Experiences of international medical
students enrolled in Chinese medical
institutions towards online teaching
during the COVID-19 pandemic
Sarfraz Aslam1, Huma Akram2, Atif Saleem3 and BaoHui Zhang1
1
School of Education, Shaanxi Normal University, Xi’an, Shaanxi, China
Faculty of Education, Northeast Normal University, Changchun, Jilin, China
3
College of Teacher Education, College of Education and Human Development, Zhejiang Normal
University, Jinhua, Zhejiang, China
2
ABSTRACT
Submitted 26 May 2021
Accepted 4 August 2021
Published 25 August 2021
Corresponding authors
Sarfraz Aslam,
sarfrazmian@nenu.edu.cn
BaoHui Zhang,
baohui.zhang@snnu.edu.cn
Academic editor
Aslı Suner
Additional Information and
Declarations can be found on
page 17
DOI 10.7717/peerj.12061
Copyright
2021 Aslam et al.
Distributed under
Creative Commons CC-BY 4.0
Introduction: . The COVID-19 pandemic has forced the world to pause.
One hundred and eighty-eight countries have imposed countrywide school closures,
affecting more than 1.5 billion children and youths. The majority of academic leaders
are currently encouraging online education to resolve this crisis. This study aimed to
investigate international medical students’ (IMS) experiences of online teaching
during the COVID-19 pandemic.
Methods: Data were collected online using a validated questionnaire and one
open-ended question, presented on the Google forms platform. The study attracted
responses from 1,107 IMS volunteer participants. IBM SPSS v. 25, GraphPad Prism
v. 9, and MindManager v. 2018 were used for data analysis. All variables were
subjected to descriptive statistical analysis. The Mann–Whitney U test was used in
subgroup analysis and the Kruskal-Wallis test was also applied for year-wise
comparisons. Open-ended text responses were analyzed qualitatively, extracting
themes by which responses were classified.
Results: Among 1,107 respondents, a total of 67.8% were males, and the majority
(63.1%) of the IMS were in the age group of 21–23 years. The results show that more
than half of the respondents reported their Internet connection quality as poor to
average. Poor Internet connection severely affected IMS online learning experience.
Persistent and recurrent issues with Internet access became a significant concern for
IMS. Lack of electricity is one of the factors that can contribute to poor learning
output and dissatisfaction with online teaching. IMS perceive online medical
education as unhelpful in several phases of the training, such as improving their
clinical skills, knowledge, and discussion skills.
Conclusions: During these unprecedented periods, online teaching has allowed
medical education to continue. However, IMS are generally dissatisfied with online
teaching. Medical students must visualize the human body, so supportive
technologies are important to compensate for the lack of clinical practices. Medical
institutions may need to invest in faculty training programs and continually adjust to
enhance the content of online training and international partnerships. A switch
from conventional face-to-face teaching to a fully functional virtual education
framework in the medical education field will take time and experience.
How to cite this article Aslam S, Akram H, Saleem A, Zhang B. 2021. Experiences of international medical students enrolled in Chinese
medical institutions towards online teaching during the COVID-19 pandemic. PeerJ 9:e12061 DOI 10.7717/peerj.12061
Subjects Science and Medical Education, Statistics
Keywords Medical students, Medical education, COVID-19, Online teaching, China, Pandemic,
International students
INTRODUCTION
Current scenario: the world and China
The twenty-first century is experiencing what may be one of its most devastating events.
Now known to the world as the COVID-19 pandemic, the virus swiftly engulfed the whole
world with almost 11 million cases in a span of around six months. It has not only
increased the global burden of disease but has heavily dented many social systems,
including education (Baloch et al., 2021). The World Health Organization (WHO)
announced the COVID-19 outbreak initially as a public health emergency of international
concern on January 30, 2020 and later declared it a pandemic on March 11, 2020
(WHO, 2020). In China, the first case of novel coronavirus was reported in Wuhan in
December 2019 (Chen et al., 2020; Huang et al., 2020). At that time it was not expected to
become a pandemic, but by June 20th 2020 COVID-19 had infected more than 8.5 million
and killed 460 thousand people globally (Sindiani et al., 2020).
Soon after this viral outbreak, semester break was approaching in China and many
international students were returning to their homes for vacations. The Chinese Ministry
of Foreign Affairs announced that all students (local and international) must await official
permission before returning to their institutions (Wang & Dai, 2020). On 28th March
2020, in view of the rapid global spread of COVID-19, China temporarily suspended the
right of foreign nationals holding valid visas or residence permits, including international
students, to enter the country (Overseas Security Advisory Council, 2020).
With the growing influence of Chinese education, the number of students in China
is increasing (Gu et al., 2020). In 2017, China became the leading destination for
international students in Asia (Jianfeng, 2018). China is currently one of the fastestgrowing destinations for international medical students (IMS) (Fan, Kosik & Chen, 2013),
having received over 68,000 IMS, mostly from Asian and African nations (Li, 2019). More
than 10,000 students came to China to study medicine in 2018, most of whom chose a
Bachelor of Medicine Bachelor of Surgery (MBBS) program delivered in English. Chinese
medical institutions offer an English-taught 6-year undergraduate program (MBBS)
that includes 5-year theoretical and practical studies courses and a one-year clinical
rotation internship. Successful students receive a bachelor’s degree in medicine and
surgery at the end of the program. The Chinese government has authorized 49 medical
institutions to accept IMS (Chu et al., 2019). Each of those students requires a study visa
and many of those planning to work in China after graduation are required to apply for a
work visa (Li & Sun, 2019).
The COVID-19 pandemic has forced the world to pause, and countries worldwide have
implemented strict policies to avoid disease transmission (Sarwar et al., 2020). It was
observed by UNESCO (2020) that 91% of the total global student population has been
absent from school in more than 188 countries affected by the pandemic (Koçoglu &
Tekdal, 2020).
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COVID-19 and online medical education
In order to continue to offer higher education, authorities worldwide issued new
recommendations on conversion to online university teaching and most academic leaders
are currently encouraging this switch (UNESCO, 2020). In the COVID-19 pandemic
situation, universities at all levels worldwide have led their teachers and students to use
material conventionally delivered face to face via an online format.
Accelerated development of IT systems and enhancement of Internet mechanisms have
allowed online learning to become central to modern global education (Wang, Zhang &
Ye, 2020). Moving from on-campus to distance learning may be facilitated by methods
such as self-paced independent study and remote interactive workshops, or real-time
immersive settings that are needed for distance learning (Cook et al., 2010).
A rise in external resources and teaching programs such as Osmosis and BiteMedicine
has allowed many teaching sessions to be available to medical students online (Dost et al.,
2020). Learning health and medicine with Osmosis is intended to be fun, with a visual
style to help communicate difficult concepts by grounding them with visual memory
anchors, memorable characters, and engaging animations (Osmosis, https://www.osmosis.
org/about), and BiteMedicine is a free complete resource intended to help medical students
excel in their medical studies. Question banks, online textbooks, live webinars and
forums are provided to help students to pass their exams (BiteMedicine, https://www.
bitemedicine.com).
Several studies indicate that online and blended educational approaches are equivalent
to conventional classroom models. However, few studies are based on students and
teachers’ satisfaction with online education during situations such as the COVID-19
pandemic (Muflih et al., 2020). Appraising a student’s simulated learning experience may
help assess the effectiveness of an online training program (Hamutoglu et al., 2020).
Medical students’ perceptions about online learning
The online learning experiences of medical students globally have been the focus of many
recent studies. A national cross-sectional survey (Dost et al., 2020) investigated 2721 UK
medical students’ perceptions of online teaching during the COVID-19 pandemic.
It was concluded that online teaching had allowed the continuity of medical education
during these exceptional times. A cross-sectional survey among medical students in the
North of Jordan (Sindiani et al., 2020) found that most medical students favored the
conventional face-to-face teaching method over the solo online teaching methods.
A survey of Pakistani dentistry graduates found that they were unanimously unhappy
with different online teaching sessions (Sarwar et al., 2020). However, a nationwide
survey of online teaching strategies in dental education in China found the online delivery
to be necessary and effective during the outbreak. The study recommends that the
online education model and pedagogy may be enhanced for future delivery of dental
education (Wang, Zhang & Ye, 2020).
As mentioned earlier, China is host to many IMS, but to date no study has investigated
their online learning experiences. Research on this issue is important as a basis for
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development of sophisticated online learning-enabled programs. The present study
therefore aimed to:
1. Assess the experiences of IMS regarding the effectiveness of online teaching.
2. Investigate the challenges faced by IMS in adjusting to this new mode of learning.
3. Propose practical strategies for medical institutions to address the identified factors.
The findings of this research will serve as the foundation for future applied and
intervention studies, as a guide for universities and policymakers worldwide, and may be
used to better understand the positive role of online teaching in medical education.
MATERIALS AND METHODS
A cross-sectional survey design was used for data collection (Fraenkel, Wallen & Hyun,
1993) between January and March 2021 via an online Google-based questionnaire.
Participants
The target population was IMS studying in various medical institutions in China.
Undergraduate IMS who were enrolled and took online courses from the first to the
fifth year in the 6-year MBBS program were included in this study. A total of 1,107 IMS
from fifteen medical institutions across China were recruited by simple random sampling
in which each member of the population has an equal chance of being selected. This
approach removes bias from the selection procedure and should result in a representative
sample (Gravetter & Forzano, 2011). Ideally, a sample size of more than a few hundred is
required in order to apply this sampling technique (Saunders, Lewis & Thornhill,
2009). The present study sample size was determined using an Open-Epi online calculator.
If 50% of the target population subjects were interested in participating, a sample size
of 385 would be required to assess the estimated proportion at 5% absolute precision and
95% confidence (Dhand & Khatkar, 2014). Moreover, a sample size of about 400 should be
sufficient for a large population (Krejcie & Morgan, 1970). The G Power 3.1 calculator
was used to compute statistical power of 0.95, above the value of 0.80 considered adequate
in social science research (Hair et al., 2016; Uttley, 2019).
Instrument
The data collection tool consisted of two parts: a questionnaire and an open-ended
question.
Questionnaire
A validated questionnaire (Sarwar et al., 2020) in English consisting of 31 items (without
sub-dimensions or inverse items), with a mixture of question styles including 5-point
Likert-type questions, was used to collect the data (File S1). The questionnaire is
considered highly reliable, the Cronbach’s alpha value of the original version being 0.78
(Sarwar et al., 2020). The current authors checked validation prior to the final data
collection and found Cronbach’s alpha value of 0.83 indicating an acceptable level of
internal consistency. This questionnaire was designed and used to measure the
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self-reported effectiveness of medical e-learning classes during the COVID-19 pandemic.
The questionnaire explored the following three themes (1) Demographics of participants
(2) General information about technology readiness and online classes (3) Effectiveness
of online classes.
Opinions about online teaching
The current researchers added one open-ended question asking ‘‘Please tell us the three
most crucial improvements required to make online sessions more effective and anything
you want to share; please feel free to share’’ intended to gather IMS perspectives about the
challenges they face during online teaching and their suggestions for improvements to
make online sessions more effective.
Ethical considerations and participation
Ethics committee approval was received from Shaanxi Normal University’s institutional
review board (reference number AR 2021-01-001). IMS were given brief information
about the study and were invited to agree to participate using the consent form on the first
page of the online questionnaire. Participation was voluntary, and before beginning the
survey, participants were told that all data obtained would be anonymous and would be
used for research purposes only. At the start of the survey, a mandatory email id was
required to validate participation, affirming consent and preventing multiple responses.
Data collection
The survey was created using Google forms, and the link was distributed to IMS globally
via social media (WeChat, WhatsApp, Facebook and others). The authors also contacted
some known IMS and invited them to let others know about this study to minimize
non-response bias. Response rate could not be determined since the number of IMS who
were aware of the study was unknown.
Data analysis
Data were exported from Google Forms for data analysis using IBM SPSS v. 25. GraphPad
Prism v. 9 was used to generate graphs, and MindManager v. 2018 was used to draw the
coding map. Shapiro–Wilk and Kolmogorov–Smirnov normality tests were used to
examine whether the data were distributed normally, which found the data set to be
non-Gaussian in distribution. Descriptive statistical analysis was performed on all the
variables. Mann–Whitney U tests were used in subgroup comparisons between public and
private sector and male and female IMS. Kruskal–Wallis tests followed by Mann–Whitney
U tests were used for year-wise comparisons. P values of < 0.05 were considered
statistically significant.
Content analysis, a qualitative data analysis method, was used to categorize the open
text responses. The main purpose of content analysis is to capture the concepts and
correlations that may explain the collected data (Şimşek & Yıldırım, 2011). Two members
of the research team coded and classified the open-ended responses separately. Then, they
collectively did this based on the analysis of the core concept for each aim. To ensure
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authenticity, investigator triangulation was used (Ma et al., 2009). A three-step coding
approach was used to further refine the analysis of responses to the open-ended question.
A thematic analysis technique was used to identify, deduce and record trends in the
responses (Clarke & Braun, 2013; Ritchie et al., 2013). A three-step coding approach was
adopted (Gioia, Corley & Hamilton, 2013), similar to Strauss and Corbin’s open and axial
coding (1990). The first step involved coding all the open-ended responses into several
codes, then axial coding (Corbin & Strauss, 2014) was used to merge similar codes that
were revealed during the first step, and cluster the codes into a less tangible form (Gioia,
Corley & Hamilton, 2013). In the final step (Fig. 1) all similar codes that were found in the
second step were grouped into three themes; (a) IMS perception of online teaching, (b)
Barriers to online teaching, and (d) Future strategies for online teaching (Gioia, Corley &
Hamilton, 2013) and the frequency of the codes was calculated (Fig. 2).
RESULTS
Demographics of participants
A total of 1,107 IMS participated in this study. Among them, 750 (67.8%) participants were
males. Most (n = 698; 63.1%) were in the age range of 21 to 23 years, followed by 24 to
26 years (n = 204; 18.4%). The highest proportion of students were in their fourth year
(n = 496; 44.8%), followed by third year (n = 248; 22.4%). Students from 12 countries
participated in the study, the highest proportion being from Pakistan, 297 (26.8%),
followed by Somalia 193 (17.4%), Indonesia 132 (11.9%) and India 111 (10%). For most of
the students (n = 1,051; 94.9 %) their current location was their home country (Table 1).
Technology readiness of the study participants and general
information about online classes
Most participants (n = 1,085; 98%), reported easy access to the internet. However, 664
(59.9%) participants rated their internet connection from poor to average, while 322
(29.9%) rated the quality of their connection as excellent. More than half of the
participants (n = 581; 52.5%), reported impeded electrical supply. This is an alarming
situation preventing Internet connection. Smartphone (n = 642; 58%) and laptop (n = 310;
28%) were selected by most participants as the preferred devices for accessing online
classes. More than half (n = 587; 53%) of participants indicated that email was their
preferred method of communication about the course schedule, followed by social media
app WeChat 387 (35%). A total of 787 (71.1%) respondents reported attending three hours
of online classes in a day. More than half of respondents (n = 659; 59.5%) reported
that no assessment took place at the end of each class. A majority (n = 687; 62.1%) reported
that three subjects were covered in a day (Table 2).
IMS experience of online classes
Overall, students reported that online classes were not highly effective, and offered limited
opportunities to interact with teachers. More than half (n = 614; 55.4%, 3.40 ± 1.40) of
respondents agreed or strongly agreed with the statement that online classes hamper their
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Figure 1 Representation of the coding tree that resulted from the coding process, with first-order codes, second-order codes, and aggregate
Full-size DOI: 10.7717/peerj.12061/fig-1
themes.
attention and focus. Moreover, 801 (72.4%, 1.92 ± 1.24) of the respondents disagreed or
strongly disagreed that online classes are equally or more informative than campus classes.
Furthermore, 664 (60%, 1.83 ± 1.24) respondents strongly disagreed that online sessions
should continue even after on-campus classes have restarted (Table 3).
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Figure 2 Representation of the coding under three aggregate themes, the indication of the number of
Full-size DOI: 10.7717/peerj.12061/fig-2
segments retrieved per code is shown as well.
Comparison between public and private sector medical schools
A Mann–Whitney U test was conducted to compare satisfaction with online classes among
students in public and private sector medical schools. As shown in Table 4, no statistically
significant difference was found between IMS from public and private institutions.
Students from both sectors were dissatisfied with various features of online teaching
including: key information availability (Public 2.92 ± 1.30, Private 2.87 ± 1.32, P = 0.617),
assistance received in overcoming difficulties (Public 3.04 ± 1.13, Private 2.99 ± 1.14,
P = 0.603), teachers are well trained for online classes (Public 2.86 ± 1.47, Private 2.78 ±
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Table 1 A table outlining the demographics (gender, age, year of program, country of origin and
current location) of IMS responding to the survey (n = 1,107).
Variable
Frequency
Percentage (%)
Male
750
67.8
Female
357
32.2
18–20 years
153
13.8
21–23 years
698
63.1
24–26 years
204
18.4
27 and above
52
4.7
First-year
113
10.2
Second-year
125
11.3
Third-year
248
22.4
Fourth-year
496
44.8
Fifth-year
125
11.3
Pakistan
297
26.8
Somalia
193
17.4
Gender
Age
Year of program
Country of origin
Bangladesh
107
9.7
Indonesia
132
11.9
India
111
10
Nigeria
81
7.3
Sudan
51
4.6
Ghana
43
3.9
Kenya
39
3.5
Tanzania
27
2.4
Sri-Lanka
13
1.2
Uganda
13
1.2
Home Country
1,051
94.9
Other than the home country
5
0.5
China
51
4.6
Current location of IMS
1.50, P = 0.386), and whether attending classes from home hampers attention (Public
3.40 ± 1.39, Private 3.35 ± 1.42, P = 0.552).
Effectiveness of online classes: an analysis of perceptions based on gender
A Mann-Whitney U test was used to examine IMS perceptions of effectiveness of the
online classes. Table 5 shows that IMS were dissatisfied with the effectiveness of online
classes as compared with active campus learning sessions, with no significant difference in
dissatisfaction between genders (male, 1.89 ± 1.21, female, 1.96 ± 1.31, P = 0.613).
Similarly, no significant gender difference was found in the view that online sessions
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Table 2 A table displaying IMS choices on their technology readiness & information about online classes.
Statement
Response
Frequency & Percentage (%)
Yes
1,085 (98%)
No
22 (02%)
Poor
111 (10%)
Fair
333 (30.1%)
Average
220 (19.9%)
Good
121 (10.9%)
Excellent
322 (29.9%)
Yes
526 (47.5 %)
No
581 (52.5 %)
Do you have easy access to the internet
If yes, how would you grade your internet connectivity?
(1 = poor to 5 = excellent)
Do you have an unimpeded electrical supply?
Which of the following device do you use for online classes
Smartphone
642 (58%)
Laptop
310 (28%)
Tablet
122(11%)
Desktop
30 (2.7%)
Other
3 (0.3%)
Via e-mail
587 (53%)
What is the mode of notification of the class schedule?
Via social media (e.g. WeChat, QQ) 387 (35%)
Via an Institution website
133 (12%)
Via SMS on cell phone
00 (0%)
Other
00 (0%)
One day before
365 (33%)
How long before the start of a class are you informed about the lecture schedule
Two days before
609 (55%)
Few hours before
100 (09%)
One hour before
22 (02%)
Other
11 (01%)
March
804 (72.6 %)
April
289 (26.1 %)
May
14 (1.3 %)
June
00 (0%)
August
00 (0%)
1h
00 (0%)
2h
301 (27.2 %)
3h
787 (71.1 %)
4h
07 (0.6 %)
When did the online classes start after the 2020 vacations
What is the duration of online teaching per day
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Table 2 (continued )
Statement
Response
Frequency & Percentage (%)
5 or more h
12 (1.1 %)
Yes
448 (40.5 %)
No
659 (59.5 %)
One
00 (0%)
Two
401(36.2 %)
Three
687(62.1 %)
Four
07(0.6 %)
Five or more
12(1.1 %)
Are you being assessed at the end of each class through a test or quiz?
How many subjects are covered in one day?
should not continue after commencement of on campus classes (male, 1.81 ± 1.23, female,
1.85 ± 1.27, P = 0.785). These findings indicate that IMS perceive online medical education
as unhelpful in several phases of the training, such as improving their clinical skills,
knowledge and discussion skills.
An analysis of perceptions based on year of study
A Kruskal–Wallis test was used to examine IMS perceptions regarding the effectiveness of
online classes. Table 6 shows that dissatisfaction with the effectiveness of online classes
(based on responses to questions such as ‘online learning fits in my schedules better than a
typical day to day classes’) was similar in all five years of study (First year 1.84 ± 1.18;
Second year 1.81 ± 1.13; Third year 1.77 ± 1.13; Fourth year 1.89 ± 1.16; Fifth year 1.93 ±
1.22; P = 0.613). These findings indicate that IMS in all phases of the program believe that
online medical education is not fulfilling their needs.
IMS’ perspectives on the improvement of online teaching and future
strategies
An optional open-ended question (‘‘Please tell us the three most crucial improvements
required to make online sessions more effective and anything you want to share; please feel
free to share’’) was addressed by 251 (22.67%) participants. A rigorous procedure was
adopted for systematic coding. The qualitative data analysis method is described in
Methods. See Fig. 2 for three main themes, related codes and their frequency.
DISCUSSION
The COVID 19 pandemic forced educational institutions worldwide to adapt and
implement online platforms for teaching (Akram et al., 2021; Wu et al., 2020). Here, we
discuss how this situation has shaped the use of online teaching. Some of the findings
were correlated with similar challenges in different environments; it is important to
remember that our results reflect students’ diverse experiences from 12 countries. This
investigation revealed dissatisfaction with online teaching among IMS enrolled with
different medical institutions in China during the pandemic period. Previously, the courses
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Table 3 A table displaying students’ perceptions on their experiences of online teaching, ranked on a Likert scale from 1 to 5, where
1 = strongly disagree and 5 = strongly agree. Likert scores have been shown as frequency, percentage and mean ± SD.
Statement
Strongly
Agree (n, %) Neutral
agree (n, %)
(n, %)
Disagree
(n, %)
Strongly
disagree
(n, %)
Mean ± SD
My institution has an online learning management system
(LMS) or Web site where all information about online
classes is available
102 (9.2%)
349 (31.5%)
227 (20.5%)
238 (21.5%)
191 (17.3%)
2.93 ± 1.25
All key information about the course is available on LMS or 136 (12.3%)
the institution Web site
290 (26.2%)
221 (20%)
256 (23.1%)
204 (18.4%)
2.90 ± 1.30
All course readings, assignments, and lectures are available 136 (12.3%)
online
374 (33.8%)
221 (20%)
256 (23.1%)
120 (10.8%)
3.13 ± 1.21
Students are assisted in overcoming obstacles in accessing
the classes or materials
102 (9.2%)
323 (29.2%)
290 (26.2%)
290 (26.2%)
102 (9.2%)
3.02 ± 1.13
Time allotted for online classes is sufficient
119 (10.7%)
392 (35.4%)
205 (18.5%)
323 (29.2%)
68 (6.1%)
3.15 ± 1.13
I am able to interact with teachers during online classes
136 (12.3%)
374 (33.8%)
153 (13.8%)
239 (21.6%)
205 (18.5%)
2.99 ± 1.33
I am able to interact with teachers after online class in the
Q&A session
170 (15.4%)
255 (23%)
239 (21.6%)
239 (21.6%)
204 (18.4%)
2.95 ± 1.34
Every individual is given a chance to participate and pitch in 136 (12.3%)
their ideas during online classes
220 (19.9%)
256 (23.1%)
256 (23.1%)
239 (21.6%)
2.78 ± 1.31
The teachers are well trained for online classes and are able 221 (20%)
to use the video conferencing app with ease
170 (15.4%)
239 (21.6%)
170 (15.4%)
307(27.7%)
2.84 ± 1.48
Attending classes from home hampers my attention and
focus
307 (27.7%)
187 (16.9%)
136 (12.3%)
170 (15.4%)
3.40 ± 1.40
Online classes are equally or more informative as compared 68 (6.1%)
with active learning on campus
85 (7.7%)
153 (13.8%)
187 (16.9%)
614 (55.5%)
1.92 ± 1.24
Online learning fits in my schedules better than a typical day 34 (3.1%)
to day classes
102 (9.2%)
170 (15.4%)
170 (15.4%)
631 (57%)
1.86 ± 1.16
85 (7.7%)
Demonstration of practical/clinical/lab work by the
instructor during online classes would help me learn in a
better way
120 (10.8%)
136 (12.3%)
170 (15.4%)
596 (53.8%)
2.03 ± 1.33
I would like to have these online sessions continued even
after campus classes have started
102 (9.2%)
68 (6.1%)
205 (18.5%)
664 (60%)
1.83 ± 1.24
307 (27.7%)
68 (6.1%)
Note:
SD, standard deviation.
included a rudimentary online medical education presence but the COVID-19 outbreak
triggered much higher reliance on online teaching (Dost et al., 2020) and students became
acquainted with various online learning methods and forums (Dhawan, 2020).
Experiences of IMS regarding the effectiveness of online teaching:
technology readiness of IMS on uptake of online teaching
More than half of the respondents reported the quality of their internet connections as
poor to average. Poor internet connection severely impacted students’ online learning
experience. Persistent and recurring problems of internet access have emerged as a
significant challenge faced by students and teachers, with previous studies reporting
similar challenges to students’ online learning (Dridi et al., 2020). Family distractions,
internet access, tutorial scheduling (Dost et al., 2020) and delayed communication
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Table 4 A Mann–Whitney U test results (p < 0.05): comparison of public and private sector medical institutes regarding the effectiveness of
online teaching.
Survey questions
Public,
n = 806
Mean ± SD
Private,
n= 301
Mean ± SD
p-Value
My institution has an online learning management system (LMS) or Web site where all
information about online classes is available
2.95 ± 1.25
2.91 ± 1.27
0.706a
All key information about the course is available on LMS or the institution Web site
2.92 ± 1.30
2.87 ± 1.32
0.617a
All course readings, assignments, and lectures are available online
3.14 ± 1.20
3.10 ± 1.23
0.649a
Students are assisted in overcoming obstacles in accessing the classes or materials
3.04 ± 1.13
2.99 ± 1.14
0.603a
Time allotted for online classes is sufficient
3.15 ± 1.14
3.14 ± 1.13
0.941a
I am able to interact with teachers during online classes
2.99 ± 1.34
3.00 ± 1.32
0.950a
I am able to interact with teachers after online class in the Q&A session
2.96 ± 1.33
2.91 ± 1.34
0.596a
Every individual is given a chance to participate and pitch in their ideas during online classes
2.80 ± 1.31
2.72 ± 1.32
0.349a
The teachers are well trained for online classes and are able to use the video conferencing app with ease
2.86 ± 1.47
2.78 ± 1.50
0.386a
Attending classes from home hampers my attention and focus
3.40 ± 1.39
3.35 ± 1.42
0.552a
Online classes are equally or more informative as compared with active learning on campus
1.92 ± 1.24
1.91 ± 1.24
0.878a
Online learning fits in my schedules better than a typical day to day classes
1.87 ± 1.17
1.82 ± 1.13
0.544a
Demonstration of practical/clinical/lab work by the instructor during online classes would help me learn in a 2.04 ± 1.34
better way
1.99 ± 1.31
0.537a
I would like to have these online sessions continued even after campus classes have started
1.75 ± 1.19
0.193a
1.85 ± 1.26
Notes:
a
p-Value calculated by using Mann–Whitney U test.
SD = standard deviation.
Table 5 Comparison of male and female perceptions of online classes: a Mann–Whitney U test results (p < 0.05).
Survey questions
Male,
Female,
n = 750
n = 357
Mean ± SD Mean ± SD
p-Value
My institution has an online learning management system (LMS) or Web site where all information about 2.92 ± 1.28 2.97 ± 1.30
online classes is available
0.468a
All key information about the course is available on LMS or the institution Web site
2.89 ± 1.28 2.93 ± 1.36
0.692a
All course readings, assignments, and lectures are available online
3.12 ± 1.18 3.15 ± 1.26
0.587a
Students are assisted in overcoming obstacles in accessing the classes or materials
3.01 ± 1.123 3.06 ± 1.15
0.565a
Time allotted for online classes is sufficient
3.14 ± 1.11 3.17 ± 1.19
0.719a
I am able to interact with teachers during online classes
2.97 ± 1.32 3.04 ± 1.37
0.401a
I am able to interact with teachers after online class in the Q&A session
2.93 ± 1.32 2.98 ± 1.38
0.561a
Every individual is given a chance to participate and pitch in their ideas during online classes
2.77 ± 1.28 2.79 ± 1.38
0.895a
The teachers are well trained for online classes and are able to use the video conferencing app with ease
2.82 ± 1.45 2.87 ± 1.52
0.624a
Attending classes from home hampers my attention and focus
3.38 ± 1.38 3.43 ± 1.44
0.402a
Online classes are equally or more informative as compared with active learning on campus
1.89 ± 1.21 1.96 ± 1.31
0.613a
Online learning fits in my schedules better than a typical day to day classes
1.84 ± 1.13 1.88 ± 1.21
0.837a
Demonstration of practical/clinical/lab work by the instructor during online classes would help me learn in a 1.98 ± 1.29 2.12 ± 1.42
better way
0.254a
I would like to have these online sessions continued even after campus classes have started
0.785a
1.81 ± 1.23 1.85 ± 1.27
Notes:
a
p-Value calculated by using Mann–Whitney U test.
SD = standard deviation.
Aslam et al. (2021), PeerJ, DOI 10.7717/peerj.12061
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Table 6 A Kruskal–Wallis test results (p < 0.05): year-wise comparison of IMS perceptions of online classes.
Survey questions
First year,
n = 113
Second
Third year,
year,
n = 248
n = 125
Mean ± SD Mean ± SD Mean ± SD
My institution has an online learning management system (LMS) or 2.90 ± 1.26 2.85± 1.28
Web site where all information about online classes is available
2.97± 1.24
Fourth
Fifth year, p-Value
year,
n = 125
n = 496
Mean ± SD Mean ± SD
2.93± 1.26
2.98 ± 1.28 0.882a
All key information about the course is available on LMS or the
institution Web site
2.87 ± 1.33 2.83 ± 1.34 2.94 ± 1.30 2.90 ± 1.29 2.96 ± 1.32 0.915a
All course readings, assignments, and lectures are available online
3.11 ± 1.24 3.09 ± 1.25 3.14 ± 1.22 3.14 ± 1.19 3.15 ± 1.24 0.996a
Students are assisted in overcoming obstacles in accessing the classes 2.95± 1.18
or materials
2.96± 1.18
3.08± 1.16
3.03± 1.10
3.05 ± 1.12 0.812a
Time allotted for online classes is sufficient
3.13 ± 1.16 3.11 ± 1.18 3.15 ± 1.12 3.16 ± 1.13 3.16 ± 1.15 0.994a
I am able to interact with teachers during online classes
3.03 ± 1.31 2.96 ± 1.36 2.99 ± 1.35 3.00 ± 1.31 2.96 ± 1.40 0.995a
I am able to interact with teachers after online class in the Q&A
session
2.99 ± 1.36 2.97 ± 1.35 2.89 ± 1.36 2.95 ± 1.31 2.99 ± 1.37 0.946a
Every individual is given a chance to participate and pitch in their
ideas during online classes
2.72 ± 1.33 2.69 ± 1.34 2.74 ± 1.34 2.81 ± 1.29 2.85 ± 1.33 0.759a
The teachers are well trained for online classes and are able to use the 2.97 ± 1.46 2.85 ± 1.52 2.76 ± 1.52 2.84 ± 1.44 2.85 ± 1.50 0.793a
video conferencing app with ease
Attending classes from home hampers my attention and focus
3.38 ± 1.38 3.46 ± 1.50 3.34 ± 1.39 3.41 ± 1.38 3.43 ± 1.38 0.854a
Online classes are equally or more informative as compared with
active learning on campus
1.88 ± 1.24 1.85 ± 1.21 1.84 ± 1.21 1.97 ± 1.25 1.96 ± 1.28 0.579a
Online learning fits in my schedules better than a typical day to day 1.84 ± 1.18 1.81 ± 1.13 1.77 ± 1.13 1.89 ± 1.16 1.93 ± 1.22 0.621a
classes
Demonstration of practical/clinical/lab work by the instructor during 2.00 ± 1.35 1.98 ± 1.33 1.95 ± 1.32 2.08 ± 1.33 2.05 ± 1.35 0.647a
online classes would help me learn in a better way
I would like to have these online sessions continued even after
campus classes have started
1.77 ± 1.23 1.79 ± 1.20 1.87 ± 1.26 1.82 ± 1.25 1.84 ± 1.26 0.930a
Notes:
a
p-Value calculated by using Kruskal–Wallis test.
SD, standard deviation.
(Howland & Moore, 2002; Vonderwell, 2003) are significant challenges in online teaching,
and may disadvantage students with limited Internet access.
The current study also found that most students reported inadequate electrical supply
during their virtual classes. This is a significant observation as it has been documented
that the lack of sufficient electrical supply is one of the factors associated with tension
among medical students, which may contribute to low academic success (Qamar, Khan &
Bashir Kiani, 2015). Our findings confirm that electricity shortage and slow internet
connections (common problems in rural areas) were perceived as leading barriers to
course related information (Luqman et al., 2019). Power crises have been highlighted as
contributing to reduced technology usage for access to education (Adil, Usman & Jalil,
2020). The academic literature is mostly silent on the relationship between the power crisis
and online education.
The two most critical factors limiting access to online medical education programs may
be power outages and poor Internet access, affecting students’ performance particularly in
developing countries such as India, Tanzania, Pakistan, Somalia and others.
Aslam et al. (2021), PeerJ, DOI 10.7717/peerj.12061
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The present study found that respondents most commonly used smartphones to attend
online classes, and the most popular means by which to receive class schedule notifications
was social media (e.g., WeChat). Medical students have demonstrated positive attitudes
toward learning with mobile technology (Abbasi et al., 2020; Hamilton et al., 2016; Suner,
Yilmaz & Pişkin, 2019). Time management and convenient access to information are
important reasons for medical students increasingly preferring to use smartphones (Bansal
et al., 2020).
IMS perception of online teaching and challenges in adjusting to this
new mode of learning
Students have reportedly expressed high levels of dissatisfaction with the institutional
learning management system, the online availability of teaching information, their ability
to engage with teachers during online classes, and the ability to overcome obstacles to
access the classes and supplementary resources (Alenezi, 2018). Several authors have
indicated that virtual education systems require a robust system of hardware and software
that ensures reliable access to content and resources for successful learning (Asiry, 2017;
Sarwar et al., 2020).
In the present study students also expressed general disappointment with teachers’
ability to offer online lectures effectively, an outcome of the need for rapid adaptation to
online learning technology. This finding echoes those of other scholars who have argued
that it is a demanding and time-consuming exercise to create persuasive, creative, and
educational online content, and that this process requires a transitional preparation and
adaptation period (Akram et al., 2021; Crawford et al., 2020; Sarwar et al., 2020)
In the present study, students acknowledged that their concentration and focus on
learning are impeded by online classes. When asked whether online classes were more
informative, most students disagreed. In addition, most students disagreed that online
sessions should continue after regular campus operations have been completely resumed.
These findings confirm those of previous studies in which the vast majority of students in
dentistry and medicine appear to prefer traditional teaching methods and learning from
textbooks and lectures (Abbasi et al., 2020; Bansal et al., 2020; Sarwar et al., 2020).
In contrast, some research indicates that students favor a more comprehensive
pedagogical approach. To develop students’ understanding and expertise, traditional study
sessions may be combined with online workshops including supplementary teaching
materials and assignments (Dost et al., 2020; Hamilton et al., 2016). The development of
groundbreaking educational initiatives to boost remote medical education has been
initiated and may lead to success (Huddart et al., 2020).
Our findings indicate that students would like more collaborative online teaching
lessons. This could be accomplished by integrating approaches such as polls, quizzes, or
breakout rooms into student response systems (Dost et al., 2020; McBrien, Cheng & Jones,
2009), to promote student engagement (Morawo, Sun & Lowden, 2020). This active
contact between teachers and students enables uncertainty to be quickly resolved to
improve student engagement and build a more vibrant learning atmosphere.
Aslam et al. (2021), PeerJ, DOI 10.7717/peerj.12061
15/22
Future directions and practical strategies for online teaching in
medical institutes
Sufficient and updated resources contribute significantly to the online learning of students
(Azevedo & Marques, 2017), and their proper channelization is crucial for successful
accomplishment (Akram & Yang, 2021). IMS pointed out that compact pre-recorded
videos would be helpful to learn remotely, previous research (Guo, Kim & Rubin, 2014)
indicates the same. They further emphasized that the current pandemic climate needs a
collective, harmonized, and global mutual effort to create successful online pedagogy
practice policies. Moreover online education in developing countries remains a relatively
recent concept, and strong efforts must be made to identify successful and efficient
teaching strategies to address the barriers and obstacles preventing students’ access to
effective online learning. Thus, the circumstances of the pandemic, lockdown and social
distancing have impacted the learning of medical students.
Recommendations
The digitalization of medical teaching could play a significant role in the future of medical
institutions (Dost et al., 2020). We believe that this critical student-facing issue should be
addressed as soon as possible. Teachers should be encouraged to take part in online
educational courses and programs that provide them with the tools they need to learn
about different facets of immersive learning methods. This will help medical educators
increase the effectiveness of existing online sessions. A smartphone-compatible virtual
learning environment may be useful for online medical teaching. Problem-based
learning or team-based learning has been found to improve learning outcomes (Clark,
2006; Yew & Goh, 2016), and student motivation and understanding (Chang, 2016). There
is a need for a student support system to enhance the learning environment (Aslam et al.,
2020). In a lockdown environment, medical institutions must launch external resources
and training initiatives such as Osmosis and BiteMedicine. Universities need to
immediately sign collaboration agreements with the medical institutions within students’
home countries to discuss teaching and learning arrangements for crisis periods.
Strengths, limitations and future research directions
To the researchers’ knowledge, this is the first study to examine IMS’ experiences of online
teaching in China. The large sample size of 1,107 medical students from 12 countries
from both pre-clinical and clinical years is one of the study’s strengths. In addition, the use
of different approaches for the recruitment of medical students reduced possible bias in
the responses. We studied the experiences of IMS in detail by analyzing their responses
within groups based on the type of institution (public and private) as well as gender and
year of study. We believe the results of our study provide important insights that will
help medical institutions, educators and students to address and devise strategies to
overcome the challenges to IMS learning posed by the COVID-19 pandemic.
The results may disproportionately represent views from students in some locations,
with more responses from some countries (such as Pakistan) than others (such as
Aslam et al. (2021), PeerJ, DOI 10.7717/peerj.12061
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Tanzania; Table 1), possibly introducing sampling bias. Another constraint of the study is
that no assessment was made of whether institutions had trained their teachers
professionally to conduct online classes, and the students’ response regarding teachers’
capacity to conduct classes online does not accurately reflect the teachers’ ability. When
IMS interactions were explored in this research, no distinctions were made between
various modes of online instruction. More in-depth observational surveys, such as focus
groups/interviews/reflection journals, should be undertaken in collaboration with medical
institutions to accurately measure the impact of COVID-19 on student use of online
teaching. Future researchers should investigate alternatives to clinical sessions during any
crisis requiring social distancing and how to conduct and control the quality of exams.
CONCLUSIONS
This study explored the perspectives of IMS regarding the effectiveness of online teaching,
challenges in adjusting to this new mode of learning and proposed practical strategies
for medical institutions based on the identified factors. IMS’ dissatisfaction with the
various components of the online teaching indicates a need for medical institutions to
enhance online learning. IMS suggested strategies including investment in faculty
professional development and modification of online course content. More international
collaboration may increase the quality and accessibility of online medical education.
Virtual teaching, especially clinical simulation arrangements, should be developed
collaboratively by advanced and developing countries, and would be helpful to IMS and
assessment strategies. We recognize that COVID-19 has proven to be an extraordinary
threat at the global level (Baloch et al., 2021) to which medical institutions have responded,
but online education needs to be developed further. It will take time and experience to
switch from a conventional face-to-face teaching approach to a fully functional virtual
education framework in the medical education field.
ACKNOWLEDGEMENTS
The authors would like to thank the Shaanxi Normal University Xi’an of China for
technical support with data collection. The authors would also like to thank all the IMS for
actively taking part in this study. Special thanks to Muhammad Mussab Umair for his
assistance in distributing the survey link. We would also like to express our gratitude to
Prof. Dr. Arshad Hasan, for his permission to use the questionnaire for this study.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
The authors received no funding for this work.
Competing Interests
The authors declare that they have no competing interests.
Aslam et al. (2021), PeerJ, DOI 10.7717/peerj.12061
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Author Contributions
Sarfraz Aslam conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, and approved the final draft.
Huma Akram conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, and approved the final draft.
Atif Saleem conceived and designed the experiments, performed the experiments,
analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the
paper, and approved the final draft.
BaoHui Zhang conceived and designed the experiments, performed the experiments,
analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.
Human Ethics
The following information was supplied relating to ethical approvals (i.e., approving body
and any reference numbers):
The Shaanxi Normal University granted Ethical approval to carry out the study within
its facilities (Approval No: AR 2021-01-001).
Data Availability
The following information was supplied regarding data availability:
The raw measurements are available in the Supplemental Files.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/10.7717/
peerj.12061#supplemental-information.
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