourah bint Abdulrahman University’s
(PNU; Riyadh, Kingdom of Saudi Arabia). It found an excellent existing
infrastructure and a high level of readiness among instructors at the university.
This study’s findings also revealed that satisfaction was positively correlated with
perceived effectiveness during the complete shift to e-learning. While this
correlation indicated a positive shift experience at the institution, students and
instructors at the College of Health and Rehabilitation Sciences (CHRS) within
PNU – which offers thirteen allied health speciality programmes (PNU, 2020)
– might have had a different experience. This potential difference is due to the
nature of learning, which requires hands-on practice to master clinical skills. The
unplanned, sudden shift to online learning led to changes in not only theoretical
teaching but also practical and clinical training, which have been replaced by
videos, online simulation, case study reports and online discussions. As a result,
the shift to e-learning might influence both the learning process and learning
outcomes (Huang, 2010; Luhanga, 2018; Parandeh et al., 2015). Therefore,
assessing satisfaction with e-learning provides insights for educational
institutions on identifying areas of improvement in online learning (Bolliger, 2004;
Liaw et al., 2007).
1.2 Students’ and Instructors’ Complementarity in the Learning Process
While exploring the unique learning experience during a shift to e-learning is
interesting, such investigations can only allow insights via analyses of
perceptions’ complementarity between students (as learners) and instructors (as
teachers) since exploring both learners’ and teachers’ perspectives can provide
comprehensive evaluations of the e-learning experience as one entity (Khan,
2005). Instructors have been very clearly established to represent half of the crucial
learning experience via the teaching process for which they are responsible. The
other half of the learning experience is based on students’ learning process
(Ellaway & Masters, 2008). Hence, both halves of this experience (those of
instructors and teachers) are complementary, and their harmony is essential to the
learning process.
Mishra et al. (2020) examined both students’ and teachers’ perceptions of the
online learning experience during the COVID-19 pandemic. Their findings
revealed that the main factor causing instructors’ better motivation compared to
students is a belief that online education can proficiently deliver intended learning
outcomes. Students, however, reported less interest in and attention to online
classes as a new, unfamiliar teaching mode. However, as the literature suggests,
while learners report preferences regarding their learning styles, they also tend to
adapt their learning to the available teaching strategies, based on the context and
motivations (Entwistle, 1997). Recently, Motte-Signoret et al. (2021) indicated that
both medical students and their instructors perceived e-learning as a suitable
alternative medical education delivery method during the pandemic.

19
At the beginning of the COVID-19 pandemic, many academic researchers were
interested in studying the emergency shift to e-learning. In this regard, most
published studies have highlighted the sudden shift’s influence on e-learning’s
effectiveness. Hence, in addition to analysing this experience, the present study
also compares this experience from the perspectives of both students and teachers
affiliated with a health college (including 13 different specialities). Furthermore,
it emphasises some key factors’ importance in determining e-learning’s
effectiveness under the pandemic’s unexpected circumstances. Thus, existing e-
learning infrastructures and support prior to the COVID-19 pandemic’s
lockdown-related emergency shift to e-learning, an indicator of readiness for e-
learning, and perceived satisfaction among students and teachers, were analysed
as possible factors influencing the shift’s perceived effectiveness. The study’s
results were, therefore, expected to provide insights into the complexity of this e-
learning’s effectiveness and the necessary considerations of the above-mentioned
factors to promote this e-learning as education systems are currently projected to
further integration of e-learning in the coming years.
The present study’s researchers hypothesised that students and instructors would
harbour different perspectives regarding readiness, satisfaction and perceived
effectiveness during this shift. Thus, the authors’ null hypothesis was that
students and instructors would demonstrate similar readiness, satisfaction and
perceived effectiveness as a result of this shift.
1.3 Conceptual Framework
This research adopted the cognitive theory of learning, which holds that learning
is affected by both intrinsic and extrinsic factors (Janelli, 2018). In the field of e-
learning teaching strategies, cognitive overload, motivation levels and real-life
situations are all considered essential factors that affect the learning process
(Mödritscher, 2006). In this research context, e-learning was imposed suddenly.
The authors sought to explain the supporting environment that contributed to the
success of any e-learning experience through a conceptual framework (Figure 1).
As the literature has discussed, an appropriate e-learning infrastructure with
adequate support significantly affects the continuation and successful
achievement of the e-learning process. Thus, ensuring a sufficient level of
readiness (preparedness) for both students and instructors positively influences
satisfaction levels and, consequently, achieves reasonable levels of perceived e-
learning effectiveness. The presence of all these elements simultaneously would
ensure overall success in a shift to e-learning. Thus, through its mode of learning,
the current research obtained insights into the factors that contribute to e-
learning’s continuation.

20
Figure 1: Conceptual framework of the e-learning experience
2. Methods
2.1 Design
A comparative analytic study was conducted during May 2020. Participants were
recruited using a convenient sampling technique. This method was the most
efficient method possible, especially during the lockdown period. Questionnaires
were available electronically via Microsoft Forms and distributed via the CHRS e-
mail lists. To facilitate the dissemination of the study’s information and requests
to affiliated students, instructors and administrative staff, the CHRS had
developed and annually updated a specific e-mail list for each of the above-
mentioned categories. Hence, the two e-mail lists corresponding, respectively, to
instructors and students were used to solicit participation in this study after
consent was obtained from all participants. The email was sent twice to each
person on the e-mail lists the first time as an invitation to participate in the study
and the second time as a gentle reminder to encourage further participation. The
emails directed to students were sent from the official email address of the Vice-
Deanship of Student Affairs. Meanwhile, the emails directed to instructors were
sent from the official email address of the Vice-Deanship of Academic Affairs. The
lists’ inclusion criteria were instructors and students who were actively engaged
in learning or teaching during the semester when the sudden shift to e-learning
occurred. The study sample comprised 47 of 66 instructors and 254 of 720 students
at the CHRS. The survey rates were 35% and 71% among students and instructors,
respectively. However, note that prospective participants’ ability to submit
answers was deactivated soon after the survey met its required representative
numbers of participants, which were n = 45 (of 66) for instructors and n = 251 for
students. These values were calculated based on a confidence level of 95% and a
margin of error of ± 5%. Ethical approval (IRB Log Number 20-0162) was obtained

21
from the institutional review board at PNU before this research was conducted.
Participation in the study was voluntary. Anonymity and confidentiality were
maintained, and consent to participate was obtained from participants at the
beginning of the study’s questionnaires.
2.2 Research Instruments
Two questionnaires were designed for this project’s data collection. The first
questionnaire was directed towards instructors (Appendix 1) while the second
questionnaire was directed towards students (Appendix 2). The questionnaires
were adapted from a previous study that had been conducted by the present
research team (Alqabbani et al., 2020) with some adjustments to suit the current
study’s aim. The two questionnaires comprised four similar sections, including
general characteristics, the readiness to shift to e-learning, the perceived
effectiveness of learning or teaching after the shift to e-learning and satisfaction
with this shift. Since the study aimed to compare instructors’ perspectives and
students’ perspectives, the questionnaires’ three latter sections were designed to
measure the same parameters; therefore, they comprised the same questions.
However, the term “teaching” was applied to instructors, and the term “learning”
was applied to students. The sections are described in detail in the following four
paragraphs.
Section 1 comprised three questions for instructors and four questions for
students. For instructors, this section collected data about academic rank, years of
teaching experience and numbers of courses taught. For students, the collected
data were grade point averages (GPAs), levels of study, academic levels and
programmes of study.
Section 2 contained five questions to measure the readiness to switch to e-learning
by assessing experiences with e-learning platforms, as well as the feasibility and
accessibility of e-learning platforms prior to the COVID-19 pandemic. These
questions focused on whether instructors and students had electronic devices,
proper internet access and diverse ways to interact with each other—including
both face-to-face and telecommunication methods—in addition to questions
about the use of different BlackBoard BB features. Each answer that reflected the
use of e-learning platforms or a supporting atmosphere was given a readiness
score of 1. The total readiness score was calculated by adding the values of the
scores for each question. The maximum readiness score was 9, and the minimum
readiness score was 0.
Section 3 comprised a total of 14 questions to evaluate how both instructors and
students perceived e-learning experiences’ effectiveness after the pandemic-
related shift. These questions pertained to e-learning experiences and quality, the
extent to which e-learning supported independent learning and helped achieve
goals, students’ motivation, communication between students and instructors,
time management and organisation. A five-point Likert scale (Likert, 1932) was
employed in which the highest score, 5, indicated strongly agree, a score of 4
indicated agree, a score of 3 indicated neutral, a score of 2 indicated disagree and
the lowest score, 1, indicated strongly disagree.

22
Section 4 comprised five questions to assess satisfaction levels among students
and instructions regarding their learning or teaching experiences after the shift to
e-learning. These questions pertaining to satisfaction assessed overall experiences
related to teaching or learning, the clarity of remote teaching or learning
instructions, the accessibility of remote teaching or learning materials, the
simplicity of remote teaching or learning tools and the support or feedback
received during remote teaching or learning. The scale was also based on a five-
point Likert scale (Likert, 1932) in which the highest score of, 5, indicated very
satisfied, a score of 4 indicated satisfied, a score of 3 indicated neutral, a score of 2
indicated unsatisfied and the lowest score, 1, indicated not at all satisfied.
The internal consistency of the questionnaires’ reliability was tested using
Cronbach’s α, as described by Bolarinwa (2015). The obtained α value was equal
for instructors and students, as follows: perceived e-learning effectiveness (14
questions; 0.85, 0.88) and satisfaction (five questions; 0.78, 0.79). These values
showed that the questionnaire’s reliability was good, indicating that the items
effectively measured the same aspects. Additionally, the questionnaires were
piloted with 10% of the study’s respective samples. This pilot approach involved
testing the questionnaires on a smaller scale with a sample of the study population
before their distribution. This step was crucial since it helped ensure that the
questionnaires adequately measured the items for which they were designed and
that respondents provided feedback. Respondents’ feedback was requested on the
appropriateness, length and wording of the questionnaires and the instructions,
as well as the questions’ adequacy, as recommended by Marshall (2005).
2.3 Statistical Analysis
The collected data were analysed using Statistical Package for Social Sciences
software (SPSS version 22). Descriptive statistics were used to present the results
in frequencies and percentages. Normal data distribution was assessed using
Kolmogorov-Smirnov and Shapiro-Wilk tests. An independent t-test was
conducted to evaluate differences in means between instructors and students. For
instructors, Pearson’s correlation coefﬁcient was applied to determine the
correlation among teaching experience, academic rank, the readiness to shift to e-
learning, the perceived effectiveness of teaching after the shift to e-learning and
satisfaction with e-learning. For students, to assess the correlation among GPAs,
academic levels, the readiness to switch to e-learning, the perceived effectiveness
of learning and satisfaction with the shift to e-learning, Pearson’s correlation
coefficient was used. The statistical significance for these analyses was set to p ≤
0.05.
3. Results
3.1 General Characteristics
In total, 47 instructors and 254 students participated in this study. Of the
participating instructors, 65.9% had more than five years of teaching experience
and 80.9% had a PhD. The numbers of courses taught by the instructors were two
and three, representing 34% and 31.9% of participating instructors, respectively.
The majority of participating students were enrolled in courses at the
Rehabilitation Sciences department (35.8%) or Health Sciences department

23
(45.3%). Most students (95.5%) had an excellent (> 4.5) or very good (3.75–4.4)
GPA (Table 1).
Table 1. Characteristics of the study sample
% n
Instructors (n = 47)
Academic rank
Teaching assistant 10.6 5
Lecturer 8.5 4
Assistant professor 61.7 29
Associate professor 14.9 7
Professor 4.3 2
Teaching experience (years)
0–2 19.1 9
3–5 14.9 7
6–10 34.0 16
> 10 31.9 15
Number of courses taught
1 19.1 9
2 34.0 16
3 31.9 15
4 6.4 3
5 8.5 4
Students (n = 254)
Department
Rehabilitation Sciences 35.8 91
Health Sciences 45.3 115
Communication Sciences 8.7 22
Radiology Sciences 10.2 26
GPA*
Excellent (> 4.5) 41.3 105
Very good (3.75–4.4) 50.4 128
Good (2.5–3.74) 5.5 14
Poor (< 2.5) 0.0 0
Academic level
3–4 31.9 81
5–6 25.2 64
7–8 31.1 79
9–10 5.9 15
11–12 5.9 15
*GPA: Grade point average.
3.2 The Readiness to Shift to e-Learning
Vis-à-vis their readiness to shift to e-learning, all participating instructors (100%)
and the majority of participating students (97.6%) reported that they owned
electronic devices. Additionally, 93.6% of instructors and 94.5% of students
reported having proper internet access. Students and instructors seemed to use

24
similar ways to interact, including office hours, emails, Telegram, WhatsApp and
communication during lectures, and no significant differences were reported in
this regard (p > 0.05). Therefore, the authors’ null hypothesis was verified. In
contrast, the use of BB was significantly higher (more than twice as high) among
instructors (61.7%) compared to students (29.9%); p < 0.00001. However, an
analysis of BB use features suggested that students used certain features more
than instructors, particularly assignments, virtual classes and quizzes or exams (p
= 0.01, p = 0.00022 and p < 0.00001, respectively). The use of discussion boards and
the uploading of course materials were almost equal among students and
instructors since no significant difference was found in these regards (p > 0.05).
The calculated overall mean readiness scores showed that the obtained values
were equal to 6.2 ± 1.9 for instructors and 6.5 ± 1.5 for students. The difference
between the overall mean readiness scores was not significant between instructors
and students (p = 0.187) (Table 2). This finding shows that instructors and students
at the CHRS were equally prepared for the sudden shift to e-learning as a result
of the COVID-19 pandemic.
Table 2: Frequency (in percentages; n) of students’ and instructors’ interactions and
readiness
Instructors (n = 47) Students (n = 254) p-value
Electronic device 100 (47) 97.6 (248) 0.29
Proper internet 93.6 (44) 94.5 (240) 0.81
Interaction
Office hours 87.2 (41) 77.6 (197) 0.13
Email 93.6 (44) 90.6 (230) 0.49
BB 61.7 (29) 29.9 (76) < 0.00001
Telegram 6.4 (3) 7.5 (19) 0.78
WhatsApp 42.6 (20) 40.6 (103) 0.79
Lectures only 19.1 (9) 18.9 (48) 0.96
Blackboard features
Virtual classes 23.4 (11) 52.8 (134) 0.00022
Discussion board 51.1 (24) 44.9 (114) 0.44
Quizzes or exams 42.6 (20) 73.6 (187) < 0.00001
Uploading course materials 85.1 (40) 83.9 (213) 0.83
Submitting assignments 72.3 (34) 87.0 (221) 0.01
Overall readiness 6.2 ± 1.9 6.5 ± 1.5 0.187
Z-score
3.3 Satisfaction
Table 3 summarises the study’s results regarding satisfaction with e-learning
among instructors and students at the CHRS. The highest score was obtained for
accessibility of e-learning materials for both instructors (4.3 ± 0.7) and students (4.0 ±
1.0). Meanwhile, the lowest score was obtained for e-learning experience for
instructors (3.7 ± 1.1) and students (3.4 ± 1.1). A similar low score was obtained
for students in support or feedback received during e-learning (3.4 ± 1.3). For all
questions related to satisfaction, the average scores for instructors exceeded the
corresponding scores for students. Hence, the differences were significant for e-
learning experience and clarity of e-learning instructions between the two groups (p =
0.048 and p = 0.011, respectively). Consequently, the mean score for overall
satisfaction with e-learning was significantly higher for instructors than students

25
(4.1 ± .0.6 versus 3.7 ± 0.8; p < 0.001). Therefore, the authors’ null hypothesis was
rejected. These results demonstrate that, unlike the readiness to shift to e-learning
(which was similar between the study’s two populations), instructors were more
satisfied with their e-learning experiences than students.
Table 3: Satisfaction with e-learning among instructors and students at the College of
Health and Rehabilitation Sciences: Mean ± SD
Instructors (n = 47) Students (n = 254) p-value
Overall satisfaction 4.1 ± 0.6 3.7 ± 0.8 < 0.001
e-Learning experience 3.7 ± 1.1 3.4 ± 1.1 0.048
Clarity of e-learning instructions 4.0 ± 0.9 3.6 ± 1.1 0.011
Accessibility of e-learning materials 4.3 ± 0.7 4.0 ± 1.0 0.057
Simplicity of e-learning tools 4.2 ± 0.7 3.9 ± 1.1 0.251
Support or feedback received during e-
learning
4.1 ± 0.9 3.4 ± 1.3 0.685
P-values were calculated using an independent t-test.
5 = very satisfied. 1 = not at all satisfied.
3.4 Perceived Effectiveness
Table 4 presents the study’s results regarding the perceived effectiveness of
learning or teaching after the pandemic-related e-learning shift among both
instructors and students. For both instructors and students, the lowest mean
scores obtained pertained to shifting to e-learning is more enjoyable than face-to-face
learning at 2.1 ± 1.1 and 2.3 ± 1.3, respectively. The highest score was obtained for
shifting to e-learning introduced me to different online applications, which helped my
teaching/learning, at 4.4 ± 0.7 for instructors and 3.5 ± 0.8 for students. The score for
shifting to e-learning helped students become independent learners was significantly
higher among students (p = 0.037). The mean scores of the perception-related
items were higher for instructors than students. Among instructors, the average
scores for seven items were significantly higher (p < 0.05) than students’
corresponding scores: shifting to e-learning gave me a positive teaching or learning
experience; improved the quality of my teaching or learning; helped me be better organised;
introduced me to different online applications, which helped my teaching or learning;
introduced me to a variety of new assessment methods; a good motivation for teaching or
learning; and helps deliver or explain the subject’s material well. The authors’ null
hypothesis was rejected since the overall average score for instructors’ perceived
teaching experiences exceeded the mean score for students’ perceived learning
(3.3 ± .0.6 versus 2.9 ± .0.6; p < 0.001) (Table 4). This finding indicates that
instructors had better e-learning experiences than students.
Table 4: The perceived effectiveness of learning or teaching after the shift to e-
learning among instructors and students at the College of Health and Rehabilitation
Sciences: Mean ± SD
Instructors
(n = 47)
Students
(n = 254)
p-value
Overall perceived effectiveness of shifting to e-
learning
3.3 ± 0.6 3.0 ± 0.6 < 0.001*
It gave me a positive teaching/learning experience. 3.7 ± 0.9 2.8 ± 1.0 < 0.001*
It improved the quality of my teaching/learning. 3.1 ± 0.9 2.5 ± 1.0 < 0.001*
It helped me be better organised. 3.2 ± 0.9 2.9 ± 1.0 0.04*

26
It improved the communication between students and
instructors.
3.2 ± 1.0 3.1 ± 0.9 0.34
It helped students become independent learners. 3.3 ± 1.1 3.7 ± 0.7 0.037*
It helped me work at my own speed. 3.6 ± 1.1 3.6 ± 0.7 0.787
It enabled me to achieve course learning outcomes. 2.9 ± 1.1 2.9 ± 1.1 0.978
It introduced me to different online applications, which
helped my teaching or learning.
4.4 ± 0.7 3.5 ± 0.8 < 0.001*
It introduced me to a variety of new assessment methods,
which affected my teaching or learning in positively.
4.0 ± 1.0 3.1 ± 1.0 < 0.001*
It helped me manage my time more effectively. 3.3 ± 1.0 3.1 ± 1.2 0.209
Remote learning is a good motivation for teaching or
learning.
4.0 ± 0.8 2.6 ± 1.0 < 0.001*
Remote learning helps deliver or explain the subject’s
material well.
3.1 ± 1.0 2.5 ± 1.1 0.001*
Remote learning is more enjoyable than face-to-face
learning.
2.1 ± 1.1 2.3 ± 1.3 0.358
It made me prefer to teach more courses via remote
learning.
2.6 ± 1.1 2.8 ± 1.2 0.251
P-values were calculated using an independent t-test.
5 = strongly agree. 1 = strongly disagree.
3.5 Correlations between e-Learning Readiness, Satisfaction and Perceived
Effectiveness
Table 5 and Table 6 summarise the correlations between the different parameters
investigated for students and instructors, respectively. Pearson’s correlation
coefficient revealed a strong positive correlation between satisfaction with
perceived teaching or learning experiences after the shift to e-learning and the
perceived effectiveness of learning or teaching for students (r = 0.68, p < 0.001), as
well as a moderate correlation for instructors (r = 0.38, p = 0.008). The readiness to
switch to e-learning was weakly correlated with satisfaction for students only (r =
0.217, p < 0.001). Instructors’ academic rank exhibited a moderate correlation with
such readiness (r = 0.468, p = 0.001) and perceived effectiveness (r = 0.340, p =
0.019). Interestingly, for both students and instructors, the perceived effectiveness
of learning or teaching after the shift to e-learning significantly correlated with e-
learning satisfaction, unlike the readiness to switch to e-learning. This finding
might indicate satisfaction’s importance as a principal factor in the learning
process’s success.
Table 5: Correlation between the different parameters investigated among College of
Health and Rehabilitation Sciences instructors (n = 47)
Teaching
experience
Academic
rank
Readiness Perceived
effectiveness
Satisfaction
Teaching experience 1 0.265 0.255 -0.149 0.105
Academic rank — 1 0.468** 0.340* 0.246
Readiness — — 1 0.085 0.11
Perceived effectiveness — — — 1 0.383**
Satisfaction — — — — 1
Correlations were calculated using Pearson’s test; * p < 0.05 and ** p < 0.01

27
Table 6: Correlation between the different parameters investigated among College of
Health and Rehabilitation Sciences students (n = 254)
GPA Academic
level
Readiness Perceived
effectiveness
Satisfaction
GPA 1 -0.085 0.019 -0.03 -0.121
Academic level — 1 0.045 0.028 -0.068
Readiness — — 1 0.682** 0.108
Perceived effectiveness — — — 1 0.217**
Satisfaction — — — — 1
Correlations were calculated using the Pearson’s correlation coefficient; ** p < 0.01.
6. Discussion
This study aimed to provide an understanding of experiences related to the
pandemic-related abrupt shift to e-learning from the perspectives of both teachers
and students, assessing how readiness may affect these experiences. The study’s
findings revealed a high level of readiness to shift to e-learning among both
instructors and students, as well as a positive correlation between perceived
effectiveness and satisfaction. However, instructors showed significantly higher
satisfaction levels (p < 0.001) and perceived this experience to be more effective
than students had done.
Based on the study’s conceptual framework, these results indicate that a high level
of readiness among students and instructors—which led to satisfaction—
correlates with the shift to e-learning’s perceived effectiveness. Both students and
instructors agreed that e-learning provided an opportunity to work at their own
pace, manage their time more effectively and improve their interactions. The shift
to e-learning introduced instructors to a variety of previously not employed
online applications with which to communicate with students. Thus, this
expansion of the communication tools applied during e-learning improved
interactions between instructors and their students. Such interactions enhance
students’ engagement and satisfaction with online courses, as the literature has
previously shown (Beaudoin et al., 2009; Dixson, 2010). Moreover, students and
instructors harboured similar perspectives on the effectiveness of time
management and work pacing. e-Learning offers flexible teaching and learning
opportunities for more self-directed learning (Albarrak, 2011). Although e-
learning forces instructors to work outside their comfort zones, instructors
expressed high satisfaction levels. Instructors were more satisfied with their e-
learning experiences than students, which may have been due to BB’s regular
training and the accessible technical support provided by the university to all
faculty members (Alqabbani et al., 2020). Additionally, Maatuk et al. (2021)
explained that lower satisfaction levels among students had resulted from the
increased workload caused by e-learning. This influence could be particularly
present during the emergency shift to e-learning because of the COVID-19
pandemic. Thus, as cognitive load theorists have suggested, instructors should
consider the amount of work they assign their students and divide information
into chunks so that their students can have more effective learning experiences
(Van Merrienboer & Ayres, 2005). Congruent results by Sørebø and Sørebø (2008)
indicated that instructors’ perceived usefulness of e-learning and satisfaction are

28
useful in introducing appropriate elements for successful planning to achieve
effective e-learning.
The current study’s results indicate that e-learning helped students become
independent learners, showing that students’ autonomy and responsibility vis-à-
vis their learning increased after the shift to e-learning. Consistent with this
finding, Joo et al. (2011) and Yang and Cao (2013) concluded that learners’
realisation of their e-learning responsibilities predicted learning flows and
steadiness, as well as success. Additionally, e-learning facilitates the achievement
of learning outcomes and learners’ development, supporting students’ autonomy
(Algahtani, 2011). However, Lawrence (2018) claimed that students’ evaluations
of their learning’s effectiveness have provided invalid data, and Lawrence
considered such evaluations a poor measurement of learning effectiveness. This
claim was supported by a meta-analysis showing no significant correlation
between students’ teaching evaluations and learning (Uttl et al., 2017).
Interestingly, despite students’ low perception of effectiveness, they considered
e-learning useful in increasing their autonomy and responsibility vis-à-vis their
learning, which is a sign of successful learning (Joo et al., 2011; Yang & Cao, 2013).
In Saudi Arabia, e-learning started suddenly during the middle of the second
semester of the 2019–2020 academic year, without any prior planning, in response
to the COVID-19 pandemic-related lockdowns. Yet, advanced planning and
infrastructure are key determinants of successful e-learning (Algahtani, 2011;
Aljaber, 2018; Edwards & McKinnell, 2007; Nasiri et al., 2014; Rice & McKendree,
2014). Negative attitudes were observed among both students and instructors in
terms of preferring face-to-face learning and not enjoying e-learning. While the
sudden shift to e-learning was expected to influence e-learning’s effectiveness,
this impact was more apparent among students than instructors since students
harboured lower perceptions of e-learning’s effectiveness. A possible explanation
for this difference is courses’ clinical and practical nature, which Corter et al.
(2011) by confirming that students’ motivations were higher among a hands-on
group than at simulation distance laboratory. Additionally, some studies have
suggested that e-learning may be avoided since it cannot replace face-to-face
learning, especially in medical education (Albarrak, 2011; Rajab, 2018), given
medical academics’ independent and conservative nature (Lane, 2007). Another
explanation could be the difficulty of achieving intended learning outcomes,
which led to improper planning for a complete e-learning mode since courses
were designed to be delivered in a traditional mode.
Moreover, students’ experience of the shift to e-learning were found to be
negative. Hence, their introduction to new assessment methods could be more
stressful since they were not trained in these methods; only 29.9% of students had
used BB before the studied shift to e-learning. After this shift, classic assessment
methods changed to electronic alternatives and a new grade distribution
occurred. Therefore, the lack of preparation at both the technical level and the
psychological level—as well as concerns about lower grades—could have
contributed to this negative perception. Furthermore, the overall increase in
anxiety during the COVID-19 pandemic could also have influenced learning
(Almoayad et al., 2020; Cao et al., 2020; Gallagher & Schleyer, 2020; Saddik et al.,

29
2020). Students were also found not to have fully understood e-learning course
material, marking a significant difference from instructors’ perception. This
finding could be associated with students’ preference for face-to-face learning
over e-learning and a lack of enjoyment, as previously discussed. Additionally,
regarding the nature of health-profession courses, students were suddenly
introduced to various substitutes to clinical training—such as simulated and
recorded cases—which are effective in health professions education (Albarrak,
2011). Bao (2020) claimed that instructors should break down e-learning material
and adopt a modular teaching method to increase students’ involvement in e-
learning.
The current study has shown that students and instructors were ready to shift to
e-learning, demonstrating satisfaction with the support provided, which led to a
positive perception of this shift. However, a lack of planning was highlighted in
the negative perceptions among learners and teachers. The continued use of
traditional methods of teaching, assessing and learning among both teachers and
learners—without proper modulation for e-learning—could explain e-learning’s
perceived failure to help achieve intended learning outcomes. Thus, given the lack
of clarity about education’s future and the expected extension of e-learning, and
to promote successful e-learning experiences in health professions education, both
instructors and students must adopt new teaching and learning approaches and
share their decisions regarding the e-learning planning process. This approach is
essential to overcome gaps among the main education stakeholders, especially in
different healthcare specialities, which may require various educational strategies
and learning styles.
6.1 Limitations
Although this study explored e-learning-related perceptions among health
professions instructors and students, it did not differentiate between specialities
vis-à-vis the nature of clinical courses taught through e-learning. Moreover, the
clinical and practical training conducted after the shift to e-learning using online
alternatives, such as simulation, were not tested for their effectiveness. The other
limitation of this study is the convenient sampling technique used during the
project’s data collection. While the study’s findings cannot be generalised, they
could nonetheless serve as a basis for adequate planning to develop a complete,
successful e-learning model in medical education.
7. Conclusion
This study aimed to assess the level of readiness for e-learning, perceived
effectiveness and satisfaction regarding e-learning experiences among both
students and instructors at a college with courses in 13 health professions during
the COVID-19 pandemic. The results showed that the readiness to shift to e-
learning was high among both students and instructors, positively correlating
with satisfaction—which, in turn, positively correlated with perceived
effectiveness. The study’s main findings are that e-learning provided similar
opportunities for both students and instructors at the CHRS to work at their own
pace, manage their time more effectively and improve their interactions. On the
other hand, the sudden shift to e-learning was not enjoyable, and it did not help
students or instructors achieve course learning outcomes; both groups would

30
have preferred to have more courses delivered via face-to-face learning.
Throughout these findings, respondents’ experiences highlighted proper
planning’s importance to e-learning. However, a complete e-learning mode might
not be suitable for all aspects of health professions education—especially not for
courses that require practical skills. By analysing both positive and negative e-
learning perceptions during experiences after the sudden shift to e-learning
among instructors and students at the CHRS, this study also recommended
planning for a blended learning approach integrating face-to-face learning and e-
learning to best achieve intended learning outcomes. One of this study’s main
recommendations is to plan for e-learning. Utilising different approaches and
teaching strategies and considering dividing information into chunks and tasks to
avoid overloading students, is recommended to obtain greater benefits from the
shift to e-learning during the COVID-19 pandemic, such as better time
management and increased independence. Strategies such as team-based learning
or flipped classes may be more enjoyable for both teachers and learners during e-
learning. Additionally, some assessment methods—such as open-book exams and
oral exams—may be more suitable for e-learning than traditional assessment
methods. Moreover, blended learning could be suitable to address intended
learning outcomes and increase motivation during clinical and practical training
while maintaining e-learning’s benefits. Based on this study, the authors
recommend further research exploring the e-learning shift’s effect on clinical
training outcomes for different health professions. Studies on e-learning
experiences a year after this shift, when educational institutions are expected to
have clearer plans and have better prepared for e-learning, are also
recommended.
Acknowledgements
This research was funded by the Deanship of Scientific Research at Princess
Nourah bint Abdulrahman University through the Fast-track Research Funding
Program.
Declaration of Interest Statement
The authors declare no conflicts of interest.
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